{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 9 - Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data preparation\n",
    "![Workflow](workflow_ComparingSample.svg)\n",
    "## 1.1 Load data into frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pheno = pd.read_csv('phenoTable.csv')\n",
    "geno  = pd.read_csv('genoTable.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 [Merge data frames](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Tasks\n",
    "![Workflow](workflow_StatisticalSignificanceHunter.svg)\n",
    "## 2.1 Running basic statistical analysis\n",
    "For this example we will start with a fairly complicated dataset from a genetics analysis done at the Institute of Biomechanics, ETHZ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1. Introduction\n",
    "There are 1000 mouse femur bones which have been measured at high resolution and a number of shape analyses run on each sample. - Phenotypical Information - Each column represents a metric which was assessed in the images - CORT_DTO__C_TH for example is the mean thickness of the cortical bone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>BMD</th>\n",
       "      <th>MECHANICS_STIFFNESS</th>\n",
       "      <th>CORT_DTO__C_TH</th>\n",
       "      <th>CORT_DTO__C_TH_SD</th>\n",
       "      <th>CORT_MOM__J</th>\n",
       "      <th>CT_TH_RAD</th>\n",
       "      <th>CT_TH_RAD_STD</th>\n",
       "      <th>CANAL_VOLUME</th>\n",
       "      <th>CANAL_COUNT</th>\n",
       "      <th>CANAL_DENSITY</th>\n",
       "      <th>...</th>\n",
       "      <th>CANAL_THETA</th>\n",
       "      <th>CANAL_THETA_CV</th>\n",
       "      <th>CANAL_PCA1</th>\n",
       "      <th>CANAL_PCA1_CV</th>\n",
       "      <th>CANAL_PCA2</th>\n",
       "      <th>CANAL_PCA2_CV</th>\n",
       "      <th>CANAL_PCA3</th>\n",
       "      <th>CANAL_PCA3_CV</th>\n",
       "      <th>FEMALE</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.030221</td>\n",
       "      <td>57.163181</td>\n",
       "      <td>0.186455</td>\n",
       "      <td>0.019785</td>\n",
       "      <td>0.103288</td>\n",
       "      <td>78.558303</td>\n",
       "      <td>17.440679</td>\n",
       "      <td>18351.469264</td>\n",
       "      <td>31.0</td>\n",
       "      <td>72.458800</td>\n",
       "      <td>...</td>\n",
       "      <td>59.576428</td>\n",
       "      <td>0.281042</td>\n",
       "      <td>443.537228</td>\n",
       "      <td>1.326217</td>\n",
       "      <td>120.150958</td>\n",
       "      <td>1.677884</td>\n",
       "      <td>30.294477</td>\n",
       "      <td>0.700402</td>\n",
       "      <td>0</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.032788</td>\n",
       "      <td>54.972011</td>\n",
       "      <td>0.183007</td>\n",
       "      <td>0.015696</td>\n",
       "      <td>0.126947</td>\n",
       "      <td>88.691516</td>\n",
       "      <td>22.238608</td>\n",
       "      <td>27002.217716</td>\n",
       "      <td>137.0</td>\n",
       "      <td>206.113056</td>\n",
       "      <td>...</td>\n",
       "      <td>54.487601</td>\n",
       "      <td>0.401896</td>\n",
       "      <td>293.627859</td>\n",
       "      <td>1.272190</td>\n",
       "      <td>84.416139</td>\n",
       "      <td>1.541258</td>\n",
       "      <td>34.940901</td>\n",
       "      <td>0.804821</td>\n",
       "      <td>0</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.036075</td>\n",
       "      <td>73.590881</td>\n",
       "      <td>0.216930</td>\n",
       "      <td>0.028019</td>\n",
       "      <td>0.171012</td>\n",
       "      <td>79.973567</td>\n",
       "      <td>8.862339</td>\n",
       "      <td>18464.688139</td>\n",
       "      <td>128.0</td>\n",
       "      <td>177.921019</td>\n",
       "      <td>...</td>\n",
       "      <td>56.120693</td>\n",
       "      <td>0.356876</td>\n",
       "      <td>326.470697</td>\n",
       "      <td>1.155693</td>\n",
       "      <td>87.714578</td>\n",
       "      <td>1.051160</td>\n",
       "      <td>32.911487</td>\n",
       "      <td>0.754326</td>\n",
       "      <td>0</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.031145</td>\n",
       "      <td>49.854823</td>\n",
       "      <td>0.193758</td>\n",
       "      <td>0.024087</td>\n",
       "      <td>0.099639</td>\n",
       "      <td>88.215056</td>\n",
       "      <td>23.288367</td>\n",
       "      <td>42840.614369</td>\n",
       "      <td>147.0</td>\n",
       "      <td>247.019809</td>\n",
       "      <td>...</td>\n",
       "      <td>50.206993</td>\n",
       "      <td>0.445938</td>\n",
       "      <td>243.130372</td>\n",
       "      <td>1.014527</td>\n",
       "      <td>81.448541</td>\n",
       "      <td>1.162161</td>\n",
       "      <td>37.690527</td>\n",
       "      <td>0.944862</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.034226</td>\n",
       "      <td>66.578296</td>\n",
       "      <td>0.175598</td>\n",
       "      <td>0.018144</td>\n",
       "      <td>0.176490</td>\n",
       "      <td>79.330125</td>\n",
       "      <td>15.968669</td>\n",
       "      <td>25474.883270</td>\n",
       "      <td>271.0</td>\n",
       "      <td>349.344731</td>\n",
       "      <td>...</td>\n",
       "      <td>53.561597</td>\n",
       "      <td>0.441762</td>\n",
       "      <td>243.212520</td>\n",
       "      <td>1.041145</td>\n",
       "      <td>80.598173</td>\n",
       "      <td>1.394151</td>\n",
       "      <td>39.716728</td>\n",
       "      <td>1.075045</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BMD  MECHANICS_STIFFNESS  CORT_DTO__C_TH  CORT_DTO__C_TH_SD  \\\n",
       "0  0.030221            57.163181        0.186455           0.019785   \n",
       "1  0.032788            54.972011        0.183007           0.015696   \n",
       "2  0.036075            73.590881        0.216930           0.028019   \n",
       "3  0.031145            49.854823        0.193758           0.024087   \n",
       "4  0.034226            66.578296        0.175598           0.018144   \n",
       "\n",
       "   CORT_MOM__J  CT_TH_RAD  CT_TH_RAD_STD  CANAL_VOLUME  CANAL_COUNT  \\\n",
       "0     0.103288  78.558303      17.440679  18351.469264         31.0   \n",
       "1     0.126947  88.691516      22.238608  27002.217716        137.0   \n",
       "2     0.171012  79.973567       8.862339  18464.688139        128.0   \n",
       "3     0.099639  88.215056      23.288367  42840.614369        147.0   \n",
       "4     0.176490  79.330125      15.968669  25474.883270        271.0   \n",
       "\n",
       "   CANAL_DENSITY  ...  CANAL_THETA  CANAL_THETA_CV  CANAL_PCA1  CANAL_PCA1_CV  \\\n",
       "0      72.458800  ...    59.576428        0.281042  443.537228       1.326217   \n",
       "1     206.113056  ...    54.487601        0.401896  293.627859       1.272190   \n",
       "2     177.921019  ...    56.120693        0.356876  326.470697       1.155693   \n",
       "3     247.019809  ...    50.206993        0.445938  243.130372       1.014527   \n",
       "4     349.344731  ...    53.561597        0.441762  243.212520       1.041145   \n",
       "\n",
       "   CANAL_PCA2  CANAL_PCA2_CV  CANAL_PCA3  CANAL_PCA3_CV  FEMALE   ID  \n",
       "0  120.150958       1.677884   30.294477       0.700402       0  351  \n",
       "1   84.416139       1.541258   34.940901       0.804821       0  356  \n",
       "2   87.714578       1.051160   32.911487       0.754326       0  357  \n",
       "3   81.448541       1.162161   37.690527       0.944862       0  359  \n",
       "4   80.598173       1.394151   39.716728       1.075045       1  360  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pheno.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genetic Information (genoTable.csv)\n",
    "Each animal has been tagged at a number of different regions of the genome (called markers: D1Mit236)\n",
    "- At each marker there are 3 (actually 4) possibilities\n",
    "- A is homozygous (the same from both parents) from the A strain\n",
    "- B is homozygous from the B strain\n",
    "- H is heterozygous (one from A, one from B)\n",
    "- ‘-’ is missing or erronous measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>ID</th>\n",
       "      <th>D1Mit64</th>\n",
       "      <th>D1Mit236</th>\n",
       "      <th>D1Mit7</th>\n",
       "      <th>D1Mit386</th>\n",
       "      <th>D1Mit14</th>\n",
       "      <th>D1Mit540</th>\n",
       "      <th>D1Mit17</th>\n",
       "      <th>D2Mit365</th>\n",
       "      <th>D2Mit323</th>\n",
       "      <th>...</th>\n",
       "      <th>D18Mit64</th>\n",
       "      <th>D18Mit147</th>\n",
       "      <th>D18Mit123</th>\n",
       "      <th>D18Mit9</th>\n",
       "      <th>D18Mit4</th>\n",
       "      <th>D19Mit68</th>\n",
       "      <th>D19Mit40</th>\n",
       "      <th>D19MIT88</th>\n",
       "      <th>D19MIT17</th>\n",
       "      <th>D19MIT108</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>351</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>353</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>354</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>...</td>\n",
       "      <td>H</td>\n",
       "      <td>-</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>-</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>-</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>356</td>\n",
       "      <td>H</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>H</td>\n",
       "      <td>H</td>\n",
       "      <td>-</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>H</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>H</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>-</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID D1Mit64 D1Mit236 D1Mit7 D1Mit386 D1Mit14 D1Mit540 D1Mit17 D2Mit365  \\\n",
       "0  351       H        H      H        H       H        H       H        H   \n",
       "1  353       B        B      B        B       H        H       H        H   \n",
       "2  354       H        A      A        A       A        H       H        H   \n",
       "3  355       A        A      H        H       -        H       H        A   \n",
       "4  356       H        A      A        A       A        A       H        H   \n",
       "\n",
       "  D2Mit323  ... D18Mit64 D18Mit147 D18Mit123 D18Mit9 D18Mit4 D19Mit68  \\\n",
       "0        H  ...        H         H         H       H       H        H   \n",
       "1        A  ...        H         A         A       A       H        H   \n",
       "2        H  ...        H         -         H       H       A        H   \n",
       "3        A  ...        H         H         A       A       A        A   \n",
       "4        -  ...        B         H         B       B       H        B   \n",
       "\n",
       "  D19Mit40 D19MIT88 D19MIT17 D19MIT108  \n",
       "0        H        H        H         H  \n",
       "1        H        H        H         H  \n",
       "2        H        A        H         H  \n",
       "3        A        -        A         A  \n",
       "4        B        B        -         H  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geno.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 Tasks\n",
    "1. Look at the histograms of the available variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kaestner/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(15,15));\n",
    "pheno.hist(ax=ax,bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are far too many variables to work with. At least for a start. We have to focus on some few e.g.\n",
    "- Bone mineral desnity (BMD)\n",
    "- Cortical bone thickness (CORT_DTO_TH)\n",
    "- Cortical bone Microstructural thickness (CORT_DTO_TH_SD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2.2 Look at the pair plot\n",
    "Explore the data and correlations between various metrics by using the ‘pairplot’ plotting component. Change the components to examine different variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(pheno, vars = ['BMD', 'CORT_DTO__C_TH', 'CORT_DTO__C_TH_SD'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. For the rest of the analysis you can connect the various components to the ‘Column Filter’ node since that is the last step in the processing\n",
    "4. Use one of the T-Test nodes in the Statistics -> Hypothesis Testing section to see if there is a statistically significant difference between Gender’s when examining Cortical Bone Microstructural Thickness (Mean)\n",
    "    - Which value is the p-value?\n",
    "    - What does the p-value mean, is it significant, by what criterion?\n",
    "5. Use another node from the Hypothesis Testing section to evaluate the effect on the D16Mit5 on the Lacuna Distribution Anisotropy? Is it significant?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.3. Questions\n",
    "1. In the ‘Independent Groups T-Test’ node we can run a t-test against all of the columns at the same time, why SHOULDN’T we do this?\n",
    "2. If we do, how do we need to interpret this in the results\n",
    "3. Is p<0.05 a sufficient signifance criteria?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Comparing two real bone samples\n",
    "For this example we will compare two real cortical bone samples taken from mice. The data will be downloaded in KNIME from the course website (KNIME can also download / upload to FTP servers making sharing results and data easier). - If you are using your own computer you will need to change the Target Folder in both of the “Download” nodes to something reasonable (just click Browse)\n",
    "\n",
    "For the purpose of the analysis and keeping the data sizes small, we will use Kevin’s Crazy Camera again for simulating the noisy detection process. The assignment aims to be more integrative and you will combine a number of different lectures to get to the final answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![Workflow](workflow_TwoSampleOneCamera.svg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 Questions\n",
    "1. We want to know if there is a statistically significant difference in\n",
    "    - cell volume\n",
    "    - cell shape\n",
    "    - cell density\n",
    "    - between the two samples given the variation in the detector\n",
    "    - which metric do we need here? why?\n",
    "2. We see in the volume comparison a very skewed representation of the data Volume/Num Pixels\n",
    "    - why is this? (Hint check the segmented images)\n",
    "    - What might be done to alleviate it (hint Row Filter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 Hints\n",
    "1. Look at the kind of noise (you can peek inside the Crappy Camera) to choose the proper filter\n",
    "2. Use an automated thresholding technique for finding the bone automatic-methods\n",
    "3. To do this we will need to enhance the image, segment out the bone (dense) tissue, find the mask so that we can look at the\n",
    "4. We then need to label the cells, and analyze their volume and shape: labeling\n",
    "5. Use a Chunk Loop for the Segmenting Features node to prevent all of the features being dumped into the same output (they can afterwards be recombined with the original images using the Join node)\n",
    "6. The final join should combine the output of the feature analysis (the end loop in this case) and the original crappy camera images. Since the Chunk Loop outputs iterations, we simply use that as the key for joining\n",
    "7. Use Morphology and strongy filter parameters it might be possible to maximize the differences in the groups\n",
    "8. You can make the plots in R (sometimes easier than KNIME) using the R View (Table) node and the following code (for volume)\n",
    "\n",
    "``` R\n",
    "library(ggplot2)\n",
    "cur.df<-data.frame(\n",
    "  sample=as.factor(knime.in$\"Image Number\"),\n",
    "    measurement=knime.in$\"Measurement_Number\",\n",
    "    volume=knime.in$\"Num Pix\"\n",
    "    )\n",
    "    \n",
    "ggplot(cur.df,aes(x=volume))+\n",
    "    geom_density(aes(color=sample,group=measurement))+\n",
    "    theme_bw(25)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}