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cds-amal/StanStatistics.ipynb

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StanStatistics.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import datetime as dt\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" What kind of data is in the log?\n",
" Pretty standard, but looks like we have to do some conversion "
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def extract_time(s): \n",
" '''Convert (hh:mm:ss.mili) to date time\n",
" '''\n",
" return dt.time(*map(int, re.split('[:\\.]',s[1:-1])))\n",
"\n",
"data = pd.read_csv('sls_test2.log', \n",
" header=None, sep=' ', \n",
" names='Time Sls'.split(),\n",
" converters = { 'Time' : extract_time})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Get some basic stats"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sls</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>16011.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.027827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.213622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.001240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.021400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>19.162140</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sls\n",
"count 16011.000000\n",
"mean 0.027827\n",
"std 0.213622\n",
"min 0.000010\n",
"25% 0.000790\n",
"50% 0.001240\n",
"75% 0.021400\n",
"max 19.162140"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Top 10 - best times"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Sls</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7000</th>\n",
" <td>13:22:10.094600</td>\n",
" <td>0.00001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7096</th>\n",
" <td>13:23:10.069600</td>\n",
" <td>0.00046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8090</th>\n",
" <td>13:35:40.039600</td>\n",
" <td>0.00047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7967</th>\n",
" <td>13:34:55.041100</td>\n",
" <td>0.00048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7093</th>\n",
" <td>13:23:10.069600</td>\n",
" <td>0.00048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8519</th>\n",
" <td>13:40:55.044100</td>\n",
" <td>0.00049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13226</th>\n",
" <td>14:50:55.044200</td>\n",
" <td>0.00049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8736</th>\n",
" <td>13:46:10.069200</td>\n",
" <td>0.00049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12823</th>\n",
" <td>14:44:10.074200</td>\n",
" <td>0.00049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11701</th>\n",
" <td>14:32:40.046200</td>\n",
" <td>0.00049</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Sls\n",
"7000 13:22:10.094600 0.00001\n",
"7096 13:23:10.069600 0.00046\n",
"8090 13:35:40.039600 0.00047\n",
"7967 13:34:55.041100 0.00048\n",
"7093 13:23:10.069600 0.00048\n",
"8519 13:40:55.044100 0.00049\n",
"13226 14:50:55.044200 0.00049\n",
"8736 13:46:10.069200 0.00049\n",
"12823 14:44:10.074200 0.00049\n",
"11701 14:32:40.046200 0.00049"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_10 = data.sort(['Sls'],ascending=[1]).head(10)\n",
"best_10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Top 10 - worst times"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Sls</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>03:29:25.052500</td>\n",
" <td>19.16214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>03:30:09.044700</td>\n",
" <td>9.71357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>04:33:21.046400</td>\n",
" <td>7.02660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14682</th>\n",
" <td>15:17:15.065700</td>\n",
" <td>6.68208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1336</th>\n",
" <td>08:50:26.075400</td>\n",
" <td>4.44524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>05:14:15.073000</td>\n",
" <td>4.40291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>03:56:00.028300</td>\n",
" <td>3.46836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4192</th>\n",
" <td>10:35:45.082400</td>\n",
" <td>3.25344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>04:57:03.041300</td>\n",
" <td>2.89334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1047</th>\n",
" <td>08:18:57.066600</td>\n",
" <td>2.75963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Time Sls\n",
"42 03:29:25.052500 19.16214\n",
"43 03:30:09.044700 9.71357\n",
"49 04:33:21.046400 7.02660\n",
"14682 15:17:15.065700 6.68208\n",
"1336 08:50:26.075400 4.44524\n",
"54 05:14:15.073000 4.40291\n",
"46 03:56:00.028300 3.46836\n",
"4192 10:35:45.082400 3.25344\n",
"50 04:57:03.041300 2.89334\n",
"1047 08:18:57.066600 2.75963"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worst_10 = data.sort(['Sls'],ascending=[0]).head(10)\n",
"worst_10"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a4b4f60>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12a648320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Times = worst_10.Time.values\n",
"Times.sort()\n",
"xlim = (Times[0], Times[-1])\n",
"worst_10.plot(x='Time', y='Sls', xlim=xlim, title='Worst 10')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Add a Count column for associated SLS value."
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>Sls</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00:32:36.009300</td>\n",
" <td>0.88537</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00:45:32.002700</td>\n",
" <td>0.49021</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00:45:38.005800</td>\n",
" <td>0.62213</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00:56:01.091600</td>\n",
" <td>0.87027</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01:32:44.061200</td>\n",
" <td>0.00728</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>01:32:45.030000</td>\n",
" <td>0.00123</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>01:32:45.065900</td>\n",
" <td>0.01564</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>01:32:45.098700</td>\n",
" <td>0.00096</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>01:34:07.062700</td>\n",
" <td>0.40652</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>01:46:55.098400</td>\n",
" <td>0.18259</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>01:46:56.042200</td>\n",
" <td>0.08073</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>01:47:57.003000</td>\n",
" <td>0.05493</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>01:47:57.012400</td>\n",
" <td>0.09102</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>01:56:27.023100</td>\n",
" <td>0.55525</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>02:03:56.071600</td>\n",
" <td>0.18632</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>02:03:57.051300</td>\n",
" <td>0.43668</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>02:03:57.066900</td>\n",
" <td>0.15468</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>02:06:05.096300</td>\n",
" <td>1.29089</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>02:06:09.029200</td>\n",
" <td>0.45142</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>02:17:21.047800</td>\n",
" <td>2.73998</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>02:17:53.005600</td>\n",
" <td>0.43057</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
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" <td>0.14853</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>02:27:03.045900</td>\n",
" <td>0.41938</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>02:27:09.011500</td>\n",
" <td>0.20555</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>02:37:23.075500</td>\n",
" <td>0.15385</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>02:37:23.084900</td>\n",
" <td>0.10241</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>02:37:23.095800</td>\n",
" <td>0.10145</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>02:37:23.098900</td>\n",
" <td>0.03189</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>02:37:24.002000</td>\n",
" <td>0.03569</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>02:38:52.075500</td>\n",
" <td>0.14045</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15981</th>\n",
" <td>22:01:11.041500</td>\n",
" <td>0.15345</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>15982</th>\n",
" <td>22:01:11.052400</td>\n",
" <td>0.12164</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15983</th>\n",
" <td>22:01:11.057100</td>\n",
" <td>0.04712</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15984</th>\n",
" <td>22:01:11.064900</td>\n",
" <td>0.07218</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15985</th>\n",
" <td>22:01:11.069600</td>\n",
" <td>0.04069</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15986</th>\n",
" <td>22:01:11.082100</td>\n",
" <td>0.13526</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15987</th>\n",
" <td>22:01:11.088400</td>\n",
" <td>0.05201</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15988</th>\n",
" <td>22:01:12.005600</td>\n",
" <td>0.18522</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15989</th>\n",
" <td>22:01:12.008700</td>\n",
" <td>0.02601</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15990</th>\n",
" <td>22:01:12.011800</td>\n",
" <td>0.03091</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15991</th>\n",
" <td>22:01:12.013400</td>\n",
" <td>0.01061</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15992</th>\n",
" <td>22:01:12.016500</td>\n",
" <td>0.03230</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15993</th>\n",
" <td>22:01:12.018100</td>\n",
" <td>0.02038</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15994</th>\n",
" <td>22:01:12.022700</td>\n",
" <td>0.04038</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15995</th>\n",
" <td>22:01:12.024300</td>\n",
" <td>0.01480</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15996</th>\n",
" <td>22:01:12.025900</td>\n",
" <td>0.02074</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15997</th>\n",
" <td>22:01:12.027400</td>\n",
" <td>0.01549</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15998</th>\n",
" <td>22:01:12.032100</td>\n",
" <td>0.04552</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15999</th>\n",
" <td>22:01:12.035200</td>\n",
" <td>0.03657</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16000</th>\n",
" <td>22:01:12.039900</td>\n",
" <td>0.04534</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16001</th>\n",
" <td>22:01:12.044600</td>\n",
" <td>0.03974</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16002</th>\n",
" <td>22:01:12.049300</td>\n",
" <td>0.05612</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16003</th>\n",
" <td>22:01:12.052400</td>\n",
" <td>0.01803</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16004</th>\n",
" <td>22:01:12.057100</td>\n",
" <td>0.05043</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16005</th>\n",
" <td>22:01:12.060200</td>\n",
" <td>0.03627</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16006</th>\n",
" <td>22:06:04.017900</td>\n",
" <td>0.16509</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16007</th>\n",
" <td>22:42:21.079700</td>\n",
" <td>0.70478</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16008</th>\n",
" <td>23:11:24.050800</td>\n",
" <td>1.36544</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16009</th>\n",
" <td>23:52:31.089100</td>\n",
" <td>0.79489</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16010</th>\n",
" <td>23:57:09.009400</td>\n",
" <td>0.28034</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>16011 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Time Sls Count\n",
"0 00:32:36.009300 0.88537 1\n",
"1 00:45:32.002700 0.49021 1\n",
"2 00:45:38.005800 0.62213 1\n",
"3 00:56:01.091600 0.87027 1\n",
"4 01:32:44.061200 0.00728 2\n",
"5 01:32:45.030000 0.00123 35\n",
"6 01:32:45.065900 0.01564 2\n",
"7 01:32:45.098700 0.00096 100\n",
"8 01:34:07.062700 0.40652 1\n",
"9 01:46:55.098400 0.18259 1\n",
"10 01:46:56.042200 0.08073 1\n",
"11 01:47:57.003000 0.05493 1\n",
"12 01:47:57.012400 0.09102 2\n",
"13 01:56:27.023100 0.55525 1\n",
"14 02:03:56.071600 0.18632 1\n",
"15 02:03:57.051300 0.43668 1\n",
"16 02:03:57.066900 0.15468 1\n",
"17 02:06:05.096300 1.29089 1\n",
"18 02:06:09.029200 0.45142 1\n",
"19 02:17:21.047800 2.73998 1\n",
"20 02:17:53.005600 0.43057 1\n",
"21 02:24:42.013100 0.14853 1\n",
"22 02:27:03.045900 0.41938 1\n",
"23 02:27:09.011500 0.20555 1\n",
"24 02:37:23.075500 0.15385 1\n",
"25 02:37:23.084900 0.10241 1\n",
"26 02:37:23.095800 0.10145 1\n",
"27 02:37:23.098900 0.03189 1\n",
"28 02:37:24.002000 0.03569 2\n",
"29 02:38:52.075500 0.14045 1\n",
"... ... ... ...\n",
"15981 22:01:11.041500 0.15345 1\n",
"15982 22:01:11.052400 0.12164 1\n",
"15983 22:01:11.057100 0.04712 1\n",
"15984 22:01:11.064900 0.07218 1\n",
"15985 22:01:11.069600 0.04069 1\n",
"15986 22:01:11.082100 0.13526 1\n",
"15987 22:01:11.088400 0.05201 1\n",
"15988 22:01:12.005600 0.18522 1\n",
"15989 22:01:12.008700 0.02601 2\n",
"15990 22:01:12.011800 0.03091 3\n",
"15991 22:01:12.013400 0.01061 2\n",
"15992 22:01:12.016500 0.03230 2\n",
"15993 22:01:12.018100 0.02038 1\n",
"15994 22:01:12.022700 0.04038 1\n",
"15995 22:01:12.024300 0.01480 2\n",
"15996 22:01:12.025900 0.02074 1\n",
"15997 22:01:12.027400 0.01549 2\n",
"15998 22:01:12.032100 0.04552 2\n",
"15999 22:01:12.035200 0.03657 1\n",
"16000 22:01:12.039900 0.04534 1\n",
"16001 22:01:12.044600 0.03974 3\n",
"16002 22:01:12.049300 0.05612 3\n",
"16003 22:01:12.052400 0.01803 2\n",
"16004 22:01:12.057100 0.05043 1\n",
"16005 22:01:12.060200 0.03627 2\n",
"16006 22:06:04.017900 0.16509 1\n",
"16007 22:42:21.079700 0.70478 1\n",
"16008 23:11:24.050800 1.36544 1\n",
"16009 23:52:31.089100 0.79489 1\n",
"16010 23:57:09.009400 0.28034 1\n",
"\n",
"[16011 rows x 3 columns]"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Count'] = data.groupby(['Sls']).transform('count')\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a657128>"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x107a12668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.Count.hist(bins=8, range=(0,250))"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a657be0>"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12a66c6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.Sls.hist(bins=10, range=(0.001,.025))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Remove outliers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keep only the ones that are within +2 to -2 \n",
"standard deviations in the column 'Sls'."
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"no_outs = data[np.abs(data.Sls-data.Sls.mean())<=(2*data.Sls.std())] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Compare original data and sans-outlier summary"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sls</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>16011.000000</td>\n",
" <td>16011.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.027827</td>\n",
" <td>69.720130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.213622</td>\n",
" <td>74.070227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000010</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000790</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.001240</td>\n",
" <td>32.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.021400</td>\n",
" <td>137.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>19.162140</td>\n",
" <td>225.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sls Count\n",
"count 16011.000000 16011.000000\n",
"mean 0.027827 69.720130\n",
"std 0.213622 74.070227\n",
"min 0.000010 1.000000\n",
"25% 0.000790 2.000000\n",
"50% 0.001240 32.000000\n",
"75% 0.021400 137.000000\n",
"max 19.162140 225.000000"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sls</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>15941.000000</td>\n",
" <td>15941.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.020457</td>\n",
" <td>70.021893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.045509</td>\n",
" <td>74.092255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000010</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000790</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.001230</td>\n",
" <td>35.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.020810</td>\n",
" <td>137.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.454920</td>\n",
" <td>225.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sls Count\n",
"count 15941.000000 15941.000000\n",
"mean 0.020457 70.021893\n",
"std 0.045509 74.092255\n",
"min 0.000010 1.000000\n",
"25% 0.000790 2.000000\n",
"50% 0.001230 35.000000\n",
"75% 0.020810 137.000000\n",
"max 0.454920 225.000000"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_outs.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Histogram of SLS (no outliers): try to fit 20 bins, range between 0 & .025"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a68ef28>"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12a6d9160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"no_outs.Sls.hist(bins=20, range=(0,.025))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Statistics is not my thing"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x107b3c9b0>"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x107a4df28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# lets do a histogram and set the bin range to something else\n",
"# \n",
"no_outs.Sls.hist(bins=20, range=(0.02,.5))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Bin the data frame by Sls with 10 bins...\n",
"```python\n",
"bins = np.linspace(no_outs.Sls.min(), no_outs.Sls.max(), 10) # do it by 10 bins\n",
"bins = np.arange(0, no_outs.Sls.max(),0.05) # do it by increments of .05\n",
"groups = no_outs.groupby(pandas.cut(no_outs.a, bins))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Sls\n",
"(0.1, 0.11] 100\n",
"(0.11, 0.12] 81\n",
"(0.12, 0.13] 87\n",
"(0.13, 0.14] 67\n",
"(0.14, 0.15] 50\n",
"(0.15, 0.16] 51\n",
"(0.16, 0.17] 43\n",
"(0.17, 0.18] 34\n",
"(0.18, 0.19] 26\n",
"(0.19, 0.2] 23\n",
"(0.2, 0.21] 14\n",
"(0.21, 0.22] 24\n",
"(0.22, 0.23] 13\n",
"(0.23, 0.24] 20\n",
"(0.24, 0.25] 18\n",
"(0.25, 0.26] 8\n",
"(0.26, 0.27] 7\n",
"(0.27, 0.28] 12\n",
"(0.28, 0.29] 11\n",
"(0.29, 0.3] 17\n",
"(0.3, 0.31] 8\n",
"(0.31, 0.32] 5\n",
"(0.32, 0.33] 9\n",
"(0.33, 0.34] 6\n",
"(0.34, 0.35] 3\n",
"(0.35, 0.36] 9\n",
"(0.36, 0.37] 4\n",
"(0.37, 0.38] 2\n",
"(0.38, 0.39] 5\n",
"(0.39, 0.4] 3\n",
"(0.4, 0.41] 7\n",
"(0.41, 0.42] 6\n",
"(0.42, 0.43] 3\n",
"(0.43, 0.44] 3\n",
"(0.44, 0.45] 2\n",
"Name: Sls, dtype: int64"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins = np.arange(0.1, no_outs.Sls.max(),0.01) # start from 100 ms, to max, increment by 10ms\n",
"groups = no_outs.groupby(pd.cut(no_outs.Sls, bins))\n",
"groups.count().Sls"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12a72d048>"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x12b28a080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"groups.count().Sls.plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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