usage/example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statannotations.Annotator import Annotator\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot non-hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, no multiple comparisons correction is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Thur vs. Fri: Mann-Whitney-Wilcoxon test two-sided, P_val:6.477e-01 U_stat=6.305e+02\n",
      "Thur vs. Sat: Mann-Whitney-Wilcoxon test two-sided, P_val:4.690e-02 U_stat=2.180e+03\n",
      "Sun vs. Fri: Mann-Whitney-Wilcoxon test two-sided, P_val:2.680e-02 U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "annot = Annotator(ax, [(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")], data=df, x=x, y=y, order=order)\n",
    "annot.configure(test='Mann-Whitney', text_format='star', loc='outside', verbose=2)\n",
    "annot.apply_test()\n",
    "ax, test_results = annot.annotate()\n",
    "plt.savefig('example_non-hue_outside.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical test results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `annotate` method returns the tuple `ax, annotations`, where `annotations` is a list of `Annotation` objects.\n",
    "The `data` attribute of an Annotation is a `StatResult` object, which containing both the original data of the groups\n",
    "(boxes for boxplots), and the statistical test results (p-value, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney-Wilcoxon test two-sided, P_val:6.477e-01 U_stat=6.305e+02\n",
      "Mann-Whitney-Wilcoxon test two-sided, P_val:4.690e-02 U_stat=2.180e+03\n",
      "Mann-Whitney-Wilcoxon test two-sided, P_val:2.680e-02 U_stat=9.605e+02\n",
      "\n",
      "StatResult attributes: dict_keys(['test_description', 'test_short_name', 'stat_str', 'stat_value', 'pvalue', '_corrected_significance', '_correction_method', 'alpha', 'group1', 'group2'])\n"
     ]
    }
   ],
   "source": [
    "for res in test_results:\n",
    "    print(res.data)\n",
    "\n",
    "print(\"\\nStatResult attributes:\", test_results[0].data.__dict__.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a multiple comparisons correction (requires `statsmodels`)\n",
    "In this example, note that previously configured parameters are remembered if not changed.\n",
    "This is the case for `pairs`, `test`, `text_format`, and `loc`.\n",
    "\n",
    "To avoid this, you can call `annot.reset_configuration()` to get back to all default/unset values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Thur vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val:1.000e+00 U_stat=6.305e+02\n",
      "Thur vs. Sat: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val:1.407e-01 U_stat=2.180e+03\n",
      "Sun vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val:8.041e-02 U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting = {\n",
    "    \"data\": df,\n",
    "    \"x\": x,\n",
    "    \"y\": y,\n",
    "    \"order\": order\n",
    "}\n",
    "ax = sns.boxplot(**plotting)\n",
    "annot.new_plot(ax, **plotting)\n",
    "annot.configure(comparisons_correction=\"Bonferroni\", verbose=2)\n",
    "test_results = annot.apply_test().annotate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Or another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Thur vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:6.477e-01 U_stat=6.305e+02\n",
      "Thur vs. Sat: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:4.690e-02 (ns) U_stat=2.180e+03\n",
      "Sun vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:2.680e-02 (ns) U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f9240ceacc0>,\n [<statannotations.Annotation.Annotation at 0x7f923fa69da0>,\n  <statannotations.Annotation.Annotation at 0x7f923fa69ac8>,\n  <statannotations.Annotation.Annotation at 0x7f9240bfd3c8>])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "annot.new_plot(ax, data=df, x=x, y=y, order=order)\n",
    "annot.configure(comparisons_correction=\"BH\")\n",
    "annot.apply_and_annotate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "To have `\"ns\"` instead of `\"* (ns)\"`, configure with `correction_format=\"replace\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Thur vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:6.477e-01 U_stat=6.305e+02\n",
      "Thur vs. Sat: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:4.690e-02 (ns) U_stat=2.180e+03\n",
      "Sun vs. Fri: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:2.680e-02 (ns) U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f9240e64ef0>,\n [<statannotations.Annotation.Annotation at 0x7f9240ce7828>,\n  <statannotations.Annotation.Annotation at 0x7f923fa57d30>,\n  <statannotations.Annotation.Annotation at 0x7f9240cf9ac8>])"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "annot.new_plot(ax, data=df, x=x, y=y, order=order)\n",
    "annot.configure(comparisons_correction=\"BH\", correction_format=\"replace\")\n",
    "annot.apply_and_annotate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical test annotation can be either drawn inside the plot area (`loc='inside'`), or on top on it (`loc='outside'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Sun vs. Thur: Custom statistical test with Benjamini-Hochberg correction, P_val:1.000e-01\n",
      "Sun vs. Fri: Custom statistical test with Benjamini-Hochberg correction, P_val:1.000e-03\n",
      "Sun vs. Sat: Custom statistical test with Benjamini-Hochberg correction, P_val:1.000e-01\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "pairs = [(\"Sun\", \"Thur\"), (\"Sun\", \"Sat\"), (\"Fri\", \"Sun\")]\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "annot.new_plot(ax, pairs=pairs, data=df, x=x, y=y, order=order)\n",
    "annot.configure(test=None, loc='inside')\n",
    "annot.set_pvalues([0.1, 0.1, 0.001])\n",
    "annot.annotate()\n",
    "plt.savefig('example_non-hue_inside.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot with hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create boxes with unequal ymax position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   carat      cut color clarity  depth  table  price     x     y     z\n0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>carat</th>\n      <th>cut</th>\n      <th>color</th>\n      <th>clarity</th>\n      <th>depth</th>\n      <th>table</th>\n      <th>price</th>\n      <th>x</th>\n      <th>y</th>\n      <th>z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.23</td>\n      <td>Ideal</td>\n      <td>E</td>\n      <td>SI2</td>\n      <td>61.5</td>\n      <td>55.0</td>\n      <td>326</td>\n      <td>3.95</td>\n      <td>3.98</td>\n      <td>2.43</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.21</td>\n      <td>Premium</td>\n      <td>E</td>\n      <td>SI1</td>\n      <td>59.8</td>\n      <td>61.0</td>\n      <td>326</td>\n      <td>3.89</td>\n      <td>3.84</td>\n      <td>2.31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.23</td>\n      <td>Good</td>\n      <td>E</td>\n      <td>VS1</td>\n      <td>56.9</td>\n      <td>65.0</td>\n      <td>327</td>\n      <td>4.05</td>\n      <td>4.07</td>\n      <td>2.31</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.29</td>\n      <td>Premium</td>\n      <td>I</td>\n      <td>VS2</td>\n      <td>62.4</td>\n      <td>58.0</td>\n      <td>334</td>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>2.63</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.31</td>\n      <td>Good</td>\n      <td>J</td>\n      <td>SI2</td>\n      <td>63.3</td>\n      <td>58.0</td>\n      <td>335</td>\n      <td>4.34</td>\n      <td>4.35</td>\n      <td>2.75</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('diamonds')\n",
    "df = df[df['color'].map(lambda x: x in 'EIJ')]\n",
    "# Modifying data to yield unequal boxes in the hue value\n",
    "df.loc[df['cut'] == 'Ideal', 'price'] = df.loc[df['cut'] == 'Ideal', 'price'].map(lambda x: min(x, 5000))\n",
    "df.loc[df['cut'] == 'Premium', 'price'] = df.loc[df['cut'] == 'Premium', 'price'].map(lambda x: min(x, 7500))\n",
    "df.loc[df['cut'] == 'Good', 'price'] = df.loc[df['cut'] == 'Good', 'price'].map(lambda x: min(x, 15000))\n",
    "df.loc[df['cut'] == 'Very Good', 'price'] = df.loc[df['cut'] == 'Very Good', 'price'].map(lambda x: min(x, 3000))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "E_Ideal vs. E_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.560e-31 U_stat=3.756e+06\n",
      "I_Ideal vs. I_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.141e-61 U_stat=1.009e+06\n",
      "J_Ideal vs. J_Premium: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:4.018e-37 U_stat=2.337e+05\n",
      "E_Ideal vs. E_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.201e-19 U_stat=1.480e+06\n",
      "I_Ideal vs. I_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.008e-13 U_stat=4.359e+05\n",
      "J_Ideal vs. J_Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.006e-04 U_stat=1.174e+05\n",
      "E_Ideal vs. E_Very Good: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:1.736e-02 U_stat=4.850e+06\n",
      "E_Good vs. I_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.906e-01 U_stat=9.882e+05\n",
      "I_Premium vs. J_Ideal: Mann-Whitney-Wilcoxon test two-sided with Benjamini-Hochberg correction, P_val:5.159e-27 U_stat=8.084e+05\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"color\"\n",
    "y = \"price\"\n",
    "hue = \"cut\"\n",
    "hue_order=['Ideal', 'Premium', 'Good', 'Very Good', 'Fair']\n",
    "order = [\"E\", \"I\", \"J\"]\n",
    "pairs=[\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Very Good\")),\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Premium\")),\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Good\")),\n",
    "    ((\"I\", \"Ideal\"), (\"I\", \"Premium\")),\n",
    "    ((\"I\", \"Ideal\"), (\"I\", \"Good\")),\n",
    "    ((\"J\", \"Ideal\"), (\"J\", \"Premium\")),\n",
    "    ((\"J\", \"Ideal\"), (\"J\", \"Good\")),\n",
    "    ((\"E\", \"Good\"), (\"I\", \"Ideal\")),\n",
    "    ((\"I\", \"Premium\"), (\"J\", \"Ideal\")),\n",
    "    ]\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order, hue=hue, hue_order=hue_order)\n",
    "annot.new_plot(ax, pairs, data=df, x=x, y=y, order=order, hue=hue, hue_order=hue_order)\n",
    "annot.configure(test='Mann-Whitney', verbose=2)\n",
    "annot.apply_test()\n",
    "annot.annotate()\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_hue_layout.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot with bucketed categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   total_bill   tip     sex smoker  day    time  size    tip_bucket\n0       16.99  1.01  Female     No  Sun  Dinner     2  (0.991, 4.0]\n1       10.34  1.66    Male     No  Sun  Dinner     3  (0.991, 4.0]\n2       21.01  3.50    Male     No  Sun  Dinner     3  (0.991, 4.0]\n3       23.68  3.31    Male     No  Sun  Dinner     2  (0.991, 4.0]\n4       24.59  3.61  Female     No  Sun  Dinner     4  (0.991, 4.0]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>day</th>\n      <th>time</th>\n      <th>size</th>\n      <th>tip_bucket</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16.99</td>\n      <td>1.01</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n      <td>(0.991, 4.0]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.34</td>\n      <td>1.66</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n      <td>(0.991, 4.0]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.01</td>\n      <td>3.50</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n      <td>(0.991, 4.0]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.68</td>\n      <td>3.31</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n      <td>(0.991, 4.0]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.59</td>\n      <td>3.61</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>4</td>\n      <td>(0.991, 4.0]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "df['tip_bucket'] = pd.cut(df['tip'], 3)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[(0.991, 4.0], (4.0, 7.0], (7.0, 10.0]]\nCategories (3, interval[float64]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this case we just have to pass the list of categories objects to the annotate function.\n",
    "tip_bucket_list = df['tip_bucket'].unique()\n",
    "tip_bucket_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Fri_(0.991, 4.0] vs. Sat_(7.0, 10.0]: t-test independent samples, P_val:6.176e-07 t=-7.490e+00\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "hue = \"tip_bucket\"\n",
    "data = df\n",
    "ax = sns.boxplot(data=df, x=x, y=y, hue=hue)\n",
    "# method calls can be queued\n",
    "(annot\n",
    " .reset_configuration()\n",
    " .new_plot(ax, [((\"Sat\", tip_bucket_list[2]), (\"Fri\", tip_bucket_list[0]))],\n",
    "           data=df, x=x, y=y, hue=hue)\n",
    " .configure(test='t-test_ind')\n",
    " .apply_test()\n",
    " .annotate())\n",
    "\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_bucketed.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Barplot with hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Support for barplot has not been fully implemented yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "SI2_I vs. SI2_J: t-test independent samples, P_val:3.710e-01 t=-8.949e-01\n",
      "SI1_I vs. SI1_J: t-test independent samples, P_val:8.513e-05 t=-3.937e+00\n",
      "VS1_I vs. VS1_J: t-test independent samples, P_val:9.140e-05 t=-3.923e+00\n",
      "VS2_I vs. VS2_J: t-test independent samples, P_val:6.806e-03 t=-2.709e+00\n",
      "VVS2_I vs. VVS2_J: t-test independent samples, P_val:9.076e-14 t=-7.672e+00\n",
      "VVS1_I vs. VVS1_J: t-test independent samples, P_val:6.536e-10 t=-6.321e+00\n",
      "I1_I vs. I1_J: t-test independent samples, P_val:2.905e-02 t=-2.206e+00\n",
      "IF_I vs. IF_J: t-test independent samples, P_val:4.412e-03 t=-2.881e+00\n",
      "SI1_E vs. SI2_E: t-test independent samples, P_val:4.178e-66 t=-1.749e+01\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset('diamonds')\n",
    "df = df[df['color'].map(lambda c: c in 'EIJ')]\n",
    "\n",
    "x = \"clarity\"\n",
    "y = \"carat\"\n",
    "hue = \"color\"\n",
    "hue_order=[\"E\", \"I\", \"J\"]\n",
    "pairs = [\n",
    "    ((\"SI2\", \"E\"), (\"SI1\", \"E\"))\n",
    "    ]\n",
    "pairs = pairs + [((clar, 'I'), (clar, 'J'))\n",
    "                         for clar in df['clarity'].unique()]\n",
    "width = 0.4\n",
    "ax = sns.barplot(data=df, x=x, y=y, hue=hue, hue_order=hue_order, seed=2021)\n",
    "annot.new_plot(ax, pairs, plot='barplot',\n",
    "               data=df, x=x, y=y, hue=hue, hue_order=hue_order, seed=2021)\n",
    "annot.apply_test().annotate()\n",
    "\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1), title=hue)\n",
    "plt.savefig('example_barplot_hue.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning y offsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./example_tuning_y_offsets_w_arguments.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat_Yes vs. Sat_No: t-test independent samples, P_val:4.304e-01 t=7.922e-01\n",
      "Thur_No vs. Fri_No: t-test independent samples, P_val:7.425e-01 t=-3.305e-01\n",
      "Thur_Yes vs. Sun_No: t-test independent samples, P_val:5.623e-01 t=-5.822e-01\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "hue = \"smoker\"\n",
    "pairs = [((\"Thur\", \"No\"), (\"Fri\", \"No\")),\n",
    "         ((\"Sat\", \"Yes\"), (\"Sat\", \"No\")),\n",
    "         ((\"Sun\", \"No\"), (\"Thur\", \"Yes\"))]\n",
    "ax = sns.boxplot(data=df, x=x, y=y, hue=hue)\n",
    "annot.new_plot(ax, pairs,\n",
    "               data=df, x=x, y=y, hue=hue)\n",
    "annot.configure(test='t-test_ind', text_format='full', loc='inside',\n",
    "                comparisons_correction=None, line_height=0.05, text_offset=8)\n",
    "\n",
    "annot.apply_test().annotate(line_offset_to_group=0.2, line_offset=0.1)\n",
    "\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_tuning_y_offsets.svg')\n",
    "plt.savefig('example_tuning_y_offsets.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Use a different test with StatTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "setosa vs. versicolor: Bartlett statistical test for equal variances, P_val:8.660e-03 Stat=6.892e+00\n",
      "versicolor vs. virginica: Bartlett statistical test for equal variances, P_val:1.478e-01 Stat=2.095e+00\n",
      "setosa vs. virginica: Bartlett statistical test for equal variances, P_val:6.379e-05 Stat=1.599e+01\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f923f9da1d0>,\n [<statannotations.Annotation.Annotation at 0x7f92423d7828>,\n  <statannotations.Annotation.Annotation at 0x7f92423d79b0>,\n  <statannotations.Annotation.Annotation at 0x7f92423d7cc0>])"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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GWq0Wrq6usLOzq3cAIrI+1vjibvWL6NHR0TInaXhmLYGwePFi/PHHH4iMjMT8+fNx4cIFrFy5UupsRERkAWaN6DMyMnDgwAHTxwMHDoSfn59koYiIyHLMGtG3bdsWeXn/e/W+tLTUIktnEhGR9Mwa0bdr1w4TJkzA6NGjodFokJSUhDZt2pimbxYvXixpSCIiundmFX3nzp3RuXNn08ectiEish5mFX1YWBh0Oh1ycnLQtWtX6PV6NGvW7G+3S05Oxttvv42ysjIMHjyYI38iIhmYNUd/+vRpeHt7Y9asWbh27RqGDx+OH374odZtLl26hGXLlmHz5s3YvXs3MjIyuD4OEZEMzCr6mJgYfPTRR3ByckK7du2wZs0arFq1qtZtEhMT4evri3bt2sHW1haxsbHo3bu3RUITEZH5zJq60el0eOSRR0wfDxs2DLGxsbVuk5OTA1tbW7zwwgv4/fffMXz4cLzyyitmB0tPTzf7sWSdioqK5I7QJBQVFeH777+XO4bsqr/fmuJzYfaiZoWFhVCpVABg1tKylZWVOHXqFD7++GM0b94cs2fPxpdffonAwECzgrm5ucHe3t6sx5J1io+PR1GB3CmUr2XLlvDw8JA7huzi4+MBQLHPhV6vv+sA2aypmxdeeAEhISG4evUq5s6di0mTJmH27Nm1btOmTRt4enrCxcUFDg4O8Pb2RlpaWt3TExFRvZg1otdqtXj44YeRkpICo9EIV1dXDBo0qNZtvLy8EBERgZs3b6JFixb45ptvMGLECIuEJmXIz8/Hn8UGLqUroavFBhjy8+WOQTIza0S/dOlSbNu2Df3798e7776LK1euYOHChbVu07t3b8yYMQOTJ0+Gr68vOnTogAkTJlgkNBERmc+sEX16ejri4+MRFxeHp556CvPmzTNrrj0oKAhBQUH1DknK5OzsDJvia7zxiIQ+TMtDSy5X0uSZNaIXQkCtViMlJQUDBw4EUHUlDhERNX5mFf2DDz6ImTNn4vLly+jfvz/mzZuH7t27S52NiIgswKypm+joaCQmJsLDwwO2trZ4/PHHMX78eKmzySY5ORmJiYmS7LugoOp6QicnJ4vv28fHB1qt1uL7JSLrZlbRN2/eHOPGjTN9PGnSJMkCKV31cs9SFD0R0Z2YVfRNjVarlWxk3JRvZ0ZE8jBrjp6IiKwXi56ISOFY9ERECseiJyJSOBY9EZHCseiJiBSOl1eSrK5a0eqVxeVGAICjnfWMj64WG9BS7hB1JNUbFqvvo1F9ibMlNfY3K7LoSTaurq5yR6iT6/8tivadrCd3S1jf8ywVF5emu3gei55kM3PmTLkj1Anf7NYwpHzDYlNlPX+DEhHRPWHRExEpHIueiEjhWPRERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoVj0RMRKRyLnohI4Vj0REQKx6InIlI4Fj0RkcJZ9TLFW7duNd1MwFpIefMDqbi6ulrdksJE9D9WXfRZWVlIzzgLjYOT3FHMZjRoAABnsv6QOYl5KnUFckcgonqy6qIHAI2DE5p3HiF3DMUqzUmSOwIR1RPn6ImIFI5FT0SkcCx6IiKFk3SOPjQ0FHl5ebCxqTrMihUr0Lt3bykPSUREt5Cs6IUQuHjxIg4fPmwqeiIianiSTd1UXy/+3HPPISAgAJ988olUhyIiolpINtS+efMmPD09sWTJElRUVODZZ5/FQw89hMGDB1vsGPn5+ajUFfASQAlV6gqQn28ndwwiqgfJir5v377o27ev6eOgoCAcPXrU7KJPT0//28fodLp7zkfm0+l0+P777+WOIbuioiIA4HNhpYqKihAfH4+goCC0bNlS7jgNSrKiP3XqFCoqKuDp6Qmgas6+LnP1bm5usLe3r/Ux7du3R0GZmm+YklBpThLat38AHh4eckeRXXx8PADwubBSmzdvRm5uLs6cOYPZs2fLHcfi9Hr9XQfIks3RFxUVYc2aNdDr9SguLsaXX34JHx8fqQ5HRHRXeXl5SEpKghAChw4dQn5+vtyRGpRkI3ovLy+cPn0a48ePh9FoxOTJk2tM5RBJJTk5GYmJiRbfr9QL0vn4+ECr1Uqy76bu888/h9FoBAAYjUZ8/vnnihzV342k1z2+8soreOWVV6Q8BFGDcXFxkTsC3aMjR47AYDAAAAwGAw4fPsyiJ7JmWq2WI2OqYfjw4UhMTITBYICNjQ28vLzkjtSguAQCESlecHAw1OqqulOr1QgODpY5UcNi0ROR4rm4uGDEiBFQqVTw9vaGs7Oz3JEalNVP3VjbG6aMhqpr/9U2DjInMU/VjUcekDsGUb0FBwcjNze3yY3mASsveldXV7kj1Fn1lRuurtZSng9Y5fNMdCsXFxesXr1a7hiysOqit8b7mFZfmhcdHS1zEiJqKjhHT0SkcCx6IiKFY9ETESkci56ISOFY9ERECseiJyJSOBY9EZHCseiJiBSORU9EpHAseiIihWPRExEpHIueiEjhWPRERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoWz6lsJSiU5ORmJiYmS7Lv6nrHVtxS0JB8fH2i1Wovvl4isG4u+gbm4uMgdgYiaGBb9HWi1Wo6MiUgxOEdPRKRwLHoiIoVj0RMRKRyLnohI4Vj0REQK1+iuuhFCAADKy8tlTkJEZD2qO7O6Q/+q0RV9RUUFAODcuXMyJyEisj4VFRVwcHCo8TmVuFP9y8hoNKKkpAS2trZQqVRyxyEisgpCCFRUVKBFixZQq2vOyje6oiciIsvii7FERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoVj0RMRKRyLXmL//ve/sXfvXrljUD189tln+Oyzz+q8XUJCAhYsWCBBoqZt0aJF+Pnnn81+fFJSEjZu3GjRfVobvmFKYgsWLED//v0RGBgodxRqYAkJCUhNTcXq1avljkJNXKNb68YaXL16FeHh4SgtLYVarcbixYuhVqsRHR0NnU4HZ2dnvP7667h06RKSk5Nx4sQJ3H///ejRowcWLVqE3377DTY2Nnj11Vfx5JNP4ttvv8XatWsBAK1atcKbb74JFxcXxMbG4ttvv0VhYSHatm2L2NhYtGnTRuavvvELCwvD2LFjMWrUKABAYGAgli9fjtjYWBQUFMDBwQFLlixBz549sWDBAhQUFCAnJwfz58/HyZMnkZKSArVaDW9vb4SFheGtt94CAMyZMwd79uzBli1boFKp4O7ujqioKBgMBixevBhnz56FSqXC9OnTMX78+BqZfvrpJ6xatQp6vR7Ozs5YsWIFOnfujNDQULRq1Qrnz5/Hhg0b0KNHjwZ/vhqzO53LnJwcbN68GQCwdu1aGI1GdO3aFYsXL8Zrr72G3NxcdOrUCVevXsXbb7+N1NRU0y9crVaLgIAAHDt2DGVlZYiJiYGbmxtCQ0MRFhaG/v37Y926dTh06BA0Gg2efvppTJkyBampqYiNjYVOp8PNmzcRGRkJb29vOZ+auhFUZ2+99ZbYunWrEEKIo0ePiri4ODF27Fhx5coVIYQQX3/9tZgyZYoQQoiIiAixY8cOIYQQ//jHP8QHH3wghBAiNzdXDB48WFy/fl2EhISI06dPCyGEiIuLE9988424ePGiCAsLE5WVlUIIIebPny+2bdvWkF+m1Tp48KCYM2eOEEKI7Oxs4evrK55++mnxyy+/CCGEOH/+vBg5cqQQour8RERECCGEuHz5svD19RVCCFFaWipefvllodPpxKZNm8SmTZvE1atXhaenp/j999+FEEKEh4eLxMREERMTI6KiooQQQty4cUNotVpx5swZsWPHDhERESH0er3w8vIyneN9+/aJwMBAIYQQISEhYtOmTQ30zFifO53LkJAQceLECXHixAnh4eEhbt68KYQQIjo6WsTExAghhEhLSxM9evQQly5dMp0HIYTw8vISH374oRBCiH/+858iLCxMCCFM+9y3b58IDg4Wer1eFBcXi4CAAHHt2jUxZ84c8euvvwohhDh+/Ljw9/dvyKeh3jiivweenp6YM2cOzpw5g2HDhmHYsGHYvHkzZs+ebXpMcXHxbdudOHECK1euBAB06tQJvXv3xunTpzFixAiEhYXB29sbI0aMwODBgwEAERER2L59O7Kzs/HTTz/hwQcfbJgv0MoNGzYMK1asQHFxMfbu3QtfX19s2bIFkZGRpseUlpYiPz8fAPDYY48BAB544AHY29sjODgYXl5eCA8Ph729vWmbH3/8Ef369UO7du0AwPRX2ObNm/HGG28AAFxcXDBixAikpqbC0dERAHDx4kXcd999puOMGTMGS5cuRVFRUY3j0+1uPZfVo/FqDz30EFq2bAkASElJwbp16wAA7u7u6Nat2x33OXToUABA165dcfDgwRr/dvLkSYwZMwZ2dnaws7PDrl27AFSd68OHD2P//v04ffo0SkpKLP61SolFfw88PDzw1Vdf4ciRI9i3bx+2b9+Ojh07mr4pKisr8eeff962nbjl5RAhBCorKzF16lR4eXnh8OHDWLt2LdLS0jB06FDMmzcPU6dOxahRo6BWq++4zjTdzs7ODl5eXkhOTsb+/fvx3nvvYdu2babzA1RNvzk5OQGAaUlXGxsbbN++Hampqfj6668RHByMjz/+2LSNjY1NjRVV8/LyANz9vFYzGo23ZfzrY25dUpb+507n8q9F/9fnTqPRmPUzUv3L+06r4956ji9fvgwXFxeEhoZiwIABGDBgADw9PREeHl6fL6vB8aqbe7BmzRrs3r0bTz31FJYuXYrMzEwUFhbi1KlTAIAdO3aYvhE0Go3pB3rgwIGIj48HAFy6dAk//PAD+vTpg4kTJ6KkpARTp07F1KlTkZGRgZMnT6J///6YNGkSunTpgiNHjtQoD6rduHHj8OGHH8LJyQn/93//hy5dupiKPiUlBc8888xt22RkZCAkJARPPPEEIiIi8PDDDyM7O9v07+7u7vjpp59w/fp1AMAbb7yBpKSkGuc1Ly8PSUlJ6N+/v2k7V1dXFBQUIC0tDQCwb98+dOjQwfSLhmp367m8G09PT+zZswcAcPbsWZw/f77OS50/8cQTOHjwICoqKlBWVoYZM2bg119/xcWLF/Hyyy/jySefRFJSktX9LHJEfw9CQ0Mxb948JCQkQKPRYO3atWjVqpXpxTZHR0fExMQAAAYNGoT169ejZcuWWLRoEZYuXYqEhAQAwMqVK9G2bVvMnTsXCxYsgI2NDZo3b46VK1eiWbNmpheiAMDNzQ2XL1+W7Wu2Nh4eHigqKsKkSZMAVP3pvXz5crz//vuwtbVFbGzsbSXQs2dP9OnTB/7+/mjWrBn69euHJ598Er/88guAqqmdRYsWYfr06TAajejTpw8CAwNRVlaG5cuXY+zYsaisrMQLL7yAXr164ezZswCqRqWxsbGIiopCWVkZWrVqhdjY2IZ9QqzYrefybl566SVERkZi7NixePDBB9GmTZs6/7Xk4+OD9PR0BAYGwmg04tlnn8Vjjz2GoKAg+Pn5wcbGBgMHDoROp0NpaSmaN29eny+twfDySiJShF27dqFjx47w8PDAb7/9hpCQEBw6dOi2m3A0RRzRE5EiuLq6YtmyZTAajVCr1VixYgVL/r84oiciUjj+uiMiUjgWPRGRwrHoiYgUjkVPVE8zZ87Er7/+KncMorvii7FERArHyytJ0UpKShAZGYmcnByo1Wr06tULfn5+WL9+PTp06ICsrCw4ODhg9erVePjhh1FeXo5169bh5MmTqKysRM+ePbF48WI4OjoiOzsbS5cuRV5eHtRqNWbPng1fX19otVps3LgR7u7uSE5OxpYtW1BRUQEHBwdERESgb9++uHDhAhYtWoTy8nIIIRAUFHTHd+cSSYFTN6RoiYmJKCkpwa5du0zLFFy+fBnp6ekIDQ3Fnj17EBgYiPnz5wMA4uLioNFokJCQgN27d6Nt27amhbLmzp2L0aNH46uvvkJcXBzWr19fY/G6ixcvIjY2FnFxcdi5cyeioqIwZ84clJaWYtu2bdBqtUhISEBcXBxOnTp1xzVwiKTAET0pmoeHB2JjYxEaGopBgwZhypQpyMvLQ/fu3fH4448DACZMmIAVK1YgPz8fRxFRL2kAAAIiSURBVI4cQVFREY4fPw4AqKioQOvWrVFQUIDMzExMnDgRANC+fXscOnSoxrFSUlJw7do1TJ061fQ5lUqF3Nxc+Pj4ICIiAmlpafD09DTdw4CoIbDoSdE6deqExMREfPfddzhx4gSmTZuGFStWQKPR3PZYjUYDo9GIhQsXYtiwYQCqpn70ej1sbKp+VP66Pk5WVhY6dOhg+thoNMLT0xMbNmwwfe73339H27Zt0b17dxw4cADHjx/Ht99+i3feeQcJCQmmJY+JpMQhBSnav/71L0RGRmLIkCGYP38+hgwZgoyMDGRmZiIzMxMA8MUXX6Bv37647777MGTIEHz66acoLy+H0WjEkiVLsH79ejg6OqJXr17YuXMngKoCnzRpkmlNeaBq9cSUlBRcuHABAHD06FEEBARAp9Nh3rx52LdvH/z8/LBs2TI4OjoiNze34Z8QapJ41Q0pWmlpKRYuXIizZ8+iWbNmaN++PcaPH49Vq1ahe/fuuHLlClxcXLBq1Sp07NgROp0OMTExSE1NRWVlJXr06IGoqCg4OjoiJycHr7/+Ov7880+oVCrMmTMH3t7eNV6M/c9//oN3330XQgjY2Nhg4cKFePzxx00vxpaWlkKj0cDT0xPz58+v8zK6RPeCRU9NznfffYeoqCjs3btX7ihEDYJTN0RECscRPRGRwnFET0SkcCx6IiKFY9ETESkci56ISOFY9ERECseiJyJSuP8HCL32V9gi/qsAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statannotations.stats.StatTest import StatTest\n",
    "\n",
    "# For example bartlett's test\n",
    "from scipy.stats import bartlett\n",
    "\n",
    "df = sns.load_dataset(\"iris\")\n",
    "x = \"species\"\n",
    "y = \"sepal_length\"\n",
    "\n",
    "pairs = [(\"setosa\", \"versicolor\"), (\"setosa\", \"virginica\"), (\"versicolor\", \"virginica\")]\n",
    "\n",
    "# Required descriptors for annotate\n",
    "custom_long_name = 'Bartlett statistical test for equal variances'\n",
    "custom_short_name = 'Bartlett'\n",
    "custom_func = bartlett\n",
    "custom_test = StatTest(custom_func, custom_long_name, custom_short_name)\n",
    "\n",
    "# Then, same as usual\n",
    "ax = sns.boxplot(data=df, x=x, y=y)\n",
    "annot.reset_configuration()\n",
    "annot.new_plot(ax, pairs, data=df, x=x, y=y)\n",
    "annot.configure(test=custom_test, comparisons_correction=None,\n",
    "                text_format='star').apply_test().annotate()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use a custom test with StatTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "setosa vs. versicolor: Log t-test, P_val:4.416e-18 Stat=-1.066e+01\n",
      "versicolor vs. virginica: Log t-test, P_val:1.979e-07 Stat=-5.598e+00\n",
      "setosa vs. virginica: Log t-test, P_val:3.486e-29 Stat=-1.605e+01\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f923ffe13c8>,\n [<statannotations.Annotation.Annotation at 0x7f9240f82780>,\n  <statannotations.Annotation.Annotation at 0x7f924111e358>,\n  <statannotations.Annotation.Annotation at 0x7f924245f208>])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statannotations.stats.StatTest import StatTest\n",
    "import numpy as np\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Any function that follows the StatTest API could go here\n",
    "def log_ttest(group_data1, group_data2, **stats_params):\n",
    "    group_data1_log = np.log(group_data1)\n",
    "    group_data2_log = np.log(group_data2)\n",
    "\n",
    "    return ttest_ind(group_data1_log, group_data2_log, **stats_params)\n",
    "\n",
    "df = sns.load_dataset(\"iris\")\n",
    "x = \"species\"\n",
    "y = \"sepal_length\"\n",
    "\n",
    "pairs = [(\"setosa\", \"versicolor\"), (\"setosa\", \"virginica\"), (\"versicolor\", \"virginica\")]\n",
    "\n",
    "# Required descriptors for annotate\n",
    "custom_long_name = 'Log t-test'\n",
    "custom_short_name = 'log-t'\n",
    "custom_func = log_ttest\n",
    "custom_test = StatTest(custom_func, custom_long_name, custom_short_name)\n",
    "\n",
    "# Then, same as usual\n",
    "ax = sns.boxplot(data=df, x=x, y=y)\n",
    "annot = Annotator(ax, pairs, data=df, x=x, y=y)\n",
    "annot.configure(test=custom_test, comparisons_correction=None,\n",
    "                text_format='star').apply_test().annotate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom p-values as input\n",
    "! multiple comparisons corrections will be applied if they are configured in annotator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'versicolor') stat=6.89e+00 p-value=8.66e-03\n",
      "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'virginica') stat=1.60e+01 p-value=6.38e-05\n",
      "Performing Bartlett statistical test for equal variances on pair: ('versicolor', 'virginica') stat=2.09e+00 p-value=1.48e-01\n",
      "pvalues: [0.008659557933880048, 6.378941946712554e-05, 0.14778816016231236]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import bartlett\n",
    "\n",
    "test_short_name = 'Bartlett'\n",
    "pvalues = []\n",
    "for pair in pairs:\n",
    "    data1 = df.groupby(x)[y].get_group(pair[0])\n",
    "    data2 = df.groupby(x)[y].get_group(pair[1])\n",
    "    stat, p = bartlett(data1, data2)\n",
    "    print(\"Performing Bartlett statistical test for equal variances on pair:\",\n",
    "          pair, \"stat={:.2e} p-value={:.2e}\".format(stat, p))\n",
    "    pvalues.append(p)\n",
    "print(\"pvalues:\", pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "setosa vs. versicolor: Custom statistical test, P_val:8.660e-03\n",
      "versicolor vs. virginica: Custom statistical test, P_val:1.478e-01\n",
      "setosa vs. virginica: Custom statistical test, P_val:6.379e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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GWq0Wrq6usLOzq3cAIrI+1vjibvWL6NHR0TInaXhmLYGwePFi/PHHH4iMjMT8+fNx4cIFrFy5UupsRERkAWaN6DMyMnDgwAHTxwMHDoSfn59koYiIyHLMGtG3bdsWeXn/e/W+tLTUIktnEhGR9Mwa0bdr1w4TJkzA6NGjodFokJSUhDZt2pimbxYvXixpSCIiundmFX3nzp3RuXNn08ectiEish5mFX1YWBh0Oh1ycnLQtWtX6PV6NGvW7G+3S05Oxttvv42ysjIMHjyYI38iIhmYNUd/+vRpeHt7Y9asWbh27RqGDx+OH374odZtLl26hGXLlmHz5s3YvXs3MjIyuD4OEZEMzCr6mJgYfPTRR3ByckK7du2wZs0arFq1qtZtEhMT4evri3bt2sHW1haxsbHo3bu3RUITEZH5zJq60el0eOSRR0wfDxs2DLGxsbVuk5OTA1tbW7zwwgv4/fffMXz4cLzyyitmB0tPTzf7sWSdioqK5I7QJBQVFeH777+XO4bsqr/fmuJzYfaiZoWFhVCpVABg1tKylZWVOHXqFD7++GM0b94cs2fPxpdffonAwECzgrm5ucHe3t6sx5J1io+PR1GB3CmUr2XLlvDw8JA7huzi4+MBQLHPhV6vv+sA2aypmxdeeAEhISG4evUq5s6di0mTJmH27Nm1btOmTRt4enrCxcUFDg4O8Pb2RlpaWt3TExFRvZg1otdqtXj44YeRkpICo9EIV1dXDBo0qNZtvLy8EBERgZs3b6JFixb45ptvMGLECIuEJmXIz8/Hn8UGLqUroavFBhjy8+WOQTIza0S/dOlSbNu2Df3798e7776LK1euYOHChbVu07t3b8yYMQOTJ0+Gr68vOnTogAkTJlgkNBERmc+sEX16ejri4+MRFxeHp556CvPmzTNrrj0oKAhBQUH1DknK5OzsDJvia7zxiIQ+TMtDSy5X0uSZNaIXQkCtViMlJQUDBw4EUHUlDhERNX5mFf2DDz6ImTNn4vLly+jfvz/mzZuH7t27S52NiIgswKypm+joaCQmJsLDwwO2trZ4/PHHMX78eKmzySY5ORmJiYmS7LugoOp6QicnJ4vv28fHB1qt1uL7JSLrZlbRN2/eHOPGjTN9PGnSJMkCKV31cs9SFD0R0Z2YVfRNjVarlWxk3JRvZ0ZE8jBrjp6IiKwXi56ISOFY9ERECseiJyJSOBY9EZHCseiJiBSOl1eSrK5a0eqVxeVGAICjnfWMj64WG9BS7hB1JNUbFqvvo1F9ibMlNfY3K7LoSTaurq5yR6iT6/8tivadrCd3S1jf8ywVF5emu3gei55kM3PmTLkj1Anf7NYwpHzDYlNlPX+DEhHRPWHRExEpHIueiEjhWPRERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoVj0RMRKRyLnohI4Vj0REQKx6InIlI4Fj0RkcJZ9TLFW7duNd1MwFpIefMDqbi6ulrdksJE9D9WXfRZWVlIzzgLjYOT3FHMZjRoAABnsv6QOYl5KnUFckcgonqy6qIHAI2DE5p3HiF3DMUqzUmSOwIR1RPn6ImIFI5FT0SkcCx6IiKFk3SOPjQ0FHl5ebCxqTrMihUr0Lt3bykPSUREt5Cs6IUQuHjxIg4fPmwqeiIianiSTd1UXy/+3HPPISAgAJ988olUhyIiolpINtS+efMmPD09sWTJElRUVODZZ5/FQw89hMGDB1vsGPn5+ajUFfASQAlV6gqQn28ndwwiqgfJir5v377o27ev6eOgoCAcPXrU7KJPT0//28fodLp7zkfm0+l0+P777+WOIbuioiIA4HNhpYqKihAfH4+goCC0bNlS7jgNSrKiP3XqFCoqKuDp6Qmgas6+LnP1bm5usLe3r/Ux7du3R0GZmm+YklBpThLat38AHh4eckeRXXx8PADwubBSmzdvRm5uLs6cOYPZs2fLHcfi9Hr9XQfIks3RFxUVYc2aNdDr9SguLsaXX34JHx8fqQ5HRHRXeXl5SEpKghAChw4dQn5+vtyRGpRkI3ovLy+cPn0a48ePh9FoxOTJk2tM5RBJJTk5GYmJiRbfr9QL0vn4+ECr1Uqy76bu888/h9FoBAAYjUZ8/vnnihzV342k1z2+8soreOWVV6Q8BFGDcXFxkTsC3aMjR47AYDAAAAwGAw4fPsyiJ7JmWq2WI2OqYfjw4UhMTITBYICNjQ28vLzkjtSguAQCESlecHAw1OqqulOr1QgODpY5UcNi0ROR4rm4uGDEiBFQqVTw9vaGs7Oz3JEalNVP3VjbG6aMhqpr/9U2DjInMU/VjUcekDsGUb0FBwcjNze3yY3mASsveldXV7kj1Fn1lRuurtZSng9Y5fNMdCsXFxesXr1a7hiysOqit8b7mFZfmhcdHS1zEiJqKjhHT0SkcCx6IiKFY9ETESkci56ISOFY9ERECseiJyJSOBY9EZHCseiJiBSORU9EpHAseiIihWPRExEpHIueiEjhWPRERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoWz6lsJSiU5ORmJiYmS7Lv6nrHVtxS0JB8fH2i1Wovvl4isG4u+gbm4uMgdgYiaGBb9HWi1Wo6MiUgxOEdPRKRwLHoiIoVj0RMRKRyLnohI4Vj0REQK1+iuuhFCAADKy8tlTkJEZD2qO7O6Q/+q0RV9RUUFAODcuXMyJyEisj4VFRVwcHCo8TmVuFP9y8hoNKKkpAS2trZQqVRyxyEisgpCCFRUVKBFixZQq2vOyje6oiciIsvii7FERArHoiciUjgWPRGRwrHoiYgUjkVPRKRwLHoiIoVj0RMRKRyLXmL//ve/sXfvXrljUD189tln+Oyzz+q8XUJCAhYsWCBBoqZt0aJF+Pnnn81+fFJSEjZu3GjRfVobvmFKYgsWLED//v0RGBgodxRqYAkJCUhNTcXq1avljkJNXKNb68YaXL16FeHh4SgtLYVarcbixYuhVqsRHR0NnU4HZ2dnvP7667h06RKSk5Nx4sQJ3H///ejRowcWLVqE3377DTY2Nnj11Vfx5JNP4ttvv8XatWsBAK1atcKbb74JFxcXxMbG4ttvv0VhYSHatm2L2NhYtGnTRuavvvELCwvD2LFjMWrUKABAYGAgli9fjtjYWBQUFMDBwQFLlixBz549sWDBAhQUFCAnJwfz58/HyZMnkZKSArVaDW9vb4SFheGtt94CAMyZMwd79uzBli1boFKp4O7ujqioKBgMBixevBhnz56FSqXC9OnTMX78+BqZfvrpJ6xatQp6vR7Ozs5YsWIFOnfujNDQULRq1Qrnz5/Hhg0b0KNHjwZ/vhqzO53LnJwcbN68GQCwdu1aGI1GdO3aFYsXL8Zrr72G3NxcdOrUCVevXsXbb7+N1NRU0y9crVaLgIAAHDt2DGVlZYiJiYGbmxtCQ0MRFhaG/v37Y926dTh06BA0Gg2efvppTJkyBampqYiNjYVOp8PNmzcRGRkJb29vOZ+auhFUZ2+99ZbYunWrEEKIo0ePiri4ODF27Fhx5coVIYQQX3/9tZgyZYoQQoiIiAixY8cOIYQQ//jHP8QHH3wghBAiNzdXDB48WFy/fl2EhISI06dPCyGEiIuLE9988424ePGiCAsLE5WVlUIIIebPny+2bdvWkF+m1Tp48KCYM2eOEEKI7Oxs4evrK55++mnxyy+/CCGEOH/+vBg5cqQQour8RERECCGEuHz5svD19RVCCFFaWipefvllodPpxKZNm8SmTZvE1atXhaenp/j999+FEEKEh4eLxMREERMTI6KiooQQQty4cUNotVpx5swZsWPHDhERESH0er3w8vIyneN9+/aJwMBAIYQQISEhYtOmTQ30zFifO53LkJAQceLECXHixAnh4eEhbt68KYQQIjo6WsTExAghhEhLSxM9evQQly5dMp0HIYTw8vISH374oRBCiH/+858iLCxMCCFM+9y3b58IDg4Wer1eFBcXi4CAAHHt2jUxZ84c8euvvwohhDh+/Ljw9/dvyKeh3jiivweenp6YM2cOzpw5g2HDhmHYsGHYvHkzZs+ebXpMcXHxbdudOHECK1euBAB06tQJvXv3xunTpzFixAiEhYXB29sbI0aMwODBgwEAERER2L59O7Kzs/HTTz/hwQcfbJgv0MoNGzYMK1asQHFxMfbu3QtfX19s2bIFkZGRpseUlpYiPz8fAPDYY48BAB544AHY29sjODgYXl5eCA8Ph729vWmbH3/8Ef369UO7du0AwPRX2ObNm/HGG28AAFxcXDBixAikpqbC0dERAHDx4kXcd999puOMGTMGS5cuRVFRUY3j0+1uPZfVo/FqDz30EFq2bAkASElJwbp16wAA7u7u6Nat2x33OXToUABA165dcfDgwRr/dvLkSYwZMwZ2dnaws7PDrl27AFSd68OHD2P//v04ffo0SkpKLP61SolFfw88PDzw1Vdf4ciRI9i3bx+2b9+Ojh07mr4pKisr8eeff962nbjl5RAhBCorKzF16lR4eXnh8OHDWLt2LdLS0jB06FDMmzcPU6dOxahRo6BWq++4zjTdzs7ODl5eXkhOTsb+/fvx3nvvYdu2babzA1RNvzk5OQGAaUlXGxsbbN++Hampqfj6668RHByMjz/+2LSNjY1NjRVV8/LyANz9vFYzGo23ZfzrY25dUpb+507n8q9F/9fnTqPRmPUzUv3L+06r4956ji9fvgwXFxeEhoZiwIABGDBgADw9PREeHl6fL6vB8aqbe7BmzRrs3r0bTz31FJYuXYrMzEwUFhbi1KlTAIAdO3aYvhE0Go3pB3rgwIGIj48HAFy6dAk//PAD+vTpg4kTJ6KkpARTp07F1KlTkZGRgZMnT6J///6YNGkSunTpgiNHjtQoD6rduHHj8OGHH8LJyQn/93//hy5dupiKPiUlBc8888xt22RkZCAkJARPPPEEIiIi8PDDDyM7O9v07+7u7vjpp59w/fp1AMAbb7yBpKSkGuc1Ly8PSUlJ6N+/v2k7V1dXFBQUIC0tDQCwb98+dOjQwfSLhmp367m8G09PT+zZswcAcPbsWZw/f77OS50/8cQTOHjwICoqKlBWVoYZM2bg119/xcWLF/Hyyy/jySefRFJSktX9LHJEfw9CQ0Mxb948JCQkQKPRYO3atWjVqpXpxTZHR0fExMQAAAYNGoT169ejZcuWWLRoEZYuXYqEhAQAwMqVK9G2bVvMnTsXCxYsgI2NDZo3b46VK1eiWbNmpheiAMDNzQ2XL1+W7Wu2Nh4eHigqKsKkSZMAVP3pvXz5crz//vuwtbVFbGzsbSXQs2dP9OnTB/7+/mjWrBn69euHJ598Er/88guAqqmdRYsWYfr06TAajejTpw8CAwNRVlaG5cuXY+zYsaisrMQLL7yAXr164ezZswCqRqWxsbGIiopCWVkZWrVqhdjY2IZ9QqzYrefybl566SVERkZi7NixePDBB9GmTZs6/7Xk4+OD9PR0BAYGwmg04tlnn8Vjjz2GoKAg+Pn5wcbGBgMHDoROp0NpaSmaN29eny+twfDySiJShF27dqFjx47w8PDAb7/9hpCQEBw6dOi2m3A0RRzRE5EiuLq6YtmyZTAajVCr1VixYgVL/r84oiciUjj+uiMiUjgWPRGRwrHoiYgUjkVPVE8zZ87Er7/+KncMorvii7FERArHyytJ0UpKShAZGYmcnByo1Wr06tULfn5+WL9+PTp06ICsrCw4ODhg9erVePjhh1FeXo5169bh5MmTqKysRM+ePbF48WI4OjoiOzsbS5cuRV5eHtRqNWbPng1fX19otVps3LgR7u7uSE5OxpYtW1BRUQEHBwdERESgb9++uHDhAhYtWoTy8nIIIRAUFHTHd+cSSYFTN6RoiYmJKCkpwa5du0zLFFy+fBnp6ekIDQ3Fnj17EBgYiPnz5wMA4uLioNFokJCQgN27d6Nt27amhbLmzp2L0aNH46uvvkJcXBzWr19fY/G6ixcvIjY2FnFxcdi5cyeioqIwZ84clJaWYtu2bdBqtUhISEBcXBxOnTp1xzVwiKTAET0pmoeHB2JjYxEaGopBgwZhypQpyMvLQ/fu3fH4448DACZMmIAVK1YgPz8fRxFRL2kAAAIiSURBVI4cQVFREY4fPw4AqKioQOvWrVFQUIDMzExMnDgRANC+fXscOnSoxrFSUlJw7do1TJ061fQ5lUqF3Nxc+Pj4ICIiAmlpafD09DTdw4CoIbDoSdE6deqExMREfPfddzhx4gSmTZuGFStWQKPR3PZYjUYDo9GIhQsXYtiwYQCqpn70ej1sbKp+VP66Pk5WVhY6dOhg+thoNMLT0xMbNmwwfe73339H27Zt0b17dxw4cADHjx/Ht99+i3feeQcJCQmmJY+JpMQhBSnav/71L0RGRmLIkCGYP38+hgwZgoyMDGRmZiIzMxMA8MUXX6Bv37647777MGTIEHz66acoLy+H0WjEkiVLsH79ejg6OqJXr17YuXMngKoCnzRpkmlNeaBq9cSUlBRcuHABAHD06FEEBARAp9Nh3rx52LdvH/z8/LBs2TI4OjoiNze34Z8QapJ41Q0pWmlpKRYuXIizZ8+iWbNmaN++PcaPH49Vq1ahe/fuuHLlClxcXLBq1Sp07NgROp0OMTExSE1NRWVlJXr06IGoqCg4OjoiJycHr7/+Ov7880+oVCrMmTMH3t7eNV6M/c9//oN3330XQgjY2Nhg4cKFePzxx00vxpaWlkKj0cDT0xPz58+v8zK6RPeCRU9NznfffYeoqCjs3btX7ihEDYJTN0RECscRPRGRwnFET0SkcCx6IiKFY9ETESkci56ISOFY9ERECseiJyJSuP8HCL32V9gi/qsAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=df, x=x, y=y)\n",
    "annot.new_plot(ax=ax, pairs=pairs,\n",
    "               data=df, x=x, y=y)\n",
    "(annot\n",
    " .configure(test=None, test_short_name=test_short_name)\n",
    " .set_pvalues(pvalues=pvalues)\n",
    " .annotate())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Non-hue violin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "setosa vs. versicolor: Custom statistical test, P_val:8.660e-03\n",
      "versicolor vs. virginica: Custom statistical test, P_val:1.478e-01\n",
      "setosa vs. virginica: Custom statistical test, P_val:6.379e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(data=df, x=x, y=y)\n",
    "annot.new_plot(ax=ax, pairs=pairs, plot=\"violinplot\",\n",
    "               data=df, x=x, y=y)\n",
    "(annot\n",
    " .configure(test=None, test_short_name=test_short_name)\n",
    " .set_pvalues(pvalues=pvalues)\n",
    " .annotate())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom annotation texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Thur vs. Fri: first pair\n",
      "Thur vs. Sat: second pair\n",
      "Sun vs. Fri: third pair\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "annot.new_plot(ax, pairs=[(\"Thur\", \"Fri\"),(\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
    "               data=df, x=x, y=y, order=order)\n",
    "annot.configure(loc='outside')\n",
    "annot.set_custom_annotations([\"first pair\", \"second pair\", \"third pair\"])\n",
    "annot.annotate()\n",
    "\n",
    "plt.savefig('example_custom_text_annot.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "With a log-scale swarm plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n",
      "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n",
      "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = pd.read_csv('samples.tsv', sep = '\\t')\n",
    "samples = samples.loc[samples.mutation_rate_samp < 1]\n",
    "samples = samples.drop(columns=['mutation_rate_samp'])\n",
    "samples = (\n",
    "    samples.melt(id_vars=['sampleID', 'subtype'],\n",
    "                 value_vars=['nonsynon_mutation_rate_samp','synon_mutation_rate_samp'],\n",
    "                 var_name='Synon_Nonsynon', value_name='mutation_rate_samp')\n",
    "    .replace('nonsynon_mutation_rate_samp', 'Nonsynonymous')\n",
    "    .replace('synon_mutation_rate_samp', 'Synonymous'))\n",
    "\n",
    "samples = samples.loc[samples.mutation_rate_samp > 0]\n",
    "\n",
    "significanceComparisons = [(('H3N2','Synonymous'), ('H3N2','Nonsynonymous')),\n",
    "                           (('H1N1','Synonymous'), ('H1N1','Nonsynonymous')),\n",
    "                           (('Influenza B','Synonymous'), ('Influenza B','Nonsynonymous'))]\n",
    "\n",
    "fig_args = {'x': 'subtype',\n",
    "            'y': 'mutation_rate_samp',\n",
    "            'hue':'Synon_Nonsynon',\n",
    "            'data': samples,\n",
    "            'order': ['H3N2', 'H1N1', 'Influenza B'],\n",
    "            'hue_order':['Nonsynonymous','Synonymous'],\n",
    "            'dodge': True}\n",
    "\n",
    "configuration = {'test':'Mann-Whitney',\n",
    "                 'comparisons_correction':None,\n",
    "                 'text_format':'star'}\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(12,6))\n",
    "ax.set_yscale('log')\n",
    "\n",
    "sns.stripplot(ax=ax, **fig_args)\n",
    "annotator = Annotator(ax=ax, pairs=significanceComparisons,\n",
    "                      **fig_args, plot='stripplot')\n",
    "annotator.configure(**configuration).apply_test().annotate()\n",
    "fig.savefig(f'flu_dataset_log_scale_in_axes_strip.svg', format='svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "With a horizontal orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n",
      "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n",
      "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f9241b22588>,\n [<statannotations.Annotation.Annotation at 0x7f92423c7b00>,\n  <statannotations.Annotation.Annotation at 0x7f92423c7828>,\n  <statannotations.Annotation.Annotation at 0x7f924242e4a8>])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,6))\n",
    "fig_args_horiz = {\n",
    "    **fig_args,\n",
    "    'y': 'subtype',\n",
    "    'x': 'mutation_rate_samp'\n",
    "}\n",
    "ax.set_xscale('log')\n",
    "sns.stripplot(ax=ax, orient='h', **fig_args_horiz)\n",
    "annotator.new_plot(ax, plot='swarmplot', orient='h', **fig_args_horiz)\n",
    "annotator.apply_and_annotate()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Hide non significant results"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n",
      "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n"
     ]
    },
    {
     "data": {
      "text/plain": "(<matplotlib.axes._subplots.AxesSubplot at 0x7f92410c7b70>,\n [<statannotations.Annotation.Annotation at 0x7f923fa69dd8>,\n  <statannotations.Annotation.Annotation at 0x7f923f99e748>,\n  <statannotations.Annotation.Annotation at 0x7f923f99e6d8>])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,6))\n",
    "fig_args_horiz = {\n",
    "    **fig_args,\n",
    "    'y': 'subtype',\n",
    "    'x': 'mutation_rate_samp'\n",
    "}\n",
    "ax.set_xscale('log')\n",
    "sns.stripplot(ax=ax, orient='h', **fig_args_horiz)\n",
    "annotator.new_plot(ax, plot='swarmplot', orient='h', **fig_args_horiz)\n",
    "annotator.configure(\n",
    "    hide_non_significant=True,\n",
    "    pvalue_thresholds=[[1e-4, \"****\"], [1e-3, \"***\"], [1e-2, \"**\"], [0.05, \"*\"]],  # removing ns as won't be shown\n",
    ")\n",
    "annotator.apply_and_annotate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Back to vertical, with a different format for pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n",
      "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n",
      "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,6))\n",
    "ax.set_yscale('log')\n",
    "\n",
    "sns.swarmplot(ax=ax, **fig_args)\n",
    "annotator.new_plot(ax, plot='swarmplot', **fig_args)\n",
    "annotator.reset_configuration()  # undo hide_non_signif and pvalues\n",
    "annotator.configure(**{**configuration, \"text_format\":\"simple\", \"text_offset\":6})\\\n",
    "    .apply_test().annotate()\n",
    "fig.savefig(f'flu_dataset_log_scale_in_axes.svg', format='svg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As a violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H1N1_Nonsynonymous vs. H1N1_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:5.014e-04 U_stat=2.624e+03\n",
      "H3N2_Nonsynonymous vs. H3N2_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:1.294e-03 U_stat=1.535e+04\n",
      "Influenza B_Nonsynonymous vs. Influenza B_Synonymous: Mann-Whitney-Wilcoxon test two-sided, P_val:2.026e-01 U_stat=3.340e+02\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,6))\n",
    "ax.set_yscale('log')\n",
    "\n",
    "sns.violinplot(ax=ax, **fig_args)\n",
    "# plt.legend(loc=\"lower right\")\n",
    "annotator.new_plot(ax, plot='violinplot', **fig_args)\n",
    "annotator.apply_test().annotate()  # No need to `configure` to use the same parameters\n",
    "y_lims = ax.get_ylim()\n",
    "ax.set_ylim(y_lims[0], 1.25 * y_lims[1])\n",
    "fig.savefig(f'flu_dataset_log_scale_in_axes_violin.png', format='png')"
   ]
  }
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