diff --git a/examples/07_data_management.ipynb b/examples/07_data_management.ipynb
index d317456a..329d87a9 100644
--- a/examples/07_data_management.ipynb
+++ b/examples/07_data_management.ipynb
@@ -18,6 +18,13 @@
"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,
@@ -52,6 +59,13 @@
" 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,
@@ -60,7 +74,7 @@
"source": [
"rows = []\n",
"for t_start in range(12, 17):\n",
- " for t_stop in range(30, 35):\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",
@@ -124,30 +138,6 @@
" \n",
" \n",
" | 2 | \n",
- " 12 | \n",
- " 32 | \n",
- " 21 | \n",
- " 0.064960 | \n",
- " 0.2223(11) | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 12 | \n",
- " 33 | \n",
- " 22 | \n",
- " 0.066495 | \n",
- " 0.2224(10) | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 12 | \n",
- " 34 | \n",
- " 23 | \n",
- " 0.066606 | \n",
- " 0.2225(10) | \n",
- "
\n",
- " \n",
- " | 5 | \n",
" 13 | \n",
" 30 | \n",
" 18 | \n",
@@ -155,7 +145,7 @@
" 0.2215(12) | \n",
"
\n",
" \n",
- " | 6 | \n",
+ " 3 | \n",
" 13 | \n",
" 31 | \n",
" 19 | \n",
@@ -163,31 +153,7 @@
" 0.2219(11) | \n",
"
\n",
" \n",
- " | 7 | \n",
- " 13 | \n",
- " 32 | \n",
- " 20 | \n",
- " 0.063551 | \n",
- " 0.2221(12) | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " 13 | \n",
- " 33 | \n",
- " 21 | \n",
- " 0.066406 | \n",
- " 0.2223(12) | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " 13 | \n",
- " 34 | \n",
- " 22 | \n",
- " 0.067237 | \n",
- " 0.2224(12) | \n",
- "
\n",
- " \n",
- " | 10 | \n",
+ " 4 | \n",
" 14 | \n",
" 30 | \n",
" 17 | \n",
@@ -195,7 +161,7 @@
" 0.2213(13) | \n",
"
\n",
" \n",
- " | 11 | \n",
+ " 5 | \n",
" 14 | \n",
" 31 | \n",
" 18 | \n",
@@ -203,31 +169,7 @@
" 0.2218(13) | \n",
"
\n",
" \n",
- " | 12 | \n",
- " 14 | \n",
- " 32 | \n",
- " 19 | \n",
- " 0.066883 | \n",
- " 0.2220(14) | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " 14 | \n",
- " 33 | \n",
- " 20 | \n",
- " 0.070019 | \n",
- " 0.2223(15) | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " 14 | \n",
- " 34 | \n",
- " 21 | \n",
- " 0.070775 | \n",
- " 0.2224(15) | \n",
- "
\n",
- " \n",
- " | 15 | \n",
+ " 6 | \n",
" 15 | \n",
" 30 | \n",
" 16 | \n",
@@ -235,7 +177,7 @@
" 0.2213(16) | \n",
"
\n",
" \n",
- " | 16 | \n",
+ " 7 | \n",
" 15 | \n",
" 31 | \n",
" 17 | \n",
@@ -243,31 +185,7 @@
" 0.2218(17) | \n",
"
\n",
" \n",
- " | 17 | \n",
- " 15 | \n",
- " 32 | \n",
- " 18 | \n",
- " 0.070170 | \n",
- " 0.2221(18) | \n",
- "
\n",
- " \n",
- " | 18 | \n",
- " 15 | \n",
- " 33 | \n",
- " 19 | \n",
- " 0.072516 | \n",
- " 0.2224(18) | \n",
- "
\n",
- " \n",
- " | 19 | \n",
- " 15 | \n",
- " 34 | \n",
- " 20 | \n",
- " 0.072509 | \n",
- " 0.2225(18) | \n",
- "
\n",
- " \n",
- " | 20 | \n",
+ " 8 | \n",
" 16 | \n",
" 30 | \n",
" 15 | \n",
@@ -275,68 +193,29 @@
" 0.2214(21) | \n",
"
\n",
" \n",
- " | 21 | \n",
+ " 9 | \n",
" 16 | \n",
" 31 | \n",
" 16 | \n",
" 0.070874 | \n",
" 0.2220(20) | \n",
"
\n",
- " \n",
- " | 22 | \n",
- " 16 | \n",
- " 32 | \n",
- " 17 | \n",
- " 0.072437 | \n",
- " 0.2223(21) | \n",
- "
\n",
- " \n",
- " | 23 | \n",
- " 16 | \n",
- " 33 | \n",
- " 18 | \n",
- " 0.073684 | \n",
- " 0.2225(21) | \n",
- "
\n",
- " \n",
- " | 24 | \n",
- " 16 | \n",
- " 34 | \n",
- " 19 | \n",
- " 0.072767 | \n",
- " 0.2227(20) | \n",
- "
\n",
" \n",
"\n",
""
],
"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 12 32 21 0.064960 0.2223(11)\n",
- "3 12 33 22 0.066495 0.2224(10)\n",
- "4 12 34 23 0.066606 0.2225(10)\n",
- "5 13 30 18 0.051577 0.2215(12)\n",
- "6 13 31 19 0.060901 0.2219(11)\n",
- "7 13 32 20 0.063551 0.2221(12)\n",
- "8 13 33 21 0.066406 0.2223(12)\n",
- "9 13 34 22 0.067237 0.2224(12)\n",
- "10 14 30 17 0.052349 0.2213(13)\n",
- "11 14 31 18 0.063640 0.2218(13)\n",
- "12 14 32 19 0.066883 0.2220(14)\n",
- "13 14 33 20 0.070019 0.2223(15)\n",
- "14 14 34 21 0.070775 0.2224(15)\n",
- "15 15 30 16 0.056088 0.2213(16)\n",
- "16 15 31 17 0.067552 0.2218(17)\n",
- "17 15 32 18 0.070170 0.2221(18)\n",
- "18 15 33 19 0.072516 0.2224(18)\n",
- "19 15 34 20 0.072509 0.2225(18)\n",
- "20 16 30 15 0.059969 0.2214(21)\n",
- "21 16 31 16 0.070874 0.2220(20)\n",
- "22 16 32 17 0.072437 0.2223(21)\n",
- "23 16 33 18 0.073684 0.2225(21)\n",
- "24 16 34 19 0.072767 0.2227(20)"
+ " 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,
@@ -348,30 +227,45 @@
"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\")"
+ "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": [],
- "source": [
- "new_df = df = pe.input.pandas.read_sql(f\"SELECT t_start, t_stop, mass FROM mass_table WHERE t_start > 13\"\n",
- " ,\"my_db.sqlite\"\n",
- " ,auto_gamma=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
"outputs": [
{
"data": {
@@ -402,118 +296,41 @@
" \n",
" \n",
" | 0 | \n",
- " 14 | \n",
- " 30 | \n",
- " 0.2213(13) | \n",
- "
\n",
- " \n",
- " | 1 | \n",
- " 14 | \n",
- " 31 | \n",
- " 0.2218(13) | \n",
- "
\n",
- " \n",
- " | 2 | \n",
- " 14 | \n",
- " 32 | \n",
- " 0.2220(14) | \n",
- "
\n",
- " \n",
- " | 3 | \n",
- " 14 | \n",
- " 33 | \n",
- " 0.2223(15) | \n",
- "
\n",
- " \n",
- " | 4 | \n",
- " 14 | \n",
- " 34 | \n",
- " 0.2224(15) | \n",
- "
\n",
- " \n",
- " | 5 | \n",
" 15 | \n",
" 30 | \n",
" 0.2213(16) | \n",
"
\n",
" \n",
- " | 6 | \n",
+ " 1 | \n",
" 15 | \n",
" 31 | \n",
" 0.2218(17) | \n",
"
\n",
" \n",
- " | 7 | \n",
- " 15 | \n",
- " 32 | \n",
- " 0.2221(18) | \n",
- "
\n",
- " \n",
- " | 8 | \n",
- " 15 | \n",
- " 33 | \n",
- " 0.2224(18) | \n",
- "
\n",
- " \n",
- " | 9 | \n",
- " 15 | \n",
- " 34 | \n",
- " 0.2225(18) | \n",
- "
\n",
- " \n",
- " | 10 | \n",
+ " 2 | \n",
" 16 | \n",
" 30 | \n",
" 0.2214(21) | \n",
"
\n",
" \n",
- " | 11 | \n",
+ " 3 | \n",
" 16 | \n",
" 31 | \n",
" 0.2220(20) | \n",
"
\n",
- " \n",
- " | 12 | \n",
- " 16 | \n",
- " 32 | \n",
- " 0.2223(21) | \n",
- "
\n",
- " \n",
- " | 13 | \n",
- " 16 | \n",
- " 33 | \n",
- " 0.2225(21) | \n",
- "
\n",
- " \n",
- " | 14 | \n",
- " 16 | \n",
- " 34 | \n",
- " 0.2227(20) | \n",
- "
\n",
" \n",
"\n",
""
],
"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,