mirror of
https://github.com/fjosw/pyerrors.git
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360 lines
95 KiB
Text
360 lines
95 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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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": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('./base_style.mplstyle')\n",
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"import shutil\n",
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"usetex = shutil.which('latex') not in ('', None)\n",
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"plt.rc('text', usetex=usetex)"
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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 look at the analysis of the current quark mass (PCAC mass) on a test gauge field ensemble with fixed Schrödinger functional boundary conditions in the temporal direction."
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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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"## Loading data"
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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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"We can load data from preprocessed files which contains lists of `pyerror` `Obs` and convert them to `Corr` objects as explained in the previous example. We use the parameter `padding` to keep track of the fixed boundary conditions at both temporal ends of the lattice. This allows us to specify absolut temporal positions without having to keep track of any shifts in the data."
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data has been written using pyerrors 2.0.0.\n",
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"Format version 0.1\n",
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"Written by fjosw on 2022-01-06 11:27:27 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
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"\n",
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"Description: SF correlation function f_A on a test ensemble\n",
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"Data has been written using pyerrors 2.0.0.\n",
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"Format version 0.1\n",
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"Written by fjosw on 2022-01-06 11:27:34 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n",
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"\n",
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"Description: SF correlation function f_P on a test ensemble\n"
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]
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}
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],
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"source": [
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"p_obs_names = [r'f_A', r'f_P']\n",
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"\n",
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"p_obs = {}\n",
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"for i, item in enumerate(p_obs_names):\n",
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" tmp_data = pe.input.json.load_json(\"./data/\" + item)\n",
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" p_obs[item] = pe.Corr(tmp_data, padding=[1, 1])\n",
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" p_obs[item].tag = item"
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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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"We can now use the method `Corr.show` to have a quick look at the data we just read in "
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
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},
|
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"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"p_obs['f_A'].show(comp=p_obs['f_P'], y_range=[-0.8, 8])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Constructing the PCAC mass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The PCAC mass is defined as\n",
|
|
"\\begin{align*}\n",
|
|
"am(x_0)=\\frac{a\\tilde{\\partial}_0 f_\\mathrm{A}(x_0)+a^2c_\\mathrm{A}\\partial_0^{\\ast}\\partial_0^{}f_\\mathrm{P}(x_0)}{2f_\\mathrm{P}(x_0)}+\\mathrm{O}(a^2)\\,.\n",
|
|
"\\end{align*}\n",
|
|
"\n",
|
|
"We now need to obtain the first derivative of f_A and the second derivative of f_P"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"first_deriv_fA = p_obs['f_A'].deriv()\n",
|
|
"first_deriv_fA.tag = r\"First derivative of f_A\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"second_deriv_fP = p_obs['f_P'].second_deriv()\n",
|
|
"second_deriv_fP.tag = r\"Second derivative of f_P\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can use these to obtain the unimproved PCAC mass:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"am_pcac = first_deriv_fA / 2 / p_obs['f_P']\n",
|
|
"am_pcac.gamma_method()\n",
|
|
"am_pcac.tag = \"Unimproved PCAC mass\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"And with the inclusion of the improvement coefficient $c_\\mathrm{A}$ also the $\\mathrm{O}(a)$ improved PCAC mass:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cA = -0.03888694628624465\n",
|
|
"am_pcac_impr = (first_deriv_fA + cA * second_deriv_fP) / 2 / p_obs['f_P']\n",
|
|
"am_pcac_impr.gamma_method()\n",
|
|
"am_pcac_impr.tag = \"Improved PCAC mass\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can take a look at the time dependence of the PCAC mass with the method `Corr.show`:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 640x395.55 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"am_pcac_impr.show(comp=am_pcac)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Plateau values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now construct a plateau as a derived observable from the masses."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fit with 1 parameters\n",
|
|
"Method: Levenberg-Marquardt\n",
|
|
"`ftol` termination condition is satisfied.\n",
|
|
"chisquare/d.o.f.: 0.2704765091136813\n",
|
|
"Result\t 5.03431904e-03 +/- 5.38835422e-04 +/- 8.24919899e-05 (10.703%)\n",
|
|
" t_int\t 5.15384615e-01 +/- 1.25000000e-01 S = 2.00\n",
|
|
"64 samples in 1 ensemble:\n",
|
|
" · Ensemble 'test_ensemble' : 64 configurations (from 1 to 64)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"pcac_plateau = am_pcac_impr.plateau([7, 16]) # We manually specify the plateau range here\n",
|
|
"pcac_plateau.gamma_method()\n",
|
|
"pcac_plateau.details()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can now plot the data with the two plateaus"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"am_pcac_impr.show(comp=am_pcac, plateau=pcac_plateau)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The Monte Carlo history of the observable can be accessed with `plot_history` to identify possible outliers or have a look at the shape of the distribution"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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tUhp3zz9U9XtJ22FmaZE7klYz//4wQ/4AAABa0XbANu65YoMJ+2ZJ84Ew6eBMZjxcYXeru//m7qfpP7FgLgAAiEirAVto5RqFiQT5fYdNpckKrWj3s+kzLWs8mBYAAPTOPLpE7yhZE03SWevXQeb1MF13rWyajHPdpKE17V6uS3RH0oNZl/kAAADoUuvrsElnC+GmAdTn2RmbZrYjac/dNyqkGSpZFPempE1J95R5kkFoUdvJHG5t0izRgvyyDhsAAKikzXXY5hKw9Q0BGwAAqKq3C+cCANCUN2/f6denI715+67rrABz19U6bAAAlPbm7Tt9+c2PevL8tdbXLuqHr7/QJx9/1HW2gLmhhQ0AEL1Hz17pyfPXkqQnz1/r0TNWX8JyIWADAETv6pVLWl+7KElaX7uoq1d4giCWC5MOCjDpAADi8+btOz169kpXr1yiOxRRanPSAWPYAAC98MnHH+mPnw66zgbQCbpEAQAAIkfABgAAEDkCNgAAgMgRsAEAAESOgA0AACByBGwAAACRI2ADAACIHAEbAABA5AjYAAAAIkfAhqi8eftOvz4d6c3bd11nBTXxWQJAc3g0FaLx5u07ffnNj3ry/LXW1y7qh6+/4HmBPcVnCQDNooUN0Xj07JWePH8tSXry/LUePXvVcY4wKz5LAGgWARuicfXKJa2vXZQkra9d1NUrlzrOEWbFZwkAzTJ37zoP0TGzFUknJycnWllZ6To7S+XN23d69OyVrl65RBdaz/FZAlg2p6enWl1dlaRVdz9t8tiMYUNUPvn4I/3x00HX2UAD+CwBoDl0iQIAAESOgA0AACByBGwAAACRI2ADAACIHAEbAABA5AjYAAAAIkfABgAAEDkCNgAAgMgRsAEAAESOgA0AACByBGwAAACRI2ADAHTuzdt3+vXpSG/evus6K0CUePg7AKBTb96+05ff/Kgnz19rfe2ifvj6C33y8UddZwuICi1sAIBOPXr2Sk+ev5YkPXn+Wo+eveo4R0B8CNgAAJ26euWS1tcuSpLW1y7q6pVLHecIiI+5e9d5iI6ZrUg6OTk50crKStfZAYCJ3rx9p0fPXunqlUu97UpchL+hjGX5O5fV6empVldXJWnV3U+bPDZj2ACgxxZl/NcnH3+kP3466DobrVqUzwrdoEsUAHqM8V/9wWeFOgjYAKDHGP/VH3xWqIMxbAUYwwagTxgX1R98Vout92PYzOyWpFF4OXD3e3XTmNlA0g1J1939WhPnBIA+WobxX4uCzwqzar1LNAROcvcDdz+QdGRm+3XSmNmmkmBtIOlyE+cEAACIVetdomb2UtK6u48y29zdrW4aM9uWdNvdP6t7zlx6ukQXFN0RAIC2tNkl2moLm5kNlXRHjgr2bTWVpsn0WFzplPqvvv1JX37zI88sBAD0RttdosMx20dKujObSlMrvZldMLOV9J8kpu4sIKbUAwD6qqtlPV6oYOxZC2nKpr8t6STz7181zoNIMaUeQF1v3r7Tr09HtNBj7rp60sEsgVedYG1a+juS/p55fUkEbQvnk48/0g9ff8EYNgAzKfOkAsbJoi1tB2zHY7YPJuybJU2t9O7+m6Tf0tdmpeYmoIeYUg9gVkXDKrLXEx49hTa12iXq7seSRmEiQH7fYVNpmkwPAECRacMqGCeLNs1jDNsdSWezM8NSHAeZ18N03bSyaTLGdXOWTQ8AQCnpsIp//O3Pha1njJNFm+byaKoQkKXdkZ+7+15m346kPXffqJBmKGlb0k1Jm5LuSfrZ3R+USV8iv6zDBgCojDFsy63Nddh4lmgBAjYAAFBVbxfOBQAAQH0EbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQK2BcEDiQEAWFxdPfwdDeL5dQAALDZa2BYAz68DAGD+5tm7RQvbAkifX5e2sPH8OgAA2lXUu9UmArYFkD6QuO7z63gGHgAA5RT1bg1X2+u4JGBbEJ98/JH++Olg5vSMgwMAoLyi3q3//Z//19r5CNggqfhOoU4AuMhoiVwcfJYAZlXUu/W//9Pe+QjYIIlxcGXRErk4+CyBfovhhqtu71YVBGyQ1Nw4uEVHS+Ti4LME+msZb7hY1gNn0juFRf/S15G2RErqtCWShZLri+WzBFDdMi5nRQsbUEEMLZHLeGfZhhg+y7Ji6PoBYrKMw3gI2ICK5jlmoQhdec0p81l2HSwRoAPn9emGqyl0iQI9Q1fe/KTB0lff/qQvv/mxky7oZez6AcpYtmE8tLABPbOMd5ZdaaI1s24L3TJ2/QA4j4AN6KGuu2WXRd1gqYnuTAJ0ABIBGwCMVTdYamq8IQE6AMawAXPGkhz9UmecDOMNATTF3L3rPETHzFYknZycnGhlZaXr7GCBMONv+XQ9yxTA/Jyenmp1dVWSVt39tMlj08IGzBEz/pbPss1kA9AOAjZgjugiAwCGhsyCLtECdImiTXSRAVhmizw0hC5RYIHQRQZgmZUZGkIL3Hks6wEAWAq0bsdh2vqGi9wCVwcBGwBg4REExGPa+oY8L7kYXaIAgIUwqRuNGdpxmTQ0hMlZxWhhAwD03rQWNJ7J2h88jq0YARsAoPemdaMRBPQLj2M7jy5RAEDvlelGY4Y2+ox12AqwDhsA9A+zQNG1Ntdho0sUALAQ6EbDIqNLFAAAIHIEbAAAAJEjYAPQWzy+BsCyYAwbgF5i5XoAy4QWNgCFprVedd26xcr1AJbJXFrYzOyWpFF4OXD3e3XTTNpvZluSdiU9lHQs6Zqkn939wex/BbA8prVexdC6xcr1QDtYHiVOrQdsIbCSux+E11tmtu/uu7OmKXHMgaQtSdtKAra7dYI1vrzlUE6LY9qq8TE8nJmV64Hmlb0Z43o/f/NoYbstaT194e6HZvZQSQvYrGnKHHPd3Ud1Mx9DS0IfUE6LZVrrVSytW6y7BTSrzM0Y1/tutDqGzcyGSrorRwX7tmZJM8sx65jXOJnYxwtNE0s5oRlp69U//vbnwovxtP0AZtP1Na7MI74YP9qNtlvYhmO2j5R0W86Spuwxb5jZC0mXJW24+964TJrZBUkXMpvOvqHzaEnow3ihaWIoJzRrWusVrVuLg+6tOMRwjSsz1CCWFvZl09WyHmkgNUuaUYljHkmSux9LkpntmNl9d78+Ju1tSf9etGMe42T6MF5omhjKCUB1MQQJSMRyjStzs8b40fnralmPqsFamTRn+939OA3Wgu8lbZvZYEzaO5JWM//+kN2Zfnnb+lJOa4Iu00Qdg67LCUB1dG/Fo0/XuLav9zjP3L29gyfjzR67u+W2u6Rr7n5YNY2SWZ8Tj2lm2/lZoWH/Z+5+VCLfK5JOTk5OtLKyUupvrWtalwRdFgnKAWgWLWxx4Ro3P22U9enpqVZXVyVp1d1PGzlo0GrAJklm9lJJoHSc2eb5gKtKmkn7QyvaSyXj1tIu0XTb78vMHO0iYAOArhAkYNm0daPSZsA2jy7RO0rWRJMkmdm2pIPM62G6rlrZNJP2h4DsXq5LdEfSgyaW+QCARUP3FpZNH4cCtN7CJp0tdJsGUJ9nZ2ya2Y6kPXffKJumxDEHSoK01NqkWaIF+aWFDQCABdXHFra5BGx9Q8AGAM2i2xWx6dsYtq6W9VgqXKgALDMmNiBGfVtLsqtlPRbKpJWp0wvVV9/+pC+/+ZEV+gEsnT6OFwJiQ8BW07SAjAvVe10/cgVAN/q0vlgfcC1dTnSJ1jRtZWoe4ZGgSwRYXqyM3xyupcuLgK2maQEZF6pELI9cAdCNPowX6sN4Y66ly4uAraYyAVkfLlRto6URQMz60nLVxLW0D4EpzmNZjwIs69EOLhIAYvXr05G++vans9f/+Nufo73RrnMt7Utg2ld9f9IBIInV1AHEq08TI+pcS5kI1190iQIAlt6yjDdmeEp/0SVagC5RAMCiYnhKe+gSBQAAjZjH8BTWimseXaIAAKAxTGxoBy1sABYWd/nNoSxRFhMb2kELG0pj3AP6hLv85lCWqIKJDe0gYEMpXLDRN6wI35wyZckNHVLLMuN23ugSXRJ1uzNo4kYX6nxv+7SuVuymlWV6Q/fVtz/py29+pNsUrLvZAlrYlkATrWM0cWPe6n5vuctvzrSypDUTaB8B2xJo4mLKj99i6UP3VVPfWwKHZkwqS27ogPYRsC2Bpi6m/Pgthr6MRyQIaE7bATo3dED7eNJBgUV80kEfWlTQnEmf97I85HpeYs9jXwJ0YBG0+aQDWtiWBK1jy2PaD3SfWq5i/972IRhifBmwGAjYgAUz7Qea7qvm9CEY6lOADmA8AjZgwZT5gY695aov+hAMEaADi4ExbAUWcQwblkvs46oWCWUNIMUYNgCV0II2P5Q1gHngSQcAACyQuk+2QZxoYQMAYEH0YeYyZkMLGwAAC4LnPi8uAjagYXRHAOhKOnNZUrQzlzEbZokWYJYoZkV3BICuMXO5O23OEqWFDWgQ3REAupbOXCZYWywEbECD6I4AALSBLtECdImiDrojAGA5sXAu0CMspAoAaBpdogAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyM1lWQ8zuyVpFF4O3P1e3TR19wPoFuvVAUB5rQdsIXCSux+E11tmtu/uu7OmqbsfQLd45ioAVNP6kw7M7KWkdXcfZba5u9usaeruL5FnnnQAtOjXpyN99e1PZ6//8bc/s9gwgN7r7cPfzWyopDtyVLBva5Y0dfdX/RuwfN68fadfn4705u27rrOysHjmKgBU03aX6HDM9pGkwYxp6u4/x8wuSLqQ2cSvx5Kiq24+Pvn4I/3w9ReMYQOAkrqaJfpC0uWG09TZf1vSSebfvyrmDQvi0bNXevL8tSTpyfPXevTsVcc5WlzpM1cJ1gBguq4CtqrBWpk0dfbfkbSa+feHCvnCAqGrDgAQo7a7RI/HbB9M2DctTd3957j7b5J+S1+blZqbgAVEVx0AIEbzmiX6mbsfZ7aVmSU6Nk3d/SXyzCzRnmJtLwBAV3o7SzS4I+lsdqaZbUs6yLwepuumlU3TwH4soHTCwFff/qQvv/mRWZ4AgIXResAWnjAwMLPtEDh9nlvAdkvSbpU0dfdjMTFhAACwqFrvEu0jukT7iSU5AABdarNLlICtAAFbfzGGDQDQhFl+T9oM2Oby8HdgXtK1vQAAmFWMPTZdrcMGAAAQpRjHRBOwAQAAZMS4iDpj2Aowhg0AgOUW2xg2WtgAAMDCefP2nX59Opp5Tc7YnnfMpAMAALBQYpw0UBctbAAAYKHEOGmgLgI2AACwUGKcNFAXkw4KMOkAAIB+62IhdRbOBQAAqGDRFlKnSxQAACByBGwAAACRI2ADAACIHAEbAABA5AjYAAAAIkfABgAAEDkCNgAAgMgRsAEAAESOgA0AACByBGwAAACRI2ADIvTm7Tv9+nSkN2/fdZ0VAEAEeJYoEJk3b9/py29+1JPnr7W+dlE/fP3F3B5cDACIEy1sQGQePXulJ89fS5KePH+tR89edZwjAEDXCNiAyFy9cknraxclSetrF3X1yqWOcwQA6Jq5e9d5iI6ZrUg6OTk50crKStfZwRJ68/adHj17patXLtEdCgA9cXp6qtXVVUladffTJo/NGDYgQp98/JH++Omg62wAACJBlygAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHK/a/sEZnZL0ii8HLj7vbppJu03sy1Ju5IeSjqWdE3Sz+7+YPa/AgAAoDuttrCFwErufuDuB5KOzGy/TpoSxxxI2pK0H/49JlgDAAB9Zu7e3sHNXkpad/dRZpu7u82apsT+bUmH2f0z5HtF0snJyYlWVlZmPQwAAFgip6enWl1dlaRVdz9t8tittbCZ2VBJd+WoYN/WLGlmOSYAAEDftTmGbThm+0hJt+Usacoe84aZvZB0WdKGu++NzaUkM7sg6UJm06VJ7wcAAJinLmaJpoFUk2my+4+UdIk+CGPcHpvZ/SnHvy3pJPPvXxXzBwAA0JrSLWxhbNjNEm+94+5HE/ZXDdbKpDnb7+7HuX3fS9o3s8Ku1OCOpL9nXl8SQRsAAIhE6YAtzLSsMtsyHzilBhP2TUsz9Zhmtp2dFeruIzOTku7UwkDS3X+T9Fv6OrwfAAAgCq11iYaWrlGYKJDfdzhLmmn7zWwg6X52f9gmjQ/2AAAAotb2GLY7StZEk3TWrXqQeT1M11Urm2bS/tDleS/XLboj6UGdZT4AAAC61Oo6bNLZQrdpAPV5dsamme1I2nP3jbJpShxzoCRIS61NmyVakGfWYQMAAJW0uQ5b6wFbHxGwAQCAqnq5cC4AAACaQcAGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAi97u2T2BmtySNwsuBu9+rm8bMBpJuSLru7teaOCcAAECsWm1hC4GT3P3A3Q8kHZnZfp00ZrapJFgbSLrcxDkBAABiZu7e3sHNXkpad/dRZpu7u9VNY2bbkm67+2d1z1mQhxVJJycnJ1pZWSmbDAAALLHT01Otrq5K0qq7nzZ57NZa2MxsqKQ7clSwb6upNE2mBwAAiFGbY9iGY7aPlHRnNpWmdnozuyDpQmbTpRLnAgAAmIsuZom+UMHYsxbSVEl/W9JJ5t+/apwLAACgUaVb2MKYsZsl3nrH3Y8m7J8l8KoTrJVJf0fS3zOvL4mgDQAARKJ0wObuDyQ9qHDs4zHbBxP2zZKmdnp3/03Sb+lrs9LzEwAAAFrXWpeoux9LGoWJAPl9h02laTI9AABAjNoew3ZH0tnszNCtepB5PUzXTSubJmNcN2fZ9AAAAL3Q6jps0tlCtml35OfuvpfZtyNpz903KqQZSkrH021Kuifp59BlOzV9yTyzDhsAAKikzXXYWg/Y+oiADQAAVNXLhXMBAADQDAI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AACAyBGwAQAARI6ADQAAIHIEbAAAAJEjYAMAAIgcARsAAEDkCNgAAAAiR8AGAAAQOQI2AOjQm7fv9OvTkd68fdd1VgBE7HddZwAAltWbt+/05Tc/6snz11pfu6gfvv5Cn3z8UdfZAhAhWtgAoCOPnr3Sk+evJUlPnr/Wo2evOs4RgFgRsAFAR65euaT1tYuSpPW1i7p65VLHOQIQK3P3rvMQHTNbkXRycnKilZWVrrMDYIG9eftOj5690tUrl+gOBXru9PRUq6urkrTq7qdNHpsxbADQoU8+/kh//HTQdTYARI4uUQAAgMgRsAEAAESu9S5RM7slaRReDtz9Xt00ZjaQdEPSdXe/ltu3JWlX0kNJx5KuSfrZ3R/M/lcAAAB0p9UWthB4yd0P3P1A0pGZ7ddJY2abSoK1gaTLBYcYSNqStB/+PSZYAwAAfdbqLFEzeylp3d1HmW3u7lY3jZltS7rt7p8VbD/Mpp8h38wSBQAAlbQ5S7S1FjYzGyrpzhwV7NtqKg0AAMCia3MM23DM9pGSbsum0hS5YWYvlHSZbrj73qQ3m9kFSRcym1i9EgAARKOLddjSQKqtNEeS5O7HkmRmO2Z2392vT0hzW9K/V8wTAADAXJQO2MLYsJsl3nrH3Y8m7K8arFVKkwZqGd9L2jezwq7W4I6kv2deX5L0r0o5BAAAaEnpgC3MtKwy2zIfOKUGE/bNkuYDZradnRXq7iMzk5Lu1sJA0t1/k/Rb5hhlTgUAADAXrXWJuvuxmY3MbJhv9XL3w6bSZIX12e6b2UamS3QQdpcK+LJOTxud4AEAABZYm3FD22PY7ihZE+1AOutWPUh3hlmh27mFcSemyTjXTRpa0+7lgr0dSQ8qLvNxSZI+/fTTCkkAAAAkJXFEo9Fbq+uwSWcL4aYB1OfZGZtmtiNpz903KqQZSkrH021KuqfMkwxCi9pO5nBr02aJFuTZJP2bpFdhUzqm7Q+ZbaiOcmwOZdkMyrE5lGUzKMfmdFWWlyT9tzccYLUesC2CdCFdtbAQ3jKhHJtDWTaDcmwOZdkMyrE5i1aWPPwdAAAgcgRsAAAAkSNgK+c3Sf9XmaU/MBPKsTmUZTMox+ZQls2gHJuzUGXJGDYAAIDI0cIGAAAQOQI2AACAyBGwAQAARK7tJx30XljEdxReDnJPZUCBsHjxDUnX3f1awX7KtIJQXpK0IUnuvluwfxReUp4FMt9JKSnHoaS/Zp+AQjnOxswe5us5ZTmdmW1J2pX0UMlC8deUWQQ+vIdyrMDM7kp6HF6+WLSyJGCbIP2hdPf0MVlbZraf/8HEe2a2KelPkgYqeHwYZVqNmd3NPeljP/sDSXmWdlfS3cwzhvcl3VfyI0k5zig8OnArt42yLGegpOy2lQRsdwsCDMqxhHBD9p+S/hIeUbkp6RdJFvYvRFkyS3QCM3spaT13F+7ubt3lqh/Chfy2u3+W206ZlhQuQveVtFSOwrb0QrTh7seUZzlm9lDSw/SuOlzAb7v778NryrGiTKvlfracKMtywjXycNxzrinH8sIN2ONsq5mZbbn7Yfj/hShLxrCNEZ5ZOiiqTKEpGxVRpjP5k5Luu1T6jN0B5Vmeu1/LdYF8Lim9mFOOs7kh6fvsBsqyGZRjZTuSHpjZMC2fTLC2MGVJl+h4wzHbR0qaslEdZVpBuMD8Prc5vcAcKwnmioxEeY4VWjYGkq6HTXwvKwo/dIcFuyjLam6Y2Qslw0c2MsMfKMeSQkAmSZtKrovH6ZCHELQtTFkSsFWXVi40hzIt77ak3TBOY9x7KM8CmS68gZKL+WhKEspxvEHokh+UfD9led6RJGXGVe6Y2X13vz4hDeV4XhqQjdz9SJLMbE/SE52/4c3qXVkSsFXXqw+4JyjTEsIMqO/SgbMTUJ4FQoCWDjreSce1TEhCORYws50S38E8yjInDdQyvpe0PyUIphzH+2f6P+GGdjCly7N3ZckYtvHylSk1mLAPk1GmMwrdeI9z47AozxLChftu7ofwUO9n6VGOJYVJL/+c8BbKsqRQp89kWnyHohyrGFceIy1YWRKwjRHufkaZ/vHsvqKxG5iCMp1NZhBt2jo0MLMh5VnaUNItfXhHPQj/HVGOlVyWtGVmt8JM27tSMuvWzLYpy3LSGeDZcsrcUBxTjuWFsjrW+bFqA0n/XKSyJGCb7I4yawyFO6KqXQHLalxzM2VaQWjR2JR0FGZADZXMiHoR3kJ5ThHGtdzLdUHdlHSUuWBTjiW4+6G730v/SdoP2+9l1hCjLKcIrWn57+SOpAeZljbKsbw9JXVa0gdLphyFTQtRlqzDNkW4i0wr1efZRUxxXggotpVUnk1J91S8ejdlOkW4436igplMuXWvKM8pQlnuZDZtSNoreNIB5VhS+NG7qaS+31Oyzl26lAJlOUXBd3ItX06UY3lmtqP318qFLEsCNgAAgMjRJQoAABA5AjYAAIDIEbABAABEjoANAAAgcgRsAAAAkSNgAwAAiBwBGwAAQOQI2AAAACJHwAYAABA5AjYAAIDIEbABAABEjoANAAAgcv8fhrYXFAzN4joAAAAASUVORK5CYII=",
|
|
"text/plain": [
|
|
"<Figure size 640x395.55 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"pcac_plateau.plot_history()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"If everything is satisfactory we can save the `Obs` in a file for future use. The `Obs` `pcac_plateau` conatains all relevant information for any follow up analyses."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pcac_plateau.tag = \"O(a) improved PCAC mass extracted on the test ensemble\"\n",
|
|
"pcac_plateau.dump(\"pcac_plateau_test_ensemble\", datatype=\"json.gz\")"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
}
|