StripesPlot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f894be79-8892-48d6-84dc-8ff19be63c0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T14:05:32.438786Z",
     "iopub.status.busy": "2022-04-08T14:05:32.438168Z",
     "iopub.status.idle": "2022-04-08T14:05:32.444344Z",
     "shell.execute_reply": "2022-04-08T14:05:32.443422Z",
     "shell.execute_reply.started": "2022-04-08T14:05:32.438745Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5f2892b-26f4-4936-bd53-6051c60f22ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T14:05:33.045874Z",
     "iopub.status.busy": "2022-04-08T14:05:33.045259Z",
     "iopub.status.idle": "2022-04-08T14:05:33.069661Z",
     "shell.execute_reply": "2022-04-08T14:05:33.068588Z",
     "shell.execute_reply.started": "2022-04-08T14:05:33.045833Z"
    },
    "tags": []
   },
   "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>n</th>\n",
       "      <th>xPixel</th>\n",
       "      <th>yPixel</th>\n",
       "      <th>segment</th>\n",
       "      <th>peakNumbInSegment</th>\n",
       "      <th>distanceInSegment</th>\n",
       "      <th>distanceCumulated</th>\n",
       "      <th>stripeLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>302.890215</td>\n",
       "      <td>954.066329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>310.783379</td>\n",
       "      <td>967.130875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.065934</td>\n",
       "      <td>0.065934</td>\n",
       "      <td>0.065934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>317.966526</td>\n",
       "      <td>979.020224</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.125938</td>\n",
       "      <td>0.125938</td>\n",
       "      <td>0.060003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>327.012313</td>\n",
       "      <td>993.992561</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.201501</td>\n",
       "      <td>0.201501</td>\n",
       "      <td>0.075563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>335.348085</td>\n",
       "      <td>1007.789699</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.271132</td>\n",
       "      <td>0.271132</td>\n",
       "      <td>0.069632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n      xPixel       yPixel  segment  peakNumbInSegment  distanceInSegment  \\\n",
       "0  1  302.890215   954.066329        1                  1           0.000000   \n",
       "1  2  310.783379   967.130875        1                  2           0.065934   \n",
       "2  3  317.966526   979.020224        1                  3           0.125938   \n",
       "3  4  327.012313   993.992561        1                  4           0.201501   \n",
       "4  5  335.348085  1007.789699        1                  5           0.271132   \n",
       "\n",
       "   distanceCumulated  stripeLength  \n",
       "0           0.000000      0.000000  \n",
       "1           0.065934      0.065934  \n",
       "2           0.125938      0.060003  \n",
       "3           0.201501      0.075563  \n",
       "4           0.271132      0.069632  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"BEL17-2-2_1.66x_milieu0001_StripesCounterFile_01.csv\", skiprows=8)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3bcb8b70-b0a8-4164-9012-9fc28f67b012",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T13:59:25.559572Z",
     "iopub.status.busy": "2022-04-08T13:59:25.558937Z",
     "iopub.status.idle": "2022-04-08T13:59:25.567589Z",
     "shell.execute_reply": "2022-04-08T13:59:25.566411Z",
     "shell.execute_reply.started": "2022-04-08T13:59:25.559531Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['n', 'xPixel', 'yPixel', 'segment', 'peakNumbInSegment',\n",
       "       'distanceInSegment', 'distanceCumulated', 'stripeLength'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58b42a96-fafd-495b-8e14-8ee02b4c3349",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T13:59:26.293617Z",
     "iopub.status.busy": "2022-04-08T13:59:26.293024Z",
     "iopub.status.idle": "2022-04-08T13:59:26.645271Z",
     "shell.execute_reply": "2022-04-08T13:59:26.644198Z",
     "shell.execute_reply.started": "2022-04-08T13:59:26.293577Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
    "\n",
    "a = df[df['stripeLength'] != 0]\n",
    "ax.plot(a['distanceCumulated'], a['stripeLength']*1000)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3eba168f-ce3a-48e6-9fcf-537077433efc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-08T12:28:31.783917Z",
     "iopub.status.busy": "2022-04-08T12:28:31.783361Z",
     "iopub.status.idle": "2022-04-08T12:28:31.816025Z",
     "shell.execute_reply": "2022-04-08T12:28:31.814996Z",
     "shell.execute_reply.started": "2022-04-08T12:28:31.783878Z"
    }
   },
   "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>n</th>\n",
       "      <th>xPixel</th>\n",
       "      <th>yPixel</th>\n",
       "      <th>segment</th>\n",
       "      <th>peakNumbInSegment</th>\n",
       "      <th>distanceInSegment</th>\n",
       "      <th>distanceCumulated</th>\n",
       "      <th>stripeLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>274.986001</td>\n",
       "      <td>898.007420</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.063778</td>\n",
       "      <td>0.063778</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>281.900335</td>\n",
       "      <td>911.052824</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.127555</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>289.228450</td>\n",
       "      <td>924.878917</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.195149</td>\n",
       "      <td>0.195149</td>\n",
       "      <td>0.067594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>296.142785</td>\n",
       "      <td>937.924321</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.258927</td>\n",
       "      <td>0.258927</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>305.150867</td>\n",
       "      <td>954.920038</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.342017</td>\n",
       "      <td>0.342017</td>\n",
       "      <td>0.083090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>311.651420</td>\n",
       "      <td>967.184754</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.401978</td>\n",
       "      <td>0.401978</td>\n",
       "      <td>0.059961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>318.565755</td>\n",
       "      <td>980.230159</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.465756</td>\n",
       "      <td>0.465756</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>325.893870</td>\n",
       "      <td>994.056251</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.533350</td>\n",
       "      <td>0.533350</td>\n",
       "      <td>0.067594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>340.769412</td>\n",
       "      <td>1022.122216</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.670561</td>\n",
       "      <td>0.670561</td>\n",
       "      <td>0.137211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>347.050654</td>\n",
       "      <td>1033.973152</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.728499</td>\n",
       "      <td>0.728499</td>\n",
       "      <td>0.057938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>362.339977</td>\n",
       "      <td>1062.819805</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.869527</td>\n",
       "      <td>0.869527</td>\n",
       "      <td>0.141028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>370.301185</td>\n",
       "      <td>1077.840366</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.942961</td>\n",
       "      <td>0.942961</td>\n",
       "      <td>0.073434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>377.215520</td>\n",
       "      <td>1090.885770</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1.006739</td>\n",
       "      <td>1.006739</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>384.129854</td>\n",
       "      <td>1103.931175</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1.070516</td>\n",
       "      <td>1.070516</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>394.077269</td>\n",
       "      <td>1122.699149</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.162271</td>\n",
       "      <td>1.162271</td>\n",
       "      <td>0.091755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>407.380385</td>\n",
       "      <td>1147.798388</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1.284978</td>\n",
       "      <td>1.284978</td>\n",
       "      <td>0.122707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>413.880939</td>\n",
       "      <td>1160.063105</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1.344939</td>\n",
       "      <td>1.344939</td>\n",
       "      <td>0.059961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>421.842147</td>\n",
       "      <td>1175.083665</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1.418373</td>\n",
       "      <td>1.418373</td>\n",
       "      <td>0.073434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>428.756482</td>\n",
       "      <td>1188.129070</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1.482151</td>\n",
       "      <td>1.482151</td>\n",
       "      <td>0.063778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>435.037723</td>\n",
       "      <td>1199.980006</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1.540089</td>\n",
       "      <td>1.540089</td>\n",
       "      <td>0.057938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>439.629709</td>\n",
       "      <td>1265.200157</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.056179</td>\n",
       "      <td>1.596267</td>\n",
       "      <td>0.056179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>442.173519</td>\n",
       "      <td>1269.906206</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.079287</td>\n",
       "      <td>1.619376</td>\n",
       "      <td>0.023108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>448.131688</td>\n",
       "      <td>1280.928817</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.133412</td>\n",
       "      <td>1.673500</td>\n",
       "      <td>0.054125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>453.027109</td>\n",
       "      <td>1289.985346</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.177882</td>\n",
       "      <td>1.717971</td>\n",
       "      <td>0.044471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>460.048025</td>\n",
       "      <td>1302.974041</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.241661</td>\n",
       "      <td>1.781750</td>\n",
       "      <td>0.063779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>468.131688</td>\n",
       "      <td>1317.928817</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.315094</td>\n",
       "      <td>1.855183</td>\n",
       "      <td>0.073433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>475.797036</td>\n",
       "      <td>1332.109710</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.384727</td>\n",
       "      <td>1.924816</td>\n",
       "      <td>0.069633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>483.236267</td>\n",
       "      <td>1345.872288</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.452306</td>\n",
       "      <td>1.992395</td>\n",
       "      <td>0.067579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>490.901614</td>\n",
       "      <td>1360.053181</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.521939</td>\n",
       "      <td>2.062028</td>\n",
       "      <td>0.069633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>497.278098</td>\n",
       "      <td>1371.849677</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.579864</td>\n",
       "      <td>2.119952</td>\n",
       "      <td>0.057925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>504.299014</td>\n",
       "      <td>1384.838371</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.643643</td>\n",
       "      <td>2.183731</td>\n",
       "      <td>0.063779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     n      xPixel       yPixel  segment  peakNumbInSegment  \\\n",
       "1    2  274.986001   898.007420        1                  2   \n",
       "2    3  281.900335   911.052824        1                  3   \n",
       "3    4  289.228450   924.878917        1                  4   \n",
       "4    5  296.142785   937.924321        1                  5   \n",
       "5    6  305.150867   954.920038        1                  6   \n",
       "6    7  311.651420   967.184754        1                  7   \n",
       "7    8  318.565755   980.230159        1                  8   \n",
       "8    9  325.893870   994.056251        1                  9   \n",
       "9   10  340.769412  1022.122216        1                 10   \n",
       "10  11  347.050654  1033.973152        1                 11   \n",
       "11  12  362.339977  1062.819805        1                 12   \n",
       "12  13  370.301185  1077.840366        1                 13   \n",
       "13  14  377.215520  1090.885770        1                 14   \n",
       "14  15  384.129854  1103.931175        1                 15   \n",
       "15  16  394.077269  1122.699149        1                 16   \n",
       "16  17  407.380385  1147.798388        1                 17   \n",
       "17  18  413.880939  1160.063105        1                 18   \n",
       "18  19  421.842147  1175.083665        1                 19   \n",
       "19  20  428.756482  1188.129070        1                 20   \n",
       "20  21  435.037723  1199.980006        1                 21   \n",
       "22  23  439.629709  1265.200157        2                  2   \n",
       "23  24  442.173519  1269.906206        2                  3   \n",
       "24  25  448.131688  1280.928817        2                  4   \n",
       "25  26  453.027109  1289.985346        2                  5   \n",
       "26  27  460.048025  1302.974041        2                  6   \n",
       "27  28  468.131688  1317.928817        2                  7   \n",
       "28  29  475.797036  1332.109710        2                  8   \n",
       "29  30  483.236267  1345.872288        2                  9   \n",
       "30  31  490.901614  1360.053181        2                 10   \n",
       "31  32  497.278098  1371.849677        2                 11   \n",
       "32  33  504.299014  1384.838371        2                 12   \n",
       "\n",
       "    distanceInSegment  distanceCumulated  stripeLength  \n",
       "1            0.063778           0.063778      0.063778  \n",
       "2            0.127555           0.127555      0.063778  \n",
       "3            0.195149           0.195149      0.067594  \n",
       "4            0.258927           0.258927      0.063778  \n",
       "5            0.342017           0.342017      0.083090  \n",
       "6            0.401978           0.401978      0.059961  \n",
       "7            0.465756           0.465756      0.063778  \n",
       "8            0.533350           0.533350      0.067594  \n",
       "9            0.670561           0.670561      0.137211  \n",
       "10           0.728499           0.728499      0.057938  \n",
       "11           0.869527           0.869527      0.141028  \n",
       "12           0.942961           0.942961      0.073434  \n",
       "13           1.006739           1.006739      0.063778  \n",
       "14           1.070516           1.070516      0.063778  \n",
       "15           1.162271           1.162271      0.091755  \n",
       "16           1.284978           1.284978      0.122707  \n",
       "17           1.344939           1.344939      0.059961  \n",
       "18           1.418373           1.418373      0.073434  \n",
       "19           1.482151           1.482151      0.063778  \n",
       "20           1.540089           1.540089      0.057938  \n",
       "22           0.056179           1.596267      0.056179  \n",
       "23           0.079287           1.619376      0.023108  \n",
       "24           0.133412           1.673500      0.054125  \n",
       "25           0.177882           1.717971      0.044471  \n",
       "26           0.241661           1.781750      0.063779  \n",
       "27           0.315094           1.855183      0.073433  \n",
       "28           0.384727           1.924816      0.069633  \n",
       "29           0.452306           1.992395      0.067579  \n",
       "30           0.521939           2.062028      0.069633  \n",
       "31           0.579864           2.119952      0.057925  \n",
       "32           0.643643           2.183731      0.063779  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48cd23dc-c225-468f-8f12-604211cde7c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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