07c_bland_altman/bland_altman_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  B\n",
       "0   1  4\n",
       "1   9  5\n",
       "2   7  5\n",
       "3   1  7\n",
       "4   2  4\n",
       "5   8  5\n",
       "6   9  4\n",
       "7   2  6\n",
       "8   1  6\n",
       "9   7  5\n",
       "10  8  4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# make up some data\n",
    "measurement_A = [1, 9, 7, 1, 2, 8, 9, 2, 1, 7, 8]\n",
    "measurement_B = [4, 5, 5, 7, 4, 5, 4, 6, 6, 5, 4]\n",
    "\n",
    "# show measurements as table\n",
    "pd.DataFrame([measurement_A, measurement_B], [\"A\", \"B\"]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean(A) = 5.0\n",
      "Mean(B) = 5.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean(A) = \" + str(np.mean(measurement_A)))\n",
    "print(\"Mean(B) = \" + str(np.mean(measurement_B)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take your personal notes here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(measurement_A, measurement_B, \"*\")\n",
    "plt.plot([0, 10], [0, 10])\n",
    "plt.axis([0, 10, 0, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def draw_histogram(data):\n",
    "    counts, bins = np.histogram(data, bins=10, range=(0,10))\n",
    "    plt.hist(bins[:-1], bins, weights=counts)\n",
    "    plt.axis([0, 10, 0, 4])\n",
    "    plt.show()\n",
    "    \n",
    "draw_histogram(measurement_A)\n",
    "draw_histogram(measurement_B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "The data for the following expriment is taken from 1 Altman & Bland, The Statistician 32, 1983, Fig. 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measurement_1 = [130, 132, 138, 145, 148, 150, 155, 160, 161, 170, 175, 178, 182, 182, 188, 195, 195, 200, 200, 204, 210, 210, 215, 220, 200]\n",
    "measurement_2 = [122, 130, 135, 132, 140, 151, 145, 150, 160, 150, 160, 179, 168, 175, 187, 170, 182, 179, 195, 190, 180, 195, 210, 190, 200]\n",
    "\n",
    "plt.plot(measurement_1, measurement_2, \"o\")\n",
    "plt.plot([120, 220], [120, 220])\n",
    "plt.axis([120, 220, 120, 220])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r = 0.9435300113035253\n"
     ]
    }
   ],
   "source": [
    "# Determining Pearson's correlation coefficient r with a for-loop\n",
    "import numpy as np\n",
    "\n",
    "# get the mean of the measurements\n",
    "mean_1 = np.mean(measurement_1)\n",
    "mean_2 = np.mean(measurement_2)\n",
    "\n",
    "# get the number of measurements\n",
    "n = len(measurement_1)\n",
    "\n",
    "# get the standard deviation of the measurements\n",
    "std_dev_1 = np.std(measurement_1)\n",
    "std_dev_2 = np.std(measurement_2)\n",
    "\n",
    "# sum the expectation of \n",
    "sum = 0\n",
    "for m_1, m_2 in zip(measurement_1, measurement_2):\n",
    "    sum = sum + (m_1 - mean_1) * (m_2 - mean_2) / n\n",
    "\n",
    "r = sum / (std_dev_1 * std_dev_2)\n",
    "\n",
    "print (\"r = \" + str(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9435300113035255, 1.6002440484659832e-12)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine Pearson's r using scipy\n",
    "from scipy import stats\n",
    "\n",
    "stats.pearsonr(measurement_1, measurement_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bland-Altman plots and the coefficient of repeatability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function for drawing Bland-Altman plots\n",
    "# source https://stackoverflow.com/questions/16399279/bland-altman-plot-in-python\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def bland_altman_plot(data1, data2, *args, **kwargs):\n",
    "    data1     = np.asarray(data1)\n",
    "    data2     = np.asarray(data2)\n",
    "    mean      = np.mean([data1, data2], axis=0)\n",
    "    diff      = data1 - data2                   # Difference between data1 and data2\n",
    "    md        = np.mean(diff)                   # Mean of the difference\n",
    "    sd        = np.std(diff, axis=0)            # Standard deviation of the difference\n",
    "\n",
    "    plt.scatter(mean, diff, *args, **kwargs)\n",
    "    plt.axhline(md,           color='gray', linestyle='--')\n",
    "    plt.axhline(md + 1.96*sd, color='gray', linestyle='--')\n",
    "    plt.axhline(md - 1.96*sd, color='gray', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAWyklEQVR4nO3df2zU933H8ee7xMvstvwIdmJi6ICqWBpkNdERFdggDWy4ESyAFKutVYE2jWpqpqbqWKGVlqjTFBbSRpW2VaIKgkpeV281TiCVaYEW1kJTTHADNDPtIA0YHIxTIJG9zCHv/XFH4/PZjs/fO9997vt6SJbPn8/d9/u5z33z4vL9fD7fr7k7IiISrvcVugEiIhKNglxEJHAKchGRwCnIRUQCpyAXEQncbYXYaWVlpc+ePbsQuxYRCdaJEyeuunvV0PKCBPns2bNpb28vxK5FRIJlZr8ZrlynVkREAqcgFxEJnIJcRCRwCnIRkcApyEVEAjfmWStm9vvAEeD21Ov+090fM7M7gO8Cs4FXgAZ3/23umyoipaD1ZBfb93dy6Vo/d08tZ/OqWtYurIl9W6LI5hv5W8AD7v5RoA6oN7OPAVuAg+7+EeBg6m8RkQytJ7vY2nKKrmv9ONB1rZ+tLadoPdkV67ZENeYg96Q3U3+WpX4ceAjYnSrfDazNaQtFpGRs399J/8DNtLL+gZts398Z67ZEldU5cjObZGYdwBXgh+7+AnCXu18GSP2+c4TXbjKzdjNr7+npidpuEQnQpWv9WZXnUzG1Jaqsgtzdb7p7HTATuM/MFmTx2h3unnD3RFVVxgpTEYmBu6eWZ1WeT8XUlqjGNWvF3a8BPwbqgdfMbAZA6veVnLVORErK5lW1lJdNSisrL5vE5lW1sW5LVGMOcjOrMrOpqcflwErgv4HngA2pp20Ans11I0WkNKxdWMMT6++hZmo5BtRMLeeJ9fcUZKZIMbUlKhvrPTvN7I9IDmZOIvkPQLO7f9XMpgPNwIeAV4GH3f310baVSCRcF80SEcmOmZ1w98TQ8jHPI3f3l4CFw5T3AiuiNU9ERMZLKztFRAKnIBcRCVxBbizR29vLrl270srmz5/PokWLGBgYoKmpKeM1dXV11NXV0dfXR3Nzc0Z9IpFgwYIFXL9+nT179mTUL168mNraWq5evcq+ffsy6pctW8bcuXPp7u6mra0to37FihXMmjWLCxcucPDgwYz6+vp6qqurOXfuHEeOHMmoX716NZWVlXR2dnLs2LGM+nXr1jFlyhROnz497E03GhoaqKiooKOjg46Ojoz6xsZGysrKOH78OGfOnMmo37hxIwBHjx7l7NmzaXVlZWU0NjYCcPjwYc6fP59WX1FRQUNDAwAHDhzg4sWLafWTJ09m/fr1ALS1tdHd3Z1WP336dNasWQPA3r176e3tTauvrq6mvr4egJaWFm7cuJFWP3PmTFauXAlAc3MzfX19afVz5sxh+fLlADQ1NTEwMJBWP2/ePJYsWQKQcdyBjj0de+Eee7foG7mISODGPGsllzRrRUQkeyPNWtE3chGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwGVz8+VZZvYjM3vZzM6Y2edT5Y+bWZeZdaR+Hsxfc0VkqNaTXSzddog5W55n6bZDtJ7sKnSTZIJlc2OJt4EvuvuLZvZB4ISZ/TBV97S7P5X75onIaFpPdrG15RT9AzcB6LrWz9aWUwBB3g1exmfM38jd/bK7v5h6/AbwMqAjRaSAtu/v/F2I39I/cJPt+zsL1CIphHGdIzez2cBC4IVU0SNm9pKZ7TSzaSO8ZpOZtZtZe09Pz7gaKyLpLl3rz6pcSlPWQW5mHwC+Bzzq7jeAbwIfBuqAy8DXhnudu+9w94S7J6qqqiI0WURuuXtqeVblUpqyCnIzKyMZ4k3u3gLg7q+5+013fwf4FnBf7pspIsPZvKqW8rJJaWXlZZPYvKq2QC2SQhjzYKeZGfAM8LK7f31Q+Qx3v5z6cx1wOrdNFJGR3BrQ3L6/k0vX+rl7ajmbV9VqoDNmspm1shT4DHDKzDpSZV8GPmVmdYADrwCfzWkLRWRUaxfWKLhjbsxB7u4/AWyYqu/nrjkiIpItrewUEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCl83VD0UkplpPdulSuUVMQS4io9INnoufTq2IyKh0g+fipyAXkVHpBs/FT0EuIqPSDZ6Ln4JcREalGzwXv2xuvjwL+DZQDbwD7HD3b5jZHcB3gdkk79nZ4O6/zX1TpZRpVkTx0g2ei5+5+9ieaDYDmOHuL5rZB4ETwFpgI/C6u28zsy3ANHf/0mjbSiQS3t7eHq3lUjKGzoqA5De+J9bfo7AQGcTMTrh7Ymj5mE+tuPtld38x9fgN4GWgBngI2J162m6S4S4yZpoVIRLNuM6Rm9lsYCHwAnCXu1+GZNgDd47wmk1m1m5m7T09PeNrrZQkzYoQiSbrIDezDwDfAx519xtjfZ2773D3hLsnqqqqst2tlDDNihCJJqsgN7MykiHe5O4tqeLXUufPb51Hv5LbJkqp06wIkWjGHORmZsAzwMvu/vVBVc8BG1KPNwDP5q55EgdrF9bwxPp7qJlajgE1U8s10CmShWxmrfwx8F/AKZLTDwG+TPI8eTPwIeBV4GF3f320bWnWiohI9kaatTLmeeTu/hPARqheMd6GiYhINFrZKSISOAW5iEjgFOQiIoFTkIuIBE5BLiISOAW5iEjgFOQiIoFTkIuIBE5BLiISOAW5iEjgFOQiIoFTkIuIBE5BLiISOAW5iEjgFOQiIoFTkIuIBE5BLiISuGzu2bnTzK6Y2elBZY+bWZeZdaR+HsxPM0VEZCRjvtUbsAv4Z+DbQ8qfdvenctYikTxqPdnF9v2dXLrWz91Ty9m8qlY3eS5Rcfqss7ln5xEzm52/pojkV+vJLra2nKJ/4CYAXdf62dpyCqBk/wOPq7h91rk4R/6Imb2UOvUyLQfbE8mL7fs7f/cf9i39AzfZvr+zQC2SfInbZx01yL8JfBioAy4DXxvpiWa2yczazay9p6cn4m5FsnfpWn9W5RKuuH3WkYLc3V9z95vu/g7wLeC+UZ67w90T7p6oqqqKsluRcbl7anlW5RKuuH3WkYLczGYM+nMdcHqk54oU2uZVtZSXTUorKy+bxOZVtXnZX+vJLpZuO8ScLc+zdNshWk925WU/pSRXfTbRn3WhjXmw08y+A9wPVJrZReAx4H4zqwMceAX4bB7aKJITtwa5JmImQ9wG23Ihl302kZ91MTB3n/CdJhIJb29vn/D9ikyUpdsO0TXM+diaqeX8dMsDBWhR8VOfvTczO+HuiaHlWtkpkgdxG2zLBfXZ+CnIRfIgboNtuaA+Gz8FeZ5ooCveQh9sK8TxG3qfFVI2S/RljDTQJSEPthXq+A25zwpNg515oEEbCZmO3+Klwc4JpEEbCZmO3/AoyPNAgzYSMh2/4VGQ54EGbSRkOn7Do8HOPNCgjYRMx294NNgpIhIIDXaKiJQoBbmISOAU5CIigVOQi4gETkEuIhI4BbmISOAU5CIigSvIgqDe3l527dqVVjZ//nwWLVrEwMAATU1NGa+pq6ujrq6Ovr4+mpubM+oTiQQLFizg+vXr7NmzJ6N+8eLF1NbWcvXqVfbt25dRv2zZMubOnUt3dzdtbW0Z9StWrGDWrFlcuHCBgwcPZtTX19dTXV3NuXPnOHLkSEb96tWrqayspLOzk2PHjmXUr1u3jilTpnD69GmGm2Pf0NBARUUFHR0ddHR0ZNQ3NjZSVlbG8ePHOXPmTEb9xo0bATh69Chnz55NqysrK6OxsRGAw4cPc/78+bT6iooKGhoaADhw4AAXL15Mq588eTLr168HoK2tje7u7rT66dOns2bNGgD27t1Lb29vWn11dTX19fUAtLS0cOPGjbT6mTNnsnLlSgCam5vp6+tLq58zZw7Lly8HoKmpiYGBgbT6efPmsWTJEoCM4w507OnYC/fYuyWbe3buBFYDV9x9QarsDuC7wGyS9+xscPffjnWbUlhX33yLC6/30/e286/bDrF5VS3TxrGNYxe6+eLPn+fuqeV8uuZN3p+X1orISMa8stPMlgFvAt8eFORPAq+7+zYz2wJMc/cvvde2tLKz8IZecxqS19N4Yv09Y16KnYttiMjYRV7Z6e5HgNeHFD8E7E493g2sHXcLZUJt39+ZFsAA/QM32b6/c0K3ISLRRR3svMvdLwOkft850hPNbJOZtZtZe09PT8TdSlS5uOa0rlstUhwmbNaKu+9w94S7J6qqqiZqtzKCXFxzWtetFikOUYP8NTObAZD6fSV6k2Qi5OKa07putUhxiBrkzwEbUo83AM9G3J5MkLULa3hi/T3UTC3HSN6PMdtBylxsQ0Siy2bWyneA+4FK4DXgMaAVaAY+BLwKPOzuQwdEM2jWiohI9kaatTLmeeTu/qkRqlaMu1UiIhKZluiLiAROQS4iErhgbr7cerIr1jeDjfv7F5GRBRHkQ5eCd13rZ2vLKYBYhFnc37+IjC6IUytxXwoe9/cvIqMLIsjjvhQ87u9fREYXRJDHfSl43N+/iIwuiCCP+1LwuL9/ERldEIOdtwb04jprI+7vPy40M0nGa8xL9HNJS/RF0ukmHTIWkW8sISL5o5lJEoWCXKQIaGaSRKEgFykCmpkkUSjIRYqAZiZJFEHMWhEpdZqZJFEoyEWKxNqFNQpuGRedWhERCZyCXEQkcDk5tWJmrwBvADeBt4ebsC4iMlaluMo1n+8pl+fIP+7uV3O4PRGJoVK8/n6+35NOrYhIUSnFVa75fk+5CnIHfmBmJ8xs03BPMLNNZtZuZu09PT052q2IlJpSXOWa7/eUqyBf6u73Ap8APmdmy4Y+wd13uHvC3RNVVVU52q2IlJpSXOWa7/eUkyB390up31eAPcB9udiuiMRPKa5yzfd7ijzYaWbvB97n7m+kHv8Z8NXILRORWCrFVa75fk+Rr0duZnNJfguH5D8M/+bu/zjaa3Q9chGR7I10PfLI38jd/Rzw0ajbERGR8dH0QxGRwCnIRUQCp6sfRlCKy4hFJDwK8nEqxWXEIhImnVoZp1JcRiwiYVKQj1MpLiMWkTApyMepFJcRi0iYYhfkrSe7WLrtEHO2PM/SbYdoPdk1ru2U4jJiEQlTrAY7czlAWYrLiEUkTLEK8tEGKMcTwLpZrogUg1idWtEApYiUolgFuQYoRaQUxSrINUApIqUoVufINUBZ/HTZg8JR34crVkEOGqAsZrrsQeGo78MWq1MrUtx02YPCUd+HTUEuRUOzigpHfR82BbkUDc0qKhz1fdhyEuRmVm9mnWb2azPbkottSvxoVlHhhNj3ubrcRimIPNhpZpOAfwH+FLgIHDez59z9l1G3LfGiWUWFE1rfa3A2nbl7tA2YLQYed/dVqb+3Arj7EyO9JpFIeHt7e6T9ikh8Ld12iK5hzt/XTC3np1seKECLJoaZnXD3xNDyXJxaqQEuDPr7YqpsaAM2mVm7mbX39PTkYLciElcanE2XiyC3Ycoyvua7+w53T7h7oqqqKge7FZG40uBsulwsCLoIzBr090zgUg62KxNAq/mKhz6Lsdu8qjbtHDkU/+BsPuUiyI8DHzGzOUAX8Eng0znYruSZBoyKhz6L7IQ2OJtvkYPc3d82s0eA/cAkYKe7n4ncMsm7XF+fXcZPn0X2dLmNd+XkWivu/n3g+7nYlkwcDRgVD30WEoVWdsaYBoyKhz4LiUJBHmMhruYrVfosJIrYXcZW3qUBo+Khz0KiiLyyczy0slNEJHv5XNkpIiIFpCAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwCnIRUQCpyAXEQlcpCA3s8fNrMvMOlI/D+aqYSIiMja5uLHE0+7+VA62IyIi46BTKyIigctFkD9iZi+Z2U4zmzbSk8xsk5m1m1l7T09PDnYrIiIwhlu9mdkBoHqYqq8APwOuAg78AzDD3f/ivXaqW72JiGRvpFu9vec5cndfOcYdfAvYN462iYhIBFFnrcwY9Oc64HS05oiISLaizlp50szqSJ5aeQX4bOQWiYhIViIFubt/JlcNERGR8dH0QxGRwCnIRUQCpyAXEQlcLpboZ623t5ddu3allc2fP59FixYxMDBAU1NTxmvq6uqoq6ujr6+P5ubmjPpEIsGCBQu4fv06e/bsyahfvHgxtbW1XL16lX37MmdJLlu2jLlz59Ld3U1bW1tG/YoVK5g1axYXLlzg4MGDGfX19fVUV1dz7tw5jhw5klG/evVqKisr6ezs5NixYxn169atY8qUKZw+fZrh5tg3NDRQUVFBR0cHHR0dGfWNjY2UlZVx/Phxzpw5k1G/ceNGAI4ePcrZs2fT6srKymhsbATg8OHDnD9/Pq2+oqKChoYGAA4cOMDFixfT6idPnsz69esBaGtro7u7O61++vTprFmzBoC9e/fS29ubVl9dXU19fT0ALS0t3LhxI61+5syZrFyZnAXb3NxMX19fWv2cOXNYvnw5AE1NTQwMDKTVz5s3jyVLlgBkHHegY0/HXrjH3i36Ri4iErj3XNmZD1rZKSKSvZFWduobuYhI4BTkIiKBU5CLiAROQS4iEjgFuYhI4BTkIiKBU5CLiAROQS4iEriCLAgysx7gNxO+4+JRSfIWeZJJfTM89cvI4tQ3f+DuVUMLCxLkcWdm7cOtzhL1zUjULyNT3+jUiohI8BTkIiKBU5AXxo5CN6CIqW+Gp34ZWez7RufIRUQCp2/kIiKBU5CLiAROQZ4HZrbTzK6Y2elh6v7WzNzMKgeVbTWzX5tZp5mtmtjWTpyR+sXM/ib13s+Y2ZODymPRLzB835hZnZn9zMw6zKzdzO4bVBeLvjGzWWb2IzN7OXV8fD5VfoeZ/dDMfpX6PW3Qa2LRN2ncXT85/gGWAfcCp4eUzwL2k1wMVZkq+0PgF8DtwBzgf4BJhX4PE9UvwMeBA8Dtqb/vjFu/jNI3PwA+kXr8IPDjuPUNMAO4N/X4g8DZ1Pt/EtiSKt8C/FPc+mbwj76R54G7HwFeH6bqaeDvgMEjzA8B/+7ub7n7eeDXwH3DvDZ4I/TLXwPb3P2t1HOupMpj0y8wYt84MDn1eApwKfU4Nn3j7pfd/cXU4zeAl4Eakn2wO/W03cDa1OPY9M1gCvIJYmZ/DnS5+y+GVNUAFwb9fTFVFhfzgD8xsxfM7LCZLUqVx71fAB4FtpvZBeApYGuqPJZ9Y2azgYXAC8Bd7n4ZkmEP3Jl6Wiz7RkE+AcysAvgK8PfDVQ9TFqc5obcB04CPAZuBZjMz1C+Q/L+VL7j7LOALwDOp8tj1jZl9APge8Ki73xjtqcOUlXTfgIJ8onyY5Pm6X5jZK8BM4EUzqyb5jWHWoOfO5N3/hY6Di0CLJ/0ceIfkRZDi3i8AG4CW1OP/4N1TBLHqGzMrIxniTe5+qz9eM7MZqfoZwK1TcrHqm1sU5BPA3U+5+53uPtvdZ5M82O51927gOeCTZna7mc0BPgL8vIDNnWitwAMAZjYP+D2SV7KLe79AMoCWpx4/APwq9Tg2fZP6v7NngJfd/euDqp4j+Q8dqd/PDiqPRd8MdluhG1CKzOw7wP1ApZldBB5z92eGe667nzGzZuCXwNvA59z95oQ1dgIN1y/ATmBnatrd/wEbPDn9IDb9AiP2zV8B3zCz24D/BTZBvI4ZYCnwGeCUmXWkyr4MbCN5Gu4vgVeBhyF2ffM7WqIvIhI4nVoREQmcglxEJHAKchGRwCnIRUQCpyAXEQmcglxEJHAKchGRwP0/EuhX3YPxOrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a Bland-Altman plot\n",
    "bland_altman_plot(measurement_1, measurement_2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Use napari-simpleitk and scikit-image to measure the Feret#s diameter of nuclei in the `skimage.data.cells3d` dataset. Draw a Bland-Altman plot to compare both methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}