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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data management"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pyerrors as pe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the data management example we reuse the data from the correlator example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data has been written using pyerrors 2.0.0.\n",
+      "Format version 0.1\n",
+      "Written by fjosw on 2022-01-06 11:11:19 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
+      "\n",
+      "Description:  Test data for the correlator example\n"
+     ]
+    }
+   ],
+   "source": [
+    "correlator_data = pe.input.json.load_json(\"./data/correlator_test\")\n",
+    "my_correlator = pe.Corr(correlator_data)\n",
+    "my_correlator.gamma_method()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import autograd.numpy as anp\n",
+    "def func_exp(a, x):\n",
+    "    return a[1] * anp.exp(-a[0] * x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rows = []\n",
+    "for t_start in range(12, 17):\n",
+    "    for t_stop in range(30, 32):\n",
+    "        fr = my_correlator.fit(func_exp, [t_start, t_stop], silent=True)\n",
+    "        fr.gamma_method()\n",
+    "        row = {\"t_start\": t_start,\n",
+    "               \"t_stop\": t_stop,\n",
+    "               \"datapoints\": t_stop - t_start + 1,\n",
+    "               \"chisquare_by_dof\": fr.chisquare_by_dof,\n",
+    "               \"mass\": fr[0]}\n",
+    "        rows.append(row)\n",
+    "my_df = pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "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>t_start</th>\n",
+       "      <th>t_stop</th>\n",
+       "      <th>datapoints</th>\n",
+       "      <th>chisquare_by_dof</th>\n",
+       "      <th>mass</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.057872</td>\n",
+       "      <td>0.2218(12)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "      <td>31</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.063951</td>\n",
+       "      <td>0.2221(11)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13</td>\n",
+       "      <td>30</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.051577</td>\n",
+       "      <td>0.2215(12)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13</td>\n",
+       "      <td>31</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.060901</td>\n",
+       "      <td>0.2219(11)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>30</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.052349</td>\n",
+       "      <td>0.2213(13)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>14</td>\n",
+       "      <td>31</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.063640</td>\n",
+       "      <td>0.2218(13)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.056088</td>\n",
+       "      <td>0.2213(16)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>15</td>\n",
+       "      <td>31</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.067552</td>\n",
+       "      <td>0.2218(17)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>16</td>\n",
+       "      <td>30</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.059969</td>\n",
+       "      <td>0.2214(21)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>16</td>\n",
+       "      <td>31</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.070874</td>\n",
+       "      <td>0.2220(20)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   t_start  t_stop  datapoints  chisquare_by_dof        mass\n",
+       "0       12      30          19          0.057872  0.2218(12)\n",
+       "1       12      31          20          0.063951  0.2221(11)\n",
+       "2       13      30          18          0.051577  0.2215(12)\n",
+       "3       13      31          19          0.060901  0.2219(11)\n",
+       "4       14      30          17          0.052349  0.2213(13)\n",
+       "5       14      31          18          0.063640  0.2218(13)\n",
+       "6       15      30          16          0.056088  0.2213(16)\n",
+       "7       15      31          17          0.067552  0.2218(17)\n",
+       "8       16      30          15          0.059969  0.2214(21)\n",
+       "9       16      31          16          0.070874  0.2220(20)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pe.input.pandas.to_sql(my_df, \"mass_table\", \"my_db.sqlite\", if_exists='fail')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At a later stage of the analysis the content of the database can be reconstructed into a DataFrame via SQL queries.\n",
+    "In this example we extract `t_start`, `t_stop` and the fitted mass for all fits which start at times larger than 14."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df = pe.input.pandas.read_sql(f\"SELECT t_start, t_stop, mass FROM mass_table WHERE t_start > 14\",\n",
+    "                                  \"my_db.sqlite\",\n",
+    "                                  auto_gamma=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "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>t_start</th>\n",
+       "      <th>t_stop</th>\n",
+       "      <th>mass</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.2213(16)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.2218(17)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.2214(21)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0.2220(20)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}