{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic pyerrors example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import pyerrors, as well as autograd wrapped numpy and matplotlib. The sys statement is not necessary if pyerrors was installed via pip." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('..')\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyerrors as pe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use numpy to generate some fake data" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": 4, "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": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Obs[1.415(20)]\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": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 1.41522010e+00 +/- 2.03946273e-02 +/- 1.01973136e-03 (1.441%)\n", " t_int\t 5.07378446e-01 +/- 4.51400871e-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": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 3.27194697e-01 +/- 1.96872835e+00 +/- 3.38140198e-01 (601.699%)\n", " t_int\t 5.41336983e+00 +/- 1.59801329e+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": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "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": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result\t 3.27194697e-01 +/- 2.14280114e+00 +/- 2.48970994e-01 (654.901%)\n", " t_int\t 6.41297945e+00 +/- 2.18167829e+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": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Binning analysis:\n", "Result:\t 3.27194697e-01 +/- 1.81819841e+00 +/- 3.98347312e-01 (555.693%)\n", "Result:\t 3.27194697e-01 +/- 1.66475180e+00 +/- 5.21149746e-01 (508.795%)\n", "Result:\t 3.27194697e-01 +/- 1.41273466e+00 +/- 6.28627238e-01 (431.772%)\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 +/- 2.02097394e+00 +/- 3.22723658e-01 (617.667%)\n", " t_int\t 5.70449936e+00 +/- 1.53928442e+00 S = 1.50\n", "Result\t 3.27194697e-01 +/- 1.96872835e+00 +/- 3.38140198e-01 (601.699%)\n", " t_int\t 5.41336983e+00 +/- 1.59801329e+00 S = 2.00\n", "Result\t 3.27194697e-01 +/- 1.89700786e+00 +/- 3.67353992e-01 (579.780%)\n", " t_int\t 5.02613753e+00 +/- 1.69573607e+00 S = 3.00\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pyerrors.jackknifing as jn\n", "jack1 = jn.generate_jack(c_obs1, max_binsize=120)\n", "jack2 = jn.generate_jack(c_obs2, max_binsize=120)\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=25)\n", "jack3.print(binsize=50)\n", "jack3.print(binsize=100)\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." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.11" } }, "nbformat": 4, "nbformat_minor": 4 }