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dmd/Untitled.ipynb.json

Created July 17, 2015 16:26
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Untitled.ipynb.json
{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import pandas as pd\n",
"d= pd.DataFrame.from_dict(eval(open('datadict').read()), orient='index', dtype=float)\n",
"d.DateTime = pd.to_datetime(d.DateTime)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>even_ghost_p-p</th>\n",
" <th>odd_ghost_min</th>\n",
" <th>object_radius_mm</th>\n",
" <th>even_ghost_max</th>\n",
" <th>DateTime</th>\n",
" <th>peripheral_roi_raw_std</th>\n",
" <th>central_roi_raw_max</th>\n",
" <th>even_ghost_p-p%</th>\n",
" <th>peripheral_roi_raw_min</th>\n",
" <th>central_roi_polyfit_lin</th>\n",
" <th>...</th>\n",
" <th>peripheral_roi_detrended_min</th>\n",
" <th>central_roi_raw_min</th>\n",
" <th>central_roi_detrended_std%</th>\n",
" <th>center_of_mass_x</th>\n",
" <th>peripheral_roi_raw_mean</th>\n",
" <th>peripheral_roi_detrended_mean</th>\n",
" <th>central_roi_detrended_mean</th>\n",
" <th>peripheral_roi_detrended_std%</th>\n",
" <th>peripheral_roi_detrended_max</th>\n",
" <th>peripheral_roi_SFNR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.472297</td>\n",
" <td> 3.235896</td>\n",
" <td> 84.218750</td>\n",
" <td> 2.450390</td>\n",
" <td>2014-07-07 07:11:22</td>\n",
" <td> 0.637441</td>\n",
" <td> 1539.37</td>\n",
" <td> 21.350171</td>\n",
" <td> 1249.388889</td>\n",
" <td> 0.001063</td>\n",
" <td>...</td>\n",
" <td> 1250.613558</td>\n",
" <td> 1533.73</td>\n",
" <td> 0.052447</td>\n",
" <td> 33</td>\n",
" <td> 1250.848721</td>\n",
" <td> 1251.693979</td>\n",
" <td> 1535.515927</td>\n",
" <td> 0.039363</td>\n",
" <td> 1252.889668</td>\n",
" <td> 225.596703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 0.580009</td>\n",
" <td> 2.415251</td>\n",
" <td> 83.354945</td>\n",
" <td> 3.298066</td>\n",
" <td>2014-09-17 08:43:35</td>\n",
" <td> 0.611646</td>\n",
" <td> 1514.19</td>\n",
" <td> 19.414199</td>\n",
" <td> 1228.184028</td>\n",
" <td> 0.000167</td>\n",
" <td>...</td>\n",
" <td> 1229.304928</td>\n",
" <td> 1508.57</td>\n",
" <td> 0.062365</td>\n",
" <td> 32</td>\n",
" <td> 1230.006561</td>\n",
" <td> 1231.042907</td>\n",
" <td> 1510.981299</td>\n",
" <td> 0.039084</td>\n",
" <td> 1232.428128</td>\n",
" <td> 222.708414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.488590</td>\n",
" <td> 1.914204</td>\n",
" <td> 83.354945</td>\n",
" <td> 2.574831</td>\n",
" <td>2014-10-29 08:12:57</td>\n",
" <td> 0.468266</td>\n",
" <td> 1520.74</td>\n",
" <td> 20.814401</td>\n",
" <td> 1231.631944</td>\n",
" <td> 0.001160</td>\n",
" <td>...</td>\n",
" <td> 1231.658173</td>\n",
" <td> 1512.01</td>\n",
" <td> 0.057538</td>\n",
" <td> 32</td>\n",
" <td> 1233.061495</td>\n",
" <td> 1232.960319</td>\n",
" <td> 1514.326143</td>\n",
" <td> 0.035953</td>\n",
" <td> 1234.081719</td>\n",
" <td> 223.213753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 0.517155</td>\n",
" <td> 2.955147</td>\n",
" <td> 82.482094</td>\n",
" <td> 2.452956</td>\n",
" <td>2014-12-24 07:19:53</td>\n",
" <td> 0.626498</td>\n",
" <td> 1614.69</td>\n",
" <td> 23.688801</td>\n",
" <td> 1311.482639</td>\n",
" <td> 0.000818</td>\n",
" <td>...</td>\n",
" <td> 1312.611632</td>\n",
" <td> 1607.70</td>\n",
" <td> 0.061475</td>\n",
" <td> 33</td>\n",
" <td> 1313.083808</td>\n",
" <td> 1313.971200</td>\n",
" <td> 1610.738873</td>\n",
" <td> 0.035804</td>\n",
" <td> 1315.151265</td>\n",
" <td> 237.203254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 0.514811</td>\n",
" <td> 2.597157</td>\n",
" <td> 83.354945</td>\n",
" <td> 2.315317</td>\n",
" <td>2015-01-10 14:32:37</td>\n",
" <td> 1.463018</td>\n",
" <td> 1617.83</td>\n",
" <td> 24.644293</td>\n",
" <td> 1294.309028</td>\n",
" <td> 0.000101</td>\n",
" <td>...</td>\n",
" <td> 1298.306807</td>\n",
" <td> 1611.18</td>\n",
" <td> 0.052849</td>\n",
" <td> 32</td>\n",
" <td> 1297.170888</td>\n",
" <td> 1299.589700</td>\n",
" <td> 1614.841306</td>\n",
" <td> 0.039137</td>\n",
" <td> 1300.668312</td>\n",
" <td> 227.715817</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
" even_ghost_p-p odd_ghost_min object_radius_mm even_ghost_max \\\n",
"0 0.472297 3.235896 84.218750 2.450390 \n",
"1 0.580009 2.415251 83.354945 3.298066 \n",
"2 0.488590 1.914204 83.354945 2.574831 \n",
"3 0.517155 2.955147 82.482094 2.452956 \n",
"4 0.514811 2.597157 83.354945 2.315317 \n",
"\n",
" DateTime peripheral_roi_raw_std central_roi_raw_max \\\n",
"0 2014-07-07 07:11:22 0.637441 1539.37 \n",
"1 2014-09-17 08:43:35 0.611646 1514.19 \n",
"2 2014-10-29 08:12:57 0.468266 1520.74 \n",
"3 2014-12-24 07:19:53 0.626498 1614.69 \n",
"4 2015-01-10 14:32:37 1.463018 1617.83 \n",
"\n",
" even_ghost_p-p% peripheral_roi_raw_min central_roi_polyfit_lin \\\n",
"0 21.350171 1249.388889 0.001063 \n",
"1 19.414199 1228.184028 0.000167 \n",
"2 20.814401 1231.631944 0.001160 \n",
"3 23.688801 1311.482639 0.000818 \n",
"4 24.644293 1294.309028 0.000101 \n",
"\n",
" ... peripheral_roi_detrended_min central_roi_raw_min \\\n",
"0 ... 1250.613558 1533.73 \n",
"1 ... 1229.304928 1508.57 \n",
"2 ... 1231.658173 1512.01 \n",
"3 ... 1312.611632 1607.70 \n",
"4 ... 1298.306807 1611.18 \n",
"\n",
" central_roi_detrended_std% center_of_mass_x peripheral_roi_raw_mean \\\n",
"0 0.052447 33 1250.848721 \n",
"1 0.062365 32 1230.006561 \n",
"2 0.057538 32 1233.061495 \n",
"3 0.061475 33 1313.083808 \n",
"4 0.052849 32 1297.170888 \n",
"\n",
" peripheral_roi_detrended_mean central_roi_detrended_mean \\\n",
"0 1251.693979 1535.515927 \n",
"1 1231.042907 1510.981299 \n",
"2 1232.960319 1514.326143 \n",
"3 1313.971200 1610.738873 \n",
"4 1299.589700 1614.841306 \n",
"\n",
" peripheral_roi_detrended_std% peripheral_roi_detrended_max \\\n",
"0 0.039363 1252.889668 \n",
"1 0.039084 1232.428128 \n",
"2 0.035953 1234.081719 \n",
"3 0.035804 1315.151265 \n",
"4 0.039137 1300.668312 \n",
"\n",
" peripheral_roi_SFNR \n",
"0 225.596703 \n",
"1 222.708414 \n",
"2 223.213753 \n",
"3 237.203254 \n",
"4 227.715817 \n",
"\n",
"[5 rows x 66 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dt = d.groupby('DateTime')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"errors=dt['odd_ghost_min','odd_ghost_max'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10c6f02d0>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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CzPlyzYMYLRljzuaNdGfzRrqzebtz96TDFkIIMR/KYU+RbG2hvG28N9K9tL2R7v7ksHWH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\nwxZCiCT8f6ZY4HweVFsyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c6f9410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dt['odd_ghost_mean'].mean().plot(kind='bar',yerr=errors)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>odd_ghost_min</th>\n",
" <th>odd_ghost_max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>DateTime</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-07-07 07:11:22</th>\n",
" <td> 3.235896</td>\n",
" <td> 3.671185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-09-17 08:43:35</th>\n",
" <td> 2.415251</td>\n",
" <td> 2.974990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-10-29 08:12:57</th>\n",
" <td> 1.914204</td>\n",
" <td> 2.444083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-24 07:19:53</th>\n",
" <td> 2.955147</td>\n",
" <td> 3.438752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-01-10 14:32:37</th>\n",
" <td> 2.597157</td>\n",
" <td> 3.138273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-23 12:26:06</th>\n",
" <td> 3.888966</td>\n",
" <td> 4.420275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-26 07:17:18</th>\n",
" <td> 4.299617</td>\n",
" <td> 4.697812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-30 08:55:06</th>\n",
" <td> 4.335638</td>\n",
" <td> 4.770609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-03 07:24:25</th>\n",
" <td> 10.425711</td>\n",
" <td> 10.952869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-04 06:44:28</th>\n",
" <td> 4.439754</td>\n",
" <td> 5.028988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-05 07:27:42</th>\n",
" <td> 11.057443</td>\n",
" <td> 11.778681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-06 11:56:29</th>\n",
" <td> 3.610959</td>\n",
" <td> 4.003027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-08 06:51:03</th>\n",
" <td> 11.640202</td>\n",
" <td> 12.167419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-10 07:39:34</th>\n",
" <td> 10.071729</td>\n",
" <td> 10.753062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-12 07:58:06</th>\n",
" <td> 10.645127</td>\n",
" <td> 11.276929</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" odd_ghost_min odd_ghost_max\n",
"DateTime \n",
"2014-07-07 07:11:22 3.235896 3.671185\n",
"2014-09-17 08:43:35 2.415251 2.974990\n",
"2014-10-29 08:12:57 1.914204 2.444083\n",
"2014-12-24 07:19:53 2.955147 3.438752\n",
"2015-01-10 14:32:37 2.597157 3.138273\n",
"2015-05-23 12:26:06 3.888966 4.420275\n",
"2015-05-26 07:17:18 4.299617 4.697812\n",
"2015-05-30 08:55:06 4.335638 4.770609\n",
"2015-06-03 07:24:25 10.425711 10.952869\n",
"2015-06-04 06:44:28 4.439754 5.028988\n",
"2015-06-05 07:27:42 11.057443 11.778681\n",
"2015-06-06 11:56:29 3.610959 4.003027\n",
"2015-06-08 06:51:03 11.640202 12.167419\n",
"2015-06-10 07:39:34 10.071729 10.753062\n",
"2015-06-12 07:58:06 10.645127 11.276929"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"errors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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