{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb7e89caa9e6d772",
   "metadata": {},
   "source": [
    "# Visualize Label Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c54a8d7c872547",
   "metadata": {},
   "source": [
    "Learn how to visualize and compare partitioned datasets when applying different `Partitioner`s or parameters.\n",
    "\n",
    "If you partition datasets to simulate heterogeneity through label skew and/or size skew, you can now effortlessly visualize the partitioned dataset using `flwr-datasets`.\n",
    "\n",
    "All the described visualization functions are compatible with all ``Partitioner`` you can find in\n",
    "[flwr_datasets.partitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.html#module-flwr_datasets.partitioner)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7220467f2c6ba432",
   "metadata": {},
   "source": [
    "## Install Flower Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c46514b679f394ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -q \"flwr-datasets[vision]\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ffd5b6836a5ee0",
   "metadata": {},
   "source": [
    "## Plot Label Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38fbbdfe6b930916",
   "metadata": {},
   "source": [
    "### Bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5778edf97a7ee04",
   "metadata": {},
   "source": [
    "Let's visualize the result of `DirichletPartitioner`.\n",
    "We will create a `FederatedDataset` and assign `DirichletPartitioner` to the `train` split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42397afaaf50529e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flwr_datasets import FederatedDataset\n",
    "from flwr_datasets.partitioner import DirichletPartitioner\n",
    "from flwr_datasets.visualization import plot_label_distributions\n",
    "\n",
    "\n",
    "fds = FederatedDataset(\n",
    "    dataset=\"cifar10\",\n",
    "    partitioners={\n",
    "        \"train\": DirichletPartitioner(\n",
    "            num_partitions=10,\n",
    "            partition_by=\"label\",\n",
    "            alpha=0.3,\n",
    "            seed=42,\n",
    "            min_partition_size=0,\n",
    "        ),\n",
    "    },\n",
    ")\n",
    "\n",
    "partitioner = fds.partitioners[\"train\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d5855ee8a605d3",
   "metadata": {},
   "source": [
    "Once we have the partitioner with the dataset assigned, we are ready to pass it to the plotting function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75b48256ed68897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dhw7F4sWL4e3tDT8/P6SlpSEsLAwNGjRQ+E6AlN+DRERlpqLdYIiqjMJt5t61rVyhX3/9VWjfvr2gr68v6OvrC/Xr1xf8/f2FpKQksU6nTp2EBg0alLj/wq3fHj169NY6crlcmDt3rmBvby9oa2sLzZo1E/bv3//WbesWLFhQbDt79+4V3NzcBA0NDYWtE99sRxAE4Y8//hBatGghaGlpKWxb9+ZWdYIgCHl5eUJwcLDg6OgoaGpqCnZ2dsLUqVMVticUhFdb1RW3feDbttB7E4C3PmbPni0IgiCcOXNGaNOmjaCrqyvY2toKkyZNEo4cOSIAEE6cOKHQZ4MGDYTz588LHh4ego6OjmBvby+sXLmySL+5ubnCvHnzhAYNGgja2tqCqamp0KJFCyE4OFjIyMhQGN/r2yXOmTNHaNWqlWBiYiLo6uoK9evXF0JCQsStGN+mcLvEwoempqZgYWEhdOzYUQgJCSmyFaYgFN0u8cKFC8LAgQOF2rVrC9ra2oKlpaXQs2dP4fz58wrXve119vHxEfT19YuN713vu0WLFgl2dnaCtra20KFDB+HSpUtFrv/pp5+EOnXqCFpaWkLTpk2FI0eOVJn3IBFRecgEgd9mISIiIiJSNa4xJyIiIiKSACbmREREREQSwMSciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAG8wVAFkcvluH//PgwNDSv8NtZERESkHIIg4NmzZ7C1tYWaGucrSbWYmFeQ+/fvw87OTtVhEBERURncu3cPtWrVUnUY9J5TaWJ+6tQpLFiwAPHx8Xjw4AF2796N3r17i+cFQcDMmTOxfv16pKeno127dlizZg2cnZ3FOk+ePMHo0aPx22+/QU1NDf369cOyZctgYGAg1rl8+TL8/f0RFxcHCwsLjB49WuEW0ADwyy+/YPr06UhJSYGzszPmzZsn3pa6JAwNDQG8+odtZGRUxmeEiIiIKlNmZibs7OzE3+NEqqTSxDw7OxtNmjTB0KFD0bdv3yLn58+fj+XLlyMiIgKOjo6YPn06vL29cf36dejo6AAABg0ahAcPHiAyMhJ5eXkYMmQIRowYga1btwJ49Q+uS5cu8PLyQlhYGK5cuYKhQ4fCxMQEI0aMAAD88ccfGDhwIEJDQ9GzZ09s3boVvXv3xoULF9CwYcMSjaVw+YqRkRETcyIioiqGy1BJCmSCIAiqDgJ49Q/i9RlzQRBga2uLb775BhMmTAAAZGRkwMrKChs3bsSAAQOQmJgINzc3xMXFwd3dHQBw+PBhdO/eHf/9739ha2uLNWvWYNq0aXj48CG0tLQAAFOmTMGePXtw48YNAED//v2RnZ2N/fv3i/G0adMGTZs2RVhYWIniz8zMhLGxMTIyMpiYExERVRH8/U1SItlvOSQnJ+Phw4fw8vISy4yNjdG6dWvExMQAAGJiYmBiYiIm5QDg5eUFNTU1xMbGinU6duwoJuUA4O3tjaSkJDx9+lSs83o/hXUK+yEiIiIiUjbJfvnz4cOHAAArKyuFcisrK/Hcw4cPYWlpqXBeQ0MDZmZmCnUcHR2LtFF4ztTUFA8fPnxnP8XJyclBTk6OeJyZmVma4RERERERKZDsjLnUhYaGwtjYWHxwRxYiIiIiKg/JJubW1tYAgNTUVIXy1NRU8Zy1tTXS0tIUzufn5+PJkycKdYpr4/U+3lan8Hxxpk6dioyMDPFx79690g6RiIiIiEgk2cTc0dER1tbWiIqKEssyMzMRGxsLDw8PAICHhwfS09MRHx8v1jl+/Djkcjlat24t1jl16hTy8vLEOpGRkXBxcYGpqalY5/V+CusU9lMcbW1tcQcW7sRCREREROWl0sQ8KysLCQkJSEhIAPDqC58JCQm4e/cuZDIZAgMDMWfOHOzbtw9XrlzB4MGDYWtrK+7c4urqiq5du2L48OE4d+4czpw5g4CAAAwYMAC2trYAgM8//xxaWlrw8/PDtWvXsH37dixbtgzjx48X4xg7diwOHz6MRYsW4caNGwgKCsL58+cREBBQ2U8JEREREb2vBBU6ceKEAKDIw8fHRxAEQZDL5cL06dMFKysrQVtbW+jcubOQlJSk0Mbjx4+FgQMHCgYGBoKRkZEwZMgQ4dmzZwp1Ll26JLRv317Q1tYWatasKXz//fdFYtmxY4dQr149QUtLS2jQoIFw4MCBUo0lIyNDACBkZGSU7kkgIiIileHvb5ISyexjXtVxH1QiIqKqh7+/SUoku8aciIiIiOh9wsSciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAFMzImIiIiIJEBD1QEQEREpW50N/ZTex59+vyq9DyKq3jhjTkREREQkAUzMiYiIiIgkgIk5EREREZEEMDEnIiIiIpIAJuZERERERBLAxJyIiIiISAKYmBMRERERSQATcyIiIiIiCWBiTkREREQkAUzMiYiIiIgkgIk5EREREZEEMDEnIiIiIpIAJuZERERERBLAxJyIiIiISAKYmBMRERERSQATcyIiIiIiCWBiTkREREQkAUzMiYiIiIgkQEPVARARESlb33pmqg6BiOhfccaciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAFMzImIiIiIJICJORERERGRBDAxJyIiIiKSACbmREREREQSwMSciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAFMzImIiIiIJICJORERERGRBDAxJyIiIiKSACbmREREREQSwMSciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAFMzImIiIiIJICJORERERGRBDAxJyIiIiKSAA1VB0BERKRsk9w7qDoEIqJ/xRlzIiIiIiIJYGJORERERCQBXMpCREREVAnkcjlyc3NVHQZVIk1NTairq5e4PhNzIiIiIiXLzc1FcnIy5HK5qkOhSmZiYgJra2vIZLJ/rcvEnIiIiEiJBEHAgwcPoK6uDjs7O6ipcSXx+0AQBDx//hxpaWkAABsbm3+9hok5ERERkRLl5+fj+fPnsLW1hZ6enqrDoUqkq6sLAEhLS4OlpeW/LmvhRzYiIiIiJSooKAAAaGlpqTgSUoXCD2N5eXn/WpeJOREREVElKMkaY6p+SvO6MzEnIiIiIpIAJuZEREREVCE2btwIExOTcrcjk8mwZ8+ecrdT1TAxJyIiIiKRr68vevfureow3ktMzImIiIiIJEDSiXlBQQGmT58OR0dH6OrqwsnJCbNnz4YgCGIdQRAwY8YM2NjYQFdXF15eXrh165ZCO0+ePMGgQYNgZGQEExMT+Pn5ISsrS6HO5cuX0aFDB+jo6MDOzg7z58+vlDESERERVRWLFy9Go0aNoK+vDzs7O3z99ddFcioA2LNnD5ydnaGjowNvb2/cu3dP4fzevXvRvHlz6OjooE6dOggODkZ+fn6xfebm5iIgIAA2NjbQ0dGBvb09QkNDlTI+VZN0Yj5v3jysWbMGK1euRGJiIubNm4f58+djxYoVYp358+dj+fLlCAsLQ2xsLPT19eHt7Y2XL1+KdQYNGoRr164hMjIS+/fvx6lTpzBixAjxfGZmJrp06QJ7e3vEx8djwYIFCAoKwrp16yp1vERERERSpqamhuXLl+PatWuIiIjA8ePHMWnSJIU6z58/R0hICDZt2oQzZ84gPT0dAwYMEM///vvvGDx4MMaOHYvr169j7dq12LhxI0JCQortc/ny5di3bx927NiBpKQkbNmyBQ4ODsocpspI+gZDf/zxB3r16oUePXoAABwcHPDzzz/j3LlzAF7Nli9duhTfffcdevXqBQDYtGkTrKyssGfPHgwYMACJiYk4fPgw4uLi4O7uDgBYsWIFunfvjoULF8LW1hZbtmxBbm4ufvzxR2hpaaFBgwZISEjA4sWLFRJ4IiIiovdZYGCg+LODgwPmzJmDkSNHYvXq1WJ5Xl4eVq5cidatWwMAIiIi4OrqinPnzqFVq1YIDg7GlClT4OPjAwCoU6cOZs+ejUmTJmHmzJlF+rx79y6cnZ3Rvn17yGQy2NvbK3eQKiTpGfO2bdsiKioKN2/eBABcunQJp0+fRrdu3QAAycnJePjwIby8vMRrjI2N0bp1a8TExAAAYmJiYGJiIiblAODl5QU1NTXExsaKdTp27Kiw8b+3tzeSkpLw9OlTpY+TiIiIqCo4duwYOnfujJo1a8LQ0BBffvklHj9+jOfPn4t1NDQ00LJlS/G4fv36MDExQWJiIoBX+dysWbNgYGAgPoYPH44HDx4otFPI19cXCQkJcHFxwZgxY3D06FHlD1RFJD1jPmXKFGRmZqJ+/fpQV1dHQUEBQkJCMGjQIADAw4cPAQBWVlYK11lZWYnnHj58CEtLS4XzGhoaMDMzU6jj6OhYpI3Cc6ampkViy8nJQU5OjnicmZlZnqESERERSVpKSgp69uyJUaNGISQkBGZmZjh9+jT8/PyQm5sr3uHy32RlZSE4OBh9+/Ytck5HR6dIWfPmzZGcnIxDhw7h2LFj+Oyzz+Dl5YWdO3eWe0xSI+nEfMeOHdiyZQu2bt0qLi8JDAyEra2t+OcPVQkNDUVwcLBKYyAiIiKqLPHx8ZDL5Vi0aBHU1F4tutixY0eRevn5+Th//jxatWoFAEhKSkJ6ejpcXV0BvEq0k5KSULdu3RL3bWRkhP79+6N///745JNP0LVrVzx58gRmZmYVMDLpkHRiPnHiREyZMkX8wkCjRo3w119/ITQ0FD4+PrC2tgYApKamwsbGRrwuNTUVTZs2BQBYW1sjLS1Nod38/Hw8efJEvN7a2hqpqakKdQqPC+u8aerUqRg/frx4nJmZCTs7u3KMloiIiEgaMjIykJCQoFBWo0YN5OXlYcWKFfj4449x5swZhIWFFblWU1MTo0ePxvLly6GhoYGAgAC0adNGTNRnzJiBnj17onbt2vjkk0+gpqaGS5cu4erVq5gzZ06R9hYvXgwbGxs0a9YMampq+OWXX2BtbV0hNzKSGkmvMX/+/Ln4iayQuro65HI5AMDR0RHW1taIiooSz2dmZiI2NhYeHh4AAA8PD6SnpyM+Pl6sc/z4ccjlcvFLCR4eHjh16hTy8vLEOpGRkXBxcSl2GQsAaGtrw8jISOFBREREVB1ER0ejWbNmCo/Nmzdj8eLFmDdvHho2bIgtW7YUu22hnp4eJk+ejM8//xzt2rWDgYEBtm/fLp739vbG/v37cfToUbRs2RJt2rTBkiVL3vqlTkNDQ8yfPx/u7u5o2bIlUlJScPDgwSI5YnUgE17fFFxifH19cezYMaxduxYNGjTAxYsXMWLECAwdOhTz5s0D8GpLxe+//x4RERFwdHTE9OnTcfnyZVy/fl1cp9StWzekpqYiLCwMeXl5GDJkCNzd3bF161YArz4Vuri4oEuXLpg8eTKuXr2KoUOHYsmSJSXelSUzMxPGxsbIyMhgkk5EJDFpLzYpvQ9L3cFK74MqXmX8/n758iWSk5Ph6OhY7Bpqqt5K8/pLeinLihUrMH36dHz99ddIS0uDra0tvvrqK8yYMUOsM2nSJGRnZ2PEiBFIT09H+/btcfjwYYWBb9myBQEBAejcuTPU1NTQr18/LF++XDxvbGyMo0ePwt/fHy1atECNGjUwY8YMbpVIVY5sVBul9yGsOav0PoiIiN5Hkp4xr0o4Y05SwMScqHicMae34Yw5KVtpXv/qtziHiIiIiKgKYmJORERERCQBTMyJiIiIiCSAiTkRERERkQQwMSciIiIikgAm5kREREREEsDEnIiIiIhIApiYExEREZFKpaSkQCaTISEhQdWhqJSk7/xJREREVF1Vxk3hXleWG8R5enqiadOmWLp0acUHREVwxpyIiIiIykQQBOTn56s6jGqDiTkRERERFeHr64uTJ09i2bJlkMlkkMlk2LhxI2QyGQ4dOoQWLVpAW1sbp0+fhq+vL3r37q1wfWBgIDw9PcVjuVyO+fPno27dutDW1kbt2rUREhJSbN8FBQUYOnQo6tevj7t37ypxlNLCpSxEREREVMSyZctw8+ZNNGzYELNmzQIAXLt2DQAwZcoULFy4EHXq1IGpqWmJ2ps6dSrWr1+PJUuWoH379njw4AFu3LhRpF5OTg4GDhyIlJQU/P7777CwsKi4QUkcE3MiIiIiKsLY2BhaWlrQ09ODtbU1AIiJ9KxZs/DRRx+VuK1nz55h2bJlWLlyJXx8fAAATk5OaN++vUK9rKws9OjRAzk5OThx4gSMjY0raDRVA5eyEBEREVGpuLu7l6p+YmIicnJy0Llz53fWGzhwILKzs3H06NH3LikHmJgTERERUSnp6+srHKupqUEQBIWyvLw88WddXd0Stdu9e3dcvnwZMTEx5Q+yCmJiTkRERETF0tLSQkFBwb/Ws7CwwIMHDxTKXt+T3NnZGbq6uoiKinpnO6NGjcL333+P//znPzh58mSZYq7KuMaciIiIiIrl4OCA2NhYpKSkwMDAAHK5vNh6H374IRYsWIBNmzbBw8MDP/30E65evYpmzZoBAHR0dDB58mRMmjQJWlpaaNeuHR49eoRr167Bz89Poa3Ro0ejoKAAPXv2xKFDh4qsQ6/OOGNORERERMWaMGEC1NXV4ebmBgsLi7duXejt7Y3p06dj0qRJaNmyJZ49e4bBgwcr1Jk+fTq++eYbzJgxA66urujfvz/S0tKKbS8wMBDBwcHo3r07/vjjjwofl1TJhDcXBFGZZGZmwtjYGBkZGTAyMlJ1OPSeqoy7yJXlznFEqpb2YpPS+7DUHfzvlUhyKuP398uXL5GcnAxHR0fo6OgopQ+SrtK8/pwxJyIiIiKSACbmREREREQSwMSciIiIiEgCmJgTEREREUkAE3MiIiIiIglgYk5EREREJAFMzImIiIiIJICJORERERGRBDAxJyIiIiKSACbmRERERFQqvr6+6N279zvrODg4YOnSpZUST3WhoeoAiIiIiN5HdTb0q9T+/vT7tVL7i4uLg76+fqX2WdUxMSciIiKiCmdhYaHqEKocLmUhIiIiomLt3LkTjRo1gq6uLszNzeHl5YXs7Gzx/MKFC2FjYwNzc3P4+/sjLy9PPPfmUhaZTIY1a9agW7du0NXVRZ06dbBz587KHI7kMTEnIiIioiIePHiAgQMHYujQoUhMTER0dDT69u0LQRAAACdOnMCdO3dw4sQJREREYOPGjdi4ceM725w+fTr69euHS5cuYdCgQRgwYAASExMrYTRVA5eyEBEREVERDx48QH5+Pvr27Qt7e3sAQKNGjcTzpqamWLlyJdTV1VG/fn306NEDUVFRGD58+Fvb/PTTTzFs2DAAwOzZsxEZGYkVK1Zg9erVyh1MFcEZcyIiIiIqokmTJujcuTMaNWqETz/9FOvXr8fTp0/F8w0aNIC6urp4bGNjg7S0tHe26eHhUeSYM+b/j4k5ERERERWhrq6OyMhIHDp0CG5ublixYgVcXFyQnJwMANDU1FSoL5PJIJfLVRFqtcHEnIiIiIiKJZPJ0K5dOwQHB+PixYvQ0tLC7t27y9ze2bNnixy7urqWN8xqg2vMiYiIiKiI2NhYREVFoUuXLrC0tERsbCwePXoEV1dXXL58uUxt/vLLL3B3d0f79u2xZcsWnDt3Dhs2bKjgyKsuJuZEREREVISRkRFOnTqFpUuXIjMzE/b29li0aBG6deuG7du3l6nN4OBgbNu2DV9//TVsbGzw888/w83NrYIjr7qYmBMRUbVn8aISOtGthD6oWqnsO3GWlqurKw4fPlzsueK2RXx9z3IASElJKVLH1tYWR48erYDoqieuMSciIiIikgAm5kREREREEsClLERERESkdIV3DKW344w5EREREZEEMDEnIiIiIpIAJuZERERERBLAxJyIiIiISAKYmBMRERERSQB3ZVGxCb8PV2r7CzusV2r7RERERFQxOGNOREREREV4enoiMDBQ1WG8VzhjTkRERKQCyv6r+Zv4V3Tp44w5ERERESldbm6uqkOQPCbmRERERFQsuVyOSZMmwczMDNbW1ggKChLP3b17F7169YKBgQGMjIzw2WefITU1VTwfFBSEpk2b4ocffoCjoyN0dHQAADt37kSjRo2gq6sLc3NzeHl5ITs7W7zuhx9+gKurK3R0dFC/fn2sXr260saralzKQkRERETFioiIwPjx4xEbG4uYmBj4+vqiXbt26Ny5s5iUnzx5Evn5+fD390f//v0RHR0tXn/79m38+uuv2LVrF9TV1fHgwQMMHDgQ8+fPR58+ffDs2TP8/vvvEAQBALBlyxbMmDEDK1euRLNmzXDx4kUMHz4c+vr68PHxUdGzUHmYmBMRERFRsRo3boyZM2cCAJydnbFy5UpERUUBAK5cuYLk5GTY2dkBADZt2oQGDRogLi4OLVu2BPBq+cqmTZtgYWEBALhw4QLy8/PRt29f2NvbAwAaNWok9jdz5kwsWrQIffv2BQA4Ojri+vXrWLt27XuRmHMpCxEREREVq3HjxgrHNjY2SEtLQ2JiIuzs7MSkHADc3NxgYmKCxMREscze3l5MygGgSZMm6Ny5Mxo1aoRPP/0U69evx9OnTwEA2dnZuHPnDvz8/GBgYCA+5syZgzt37ih5pNLAGXMiIiIiKpampqbCsUwmg1wuL/H1+vr6Csfq6uqIjIzEH3/8gaNHj2LFihWYNm0aYmNjoaenBwBYv349WrduXeS69wFnzImIiIioVFxdXXHv3j3cu3dPLLt+/TrS09Ph5ub2zmtlMhnatWuH4OBgXLx4EVpaWti9ezesrKxga2uLP//8E3Xr1lV4ODo6KntIksAZcyIiIiIqFS8vLzRq1AiDBg3C0qVLkZ+fj6+//hqdOnWCu7v7W6+LjY1FVFQUunTpAktLS8TGxuLRo0dwdXUFAAQHB2PMmDEwNjZG165dkZOTg/Pnz+Pp06cYP358ZQ1PZZiYExEREVGpyGQy7N27F6NHj0bHjh2hpqaGrl27YsWKFe+8zsjICKdOncLSpUuRmZkJe3t7LFq0CN26dQMADBs2DHp6eliwYAEmTpwIfX19NGrU6L25A6lMKNyfhsolMzMTxsbGyMjIgJGRUYmvU/Zdv3iXr/eLbFQbpfchrDmr9D6IKprwZJPS+5CZDVZ6H1Txyvr7uzRevnyJ5ORkhb286f1Rmtefa8yJiIiIiCRA8ktZ/v77b0yePBmHDh3C8+fPUbduXYSHh4vrlwRBwMyZM7F+/Xqkp6ejXbt2WLNmDZydncU2njx5gtGjR+O3336Dmpoa+vXrh2XLlsHAwECsc/nyZfj7+yMuLg4WFhYYPXo0Jk2apPTxedU2VXofRERERCR9kp4xf/r0Kdq1awdNTU0cOnQI169fx6JFi2Bq+v/J7Pz587F8+XKEhYUhNjYW+vr68Pb2xsuXL8U6gwYNwrVr1xAZGYn9+/fj1KlTGDFihHg+MzMTXbp0gb29PeLj47FgwQIEBQVh3bp1lTpeIiIiInp/SXrGfN68ebCzs0N4eLhY9vp2OYIgYOnSpfjuu+/Qq1cvAK/uOmVlZYU9e/ZgwIABSExMxOHDhxEXFyfOsq9YsQLdu3fHwoULYWtriy1btiA3Nxc//vgjtLS00KBBAyQkJGDx4sUKCTwRERERkbJIesZ83759cHd3x6effgpLS0s0a9YM69f//5cZk5OT8fDhQ3h5eYllxsbGaN26NWJiYgAAMTExMDExUdi6x8vLC2pqaoiNjRXrdOzYEVpaWmIdb29vJCUliXejelNOTg4yMzMVHkREREREZSXpxPzPP/8U14sfOXIEo0aNwpgxYxAREQEAePjwIQDAyspK4TorKyvx3MOHD2FpaalwXkNDA2ZmZgp1imvj9T7eFBoaCmNjY/Hx+i1piYiIiIhKS9KJuVwuR/PmzTF37lw0a9YMI0aMwPDhwxEWFqbq0DB16lRkZGSIj9fvfEVEREREVFqSXmNuY2NT5Laurq6u+PXXXwEA1tbWAIDU1FTY2NiIdVJTU9G0aVOxTlpamkIb+fn5ePLkiXi9tbU1UlNTFeoUHhfWeZO2tja0tbXLODIiIqLS4X0KiKo/Sc+Yt2vXDklJSQplN2/ehL29PYBXXwS1trZGVFSUeD4zMxOxsbHw8PAAAHh4eCA9PR3x8fFinePHj0Mul6N169ZinVOnTiEvL0+sExkZCRcXF4UdYIiIiIiIlEXSifm4ceNw9uxZzJ07F7dv38bWrVuxbt06+Pv7A3h1O9jAwEDMmTMH+/btw5UrVzB48GDY2tqid+/eAF7NsHft2hXDhw/HuXPncObMGQQEBGDAgAGwtbUFAHz++efQ0tKCn58frl27hu3bt2PZsmUYP368qoZOREREpHKCIGDEiBEwMzODTCZDQkKCqkOq1iS9lKVly5bYvXs3pk6dilmzZsHR0RFLly7FoEGDxDqTJk1CdnY2RowYgfT0dLRv3x6HDx9WuOXpli1bEBAQgM6dO4s3GFq+fLl43tjYGEePHoW/vz9atGiBGjVqYMaMGdwqkYiIiJTm8F/Kv5Hh67razy/1NYcPH8bGjRsRHR2NOnXqoEaNGkqIjApJOjEHgJ49e6Jnz55vPS+TyTBr1izMmjXrrXXMzMywdevWd/bTuHFj/P7772WOk4iIiKi6uXPnDmxsbNC2bdtiz+fm5ipsN03lI/nEnKSPX0giIiKqfnx9fcUtqmUyGezt7eHg4ICGDRtCQ0MDP/30Exo1aoQTJ07g5MmTmDhxIi5dugQzMzP4+Phgzpw50NB4lWo+e/YMI0eOxJ49e2BkZIRJkyZh7969aNq0KZYuXarCUUqLpNeYExEREZFqLFu2DLNmzUKtWrXw4MEDxMXFAQAiIiKgpaWFM2fOICwsDH///Te6d++Oli1b4tKlS1izZg02bNiAOXPmiG2NHz8eZ86cwb59+xAZGYnff/8dFy5cUNXQJIsz5kRERERUhLGxMQwNDaGurq6wfbSzszPmz///9erTpk2DnZ0dVq5cCZlMhvr16+P+/fuYPHkyZsyYgezsbERERGDr1q3o3LkzACA8PFzchIP+HxNzIiIiIiqxFi1aKBwnJibCw8MDMplMLGvXrh2ysrLw3//+F0+fPkVeXh5atWolnjc2NoaLi0ulxVxVMDEnIqJqT7is/D+ZyzwHK70PIinQ19dXdQjVFhNzomrE0b2mqkMgIqL3TOFd2QVBEGfNz5w5A0NDQ9SqVQumpqbQ1NREXFwcateuDQDIyMjAzZs30bFjR1WGLjlMzFWsuWVDVYdAREREVGZff/01li5ditGjRyMgIABJSUmYOXMmxo8fDzU1NRgaGsLHxwcTJ06EmZkZLC0tMXPmTKipqSksfyEm5ipn8ULJHegquX0iIiJ6r9WsWRMHDx7ExIkT0aRJE5iZmcHPzw/fffedWGfx4sUYOXIkevbsKW6XeO/ePYUbQhITcyIiIiKVKMudOCtbYGAgAgMDxePo6Ohi63Xq1Annzp17azuGhobYsmWLeJydnY3g4GDeZf0NZdrHvE6dOnj8+HGR8vT0dNSpU6fcQRERERFR9XHx4kX8/PPPuHPnDi5cuIBBgwYBAHr16qXiyKSlTDPmKSkpKCgoKFKek5ODv//+u9xBEREREVH1snDhQiQlJUFLSwstWrTA77//jho1aqg6LEkpVWK+b98+8ecjR47A2NhYPC4oKEBUVBQcHBwqLDgiIiIiqvqaNWuG+Ph4VYcheaVKzHv37g0AkMlk8PHxUTinqakJBwcHLFq0qMKCIyIiIiJ6X5QqMZfL5QAAR0dHxMXF8c8PREREREQVpExrzJOTkys6DiIiIiKi91qZt0uMiopCVFQU0tLSxJn0Qj/++GO5AyMiIiIiep+UKTEPDg7GrFmz4O7uDhsbG961iYiIiIionMqUmIeFhWHjxo348ssvKzoeIiIiIqL3UpluMJSbm4u2bdtWdCxEREREJHGenp4KdwOlilOmGfNhw4Zh69atmD59ekXHQ0RERPReSHuxqVL7s9QdXKn9UemVKTF/+fIl1q1bh2PHjqFx48bQ1NRUOL948eIKCY6IiIiI6H1RpqUsly9fRtOmTaGmpoarV6/i4sWL4iMhIaGCQyQiIiIiVcjOzsbgwYNhYGAAGxubIjeSfPr0KQYPHgxTU1Po6emhW7duuHXrlkKd9evXw87ODnp6eujTpw8WL14MExOTShxF1VGmGfMTJ05UdBxEREREJDETJ07EyZMnsXfvXlhaWuLbb7/FhQsX0LRpUwCAr68vbt26hX379sHIyAiTJ09G9+7dcf36dWhqauLMmTMYOXIk5s2bh//85z84duwYl0K/Q5n3MaeKIVy+oNT2ZZ5cT0ZERESll5WVhQ0bNuCnn35C586dAQARERGoVasWAIgJ+ZkzZ8RNQbZs2QI7Ozvs2bMHn376KVasWIFu3bphwoQJAIB69erhjz/+wP79+1UzKIkrU2L+wQcfvHPv8uPHj5c5ICIiIiJSvTt37iA3NxetW7cWy8zMzODi4gIASExMhIaGhsJ5c3NzuLi4IDExEQCQlJSEPn36KLTbqlUrJuZvUabEvPDPF4Xy8vKQkJCAq1evwsfHpyLiIiIiIiJ6r5QpMV+yZEmx5UFBQcjKyipXQERERESkek5OTtDU1ERsbCxq164N4NWXPW/evIlOnTrB1dUV+fn5iI2NFZeyPH78GElJSXBzcwMAuLi4IC4uTqHdN4/p/5VpV5a3+eKLL/Djjz9WZJNEREREpAIGBgbw8/PDxIkTcfz4cVy9ehW+vr5QU3uVPjo7O6NXr14YPnw4Tp8+jUuXLuGLL75AzZo10atXLwDA6NGjcfDgQSxevBi3bt3C2rVrcejQoXcuiX6fVWhiHhMTAx0dnYpskoiIiIhUZMGCBejQoQM+/vhjeHl5oX379mjRooV4Pjw8HC1atEDPnj3h4eEBQRBw8OBB8R437dq1Q1hYGBYvXowmTZrg8OHDGDduHPPFtyjTUpa+ffsqHAuCgAcPHuD8+fPcAoeIiIioBKrCnTgNDAywefNmbN68WSybOHGi+LOpqSk2bXr3HUyHDx+O4cOHKxzXrVu34oOtBsqUmBsbGyscq6mpwcXFBbNmzUKXLl0qJDAiIiIiqvoWLlyIjz76CPr6+jh06BAiIiKwevVqVYclSWVKzMPDwys6DiIiIiKqhs6dO4f58+fj2bNnqFOnDpYvX45hw4apOixJKtcNhuLj48V9Khs0aIBmzZpVSFBUtRya0lHVIRAREZFE7dixQ9UhVBllSszT0tIwYMAAREdHw8TEBACQnp6ODz74ANu2bYOFhUVFxkhEREREVO2VaVeW0aNH49mzZ7h27RqePHmCJ0+e4OrVq8jMzMSYMWMqOkYiIiIiomqvTDPmhw8fxrFjx+Dq6iqWubm5YdWqVfzyJxERERFRGZRpxlwul4v7U75OU1MTcrm83EEREREREb1vypSYf/jhhxg7dizu378vlv39998YN24cOnfuXGHBERERERG9L8qUmK9cuRKZmZlwcHCAk5MTnJyc4OjoiMzMTKxYsaKiYyQiIiIiqvbKtMbczs4OFy5cwLFjx3Djxg0AgKurK7y8vCo0OCIiIiKSFk9PTzRt2hRLly5VdSjVTqkS8+PHjyMgIABnz56FkZERPvroI3z00UcAgIyMDDRo0ABhYWHo0KGDUoIlIiIiqi6EJ+++lX1Fk5kNrtT+qPRKtZRl6dKlGD58OIyMjIqcMzY2xldffYXFixdXWHBERERE9H7Jzc1VdQgqU6rE/NKlS+jatetbz3fp0gXx8fHlDoqIiIiIVC87OxuDBw+GgYEBbGxssGjRIoXzOTk5mDBhAmrWrAl9fX20bt0a0dHRCnVOnz6NDh06QFdXF3Z2dhgzZgyys7PF8w4ODpg9ezYGDx4MIyMjjBgxojKGJkmlSsxTU1OL3SaxkIaGBh49elTuoIiIiIhI9SZOnIiTJ09i7969OHr0KKKjo3HhwgXxfEBAAGJiYrBt2zZcvnwZn376Kbp27Ypbt24BAO7cuYOuXbuiX79+uHz5MrZv347Tp08jICBAoZ+FCxeiSZMmuHjxIqZPn16pY5SSUq0xr1mzJq5evYq6desWe/7y5cuwsbGpkMCIiIiISHWysrKwYcMG/PTTT+J22BEREahVqxYA4O7duwgPD8fdu3dha2sLAJgwYQIOHz6M8PBwzJ07F6GhoRg0aBACAwMBAM7Ozli+fDk6deqENWvWQEdHB8Crrbi/+eabyh+kxJQqMe/evTumT5+Orl27ik9koRcvXmDmzJno2bNnhQZIRERERJXvzp07yM3NRevWrcUyMzMzuLi4AACuXLmCgoIC1KtXT+G6nJwcmJubA3i1DPry5cvYsmWLeF4QBMjlciQnJ4t3kXd3d1f2cKqEUiXm3333HXbt2oV69eohICBAfGFu3LiBVatWoaCgANOmTVNKoERERO8zR/eaqg6BSEFWVhbU1dURHx8PdXV1hXMGBgZina+++gpjxowpcn3t2rXFn/X19ZUbbBVRqsTcysoKf/zxB0aNGoWpU6dCEAQAgEwmg7e3N1atWgUrKyulBEpERERElcfJyQmampqIjY0Vk+inT5/i5s2b6NSpE5o1a4aCggKkpaW9davs5s2b4/r1629dBk2KSn2DIXt7exw8eBBPnz7F7du3IQgCnJ2dYWpqqoz4iIiIiEgFDAwM4Ofnh4kTJ8Lc3ByWlpaYNm0a1NRe7R1Sr149DBo0CIMHD8aiRYvQrFkzPHr0CFFRUWjcuDF69OiByZMno02bNggICMCwYcOgr6+P69evIzIyEitXrlTxCKWnTHf+BABTU1O0bNmyImMhIiIiem9UhRv+LFiwAFlZWfj4449haGiIb775BhkZGeL58PBwzJkzB9988w3+/vtv1KhRA23atBG/c9i4cWOcPHkS06ZNQ4cOHSAIApycnNC/f39VDUnSypyYExEREVH1ZmBggM2bN2Pz5s1i2cSJE8WfNTU1ERwcjODg4Le20bJlSxw9evSt51NSUiok1uqgVPuYExERERGRcjAxJyIiIiKSACbmREREREQSwDXmRNVI33pmqg6BiIiIyogz5kREREREEsDEnIiIiIhIApiYExERERFJANeYE5HkyEa1UXofwpqzSu+DiIioNDhjTkREREQkAUzMiYiIiIgkgEtZiIiIiFRAHh1Yqf2peS6t1P6CgoKwZ88eJCQkVGq/VVmVmjH//vvvIZPJEBgYKJa9fPkS/v7+MDc3h4GBAfr164fU1FSF6+7evYsePXpAT08PlpaWmDhxIvLz8xXqREdHo3nz5tDW1kbdunWxcePGShgREREREdErVSYxj4uLw9q1a9G4cWOF8nHjxuG3337DL7/8gpMnT+L+/fvo27eveL6goAA9evRAbm4u/vjjD0RERGDjxo2YMWOGWCc5ORk9evTABx98gISEBAQGBmLYsGE4cuRIpY2PiIiISGrkcjnmz5+PunXrQltbG7Vr10ZISAgAYPLkyahXrx709PRQp04dTJ8+HXl5eQCAjRs3Ijg4GJcuXYJMJoNMJuOkZwlUiaUsWVlZGDRoENavX485c+aI5RkZGdiwYQO2bt2KDz/8EAAQHh4OV1dXnD17Fm3atMHRo0dx/fp1HDt2DFZWVmjatClmz56NyZMnIygoCFpaWggLC4OjoyMWLVoEAHB1dcXp06exZMkSeHt7q2TMRERERKo2depUrF+/HkuWLEH79u3x4MED3LhxAwBgaGiIjRs3wtbWFleuXMHw4cNhaGiISZMmoX///rh69SoOHz6MY8eOAQCMjY1VOZQqoUok5v7+/ujRowe8vLwUEvP4+Hjk5eXBy8tLLKtfvz5q166NmJgYtGnTBjExMWjUqBGsrKzEOt7e3hg1ahSuXbuGZs2aISYmRqGNwjqvL5l5U05ODnJycsTjzMzMChgpEVUndTb0U2r7f/r9qtT2iej99uzZMyxbtgwrV66Ej48PAMDJyQnt27cHAHz33XdiXQcHB0yYMAHbtm3DpEmToKurCwMDA2hoaMDa2lol8VdFkk/Mt23bhgsXLiAuLq7IuYcPH0JLSwsmJiYK5VZWVnj48KFY5/WkvPB84bl31cnMzMSLFy+gq6tbpO/Q0FAEBweXeVxEREREUpaYmIicnBx07ty52PPbt2/H8uXLcefOHWRlZSE/Px9GRkaVHGX1Iuk15vfu3cPYsWOxZcsW6OjoqDocBVOnTkVGRob4uHfvnqpDIiIiIqowxU1MFoqJicGgQYPQvXt37N+/HxcvXsS0adOQm5tbiRFWP5JOzOPj45GWlobmzZtDQ0MDGhoaOHnyJJYvXw4NDQ1YWVkhNzcX6enpCtelpqaKfzaxtrYusktL4fG/1TEyMnrrm1JbWxtGRkYKDyIiIqLqwtnZGbq6uoiKiipy7o8//oC9vT2mTZsGd3d3ODs746+//lKoo6WlhYKCgsoKt1qQ9FKWzp0748qVKwplQ4YMQf369TF58mTY2dlBU1MTUVFR6Nfv1VrOpKQk3L17Fx4eHgAADw8PhISEIC0tDZaWlgCAyMhIGBkZwc3NTaxz8OBBhX4iIyPFNoiIiIjeNzo6Opg8eTImTZoELS0ttGvXDo8ePcK1a9fg7OyMu3fvYtu2bWjZsiUOHDiA3bt3K1zv4OCA5ORkJCQkoFatWjA0NIS2traKRlM1SDoxNzQ0RMOGDRXK9PX1YW5uLpb7+flh/PjxMDMzg5GREUaPHg0PDw+0adMGANClSxe4ubnhyy+/xPz58/Hw4UN899138Pf3F98cI0eOxMqVKzFp0iQMHToUx48fx44dO3DgwIHKHTARERG9Nyr7hj9lMX36dGhoaGDGjBm4f/8+bGxsMHLkSPj5+WHcuHEICAhATk4OevTogenTpyMoKEi8tl+/fti1axc++OADpKenIzw8HL6+viobS1Ug6cS8JJYsWQI1NTX069cPOTk58Pb2xurVq8Xz6urq2L9/P0aNGgUPDw/o6+vDx8cHs2bNEus4OjriwIEDGDduHJYtW4ZatWrhhx9+4FaJRERE9F5TU1PDtGnTMG3atCLn5s+fj/nz5yuUvb6jnba2Nnbu3KnsEKuVKpeYR0dHKxzr6Ohg1apVWLVq1Vuvsbe3L7JU5U2enp64ePFiRYRIRERERFRqkv7yJxERERHR+4KJORERERGRBFS5pSxEVP05utdUdQhERESVjjPmREREREQSwMSciIiIiEgCmJgTEREREUkAE3MiIiIiIgnglz+JSHL61jNTdQhERESVjok5EUmOV21TVYdARPTe8/T0RNOmTbF06dJizzs4OCAwMFDhbp8lERQUhD179iAhIaHcMVY3TMyJiIiIVODFtO6V2p9uyLvvgl5acXFx0NfXr9A233dMzImIqNrLibyp9D50PZXeBZGkWFhYvPN8Xl4eNDU1Kyma6oFf/iQiIiKiYuXn5yMgIADGxsaoUaMGpk+fDkEQALxayvL6MheZTIY1a9bgP//5D/T19RESEgIA+P7772FlZQVDQ0P4+fnh5cuXqhhKlcDEnIiIiIiKFRERAQ0NDZw7dw7Lli3D4sWL8cMPP7y1flBQEPr06YMrV65g6NCh2LFjB4KCgjB37lycP38eNjY2WL16dSWOoGrhUhYiIiXh7jJEVNXZ2dlhyZIlkMlkcHFxwZUrV7BkyRIMHz682Pqff/45hgwZIh4PGDAAfn5+8PPzAwDMmTMHx44d46z5W3DGnIiIiIiK1aZNG8hkMvHYw8MDt27dQkFBQbH13d3dFY4TExPRunVrhTIPD4+KD7Sa4Iw5EZGScNtHInrfcJeW8mFiTkSkJM0tG6o6BCKicomNjVU4Pnv2LJydnaGurl6i611dXREbG4vBgwcrtEHF41IWIiIiIirW3bt3MX78eCQlJeHnn3/GihUrMHbs2BJfP3bsWPz4448IDw/HzZs3MXPmTFy7dk2JEVdtnDEnIiIiUoGKvuGPMgwePBgvXrxAq1atoK6ujrFjx2LEiBElvr5///64c+cOJk2ahJcvX6Jfv34YNWoUjhw5osSoqy4m5kRERERURHR0tPjzmjVripxPSUlROC7c3/xN3377Lb799luFsnnz5pU7vuqIS1mIiIiIiCSAiTkRERERkQQwMSciIiIikgCuMSciyeE2g0RE9D7ijDkRERERkQQwMSciIiIikgAm5kREREREEsA15iqWE3lTqe3reiq1eSIiIiKqIJwxJyIiIiKSACbmRERERPRe2LhxI0xMTN5ZJygoCE2bNhWPfX190bt3b6XGVYhLWYiIiIhU4FbrBpXan3PstUrtD3iVCAcGBiI9Pb3S+y6rCRMmYPTo0Srpm4k5EREREdH/GBgYwMDAQCV9cykLERFVe/899pfSH0TV0eHDh9G+fXuYmJjA3NwcPXv2xJ07dwAA0dHRkMlkCrPhCQkJkMlkSElJQXR0NIYMGYKMjAzIZDLIZDIEBQUBAJ4+fYrBgwfD1NQUenp66NatG27duiW2U7jkZP/+/XBxcYGenh4++eQTPH/+HBEREXBwcICpqSnGjBmDgoIC8bp/a7fQnj174OzsDB0dHXh7e+PevXviuTeXsrxJLpcjNDQUjo6O0NXVRZMmTbBz584yPsOKOGNORKQkFi+U3IGuktsnUgLZqDZK70NYc1bpfbwvsrOzMX78eDRu3BhZWVmYMWMG+vTpg4SEhH+9tm3btli6dClmzJiBpKQkABBnon19fXHr1i3s27cPRkZGmDx5Mrp3747r169DU1MTAPD8+XMsX74c27Ztw7Nnz9C3b1/06dMHJiYmOHjwIP7880/069cP7dq1Q//+/UvVbkhICDZt2gQtLS18/fXXGDBgAM6cOVOi5yQ0NBQ//fQTwsLC4OzsjFOnTuGLL76AhYUFOnXqVNqnWAETcyIiIqo0h6Z0VHUIVAr9+vVTOP7xxx9hYWGB69ev/+u1WlpaMDY2hkwmg7W1tVhemDifOXMGbdu2BQBs2bIFdnZ22LNnDz799FMAQF5eHtasWQMnJycAwCeffILNmzcjNTUVBgYGcHNzwwcffIATJ06gf//+pWp35cqVaN26NQAgIiICrq6uOHfuHFq1avXOMeXk5GDu3Lk4duwYPDw8AAB16tTB6dOnsXbtWibmRERERKQct27dwowZMxAbG4t//vkHcrkcAHD37l3o6emVqc3ExERoaGiIiTEAmJubw8XFBYmJiWKZnp6emJQDgJWVFRwcHBTWf1tZWSEtLa1U7WpoaKBly5bicf369WFiYoLExMR/Tcxv376N58+f46OPPlIoz83NRbNmzUr6FLwVE3MiIiIiKtbHH38Me3t7rF+/Hra2tpDL5WjYsCFyc3PFBFkQBLF+Xl5ehfVduPSkkEwmK7as8MNCZcjKygIAHDhwADVr1lQ4p62tXe72mZgTERFRpWlu2VDVIVAJPX78GElJSVi/fj06dOgAADh9+rR43sLCAgDw4MEDmJqaAkCRtedaWloKX84EAFdXV+Tn5yM2NlZcclLYl5ubW5njLWm7+fn5OH/+vDg7npSUhPT0dLi6uv5rH25ubtDW1sbdu3fLvWylOEzMiYiIqNLUiL2g/E48Byu/j/eAqakpzM3NsW7dOtjY2ODu3buYMmWKeL5u3bqws7NDUFAQQkJCcPPmTSxatEihDQcHB2RlZSEqKgpNmjSBnp4enJ2d0atXLwwfPhxr166FoaEhpkyZgpo1a6JXr15ljrek7WpqamL06NFYvnw5NDQ0EBAQgDZt2vzrMhYAMDQ0xIQJEzBu3DjI5XK0b98eGRkZOHPmDIyMjODj41Pm+AEm5kREREQqoYob/pSGmpoatm3bhjFjxqBhw4ZwcXHB8uXL4enpCeBVgvvzzz9j1KhRaNy4MVq2bIk5c+aIX7IEXu3MMnLkSPTv3x+PHz/GzJkzERQUhPDwcIwdOxY9e/ZEbm4uOnbsiIMHDxZZqlJaJWlXT08PkydPxueff46///4bHTp0wIYNG0rcx+zZs2FhYYHQ0FD8+eefMDExQfPmzfHtt9+WK3YAkAmvLwyiMsvMzISxsTEyMjJgZGRU4uteTOuuxKgA3ZCDSm0fAA7/NUnpfXS1n6/0PqqDCb8PV3ofCzusV3ofaS82Kb0PS13lz6gJT5Q7DpkZZwVLqjLusKjsJKvOhn7/Xqmc/vT7Vel9KPv3HlC6331l/f1dGi9fvkRycjIcHR2ho6OjlD5Iukrz+vMGQ0REREREEsDEnIiIiIhIApiYExERERFJABNzIiIiIiIJYGJORERERCQBTMyJiIiIiCSAiTkRERERkQQwMSciIiIikgAm5kRERERUaikpKZDJZEhISCh3W76+vujdu3e526nqNFQdABERERFVPXZ2dnjw4AFq1Kih6lCqDSbmRERERCqwVeZSqf19LiRVaHvq6uqwtrZ+63lBEFBQUAANDaabJcWlLERERERUrMOHD6N9+/YwMTGBubk5evbsiTt37gAoupQlOjoaMpkMhw4dQosWLaCtrY3Tp08jKCgITZs2xdq1a2FnZwc9PT189tlnyMjIKFO/r/e9a9cufPDBB9DT00OTJk0QExOj0M7p06fRoUMH6Orqws7ODmPGjEF2dnbFP1EVhB9hiKqRSe4dVB0CERFVI9nZ2Rg/fjwaN26MrKwszJgxA3369HnnuvIpU6Zg4cKFqFOnDkxNTREdHY3bt29jx44d+O2335CZmQk/Pz98/fXX2LJlS6n7VVP7/3nladOmYeHChXB2dsa0adMwcOBA3L59GxoaGrhz5w66du2KOXPm4Mcff8SjR48QEBCAgIAAhIeHV/RTVSGYmBMRERFRsfr166dw/OOPP8LCwgLXr1+HgYFBsdfMmjULH330kULZy5cvsWnTJtSsWRMAsGLFCvTo0QOLFi0qdjnMu/pt2LChWD5hwgT06NEDABAcHIwGDRrg9u3bqF+/PkJDQzFo0CAEBgYCAJydnbF8+XJ06tQJa9asgY6OTumejErAxJyIiKgK6FvPTNUh0Hvo1q1bmDFjBmJjY/HPP/9ALpcDAO7evQs3N7dir3F3dy9SVrt2bTEpBwAPDw/I5XIkJSUVm5i/q9/XE/PGjRuLP9vY2AAA0tLSUL9+fVy6dAmXL19WmJUXBAFyuRzJyclwdXUtzVNRKZiYExEREVGxPv74Y9jb22P9+vWwtbWFXC5Hw4YNkZub+9Zr9PX1K61fTU1N8WeZTAYAYhKflZWFr776CmPGjCnSfu3atcsdozIwMSciIiKiIh4/foykpCSsX78eHTq8+g7T6dOny9TW3bt3cf/+fdja2gIAzp49CzU1Nbi4FN2ZpqL6bd68Oa5fv466deuWKWZVYGJORERElea/x/5Seh/OIUrv4r1gamoKc3NzrFu3DjY2Nrh79y6mTJlSprZ0dHTg4+ODhQsXIjMzE2PGjMFnn31W7DKWiup38uTJaNOmDQICAjBs2DDo6+vj+vXriIyMxMqVK8s0DmVjYk5UjVi8qIROdCuhDyIiUjk1NTVs27YNY8aMQcOGDeHi4oLly5fD09Oz1G3VrVsXffv2Rffu3fHkyRP07NkTq1evVmq/jRs3xsmTJzFt2jR06NABgiDAyckJ/fv3L3X8lYWJOREREZEKVPQNf5TBy8sL169fVygTBKHYnz09PRWO3zRq1CiMGjWq2HMbN24sVb8ODg5F+jIxMSlS1rJlSxw9evStMUkNbzBERERERCQBkk7MQ0ND0bJlSxgaGsLS0hK9e/dGUpLip8uXL1/C398f5ubmMDAwQL9+/ZCamqpQ5+7du+jRowf09PRgaWmJiRMnIj8/X6FOdHQ0mjdvDm1tbdStW7fIJzciIiIiImWS9FKWkydPwt/fHy1btkR+fj6+/fZbdOnSBdevXxe34hk3bhwOHDiAX375BcbGxggICEDfvn1x5swZAEBBQQF69OgBa2tr/PHHH3jw4AEGDx4MTU1NzJ07FwCQnJyMHj16YOTIkdiyZQuioqIwbNgw2NjYwNvbW2XjJyot4fIFpfch8xys9D6IiKj6CAoKQlBQkKrDqBIknZgfPnxY4Xjjxo2wtLREfHw8OnbsiIyMDGzYsAFbt27Fhx9+CAAIDw+Hq6srzp49izZt2uDo0aO4fv06jh07BisrKzRt2hSzZ8/G5MmTERQUBC0tLYSFhcHR0RGLFi0CALi6uuL06dNYsmQJE3MiIiIiqhSSTszflJGRAQAwM3t197P4+Hjk5eXBy8tLrFO/fn3Url0bMTExaNOmDWJiYtCoUSNYWVmJdby9vTFq1Chcu3YNzZo1Q0xMjEIbhXUKb+FanJycHOTk5IjHmZmZFTFEonLJibyp9D50PZXeBRER0XtJ0mvMXyeXyxEYGIh27dqJt2J9+PAhtLS0YGJiolDXysoKDx8+FOu8npQXni889646mZmZePGi+P3nQkNDYWxsLD7s7OzKPUYiIiKqvt61YwlVX6V53atMYu7v74+rV69i27Ztqg4FADB16lRkZGSIj3v37qk6JCIiIpIgdXV1AHjnbeyp+nr+/DkAQFNT81/rVomlLAEBAdi/fz9OnTqFWrVqieXW1tbIzc1Fenq6wqx5amqqeCcpa2trnDt3TqG9wl1bXq/z5k4uqampMDIygq5u8XdT0dbWhra2drnHRkRERNWbhoYG9PT08OjRI2hqakJNrcrMi1I5CIKA58+fIy0tDSYmJuIHtHeRdGIuCAJGjx6N3bt3Izo6Go6OjgrnW7RoAU1NTURFRaFfv34AgKSkJNy9exceHh4AAA8PD4SEhCAtLQ2WlpYAgMjISBgZGcHNzU2sc/DgQYW2IyMjxTaIiN5XdTb0U3off/r9qvQ+iFRJJpPBxsYGycnJ+Ouvv1QdDlUyExMTcTL430g6Mff398fWrVuxd+9eGBoaimvCjY2NoaurC2NjY/j5+WH8+PEwMzODkZERRo8eDQ8PD7Rp0wYA0KVLF7i5ueHLL7/E/Pnz8fDhQ3z33Xfw9/cXZ7xHjhyJlStXYtKkSRg6dCiOHz+OHTt24MCBAyobOxEREVUfWlpacHZ25nKW94ympmaJZsoLSToxX7NmDYBXt3h9XXh4OHx9fQEAS5YsgZqaGvr164ecnBx4e3tj9erVYl11dXXs378fo0aNgoeHB/T19eHj44NZs2aJdRwdHXHgwAGMGzcOy5YtQ61atfDDDz9wq0Qieu/1rWem6hCIqg01NTXo6OioOgySMEkn5iX5FquOjg5WrVqFVatWvbWOvb19kaUqb/L09MTFixdLHSMRERERUUXgtw+IiIiIiCSAiTkRERERkQQwMSciIiIikgAm5kREREREEsDEnIiIiIhIApiYExERERFJABNzIiIiIiIJkPQ+5u+D/x5T7q15nUOU2jwRERERVRDOmBMRERERSQATcyIiIiIiCeBSFiq3TuuuKr8TLskhIiKiao4z5kREREREEsAZcyIieiuv2qaqDoGI6L3BGXMiIiIiIgngjDmV2+65d5Tex+dcY05ERETVHBNzIiJ6q+aWDVUdAhHRe4NLWYiIiIiIJICJORERERGRBDAxJyIiIiKSAK4xJyKit7J4UQmd6FZCH0REVQBnzImIiIiIJICJORERERGRBDAxJyIiIiKSACbmREREREQSwC9/EhERUaWJO5ev9D6cld4DkXJwxpyIiIiISAI4Y070P3U29FN6H3/6/ar0PoiIiKhq4ow5EREREZEEMDEnIiIiIpIALmUh+p++9cxUHQIRERG9x5iYE/2PV21TVYdARERE7zEuZSEiIiIikgDOmBNVI/899pfS+3AOUXoXJCHC5QtK70PmOVjpfRARVQWcMSciIiIikgAm5kREREREEsDEnIiIiIhIArjGXMXizuUrtX1npbZORERERBWFiTkRSY7Fi0roRLcS+iCqQJPcO6g6BCJSMibmRNWIsv8CA1TOX2FeLtqm9D50Q7gTCBERSQvXmBMRERERSQBnzImIiKqAGrHK31Me3FOeSKU4Y05EREREJAFMzImIiIiIJIBLWYiIiKqAnMibSu9D11PpXRDRO3DGnIiIiIhIApiYExERERFJABNzIiIiIiIJYGJORERERCQB/PIn0f80t2yo6hCIiIjoPcbEnOh/LF5UQie6ldAHERERVUlMzImI6K22fXBI6X18LixVeh9ERFUB15gTEREREUkAE3MiIiIiIgngUhYiIiURLl9Qavsyz8FKbZ+IiCoXE3MiIiVR9i3Ueft0IqLqhYk5ERFRFfDfY38pvQ/nEKV3QUTvwMSciIioCog7l6/0PpyV3gMRvQu//ElEREREJAFMzImIiIiIJICJORERERGRBHCNORGRkij7y3r8oh4RUfXCxPwNq1atwoIFC/Dw4UM0adIEK1asQKtWrVQdFhERlQO/OElEVQGXsrxm+/btGD9+PGbOnIkLFy6gSZMm8Pb2RlpamqpDIyIiIqJqjjPmr1m8eDGGDx+OIUOGAADCwsJw4MAB/Pjjj5gyZYqKoyOiqkbZs7ScoSUiql6YmP9Pbm4u4uPjMXXqVLFMTU0NXl5eiImJUWFkRO8f3kiFiIjeR0zM/+eff/5BQUEBrKysFMqtrKxw48aNIvVzcnKQk5MjHmdkZAAAMjMzS9XvcxSUIdqSK208ZaHsMQCVM44XczcrvQ/dGb2V2n51eS2yCqrHOPjvu2Q4jpKpDmMApDeOwrqCICgrHKISY2JeRqGhoQgODi5Sbmdnp4Jo3m64sbGqQ6gQ1WUcWFT1x1FtXotqMI7q8lpwHNJRHcYAlG0cz549g3E1GT9VXUzM/6dGjRpQV1dHamqqQnlqaiqsra2L1J86dSrGjx8vHsvlcjx58gTm5uaQyWRKiTEzMxN2dna4d+8ejIyMlNJHZagO46gOYwCqxziqwxgAjkNKqsMYgOoxjsoYgyAIePbsGWxtbZXSPlFpMDH/Hy0tLbRo0QJRUVHo3bs3gFfJdlRUFAICAorU19bWhra2tkKZiYlJJUQKGBkZVdn/ZF9XHcZRHcYAVI9xVIcxAByHlFSHMQDVYxzKHgNnykkqmJi/Zvz48fDx8YG7uztatWqFpUuXIjs7W9ylhYiIiIhIWZiYv6Z///549OgRZsyYgYcPH6Jp06Y4fPhwkS+EEhERERFVNCbmbwgICCh26YoUaGtrY+bMmUWW0FQ11WEc1WEMQPUYR3UYA8BxSEl1GANQPcZRHcZAVBoygfsDERERERGpnJqqAyAiIiIiIibmRERERESSwMSciIiIiEgCmJgTEREREUkAE/MqYtWqVXBwcICOjg5at26Nc+fOqTqkUjt16hQ+/vhj2NraQiaTYc+ePaoOqdRCQ0PRsmVLGBoawtLSEr1790ZSUpKqwyqVNWvWoHHjxuINOzw8PHDo0CFVh1Vu33//PWQyGQIDA1UdSqkEBQVBJpMpPOrXr6/qsErt77//xhdffAFzc3Po6uqiUaNGOH/+vKrDKhUHB4cir4VMJoO/v7+qQyuxgoICTJ8+HY6OjtDV1YWTkxNmz56NqrjPw7NnzxAYGAh7e3vo6uqibdu2iIuLU3VYRErFxLwK2L59O8aPH4+ZM2fiwoULaNKkCby9vZGWlqbq0EolOzsbTZo0wapVq1QdSpmdPHkS/v7+OHv2LCIjI5GXl4cuXbogOztb1aGVWK1atfD9998jPj4e58+fx4cffohevXrh2rVrqg6tzOLi4rB27Vo0btxY1aGUSYMGDfDgwQPxcfr0aVWHVCpPnz5Fu3btoKmpiUOHDuH69etYtGgRTE1NVR1aqcTFxSm8DpGRkQCATz/9VMWRldy8efOwZs0arFy5EomJiZg3bx7mz5+PFStWqDq0Uhs2bBgiIyOxefNmXLlyBV26dIGXlxf+/vtvVYdGpDwCSV6rVq0Ef39/8bigoECwtbUVQkNDVRhV+QAQdu/ereowyi0tLU0AIJw8eVLVoZSLqamp8MMPP6g6jDJ59uyZ4OzsLERGRgqdOnUSxo4dq+qQSmXmzJlCkyZNVB1GuUyePFlo3769qsOocGPHjhWcnJwEuVyu6lBKrEePHsLQoUMVyvr27SsMGjRIRRGVzfPnzwV1dXVh//79CuXNmzcXpk2bpqKoiJSPM+YSl5ubi/j4eHh5eYllampq8PLyQkxMjAojIwDIyMgAAJiZmak4krIpKCjAtm3bkJ2dDQ8PD1WHUyb+/v7o0aOHwr+RqubWrVuwtbVFnTp1MGjQINy9e1fVIZXKvn374O7ujk8//RSWlpZo1qwZ1q9fr+qwyiU3Nxc//fQThg4dCplMpupwSqxt27aIiorCzZs3AQCXLl3C6dOn0a1bNxVHVjr5+fkoKCiAjo6OQrmurm6V+4sSUWnwzp8S988//6CgoABWVlYK5VZWVrhx44aKoiIAkMvlCAwMRLt27dCwYUNVh1MqV65cgYeHB16+fAkDAwPs3r0bbm5uqg6r1LZt24YLFy5U6XWnrVu3xsaNG+Hi4oIHDx4gODgYHTp0wNWrV2FoaKjq8Erkzz//xJo1azB+/Hh8++23iIuLw5gxY6ClpQUfHx9Vh1cme/bsQXp6Onx9fVUdSqlMmTIFmZmZqF+/PtTV1VFQUICQkBAMGjRI1aGViqGhITw8PDB79my4urrCysoKP//8M2JiYlC3bl1Vh0ekNEzMicrI398fV69erZKzNy4uLkhISEBGRgZ27twJHx8fnDx5skol5/fu3cPYsWMRGRlZZFatKnl9JrNx48Zo3bo17O3tsWPHDvj5+akwspKTy+Vwd3fH3LlzAQDNmjXD1atXERYWVmUT8w0bNqBbt26wtbVVdSilsmPHDmzZsgVbt25FgwYNkJCQgMDAQNja2la512Lz5s0YOnQoatasCXV1dTRv3hwDBw5EfHy8qkMjUhom5hJXo0YNqKurIzU1VaE8NTUV1tbWKoqKAgICsH//fpw6dQq1atVSdTilpqWlJc46tWjRAnFxcVi2bBnWrl2r4shKLj4+HmlpaWjevLlYVlBQgFOnTmHlypXIycmBurq6CiMsGxMTE9SrVw+3b99WdSglZmNjU+RDnaurK3799VcVRVQ+f/31F44dO4Zdu3apOpRSmzhxIqZMmYIBAwYAABo1aoS//voLoaGhVS4xd3JywsmTJ5GdnY3MzEzY2Nigf//+qFOnjqpDI1IarjGXOC0tLbRo0QJRUVFimVwuR1RUVJVdE1yVCYKAgIAA7N69G8ePH4ejo6OqQ6oQcrkcOTk5qg6jVDp37owrV64gISFBfLi7u2PQoEFISEiokkk5AGRlZeHOnTuwsbFRdSgl1q5duyLbht68eRP29vYqiqh8wsPDYWlpiR49eqg6lFJ7/vw51NQUf7Wrq6tDLperKKLy09fXh42NDZ4+fYojR46gV69eqg6JSGk4Y14FjB8/Hj4+PnB3d0erVq2wdOlSZGdnY8iQIaoOrVSysrIUZgGTk5ORkJAAMzMz1K5dW4WRlZy/vz+2bt2KvXv3wtDQEA8fPgQAGBsbQ1dXV8XRlczUqVPRrVs31K5dG8+ePcPWrVsRHR2NI0eOqDq0UjE0NCyytl9fXx/m5uZVas3/hAkT8PHHH8Pe3h7379/HzJkzoa6ujoEDB6o6tBIbN24c2rZti7lz5+Kzzz7DuXPnsG7dOqxbt07VoZWaXC5HeHg4fHx8oKFR9X5FfvzxxwgJCUHt2rXRoEEDXLx4EYsXL8bQoUNVHVqpHTlyBIIgwMXFBbdv38bEiRNRv379Kve7j6hUVL0tDJXMihUrhNq1awtaWlpCq1athLNnz6o6pFI7ceKEAKDIw8fHR9WhlVhx8QMQwsPDVR1aiQ0dOlSwt7cXtLS0BAsLC6Fz587C0aNHVR1WhaiK2yX2799fsLGxEbS0tISaNWsK/fv3F27fvq3qsErtt99+Exo2bChoa2sL9evXF9atW6fqkMrkyJEjAgAhKSlJ1aGUSWZmpjB27Fihdu3ago6OjlCnTh1h2rRpQk5OjqpDK7Xt27cLderUEbS0tARra2vB399fSE9PV3VYREolE4QqeDswIiIiIqJqhmvMiYiIiIgkgIk5EREREZEEMDEnIiIiIpIAJuZERERERBLAxJyIiIiISAKYmBMRERERSQATcyIiIiIiCWBiTkTvtZSUFMhkMiQkJLyznqenJwIDAyslJiIiej8xMSciyfH19YVMJoNMJoOWlhbq1q2LWbNmIT8/v9zt9u7dW6HMzs4ODx48QMOGDQEA0dHRkMlkSE9PV6i3a9cuzJ49u1z9/5s3PyQUHhc+DA0N0aBBA/j7++PWrVtKjYWIiCofE3MikqSuXbviwYMHuHXrFr755hsEBQVhwYIFZWqroKAAcrm82HPq6uqwtraGhobGO9swMzODoaFhmfovr2PHjuHBgwe4dOkS5s6di8TERDRp0gRRUVEqiYeIiJSDiTkRSZK2tjasra1hb2+PUaNGwcvLC/v27QMALF68GI0aNYK+vj7s7Ozw9ddfIysrS7x248aNMDExwb59++Dm5gZtbW0MHToUERER2Lt3rzgDHR0drTBLnZKSgg8++AAAYGpqCplMBl9fXwBFl7I8ffoUgwcPhqmpKfT09NCtWzeFWezCGI4cOQJXV1cYGBiIHzZKy9zcHNbW1qhTpw569eqFY8eOoXXr1vDz80NBQUEZnl0iIpIiJuZEVCXo6uoiNzcXAKCmpobly5fj2rVriIiIwPHjxzFp0iSF+s+fP8e8efPwww8/4Nq1a1i+fDk+++wzMTl+8OAB2rZtq3CNnZ0dfv31VwBAUlISHjx4gGXLlhUbj6+vL86fP499+/YhJiYGgiCge/fuyMvLU4hh4cKF2Lx5M06dOoW7d+9iwoQJ5X4u1NTUMHbsWPz111+Ij48vd3tERCQN7/7bLRGRigmCgKioKBw5cgSjR48GAIWZawcHB8yZMwcjR47E6tWrxfK8vDysXr0aTZo0Ect0dXWRk5MDa2vrYvtSV1eHmZkZAMDS0hImJibF1rt16xb27duHM2fOiMn9li1bYGdnhz179uDTTz8VYwgLC4OTkxMAICAgALNmzSrbE/GG+vXrA3i1Dr1Vq1YV0iYREakWE3MikqT9+/fDwMAAeXl5kMvl+PzzzxEUFATg1Zrr0NBQ3LhxA5mZmcjPz8fLly/x/Plz6OnpAQC0tLTQuHFjpcSWmJgIDQ0NtG7dWiwzNzeHi4sLEhMTxTI9PT0xKQcAGxsbpKWlVUgMgiAAAGQyWYW0R0REqselLEQkSR988AESEhJw69YtvHjxAhEREdDX10dKSgp69uyJxo0b49dff0V8fDxWrVoFAOJSF+DV7Liqk1ZNTU2FY5lMJibU5VX4AcDR0bFC2iMiItXjjDkRSZK+vj7q1q1bpDw+Ph5yuRyLFi2CmtqruYUdO3aUqE0tLa1//bKklpYWALyznqurK/Lz8xEbGysuZXn8+DGSkpLg5uZWoljKQy6XY/ny5XB0dESzZs2U3h8REVUOzpgTUZVSt25d5OXlYcWKFfjzzz+xefNmhIWFlehaBwcHXL58GUlJSfjnn38UvqhZyN7eHjKZDPv378ejR48Udnsp5OzsjF69emH48OE4ffo0Ll26hC+++AI1a9ZEr169yj3GNz1+/BgPHz7En3/+iX379sHLywvnzp3Dhg0boK6uXuH9ERGRajAxJ6IqpUmTJli8eDHmzZuHhg0bYsuWLQgNDS3RtcOHD4eLiwvc3d1hYWGBM2fOFKlTs2ZNBAcHY8qUKbCyskJAQECxbYWHh6NFixbo2bMnPDw8IAgCDh48WGT5SkXw8vKCjY0NGjVqhClTpsDV1RWXL18Wt3YkIqLqQSZU1IJHIiIiIiIqM86YExERERFJABNzIiIiIiIJYGJORERERCQBTMyJiIiIiCSAiTkRERERkQQwMSciIiIikgAm5kREREREEsDEnIiIiIhIApiYExERERFJABNzIiIiIiIJYGJORERERCQBTMyJiIiIiCTg/wD1w8OR9+ukgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax, df = plot_label_distributions(\n",
    "    partitioner,\n",
    "    label_name=\"label\",\n",
    "    plot_type=\"bar\",\n",
    "    size_unit=\"absolute\",\n",
    "    partition_id_axis=\"x\",\n",
    "    legend=True,\n",
    "    verbose_labels=True,\n",
    "    title=\"Per Partition Labels Distribution\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be05badab744d9f7",
   "metadata": {},
   "source": [
    "You can configure many details directly using the function parameters. The ones that can interest you the most are:\n",
    "\n",
    "* `size_unit` to have the sizes normalized such that they sum up to 1 and express the fraction of the data in each partition,\n",
    "* `legend` and `verbose_labels` in case the dataset has more descriptive names and not numbers,\n",
    "* `cmap` to change the values of the bars (for an overview of the available colors, have a look at [link](https://matplotlib.org/stable/users/explain/colors/colormaps.html); check out `cmap=\"tab20b\"`) \n",
    "\n",
    " And for even greater control, you can specify `plot_kwargs` and `legend_kwargs` as `Dict`, which will be further passed to the `plot` and `legend` functions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbf6dc4ede79f05",
   "metadata": {},
   "source": [
    "You can also inspect the exact numbers that were used to create this plot. Three objects are returned (see reference [here](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_label_distributions.html#flwr_datasets.visualization.plot_label_distributions)). Let's inspect the returned DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6edd14d8b260e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airplane</th>\n",
       "      <th>automobile</th>\n",
       "      <th>bird</th>\n",
       "      <th>cat</th>\n",
       "      <th>deer</th>\n",
       "      <th>dog</th>\n",
       "      <th>frog</th>\n",
       "      <th>horse</th>\n",
       "      <th>ship</th>\n",
       "      <th>truck</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partition ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>817</td>\n",
       "      <td>794</td>\n",
       "      <td>1462</td>\n",
       "      <td>2123</td>\n",
       "      <td>432</td>\n",
       "      <td>25</td>\n",
       "      <td>456</td>\n",
       "      <td>384</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1416</td>\n",
       "      <td>6</td>\n",
       "      <td>97</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3409</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>454</td>\n",
       "      <td>3</td>\n",
       "      <td>511</td>\n",
       "      <td>15</td>\n",
       "      <td>84</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>762</td>\n",
       "      <td>159</td>\n",
       "      <td>1100</td>\n",
       "      <td>51</td>\n",
       "      <td>120</td>\n",
       "      <td>166</td>\n",
       "      <td>2</td>\n",
       "      <td>1982</td>\n",
       "      <td>1351</td>\n",
       "      <td>2175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>714</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2400</td>\n",
       "      <td>425</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>170</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>477</td>\n",
       "      <td>27</td>\n",
       "      <td>44</td>\n",
       "      <td>590</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>422</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>486</td>\n",
       "      <td>380</td>\n",
       "      <td>92</td>\n",
       "      <td>90</td>\n",
       "      <td>380</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>122</td>\n",
       "      <td>2811</td>\n",
       "      <td>597</td>\n",
       "      <td>2174</td>\n",
       "      <td>1038</td>\n",
       "      <td>1727</td>\n",
       "      <td>1</td>\n",
       "      <td>682</td>\n",
       "      <td>515</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>256</td>\n",
       "      <td>29</td>\n",
       "      <td>342</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>1511</td>\n",
       "      <td>2240</td>\n",
       "      <td>1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1136</td>\n",
       "      <td>1073</td>\n",
       "      <td>503</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              airplane  automobile  bird   cat  deer   dog  frog  horse  ship  \\\n",
       "Partition ID                                                                    \n",
       "0                  817         794  1462  2123   432    25   456    384    14   \n",
       "1                 1416           6    97     5     3     0  3409      0     3   \n",
       "2                    0           4    11     2   454     3   511     15    84   \n",
       "3                  762         159  1100    51   120   166     2   1982  1351   \n",
       "4                    2          43   714     2    19  2400   425      1   151   \n",
       "5                   67          79   170    25  2552   477    27     44   590   \n",
       "6                  422           2     4   486   380    92    90    380    50   \n",
       "7                  122        2811   597  2174  1038  1727     1    682   515   \n",
       "8                  256          29   342    75     1    84     8   1511  2240   \n",
       "9                 1136        1073   503    57     1    26    71      1     2   \n",
       "\n",
       "              truck  \n",
       "Partition ID         \n",
       "0                 9  \n",
       "1               868  \n",
       "2                21  \n",
       "3              2175  \n",
       "4               477  \n",
       "5                 0  \n",
       "6                 6  \n",
       "7                 4  \n",
       "8              1417  \n",
       "9                23  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2902213a",
   "metadata": {},
   "source": [
    "Each row represents a unique partition ID, and the columns represent unique labels (either in the verbose version if `verbose_labels=True` or typically `int` values otherwise, representing the partition IDs).\n",
    "That you can index the DataFrame `df[partition_id, label_id]` to get the number of samples in `partition_id` for the specified `label_id.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ffe4039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4, \"bird\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6c17af529a668f",
   "metadata": {},
   "source": [
    "Let's see a plot with `size_unit=\"percent\"`, which is another excellent way to understand the partitions. In this mode, the number of datapoints for each class in a given partition are normalized, so they sum up to 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a241894a47f3cc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax, df = plot_label_distributions(\n",
    "    partitioner,\n",
    "    label_name=\"label\",\n",
    "    plot_type=\"bar\",\n",
    "    size_unit=\"percent\",\n",
    "    partition_id_axis=\"x\",\n",
    "    legend=True,\n",
    "    verbose_labels=True,\n",
    "    cmap=\"tab20b\",\n",
    "    title=\"Per Partition Labels Distribution\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d1fe2e1c0f14c",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6a2e53de3cc084",
   "metadata": {},
   "source": [
    "You might want to visualize the results of partitioning as a heatmap, which can be especially useful for binary labels. Here's how:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a4b4e574866120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 948.683x316.228 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax, df = plot_label_distributions(\n",
    "    partitioner,\n",
    "    label_name=\"label\",\n",
    "    plot_type=\"heatmap\",\n",
    "    size_unit=\"absolute\",\n",
    "    partition_id_axis=\"x\",\n",
    "    legend=True,\n",
    "    verbose_labels=True,\n",
    "    title=\"Per Partition Labels Distribution\",\n",
    "    plot_kwargs={\"annot\": True},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5167593e67fa3dbb",
   "metadata": {},
   "source": [
    "Note: we used the `plot_kwargs={\"annot\": True}` to add the number directly to the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e41273551ac32a",
   "metadata": {},
   "source": [
    "If you are a `pandas` fan, then you might be interested that a similar heatmap can be created with the DataFrame object for visualization in jupyter notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcc90b52bfd650cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_25d59_row4_col2 {\n",
       "  background-color: #d1edcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row4_col5 {\n",
       "  background-color: #2e964d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_25d59_row4_col6, #T_25d59_row6_col0 {\n",
       "  background-color: #e5f5e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row4_col9, #T_25d59_row5_col5 {\n",
       "  background-color: #e2f4dd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row5_col0, #T_25d59_row5_col1, #T_25d59_row8_col3, #T_25d59_row9_col6 {\n",
       "  background-color: #f4fbf2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row5_col4 {\n",
       "  background-color: #238b45;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_25d59_row5_col8, #T_25d59_row7_col2 {\n",
       "  background-color: #daf0d4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row6_col3 {\n",
       "  background-color: #e1f3dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row7_col1 {\n",
       "  background-color: #0d7836;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_25d59_row7_col4 {\n",
       "  background-color: #b7e2b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row7_col5 {\n",
       "  background-color: #72c375;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row7_col7 {\n",
       "  background-color: #d3eecd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row8_col0 {\n",
       "  background-color: #ecf8e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row8_col2 {\n",
       "  background-color: #e9f7e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row8_col7 {\n",
       "  background-color: #88ce87;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row8_col8 {\n",
       "  background-color: #39a257;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_25d59_row9_col0 {\n",
       "  background-color: #aedea7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row9_col1 {\n",
       "  background-color: #b4e1ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row9_col2 {\n",
       "  background-color: #e0f3db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_25d59_row9_col3 {\n",
       "  background-color: #f5fbf2;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_25d59\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_25d59_level0_col0\" class=\"col_heading level0 col0\" >airplane</th>\n",
       "      <th id=\"T_25d59_level0_col1\" class=\"col_heading level0 col1\" >automobile</th>\n",
       "      <th id=\"T_25d59_level0_col2\" class=\"col_heading level0 col2\" >bird</th>\n",
       "      <th id=\"T_25d59_level0_col3\" class=\"col_heading level0 col3\" >cat</th>\n",
       "      <th id=\"T_25d59_level0_col4\" class=\"col_heading level0 col4\" >deer</th>\n",
       "      <th id=\"T_25d59_level0_col5\" class=\"col_heading level0 col5\" >dog</th>\n",
       "      <th id=\"T_25d59_level0_col6\" class=\"col_heading level0 col6\" >frog</th>\n",
       "      <th id=\"T_25d59_level0_col7\" class=\"col_heading level0 col7\" >horse</th>\n",
       "      <th id=\"T_25d59_level0_col8\" class=\"col_heading level0 col8\" >ship</th>\n",
       "      <th id=\"T_25d59_level0_col9\" class=\"col_heading level0 col9\" >truck</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Partition ID</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_25d59_row0_col0\" class=\"data row0 col0\" >817</td>\n",
       "      <td id=\"T_25d59_row0_col1\" class=\"data row0 col1\" >794</td>\n",
       "      <td id=\"T_25d59_row0_col2\" class=\"data row0 col2\" >1462</td>\n",
       "      <td id=\"T_25d59_row0_col3\" class=\"data row0 col3\" >2123</td>\n",
       "      <td id=\"T_25d59_row0_col4\" class=\"data row0 col4\" >432</td>\n",
       "      <td id=\"T_25d59_row0_col5\" class=\"data row0 col5\" >25</td>\n",
       "      <td id=\"T_25d59_row0_col6\" class=\"data row0 col6\" >456</td>\n",
       "      <td id=\"T_25d59_row0_col7\" class=\"data row0 col7\" >384</td>\n",
       "      <td id=\"T_25d59_row0_col8\" class=\"data row0 col8\" >14</td>\n",
       "      <td id=\"T_25d59_row0_col9\" class=\"data row0 col9\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_25d59_row1_col0\" class=\"data row1 col0\" >1416</td>\n",
       "      <td id=\"T_25d59_row1_col1\" class=\"data row1 col1\" >6</td>\n",
       "      <td id=\"T_25d59_row1_col2\" class=\"data row1 col2\" >97</td>\n",
       "      <td id=\"T_25d59_row1_col3\" class=\"data row1 col3\" >5</td>\n",
       "      <td id=\"T_25d59_row1_col4\" class=\"data row1 col4\" >3</td>\n",
       "      <td id=\"T_25d59_row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "      <td id=\"T_25d59_row1_col6\" class=\"data row1 col6\" >3409</td>\n",
       "      <td id=\"T_25d59_row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "      <td id=\"T_25d59_row1_col8\" class=\"data row1 col8\" >3</td>\n",
       "      <td id=\"T_25d59_row1_col9\" class=\"data row1 col9\" >868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_25d59_row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "      <td id=\"T_25d59_row2_col1\" class=\"data row2 col1\" >4</td>\n",
       "      <td id=\"T_25d59_row2_col2\" class=\"data row2 col2\" >11</td>\n",
       "      <td id=\"T_25d59_row2_col3\" class=\"data row2 col3\" >2</td>\n",
       "      <td id=\"T_25d59_row2_col4\" class=\"data row2 col4\" >454</td>\n",
       "      <td id=\"T_25d59_row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "      <td id=\"T_25d59_row2_col6\" class=\"data row2 col6\" >511</td>\n",
       "      <td id=\"T_25d59_row2_col7\" class=\"data row2 col7\" >15</td>\n",
       "      <td id=\"T_25d59_row2_col8\" class=\"data row2 col8\" >84</td>\n",
       "      <td id=\"T_25d59_row2_col9\" class=\"data row2 col9\" >21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_25d59_row3_col0\" class=\"data row3 col0\" >762</td>\n",
       "      <td id=\"T_25d59_row3_col1\" class=\"data row3 col1\" >159</td>\n",
       "      <td id=\"T_25d59_row3_col2\" class=\"data row3 col2\" >1100</td>\n",
       "      <td id=\"T_25d59_row3_col3\" class=\"data row3 col3\" >51</td>\n",
       "      <td id=\"T_25d59_row3_col4\" class=\"data row3 col4\" >120</td>\n",
       "      <td id=\"T_25d59_row3_col5\" class=\"data row3 col5\" >166</td>\n",
       "      <td id=\"T_25d59_row3_col6\" class=\"data row3 col6\" >2</td>\n",
       "      <td id=\"T_25d59_row3_col7\" class=\"data row3 col7\" >1982</td>\n",
       "      <td id=\"T_25d59_row3_col8\" class=\"data row3 col8\" >1351</td>\n",
       "      <td id=\"T_25d59_row3_col9\" class=\"data row3 col9\" >2175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_25d59_row4_col0\" class=\"data row4 col0\" >2</td>\n",
       "      <td id=\"T_25d59_row4_col1\" class=\"data row4 col1\" >43</td>\n",
       "      <td id=\"T_25d59_row4_col2\" class=\"data row4 col2\" >714</td>\n",
       "      <td id=\"T_25d59_row4_col3\" class=\"data row4 col3\" >2</td>\n",
       "      <td id=\"T_25d59_row4_col4\" class=\"data row4 col4\" >19</td>\n",
       "      <td id=\"T_25d59_row4_col5\" class=\"data row4 col5\" >2400</td>\n",
       "      <td id=\"T_25d59_row4_col6\" class=\"data row4 col6\" >425</td>\n",
       "      <td id=\"T_25d59_row4_col7\" class=\"data row4 col7\" >1</td>\n",
       "      <td id=\"T_25d59_row4_col8\" class=\"data row4 col8\" >151</td>\n",
       "      <td id=\"T_25d59_row4_col9\" class=\"data row4 col9\" >477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_25d59_row5_col0\" class=\"data row5 col0\" >67</td>\n",
       "      <td id=\"T_25d59_row5_col1\" class=\"data row5 col1\" >79</td>\n",
       "      <td id=\"T_25d59_row5_col2\" class=\"data row5 col2\" >170</td>\n",
       "      <td id=\"T_25d59_row5_col3\" class=\"data row5 col3\" >25</td>\n",
       "      <td id=\"T_25d59_row5_col4\" class=\"data row5 col4\" >2552</td>\n",
       "      <td id=\"T_25d59_row5_col5\" class=\"data row5 col5\" >477</td>\n",
       "      <td id=\"T_25d59_row5_col6\" class=\"data row5 col6\" >27</td>\n",
       "      <td id=\"T_25d59_row5_col7\" class=\"data row5 col7\" >44</td>\n",
       "      <td id=\"T_25d59_row5_col8\" class=\"data row5 col8\" >590</td>\n",
       "      <td id=\"T_25d59_row5_col9\" class=\"data row5 col9\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_25d59_row6_col0\" class=\"data row6 col0\" >422</td>\n",
       "      <td id=\"T_25d59_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_25d59_row6_col2\" class=\"data row6 col2\" >4</td>\n",
       "      <td id=\"T_25d59_row6_col3\" class=\"data row6 col3\" >486</td>\n",
       "      <td id=\"T_25d59_row6_col4\" class=\"data row6 col4\" >380</td>\n",
       "      <td id=\"T_25d59_row6_col5\" class=\"data row6 col5\" >92</td>\n",
       "      <td id=\"T_25d59_row6_col6\" class=\"data row6 col6\" >90</td>\n",
       "      <td id=\"T_25d59_row6_col7\" class=\"data row6 col7\" >380</td>\n",
       "      <td id=\"T_25d59_row6_col8\" class=\"data row6 col8\" >50</td>\n",
       "      <td id=\"T_25d59_row6_col9\" class=\"data row6 col9\" >6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_25d59_row7_col0\" class=\"data row7 col0\" >122</td>\n",
       "      <td id=\"T_25d59_row7_col1\" class=\"data row7 col1\" >2811</td>\n",
       "      <td id=\"T_25d59_row7_col2\" class=\"data row7 col2\" >597</td>\n",
       "      <td id=\"T_25d59_row7_col3\" class=\"data row7 col3\" >2174</td>\n",
       "      <td id=\"T_25d59_row7_col4\" class=\"data row7 col4\" >1038</td>\n",
       "      <td id=\"T_25d59_row7_col5\" class=\"data row7 col5\" >1727</td>\n",
       "      <td id=\"T_25d59_row7_col6\" class=\"data row7 col6\" >1</td>\n",
       "      <td id=\"T_25d59_row7_col7\" class=\"data row7 col7\" >682</td>\n",
       "      <td id=\"T_25d59_row7_col8\" class=\"data row7 col8\" >515</td>\n",
       "      <td id=\"T_25d59_row7_col9\" class=\"data row7 col9\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_25d59_row8_col0\" class=\"data row8 col0\" >256</td>\n",
       "      <td id=\"T_25d59_row8_col1\" class=\"data row8 col1\" >29</td>\n",
       "      <td id=\"T_25d59_row8_col2\" class=\"data row8 col2\" >342</td>\n",
       "      <td id=\"T_25d59_row8_col3\" class=\"data row8 col3\" >75</td>\n",
       "      <td id=\"T_25d59_row8_col4\" class=\"data row8 col4\" >1</td>\n",
       "      <td id=\"T_25d59_row8_col5\" class=\"data row8 col5\" >84</td>\n",
       "      <td id=\"T_25d59_row8_col6\" class=\"data row8 col6\" >8</td>\n",
       "      <td id=\"T_25d59_row8_col7\" class=\"data row8 col7\" >1511</td>\n",
       "      <td id=\"T_25d59_row8_col8\" class=\"data row8 col8\" >2240</td>\n",
       "      <td id=\"T_25d59_row8_col9\" class=\"data row8 col9\" >1417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_25d59_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_25d59_row9_col0\" class=\"data row9 col0\" >1136</td>\n",
       "      <td id=\"T_25d59_row9_col1\" class=\"data row9 col1\" >1073</td>\n",
       "      <td id=\"T_25d59_row9_col2\" class=\"data row9 col2\" >503</td>\n",
       "      <td id=\"T_25d59_row9_col3\" class=\"data row9 col3\" >57</td>\n",
       "      <td id=\"T_25d59_row9_col4\" class=\"data row9 col4\" >1</td>\n",
       "      <td id=\"T_25d59_row9_col5\" class=\"data row9 col5\" >26</td>\n",
       "      <td id=\"T_25d59_row9_col6\" class=\"data row9 col6\" >71</td>\n",
       "      <td id=\"T_25d59_row9_col7\" class=\"data row9 col7\" >1</td>\n",
       "      <td id=\"T_25d59_row9_col8\" class=\"data row9 col8\" >2</td>\n",
       "      <td id=\"T_25d59_row9_col9\" class=\"data row9 col9\" >23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12f3b7880>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.style.background_gradient(axis=None, cmap=\"Greens\", vmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d85e1b40d54918",
   "metadata": {},
   "source": [
    "## Plot Comparison of Label Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f49259a3de7dd17",
   "metadata": {},
   "source": [
    "Now, once you know how to visualize a single partitioned dataset, you'll learn how to compare a few of them on a single plot.\n",
    "\n",
    "Let's compare:\n",
    "\n",
    "- IidPartitioner,\n",
    "- DirichletPartitioner,\n",
    "- ShardPartitioner\n",
    "still using the `cifar10` dataset.\n",
    "\n",
    "We need to create a list of partitioners. Each partitioner needs to have a dataset assigned to it (it does not have to be the same dataset so you can also compare the same partitioning on different datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e84a9192a266f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flwr_datasets import FederatedDataset\n",
    "from flwr_datasets.partitioner import (\n",
    "    IidPartitioner,\n",
    "    DirichletPartitioner,\n",
    "    ShardPartitioner,\n",
    ")\n",
    "\n",
    "partitioner_list = []\n",
    "title_list = [\"IidPartitioner\", \"DirichletPartitioner\", \"ShardPartitioner\"]\n",
    "\n",
    "## IidPartitioner\n",
    "fds = FederatedDataset(\n",
    "    dataset=\"cifar10\",\n",
    "    partitioners={\n",
    "        \"train\": IidPartitioner(num_partitions=10),\n",
    "    },\n",
    ")\n",
    "partitioner_list.append(fds.partitioners[\"train\"])\n",
    "\n",
    "## DirichletPartitioner\n",
    "fds = FederatedDataset(\n",
    "    dataset=\"cifar10\",\n",
    "    partitioners={\n",
    "        \"train\": DirichletPartitioner(\n",
    "            num_partitions=10,\n",
    "            partition_by=\"label\",\n",
    "            alpha=1.0,\n",
    "            min_partition_size=0,\n",
    "        ),\n",
    "    },\n",
    ")\n",
    "partitioner_list.append(fds.partitioners[\"train\"])\n",
    "\n",
    "## ShardPartitioner\n",
    "fds = FederatedDataset(\n",
    "    dataset=\"cifar10\",\n",
    "    partitioners={\n",
    "        \"train\": ShardPartitioner(\n",
    "            num_partitions=10, partition_by=\"label\", num_shards_per_partition=2\n",
    "        )\n",
    "    },\n",
    ")\n",
    "partitioner_list.append(fds.partitioners[\"train\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18bae80",
   "metadata": {},
   "source": [
    "Now let's visualize them side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ee2864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from flwr_datasets.visualization import plot_comparison_label_distribution\n",
    "\n",
    "fig, axes, df_list = plot_comparison_label_distribution(\n",
    "    partitioner_list=partitioner_list,\n",
    "    label_name=\"label\",\n",
    "    subtitle=\"Comparison of Partitioning Schemes on CIFAR10\",\n",
    "    titles=title_list,\n",
    "    legend=True,\n",
    "    verbose_labels=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862899eb04695380",
   "metadata": {},
   "source": [
    "## Bonus: Natural Id Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3f8aaf",
   "metadata": {},
   "source": [
    "Nothing stops you from using the `NaturalIdPartitioner` to visualize a dataset with the `id` in it and does not need the artificial partitioning but has the pre-existing partitions. For that dataset, we use `NaturalIdPartitioner`. Let's look at the `speech-commands` dataset that has `speaker_id`, and there are quite a few speakers; therefore, we will show only the first 20 partitions. And since we have quite a few different labels, let's specify `legend_kwargs={\"ncols\": 2}` to display them in two columns (we will also shift the legend slightly to the right)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f016d21a",
   "metadata": {},
   "source": [
    "You'll be using the [Google SpeechCommands](https://huggingface.co/datasets/google/speech_commands) dataset, which is a speech-based dataset. For this, you'll need to install the `\"audio\"` extension for Flower Datasets. It can be easily done like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd5ca8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -q \"flwr-datasets[audio]\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ea3642",
   "metadata": {},
   "source": [
    "With everything ready, let's visualize the partitions for a naturally partitioned dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fe70116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from flwr_datasets import FederatedDataset\n",
    "from flwr_datasets.partitioner import NaturalIdPartitioner\n",
    "from flwr_datasets.visualization import plot_label_distributions\n",
    "\n",
    "\n",
    "fds = FederatedDataset(\n",
    "    dataset=\"google/speech_commands\",\n",
    "    subset=\"v0.01\",\n",
    "    partitioners={\n",
    "        \"train\": NaturalIdPartitioner(\n",
    "            partition_by=\"speaker_id\",\n",
    "        ),\n",
    "    },\n",
    ")\n",
    "\n",
    "partitioner = fds.partitioners[\"train\"]\n",
    "\n",
    "fix, ax, df = plot_label_distributions(\n",
    "    partitioner=partitioner,\n",
    "    label_name=\"label\",\n",
    "    max_num_partitions=20,\n",
    "    plot_type=\"bar\",\n",
    "    size_unit=\"percent\",\n",
    "    partition_id_axis=\"x\",\n",
    "    legend=True,\n",
    "    title=\"Per Partition Labels Distribution\",\n",
    "    verbose_labels=True,\n",
    "    legend_kwargs={\"ncols\": 2, \"bbox_to_anchor\": (1.25, 0.5)},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4442c99c",
   "metadata": {},
   "source": [
    "## More resources\n",
    "\n",
    "If you are looking for more resources, feel free to check:\n",
    "\n",
    "* `flwr-dataset` documentation\n",
    "    * [plot_label_distributions](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_label_distributions.html#flwr_datasets.visualization.plot_label_distributions)\n",
    "    * [plot_comparison_label_distribution](https://flower.ai/docs/datasets/ref-api/flwr_datasets.visualization.plot_comparison_label_distribution.html#flwr_datasets.visualization.plot_comparison_label_distribution)\n",
    "* if you want to do any custom modification of the returned plots\n",
    "    * [matplotlib](https://matplotlib.org/)\n",
    "    * [seaborn](https://seaborn.pydata.org/)\n",
    "    * or plot directly using pandas object [pd.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)\n",
    "\n",
    "\n",
    "This was the last tutorial. \n",
    "\n",
    "Previous tutorials:\n",
    "\n",
    "* [Quickstart Basics](https://flower.ai/docs/datasets/tutorial-quickstart.html)\n",
    "\n",
    "* [Use Partitioners](https://flower.ai/docs/datasets/tutorial-use-partitioners.html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flwr",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}