diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..b14947a
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+statsplot/_version.py export-subst
diff --git a/.github/README.md b/.github/README.md
index afd15bd..18abbca 100644
--- a/.github/README.md
+++ b/.github/README.md
@@ -1,18 +1,19 @@
+[![Latest Version on PyPI](https://img.shields.io/pypi/v/statsplot.svg)](https://pypi.python.org/pypi/statsplot/)
+
 # Statsplot
 
 A package that allows you easily to calculate and plot statistics.
 
  Seaborn                   |  Statsplot
 :-------------------------:|:-------------------------:
-![boxplot seaborn](../docs/images/nested_seaborn.png) |  ![boxplot statsplot](../docs/images/nested_statsplot.png)
-
+![boxplot seaborn](https://github.com/SilasK/statsplot/blob/master/docs/images/nested_seaborn.png) |  ![boxplot statsplot](https://github.com/SilasK/statsplot/blob/master/docs/images/nested_statsplot.png)
 
 
 
 
 > 
 <!-- 
-[![Latest Version on PyPI](https://img.shields.io/pypi/v/statsplot.svg)](https://pypi.python.org/pypi/statsplot/)
+
 [![Supported Implementations](https://img.shields.io/pypi/pyversions/statsplot.svg)](https://pypi.python.org/pypi/statsplot/)
 [![Build Status](https://secure.travis-ci.org/silask/statsplot.svg?branch=master)](http://travis-ci.org/christophevg/statsplot)
 [![Documentation Status](https://readthedocs.org/projects/statsplot/badge/?version=latest)](https://statsplot.readthedocs.io/en/latest/?badge=latest)
@@ -27,6 +28,11 @@ Visit [Read the Docs](https://statsplot.readthedocs.org) for the full documentat
 
  -->
 
+ Setup :
+
+ ```
+ pip install statsplot
+ ```
 
  Setup dev version:
 
@@ -37,5 +43,6 @@ pip install --editable .
 
 ```
 
-See the [Example](../example.ipynb)
+
+See the [Example](https://github.com/SilasK/statsplot/blob/master/example.ipynb) or run it on [Colab](https://colab.research.google.com/github/SilasK/statsplot/blob/master/example.ipynb)
 
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 100644
index 0000000..b707194
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1,7 @@
+# Copilot Instructions
+
+- Use Pixi for local development workflows in this repository.
+- Prefer `pixi run test` for running tests.
+- Prefer `pixi run test-mpl` for matplotlib image-comparison tests.
+- When adding dependencies for local workflows, update `pixi.toml` first.
+- Keep CI workflows independent from Pixi unless explicitly requested.
diff --git a/.github/workflows/publish-to-test-pypi.yml b/.github/workflows/publish-to-test-pypi.yml
new file mode 100644
index 0000000..26e7208
--- /dev/null
+++ b/.github/workflows/publish-to-test-pypi.yml
@@ -0,0 +1,46 @@
+name: Publish package
+on: [push]
+jobs:
+  Publish-to-PyPI:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@master
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.9
+    
+      - name: Install pypa/build
+        run: >-
+          python -m
+          pip install
+          build
+          --user
+          
+      - name: Build a binary wheel and a source tarball
+        run: >-
+          python -m
+          build
+          --sdist
+          --wheel
+          --outdir dist/
+          .
+          
+      - name: Publish package to TestPyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.TEST_PYPI_TOKEN }}
+          repository_url: https://test.pypi.org/legacy/
+          verbose: true
+
+      - name: Publish package
+        if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_TOKEN }}
+
+          
+
+
diff --git a/.gitignore b/.gitignore
index 543d38a..0fae21d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,8 @@ docs/_build
 old
 *.ipynb_checkpoints
 
+test/_actual
+
 *__pycache__
 .vscode
 
diff --git a/example.ipynb b/example.ipynb
index d66dabe..2373f3b 100644
--- a/example.ipynb
+++ b/example.ipynb
@@ -5,17 +5,44 @@
    "execution_count": 1,
    "id": "b24bab44",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You have statsplot v 0.3.0\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pylab as plt\n",
     "import seaborn as sns\n",
     "\n",
+    "try:\n",
     "\n",
-    "import statsplot as stp\n",
+    "    import statsplot as stp\n",
+    "except ImportError:\n",
+    "    # Install statsplot\n",
+    "    ! pip install statsplot\n",
+    "    import statsplot as stp\n",
     "\n",
-    "from statsplot.statstable import MetaTable"
+    "from statsplot import MetaTable\n",
+    "print(f\"You have statsplot v {stp.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "00cfe8e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sns.set_context(font_scale=1.5)\n",
+    "# Nice plots when you have a mac\n",
+    "%config InlineBackend.figure_format = 'retina'"
    ]
   },
   {
@@ -30,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "98b56061",
    "metadata": {},
    "outputs": [],
@@ -65,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "668f3083",
    "metadata": {},
    "outputs": [
@@ -100,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "b17c3b88",
    "metadata": {},
    "outputs": [
@@ -113,7 +140,7 @@
        "Name: Weight, dtype: float64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -150,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "b80d3874",
    "metadata": {},
    "outputs": [
@@ -175,7 +202,7 @@
     "ax, stats = stp.statsplot(\n",
     "    variable=weights,\n",
     "    test_variable=groups,\n",
-    "    labelkws={\"show_ns\": True, \"use_stars\": False},\n",
+    "    show_not_significant = True\n",
     ")"
    ]
   },
@@ -189,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "da475e1c",
    "metadata": {},
    "outputs": [
@@ -269,7 +296,7 @@
        "Sample_4  Participant_5  Control    before    19.663896"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "d1cdeca6",
    "metadata": {},
    "outputs": [
@@ -326,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "1a87a451",
    "metadata": {},
    "outputs": [
@@ -339,13 +366,13 @@
        "Name: Measurement, dtype: object"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -385,93 +412,18 @@
     "This example is based on on microbiome profiling"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "556680ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Functions to parse taxonomy\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import warnings\n",
-    "\n",
-    "TAXONMIC_LEVELS = [\"Domain\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"]\n",
-    "\n",
-    "\n",
-    "def tax2table(Taxonomy_Series, split_character=\";\", remove_prefix=False):\n",
-    "    \"\"\"\n",
-    "    Transforms (green_genes) taxonomy to a table\n",
-    "    Expect the following input format:\n",
-    "    d__Bacteria;p__Bacteroidota;c__Bacteroidia;f__\n",
-    "    Replaces empty values and can remove prefix 'c__'\n",
-    "    \"\"\"\n",
-    "\n",
-    "    # drop missing values\n",
-    "    if Taxonomy_Series.isnull().any():\n",
-    "        warnings.warn(\n",
-    "            \"Some samples have no taxonomy asigned. Samples:\\n\"\n",
-    "            + \", \".join(Taxonomy_Series.index[Taxonomy_Series.isnull()].astype(str))\n",
-    "        )\n",
-    "\n",
-    "    Tax = Taxonomy_Series.dropna().astype(str).str.split(split_character, expand=True)\n",
-    "    # Add headers as long as we have columns\n",
-    "    Tax.columns = TAXONMIC_LEVELS[: len(Tax.columns)]\n",
-    "\n",
-    "    if remove_prefix:\n",
-    "        Tax = Tax.applymap(lambda s: s[3:], na_action=\"ignore\").replace(\"\", np.nan)\n",
-    "    else:\n",
-    "        Tax[Tax.applymap(len, na_action=\"ignore\") == 3] = np.nan\n",
-    "\n",
-    "    # add missing values again\n",
-    "\n",
-    "    Tax = Tax.reindex(Taxonomy_Series.index)\n",
-    "\n",
-    "    return Tax\n",
-    "\n",
-    "\n",
-    "def load_gtdb_tax(taxonomy_file, remove_prefix=False):\n",
-    "\n",
-    "    D = pd.read_table(taxonomy_file, index_col=0)\n",
-    "\n",
-    "    Tax = tax2table(D[\"classification\"], remove_prefix=remove_prefix)\n",
-    "\n",
-    "    return Tax"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "vocal-publicity",
+   "id": "cbf3fabd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# load microbiome data\n",
-    "# The paths here are existing only on my computer\n",
-    "\n",
-    "# 1. counts/ relab\n",
-    "# 2. taxonomy\n",
-    "# 3. metadata\n",
-    "\n",
-    "Tax = load_gtdb_tax(\n",
-    "    \"../WarmMicrobiota/Metagenome/WD/genomes/taxonomy/gtdbtk.bac120.summary.tsv\",\n",
-    "    remove_prefix=True,\n",
-    ").sort_index()\n",
-    "\n",
-    "# create a label for each genome even if species is not defined\n",
-    "\n",
-    "Tax[\"Label\"] = Tax.ffill(axis=1)[\"species\"]\n",
-    "Tax.loc[Tax.species.isnull(), \"Label\"] += \" \" + Tax.index[Tax.species.isnull()]\n",
+    "relab = pd.read_table(\"test/data/micobiota_relab.tsv.gz\", index_col=0)\n",
     "\n",
+    "Tax = pd.read_table(\"test/data/micobiota_taxonomy.tsv.gz\", index_col=0)\n",
     "\n",
-    "metadata = pd.read_table(\"../WarmMicrobiota/Metagenome/metadata.tsv\", index_col=0)\n",
-    "D = pd.read_table(\n",
-    "    \"../WarmMicrobiota/Metagenome/WD/genomes/counts/median_coverage_genomes.tsv\",\n",
-    "    index_col=0,\n",
-    ")\n",
-    "# relative abundance\n",
-    "relab = (D.T / D.sum(1)).T * 100"
+    "metadata = pd.read_table(\"test/data/micobiota_metadata.tsv.gz\", index_col=0)"
    ]
   },
   {
@@ -479,29 +431,16 @@
    "execution_count": 11,
    "id": "0193b0c2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MetaTable with 32 samples x 147 features"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# transform data with centered log transform\n",
-    "\n",
     "from statsplot import transformations\n",
     "\n",
     "clr_data = transformations.clr(relab, log=np.log2)\n",
     "\n",
     "# put everithing together in a MetaTable\n",
     "\n",
-    "D = MetaTable(clr_data, obs=metadata, var=Tax)\n",
-    "D"
+    "D = MetaTable(clr_data, obs=metadata, var=Tax)"
    ]
   },
   {
@@ -535,237 +474,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "aggressive-trial",
-   "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 tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Pvalue</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Statistic</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">median_diff</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">pBH</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>Cecum</th>\n",
-       "      <th>Feces</th>\n",
-       "      <th>Cecum</th>\n",
-       "      <th>Feces</th>\n",
-       "      <th>Cecum</th>\n",
-       "      <th>Feces</th>\n",
-       "      <th>Cecum</th>\n",
-       "      <th>Feces</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "      <th>Hot_vs_RT</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>MAG001</th>\n",
-       "      <td>0.000086</td>\n",
-       "      <td>9.776735e-08</td>\n",
-       "      <td>-7.182472</td>\n",
-       "      <td>-10.031165</td>\n",
-       "      <td>3.871136</td>\n",
-       "      <td>4.234078</td>\n",
-       "      <td>0.001576</td>\n",
-       "      <td>0.000014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG002</th>\n",
-       "      <td>0.836514</td>\n",
-       "      <td>5.090102e-01</td>\n",
-       "      <td>-0.210302</td>\n",
-       "      <td>-0.680390</td>\n",
-       "      <td>-0.065014</td>\n",
-       "      <td>-0.113144</td>\n",
-       "      <td>0.924569</td>\n",
-       "      <td>0.699294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG003</th>\n",
-       "      <td>0.582932</td>\n",
-       "      <td>7.263050e-01</td>\n",
-       "      <td>-0.563255</td>\n",
-       "      <td>-0.358060</td>\n",
-       "      <td>0.018494</td>\n",
-       "      <td>0.192080</td>\n",
-       "      <td>0.745140</td>\n",
-       "      <td>0.847356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG004</th>\n",
-       "      <td>0.026453</td>\n",
-       "      <td>4.278596e-02</td>\n",
-       "      <td>2.575495</td>\n",
-       "      <td>2.252542</td>\n",
-       "      <td>-1.051217</td>\n",
-       "      <td>-1.638859</td>\n",
-       "      <td>0.092516</td>\n",
-       "      <td>0.131032</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG005</th>\n",
-       "      <td>0.006127</td>\n",
-       "      <td>5.384854e-02</td>\n",
-       "      <td>3.267759</td>\n",
-       "      <td>2.104872</td>\n",
-       "      <td>-1.349585</td>\n",
-       "      <td>-0.650092</td>\n",
-       "      <td>0.032168</td>\n",
-       "      <td>0.153960</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>MAG143</th>\n",
-       "      <td>0.011926</td>\n",
-       "      <td>1.485766e-02</td>\n",
-       "      <td>2.972029</td>\n",
-       "      <td>2.840871</td>\n",
-       "      <td>-2.133191</td>\n",
-       "      <td>-2.072522</td>\n",
-       "      <td>0.053698</td>\n",
-       "      <td>0.065756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG144</th>\n",
-       "      <td>0.154094</td>\n",
-       "      <td>1.801576e-01</td>\n",
-       "      <td>1.595590</td>\n",
-       "      <td>1.483567</td>\n",
-       "      <td>-0.274400</td>\n",
-       "      <td>-0.178421</td>\n",
-       "      <td>0.300687</td>\n",
-       "      <td>0.343093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG145</th>\n",
-       "      <td>0.114007</td>\n",
-       "      <td>5.550921e-02</td>\n",
-       "      <td>-1.685909</td>\n",
-       "      <td>-2.101932</td>\n",
-       "      <td>0.352819</td>\n",
-       "      <td>1.037689</td>\n",
-       "      <td>0.246456</td>\n",
-       "      <td>0.153960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG146</th>\n",
-       "      <td>0.121707</td>\n",
-       "      <td>8.657415e-02</td>\n",
-       "      <td>1.671672</td>\n",
-       "      <td>1.863180</td>\n",
-       "      <td>-2.149775</td>\n",
-       "      <td>-2.022352</td>\n",
-       "      <td>0.259169</td>\n",
-       "      <td>0.215702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MAG147</th>\n",
-       "      <td>0.684818</td>\n",
-       "      <td>3.889712e-01</td>\n",
-       "      <td>0.414712</td>\n",
-       "      <td>0.898177</td>\n",
-       "      <td>-0.281860</td>\n",
-       "      <td>-0.238357</td>\n",
-       "      <td>0.829402</td>\n",
-       "      <td>0.589886</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>147 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Pvalue               Statistic            median_diff            \\\n",
-       "           Cecum         Feces     Cecum      Feces       Cecum     Feces   \n",
-       "       Hot_vs_RT     Hot_vs_RT Hot_vs_RT  Hot_vs_RT   Hot_vs_RT Hot_vs_RT   \n",
-       "MAG001  0.000086  9.776735e-08 -7.182472 -10.031165    3.871136  4.234078   \n",
-       "MAG002  0.836514  5.090102e-01 -0.210302  -0.680390   -0.065014 -0.113144   \n",
-       "MAG003  0.582932  7.263050e-01 -0.563255  -0.358060    0.018494  0.192080   \n",
-       "MAG004  0.026453  4.278596e-02  2.575495   2.252542   -1.051217 -1.638859   \n",
-       "MAG005  0.006127  5.384854e-02  3.267759   2.104872   -1.349585 -0.650092   \n",
-       "...          ...           ...       ...        ...         ...       ...   \n",
-       "MAG143  0.011926  1.485766e-02  2.972029   2.840871   -2.133191 -2.072522   \n",
-       "MAG144  0.154094  1.801576e-01  1.595590   1.483567   -0.274400 -0.178421   \n",
-       "MAG145  0.114007  5.550921e-02 -1.685909  -2.101932    0.352819  1.037689   \n",
-       "MAG146  0.121707  8.657415e-02  1.671672   1.863180   -2.149775 -2.022352   \n",
-       "MAG147  0.684818  3.889712e-01  0.414712   0.898177   -0.281860 -0.238357   \n",
-       "\n",
-       "             pBH            \n",
-       "           Cecum     Feces  \n",
-       "       Hot_vs_RT Hot_vs_RT  \n",
-       "MAG001  0.001576  0.000014  \n",
-       "MAG002  0.924569  0.699294  \n",
-       "MAG003  0.745140  0.847356  \n",
-       "MAG004  0.092516  0.131032  \n",
-       "MAG005  0.032168  0.153960  \n",
-       "...          ...       ...  \n",
-       "MAG143  0.053698  0.065756  \n",
-       "MAG144  0.300687  0.343093  \n",
-       "MAG145  0.246456  0.153960  \n",
-       "MAG146  0.259169  0.215702  \n",
-       "MAG147  0.829402  0.589886  \n",
-       "\n",
-       "[147 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ST.stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
    "id": "incoming-meeting",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -781,7 +495,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -804,13 +518,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "0972dec6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -818,7 +532,7 @@
      "metadata": {
       "image/png": {
        "height": 386,
-       "width": 520
+       "width": 511
       },
       "needs_background": "light"
      },
@@ -826,7 +540,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -859,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "b5986bb2",
    "metadata": {},
    "outputs": [],
@@ -871,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "20c4a682",
    "metadata": {},
    "outputs": [
@@ -881,7 +595,7 @@
        "<AxesSubplot:xlabel='Principal Component', ylabel='Explained Variance Ratio'>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -908,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "40ebcd48",
    "metadata": {},
    "outputs": [
@@ -918,7 +632,7 @@
        "<AxesSubplot:xlabel='PC 1 [20.5 %]', ylabel='PC 2 [14.3 %]'>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -950,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "2299fd46",
    "metadata": {},
    "outputs": [
@@ -960,13 +674,13 @@
        "<AxesSubplot:xlabel='PC 1 [20.5 %]', ylabel='PC 2 [14.3 %]'>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -987,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "9b54a6a5",
    "metadata": {},
    "outputs": [
@@ -1004,7 +718,7 @@
        "<AxesSubplot:xlabel='PC 1 [20.5 %]', ylabel='PC 2 [14.3 %]'>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1041,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "5ae251a8",
    "metadata": {},
    "outputs": [
@@ -1068,13 +782,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "d2f803f9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "[<AxesSubplot:xlabel='$\\\\log_2FC$', ylabel='$-\\\\log(P)$'>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x432 with 1 Axes>"
       ]
@@ -1095,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "a66f7a25",
    "metadata": {},
    "outputs": [
@@ -1125,6 +849,7 @@
        "      <th>log2FC</th>\n",
        "      <th>median_diff</th>\n",
        "      <th>pBH</th>\n",
+       "      <th>Description</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
@@ -1133,6 +858,7 @@
        "      <th>Hot_vs_RT</th>\n",
        "      <th>Hot_vs_RT</th>\n",
        "      <th>Hot_vs_RT</th>\n",
+       "      <th>Label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1143,6 +869,7 @@
        "      <td>3.979037</td>\n",
        "      <td>4.355420</td>\n",
        "      <td>0.000038</td>\n",
+       "      <td>CAG-510 MAG001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG002</th>\n",
@@ -1151,6 +878,7 @@
        "      <td>0.413565</td>\n",
        "      <td>0.155043</td>\n",
        "      <td>0.596227</td>\n",
+       "      <td>Lachnospiraceae MAG002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG003</th>\n",
@@ -1159,6 +887,7 @@
        "      <td>1.745871</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.488326</td>\n",
+       "      <td>Bacteroides sp002491635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG004</th>\n",
@@ -1167,6 +896,7 @@
        "      <td>-1.138110</td>\n",
        "      <td>-0.081953</td>\n",
        "      <td>0.032220</td>\n",
+       "      <td>Lachnospiraceae MAG004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG005</th>\n",
@@ -1175,6 +905,7 @@
        "      <td>-1.211975</td>\n",
        "      <td>-0.668275</td>\n",
        "      <td>0.003872</td>\n",
+       "      <td>UBA7050 MAG005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1183,6 +914,7 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG143</th>\n",
@@ -1191,6 +923,7 @@
        "      <td>-1.311170</td>\n",
        "      <td>-0.851287</td>\n",
        "      <td>0.010096</td>\n",
+       "      <td>Zag111 MAG143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG144</th>\n",
@@ -1199,6 +932,7 @@
        "      <td>-9.247887</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.081583</td>\n",
+       "      <td>Oscillospiraceae MAG144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG145</th>\n",
@@ -1207,6 +941,7 @@
        "      <td>0.824869</td>\n",
        "      <td>0.233811</td>\n",
        "      <td>0.017160</td>\n",
+       "      <td>CAG-180 MAG145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG146</th>\n",
@@ -1215,6 +950,7 @@
        "      <td>-0.985527</td>\n",
        "      <td>-0.313707</td>\n",
        "      <td>0.057089</td>\n",
+       "      <td>UBA3700 MAG146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MAG147</th>\n",
@@ -1223,31 +959,46 @@
        "      <td>-0.800029</td>\n",
        "      <td>-0.003989</td>\n",
        "      <td>0.512043</td>\n",
+       "      <td>UBA3263 sp001689615</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>147 rows × 5 columns</p>\n",
+       "<p>147 rows × 6 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "          Pvalue Statistic    log2FC median_diff       pBH\n",
-       "       Hot_vs_RT Hot_vs_RT Hot_vs_RT   Hot_vs_RT Hot_vs_RT\n",
-       "MAG001  0.000002       0.0  3.979037    4.355420  0.000038\n",
-       "MAG002  0.462380     108.0  0.413565    0.155043  0.596227\n",
-       "MAG003  0.338839     106.0  1.745871    0.000000  0.488326\n",
-       "MAG004  0.010959     196.0 -1.138110   -0.081953  0.032220\n",
-       "MAG005  0.000975     216.0 -1.211975   -0.668275  0.003872\n",
-       "...          ...       ...       ...         ...       ...\n",
-       "MAG143  0.003091     207.0 -1.311170   -0.851287  0.010096\n",
-       "MAG144  0.038849     160.0 -9.247887    0.000000  0.081583\n",
-       "MAG145  0.005603      54.0  0.824869    0.233811  0.017160\n",
-       "MAG146  0.024467     188.0 -0.985527   -0.313707  0.057089\n",
-       "MAG147  0.362262     150.0 -0.800029   -0.003989  0.512043\n",
+       "          Pvalue Statistic    log2FC median_diff       pBH  \\\n",
+       "       Hot_vs_RT Hot_vs_RT Hot_vs_RT   Hot_vs_RT Hot_vs_RT   \n",
+       "MAG001  0.000002       0.0  3.979037    4.355420  0.000038   \n",
+       "MAG002  0.462380     108.0  0.413565    0.155043  0.596227   \n",
+       "MAG003  0.338839     106.0  1.745871    0.000000  0.488326   \n",
+       "MAG004  0.010959     196.0 -1.138110   -0.081953  0.032220   \n",
+       "MAG005  0.000975     216.0 -1.211975   -0.668275  0.003872   \n",
+       "...          ...       ...       ...         ...       ...   \n",
+       "MAG143  0.003091     207.0 -1.311170   -0.851287  0.010096   \n",
+       "MAG144  0.038849     160.0 -9.247887    0.000000  0.081583   \n",
+       "MAG145  0.005603      54.0  0.824869    0.233811  0.017160   \n",
+       "MAG146  0.024467     188.0 -0.985527   -0.313707  0.057089   \n",
+       "MAG147  0.362262     150.0 -0.800029   -0.003989  0.512043   \n",
+       "\n",
+       "                    Description  \n",
+       "                          Label  \n",
+       "MAG001           CAG-510 MAG001  \n",
+       "MAG002   Lachnospiraceae MAG002  \n",
+       "MAG003  Bacteroides sp002491635  \n",
+       "MAG004   Lachnospiraceae MAG004  \n",
+       "MAG005           UBA7050 MAG005  \n",
+       "...                         ...  \n",
+       "MAG143            Zag111 MAG143  \n",
+       "MAG144  Oscillospiraceae MAG144  \n",
+       "MAG145           CAG-180 MAG145  \n",
+       "MAG146           UBA3700 MAG146  \n",
+       "MAG147      UBA3263 sp001689615  \n",
        "\n",
-       "[147 rows x 5 columns]"
+       "[147 rows x 6 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1259,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "fde73e94",
    "metadata": {},
    "outputs": [
diff --git a/pixi.lock b/pixi.lock
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+  sha256: 02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458
+  requires_dist:
+  - numpy>=1.26.4,<2.7
+  - pytest>=8.0.0 ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-timeout ; extra == 'test'
+  - pytest-xdist ; extra == 'test'
+  - asv ; extra == 'test'
+  - mpmath ; extra == 'test'
+  - gmpy2 ; extra == 'test'
+  - threadpoolctl ; extra == 'test'
+  - scikit-umfpack ; extra == 'test'
+  - pooch ; extra == 'test'
+  - hypothesis>=6.30 ; extra == 'test'
+  - array-api-strict>=2.3.1 ; extra == 'test'
+  - cython ; extra == 'test'
+  - meson ; extra == 'test'
+  - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
+  - intersphinx-registry ; extra == 'doc'
+  - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - sphinx-design>=0.4.0 ; extra == 'doc'
+  - matplotlib>=3.5 ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - jupytext ; extra == 'doc'
+  - myst-nb>=1.2.0 ; extra == 'doc'
+  - pooch ; extra == 'doc'
+  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
+  - jupyterlite-pyodide-kernel ; extra == 'doc'
+  - linkify-it-py ; extra == 'doc'
+  - tabulate ; extra == 'doc'
+  - click<8.3.0 ; extra == 'dev'
+  - spin ; extra == 'dev'
+  - mypy==1.10.0 ; extra == 'dev'
+  - typing-extensions ; extra == 'dev'
+  - types-psutil ; extra == 'dev'
+  - pycodestyle ; extra == 'dev'
+  - ruff>=0.12.0 ; extra == 'dev'
+  - cython-lint>=0.12.2 ; extra == 'dev'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/35/48/b992b488d6f299dbe3f11a20b24d3dda3d46f1a635ede1c46b5b17a7b163/scipy-1.17.1-cp312-cp312-macosx_10_14_x86_64.whl
+  name: scipy
+  version: 1.17.1
+  sha256: 35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8
+  requires_dist:
+  - numpy>=1.26.4,<2.7
+  - pytest>=8.0.0 ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-timeout ; extra == 'test'
+  - pytest-xdist ; extra == 'test'
+  - asv ; extra == 'test'
+  - mpmath ; extra == 'test'
+  - gmpy2 ; extra == 'test'
+  - threadpoolctl ; extra == 'test'
+  - scikit-umfpack ; extra == 'test'
+  - pooch ; extra == 'test'
+  - hypothesis>=6.30 ; extra == 'test'
+  - array-api-strict>=2.3.1 ; extra == 'test'
+  - cython ; extra == 'test'
+  - meson ; extra == 'test'
+  - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
+  - intersphinx-registry ; extra == 'doc'
+  - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - sphinx-design>=0.4.0 ; extra == 'doc'
+  - matplotlib>=3.5 ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - jupytext ; extra == 'doc'
+  - myst-nb>=1.2.0 ; extra == 'doc'
+  - pooch ; extra == 'doc'
+  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
+  - jupyterlite-pyodide-kernel ; extra == 'doc'
+  - linkify-it-py ; extra == 'doc'
+  - tabulate ; extra == 'doc'
+  - click<8.3.0 ; extra == 'dev'
+  - spin ; extra == 'dev'
+  - mypy==1.10.0 ; extra == 'dev'
+  - typing-extensions ; extra == 'dev'
+  - types-psutil ; extra == 'dev'
+  - pycodestyle ; extra == 'dev'
+  - ruff>=0.12.0 ; extra == 'dev'
+  - cython-lint>=0.12.2 ; extra == 'dev'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/b2/02/cf107b01494c19dc100f1d0b7ac3cc08666e96ba2d64db7626066cee895e/scipy-1.17.1-cp312-cp312-macosx_12_0_arm64.whl
+  name: scipy
+  version: 1.17.1
+  sha256: fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76
+  requires_dist:
+  - numpy>=1.26.4,<2.7
+  - pytest>=8.0.0 ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-timeout ; extra == 'test'
+  - pytest-xdist ; extra == 'test'
+  - asv ; extra == 'test'
+  - mpmath ; extra == 'test'
+  - gmpy2 ; extra == 'test'
+  - threadpoolctl ; extra == 'test'
+  - scikit-umfpack ; extra == 'test'
+  - pooch ; extra == 'test'
+  - hypothesis>=6.30 ; extra == 'test'
+  - array-api-strict>=2.3.1 ; extra == 'test'
+  - cython ; extra == 'test'
+  - meson ; extra == 'test'
+  - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - sphinx>=5.0.0,<8.2.0 ; extra == 'doc'
+  - intersphinx-registry ; extra == 'doc'
+  - pydata-sphinx-theme>=0.15.2 ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - sphinx-design>=0.4.0 ; extra == 'doc'
+  - matplotlib>=3.5 ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - jupytext ; extra == 'doc'
+  - myst-nb>=1.2.0 ; extra == 'doc'
+  - pooch ; extra == 'doc'
+  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
+  - jupyterlite-pyodide-kernel ; extra == 'doc'
+  - linkify-it-py ; extra == 'doc'
+  - tabulate ; extra == 'doc'
+  - click<8.3.0 ; extra == 'dev'
+  - spin ; extra == 'dev'
+  - mypy==1.10.0 ; extra == 'dev'
+  - typing-extensions ; extra == 'dev'
+  - types-psutil ; extra == 'dev'
+  - pycodestyle ; extra == 'dev'
+  - ruff>=0.12.0 ; extra == 'dev'
+  - cython-lint>=0.12.2 ; extra == 'dev'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl
+  name: seaborn
+  version: 0.13.2
+  sha256: 636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987
+  requires_dist:
+  - numpy>=1.20,!=1.24.0
+  - pandas>=1.2
+  - matplotlib>=3.4,!=3.6.1
+  - pytest ; extra == 'dev'
+  - pytest-cov ; extra == 'dev'
+  - pytest-xdist ; extra == 'dev'
+  - flake8 ; extra == 'dev'
+  - mypy ; extra == 'dev'
+  - pandas-stubs ; extra == 'dev'
+  - pre-commit ; extra == 'dev'
+  - flit ; extra == 'dev'
+  - numpydoc ; extra == 'docs'
+  - nbconvert ; extra == 'docs'
+  - ipykernel ; extra == 'docs'
+  - sphinx<6.0.0 ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-issues ; extra == 'docs'
+  - sphinx-design ; extra == 'docs'
+  - pyyaml ; extra == 'docs'
+  - pydata-sphinx-theme==0.10.0rc2 ; extra == 'docs'
+  - scipy>=1.7 ; extra == 'stats'
+  - statsmodels>=0.12 ; extra == 'stats'
+  requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda
+  sha256: 82088a6e4daa33329a30bc26dc19a98c7c1d3f05c0f73ce9845d4eab4924e9e1
+  md5: 8e194e7b992f99a5015edbd4ebd38efd
+  depends:
+  - python >=3.10
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/setuptools?source=hash-mapping
+  size: 639697
+  timestamp: 1773074868565
+- pypi: https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl
+  name: six
+  version: 1.17.0
+  sha256: 4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274
+  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*'
+- pypi: ./
+  name: statsplot
+  version: 2.3.3+1.g2ad37e1.dirty
+  sha256: e30c5f297f2a8c9a04577e98a6a61647cb4d551b18640fea9c33592976eb3828
+- pypi: https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl
+  name: threadpoolctl
+  version: 3.6.0
+  sha256: 43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb
+  requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda
+  sha256: cafeec44494f842ffeca27e9c8b0c27ed714f93ac77ddadc6aaf726b5554ebac
+  md5: cffd3bdd58090148f4cfcd831f4b26ab
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
+  - libzlib >=1.3.1,<2.0a0
+  constrains:
+  - xorg-libx11 >=1.8.12,<2.0a0
+  license: TCL
+  license_family: BSD
+  purls: []
+  size: 3301196
+  timestamp: 1769460227866
+- conda: https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h7142dee_3.conda
+  sha256: 7f0d9c320288532873e2d8486c331ec6d87919c9028208d3f6ac91dc8f99a67b
+  md5: 6e6efb7463f8cef69dbcb4c2205bf60e
+  depends:
+  - __osx >=10.13
+  - libzlib >=1.3.1,<2.0a0
+  license: TCL
+  license_family: BSD
+  purls: []
+  size: 3282953
+  timestamp: 1769460532442
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda
+  sha256: 799cab4b6cde62f91f750149995d149bc9db525ec12595e8a1d91b9317f038b3
+  md5: a9d86bc62f39b94c4661716624eb21b0
+  depends:
+  - __osx >=11.0
+  - libzlib >=1.3.1,<2.0a0
+  license: TCL
+  license_family: BSD
+  purls: []
+  size: 3127137
+  timestamp: 1769460817696
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda
+  sha256: 1d30098909076af33a35017eed6f2953af1c769e273a0626a04722ac4acaba3c
+  md5: ad659d0a2b3e47e38d829aa8cad2d610
+  license: LicenseRef-Public-Domain
+  purls: []
+  size: 119135
+  timestamp: 1767016325805
+- conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda
+  sha256: 9e156ffaefb8463437144326ada4b85d1de17961b9997ac5f1cbbaf747bd8bed
+  md5: d0e3b2f0030cf4fca58bde71d246e94c
+  depends:
+  - packaging >=24.0
+  - python >=3.10
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/wheel?source=compressed-mapping
+  size: 33491
+  timestamp: 1776878563806
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
+  sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
+  md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 601375
+  timestamp: 1764777111296
diff --git a/pixi.toml b/pixi.toml
new file mode 100644
index 0000000..21c2d4d
--- /dev/null
+++ b/pixi.toml
@@ -0,0 +1,23 @@
+[workspace]
+name = "statsplot"
+channels = ["conda-forge"]
+platforms = ["osx-arm64", "osx-64", "linux-64"]
+
+[dependencies]
+python = ">=3.11,<3.13"
+pip = ">=24"
+
+[pypi-dependencies]
+statsplot = { path = ".", editable = true }
+pytest = ">=8.3"
+pytest-mpl = ">=0.17"
+matplotlib = ">=3.9"
+seaborn = ">=0.13"
+pandas = ">=2.2"
+scipy = ">=1.13"
+scikit-learn = ">=1.5"
+
+[tasks]
+test = "pytest -q"
+test-mpl = "pytest -q -k mpl"
+test-all = "pytest -q"
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..fae12a0
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,5 @@
+[pytest]
+testpaths = test
+addopts = -ra
+markers =
+    mpl_image_compare: matplotlib image comparison test
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..bd380ae
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,7 @@
+[versioneer]
+VCS = git
+style = pep440
+versionfile_source = statsplot/_version.py
+versionfile_build = statsplot/_version.py
+tag_prefix =
+parentdir_prefix = statsplot-
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 92b7692..ac7002a 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,7 @@
 import os
 import re
 import setuptools
+import versioneer
 
 NAME = "statsplot"
 AUTHOR = "Silas Kieser"
@@ -23,16 +24,14 @@ def read(file):
         return fh.read()
 
 
-VERSION = re.search(
-    r'__version__ = [\'"]([^\'"]*)[\'"]', read(NAME.replace("-", "_") + "/__init__.py")
-).group(1)
 
 LONG_DESCRIPTION = read(README)
 
 if __name__ == "__main__":
     setuptools.setup(
         name=NAME,
-        version=VERSION,
+        version=versioneer.get_version(),
+        cmdclass=versioneer.get_cmdclass(),
         packages=setuptools.find_packages(),
         author=AUTHOR,
         description=DESCRIPTION,
diff --git a/statsplot/__init__.py b/statsplot/__init__.py
index 80c28fc..19c52af 100644
--- a/statsplot/__init__.py
+++ b/statsplot/__init__.py
@@ -1,6 +1,8 @@
-__version__ = "0.2.0"
 
-from .statstable import StatsTable
+from .statstable import StatsTable, MetaTable
 from .plot import statsplot, vulcanoplot
 from .stats import calculate_stats
 from .dimred import DimRed
+
+from . import _version
+__version__ = _version.get_versions()['version']
diff --git a/statsplot/_version.py b/statsplot/_version.py
new file mode 100644
index 0000000..6a92464
--- /dev/null
+++ b/statsplot/_version.py
@@ -0,0 +1,658 @@
+
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain.
+# Generated by versioneer-0.28
+# https://github.com/python-versioneer/python-versioneer
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+from typing import Callable, Dict
+import functools
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "$Format:%d$"
+    git_full = "$Format:%H$"
+    git_date = "$Format:%ci$"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = ""
+    cfg.parentdir_prefix = "statsplot-"
+    cfg.versionfile_source = "statsplot/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY: Dict[str, str] = {}
+HANDLERS: Dict[str, Dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None), **popen_kwargs)
+            break
+        except OSError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, [
+        "describe", "--tags", "--dirty", "--always", "--long",
+        "--match", f"{tag_prefix}[[:digit:]]*"
+    ], cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver):
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces):
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
+            else:
+                rendered += ".post0.dev%d" % (pieces["distance"])
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
diff --git a/statsplot/dimred.py b/statsplot/dimred.py
index 79ce160..9ea1800 100644
--- a/statsplot/dimred.py
+++ b/statsplot/dimred.py
@@ -133,14 +133,14 @@ def __init__(
         self, data, method=PCA, transformation=None, n_components=None, **kargs
     ):
 
-        if n_components is None:
-            n_components = data.shape[0]
-
         if data.shape[0] > data.shape[1]:
-            print(
+            warnings.warn(
                 "you don't need to reduce dimensionality or your dataset is transposed."
             )
 
+        if n_components is None:
+            n_components = min(data.shape)
+
         self.decomposition = method(n_components=n_components, **kargs)
 
         self.rawdata = data
@@ -154,7 +154,7 @@ def __init__(
 
         else:
 
-            self.data_ = data.applymap(transformation)
+            self.data_ = data.map(transformation)
 
         Xt = self.decomposition.fit_transform(self.data_)
 
diff --git a/statsplot/example_iris.py b/statsplot/example_iris.py
new file mode 100644
index 0000000..6994718
--- /dev/null
+++ b/statsplot/example_iris.py
@@ -0,0 +1,12 @@
+import seaborn as sns; sns.set_theme(color_codes=True)
+iris = sns.load_dataset("iris")
+species = iris.pop("species")
+g = sns.clustermap(iris)
+
+
+g = sns.clustermap(iris,
+                   figsize=(7, 5),
+                   row_cluster=False,
+                   dendrogram_ratio=(.1, .2),
+                   cbar_pos=(0, .2, .03, .4)
+                  )
\ No newline at end of file
diff --git a/statsplot/plot.py b/statsplot/plot.py
index 5da7cb0..893536c 100644
--- a/statsplot/plot.py
+++ b/statsplot/plot.py
@@ -1,4 +1,5 @@
 from logging import getLogger
+from textwrap import dedent
 
 logger = getLogger("__name__")
 
@@ -108,7 +109,7 @@ def vulcanoplot(
     threshold_p=0.05,
     figsize=(6, 6),
     label_points="auto",
-    max_labels=15,
+    max_labels=5,
     **kws,
 ):
 
@@ -189,6 +190,9 @@ def vulcanoplot(
         ax.annotate(g2, (-ax_lim * 0.9, 0), ha="left")
 
 
+# TODO: handle unaligned input.
+
+
 def statsplot(
     variable,
     test_variable,
@@ -196,6 +200,7 @@ def statsplot(
     order_test=None,
     grouping_variable=None,
     order_grouping=None,
+    show_dots=True,
     box_params=None,
     swarm_params=None,
     labelkws=None,
@@ -203,8 +208,16 @@ def statsplot(
     palette=None,
     p_values=None,
     test="ttest_ind",
+    show_not_significant=False,
     ax=None,
 ):
+    """Main function for plotting statistical tests."""
+
+    default_box_params = {}
+    if show_dots:
+        # Dot overlays already show sample-level values; hide boxplot outliers.
+        default_box_params.update(dict(showfliers=False, fliersize=0))
+    default_swarm_params = {}
 
     if ax is None:
         ax = plt.subplot(111)
@@ -222,37 +235,66 @@ def statsplot(
 
     if order_test is None:
         order_test = unique(test_variable)
+    else:
+        assert set(order_test) == set(
+            test_variable
+        ), "test_variable has more values than order test. order_test cannot be used to subset the data. Do this prior."
 
     # use subgrouping
     if grouping_variable is None:
         params.update(dict(x=test_variable, order=order_test))
+        default_swarm_params.update(dict(color="k"))
     else:
 
+        if type(grouping_variable) == str:
+            assert (
+                data is not None
+            ), "If grouping_variable is a string, data must be provided"
+            grouping_variable = data[grouping_variable]
+
         if order_grouping is None:
             order_grouping = unique(grouping_variable)
+        else:
 
-        params.update(
-            dict(
-                x=grouping_variable,
-                order=order_grouping,
-                hue=test_variable,
-                hue_order=order_test,
-            )
+            assert set(order_grouping) >= set(
+                grouping_variable
+            ), "grouping_variable has more values than order_grouping. order_grouping cannot be used to subset the data. Do this prior."
+
+        params.update(dict(x=grouping_variable, order=order_grouping))
+
+        default_swarm_params.update(
+            dict(dodge=True, palette="dark:k", hue=test_variable, hue_order=order_test)
         )
 
+    # apply new keyword params
     if box_params is None:
         box_params = {}
+    box_params = {**default_box_params, **box_params}
+
     if swarm_params is None:
         swarm_params = {}
+    swarm_params = {**default_swarm_params, **swarm_params}
+
+    # Ensure dodge-capable swarm defaults stay intact when users pass partial kwargs.
+    if show_dots and (grouping_variable is not None):
+        swarm_params.setdefault("dodge", True)
+        if swarm_params.get("dodge"):
+            swarm_params.setdefault("hue", test_variable)
+            swarm_params.setdefault("hue_order", order_test)
+            swarm_params.setdefault("palette", "dark:k")
+
+    sns.boxplot(
+        palette=palette,
+        legend=grouping_variable is not None,
+        hue_order=order_test,
+        hue=test_variable,
+        **params,
+        **box_params,
+    )
 
-    sns.boxplot(palette=palette, **params, **box_params)
-
-    legend = ax.get_legend_handles_labels()
-
-    sns.swarmplot(**params, color="k", dodge=True, **swarm_params)
-
-    if grouping_variable is not None:
-        ax.legend(*legend, bbox_to_anchor=(1, 1))
+    if show_dots:
+        logger.debug(f"Showing dots with params: {dict(**params, **swarm_params)}")
+        sns.swarmplot(legend=False, **params, **swarm_params)
 
     # Statistics
     if p_values is None:
@@ -270,7 +312,88 @@ def statsplot(
 
     if labelkws is None:
         labelkws = dict(deltay="auto")
-
-    plot_all_sig_labels(p_values, order_test, order_grouping, ax=ax, **labelkws)
+        if show_not_significant:
+            labelkws.update(use_stars=False)
+
+    plot_all_sig_labels(
+        p_values,
+        order_test,
+        order_grouping,
+        show_ns=show_not_significant,
+        ax=ax,
+        **labelkws,
+    )
 
     return ax, p_values
+
+
+statsplot.__doc__ = dedent(
+    """\
+    Plot Boxplot with statistical significance.
+
+    Parameters  
+    ----------
+    variable : str or pandas.Series
+        The variable to be tested.
+    test_variable : str or pandas.Series
+        The variable to be tested against.
+    data : pandas.DataFrame
+        The dataframe containing the variable, test_variable and grouping_variable.
+    order_test : list           
+        The order of the test_variable.
+    grouping_variable : str or pandas.Series
+        The variable to be used for grouping.
+    order_grouping : list
+        The order of the grouping_variable.
+    show_dots : bool
+        If True, show dots on top of boxplot.
+    box_params : dict
+        Parameters for the boxplot.
+    swarm_params : dict
+        Parameters for the swarmplot.
+    labelkws : dict
+        Parameters for the labels.
+    stats_kws : dict
+        Parameters for the statistical test.
+    palette : list
+        The color palette.
+    p_values : pandas.Series
+        The p-values of the statistical test.
+    test : str
+        The statistical test.
+    show_not_significant : bool
+        If True, show not significant labels.
+    ax : matplotlib.axes.Axes
+        The axis to plot on.
+
+    Returns
+    -------
+    ax : matplotlib.axes.Axes
+        The axis with the plot.
+    p_values : pandas.Series
+        The p-values of the statistical test.
+
+    Examples
+    --------
+
+
+    .. plot::
+        :context: close-figs
+        :format: doctest
+        :include-source: True
+
+        >>> import seaborn as sns
+        >>> import statsplot as stp
+        >>> iris = sns.load_dataset("iris")
+        >>> ax,stats = stp.statsplot(data=iris, variable="sepal_length", test_variable="species")
+        >>> print(stats)
+        versicolor_vs_setosa       8.985235e-18
+        virginica_vs_setosa        6.892546e-28
+        virginica_vs_versicolor    1.724856e-07
+        Name: sepal_length, dtype: float64
+        >>> plt.show()
+
+
+
+    """
+)
diff --git a/statsplot/siglabels.py b/statsplot/siglabels.py
index 6b2f959..1ecbe4d 100644
--- a/statsplot/siglabels.py
+++ b/statsplot/siglabels.py
@@ -62,7 +62,7 @@ def __plot_sig_labels_hue(
     show_ns=True,
     width=0.8,
     use_stars=True,
-    labelkws=None,
+    **labelkws,
 ):
 
     assert type(P_values) == pd.Series, "P values should be a series"
@@ -72,13 +72,10 @@ def __plot_sig_labels_hue(
 
     P_values = P_values.apply(format_p_value, use_stars=use_stars)
 
-    if labelkws is None:
-        labelkws = {}
-
     # start with y0
     y = y0
 
-    for idx, text in P_values.iteritems():
+    for idx, text in P_values.items():
 
         def calculate_hue_offset(group, order):
             return (order.index(group) - len(order) * 0.5 + 0.5) / len(order) * width
@@ -100,7 +97,7 @@ def ___plot_sig_labels_xaxis(
     show_ns=True,
     width=0.8,
     use_stars=True,
-    labelkws=None,
+    **labelkws,
 ):
 
     assert type(P_values) == pd.Series, "P values should be a series"
@@ -114,11 +111,8 @@ def ___plot_sig_labels_xaxis(
 
     P_values = P_values.apply(format_p_value, use_stars=use_stars)
 
-    if labelkws is None:
-        labelkws = {}
-
     y = y0
-    for idx, text in P_values.iteritems():
+    for idx, text in P_values.items():
 
         def calculate_x_offset(group, order):
             return order.index(group)
@@ -139,9 +133,8 @@ def plot_all_sig_labels(
     y0="auto",
     deltay="auto",
     ax=None,
-    **kws
+    **kws,
 ):
-
     """"""
 
     # define y0 and deltay
@@ -177,5 +170,5 @@ def plot_all_sig_labels(
                 deltay=deltay,
                 y0=y0,
                 show_ns=show_ns,
-                **kws
+                **kws,
             )
diff --git a/statsplot/stats.py b/statsplot/stats.py
index dc4a370..2ce1e31 100644
--- a/statsplot/stats.py
+++ b/statsplot/stats.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 from scipy import stats
-from xarray import corr
+
 
 import logging
 
@@ -77,6 +77,7 @@ def __stats_test_all_on_once(values1, values2, test, **test_kws):
     res = test(values1, values2, **test_kws)
     ResultsDB["Statistic"] = res.statistic
     ResultsDB["Pvalue"] = res.pvalue
+
     return ResultsDB
 
 
@@ -109,9 +110,9 @@ def two_group_test(
     test_kws=None,
     correct_for_multiple_testing=True,
 ):
-
     """test: a parwise statistical test found in scipy e.g ['mannwhitneyu','ttest_ind']
-    or a function wich takes two argumens. Additional keyword arguments can be specified by test_kws"""
+    or a function wich takes two argumens. Additional keyword arguments can be specified by test_kws
+    """
 
     # Define test
     if test_kws is None:
@@ -189,6 +190,9 @@ def two_group_test(
         Pairwise_comp = Test(values1, values2)
 
         Pairwise_comp["median_diff"] = values2.median() - values1.median()
+        Pairwise_comp["mean_diff"] = values2.mean() - values1.mean()
+        Pairwise_comp["Median1"] = values1.median()
+        Pairwise_comp["Median2"] = values2.median()
 
         if min_value >= 0:
             Pairwise_comp["log2FC"] = np.log2(values2.mean() + log_delta) - np.log2(
@@ -230,7 +234,6 @@ def calculate_stats(
     test="ttest_ind",
     **test_kws,
 ):
-
     """Calculate pairewise statistical tests optioonally grouped by a grouping variable"""
 
     kws = dict(
diff --git a/statsplot/statstable.py b/statsplot/statstable.py
index f18d749..9b511f8 100644
--- a/statsplot/statstable.py
+++ b/statsplot/statstable.py
@@ -1,12 +1,8 @@
-from curses import meta
-import imp
 import logging
 
-from matplotlib.pyplot import violinplot
-
 logger = logging.getLogger("statstable")
 
-from numpy import unique
+from numpy import dtype, unique
 import pandas as pd
 import matplotlib.pylab as plt
 
@@ -17,65 +13,136 @@
 import seaborn as sns
 
 
+def is_anndata(instance):
+    """Function to check if an object is a anndata without importing the package"""
+
+    anndata_attr = ["obs", "var", "to_df", "X"]
+    return all([hasattr(instance, a) for a in anndata_attr])
+
+
+def set_string_indexes(df):
+
+    df.index = df.index.astype(str)
+    df.columns = df.columns.astype(str)
+
+
+# TODO: groupby with function such as sum
+
+
 class MetaTable:
+    def __check_consistency(self):
+
+        if self.data.shape[0] != self.obs.shape[0]:
+            raise Exception("data and obs are not alligned")
+        if self.data.shape[1] != self.var.shape[0]:
+            raise Exception("data and var are not alligned")
+
+    def __set_names_and_size(self):
+        """Set shape and indexes"""
+        self.var_names = self.data.columns
+        self.obs_names = self.data.index
+        self.shape = self.data.shape
+
     def __init__(self, data, obs=None, var=None) -> None:
 
         if type(data) == MetaTable:
             self.data = data.data
             self.obs = data.obs
             self.var = data.var
-            self.var_names = data.var_names
-            self.obs_names = data.obs_names
-            return
 
-        self.data = pd.DataFrame(data)
+        elif is_anndata(data):
+            self.data = data.to_df()
+            self.obs = data.obs
+            self.var = data.var
 
-        assert self.data.shape[0] > 0, "data is empty"
-        assert self.data.index.is_unique, "data has duplicate indices"
-        assert self.data.columns.is_unique, "data has duplicate columns"
+        elif type(data) == pd.DataFrame:
+            # parse data
+            assert data.shape[0] > 0, "data is empty"
+            assert data.index.is_unique, "data has duplicate indices"
+            assert data.columns.is_unique, "data has duplicate columns"
 
-        if obs is None:
-            self.obs = pd.DataFrame(index=self.data.index)
-        else:
-            self.obs = pd.DataFrame(obs)
-            assert self.obs.index.is_unique, "obs has duplicate indices"
+            self.data = data
+            set_string_indexes(self.data)
 
-            # calculate intersection of obs and data
-            intersection = self.obs.index.intersection(self.data.index)
-            self.data = self.data.loc[intersection].copy()
-            self.obs = self.obs.loc[intersection].copy()
+            # parse obs
 
-        if var is None:
-            self.var = pd.DataFrame(index=self.data.columns)
-        else:
-            self.var = pd.DataFrame(var)
+            if obs is None:
+                self.obs = pd.DataFrame(index=self.data.index)
+            elif type(obs) == pd.DataFrame:
 
-            assert self.var.index.is_unique, "var has duplicate indices"
+                assert obs.index.is_unique, "obs has duplicate indices"
+                self.obs = obs
+                set_string_indexes(obs)
 
-            intersection = self.var.index.intersection(self.data.columns)
-            self.data = self.data.loc[:, intersection].copy()
-            self.var = self.var.loc[intersection].copy()
+                if self.obs.shape[0] != self.data.shape[0]:
+                    self.obs = self.obs.loc[self.data.index].copy()
 
-        self.var_names = self.data.columns
-        self.obs_names = self.data.index
+            else:
+                raise AttributeError("`obs` should be of type DataFrame or None")
+
+            # parse var
+
+            if var is None:
+                self.var = pd.DataFrame(index=self.data.columns)
+            elif type(var) == pd.DataFrame:
+
+                assert var.index.is_unique, "var has duplicate indices"
+                self.var = var
+                set_string_indexes(var)
+
+                if self.var.shape[0] != self.data.shape[0]:
+                    self.var = self.var.loc[self.data.columns].copy()
+
+            else:
+                raise AttributeError("`var` should be of type DataFrame or None")
+
+            self.__check_consistency()
+
+        else:
+            raise AttributeError("`data` needst to be one of [pandas.DataFrame,  ")
+
+        # other attributes commmon to all
+        self.__set_names_and_size()
+
+        # link functions from self data to self
+        functions_to_link = ["mean", "median", "sum", "std"]
+        for f in functions_to_link:
+            setattr(self, f, getattr(self.data, f))
+
+    def subset(self, index=None, columns=None):
+        assert not (
+            (index is None) and (columns is None)
+        ), "either indexes or columns needs to be given"
+
+        # fill indexes if None
+        if columns is None:
+            columns = self.var_names
+        elif index is None:
+            index = self.obs_names
+
+        return MetaTable(
+            data=self.data.loc[index, columns],
+            obs=self.obs.loc[index],
+            var=self.var.loc[columns],
+        )
 
     def groupby(self, groupby, axis=0):
         if axis == 0:
-            if type(groupby) == str:
-                assert groupby in self.obs.columns, "Groupby column not found in obs"
-                groupby = self.obs[groupby]
+            G = self.obs.groupby(groupby)
 
-            return self.data.groupby(groupby, axis=axis)
+            for group in G.indices:
+                yield (group, self.subset(index=self.obs_names[G.indices[group]]))
 
         elif axis == 1:
-            if type(groupby) == str:
-                assert groupby in self.var.columns, "Groupby column not found in var"
-                groupby = self.var[groupby]
-
-            return self.data.groupby(groupby, axis=axis)
+            # Group by on axis 0. var indexes contain data.columns
+            G = self.var.groupby(groupby)
+            for group in G.indices:
+                yield group, self.subset(columns=self.var_names[G.indices[group]])
+        else:
+            raise Exception("axis should be 1 or 2")
 
     def __repr__(self):
-        value = f"MetaTable with {self.data.shape[0]} samples x {self.data.shape[1]} features\n"
+        value = f"MetaTable with {self.shape[0]} samples x {self.shape[1]} features\n"
         f"Sample annotations: {list(self.obs.columns)}\n"
         f"Feature annotations: {list(self.var.columns)} "
         return value
@@ -140,10 +207,6 @@ def __init__(
         else:
             self.order_grouping, self.grouping_variable = None, None
 
-        self.__calculate_stats__(
-            test=test, test_kws=test_kws, comparisons=comparisons, ref_group=ref_group
-        )
-
         self.data_unit = data_unit
 
         if label_variable is None:
@@ -171,6 +234,10 @@ def __init__(
                 "Your labels are not unique. but I should be able to handle this."
             )
 
+        self.__calculate_stats__(
+            test=test, test_kws=test_kws, comparisons=comparisons, ref_group=ref_group
+        )
+
     def __repr__(self) -> str:
         annadata_str = super().__repr__()
         annadata_str += f"\n    test_variable: {self.test_variable.name} with groups {self.order_test}  "
@@ -188,8 +255,8 @@ def __apply_to_subsets(self, function, **kws):
 
         results = {}
 
-        for subset, subset_data in self.groupby(self.grouping_variable):
-            results[subset] = function(subset_data, **kws)
+        for subset, subset_metatable in self.groupby(self.grouping_variable):
+            results[subset] = function(subset_metatable.data, **kws)
         return results
 
     def __calculate_stats__(
@@ -228,14 +295,35 @@ def __calculate_stats__(
             results.columns = results.columns.swaplevel(0, 1)
             results.sort_index(axis=1, inplace=True)
 
-        self.stats = results.astype(float)
+        ## Add description to stats
+
+        description = self.var.copy()
+
+        if len(results.columns.levshape) == 3:
+
+            description.columns = pd.MultiIndex.from_arrays(
+                [
+                    ["Description"] * description.shape[1],
+                    ["All"] * description.shape[1],
+                    description.columns,
+                ]
+            )
+        elif len(results.columns.levshape) == 2:
+            description.columns = pd.MultiIndex.from_arrays(
+                [["Description"] * description.shape[1], description.columns]
+            )
+
+        self.stats = results.astype(float).join(description)
 
     def plot(
         self,
         variable,
+        show_dots=True,
         distance_between_sig_labels="auto",
         box_params=None,
         swarm_params=None,
+        corrected_pvalues=False,
+        show_not_significant=False,
         ax=None,
         **labelkws,
     ):
@@ -245,17 +333,24 @@ def plot(
         if ax is None:
             ax = plt.subplot(111)
 
+        if corrected_pvalues:
+            p_value_name = "pBH"
+        else:
+            p_value_name = "Pvalue"
+
         statsplot(
             self.data[variable],
             self.test_variable,
             order_test=self.order_test,
             grouping_variable=self.grouping_variable,
             order_grouping=self.order_grouping,
+            show_dots=show_dots,
             box_params=box_params,
             swarm_params=swarm_params,
+            show_not_significant=show_not_significant,
             labelkws=labelkws,
             palette=self.colors,
-            p_values=self.stats.Pvalue.loc[variable].T,
+            p_values=self.stats[p_value_name].loc[variable].T,
             ax=ax,
         )
 
@@ -271,11 +366,13 @@ def __get_groups(self, subset=None):
             return None
         else:
 
-            all_groups = list(self.stats.columns.levels[-2])
+            all_groups = list(self.stats.Pvalue.columns.get_level_values(-2).unique())
             if subset is None:
 
                 return all_groups
             elif type(subset) == str:
+                assert subset in all_groups, f"{g} is not in the Groups"
+
                 return [subset]
             else:
                 for g in subset:
@@ -287,11 +384,12 @@ def __get_comparisons(self, subset=None):
         "Check if given subset are in comparisons of statstable"
         "Otherwise return all in a row, if not defined return None"
 
-        all_comparisons = list(self.stats.columns.levels[-1])
+        all_comparisons = list(self.stats.Pvalue.columns.get_level_values(-1).unique())
         if subset is None:
 
             return all_comparisons
         elif type(subset) == str:
+            assert subset in all_comparisons, f"{g} is not in the Comparisons"
             return [subset]
         else:
             for g in subset:
@@ -299,31 +397,43 @@ def __get_comparisons(self, subset=None):
 
             return list(subset)
 
+    # TODO: Hide output axes labesls
     def vulcanoplot(
         self,
         comparisons=None,
         groups=None,
-        corrected_pvalues=True,
+        corrected_pvalues=False,
         threshold_p=None,
         hue=None,
+        figsize=(6, 6),
+        label_points="auto",
+        max_labels=5,
+        effect_label=None,
+        pvalue_label=None,
         **kws,
     ):
 
         if "log2FC" in self.stats.columns:
             effect_name = "log2FC"
-            y_label = "$\log_2FC$"
+            x_label = "$\log_2FC$"
 
         else:
             effect_name = "median_diff"
-            y_label = "median difference"
+            x_label = "median difference"
             logger.info("Don't have log2FC in stats, using median_diff for vulcanoplot")
 
+        if effect_label is not None:
+            x_label = effect_label
+
         def rename_vulcano_axis_labels():
             ax = plt.gca()
-            ax.set_xlabel(y_label)
+            ax.set_xlabel(x_label)
 
-            if corrected_pvalues:
+            if pvalue_label is not None:
+                ax.set_ylabel(pvalue_label)
+            elif corrected_pvalues:
                 ax.set_ylabel("$-\log(P_{BH})$")
+            # ellse default label from vulcano plot
 
         groups = self.__get_groups(groups)
 
@@ -348,6 +458,11 @@ def rename_vulcano_axis_labels():
         kws["hue"] = hue
         kws["labels"] = self.labels
 
+        # map kws to vulcanoplot
+        kws["label_points"] = label_points
+        kws["max_labels"] = max_labels
+        kws["figsize"] = figsize
+
         if groups is not None:
 
             for g in groups:
@@ -374,3 +489,5 @@ def rename_vulcano_axis_labels():
                 )
                 rename_vulcano_axis_labels()
                 axes.append(plt.gca())
+
+        return axes
diff --git a/statsplot/transformations.py b/statsplot/transformations.py
index 7b89db9..752c0d9 100644
--- a/statsplot/transformations.py
+++ b/statsplot/transformations.py
@@ -1,19 +1,114 @@
-try:
-    from skbio.stats import composition
-except ImportError as e:
-    raise Exception(
-        "'scikit-bio' is required for this sub-package. Install id with pip or conda"
-    ) from e
-
-
 from numpy import log
 import pandas as pd
+import numpy as np
+
+
+# copied from scikit-bio
+# because I cannot install it
+def closure(mat):
+    """
+    Performs closure to ensure that all elements add up to 1.
+    Parameters
+    ----------
+    mat : array_like
+       a matrix of proportions where
+       rows = compositions
+       columns = components
+    Returns
+    -------
+    array_like, np.float64
+       A matrix of proportions where all of the values
+       are nonzero and each composition (row) adds up to 1
+    Raises
+    ------
+    ValueError
+       Raises an error if any values are negative.
+    ValueError
+       Raises an error if the matrix has more than 2 dimension.
+    ValueError
+       Raises an error if there is a row that has all zeros.
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skbio.stats.composition import closure
+    >>> X = np.array([[2, 2, 6], [4, 4, 2]])
+    >>> closure(X)
+    array([[ 0.2,  0.2,  0.6],
+           [ 0.4,  0.4,  0.2]])
+    """
+    mat = np.atleast_2d(mat)
+    if np.any(mat < 0):
+        raise ValueError("Cannot have negative proportions")
+    if mat.ndim > 2:
+        raise ValueError("Input matrix can only have two dimensions or less")
+    if np.all(mat == 0, axis=1).sum() > 0:
+        raise ValueError("Input matrix cannot have rows with all zeros")
+    mat = mat / mat.sum(axis=1, keepdims=True)
+    return mat.squeeze()
+
+
+def multiplicative_replacement(mat, delta=None):
+    r"""Replace all zeros with small non-zero values
+    It uses the multiplicative replacement strategy [1]_ ,
+    replacing zeros with a small positive :math:`\delta`
+    and ensuring that the compositions still add up to 1.
+    Parameters
+    ----------
+    mat: array_like
+       a matrix of proportions where
+       rows = compositions and
+       columns = components
+    delta: float, optional
+       a small number to be used to replace zeros
+       If delta is not specified, then the default delta is
+       :math:`\delta = \frac{1}{N^2}` where :math:`N`
+       is the number of components
+    Returns
+    -------
+    numpy.ndarray, np.float64
+       A matrix of proportions where all of the values
+       are nonzero and each composition (row) adds up to 1
+    Raises
+    ------
+    ValueError
+       Raises an error if negative proportions are created due to a large
+       `delta`.
+    Notes
+    -----
+    This method will result in negative proportions if a large delta is chosen.
+    References
+    ----------
+    .. [1] J. A. Martin-Fernandez. "Dealing With Zeros and Missing Values in
+           Compositional Data Sets Using Nonparametric Imputation"
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skbio.stats.composition import multiplicative_replacement
+    >>> X = np.array([[.2,.4,.4, 0],[0,.5,.5,0]])
+    >>> multiplicative_replacement(X)
+    array([[ 0.1875,  0.375 ,  0.375 ,  0.0625],
+           [ 0.0625,  0.4375,  0.4375,  0.0625]])
+    """
+    mat = closure(mat)
+    z_mat = mat == 0
+
+    num_feats = mat.shape[-1]
+    tot = z_mat.sum(axis=-1, keepdims=True)
 
+    if delta is None:
+        delta = (1.0 / num_feats) ** 2
 
-from typing import Union
+    zcnts = 1 - tot * delta
+    if np.any(zcnts) < 0:
+        raise ValueError(
+            "The multiplicative replacement created negative "
+            "proportions. Consider using a smaller `delta`."
+        )
+    mat = np.where(z_mat, delta, zcnts * mat)
+    return mat.squeeze()
 
 
-def clr(data: pd.DataFrame, log=log):
+def clr(data: pd.DataFrame, log=log, features="all"):
     """
     Centered log ratio (CLR) with multiplicative replacement implemented in scikit-bio
     """
@@ -29,13 +124,29 @@ def clr(data: pd.DataFrame, log=log):
         raise Exception("data must be a pandas.DataFrame")
 
     # Fill in zeros with multiplicative replacement
-    matrix = composition.multiplicative_replacement(matrix)
+    matrix = multiplicative_replacement(matrix)
+
+    matrix = pd.DataFrame(matrix, index=d.index, columns=d.columns)
 
     # CLR
     matrix = log(matrix)
 
-    matrix = (matrix.T - matrix.mean(1)).T
+    # Center
+    if features.lower() == "all":
 
-    if type(data) == pd.DataFrame:
+        mean = matrix.mean(1)
+
+    elif features.lower() == "nz":
+
+        mean = matrix[matrix != 0].mean(1)
+    elif features.lower() == "iql":
+        # use mean of features in interquartile range
+        q1 = matrix.quantile(0.25, axis=1)
+        q3 = matrix.quantile(0.75, axis=1)
+        mean = matrix[(matrix > q1) & (matrix < q3)].mean(1)
+    else:
+        raise Exception("features must be 'all', 'nz', or 'iql'")
+
+    matrix = (matrix.T - mean).T
 
-        return pd.DataFrame(matrix, index=d.index, columns=d.columns)
+    return matrix
diff --git a/test/baseline_images/grouped_dots_default.png b/test/baseline_images/grouped_dots_default.png
new file mode 100644
index 0000000..6483300
Binary files /dev/null and b/test/baseline_images/grouped_dots_default.png differ
diff --git a/test/baseline_images/grouped_dots_partial_swarm.png b/test/baseline_images/grouped_dots_partial_swarm.png
new file mode 100644
index 0000000..49752d6
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diff --git a/test/baseline_images/ungrouped_dots.png b/test/baseline_images/ungrouped_dots.png
new file mode 100644
index 0000000..410edd0
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diff --git a/test/conftest.py b/test/conftest.py
new file mode 100644
index 0000000..110f0b8
--- /dev/null
+++ b/test/conftest.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+
+
+# CI-safe, headless backend for deterministic rendering.
+matplotlib.use("Agg")
+
+
+@pytest.fixture
+def synthetic_data() -> pd.DataFrame:
+    rng = np.random.default_rng(7)
+
+    n = 20
+    rows = []
+    for group_bin, offset in (("G1", 0.0), ("G2", 0.35)):
+        for test_group, mean in (("A", 0.0 + offset), ("B", 0.9 + offset)):
+            values = rng.normal(loc=mean, scale=0.25, size=n)
+            rows.extend(
+                {
+                    "value": float(v),
+                    "test_group": test_group,
+                    "group_bin": group_bin,
+                }
+                for v in values
+            )
+
+    data = pd.DataFrame(rows)
+
+    # Inject explicit outliers so showfliers behavior is testable.
+    outliers = pd.DataFrame(
+        [
+            {"value": 4.2, "test_group": "A", "group_bin": "G1"},
+            {"value": -3.4, "test_group": "B", "group_bin": "G1"},
+            {"value": 4.8, "test_group": "A", "group_bin": "G2"},
+            {"value": -3.9, "test_group": "B", "group_bin": "G2"},
+        ]
+    )
+    return pd.concat([data, outliers], ignore_index=True)
+
+
+@pytest.fixture
+def p_values_ungrouped() -> pd.Series:
+    return pd.Series({"B_vs_A": 0.01}, name="value")
+
+
+@pytest.fixture
+def p_values_grouped() -> pd.Series:
+    idx = pd.MultiIndex.from_tuples(
+        [("G1", "B_vs_A"), ("G2", "B_vs_A")],
+        names=["group_bin", "comparison"],
+    )
+    return pd.Series([0.01, 0.02], index=idx, name="value")
diff --git a/test/data/micobiota_metadata.tsv.gz b/test/data/micobiota_metadata.tsv.gz
new file mode 100644
index 0000000..a71ed95
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diff --git a/test/data/micobiota_relab.tsv.gz b/test/data/micobiota_relab.tsv.gz
new file mode 100644
index 0000000..9b5f93a
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diff --git a/test/data/micobiota_taxonomy.tsv.gz b/test/data/micobiota_taxonomy.tsv.gz
new file mode 100644
index 0000000..65793e6
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diff --git a/test/test_statsplot_matrix.py b/test/test_statsplot_matrix.py
new file mode 100644
index 0000000..4509729
--- /dev/null
+++ b/test/test_statsplot_matrix.py
@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import pandas as pd
+import pytest
+import seaborn as sns
+from matplotlib.axes import Axes
+
+from statsplot import statsplot
+
+
+@pytest.mark.parametrize(
+    "case",
+    [
+        {
+            "name": "ungrouped_no_dots",
+            "grouped": False,
+            "show_dots": False,
+            "swarm_params": None,
+        },
+        {
+            "name": "ungrouped_with_dots",
+            "grouped": False,
+            "show_dots": True,
+            "swarm_params": None,
+        },
+        {
+            "name": "grouped_no_dots",
+            "grouped": True,
+            "show_dots": False,
+            "swarm_params": None,
+        },
+        {
+            "name": "grouped_with_dots_default",
+            "grouped": True,
+            "show_dots": True,
+            "swarm_params": None,
+        },
+        {
+            "name": "grouped_with_dots_dodge_only",
+            "grouped": True,
+            "show_dots": True,
+            "swarm_params": {"dodge": True},
+        },
+        {
+            "name": "grouped_with_dots_partial_swarm",
+            "grouped": True,
+            "show_dots": True,
+            "swarm_params": {"size": 2},
+        },
+        {
+            "name": "grouped_with_dots_override_dodge_false",
+            "grouped": True,
+            "show_dots": True,
+            "swarm_params": {"dodge": False},
+        },
+    ],
+    ids=lambda c: c["name"],
+)
+def test_statsplot_behavior_matrix(
+    case,
+    synthetic_data: pd.DataFrame,
+    p_values_ungrouped: pd.Series,
+    p_values_grouped: pd.Series,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    captured: dict[str, dict] = {}
+
+    original_boxplot = sns.boxplot
+    original_swarmplot = sns.swarmplot
+
+    def boxplot_spy(*args, **kwargs):
+        captured["box"] = dict(kwargs)
+        return original_boxplot(*args, **kwargs)
+
+    def swarmplot_spy(*args, **kwargs):
+        captured["swarm"] = dict(kwargs)
+        return original_swarmplot(*args, **kwargs)
+
+    monkeypatch.setattr(sns, "boxplot", boxplot_spy)
+    monkeypatch.setattr(sns, "swarmplot", swarmplot_spy)
+
+    grouped = case["grouped"]
+    show_dots = case["show_dots"]
+
+    p_values = p_values_grouped if grouped else p_values_ungrouped
+    grouping_variable = "group_bin" if grouped else None
+    order_grouping = ["G1", "G2"] if grouped else None
+
+    ax, returned_p_values = statsplot(
+        variable="value",
+        test_variable="test_group",
+        data=synthetic_data,
+        order_test=["A", "B"],
+        grouping_variable=grouping_variable,
+        order_grouping=order_grouping,
+        show_dots=show_dots,
+        swarm_params=case["swarm_params"],
+        p_values=p_values,
+    )
+
+    # Smoke checks: all matrix rows should execute and return axis + p-values.
+    assert isinstance(ax, Axes)
+    assert isinstance(returned_p_values, pd.Series)
+
+    box_kwargs = captured["box"]
+    if show_dots:
+        assert box_kwargs["showfliers"] is False
+        assert box_kwargs["fliersize"] == 0
+    else:
+        assert "showfliers" not in box_kwargs
+        assert "fliersize" not in box_kwargs
+
+    if not show_dots:
+        assert "swarm" not in captured
+        return
+
+    swarm_kwargs = captured["swarm"]
+
+    if not grouped:
+        # No-grouping path should not require grouped hue/dodge defaults.
+        assert "dodge" not in swarm_kwargs
+        assert "hue" not in swarm_kwargs
+        assert "hue_order" not in swarm_kwargs
+        assert swarm_kwargs.get("color") == "k"
+        return
+
+    # Grouped + dots path should use dodge defaults unless explicitly overridden.
+    if case["swarm_params"] == {"dodge": False}:
+        assert swarm_kwargs["dodge"] is False
+    else:
+        assert swarm_kwargs["dodge"] is True
+
+    # Grouped path should preserve hue defaults even with partial swarm kwargs.
+    assert "hue" in swarm_kwargs
+    assert set(pd.unique(swarm_kwargs["hue"])) == {"A", "B"}
+    assert swarm_kwargs["hue_order"] == ["A", "B"]
+    assert swarm_kwargs["palette"] == "dark:k"
+
+    if case["swarm_params"] == {"size": 2}:
+        assert swarm_kwargs["size"] == 2
diff --git a/test/test_statsplot_mpl.py b/test/test_statsplot_mpl.py
new file mode 100644
index 0000000..47b33fc
--- /dev/null
+++ b/test/test_statsplot_mpl.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from matplotlib.testing.compare import compare_images
+
+from statsplot import statsplot
+
+
+BASELINE_DIR = Path(__file__).parent / "baseline_images"
+
+
+def _assert_matches_baseline(fig, baseline_name: str, tolerance: float = 2.0):
+    actual_dir = Path(__file__).parent / "_actual"
+    actual_dir.mkdir(exist_ok=True)
+
+    actual_path = actual_dir / baseline_name
+    fig.savefig(actual_path, dpi=100, bbox_inches="tight")
+
+    expected_path = BASELINE_DIR / baseline_name
+    assert expected_path.exists(), f"Missing baseline image: {expected_path}"
+
+    result = compare_images(str(expected_path), str(actual_path), tol=tolerance)
+    assert result is None, result
+
+
+def test_mpl_grouped_dots_default(synthetic_data, p_values_grouped):
+    fig, ax = plt.subplots(figsize=(7, 4), dpi=100)
+    statsplot(
+        variable="value",
+        test_variable="test_group",
+        data=synthetic_data,
+        order_test=["A", "B"],
+        grouping_variable="group_bin",
+        order_grouping=["G1", "G2"],
+        show_dots=True,
+        p_values=p_values_grouped,
+        ax=ax,
+    )
+    _assert_matches_baseline(fig, "grouped_dots_default.png")
+    plt.close(fig)
+
+
+def test_mpl_grouped_dots_partial_swarm_params(synthetic_data, p_values_grouped):
+    fig, ax = plt.subplots(figsize=(7, 4), dpi=100)
+    statsplot(
+        variable="value",
+        test_variable="test_group",
+        data=synthetic_data,
+        order_test=["A", "B"],
+        grouping_variable="group_bin",
+        order_grouping=["G1", "G2"],
+        show_dots=True,
+        swarm_params={"size": 2},
+        p_values=p_values_grouped,
+        ax=ax,
+    )
+    _assert_matches_baseline(fig, "grouped_dots_partial_swarm.png")
+    plt.close(fig)
+
+
+def test_mpl_ungrouped_dots(synthetic_data, p_values_ungrouped):
+    fig, ax = plt.subplots(figsize=(7, 4), dpi=100)
+    statsplot(
+        variable="value",
+        test_variable="test_group",
+        data=synthetic_data,
+        order_test=["A", "B"],
+        show_dots=True,
+        p_values=p_values_ungrouped,
+        ax=ax,
+    )
+    _assert_matches_baseline(fig, "ungrouped_dots.png")
+    plt.close(fig)
diff --git a/versioneer.py b/versioneer.py
new file mode 100644
index 0000000..18e34c2
--- /dev/null
+++ b/versioneer.py
@@ -0,0 +1,2205 @@
+
+# Version: 0.28
+
+"""The Versioneer - like a rocketeer, but for versions.
+
+The Versioneer
+==============
+
+* like a rocketeer, but for versions!
+* https://github.com/python-versioneer/python-versioneer
+* Brian Warner
+* License: Public Domain (Unlicense)
+* Compatible with: Python 3.7, 3.8, 3.9, 3.10 and pypy3
+* [![Latest Version][pypi-image]][pypi-url]
+* [![Build Status][travis-image]][travis-url]
+
+This is a tool for managing a recorded version number in setuptools-based
+python projects. The goal is to remove the tedious and error-prone "update
+the embedded version string" step from your release process. Making a new
+release should be as easy as recording a new tag in your version-control
+system, and maybe making new tarballs.
+
+
+## Quick Install
+
+Versioneer provides two installation modes. The "classic" vendored mode installs
+a copy of versioneer into your repository. The experimental build-time dependency mode
+is intended to allow you to skip this step and simplify the process of upgrading.
+
+### Vendored mode
+
+* `pip install versioneer` to somewhere in your $PATH
+   * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is
+     available, so you can also use `conda install -c conda-forge versioneer`
+* add a `[tool.versioneer]` section to your `pyproject.toml` or a
+  `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md))
+   * Note that you will need to add `tomli; python_version < "3.11"` to your
+     build-time dependencies if you use `pyproject.toml`
+* run `versioneer install --vendor` in your source tree, commit the results
+* verify version information with `python setup.py version`
+
+### Build-time dependency mode
+
+* `pip install versioneer` to somewhere in your $PATH
+   * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is
+     available, so you can also use `conda install -c conda-forge versioneer`
+* add a `[tool.versioneer]` section to your `pyproject.toml` or a
+  `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md))
+* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`)
+  to the `requires` key of the `build-system` table in `pyproject.toml`:
+  ```toml
+  [build-system]
+  requires = ["setuptools", "versioneer[toml]"]
+  build-backend = "setuptools.build_meta"
+  ```
+* run `versioneer install --no-vendor` in your source tree, commit the results
+* verify version information with `python setup.py version`
+
+## Version Identifiers
+
+Source trees come from a variety of places:
+
+* a version-control system checkout (mostly used by developers)
+* a nightly tarball, produced by build automation
+* a snapshot tarball, produced by a web-based VCS browser, like github's
+  "tarball from tag" feature
+* a release tarball, produced by "setup.py sdist", distributed through PyPI
+
+Within each source tree, the version identifier (either a string or a number,
+this tool is format-agnostic) can come from a variety of places:
+
+* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
+  about recent "tags" and an absolute revision-id
+* the name of the directory into which the tarball was unpacked
+* an expanded VCS keyword ($Id$, etc)
+* a `_version.py` created by some earlier build step
+
+For released software, the version identifier is closely related to a VCS
+tag. Some projects use tag names that include more than just the version
+string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
+needs to strip the tag prefix to extract the version identifier. For
+unreleased software (between tags), the version identifier should provide
+enough information to help developers recreate the same tree, while also
+giving them an idea of roughly how old the tree is (after version 1.2, before
+version 1.3). Many VCS systems can report a description that captures this,
+for example `git describe --tags --dirty --always` reports things like
+"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
+0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
+uncommitted changes).
+
+The version identifier is used for multiple purposes:
+
+* to allow the module to self-identify its version: `myproject.__version__`
+* to choose a name and prefix for a 'setup.py sdist' tarball
+
+## Theory of Operation
+
+Versioneer works by adding a special `_version.py` file into your source
+tree, where your `__init__.py` can import it. This `_version.py` knows how to
+dynamically ask the VCS tool for version information at import time.
+
+`_version.py` also contains `$Revision$` markers, and the installation
+process marks `_version.py` to have this marker rewritten with a tag name
+during the `git archive` command. As a result, generated tarballs will
+contain enough information to get the proper version.
+
+To allow `setup.py` to compute a version too, a `versioneer.py` is added to
+the top level of your source tree, next to `setup.py` and the `setup.cfg`
+that configures it. This overrides several distutils/setuptools commands to
+compute the version when invoked, and changes `setup.py build` and `setup.py
+sdist` to replace `_version.py` with a small static file that contains just
+the generated version data.
+
+## Installation
+
+See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
+
+## Version-String Flavors
+
+Code which uses Versioneer can learn about its version string at runtime by
+importing `_version` from your main `__init__.py` file and running the
+`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
+import the top-level `versioneer.py` and run `get_versions()`.
+
+Both functions return a dictionary with different flavors of version
+information:
+
+* `['version']`: A condensed version string, rendered using the selected
+  style. This is the most commonly used value for the project's version
+  string. The default "pep440" style yields strings like `0.11`,
+  `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
+  below for alternative styles.
+
+* `['full-revisionid']`: detailed revision identifier. For Git, this is the
+  full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
+
+* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
+  commit date in ISO 8601 format. This will be None if the date is not
+  available.
+
+* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
+  this is only accurate if run in a VCS checkout, otherwise it is likely to
+  be False or None
+
+* `['error']`: if the version string could not be computed, this will be set
+  to a string describing the problem, otherwise it will be None. It may be
+  useful to throw an exception in setup.py if this is set, to avoid e.g.
+  creating tarballs with a version string of "unknown".
+
+Some variants are more useful than others. Including `full-revisionid` in a
+bug report should allow developers to reconstruct the exact code being tested
+(or indicate the presence of local changes that should be shared with the
+developers). `version` is suitable for display in an "about" box or a CLI
+`--version` output: it can be easily compared against release notes and lists
+of bugs fixed in various releases.
+
+The installer adds the following text to your `__init__.py` to place a basic
+version in `YOURPROJECT.__version__`:
+
+    from ._version import get_versions
+    __version__ = get_versions()['version']
+    del get_versions
+
+## Styles
+
+The setup.cfg `style=` configuration controls how the VCS information is
+rendered into a version string.
+
+The default style, "pep440", produces a PEP440-compliant string, equal to the
+un-prefixed tag name for actual releases, and containing an additional "local
+version" section with more detail for in-between builds. For Git, this is
+TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
+--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
+tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
+that this commit is two revisions ("+2") beyond the "0.11" tag. For released
+software (exactly equal to a known tag), the identifier will only contain the
+stripped tag, e.g. "0.11".
+
+Other styles are available. See [details.md](details.md) in the Versioneer
+source tree for descriptions.
+
+## Debugging
+
+Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
+to return a version of "0+unknown". To investigate the problem, run `setup.py
+version`, which will run the version-lookup code in a verbose mode, and will
+display the full contents of `get_versions()` (including the `error` string,
+which may help identify what went wrong).
+
+## Known Limitations
+
+Some situations are known to cause problems for Versioneer. This details the
+most significant ones. More can be found on Github
+[issues page](https://github.com/python-versioneer/python-versioneer/issues).
+
+### Subprojects
+
+Versioneer has limited support for source trees in which `setup.py` is not in
+the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
+two common reasons why `setup.py` might not be in the root:
+
+* Source trees which contain multiple subprojects, such as
+  [Buildbot](https://github.com/buildbot/buildbot), which contains both
+  "master" and "slave" subprojects, each with their own `setup.py`,
+  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
+  distributions (and upload multiple independently-installable tarballs).
+* Source trees whose main purpose is to contain a C library, but which also
+  provide bindings to Python (and perhaps other languages) in subdirectories.
+
+Versioneer will look for `.git` in parent directories, and most operations
+should get the right version string. However `pip` and `setuptools` have bugs
+and implementation details which frequently cause `pip install .` from a
+subproject directory to fail to find a correct version string (so it usually
+defaults to `0+unknown`).
+
+`pip install --editable .` should work correctly. `setup.py install` might
+work too.
+
+Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
+some later version.
+
+[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
+this issue. The discussion in
+[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
+issue from the Versioneer side in more detail.
+[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
+[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
+pip to let Versioneer work correctly.
+
+Versioneer-0.16 and earlier only looked for a `.git` directory next to the
+`setup.cfg`, so subprojects were completely unsupported with those releases.
+
+### Editable installs with setuptools <= 18.5
+
+`setup.py develop` and `pip install --editable .` allow you to install a
+project into a virtualenv once, then continue editing the source code (and
+test) without re-installing after every change.
+
+"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
+convenient way to specify executable scripts that should be installed along
+with the python package.
+
+These both work as expected when using modern setuptools. When using
+setuptools-18.5 or earlier, however, certain operations will cause
+`pkg_resources.DistributionNotFound` errors when running the entrypoint
+script, which must be resolved by re-installing the package. This happens
+when the install happens with one version, then the egg_info data is
+regenerated while a different version is checked out. Many setup.py commands
+cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
+a different virtualenv), so this can be surprising.
+
+[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
+this one, but upgrading to a newer version of setuptools should probably
+resolve it.
+
+
+## Updating Versioneer
+
+To upgrade your project to a new release of Versioneer, do the following:
+
+* install the new Versioneer (`pip install -U versioneer` or equivalent)
+* edit `setup.cfg` and `pyproject.toml`, if necessary,
+  to include any new configuration settings indicated by the release notes.
+  See [UPGRADING](./UPGRADING.md) for details.
+* re-run `versioneer install --[no-]vendor` in your source tree, to replace
+  `SRC/_version.py`
+* commit any changed files
+
+## Future Directions
+
+This tool is designed to make it easily extended to other version-control
+systems: all VCS-specific components are in separate directories like
+src/git/ . The top-level `versioneer.py` script is assembled from these
+components by running make-versioneer.py . In the future, make-versioneer.py
+will take a VCS name as an argument, and will construct a version of
+`versioneer.py` that is specific to the given VCS. It might also take the
+configuration arguments that are currently provided manually during
+installation by editing setup.py . Alternatively, it might go the other
+direction and include code from all supported VCS systems, reducing the
+number of intermediate scripts.
+
+## Similar projects
+
+* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
+  dependency
+* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
+  versioneer
+* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools
+  plugin
+
+## License
+
+To make Versioneer easier to embed, all its code is dedicated to the public
+domain. The `_version.py` that it creates is also in the public domain.
+Specifically, both are released under the "Unlicense", as described in
+https://unlicense.org/.
+
+[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
+[pypi-url]: https://pypi.python.org/pypi/versioneer/
+[travis-image]:
+https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
+[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
+
+"""
+# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring
+# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements
+# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error
+# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with
+# pylint:disable=attribute-defined-outside-init,too-many-arguments
+
+import configparser
+import errno
+import json
+import os
+import re
+import subprocess
+import sys
+from pathlib import Path
+from typing import Callable, Dict
+import functools
+
+have_tomllib = True
+if sys.version_info >= (3, 11):
+    import tomllib
+else:
+    try:
+        import tomli as tomllib
+    except ImportError:
+        have_tomllib = False
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_root():
+    """Get the project root directory.
+
+    We require that all commands are run from the project root, i.e. the
+    directory that contains setup.py, setup.cfg, and versioneer.py .
+    """
+    root = os.path.realpath(os.path.abspath(os.getcwd()))
+    setup_py = os.path.join(root, "setup.py")
+    versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        # allow 'python path/to/setup.py COMMAND'
+        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
+        setup_py = os.path.join(root, "setup.py")
+        versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        err = ("Versioneer was unable to run the project root directory. "
+               "Versioneer requires setup.py to be executed from "
+               "its immediate directory (like 'python setup.py COMMAND'), "
+               "or in a way that lets it use sys.argv[0] to find the root "
+               "(like 'python path/to/setup.py COMMAND').")
+        raise VersioneerBadRootError(err)
+    try:
+        # Certain runtime workflows (setup.py install/develop in a setuptools
+        # tree) execute all dependencies in a single python process, so
+        # "versioneer" may be imported multiple times, and python's shared
+        # module-import table will cache the first one. So we can't use
+        # os.path.dirname(__file__), as that will find whichever
+        # versioneer.py was first imported, even in later projects.
+        my_path = os.path.realpath(os.path.abspath(__file__))
+        me_dir = os.path.normcase(os.path.splitext(my_path)[0])
+        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
+        if me_dir != vsr_dir and "VERSIONEER_PEP518" not in globals():
+            print("Warning: build in %s is using versioneer.py from %s"
+                  % (os.path.dirname(my_path), versioneer_py))
+    except NameError:
+        pass
+    return root
+
+
+def get_config_from_root(root):
+    """Read the project setup.cfg file to determine Versioneer config."""
+    # This might raise OSError (if setup.cfg is missing), or
+    # configparser.NoSectionError (if it lacks a [versioneer] section), or
+    # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
+    # the top of versioneer.py for instructions on writing your setup.cfg .
+    root = Path(root)
+    pyproject_toml = root / "pyproject.toml"
+    setup_cfg = root / "setup.cfg"
+    section = None
+    if pyproject_toml.exists() and have_tomllib:
+        try:
+            with open(pyproject_toml, 'rb') as fobj:
+                pp = tomllib.load(fobj)
+            section = pp['tool']['versioneer']
+        except (tomllib.TOMLDecodeError, KeyError):
+            pass
+    if not section:
+        parser = configparser.ConfigParser()
+        with open(setup_cfg) as cfg_file:
+            parser.read_file(cfg_file)
+        parser.get("versioneer", "VCS")  # raise error if missing
+
+        section = parser["versioneer"]
+
+    cfg = VersioneerConfig()
+    cfg.VCS = section['VCS']
+    cfg.style = section.get("style", "")
+    cfg.versionfile_source = section.get("versionfile_source")
+    cfg.versionfile_build = section.get("versionfile_build")
+    cfg.tag_prefix = section.get("tag_prefix")
+    if cfg.tag_prefix in ("''", '""', None):
+        cfg.tag_prefix = ""
+    cfg.parentdir_prefix = section.get("parentdir_prefix")
+    cfg.verbose = section.get("verbose")
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+# these dictionaries contain VCS-specific tools
+LONG_VERSION_PY: Dict[str, str] = {}
+HANDLERS: Dict[str, Dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        HANDLERS.setdefault(vcs, {})[method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None), **popen_kwargs)
+            break
+        except OSError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+LONG_VERSION_PY['git'] = r'''
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain.
+# Generated by versioneer-0.28
+# https://github.com/python-versioneer/python-versioneer
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+from typing import Callable, Dict
+import functools
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
+    git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
+    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "%(STYLE)s"
+    cfg.tag_prefix = "%(TAG_PREFIX)s"
+    cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
+    cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY: Dict[str, str] = {}
+HANDLERS: Dict[str, Dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        try:
+            dispcmd = str([command] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen([command] + args, cwd=cwd, env=env,
+                                       stdout=subprocess.PIPE,
+                                       stderr=(subprocess.PIPE if hide_stderr
+                                               else None), **popen_kwargs)
+            break
+        except OSError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %%s" %% dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %%s" %% (commands,))
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print("unable to run %%s (error)" %% dispcmd)
+            print("stdout was %%s" %% stdout)
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %%s but none started with prefix %%s" %%
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %%d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%%s', no digits" %% ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %%s" %% ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %%s" %% r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print("Directory %%s not under git control" %% root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, [
+        "describe", "--tags", "--dirty", "--always", "--long",
+        "--match", f"{tag_prefix}[[:digit:]]*"
+    ], cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%%s'"
+                               %% describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%%s' doesn't start with prefix '%%s'"
+                print(fmt %% (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
+                               %% (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%%d.g%%s" %% (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver):
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces):
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += ".post%%d.dev%%d" %% (post_version + 1, pieces["distance"])
+            else:
+                rendered += ".post0.dev%%d" %% (pieces["distance"])
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%%d" %% pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%%s" %% pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%%s" %% pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%%s" %% pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%%s" %% pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%%s'" %% style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
+'''
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        with open(versionfile_abs, "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r'\d', r)}
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r'\d', r):
+                continue
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                   hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(GITS, [
+        "describe", "--tags", "--dirty", "--always", "--long",
+        "--match", f"{tag_prefix}[[:digit:]]*"
+    ], cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
+                             cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def do_vcs_install(versionfile_source, ipy):
+    """Git-specific installation logic for Versioneer.
+
+    For Git, this means creating/changing .gitattributes to mark _version.py
+    for export-subst keyword substitution.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    files = [versionfile_source]
+    if ipy:
+        files.append(ipy)
+    if "VERSIONEER_PEP518" not in globals():
+        try:
+            my_path = __file__
+            if my_path.endswith((".pyc", ".pyo")):
+                my_path = os.path.splitext(my_path)[0] + ".py"
+            versioneer_file = os.path.relpath(my_path)
+        except NameError:
+            versioneer_file = "versioneer.py"
+        files.append(versioneer_file)
+    present = False
+    try:
+        with open(".gitattributes", "r") as fobj:
+            for line in fobj:
+                if line.strip().startswith(versionfile_source):
+                    if "export-subst" in line.strip().split()[1:]:
+                        present = True
+                        break
+    except OSError:
+        pass
+    if not present:
+        with open(".gitattributes", "a+") as fobj:
+            fobj.write(f"{versionfile_source} export-subst\n")
+        files.append(".gitattributes")
+    run_command(GITS, ["add", "--"] + files)
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+SHORT_VERSION_PY = """
+# This file was generated by 'versioneer.py' (0.28) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+import json
+
+version_json = '''
+%s
+'''  # END VERSION_JSON
+
+
+def get_versions():
+    return json.loads(version_json)
+"""
+
+
+def versions_from_file(filename):
+    """Try to determine the version from _version.py if present."""
+    try:
+        with open(filename) as f:
+            contents = f.read()
+    except OSError:
+        raise NotThisMethod("unable to read _version.py")
+    mo = re.search(r"version_json = '''\n(.*)'''  # END VERSION_JSON",
+                   contents, re.M | re.S)
+    if not mo:
+        mo = re.search(r"version_json = '''\r\n(.*)'''  # END VERSION_JSON",
+                       contents, re.M | re.S)
+    if not mo:
+        raise NotThisMethod("no version_json in _version.py")
+    return json.loads(mo.group(1))
+
+
+def write_to_version_file(filename, versions):
+    """Write the given version number to the given _version.py file."""
+    os.unlink(filename)
+    contents = json.dumps(versions, sort_keys=True,
+                          indent=1, separators=(",", ": "))
+    with open(filename, "w") as f:
+        f.write(SHORT_VERSION_PY % contents)
+
+    print("set %s to '%s'" % (filename, versions["version"]))
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces):
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver):
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces):
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
+            else:
+                rendered += ".post0.dev%d" % (pieces["distance"])
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_post_branch(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+class VersioneerBadRootError(Exception):
+    """The project root directory is unknown or missing key files."""
+
+
+def get_versions(verbose=False):
+    """Get the project version from whatever source is available.
+
+    Returns dict with two keys: 'version' and 'full'.
+    """
+    if "versioneer" in sys.modules:
+        # see the discussion in cmdclass.py:get_cmdclass()
+        del sys.modules["versioneer"]
+
+    root = get_root()
+    cfg = get_config_from_root(root)
+
+    assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
+    handlers = HANDLERS.get(cfg.VCS)
+    assert handlers, "unrecognized VCS '%s'" % cfg.VCS
+    verbose = verbose or cfg.verbose
+    assert cfg.versionfile_source is not None, \
+        "please set versioneer.versionfile_source"
+    assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
+
+    versionfile_abs = os.path.join(root, cfg.versionfile_source)
+
+    # extract version from first of: _version.py, VCS command (e.g. 'git
+    # describe'), parentdir. This is meant to work for developers using a
+    # source checkout, for users of a tarball created by 'setup.py sdist',
+    # and for users of a tarball/zipball created by 'git archive' or github's
+    # download-from-tag feature or the equivalent in other VCSes.
+
+    get_keywords_f = handlers.get("get_keywords")
+    from_keywords_f = handlers.get("keywords")
+    if get_keywords_f and from_keywords_f:
+        try:
+            keywords = get_keywords_f(versionfile_abs)
+            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
+            if verbose:
+                print("got version from expanded keyword %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        ver = versions_from_file(versionfile_abs)
+        if verbose:
+            print("got version from file %s %s" % (versionfile_abs, ver))
+        return ver
+    except NotThisMethod:
+        pass
+
+    from_vcs_f = handlers.get("pieces_from_vcs")
+    if from_vcs_f:
+        try:
+            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
+            ver = render(pieces, cfg.style)
+            if verbose:
+                print("got version from VCS %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        if cfg.parentdir_prefix:
+            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+            if verbose:
+                print("got version from parentdir %s" % ver)
+            return ver
+    except NotThisMethod:
+        pass
+
+    if verbose:
+        print("unable to compute version")
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None, "error": "unable to compute version",
+            "date": None}
+
+
+def get_version():
+    """Get the short version string for this project."""
+    return get_versions()["version"]
+
+
+def get_cmdclass(cmdclass=None):
+    """Get the custom setuptools subclasses used by Versioneer.
+
+    If the package uses a different cmdclass (e.g. one from numpy), it
+    should be provide as an argument.
+    """
+    if "versioneer" in sys.modules:
+        del sys.modules["versioneer"]
+        # this fixes the "python setup.py develop" case (also 'install' and
+        # 'easy_install .'), in which subdependencies of the main project are
+        # built (using setup.py bdist_egg) in the same python process. Assume
+        # a main project A and a dependency B, which use different versions
+        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in
+        # sys.modules by the time B's setup.py is executed, causing B to run
+        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a
+        # sandbox that restores sys.modules to it's pre-build state, so the
+        # parent is protected against the child's "import versioneer". By
+        # removing ourselves from sys.modules here, before the child build
+        # happens, we protect the child from the parent's versioneer too.
+        # Also see https://github.com/python-versioneer/python-versioneer/issues/52
+
+    cmds = {} if cmdclass is None else cmdclass.copy()
+
+    # we add "version" to setuptools
+    from setuptools import Command
+
+    class cmd_version(Command):
+        description = "report generated version string"
+        user_options = []
+        boolean_options = []
+
+        def initialize_options(self):
+            pass
+
+        def finalize_options(self):
+            pass
+
+        def run(self):
+            vers = get_versions(verbose=True)
+            print("Version: %s" % vers["version"])
+            print(" full-revisionid: %s" % vers.get("full-revisionid"))
+            print(" dirty: %s" % vers.get("dirty"))
+            print(" date: %s" % vers.get("date"))
+            if vers["error"]:
+                print(" error: %s" % vers["error"])
+    cmds["version"] = cmd_version
+
+    # we override "build_py" in setuptools
+    #
+    # most invocation pathways end up running build_py:
+    #  distutils/build -> build_py
+    #  distutils/install -> distutils/build ->..
+    #  setuptools/bdist_wheel -> distutils/install ->..
+    #  setuptools/bdist_egg -> distutils/install_lib -> build_py
+    #  setuptools/install -> bdist_egg ->..
+    #  setuptools/develop -> ?
+    #  pip install:
+    #   copies source tree to a tempdir before running egg_info/etc
+    #   if .git isn't copied too, 'git describe' will fail
+    #   then does setup.py bdist_wheel, or sometimes setup.py install
+    #  setup.py egg_info -> ?
+
+    # pip install -e . and setuptool/editable_wheel will invoke build_py
+    # but the build_py command is not expected to copy any files.
+
+    # we override different "build_py" commands for both environments
+    if 'build_py' in cmds:
+        _build_py = cmds['build_py']
+    else:
+        from setuptools.command.build_py import build_py as _build_py
+
+    class cmd_build_py(_build_py):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_py.run(self)
+            if getattr(self, "editable_mode", False):
+                # During editable installs `.py` and data files are
+                # not copied to build_lib
+                return
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            if cfg.versionfile_build:
+                target_versionfile = os.path.join(self.build_lib,
+                                                  cfg.versionfile_build)
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+    cmds["build_py"] = cmd_build_py
+
+    if 'build_ext' in cmds:
+        _build_ext = cmds['build_ext']
+    else:
+        from setuptools.command.build_ext import build_ext as _build_ext
+
+    class cmd_build_ext(_build_ext):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_ext.run(self)
+            if self.inplace:
+                # build_ext --inplace will only build extensions in
+                # build/lib<..> dir with no _version.py to write to.
+                # As in place builds will already have a _version.py
+                # in the module dir, we do not need to write one.
+                return
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            if not cfg.versionfile_build:
+                return
+            target_versionfile = os.path.join(self.build_lib,
+                                              cfg.versionfile_build)
+            if not os.path.exists(target_versionfile):
+                print(f"Warning: {target_versionfile} does not exist, skipping "
+                      "version update. This can happen if you are running build_ext "
+                      "without first running build_py.")
+                return
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile, versions)
+    cmds["build_ext"] = cmd_build_ext
+
+    if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
+        from cx_Freeze.dist import build_exe as _build_exe
+        # nczeczulin reports that py2exe won't like the pep440-style string
+        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
+        # setup(console=[{
+        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
+        #   "product_version": versioneer.get_version(),
+        #   ...
+
+        class cmd_build_exe(_build_exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _build_exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["build_exe"] = cmd_build_exe
+        del cmds["build_py"]
+
+    if 'py2exe' in sys.modules:  # py2exe enabled?
+        try:
+            from py2exe.setuptools_buildexe import py2exe as _py2exe
+        except ImportError:
+            from py2exe.distutils_buildexe import py2exe as _py2exe
+
+        class cmd_py2exe(_py2exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _py2exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["py2exe"] = cmd_py2exe
+
+    # sdist farms its file list building out to egg_info
+    if 'egg_info' in cmds:
+        _egg_info = cmds['egg_info']
+    else:
+        from setuptools.command.egg_info import egg_info as _egg_info
+
+    class cmd_egg_info(_egg_info):
+        def find_sources(self):
+            # egg_info.find_sources builds the manifest list and writes it
+            # in one shot
+            super().find_sources()
+
+            # Modify the filelist and normalize it
+            root = get_root()
+            cfg = get_config_from_root(root)
+            self.filelist.append('versioneer.py')
+            if cfg.versionfile_source:
+                # There are rare cases where versionfile_source might not be
+                # included by default, so we must be explicit
+                self.filelist.append(cfg.versionfile_source)
+            self.filelist.sort()
+            self.filelist.remove_duplicates()
+
+            # The write method is hidden in the manifest_maker instance that
+            # generated the filelist and was thrown away
+            # We will instead replicate their final normalization (to unicode,
+            # and POSIX-style paths)
+            from setuptools import unicode_utils
+            normalized = [unicode_utils.filesys_decode(f).replace(os.sep, '/')
+                          for f in self.filelist.files]
+
+            manifest_filename = os.path.join(self.egg_info, 'SOURCES.txt')
+            with open(manifest_filename, 'w') as fobj:
+                fobj.write('\n'.join(normalized))
+
+    cmds['egg_info'] = cmd_egg_info
+
+    # we override different "sdist" commands for both environments
+    if 'sdist' in cmds:
+        _sdist = cmds['sdist']
+    else:
+        from setuptools.command.sdist import sdist as _sdist
+
+    class cmd_sdist(_sdist):
+        def run(self):
+            versions = get_versions()
+            self._versioneer_generated_versions = versions
+            # unless we update this, the command will keep using the old
+            # version
+            self.distribution.metadata.version = versions["version"]
+            return _sdist.run(self)
+
+        def make_release_tree(self, base_dir, files):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            _sdist.make_release_tree(self, base_dir, files)
+            # now locate _version.py in the new base_dir directory
+            # (remembering that it may be a hardlink) and replace it with an
+            # updated value
+            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile,
+                                  self._versioneer_generated_versions)
+    cmds["sdist"] = cmd_sdist
+
+    return cmds
+
+
+CONFIG_ERROR = """
+setup.cfg is missing the necessary Versioneer configuration. You need
+a section like:
+
+ [versioneer]
+ VCS = git
+ style = pep440
+ versionfile_source = src/myproject/_version.py
+ versionfile_build = myproject/_version.py
+ tag_prefix =
+ parentdir_prefix = myproject-
+
+You will also need to edit your setup.py to use the results:
+
+ import versioneer
+ setup(version=versioneer.get_version(),
+       cmdclass=versioneer.get_cmdclass(), ...)
+
+Please read the docstring in ./versioneer.py for configuration instructions,
+edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
+"""
+
+SAMPLE_CONFIG = """
+# See the docstring in versioneer.py for instructions. Note that you must
+# re-run 'versioneer.py setup' after changing this section, and commit the
+# resulting files.
+
+[versioneer]
+#VCS = git
+#style = pep440
+#versionfile_source =
+#versionfile_build =
+#tag_prefix =
+#parentdir_prefix =
+
+"""
+
+OLD_SNIPPET = """
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions
+"""
+
+INIT_PY_SNIPPET = """
+from . import {0}
+__version__ = {0}.get_versions()['version']
+"""
+
+
+def do_setup():
+    """Do main VCS-independent setup function for installing Versioneer."""
+    root = get_root()
+    try:
+        cfg = get_config_from_root(root)
+    except (OSError, configparser.NoSectionError,
+            configparser.NoOptionError) as e:
+        if isinstance(e, (OSError, configparser.NoSectionError)):
+            print("Adding sample versioneer config to setup.cfg",
+                  file=sys.stderr)
+            with open(os.path.join(root, "setup.cfg"), "a") as f:
+                f.write(SAMPLE_CONFIG)
+        print(CONFIG_ERROR, file=sys.stderr)
+        return 1
+
+    print(" creating %s" % cfg.versionfile_source)
+    with open(cfg.versionfile_source, "w") as f:
+        LONG = LONG_VERSION_PY[cfg.VCS]
+        f.write(LONG % {"DOLLAR": "$",
+                        "STYLE": cfg.style,
+                        "TAG_PREFIX": cfg.tag_prefix,
+                        "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                        "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                        })
+
+    ipy = os.path.join(os.path.dirname(cfg.versionfile_source),
+                       "__init__.py")
+    if os.path.exists(ipy):
+        try:
+            with open(ipy, "r") as f:
+                old = f.read()
+        except OSError:
+            old = ""
+        module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0]
+        snippet = INIT_PY_SNIPPET.format(module)
+        if OLD_SNIPPET in old:
+            print(" replacing boilerplate in %s" % ipy)
+            with open(ipy, "w") as f:
+                f.write(old.replace(OLD_SNIPPET, snippet))
+        elif snippet not in old:
+            print(" appending to %s" % ipy)
+            with open(ipy, "a") as f:
+                f.write(snippet)
+        else:
+            print(" %s unmodified" % ipy)
+    else:
+        print(" %s doesn't exist, ok" % ipy)
+        ipy = None
+
+    # Make VCS-specific changes. For git, this means creating/changing
+    # .gitattributes to mark _version.py for export-subst keyword
+    # substitution.
+    do_vcs_install(cfg.versionfile_source, ipy)
+    return 0
+
+
+def scan_setup_py():
+    """Validate the contents of setup.py against Versioneer's expectations."""
+    found = set()
+    setters = False
+    errors = 0
+    with open("setup.py", "r") as f:
+        for line in f.readlines():
+            if "import versioneer" in line:
+                found.add("import")
+            if "versioneer.get_cmdclass()" in line:
+                found.add("cmdclass")
+            if "versioneer.get_version()" in line:
+                found.add("get_version")
+            if "versioneer.VCS" in line:
+                setters = True
+            if "versioneer.versionfile_source" in line:
+                setters = True
+    if len(found) != 3:
+        print("")
+        print("Your setup.py appears to be missing some important items")
+        print("(but I might be wrong). Please make sure it has something")
+        print("roughly like the following:")
+        print("")
+        print(" import versioneer")
+        print(" setup( version=versioneer.get_version(),")
+        print("        cmdclass=versioneer.get_cmdclass(),  ...)")
+        print("")
+        errors += 1
+    if setters:
+        print("You should remove lines like 'versioneer.VCS = ' and")
+        print("'versioneer.versionfile_source = ' . This configuration")
+        print("now lives in setup.cfg, and should be removed from setup.py")
+        print("")
+        errors += 1
+    return errors
+
+
+def setup_command():
+    """Set up Versioneer and exit with appropriate error code."""
+    errors = do_setup()
+    errors += scan_setup_py()
+    sys.exit(1 if errors else 0)
+
+
+if __name__ == "__main__":
+    cmd = sys.argv[1]
+    if cmd == "setup":
+        setup_command()