{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic pyerrors example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import pyerrors, as well as autograd wrapped numpy and matplotlib." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyerrors as pe" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.style.use('./base_style.mplstyle')\n", "plt.rc('text', usetex=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use numpy to generate some fake data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "test_sample1 = np.random.normal(2.0, 0.5, 1000)\n", "test_sample2 = np.random.normal(1.0, 0.1, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this we can construct `Obs`, which are the basic object of `pyerrors`. For each sample we give to the obs, we also have to specify an ensemble/replica name. In this example we assume that both datasets originate from the same gauge field ensemble labeled 'ens1'." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "obs1 = pe.Obs([test_sample1], ['ens1'])\n", "obs2 = pe.Obs([test_sample2], ['ens1'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now combine these two observables into a third one:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "obs3 = np.log(obs1 ** 2 / obs2 ** 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`pyerrors` overloads all basic math operations, the user can work with these `Obs` as if they were real numbers. The proper resampling is performed in the background via automatic differentiation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we are now interested in the error of obs3, we can use the `gamma_method` to compute it and then print the object to the notebook" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs[1.387(19)]\n" ] } ], "source": [ "obs3.gamma_method()\n", "print(obs3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With print level 1 we can take a look at the integrated autocorrelation time estimated by the automatic windowing procedure." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 1.38669742e+00 +/- 1.94840399e-02 +/- 9.74201997e-04 (1.405%)\n", " t_int\t 5.01998002e-01 +/- 4.47213596e-02 S = 2.00\n" ] } ], "source": [ "obs3.print(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected the random data from numpy exhibits no autocorrelation ($\\tau_\\text{int}\\,\\approx0.5$). It can still be interesting to have a look at the window size dependence of the integrated autocorrelation time" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "obs3.plot_tauint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows the windowsize dependence of the integrated autocorrelation time. The red vertical line signalizes the window chosen by the automatic windowing procedure with $S=2.0$.\n", "Choosing a larger windowsize would not significantly alter $\\tau_\\text{int}$, so everything seems to be correct here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlated data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now generate fake data with given covariance matrix and integrated autocorrelation times:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cov = np.array([[0.5, -0.2], [-0.2, 0.3]]) # Covariance matrix\n", "tau = [4, 8] # Autocorrelation times\n", "c_obs1, c_obs2 = pe.misc.gen_correlated_data([2.8, 2.1], cov, 'ens1', tau)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and once again combine the two `Obs` to a new one with arbitrary math operations" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 3.27194697e-01 +/- 1.79228480e+00 +/- 3.07835024e-01 (547.773%)\n", " t_int\t 5.31748262e+00 +/- 1.57262234e+00 S = 2.00\n" ] } ], "source": [ "c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n", "c_obs3.gamma_method()\n", "c_obs3.print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time we see a significant autocorrelation so it is worth to have a closer look at the normalized autocorrelation function (rho) and the integrated autocorrelation time" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c_obs3.plot_rho()\n", "c_obs3.plot_tauint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now redo the error analysis and alter the value of S or attach a tail to the autocorrelation function to take into account long range autocorrelations" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 3.27194697e-01 +/- 1.88231459e+00 +/- 2.01855751e-01 (575.289%)\n", " t_int\t 5.86511391e+00 +/- 2.16269625e+00 tau_exp = 20.00, N_sigma = 1\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "c_obs3.gamma_method(tau_exp=20)\n", "c_obs3.print()\n", "c_obs3.plot_tauint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jackknife" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For comparison and as a crosscheck, we can do a jackknife binning analysis. We compare the result for different binsizes with the result from the gamma method. Besides the more robust approach of the gamma method, it can also be shown that the systematic error of the error decreases faster with $N$ in comparison to the binning approach (see hep-lat/0306017)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Binning analysis:\n", "Result:\t 3.27194697e-01 +/- 1.30323584e+00 +/- 1.74847436e-01 (398.306%)\n", "Result:\t 3.27194697e-01 +/- 1.42921199e+00 +/- 3.13124657e-01 (436.808%)\n", "Result:\t 3.27194697e-01 +/- 1.36761713e+00 +/- 4.28131883e-01 (417.983%)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Result from the automatic windowing procedure for comparison:\n", "Result\t 3.27194697e-01 +/- 1.78414777e+00 +/- 2.73504675e-01 (545.286%)\n", " t_int\t 5.26930916e+00 +/- 1.36902941e+00 S = 1.50\n", "Result\t 3.27194697e-01 +/- 1.79228480e+00 +/- 3.07835024e-01 (547.773%)\n", " t_int\t 5.31748262e+00 +/- 1.57262234e+00 S = 2.00\n", "Result\t 3.27194697e-01 +/- 1.67905409e+00 +/- 3.16358031e-01 (513.167%)\n", " t_int\t 4.66682386e+00 +/- 1.53936903e+00 S = 3.00\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pyerrors.jackknifing as jn\n", "jack1 = jn.generate_jack(c_obs1, max_binsize=50)\n", "jack2 = jn.generate_jack(c_obs2, max_binsize=50)\n", "jack3 = jn.derived_jack(lambda x: np.sin(x[0] / x[1] - 1), [jack1, jack2])\n", "\n", "print('Binning analysis:')\n", "jack3.print(binsize=10)\n", "jack3.print(binsize=25)\n", "jack3.print(binsize=50)\n", "\n", "jack3.plot_tauint()\n", "\n", "print('Result from the automatic windowing procedure for comparison:')\n", "c_obs3.gamma_method(S=1.5)\n", "c_obs3.print()\n", "c_obs3.gamma_method(S=2)\n", "c_obs3.print()\n", "c_obs3.gamma_method(S=3)\n", "c_obs3.print()\n", "\n", "c_obs3.gamma_method(S=2)\n", "c_obs3.plot_tauint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this specific example the binned Jackknife procedure seems to underestimate the final error, the deduced intergrated autocorrelation time depends strongly on the chosen binsize. The automatic windowing procedure displayed for comparison gives more robust results for this example." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }