mirror of
https://github.com/fjosw/pyerrors.git
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docs: data management example refined.
This commit is contained in:
Fabian Joswig
parent
9a7f5679c9
commit
6a57264868
1 changed files with 76 additions and 252 deletions
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@ -18,6 +18,13 @@
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"import pyerrors as pe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For the data management example we reuse the data from the correlator example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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@ -52,6 +59,13 @@
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" return a[1] * anp.exp(-a[0] * x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In this example we perform uncorrelated fits of a single exponential function to the correlator and vary the range of the fit. The fit result can be conveniently stored in a pandas DataFrame together with the corresponding metadata."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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@ -60,7 +74,7 @@
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"source": [
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"rows = []\n",
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"for t_start in range(12, 17):\n",
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" for t_stop in range(30, 35):\n",
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" for t_stop in range(30, 32):\n",
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" fr = my_correlator.fit(func_exp, [t_start, t_stop], silent=True)\n",
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" fr.gamma_method()\n",
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" row = {\"t_start\": t_start,\n",
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@ -124,30 +138,6 @@
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>12</td>\n",
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" <td>32</td>\n",
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" <td>21</td>\n",
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" <td>0.064960</td>\n",
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" <td>0.2223(11)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>12</td>\n",
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" <td>33</td>\n",
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" <td>22</td>\n",
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" <td>0.066495</td>\n",
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" <td>0.2224(10)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>12</td>\n",
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" <td>34</td>\n",
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" <td>23</td>\n",
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" <td>0.066606</td>\n",
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" <td>0.2225(10)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>13</td>\n",
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" <td>30</td>\n",
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" <td>18</td>\n",
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@ -155,7 +145,7 @@
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" <td>0.2215(12)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <th>3</th>\n",
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" <td>13</td>\n",
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" <td>31</td>\n",
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" <td>19</td>\n",
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@ -163,31 +153,7 @@
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" <td>0.2219(11)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>13</td>\n",
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" <td>32</td>\n",
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" <td>20</td>\n",
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" <td>0.063551</td>\n",
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" <td>0.2221(12)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>13</td>\n",
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" <td>33</td>\n",
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" <td>21</td>\n",
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" <td>0.066406</td>\n",
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" <td>0.2223(12)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>13</td>\n",
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" <td>34</td>\n",
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" <td>22</td>\n",
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" <td>0.067237</td>\n",
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" <td>0.2224(12)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <th>4</th>\n",
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" <td>14</td>\n",
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" <td>30</td>\n",
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" <td>17</td>\n",
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@ -195,7 +161,7 @@
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" <td>0.2213(13)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <th>5</th>\n",
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" <td>14</td>\n",
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" <td>31</td>\n",
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" <td>18</td>\n",
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@ -203,31 +169,7 @@
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" <td>0.2218(13)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>14</td>\n",
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" <td>32</td>\n",
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" <td>19</td>\n",
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" <td>0.066883</td>\n",
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" <td>0.2220(14)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>14</td>\n",
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" <td>33</td>\n",
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" <td>20</td>\n",
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" <td>0.070019</td>\n",
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" <td>0.2223(15)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>14</td>\n",
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" <td>34</td>\n",
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" <td>21</td>\n",
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" <td>0.070775</td>\n",
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" <td>0.2224(15)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <th>6</th>\n",
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" <td>15</td>\n",
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" <td>30</td>\n",
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" <td>16</td>\n",
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@ -235,7 +177,7 @@
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" <td>0.2213(16)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <th>7</th>\n",
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" <td>15</td>\n",
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" <td>31</td>\n",
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" <td>17</td>\n",
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@ -243,31 +185,7 @@
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" <td>0.2218(17)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>15</td>\n",
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" <td>32</td>\n",
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" <td>18</td>\n",
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" <td>0.070170</td>\n",
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" <td>0.2221(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>18</th>\n",
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" <td>15</td>\n",
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" <td>33</td>\n",
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" <td>19</td>\n",
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" <td>0.072516</td>\n",
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" <td>0.2224(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>19</th>\n",
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" <td>15</td>\n",
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" <td>34</td>\n",
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" <td>20</td>\n",
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" <td>0.072509</td>\n",
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" <td>0.2225(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20</th>\n",
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" <th>8</th>\n",
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" <td>16</td>\n",
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" <td>30</td>\n",
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" <td>15</td>\n",
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@ -275,68 +193,29 @@
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" <td>0.2214(21)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>21</th>\n",
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" <th>9</th>\n",
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" <td>16</td>\n",
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" <td>31</td>\n",
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" <td>16</td>\n",
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" <td>0.070874</td>\n",
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" <td>0.2220(20)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>16</td>\n",
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" <td>32</td>\n",
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" <td>17</td>\n",
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" <td>0.072437</td>\n",
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" <td>0.2223(21)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>23</th>\n",
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" <td>16</td>\n",
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" <td>33</td>\n",
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" <td>18</td>\n",
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" <td>0.073684</td>\n",
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" <td>0.2225(21)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>24</th>\n",
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" <td>16</td>\n",
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" <td>34</td>\n",
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" <td>19</td>\n",
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" <td>0.072767</td>\n",
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" <td>0.2227(20)</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" t_start t_stop datapoints chisquare_by_dof mass\n",
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"0 12 30 19 0.057872 0.2218(12)\n",
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"1 12 31 20 0.063951 0.2221(11)\n",
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"2 12 32 21 0.064960 0.2223(11)\n",
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"3 12 33 22 0.066495 0.2224(10)\n",
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"4 12 34 23 0.066606 0.2225(10)\n",
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"5 13 30 18 0.051577 0.2215(12)\n",
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"6 13 31 19 0.060901 0.2219(11)\n",
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"7 13 32 20 0.063551 0.2221(12)\n",
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"8 13 33 21 0.066406 0.2223(12)\n",
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"9 13 34 22 0.067237 0.2224(12)\n",
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"10 14 30 17 0.052349 0.2213(13)\n",
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"11 14 31 18 0.063640 0.2218(13)\n",
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"12 14 32 19 0.066883 0.2220(14)\n",
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"13 14 33 20 0.070019 0.2223(15)\n",
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"14 14 34 21 0.070775 0.2224(15)\n",
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"15 15 30 16 0.056088 0.2213(16)\n",
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"16 15 31 17 0.067552 0.2218(17)\n",
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"17 15 32 18 0.070170 0.2221(18)\n",
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"18 15 33 19 0.072516 0.2224(18)\n",
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"19 15 34 20 0.072509 0.2225(18)\n",
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"20 16 30 15 0.059969 0.2214(21)\n",
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"21 16 31 16 0.070874 0.2220(20)\n",
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"22 16 32 17 0.072437 0.2223(21)\n",
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"23 16 33 18 0.073684 0.2225(21)\n",
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"24 16 34 19 0.072767 0.2227(20)"
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" t_start t_stop datapoints chisquare_by_dof mass\n",
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"0 12 30 19 0.057872 0.2218(12)\n",
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"1 12 31 20 0.063951 0.2221(11)\n",
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"2 13 30 18 0.051577 0.2215(12)\n",
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"3 13 31 19 0.060901 0.2219(11)\n",
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"4 14 30 17 0.052349 0.2213(13)\n",
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"5 14 31 18 0.063640 0.2218(13)\n",
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"6 15 30 16 0.056088 0.2213(16)\n",
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"7 15 31 17 0.067552 0.2218(17)\n",
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"8 16 30 15 0.059969 0.2214(21)\n",
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"9 16 31 16 0.070874 0.2220(20)"
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]
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},
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"execution_count": 5,
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@ -348,30 +227,45 @@
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"my_df"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The content of this pandas DataFrame can be inserted into a relational database, making use of the `JSON` serialization of `pyerrors` objects. In this example we use an SQLite database."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"pe.input.pandas.to_sql(my_df, \"mass_table\", \"my_db.sqlite\")"
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"pe.input.pandas.to_sql(my_df, \"mass_table\", \"my_db.sqlite\", if_exists='fail')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"At a later stage of the analysis the content of the database can be reconstructed into a DataFrame via SQL queries.\n",
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"In this example we extract `t_start`, `t_stop` and the fitted mass for all fits which start at times larger than 14."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"new_df = pe.input.pandas.read_sql(f\"SELECT t_start, t_stop, mass FROM mass_table WHERE t_start > 14\",\n",
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" \"my_db.sqlite\",\n",
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" auto_gamma=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"new_df = df = pe.input.pandas.read_sql(f\"SELECT t_start, t_stop, mass FROM mass_table WHERE t_start > 13\"\n",
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" ,\"my_db.sqlite\"\n",
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" ,auto_gamma=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -402,118 +296,41 @@
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>14</td>\n",
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" <td>30</td>\n",
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" <td>0.2213(13)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>14</td>\n",
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" <td>31</td>\n",
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" <td>0.2218(13)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>14</td>\n",
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" <td>32</td>\n",
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" <td>0.2220(14)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>14</td>\n",
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" <td>33</td>\n",
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" <td>0.2223(15)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>14</td>\n",
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" <td>34</td>\n",
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" <td>0.2224(15)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>15</td>\n",
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" <td>30</td>\n",
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" <td>0.2213(16)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <th>1</th>\n",
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" <td>15</td>\n",
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" <td>31</td>\n",
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" <td>0.2218(17)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>15</td>\n",
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" <td>32</td>\n",
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" <td>0.2221(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>15</td>\n",
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" <td>33</td>\n",
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" <td>0.2224(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>15</td>\n",
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" <td>34</td>\n",
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" <td>0.2225(18)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <th>2</th>\n",
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" <td>16</td>\n",
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" <td>30</td>\n",
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" <td>0.2214(21)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <th>3</th>\n",
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" <td>16</td>\n",
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" <td>31</td>\n",
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" <td>0.2220(20)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>16</td>\n",
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" <td>32</td>\n",
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" <td>0.2223(21)</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>16</td>\n",
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" <td>33</td>\n",
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||||
" <td>0.2225(21)</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>14</th>\n",
|
||||
" <td>16</td>\n",
|
||||
" <td>34</td>\n",
|
||||
" <td>0.2227(20)</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" t_start t_stop mass\n",
|
||||
"0 14 30 0.2213(13)\n",
|
||||
"1 14 31 0.2218(13)\n",
|
||||
"2 14 32 0.2220(14)\n",
|
||||
"3 14 33 0.2223(15)\n",
|
||||
"4 14 34 0.2224(15)\n",
|
||||
"5 15 30 0.2213(16)\n",
|
||||
"6 15 31 0.2218(17)\n",
|
||||
"7 15 32 0.2221(18)\n",
|
||||
"8 15 33 0.2224(18)\n",
|
||||
"9 15 34 0.2225(18)\n",
|
||||
"10 16 30 0.2214(21)\n",
|
||||
"11 16 31 0.2220(20)\n",
|
||||
"12 16 32 0.2223(21)\n",
|
||||
"13 16 33 0.2225(21)\n",
|
||||
"14 16 34 0.2227(20)"
|
||||
" t_start t_stop mass\n",
|
||||
"0 15 30 0.2213(16)\n",
|
||||
"1 15 31 0.2218(17)\n",
|
||||
"2 16 30 0.2214(21)\n",
|
||||
"3 16 31 0.2220(20)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
@ -522,6 +339,13 @@
|
|||
"new_df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The storage of intermediate analysis results in relational databases allows for a convenient and scalable way of splitting up a detailed analysis in multiple independent steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue