Step4_Boxplot_Histogram.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11.2\n"
     ]
    }
   ],
   "source": [
    "# Import packages for plotting, table manipulations\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style of each plot \n",
    "sns.set_style(\"white\")\n",
    "\n",
    "print(sns.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PosBurstRate  PosBurstPosition  NegBurstRate  NegBurstPosition  \\\n",
      "0         1.812464          1277.988      0.519309          1626.549   \n",
      "2         2.349173          1229.479      0.663196          1547.285   \n",
      "5         1.864984          1271.237      0.828270          1460.770   \n",
      "9         0.981264          1255.484      0.201261          1583.291   \n",
      "10        1.917934          1233.730      0.790681          1430.264   \n",
      "...            ...               ...           ...               ...   \n",
      "2399      1.849325          1238.230      0.658883          1302.491   \n",
      "2401      1.676014          1268.236      0.825700          1417.512   \n",
      "2402      1.416046          1299.492      1.335290          1326.746   \n",
      "2404      2.131353          1289.990      0.730777          1415.261   \n",
      "2406      0.404612          1697.813      0.250286          1419.762   \n",
      "\n",
      "             Force                     UID      Tag  \n",
      "0     1.904726e-13  abaEJMuY7p9if4TFi9yja6  noBreak  \n",
      "2     5.410418e-13  cFiWfUMDo7kAA86xq7Neq4  noBreak  \n",
      "5     3.526207e-13  d733oJVci3onQGbr5cCr6H  noBreak  \n",
      "9     5.343947e-13  jBy8VMCQNGLRgSXiQRmEse  noBreak  \n",
      "10    1.722974e-13  x3jMfjUsaMVznzq5a3QgEy  noBreak  \n",
      "...            ...                     ...      ...  \n",
      "2399  4.203144e-13  cKxeCTY7r5Qn8A1Ngo6rDz  noBreak  \n",
      "2401  2.072164e-13  fBRmtoesG94QoqEWnPc4xQ  noBreak  \n",
      "2402  1.010940e-13  w2zSjFrthiicWgNUGZwRU6  noBreak  \n",
      "2404  2.743550e-13  vbkqG1KN1akTUgBVPDGDVt  noBreak  \n",
      "2406  2.473844e-13  riVdhaYd4fTyygtmVckLsb  noBreak  \n",
      "\n",
      "[1641 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "#Read csv which was created with groovy script from the tutorial\n",
    "df = pd.read_csv('/Users/thomasretzer/git/Gyrase_Scatter.csv')\n",
    "\n",
    "# Take the absolute value of the NegativeBurstRate, so you can compare them in absolute values \n",
    "df['NegBurstRate'] = abs(df['NegBurstRate'])\n",
    "\n",
    "# Filter out data depending on conditions stated in line below\n",
    "# Take away first bin of the histogram (data was evaluted manually)\n",
    "cleanup = df.loc[(df['NegBurstPosition'] > 0) & (df['PosBurstPosition'] > 0) & (df['PosBurstRate'] > 0.20)& (df['NegBurstRate'] > 0.20)]\n",
    "\n",
    "# Inspect the dataframe\n",
    "print(cleanup)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the histogram for PositiveBurstRate\n",
    "sns.histplot(cleanup[\"PosBurstRate\"], bins = round(math.sqrt(len(cleanup[\"PosBurstRate\"]))), kde = True,  stat = 'probability', color = 'dimgrey').set_title('Rate distribution')\n",
    "# Plot the histogram for NegativeBurstRate\n",
    "sns.histplot(cleanup[\"NegBurstRate\"], bins = round(math.sqrt(len(cleanup[\"NegBurstRate\"]))), kde = True,  stat = 'probability', color = 'darkgray') \n",
    "\n",
    "# Label the plot \n",
    "plt.legend(labels=[\"Positive Burst Rate\",\"Negative Burst Rate\"])\n",
    "\n",
    "# Display/save the plot \n",
    "#plt.show()\n",
    "plt.savefig('210916_histplot.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      PosBurstPosition  NegBurstPosition\n",
      "0             1277.988          1626.549\n",
      "2             1229.479          1547.285\n",
      "5             1271.237          1460.770\n",
      "9             1255.484          1583.291\n",
      "10            1233.730          1430.264\n",
      "...                ...               ...\n",
      "2399          1238.230          1302.491\n",
      "2401          1268.236          1417.512\n",
      "2402          1299.492          1326.746\n",
      "2404          1289.990          1415.261\n",
      "2406          1697.813          1419.762\n",
      "\n",
      "[1641 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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hRalxxo9Dijpj8d7UsmVLTp065T59XORG6tWrd8OfJVRGQSY1rizAFGT3Jrvd7v7RrkhN0NCi1DiXgkxEapCCTGqc8eOxMUNnLYpIDVCQmcTOnTvZuXOnp8u4JWUBZr183sOV1Cwzv0YiZqZjZCZRdukLT024ejtK/YIobRCA9fLtXVeptjPzayRiZgoyqXk2+01nyRcRuVUaWhQREVNTkImIiKkpyERExNQUZCIiYmoKMhERMTUFmYiImJqCTERETE1BJiIipqYgExERU6vSzB7p6enMnDmT0NBQAAoLCxkyZAgOh6PKHfXr14+goCAsFguXL1/mN7/5DWPHjr21qn+0fv16xo0bd0fqA5g6dSrLly/n8OHD5OXlERERwaxZs3jxxRfx9va+rVpFRKRmVHmKqp49e/LSSy8BUFRURGRkJMOGDaNRo0ZV7iw1NRUfHx+KiooYPHgwkZGRBAQEVL/qH61YsYJx48bdsfqWL18OwK5duwgMDCQiIsLdpoiI1E63NNdifn4+VquVI0eOkJKSgs1mw8fHh8TERAICApgxYwb5+fkUFBQwe/ZsevToUW79goICfHx88PPzw+l0kpWVRXR0NIWFhQwaNIgPPvgAh8OBv78/eXl5PP/888TFxeHl5YXNZmPx4sU4nU4uXLhAQkICgwYNqrQ+m83GwYMHSUxMrFKNvXr1wul0snXrVux2O+Hh4cycOZN3332XnJwc5s6dS0lJCRaLhXnz5tGuXTsGDBhA165dOXbsGAEBASxbtgybzXbrr8h1nD9/ntzcXGbMmHHH266uc+fO3dX+bHmnAUhJSaFBgwZ3te/qOHr06G19MRORW1PlINuzZw8OhwOLxYLdbic+Pp6kpCQWLlxIWFgY77//PsnJyUybNo1z586xdu1acnNzOX78uLuNiRMnYrFYyMrKon///tjtN55IdsiQITz++ONs2LCB8PBwYmJi2LdvHxcuXGDKlCmsX7+ehIQE0tPTK63P19eXefPmVavGZs2aMXz4cAIDA+nYsaN7+eLFi3E4HPTv35+MjAzi4uJwOp2cPHmSdevWERQUxOjRo/n666/p3Llz1V8BERG5Lbc0tFhm7ty5hIWFARAREUFKSgoPPfQQY8eO5dlnn6WkpKTccaprhxYnTZrE9u3by7VnGEa522WXRx8xYgSrV6/m6aefxs/Pj1mzZlWpPoCzZ89Wq8bryczMJCIiAoCwsDC+/fZbAPz9/QkKCgIgKCiIwsLCm7Z1K/z9/fH39+fll1+ukfarw+l08sorr9y1/kobPQDfHeD3v/897dq1u2v9Vldt2FsWuRfd1lmLTZs25dChQwDs3buX1q1bc/jwYS5dusSqVatITk4mMTGxwnre3t4EBARQXFyMj48POTk5ABw4cKDc4ywWCwC7d++mW7durFu3jsjISNasWQNUDL47VaPFYsHlcpVbFhISwr59+wDIyMggMDCwXI0iIuIZt3U9sgULFpCYmIhhGNhsNpKSkmjatCmvvvoq27Ztw263M336dPfjJ06ciNVqxeVy0bx5c4YOHUpBQQEbN25kzJgxhIeH4+vrW6GfDh06MHv2bJYtW4bVaiU2Nha4Gi7R0dGMHDnyjtVY1t/ixYsJCQlxL3vuueeIj48nNTWVkpISFi5ceDtPnYiI3CEWoyq7NXLHREVF4XQ6q71e2bBVbRpavNjlKfCqV6V16h+6evXkK+0GV7s/2w8nafCv93jttddMMbRYG14jkbrmRp+d+kG0iIiYmoJMRERMTUEmIiKmpiATERFTU5CJiIipKchERMTUFGQiImJqCjIRETG125rZQ+6ewYOr/0Pi2sJ66RzW4isU+wd7upQaZebXSMTMFGQmMXDgQE+XcMusBXlYCy7gqn+fp0upUWZ+jUTMTEOLUuMsJQVX/xYXeLgSEamLFGRS49xBVnLFw5WISF2kIJMa91OQ1cy12kTk3qYgkxpXNqSooUURqQkKMqlxZXti1lIFmYjceQoyqXFepVeDzFaqoUURufMUZFLjLKU6RiYiNUdBJjXLMDCKCrBYLBhFV8BweboiEaljFGRSs0qLwHDRrFkzACwlRR4uSETqGgWZ1KiyU+9btWpV7raIyJ2iIJMaVXZcTEEmIjVFcy3KLfM79A4WLDd8jOEqAX4KsgbH/obFWr23nVFafGsFisg9QUEm1dayZUsee+yxaq3Tvn37aq/z7/z8/G5rfRGpmxRkUm0PP/wwDz/8cLXXi4+Pr4FqRORep2NkIiJiagoyERExNQWZiIiYmoJMRERMTUEmIiKmpiATERFTU5CJiIipKchERMTUFGQiImJqCjIRETE1BZmIiJiagkxERExNQSYiIqamIBMREVNTkImIiKkpyERExNQUZCIiYmoKMhERMTUFmYiImJqCTERETE1BJiIipqYgExERU1OQiYiIqSnIRETE1BRkIiJiagoyERExNQWZiIiYmoJMRERMTUEmIiKmpiATERFTU5CJiIipKchERMTUFGQiImJqCjIRETE1BZmIiJiagkxERExNQSYiIqamIBMREVNTkImIiKkpyERExNQUZCIiYmoKMhERMTUFmYiImJqCTERETE1BJiIipqYgExERU1OQiYiIqSnIRETE1BRkIiJiagoyERExNQWZiIiYmpenCxC5l3344Yd88803ni6jgsjISJo2beq+/e2337Jr1y4PVvST3r1706ZNG0+XIbWIgkzEg3bv3s3//d//ebqMCpo0acKgQYPctz/99FNSU1M9WNFPWrRooSCTchRkIh7WsqHBgojvPV0GAC4Dpv49kIMHD5YLsoMHD+LnbeGVX5zDYvFMbWcu24hNb+yZzqVWU5CJeJjFAlYPhcO/s1qgjV8RBw/sL7f84IH9tPErxObBo+pWDM91LrWaTvYQkXLaNCrm2LHjXLlyBYD8/HxOnsompFGJhysTqZyCTETKCW1UgsswOHLkCACHDh3CMAxC71OQSe2kIBORctr8uOeVkZFR7m+wn4JMaicFmYiU08jboGmDqyd4wNW/D/ga+Np1jEpqJwWZiFTQxq+QjAP7MQyDjB9P9BCprRRkIlJBSKMScnK/Z//+/fyQd1EnekitpiATkQrKgsvpdF69rRM9pBZTkIlIBa38SvCywt/+9je8bdDSt9TTJYlcl4JMRCqwW+HBhqW4XC5a/xhqIrWV3p4iUqmQRkVX//oVe7gSkRtTkN3Ddu7cyc6dOz1dhtRSJa6rf3V8TO6Emvy80VyL97AdO3YAMHDgQA9XIrXRmcte/KxxMRFNijxditQBNfl5oz0yEbkuC3hstnuRqlKQiYiIqSnIRETE1BRkIiJiagoyERExNQWZiIiYmoJMRERMTUEmIiKmpiATERFTU5CJiIip3TTI0tPT6d69O2fOnHEvW7p0qfs6RVXVr18/xo4dy7hx44iKimLDhg3Vr/bfrF+/3l3jI488gsPhwOFwMGrUKNLS0qrd3tSpUwE4fPgwe/fuBWDWrFkUFWmKHhGR2qpKcy3a7XZiY2N5/fXXsdzGfDWpqan4+PhQVFTE4MGDiYyMJCAg4JbbW7FiBePGjQOgZ8+evPTSSwAUFRURGRnJsGHDaNSoUZXbW758OQC7du0iMDCQiIgId5t10fnz58nNzWXGjBmeLuWedfz4cWyaXL5a3njjDbZv3+7pMqSajh49eluf9zdSpSDr2bMnLpeLDRs2uIMDIC0tjXfeeQeLxcLgwYMZP348J06cICYmBi8vL1q0aEF2dnaFvaOCggJ8fHzw8/PD6XSSlZVFdHQ0hYWFDBo0iA8++ACHw4G/vz95eXk8//zzxMXF4eXlhc1mY/HixTidTi5cuEBCQgKDBg0q135+fj5WqxWbzcbBgwdJTEzEZrPh4+NDYmIiAQEBzJgxg/z8fAoKCpg9ezY9evSgV69eOJ1Otm7dit1uJzw8nJkzZ/Luu++Sk5PD3LlzKSkpwWKxMG/ePNq1a8eAAQPo2rUrx44dIyAggGXLlmGz2e7ASyMiIlVR5dnvExISGDlyJL179wbgypUr7NixgzfffBOLxcKECRPo3bs3KSkpTJ48mT59+rB582ays7PdbUycOBGLxUJWVhb9+/fHbrffsM8hQ4bw+OOPs2HDBsLDw4mJiWHfvn1cuHCBKVOmsH79ehISEkhPT2fPnj04HA4sFgt2u534+Hh8fX2ZN28eCxcuJCwsjPfff5/k5GSmTZvGuXPnWLt2Lbm5uRw/ftzdZ7NmzRg+fDiBgYF07NjRvXzx4sU4HA769+9PRkYGcXFxOJ1OTp48ybp16wgKCmL06NF8/fXXdO7cuapPq0f5+/vj7+/Pyy+/7OlS7lnx8fEc/+dHni7DVMaPH89jjz3m6TKkmmpy5KfKQebv709cXBwxMTF07dqVy5cvc/r0aSZMmADAhQsX+Oabb8jMzKRLly4AdOvWjbffftvdxrVDi5MmTaowPGAYRrnbwcHBAIwYMYLVq1fz9NNP4+fnx6xZsyrUd+3Q4rXOnj1LWFgYABEREaSkpPDQQw8xduxYnn32WUpKSnA4HDfd/szMTCIiIgAICwvj22+/dT8vQUFBAAQFBVFYWHjTtkRE5M6p1lmL/fr1Izg4mK1bt+Lt7U1oaChvvPEGaWlpREVF0bZtW9q2bcsXX3wBwJdffllpO97e3gQEBFBcXIyPjw85OTkAHDhwoNzjyo7H7d69m27durFu3ToiIyNZs2YNUDH4KtO0aVMOHToEwN69e2ndujWHDx/m0qVLrFq1iuTkZBITEyv063K5yi0LCQlh3759AGRkZBAYGFiuRhER8YxqX1hz7ty57NmzBz8/Px555BHGjBlDUVERHTt2pFmzZkRHRxMXF0dqaip+fn54ef3UxcSJE7FarbhcLpo3b87QoUMpKChg48aNjBkzhvDwcHx9fSv02aFDB2bPns2yZcuwWq3ExsYCV8MlOjqakSNHXrfeBQsWkJiYiGEY2Gw2kpKSaNq0Ka+++irbtm3Dbrczffr0Cv0tXryYkJAQ97LnnnuO+Ph4UlNTKSkpYeHChdV96kREpAZYjKrs1lTD9u3b6dSpE61atWLLli18/vnnLFq06E52YWpRUVHV/ulCTSkbs9YxMs8pO0a2MOJ7T5dSQdLnV8/4jeua5+FKrjpzycqcdH/i4+N1jMyEbvfz5kafndXeI7uZoKAgZs2aRf369bFarSQlJd3pLkRERNzueJBFRETUmj0OERGp+zRFlYiImJqCTERETE1BJiIipqYgExERU1OQiYiIqSnIRKRSLgOKXVBU6ulKRG7sjp9+L+YxePBgT5cgtVjzBqV8dKYee3O86dVc1+ST21OTnzcKsnvYwIEDPV2C1GK2H6cRzczzUpDJbavJzxsNLYpIpTIvegOQleft4UpEbkxBJiIVFJbCyXwr3t7enMi36TiZ1GoKMhGp4FieF4YB/fv3p9QF3+TrKITUXgoyEakgM+9qcEVFRZW7LVIbKchEpILMPC+CmjcjNDSUJgH3k3lBQSa1l4JMRCrIvOhD+/AOAISFdyAz38fDFYlcn4JMRMr5vtDK+QIICwsDoH379uRchrwii4crE6mcgkxEyikbRmzfvj3wU6DpOJnUVgoyESknK88Lu5eN0NBQANq2bYvValWQSa2lIBORcjLz7ISEhuLtffWH0PXr16dNcGuy8uyeLUzkOvQVS8TDSlyQc6V2fKd0GXAs386v2oeXWx7WPpzd/+8YZ69Y8dSRsu8La8dzJLWPgkzEw85csvD7T/w9XcY1DPdxsTLt27fn7bffJrpW1SlylYJMxIOioqLo1auXp8uoICIiotztnj17EhMT46Fqyvv3kBVRkIl4UJcuXTxdQpX4+/sTGRnp6TJEKqVBZxERMTUFmYiImJqCTERETE1BJiIipqYgExERU1OQiYiIqSnIRETE1BRkIiJiagoyERExNQWZiIiYmoJMRERMTUEmIiKmpiATERFTU5CJiIipKchERMTUFGQiImJqCjIRETE1BZmIiJiagkxERExNQSYiIqamIBMREVNTkImIiKkpyERExNQUZCIiYmoKMhERMTUvTxdwr8nOziYqKsrTZYiImEp2dvZ177MYhmHcxVpERP1hWNQAAAaKSURBVETuKA0tioiIqSnIRETE1BRkIiJiagoyERExNQWZiIiYmoJMRERMTb8ju4d8+eWXLF26lLS0NE6cOEFMTAwWi4WHHnqI//7v/8ZqtbJ8+XI+/PBDvLy8iIuLo2PHjp4uu9qKi4uJi4sjOzuboqIipkyZQmhoaJ3d3tLSUubNm8exY8ew2WwsWrQIwzDq7PaWyc3NJSoqitTUVLy8vOr89j7xxBP4+fkB0LJlS5588kkWLlyIzWajd+/eTJ06FZfLRUJCAocPH8bb25sFCxbQqlUrD1d+FxhyT1i1apXx61//2hg5cqRhGIbxzDPPGHv27DEMwzDi4+ONXbt2Gfv37zccDofhcrmM7OxsIyoqypMl37K//OUvxoIFCwzDMIzvv//e6NOnT53e3vfee8+IiYkxDMMw9uzZY0yePLlOb69hGEZRUZHxX//1X8aAAQOMo0eP1vntLSgoMIYNG1Zu2dChQ40TJ04YLpfLePrpp439+/cbO3fuNObMmWMYhmF88cUXxuTJkz1R7l2nocV7xIMPPsiyZcvctw8cOMDDDz8MwKOPPso//vEPPvvsM3r37o3FYuGBBx6gtLSU77//3lMl37LIyEhmzJjhvm2z2er09vbv35/ExEQATp8+TWBgYJ3eXoAXX3yR0aNH07RpU6Buv58BDh06xJUrV5g4cSLjx49n7969FBUV8eCDD2KxWOjduzeffPIJn332Gb/85S8B6Ny5M/v37/dw5XeHguweMXDgQLy8fhpJNgwDi8UCgK+vLxcvXiQ/P5+GDRu6H1O23Gx8fX1p2LAh+fn5TJ8+nZkzZ9bp7QXw8vJizpw5JCYmMnDgwDq9vU6nk/vvv9/9gQ11+/0MUK9ePX73u9/x5z//mfnz5xMbG0v9+vXd919vm202GyUlJZ4o+a5SkN2jrNafXvpLly7RqFEjGjZsyKVLl8otLxuTN5szZ84wfvx4hg0bxpAhQ+r89sLVvZSdO3cSHx9PYWGhe3ld296//vWv/OMf/8DhcJCRkcGcOXPK7WnVte0FCA4OZujQoVgsFoKDg/Hz8+OHH35w33+9bXa5XOW+wNZVCrJ7VPv27UlPTwfgo48+onv37nTt2pWPP/4Yl8vF6dOncblc3H///R6utPrOnTvHxIkTmT17NiNGjADq9vZu27aNlStXAlC/fn0sFgsdOnSos9u7YcMG1q9fT1paGmFhYbz44os8+uijdXZ7Af7yl7+QnJwMwHfffceVK1do0KAB33zzDYZh8PHHH7u3+aOPPgLgn//8J23btvVk2XdN3Y9qqdScOXOIj4/nD3/4A23atGHgwIHYbDa6d+/Ok08+icvl4vnnn/d0mbfktddeIy8vjz/96U/86U9/AmDu3LksWLCgTm7vgAEDiI2NZezYsZSUlBAXF0dISEidfX0rU5ffzwAjRowgNjaWMWPGYLFYSEpKwmq1Eh0dTWlpKb1796ZTp078/Oc/5+9//zujR4/GMAySkpI8XfpdodnvRUTE1DS0KCIipqYgExERU1OQiYiIqSnIRETE1BRkIiJiagoykTogOTkZh8NBZGQkffv2xeFwMH36dDIyMli+fPkd6cPpdNK3b19ef/31Su9/5pln+PnPf17ux9gid4NOvxepQ5xOJ1lZWURHR3uk7X79+vHuu+/i4+Nzx/sXuR79IFqkDktPT2fTpk289NJLPP7443Tp0oUTJ07Qs2dPLl68yFdffUVwcDBLlizhzJkz7umtfHx8SExMJCgoqNJ2d+3axerVq/Hy8qJFixYsXry43DRgIneTgkzkHpGdnc26deto0qQJDz/8MFu2bCE+Pp7HHnuMvLw8XnzxRRwOB3369OGTTz5h6dKlpKSkVNrWO++8w4QJE/jVr37Ftm3byM/Pp1GjRnd5i0SuUpCJ3CMaN27MAw88AECDBg0IDQ0FwM/Pj8LCQo4cOcLKlStZs2YNhmFgt9uv21ZsbCwrV65k48aNtGnThv79+9+VbRCpjIJM5B5RdpmT62nTpg0TJ06ka9euZGZmsnfv3us+9q233mLatGkEBATw/PPP89577zF8+PA7XbJIlSjIRAS4OvFuQkIChYWFFBQUMHfu3Os+tmPHjvz2t7+lcePG+Pr60rdv37tXqMi/0VmLIlIlOmtRaiudZiQiVfbOO+/c8HdkOTk5d7kiEe2RiYiIyWmPTERETE1BJiIipqYgExERU1OQiYiIqSnIRETE1P5/pFXx96BZHd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxp = cleanup.filter([\"PosBurstPosition\",\"NegBurstPosition\"])\n",
    "print(boxp)\n",
    "\n",
    "# time: gyrase reaction 4600-6990 \n",
    "# frame 4600 = 1110.209 s\n",
    "\n",
    "# Set postion back to Gyrase arrival in the flowcell\n",
    "boxp['PosBurstPosition'] = boxp['PosBurstPosition']-1110.209\n",
    "boxp['NegBurstPosition'] = boxp['NegBurstPosition']-1110.209\n",
    "\n",
    "sns.boxplot(data=boxp,showfliers=False, notch=True,linewidth=1.5, width = 0.5,orient=\"h\")\n",
    "plt.legend(labels=[\"Positive Burst Position\",\"Negative Burst Position\"])\n",
    "plt.xlabel('Time [s]')\n",
    "\n",
    "plt.show()\n",
    "#plt.savefig('210901_boxplot_position.svg')\n",
    "# Save file as SVG so you can adjust colors easily later "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average of the Positive Burst Rate is 1.5907685284176183 +/- 0.5459933730060093 cycles/s.\n",
      "The average of the Negative Burst Rate is 0.8038159851691007 +/- 0.39787620039773297 cycles/s.\n"
     ]
    }
   ],
   "source": [
    "print('The average of the Positive Burst Rate is',cleanup['PosBurstRate'].mean(),\n",
    "      '+/-',cleanup['PosBurstRate'].std(),'cycles/s.')\n",
    "\n",
    "print('The average of the Negative Burst Rate is', cleanup['NegBurstRate'].mean(),\n",
    "      '+/-',cleanup['NegBurstRate'].std(),'cycles/s.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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
}