diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 0f3ae2f3..4c074906 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -11,11 +11,17 @@ on: jobs: pytest: - runs-on: ubuntu-latest + runs-on: ${{ matrix.os }} strategy: - fail-fast: true + fail-fast: true matrix: - python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"] + os: [ubuntu-latest] + python-version: ["3.7", "3.8", "3.9", "3.10"] + include: + - os: macos-latest + python-version: 3.9 + - os: windows-latest + python-version: 3.9 steps: - name: Checkout source diff --git a/README.md b/README.md index 773970cb..54382571 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,9 @@ -[![flake8](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![pytest](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml) [![docs](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml) [![](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![flake8](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/flake8.yml) [![pytest](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/pytest.yml) [![docs](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml/badge.svg)](https://github.com/fjosw/pyerrors/actions/workflows/docs.yml) [![](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) # pyerrors `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data. - **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html -- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples (Do not work properly at the moment) +- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples - **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md - **Bug reports:** https://github.com/fjosw/pyerrors/issues diff --git a/examples/01_basic_example.ipynb b/examples/01_basic_example.ipynb index e529c865..834f9d39 100644 --- a/examples/01_basic_example.ipynb +++ b/examples/01_basic_example.ipynb @@ -65,8 +65,8 @@ "metadata": {}, "outputs": [], "source": [ - "obs1 = pe.Obs([test_sample1], ['ens1'])\n", - "obs2 = pe.Obs([test_sample2], ['ens1'])" + "obs1 = pe.Obs([test_sample1], ['ensemble1'])\n", + "obs2 = pe.Obs([test_sample2], ['ensemble1'])" ] }, { @@ -96,7 +96,7 @@ "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" + "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" ] }, { @@ -108,7 +108,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Obs[1.387(19)]\n" + "1.367(20)\n" ] } ], @@ -121,7 +121,7 @@ "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." + "With the method `details` we can take a look at the integrated autocorrelation time estimated by the automatic windowing procedure as well as the detailed content of the `Obs` object." ] }, { @@ -133,13 +133,15 @@ "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" + "Result\t 1.36706932e+00 +/- 2.04253682e-02 +/- 1.02126841e-03 (1.494%)\n", + " t_int\t 5.01998002e-01 +/- 4.47213595e-02 S = 2.00\n", + "1000 samples in 1 ensemble:\n", + " · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n" ] } ], "source": [ - "obs3.print(1)" + "obs3.details()" ] }, { @@ -156,7 +158,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -190,7 +192,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can now generate fake data with given covariance matrix and integrated autocorrelation times:" + "We can now generate fake data with a given covariance matrix and integrated autocorrelation times:" ] }, { @@ -199,8 +201,8 @@ "metadata": {}, "outputs": [], "source": [ - "cov = np.array([[0.5, -0.2], [-0.2, 0.3]]) # Covariance matrix\n", - "tau = [4, 8] # Autocorrelation times\n", + "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)" ] }, @@ -220,15 +222,17 @@ "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" + "Result\t 3.27194697e-01 +/- 1.53249111e+00 +/- 2.49471479e-01 (468.373%)\n", + " t_int\t 4.75187177e+00 +/- 1.33949719e+00 S = 2.00\n", + "1000 samples in 1 ensemble:\n", + " · Ensemble 'ens1' : 1000 configurations (from 1 to 1000)\n" ] } ], "source": [ "c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n", "c_obs3.gamma_method()\n", - "c_obs3.print()" + "c_obs3.details()" ] }, { @@ -245,7 +249,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -289,13 +293,15 @@ "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" + "Result\t 3.27194697e-01 +/- 1.67779862e+00 +/- 2.08884244e-01 (512.783%)\n", + " t_int\t 5.69571763e+00 +/- 2.09295390e+00 tau_exp = 20.00, N_sigma = 1\n", + "1000 samples in 1 ensemble:\n", + " · Ensemble 'ens1' : 1000 configurations (from 1 to 1000)\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -308,113 +314,21 @@ ], "source": [ "c_obs3.gamma_method(tau_exp=20)\n", - "c_obs3.print()\n", + "c_obs3.details()\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, + "execution_count": null, "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." - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -428,7 +342,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/02_correlators.ipynb b/examples/02_correlators.ipynb index db4dcdef..02afa38f 100644 --- a/examples/02_correlators.ipynb +++ b/examples/02_correlators.ipynb @@ -28,17 +28,29 @@ "id": "e5764fd0", "metadata": {}, "source": [ - "We can load data from preprocessed pickle files which contain a list of `pyerror` `Obs`:" + "We can load data from a preprocessed file which contains a list of `pyerror` `Obs`:" ] }, { "cell_type": "code", "execution_count": 3, - "id": "c49ff771", + "id": "fbfa65f5", "metadata": {}, - "outputs": [], + "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.load_object('./data/correlator_test.p') " + "correlator_data = pe.input.json.load_json(\"./data/correlator_test\")" ] }, { @@ -56,7 +68,7 @@ "metadata": {}, "outputs": [], "source": [ - "my_correlator = pe.correlators.Corr(correlator_data)" + "my_correlator = pe.Corr(correlator_data)" ] }, { @@ -71,13 +83,13 @@ "text": [ "x0/a\tCorr(x0/a)\n", "------------------\n", - "8\t548(13)\n", - "9\t433(11)\n", - "10\t343.1(8.6)\n", - "11\t273.2(6.6)\n", - "12\t217.5(5.6)\n", - "13\t172.9(4.9)\n", - "14\t137.6(4.6)\n", + "8\t 548(13)\n", + "9\t 433(11)\n", + "10\t 343.1(8.6)\n", + "11\t 273.2(6.6)\n", + "12\t 217.5(5.6)\n", + "13\t 172.9(4.9)\n", + "14\t 137.6(4.6)\n", "\n" ] } @@ -102,7 +114,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -149,7 +161,7 @@ "id": "634dd613", "metadata": {}, "source": [ - "Or symmetrised" + "or symmetrised" ] }, { @@ -168,7 +180,7 @@ "id": "3d733872", "metadata": {}, "source": [ - "And we can compare different `Corr` objects by passing `comp` to the `show` method" + "We can compare different `Corr` objects by passing `comp` to the `show` method" ] }, { @@ -179,7 +191,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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JHDh6Mnb7rQPH4q41E8eMcGTRXiIz+kKN69aty+gwk9YDpUlMMHfLsilexKCJiDzrsVfew789/+ag7f/45I6429+66jx8u2JGjlpFlFptbW1syCzTs/T8fj+WLVuG1tZW9Pb2oq6uLu5+Nyyb4lUcniMiz0rsaTLDniZym87OTrS3twMwznXLptra2ozkAuWLgsppSlh77nzDoCnSD7z7e65H5wL9EWl5jTk7+xIREVmSEBMcGTcbxeNKgELIaZJSPgzgYa2nadAOXc8Am/8ZCL83sM13NrDwe0DZdTlrJ6nV6O9/tgs9fQNLWpQWj8TKa8sGTQu3sy8REZElRjHByMmWH+75Ok1JdT0DrPsKMHEWcEsQqNunrifOUtu7nnG6hQVj464e3PZoZ1wQBAD7+07gtkc7sXFXT1r7EhERWWIWE0y4wPJTeH54TjMopynSD/x4jvpwlj8OFOniw0gEWHsTcKAL+OarHKrLsv6IxBUNWwYFQRptqYsXa68EAMv7cqiOiIgsSRITHAmHUTxuHGBheC5/e5pefVR1vy24Kz5gAtTtBXcC4XfVfpRVLe3vmQZBACAB9PSdQEv7e7b2JSIisiRVTGCR53OaTL30E3U90aTbTdv+0k+AuTfnpk0Fas3WtzO6n7bv8kumpdskyhdH96tLKmMmqQsRFaZUMYFF+Rs0XX4H8Ow3gQOvA1MNFpE88PrAfpRVty44F//037ss7QfA1r5U4Np/Cfz2/6be77P/BHyuLvV+RJSfUsUEFjGniTlNWaflNO3vOwGj3zajnCYr+zKniQb1NB3cA/z3rcAX1wATdMUsc9jT1NjYGPv50KFDqK6ujq2DRumprq7GunXr0NLSktMV7dPV2dmJ2tpahEIhdHd3O90cApjTlFLRMFVWYM9GFSDt3QacPKqu196kti98kAFTDgwrElh5bRkAFfToabdXXluGYUXC1r5EGDMJOGvOwEULlCbMiN+eo4BJW9+rpqYmtjBubW0ttm/fnpPXzwZtQV0ramtrsWTJkoy3oampKemiv24TCATiljGxw87nTTYkiwlab7H+NFlsYk4IIW4XQnQB2DbozrLrgKWPAAd2Az+vAOqnqOsDXWo76zTlzKLZpVi9IoBJxfFVmScVj8TqFYG42kt29iWKifQDH7yqfv7gVXU7x9atWzdoFfg1a9bkvB2Z1NbWZnnfiooKLFu2LIut8Y50l0ax83mTTWYxwcE/Wn4Kz+c0pSxuWXYdMPMLrAjuAotml6KibJKlKt929iUaVLBuwz8CLz6U8yK24XAYoVAorlfE5/Nh/vz0cyic1NzcjFAoZHl/LwyduZndz5vSYBQTjJsN1FkLcj0fNFlSNAw4d4HTrSCoobrLp4/P+L5UwLSCdTMWAV/6hZodc+B1YOsqtT2HvcqBQAAVFRVoamqKCyC0fKbW1lbU19cjFArF8nNCoRAqKirg9/vR1NSEUCgUG9pZs2YNQqEQent70dHRgaamJjQ3N6OkpARPPvkk6urqYj1bwWAwrccBKg/L7/fHAr7KykoEg0G0tbUhFArF8rRqampir+P3+1FdXR3rGVm2bJlhHk9zczP8fn8soPT5fLG11oxeV98mn8+XVo9N4hCXfm037TUBIBQKxY6N2fuqqKgw3K6t3ZbsPSQKh8Oxz6OtrQ3V1dVxx8/o80633VxbLonEmOBI0jSmeFLKvLgAGAtA9vX1SSIqEP0fS/mj2VI+tkzK/v6E+/rV9h9dqPbLge7ubun3+yVUSTFZXl4u29ra4vZpa2uTfr8/bltDQ4PhPvrH+v1+WVNTE7vd0tIiA4HAkB9XWVkpW1paYrfLy8tlR0dH7PkSX0P/HG1tbbKjoyP2/B0dHXHvraWlRTY1NcV9PtrtZK9bU1MT97jDhw9LAIM+SyMNDQ2D3q/2OpWVlXHP0d3dLcvLy1O+L7Ptyd5D4mehva/u7u7Ybb/fLw8fPhy7bfZ5p9tusqavr0/7mx0rU8UaqXbwyoVBE1EBCr0g5cqxUr63zfj+915R94deyGmz2traZE1NjQwEAhJA3IlVSil9Pl/s5CqlHHR/R0eHVN9pB5SXl8edDLu7u6XP5xvS47q7uwft39TUJKuqqmLvw+gk3tbWNuhx2usnBk3l5eVxgUFHR0fS19UCpERaUJCM9lj961VWVsqGhgbZ0dEx6PNKfF6z92W0PdVnZxQ0VVZWxgWD5eXlccfe6PMeSrvJGjtBU2EMzxFRfjr2obpOVcRW2y9HysvLY8NztbW1uPXWW+OGbaqqqvDkk08iEAggGAwa5gIlzhbz+XyYPn16yte287hgMAifz4dgMBjb1t3dbSmvxspstsrKSjQ1NWHcuHEIBAJYtmwZampq0NzcbPq6WpvS0d7eDp/PF/f4lpYWAAPDhEbvo62tLXYMzN5X4vZ0PjutLdpQZW9vL3p7e1O+p6G0mzKLQRMRedfoM9V1qiK22n5ZFA6HEQwGB+W0NDQ0oLGxEeFwOHYyX7ZsGa666io0NDQgFAo5lkAdDofh9/vjXj9ZW/RJ7lYDm7a2NnR2diIYDKKpqSm23ex1W1tb0555Fg6H07pPz+x9JW63+9kBqn5TfX09KioqsHTp0pSBTigUGnK7KbM8X3KAiArYtL8GfGerpO9IJP6+SATY+hDgm6b2ywGzekx+vz/upBYIBFBSUjKkACETAoGAYc+I2Ym6s7PT1vNrCdmBQAA1NTXo6OiI9bCZva7ZfVYEAgHDtofD4VjSfaJQKJTW7Ea7n104HMZVV12Furo6VFVVwefzxfY1e7+dnZ0ZbzcNjSuDJiFEpRCCc1eJKDmXFbFtbm6OG64BYNj7BKhCmInDdslY7XGw87jy8nLMmzcPra2tcdvXrVsHALFZYYA6SSfWoLLy2okz2bTeGbPX9fv9qKqqintcOBxGZ2dnys9Am72mr8oeDodj9bPKy8vjjo8WBFo9BnqpPrtEWq+R/jPUhua0dhh93pluNw1RqqSnXF8A+AB0AKi0+TgmghMVqt3r1Sy6lWMHLj+6UG3PkcOHD8umpibZ1tYmGxoa4i5Guru7ZWVl5aDtHR0dsrKyUgKIPbahoUH6fL7YbDz9PjU1NfLw4cNpP07Kgdlq+plmmpqamrjZbG1tbbK8vFz6fD7Z0NAQmw1m9PpNTU1xz9vQ0BCXpJ3sdRsaGmRLS4tsa2uLJUhbSQbXnld7vD7xWv+aTU1NcQnyZu/LbHuy95D4OSd+ltp70n4H9O898fNOt91knZ1EcNetPSeEqIIKnEJSytYUu+sfF7/2HBEVlkg/0PmIKmx5zb8Cga+4uohtOBxGe3s7C0ISOezIkSMoLi4GnFh7TgjhE0JUCSEMa8ELIWqi91cJIWoS7gsACBo9jogoqaJhwFkXq5/PutiVAZN+eGndunUMmIg8JqOz56JBzzyonqJB2Y1akCSlbI7eLhdCNEkpq6O7+KWUrUJwqQwisuDofnXRHNwTf60ZMylni/YmU1tbi7lz56KqqsrRBHAiSk9WhueEEJUA6qSUcxO2HwZwrpQyrNsmpZQiGlBpUwSWAegF0CSltDRdg8NzRAXoN/XAb/9v6v0++0/A5+qy354UOjs70d7eDiB+aQ8ico6d4bmc1WkSQvgB+PQBk+6+cillo+72fADbrQZMRFSg5n0VOH9x6v1c0MsEIDYbioi8KZfFLc2qeIWhhvMAqAAKQDkAvxCiU0ppWMBCCDECwAjdpjGZaSYReYZLht2IqDC4oSJ4L3T5T1LKIIC55rvH1AFYma1GEREREem5obhlutmQ9QCKdZcpGWsRERERUYJc9jSZ1cX3JbnPlJTyJICTQojbAdwOdwSARERElKdyFmhEc5PC0YTwxPvSrs0kpXxYSlkG4JKhtI+IiIgomWwFTWZDbvVQSd4AYqUJmk32JSIiInKNTBe39AOohKqzFBBCNECVDmgFACllY7QiuLbK4HxdYct0XzP94blIP/Du74FjHwKjz1QrobuwirBX9Ucktr3diwNHT2DimJG45NwSDCsaeuHSbD0vERHlmQyf51239ly6bBe37HoG2PzPQPi9gW2+s9WK6WXXZa2dhWLjrh7c/2wXevpOxLaVFo/EymvLsGh2qeuel4iI8ozF87yja895QtczwLqvABNnAbcEgbp96nriLLW96xmnW+hpG3f14LZHO+MCGwDY33cCtz3aiY27elz1vERElGeydJ73fE9TwvDc+Sl7miL9wI/nqA9u+eNAkS5ujESAtTcBB7qAb77Kobo09EckrmjYMiiw0QgAk4pH4sXaK20NqWXreYmIKM/YPM8XVE+T7dlzrz6quuoW3BX/QQLq9oI7gfC7aj+yraX9PdPABgAkgJ6+E2hpf890n1w+LxER5Zksnuc9HzTZ9tJP1PXEC4zv17Zr+5Eta7a+ndH9sv28RESUZ7J4ni+8oOnyO9T1gdeN79e2a/uRLbcuODej+2X7eYmIKM9k8Tzv+aBJCHG7EKILwDZLD7h4hcqe37pKjW3qRSLA1ocA3zS1H9m2ZN7ZKC0eCbOsIgE1223JvLNd8bxERJRnsnie93zQZDunqWiYmm64Z6NKBtu7DTh5VF2vvUltX/ggk8DTNKxIYOW1ZQAwKMDRbq+8tsx2sna2npeIiPJMFs/znp89p8lMnaZp6oNknaYhY50mIiJylMXzvJ3Zc4UbNAGsCJ5lrAhORESOsnCeL6igyXadJiIiIqIo1mkiIiIiyjDPB01EREREucCgiYiIiMgCBk1EREREFng+aLJd3JKIiIgoDZ4PmpgITkRERLlwmtMNcB3WbrLNDXWT3NAGIiJySI7O3Qya9Ayrh56tyrGzSrghN1TodkMbiIjIITk8d3t+eC5jup4B1n0FmDgLuCUI1O1T1xNnqe1dzzjdQtfZuKsHtz3aGResAMD+vhO47dFObNzVUxBtICIih+T43O35iuCatJZR0UT6gR/PUR/y8seBIl0sGYmoBf4OdAHffJVDdVH9EYkrGrYMClY0AsCk4pF4sfbKrA2TuaENRETkkAyduwuqInhGZs+9+qjq1ltwV/yHDqjbC+4Ewu+q/QgAsHn3ftNgBQAkgJ6+E9i8e3/W2tDS/p6lNrS0v2e6DxEReZQD527PB00ZmT330k/U9cQLjO/Xtmv7Eda1783ofulYs/XtjO5HREQe4sC52/NBU0Zcfoe6PvC68f3adm0/wqjTrQ1TWt0vHbcuODej+xERkYc4cO5m0AQAF69QmfZbV6lxUL1IBNj6EOCbpvYjAMB915RhwujhSfeZMHo47rumLGttWDLvbJQWj4RZtpKAmkW3ZN7ZWWsDERE5xIFzN4MmQCWILfwesGejShzbuw04eVRdr71JbV/4IJPAdUp9o/DgDbMhgEFBi7btwRtmo9Q3KmttGFYksPLasthrJrYBAFZeW8YkcCKifOTAuZuz5/QMaz1MUx866zQZckONJDe0gYiIHDLEc7ed2XMMmhKxIrhtbqjG7YY2EBGRQ4Zw7mbQNJSgiYiIiAqGnaCJy6hYxR4oAN7r0fFae4mIKAUHz8eeD5qEELcDuB3ZTGrnmnQAvJc75LX2EhFRCg6fjz0/ey4jxS2T4Zp0ALy3xpvX2ktERCm44HzMnKZkuCYdAO+t8dYTPo5rf/IiDh47ZbrPhNHD8ewdV2S1JAIREWVIFs/HBbX2XFZxTToA3lvj7YENXUkDJgA4eOwUHtjQlaMWERHRkLjkfMygKRmuSQfAe2u8Hf9Lf0b3IyIih7nkfMygKRmuSQfAe2u8LZ03NaP7ERGRw1xyPmbQlAzXpAPgvTXeFs6aZKm9C2dNymWziIgoXS45HzNoSiblujbPAXO+DOz+NfD2VpWolif6IxIvdR/C+h37sO3tXtz3Be+s8ZZqTToJYPn8qdiw8wO81H0I/ZH8mAxBRJRXIv3q3Ppaq6rLVPGA42vEumr2nBCiEkAIwDwAkFI223hs9iqCG9WF+MQnASGAjw4MbMuT2k1m9Y2uu6gUz/yhxzN1j4zeh+8TpwMAwn/+S2ybm98DEVFBMqvHVHYD0PV0RteI9eQyKkIIH4DnpZRzoz8fllJa7rrI+jIq+gqkh7qB/60HZixSmfwTL1DjqVtXqWh36SOeDZy0+kaJvxXagXj4posx7owRnqmwra8I/s7BP+Nfg3tM39vqFQEGTkRETtPqMZmdYyv/EzhjfMYqgnsyaNITQgQANEgpK2w8Jjdrz+Vx7Sav1WOyg7WbiIg8wIFzrKN1moQQPiFElRCizeT+muj9VUKIGoP7qwDUAViS6bZlhEtqRWTD5t37LdVj2rx7f+4alSGs3URE5AEuP8dmNGiK9hAtBeADUGJwfw2gcpWi+UqdQogm/T7R7U0AGjLZtoxxSa2IbFjXvjej+7kJazcREXmAy8+xGQ2apJSd0aAnZLJLHYBm3f5BAFXa7Wguk7Z9qRCiPJPtywiX1IrIhlGnW+vqtLqfm1ityTRnqg/rd+zjrDoiIie4/Bybs5IDQgg/AJ+UMmxwX7luWE7TG724i5VaEcVnA+POUdMkPVCKQCsvcKm/BGNHnpZ03wmjh+O+a8py1LLMSVW7CQCKBPCj4Jv41toduHHNy7iiYQsX9iUiygWtvMDpo4DRk4CtP3RlfcSsJIJHSwfUSSnn6raVA2hLnBEnhDgM4FYAQQDlAMIAKgAcklI2JnmNEQBG6DaNAfB+1hPBgYTM/jt1mf0PqdpNZ0z0TCkCo2n5RvJhhpk2MxDAoBl0RvLhPRMRuZ5ReQHAYPbcQ1mZoe747DmbQVM31Ew5yzWZoo/7FwArE7fnJGgCzGs3/fmgZ0oRmJUXMJIvtYyMgsQiAZiNxHl5xiARkeuZlRf4n+8AH74GRD4e2HeI9ZjMeC1oOgygNo2gybmeJo2+dpMYBmyqA0ovApY/YTBN8kagZyfw9SBQPDk37UvCyhT8sSNPw10LZ2DGmWNdX4/JjvjaTR/hR8E3Uz5m9ZcDWHyhtwNGIiJX6dsH/Owq8/PmE8uB99uBv/knFUwNsR6TGUdLDiRhlhzuS3KfKSnlSSnlEe0C4OhQGpeWomHAuQuACytVhdKjPcCCu02mSd4FHP0A2HRPzptpxMoU/CMnPsYroV5cPn183gRMgFpm5fLp43H9nMnYsTds6TFenDFIRORqm+5Jft78zN3A8UPAu79T51oX1D7MWdAkpQwBCEcTwhPvC6b7vEKI24UQXQC2DaV9Q3bqz+o61TRJbT+HcQq+YnUm4OGPTnFGHRFRJnnsvAlkL2gaVKMpqh4q2RtAbBjP1rBcIinlw1LKMgCXDOV5hiwQzeQ3mya5f5e6njDdsRl1+kV450z1WXqM1an6XnXfNWWYMHp4yv12vN/HGXVEREOlX4R3SjSDJ1V5gYAzM+WMZDSnKdqLVAlgGYAAgEYA26WUrbp9ajAwHDdfSlk7xNe8HcDtUAHg+TnNadJLVvp993pg/d8Dp44NbMvxjDomQJuzM6uOM+qIiNJkNIFq2Ahg+udMcoFzsyyZ44ngTsjZ2nPJGJUi2NYMPP8AMONqNW7rwIw6O7PkgMIMDKyWXgAKK6AkIsqIZLPkel7NWXkBIwyanAqagMGRdNEw4K8qHJtRZ2WWXGKPU76UF7BLm1XX1rUfv/jdOyn354w6IiILUs2SW/M51aPUrztPZam8gBE7QVPy8s8ekDA857yy64CZX1ClCIL/AuxrTz6j7ucVagbB0l9lpTlWZslFJHDpOeNw02XTMHHMyLwqL2CHNquu+YVuS/uva9/LoImIKBVtltzS/zI+F37+B+pcOO3TwLyvAaPPzFp5gaHyfNAkpXwYwMNaT5PT7QEwUIpgVDQfPtXMgPD7Kikug78oWq/JO4c+srT/J0achuvnOF8/yg2szqj788mPsX7HvoIONImITGm1DA9Fv4imOheefoYq4eNing+aXC2wAnhrsxqfnTp/8P3bmlWA9EE78NQtalsGEsTt5Odo8n2WnB33XVOGbe/0phzSfOWdw3jlncMACndIk4jIkFHSt9m50IWz5My4Y0grX828xnxx393rVYL4X1UAtwSBun3qeuIslSzX9UxaL6klfVsNmATUCX/hrElpvV4+KvWNwoM3zIYATBf4TZx1uL/vBG57tJPlCIiItKTvibPUea32PWCMhUV4Z17jTHtt8HzQ5JrilkaKhqleoz0b1dTJvduAk0eBd19SJQhmXK2S4qbOB0aMVtfLH1ezCDbfa7uWU0/4OO59epftWXIrry3j0FKCRbNLsXpFAJOKR8ZtN/uYZPRy79O70BM+nvX2ERG5UqRf9TDNWKTOZ1PnA6OKgcU/APZsUhOgtHPh3m3q3Lhno0r6dmEOUyLOnssFsxWcbwkad1Xu3aaS4pY+ApRdb/ll7lq3A0917rO8P4eUUtOvU/dUx/t44c2DKR/zpcBkrFo6J/uNIyJym671qpfJ6PzW9Qzw3HeAo/sHtuVwlpyZgpo95wn6GXXHPgReXq1m1aVKinv1UVtB0/RPjra03zWfKsWXL53G5GULtBl1ALDv8HFLQdPJjyN4qfsQP18iKjyvPqqujc5vZdcB534GaJgGTLoQuLretbPkzHh+eM4z9Iv7jp2itqVacuWj3pRLruiXRnk5dMhSU0acVpR3i/DmQvefjqXeCcCGnT1ccoWICod+aZST0RnbZue3g3vU9bjprlmE1w7PD8+5ZhkVO5IV+tq9Hlh/G3BKVyrAZEad3aVRAGDC6OF49o4rUOoblal3UzCsFArVK8TK6kRUYAyXRhkOTL/SsaLOdtkZnvN8T5NrFuy1o3gysLgxmhSnSxDfugpouRk4Z0HKGXVms+SSrSUnADx4w2wGTGmyMqtOTzsU9z/bhf5kkSwRkRclzpLTn7P2bDRJ+t4ELG5wTcBkl+d7mjSuTgQ3k+aSKz2yhEujOCidOlhccoWI8orLl0axg2vPeSVoAgYqpmpLrqSaUVd2A+7Cty3NkvvMeRPwpblTWLE6C7RZdQ9s2I2unqMp9//c+Z/EL7/qnc5QIqKk1t0MdD2d+pzlgaVROHvOS2wuubJ3/58we3gb9hUB2yIzEUkywnqZfzyXRskSbVbdOePPsBQ0cckVIsoLebg0ih2eD5pct2BvuiwuuTK193f4Kn6Hrw4HPhATcf/Jm7ApYtyDYXW2F6WPS64QUcHI06VR7PB2oAGPJoIbSbHkinz+AciEJVcm/VUAq4f/G64uGlwMfcLo4bh74fk5anzh4pIrRFQQ8nhpFDs8HzTljSRLrsj1twEzroZIWHKl6MYngPOuxsoRT6AI6peWs+Ryj0uuEFFe69sHPFcTXfor/5ZGsYOJ4G6T5pIry0/di5cjZRz6cZB+yZXHX343NhyXzOdnT8JPV8zNQeuIiNKULOnbpUuj2MFEcC8ruw79Mz6PP76yCccP78OfX/sffObkb8yT7SbMAAD8wxnP476/KcPMSz+LYafxsDpBv+TK069aWwPwnUMfcckVInIfLeH72IdA+H21LdXSKGdeCCzy3tIodvDs6jIDNYAAYDIuK/o0PjP8N8bJdlqED+DTf3kJaHsJ2G5cPZxya+m8qfjNG39KuV9Xz1HcuOZl9hASkXuYjXiYJX1rS6N89jtqNngeY06TixhV+d4WmYkPxEREXkhItosm5cnSOSmrh1PuLZw1CaXFIy1VDgeYHE5ELmFU5furm4Dho4EXflBQSd9GPB80CSFuF0J0ARg8hcxDesLHce/Tu5CYYRZBEe4/eRPw5iZEnogm2x0PA899B9IgORzLH1fJes/VquQ9csSwIoGV15YBsL7kCpPDichRRgnfI0YD0y4Drv8p8OZm4InlBZP0bYSJ4C5x17odSat8X120DStHPI6z5IGBjakqsV50I/C3/5GF1pJV6Sy5wuRwInLEr78B/OEJ83PL1lXAb76n8p00Hkv6NsJEcA/RZlyd/DiSdL9NkUvQdnwe/umCXpSfbIP/g2dTV2IdNhx4rdXV5evz3aLZpagom2RryZUP+o6zejgR5Y6W9P3xSXXb7NxySRXw/HeBwM0q+bsAzy0MmhxktxcigiK8MeoiVI19CfgAKauHo/NX6gKowplMEHeENqvuH648D7c91ply/x17+/CttTsAsHo4EWVZOlW++0/l1dIodng+p8mrjJK+U4lV+b7yPmBMqXEl1t3rgecfABKqhzNB3Hl2k8MBJogTURbZrvK9ChhzljoHFSgGTQ4wS/o2M6jKd/FkYHFjtBJrfPVwRKuHgwnirmM3ORxggjgRZUlaVb43AYsb1DmoQDER3AGpkr4TmQ7RpFk9nAnizkonORwAvhSYjFVL52SnUURUWJIlfedBlW87mAjuQvolNopHnW7pMUvmTsEXA1PMk4HLrgNmfmGgauvbL6gcphTVw/HxSeDtrQWXwOcW+uTwA0dP4KXug1i7/f2Ujzv5cYTVw4koffoq38OGq22pqnzP+iIw72s8X0QxaMqBdHsWrpw5MbYsh6miYQMVWN8KqusU1cOx+7/VhcnhjtEvufLCntSVwwFgw84ebNjZw+RwIrIv3Srfp43I+yrfdng+p8ntxS3TSfjWdPUk7SUczCxBXEv2Y/VwV7p74fmYMHq45f2ZHE5EtphW+T4jSZVvJn0bYU5TFvWEj+Pan7yIg8dO2X7sPYtn4oaLJ2Pi2JH2Hqj9ccxYBCy4Uw3J/fRSFTAtfwIo0sXJkYhK9uvZCXw9WNDJfU7TgmsAliYICACTikfixdorOVRHROb69gE/uwoovWjwOWD3eqDlZuC8hcBn7lZDdQdeV8ui7NkILH2kIEYi7OQ0MWjKIrsJ30CG6vIYdcMyOdz10hnGXf3lABZfyGE6IjJRoFW+7WAiuMO0pO8iYa0H4GufPgcXTfVlrgK0PkG8/Rcqf4nVw10vnerhTS9041R/hNXDiSgeq3xnBYOmDEunt2D+OSWZ7y3QEsR3PKZus3q4J2gJ4ueMP8NS0MTq4UQ0CKt8Z43nE8HdJN2kb9sJ33awergn3XdNma3kcIAJ4kQEVvnOMgZNGWK3yrfmnsUz8XeXTctKmwCwerhHlfpG4cEbZseqwVuh/e7d/2wX+iP5katIRDawynfWMRE8QzJW5TtbWD3ck9Kt8cUEcaICxCrfafFsIrgQohJACYC5AFqklEGHm5SUvsr36cOs9Qdc86lSfPnSablP2mX1cE9KrB7+yxffxo73+1I+7t+3vAnfJ4YzOZwo37HKd065JmgSQgQAQErZLITwAXgbwDhHG5VEuj0AI04rSl3lO1tYPdyT9NXDN+3abylo6uo5ihvXvMzkcKJ8xirfOeemnKYSABUAIKUMA+jVAim3STfhe8Lo4bh74flZapVNrB7uSXYTxJkcTpSnWOXbERnPaYr2Ei0FsERKWWFwfw2AcPSmT0rZaPI8h6WUlnuacpXTlE6Vb21wZPWKgLu+8duuHn4TcKAL+Oar7NJ1EKuHExW4SD/w4zkqYFr+OKt8D5GdnKaM9jRFe4aWAvBB9Rwl3l8DqCE4KWUzgE4hRJPBfk0Abs1k2zLlh5vfsL0syqTike4LmAD1h7P0EeDAbpX03TBNJQkuuDv+jxBQtxfcCYTfBf64wZn2EgCV57R6RQCTiq0tsSMB9PSdwObd+1PuS0Qe8McNakhuwV2D/1fPuh646j6gO6j+r9dPUdcHuhgwZUBGc5qklJ1QgZBZhaw6AOfq9g8KIdoAVGvboo9tk1K2ZrJtQ+V4le9s0SeIb6oD9r+Wunr4734M9P+FlWMdlE718HXtezmjjsjLtKTvF36gbqeq8j15HnDZbfxfnUE5SwQXQvihhuPCBveVRwOocgDh6M+B6M+hXLXRjGuqfGeLliA+broKmlJVD9/XDjx1i9rGBHHH2K0e/ueTH2P9jn3uD+SJaLB0qnyPncIq3xmWy0Rwv8n2MABfNKhqAdAihDgMoCNZwCSEGCGEGKtdAIzJeIvh0irf2bLo+6we7kFWksOLBPDKO4fxrbU7cOOal3FFwxYmhxN5RbpVvhd935n25jE3zJ7rBVAipQxJKcfpLqm+BtcB6NNd3s90w1xb5TtbklYP//sk1cMXAZvvjV8lm3LGSvXwxALhnFVH5BGRftXDNGMRq3y7gBuCpkEJ4xbVAyjWXaZkrEVRdpO+S4tH4j9WBFD12emYONZakq7rJCaH108BfrkIOHWMCeIuZpYcbjYCJ6OXe5/ehZ7w8ay3j4jSZJb0rf2v7tnBhO8cymVxS7OhNl+S+0xJKU8COCmEuB3A7chQAKiv8l086nRLj1kydwq+GJiSP3kiidXDX16t8phSVQ9/4QfAqBImHDoksXr4Ux3v44U3DyZ9zMFjp/DDzW9g1dI5uWkkEaWmr/L98mq1LVWV70kXAlfX8/9vluUsaJJShoQQYSGEPzFXaSjLpUgpHwbwsFanaShtTLfK95UzJzpX5Ttb9NXDu55VQVOq6uH7XwN+dQ2Twx2krx6+7/DxlEETAJz8OIKXug/lT9BP5GXpVvkeN51VvnMgW8NzZkNu9QDKtRvR8gLNWWqDLekmfAMeTfq2wyxBnNXDXa37T8cs7bdhZw+Tw4ncYChVvpn0nRMZrQgenQFXCWAZgACARgDb9TWXogUutZ6m+VLK2iG+pn547vx0KoKnU+Vbc8/imbjh4snezWGyitXDPcfu77VrK9cTFQJW+XaMnYrgGV9GxSlDWUblrnU78FTnPluPKciFUI26jW8JGncZ792mkhKXPgKUXZ+7NlIcu0uuAGqNxGfvuAKlvlHZaxgRxetar76Ymv1P3boK+M334mcp+6YBCx9kwDREdoKmXCaCu46W9H3y40jqnQEsnz8Vl08fX7jFAVk93HO0WXV2cvWYHE6UQ6zy7SmeD5rSnT2XTtL3X/ojuH5Ogde9YPVwz9HPqnvslXexYWfqvKXThxWxejhRtrHKt+e4oU7TkEgpH5ZSlgG4xOpj0kn6njB6OO5eeH46TcxPrB7uKdqsuhGnWfuTX7t9L6uHE2UTq3x7kueDJrvsVvnWqiw/eMNs5njosXq4J9298PyUS64kYvVwogxjlW/P8nwiuN3Zc3aTvgsy4dsOs5oiTBB3rXSSwwEmiBNlTLKkb6323dH9A9uY8J1VdhLBPd/TZHd4bvonR1t63ms+VYonbr0ML9ZeyYApmbLrgG/uAG7eAHzp5ypJEUidIP7qozlpHg1mtuRKKlqCOBENkfb/z6zK99+/on6edKH63/rNVxkwuYTnE8Gt0C+N8nLokKXHjDitKP+qfGeL1erhALB/l7r+qBd4eytnfzgkccmVl7oPYu321Gtes3o4UZr0S6Oc/EhtY5Vvz/H88JzGrE6T0Sy5IjF41Xc9DkMMQd8+4GdXAaUXDS56uXs9sP424NRHA9s4o84VOGxNlEVGaQzDhgPTrzQpDnwj0LMT+HqQOUw5UFDDc0KI24UQXQC2Jd5nNkvOLGBi0ncGmCWIb12lKtqes4Az6lzIboI4k8OJLDJaGkX737dnI5O+PSZve5qsLCGR2OPEb88ZlPjNqmiYKkHAb1WuxerhRBmWrOc9EgHWfE4tN9WvO08x6TvnuIzK2LH4+0c78D+79qd83KXnjMNNl01jEb9s0Mbwg/+i8pxSzagruwFY+qtct5J00in6+qXAZFYPJzKy7mag6+nU//umfRqY9zVW+XZIQS+j0h+ReKn7EN459FHqnQF8YsRprPKdLVqC+KgSdTvVjLrw+8BrrfzH4SBWDyfKAO0L46FudTvV/77Tz2CVb4/Iu6Bp4Y9+iz+dtH6yXTpvahZbQwCAwArgrc2pl1z5gEuuuIFWPby1Y6+l/ddu34u129W+HOKmgpfO0iiBFblpGw1Z3iWCf3jkpLXHQf2DXzhrUhZbRwCAmdeoIGjrKi654iGsHk5kk+2lUR5SOUwzr3GmvWSb54OmdNae0wYPVl5bxqGEXCgapnqN9mw0WHLltiRLrlwNPFerkikp50p9o/DgDbNjs0qtkNHLvU/vQk/4ePYaR+Q2ffuA52qi/8+sLo2yUSV9MxXBM/IuEXzqP65D0YhPJN2XQwgOSXfJlYtuBP72P3LTRhokneRwgAniVGB+/Q3gD09waRQPKuhE8GSu+VQpvnzpNCarOqXsOmDmFwaq4r79AtD5K/MkyQkz1PXHJ1k93EGsHk5kQl/le1h0KNtsaZRzPwM0TANmfVHNlOP/M8foVwmZOGYkZo63HgoVVNDEpVFcQL/kyltBdW2UJKl9MwOA3f+tLkwOd4yWHA4AL+z5k6XHbNjZgw07e9izS/nJrOc81dIop43g0igOMuo5/+SIfsuP93xOk1UTRg/H3QvPd7oZpHflfcCY0sFJkloyZekcJoe7EKuHU8EzqvL91U3A8DOAF35gkvS9Chhzlvq/R44wWyXkgMUJZEAeBk2JgwBcGsXFjJZcOR5WPUxMDnctuwniTA6nvGKU8D1iNDDtMuD61cCbm4EnlnNpFJfpCR/HvU/vMlztwE5mt+eDpsSSAxPHjoi7f1LxSKxeEeDQgFuVXQcsfQQ4sFslfTdMU8mSC+6OX3IAULcX3AUc/QDY8oAz7SUAKs9p9YoAJhWPtPyYg8dO4Yeb38hiq4hyYMsDwNEe4/9Rs64HrroP6A6q/2f1U9T1gS71f46pBY754eY3ki6rZpXnc5qklA8DeFibPbf525/FHw99HEvwYhKqB+gTxNt/ofKXUlXQHTac1cMdxurhVFC0pO+Po0M5Zv+jLqkCnv8uELhZJX/zf5SjtKTvkx9HUu9sgeeDpkT6hFXyEC1BfMdj6naq6uGdv1IXgAniDmL1cCoI6VT57j/FpVEclm65lGQ8PzxHecYsORxg9XAXY/Vwylu2q3wz4dsNzJK+h4pBE7mLUXI4q4e7HquHU15Kq8o3E76dlizp24idJIG8qwje19eHsWPHOt0cGipWD/ekdLvDLzlnHL5dcT7znMhdWOXbk+5atwNPdVr/Av3JEf1o/+71ACuCk2exergnpVs9fNs7h3HjmpeZ50TOY5VvT9JX+T59mLUvXtoqITPHn4aS71p7HQZN5F6sHu5J6VQP12h5TiwTQo5glW9PSreHW1sl5MiRpJ1LcZjTRN7A6uGeZDdBnHlO5BhW+fakdBO+010lxPNBU2JxS8pTrB7uSekkiAMshEk5xirfnmQ34RsY+iohTAQnbzHqPmdyuOul033+tU+fg4um+lgIk7IvWcI3oHqUfvM9le+kYdK34+wmfAPG9eGOHDmC4uJigInglHdYPdyT9AnibV378YvfvZPyMfp9mCBOWcEq355kt8r38vlTcfn08Rn5AsagibyH1cM9SUsQ/333QduPZYI4ZRyrfHtSOr3Wf+mP4Po5mRlC9XxOExUwVg/3pL+7bBruWTzT1mOYIE4ZxSrfnpRO0ne6Cd9mGDSRd7F6uCdNHDsSVZ+djv9YEUBp8Uhbj2WCOA0Zq3x7UjpVvoeS8G36vEwEJ89j9XDP0heks1oIUytIx+RwSgurfHuS3aRvO3mQTASnwsLq4Z6VTiHMDTt7sGFnD5PDyTpW+fakoVT5ztaXKlcFTUIIH4AqAJBSNjrbGvIUVg/3vLsXno/f7vkTDh47ZWl/JoeTJazy7UlDrfKdLW7LaSoHkL13S4WB1cM9yW4hTCaHU0qs8u1Jua7ybYergiYpZSuAbqfbQR7H6uGetWh2KVavCGCSjQRxJoeTIVb59iQnqnzbkfHhuegQ21IAS6SUFQb31wAIR2/6OAxHWVF2HbD0EdUt/3Pdr+HSu4GihO8KRUXAgrvUflseYHK4w/SFMB975V1s2NmT8jGnDyvC+h37WD2cBmx5ADjaAyz9r8F/87OuB3rvU1W+39w0sN03Tf3f4FC9Y364+Q3LQ/SaSTnMb8xo0CSECACYB8AHoMTg/hoAkFI2R2+XCyGapJTVmWwHEQBWD/cwLUG8tWOvpf3Xbt+LtdvVvkwQL3Cs8u1JWtJ3kbD2hcepZZYyGjRJKTsBdAohzEqm1gE4V7d/UAjRBoBBE2UHq4d7mt3kcIAJ4gWNVb49KZ2k7/nnlGDxhbn/+85ZTpMQwg81HBc2uK88V+2gAsXq4Z5kNzkcYIJ4wWKVb09KN+m7qydpOaWsyWUiuN9kexhqOE8LnioAVCTprUJ03xFCiLHaBcCYDLaV8g2rh3tWOsnhABPECwqrfHtSOknfAHDP4pn4u8umZaVNqbihTlMvovlPUsoggKDFx9UBWJmtRlEeMksOB4AFTBB3M31yuJ3q4dM/OToHrSPHmSV9a3/zz30n/m+eCd+uYDfp2w35im4ImgYljFtUD+Ah3e0xAFL/F6XCxurhnpVO9fD/feMALj57HGfU5SNW+fa8OVN9lpZGWTJ3Cr4YmOKKv+NcBk0hk+2+JPeZklKeBHBSCHE7gNvhsppT5GKsHu55VhPEt71zGDeuedkV31Apg1jl27P0S6P0fmStl+nKmROzWuXbjpwFGlLKEIBwNCE88T6rQ3JGz/uwlLIMwCVDaR8VKFYP9yS7CeLajLqNu1LXfCKXY5Vvz9q4qwdXNGzBjWtexrfW7sCPgm9aepxTSd9GshU0mQ251UMtlQIAiCZ7N2epDUSpsXq4Z9lJEOeMujzBKt+edODICTT/thvfSGOWnJNJ30aElHbz1pM8mepFqgSwDEAAQCOA7dHlUbR9ajAwHDdfSlk7xNfUD8+d39fXh7Fjxw7lKakQGXX33xI07urfu00llV50I5PDXUDr7m/Y+Dp27O1Luf/nZ0/CT1fMzUHLKON+/Q3gD0+Y/21uXaWqfEf6B7b5pgELH+SQuoNWbX4D/77lLVuPyeWQ+pEjR1BcXAwAxVLKpN1amS5uGYIKlEyXRklYNqXVbD8br/kwgIejZQdS/8ckMsLq4Z6lJYiP+8RwS/t/0HecS654Dat8e1pZqbWOjG+Xn4dzJpzh6r9NN8yeI3IHVg/3tKXzpuI3b6SeVbdjbx++tXYHAHdMYaYUWOXbs7Re4C1/PGBp/5IzhuP6Oe4eQvX8jDMhxO1CiC4A25xuC+UJVg/3pIWzJqG0eKTlyuEAE8Rdj1W+PUuf9N3SYa0a0I694ew2KgMymtPkJG14jjlNlBHaP+sZi4AFd6rhgP27gMe+BJyzQCWI64voRSKq6nDPTuDrQSacOkRbkgGArSrDE0YPx7N3XIFS36jsNIzs69sH/OwqoPSi+L+32N/m1ar47MQLVA/T1oeAPRtZtNIFtL9Dr/wN2slpYtBEZMasFgwTxF0tncU/AeBLgclYtXROdhpF9iVL+tZqqB3dP7CNCd+u0BM+jmt/8qLlSt9az7CTC2w7lghOlFdYPdyT0l1y5eTHEbzUfci1CagFgVW+Pc/u0iiTPJZX6PmgiRXBKatYPdyT0llyZcPOHmzY2cPkcKewyrdn6at8nz7M2heOaz5Vii9fOs1zX1I8H2iwIjjlDKuHe9LdC8/HhNHWyhEATA53BKt8e1ZilW8rvboAMOK0Ilw+fbynAiaAOU1E9iQmiE+YAfz0UhUwGSaH3wQc6AK++SqHDhyUToI4k8NzJNIP/HiOCpiWPx7/N7R7PdByM3DeQuAzdzPp22XSSfgG3Pe3ZSenyfM9TUQ5VXad+kd9YLdK+m6YppJRF9wd/88eULcX3AmE3wX+uMGZ9hIAe0uuaA4eO4Ufbn4ji60iAOpvI/yemgmX+Dc063rgqvuA7qD6e6ufoq4PdDFgclhP+DjufXqXrYBJWyvywRtmuyZgsos5TUR26RPEN9UB+19LXT38dz8G+v/CCsUO0ieIP/bKu9iwM/Xw2+nDilg9PFu0pO8XfqBup6ryPXkecNlt/BtyCbsJ34D3kr6NeD5o4jIq5AgtQXzcdBU0paoevq8deOoWtY0J4o7REsRbO/Za2n/t9r1Yu13tywTxDEqnyvfYKazy7QJa0vfJjyOpdwawfP5UXD59fN588WDvDNFQLPo+q4d7kN3kcIAJ4hmTbpXvRd93pr0Uo0/6ttJTCwB/6Y/g+jmTPZn0bYRBE9FQFE8GFjcCezappO+924CTR4F3XwLW/72qWrz8CfUNesRodb38cZVIvvne+NXYKWdKfaPw4A2zYzkWVsjo5d6nd6EnfDx7jctnkX7VwzRjkfo7mDofGFUMLP5B9G/oxoG/ob3b1N/Unk3A4gZW2XeYlvRtp2jshNHDcffC87PYqtzj7DmiTEi3evjSR4Cy63PTRhqE1cNzrGu96mVilW9P8WKVbzsKqiI4E8HJFRKrh7+8WuUxpUoQf/VRBk0OYvXwHHv1UXWdqsr3pAuBq+uZ8O0S+V7l2w7PB01MBCfX0FcP73pWBU1mya37d6nrj3q55IrDWD08y/RLo5z8SG1LVeV73HRW+XZYIVX5toPDc0TZYLZCO6ASxNffBpz6aGAbZ9S5Qr4PQ+Sc0bD1sOHA9CtNisHeCPTsBL4eZA6Tgwpt2JrFLYmcZpYgvnWVqnB8zgLOqHMhuwni2lfO+5/tQn8kP76AZozR0ija7/qejUz6dqEDR06g+bfd+IbNhG8gP5O+jbCniSibEr9pFw1TJQj4LdvV0vmmvfrLASy+kL1NAJL3tEYiwJrPqare/boePSZ9O27V5jfw71vesvWYfOhtZU8TkVuUXQd8cwdw8wZV0TjSn2TJlbuAox8Am+5xpKk0YNHsUrxYeyWeuPUylJWOsfSYphe6sX7HPrzUfYi9TpvuAY72mP+uf/4HKmCa9mngSz9Xfx/ffJUBk8PKSu13OEwqHunpgMkuzyeCc/YcuZ6WID6qRN1ONaMu/D7wWiuXi3CYliB+zvgz0NVzNOX+O/b24VtrdwAo4OrhWtL3oW51O9Xv+ulnsMq3C2hJ31v+eMDS/l/79Dm4aKovb6p82+H5oImz58gzAiuAtzanXnLlAy654ib3XVOGbe/02ppyrVUPL6Rv4GktjRJYkZu2kal0hqL7jv8F188pzBQC9s4Q5crMa1QQtHUVl1zxkHSrhwMFlCBue2mUh1QO08xrnGkvpZ30XSgJ32YYNBHlStEw1Wu0Z6PBkiu3JVly5WrguVqVXEuOWDS7FKtXBDCpeKTlx0gAPX0nsHn3/pT7elrfPuC5mujvr9WlUTaqpG8OPTvmv15+F99/7o+W99e+NDx4w2yU+kZlrV1ux9lzRLmW7pIrZTcAS3+VkyaSMX3Bv1+++DZ2vJ86I6CsdAzuu2ZW/uZ+rLsZ6HqaS6N4zHOv9eC2xzot75/PeXoFtYwKkeckLrny0mqVx2SWNDthhro+1M3q4Q7TVw/ftGu/paCpq+coblzzcn6ddPRVvsPRZWdSLY1y5oXAIi6N4iR90P+HvWFLj1kydwq+GJiSv0G/TQyaiJygX3Jl2OkqH8QoaVb7pg4AH74G/OoaJoe7hN0E8bxJDjfrKU21NMpnv8OlURyUbpXviJSxLwrEnCYi55kliGvJtaVzmBzuQgVZPdyoyvdXNwHDRwMv/IBJ3y61cVcPbmOV74xg0ETkNKME8eNh1cPE5HBXs5sg7unkcKOE7xGjgWmXAdf/FHhzM/DEciZ9u0xP+DjufXoX7ITpTPo2x0RwIrcwGvZIlRx+0Y3A3/5H7tpIhrRckQc27LZUCHPO1GJ89dPneqs44K+/AfzhCfPfya2rgN98T+U7aZj07bi71u3AU532vlzlVf6dBQWVCM6K4JQ39Ani7b8Adv936orKw4azergL5HX1cC3p++OT6rbZ7+QlVcDz3wUCN6vkb/5OOkoL5IuEtYC8kKt82+H5oIkVwSmvaAniOx5Tt1NVD+/8lboATBB3gbyrHp5Ole/+U1waxWHpJH3PP6eEC05bwN4ZIje68j5gTKlxRWVWD3etvKoebrvK9ypgzFnqd5cck27Sd1dP0lEpimLQRORGxZOBxY3RisqsHu4leVE9PK0q35uAxQ3qd5cckU7SNwDcs3gm/u6yaVlpU75hIjiRm6VbPZwJ4o5Lp3r4587/JH751Uty0LoUkiV9s8q3a9lN+nZ9Pl2OFFQiOFFeS6we/vYLKocpVfXwj0+yerjD0qkefvijU3ip+5Azibj6Kt/Dhqttqap8z/oiMO9r/D1zkD44Lx51uqXHsMp3+hg0Ebmdvnr4W0F1nap6+O7/Vhcmh7uC1QTxHe/3ObPkSrpVvk8bwSrfDkq3yveVMyeyyneaXJXTJISojF6qhBDlTreHyHXMEsRZPdzV7CaIazPqNu7qyXbTklT5PiNJlW8mfTst3YRvgEnfQ+GanCYhhB9ArZSyOnq7TUpZYePxzGmiwqCd5GYsAhbcqYbkfnqpCpiWPwEU6b4LRSIqabdnJ/D1IJN0HWa3Z2DC6OF49o4rsleVuW8f8LOrgNKLBv/u7F4PtNwMnLcQ+MzdaqjuwOtqWZQ9G4Glj7AH0yE94eO49icv2iptobln8UzccPFkTBxrfaJCvrOT0+SmoKkKwHQpZW30dguAJill0OLjGTRR4WD1cM/SclAaNr6OHXtT5zl9fvYk/HTF3Ow0hlW+PYlVvjPL0URwIYQPwFIAS4x6ioQQNQDC0Zs+KWVj9OfpAA7pdu0F4Mt0+4jyAquHe5aWID7uE8Mt7f9B33Gs37Evs5WaWeXbk1jl23kZDZqEEAEA86CCnRKD+2sAQErZHL1dLoRo0obkDAx6DiKKYvVwT1s6byp+88afUu6X8SVXWOXbk1jl2x0ymggupeyMBkQhk13qADTr9g8CqIre7E7YtyTJ8xCRhtXDPWnhrEkoLR5puXI4kIEEcVb59iRW+XaPnM2eiyZ6+6SUYYP7ygEEAei/6vit5jMRFTRWD/ekYUUCK68tA2BvyRUJ4N6nd6EnfNzeC7LKtyexyre75LLkgN9kexgqmAoBeFIrOQCgPtmTCSFGCCHGahcAYzLbXCIPKbtOzWY6sFslfddPAX65CDj1EbDg7vhZUYC6veAu4OgHwJYHnGkzpbXkCgAcPHYKD2zosvdiWx4AjvYM/n3Qfnd6dgz87vy8AjjQxRlyLvDDzW/YmiVXWjwS/7EigKrPTucMuSxwQ3HLXkRzl6SUrTYeVwdgZVZaRORFrB7uSYtml6KibJLtJVfeOfRR6urhrPLtSazy7V5uCJrSTfauB/CQ7vYYAO8PvTlEHsbq4Z6kX3Jl+LAi3PZYZ8rHdPUcTV49nFW+PYlVvt0tl8NzZkndviT3mZJSnpRSHtEuAI4OpXFEeYfVwz3JboK4YXI4q3x7Eqt8u1/OgqZozlI4mhCeeF/aCd9CiNuFEF0Atg2lfUR5xyhB/HhY9TAxOdy17CaID0oON0r4HjEamHYZcP1q4M3NwBPLmfTtMukmfANM+s6lrFQEjyZyV0sp5yZsrwEQ1tVpqgRQkaROk53XZEVwIiOsHu5J6QzTfH72JPz0jDWs8u1BrPLtHMcqgkd7kSoBLAMQEEI0ANiuJXhLKRuFEDXRYAkA5g81YBJC3A7gdrhs8WEi12D1cE/SJ4g/sGE3unqSZyAUIYKSP72C0J+PqanKrPLtCazy7S0ZDZqiQ3CN0YvZPvr77MyWM3u+hwE8rPU0DfX5iPISq4d7kpYg/g9Xnpc0Ofzqom1YOeJxnNV3YOC/IKt8ux6rfHsPe2eICgmrh3tSsuTwq4u2YfXwf8OkvwrEVfmWrPLtWgeOnEDzb7vxDVb59hzPB01MBCeygdXDPcksObwIEawc8Thw3tUouvGJuCrfYs8mSFb5dqX/evldfP+5P9p+HBO+nZeVRHAnMBGcyAazGj5MEHe1xOGcy4q6sHb4g8bHTavFdXT/wDYmfLvCc6/1WKrFpWHCd3Y5lghORB7B6uGetGh2KSpmfhJ/fGUTjh/eh72vvQacRMoq3/9zWgWmfe6rmHnp1Rh2Gv/tO0Ff5fvND49ZesxXLp+GxbNLmfDtIvzrISpUrB7uPV3PYNjmf8asaA/hPG17iirfj/z5Urz8LFD6wm/ZY+GAdKt8X+4fzyrfLsOcJiJi9XAvMKnyLYefAWlS5Tvywip8IM7EtshMACbVwylrhpLwDTDp242Y00REinZSnrEIWHCnGpL76aUqYFr+BFCk+44ViQBrbwR6dgJfDzKpONv69gE/uwoovWjwsdi9HrLlZuC8hRCfuVsN1R14HZEXVgFvbsJtp76FTZFL4p5uwujhePaOK1DqG5XjN1JYVm1+A/++5a20HnvP4pm44eLJmDh2ZIZbRYns5DQxaCKiAawe7k6//obtKt8fiDNx/8kbBwVMms/PnoSfrphreB9lht2Eb4BJ305gIjgRpYfVw90l0q+Oxccn1e0UVb73Tr8Jj+8/C68eHoltkZmIJMnA+KDvONbv2MfK0lmgJX2/FDpkaf87Pjcd5505hsfCAzwfNHEZFaIMY/VwdzDq9UtR5XvqaIlPLfo6Vlvo3dixtw/fWrsDAHs3MimdpO9ZZxWzyrdHeD7QkFI+LKUsA2DcB01E6WH1cOckJn1Hq3wbHouEKt/JqoebYYL40LHKd2HwfNBERFnC6uHO6NsHPFcT/Xwfj6vyrY5F8irfZtXDk5HRy71P70JP+Hi23lleY5XvwsBEcCJKzm718HdfAn65CDj7cuBz/8w8Jyu03KVjH6ph0e4tQ67ynW5toDlTilG7+ALm1ligL1j5zsGP8KPgm5YfyyFR9+DsOQZNRJmlP6lr1cPr9qkeJr2uZ4BN9wB9ewe2Mc8pObOg1OjzBYDjYaBhGjDri8C8ryUNSvUn9V+++DZ2vN9nuVk8qSeXblDKKt/uYydo8vzwHItbEuWAlhx+YSXQf0ptiyYfx2h5OGfOYp6TVUYFKz//Q3Vf4ueriVb5xmkj1DFJ0os3rEjg8unjcf2cyaj+7HRbTWOek7mNu3pwW5oFK7Uq3wyYvMnzQRMTwYlyzChBPNKvekuY52SdUe7SiNGq98h3tqWkbzvsJohrYxD3P9uF/kh+jEhkQk/4OO59ehfS/USY9O1tng+aiCjHjBLE3wqq4aUFd8dXqwbU7QV3AUc/ALY84Eyb3WjLA8DRnsGfWdEwNZy5ZxPwxPKkSd92pJsg3tN3Apt370+5b6H44eY3cPDYqbQey6Rv7/N8nSYickDZdcDSR1Tv0s8rBrazEGZqVgpWll0HfLEJePo24M1NA9t909TnnmZ+2KLZpVi9ImA7F6fphW6c6o8UdPFFLT+sSNh/78wPyx9MBCei9GkBwG++D7z3e1vLfBRkgng6y9RMvxKY8+WMBppaANCw8XXs2Gs9ORwozAAgnaTvb5efh3MmnFHQgaZXFFQiOBE5SEsQ/9LPWAgzlXQLVl73E5WAnyLp2w4tQXz1l+diwujhth5bSAniQylY+XFE4vo5k5n0nWcYNBHR0LEQZnJDLFiZLaW+UXjwhtkQYCFMIyxYSYk8PzyXsPbc+RyeI3IQC2EOyELBymxhIcwBLFhZeFjckkETkXNYCDOrBSuzhYUw0w8el8ydgi8GpuRV8FhImNNERM4p9EKYWS5YmS2FXAhzKLlLABCRkrlLBYI9TUSUPX37gJ9dBZRepHKaiopUT9SP5wATywa2aSIRld/TsxP4ejCr+TxZYfR+Ac+95/6IxBUNW7C/74StIo4TRg/Hs3dcgVLfqKy1LZO03rX//N3b2NT1YVrP4bX3TIOxp4mI3GEohTCfuUPVdHp7a3ypAjeK9Kt2PvX1nBaszJZ0CmECwMFjp1D71E6s37EPL3UfcnUl8Y27enBFwxbcuObltAImLXn+wRtmM2AqIOxpIqLsM8rxMcvv2fmkKurolZpOQ35vuU36tiPdHB+NW3OdtLXjhnL2c+t7I/uYCM6gich9rBTC1PKBZlytemsmXqDygLauAvZsHFI17KyItXeR6iE7fhh4fIkjBSuzRRvCeqjtDWx/57Ctx2q9VKtXBBwNLvRJ7qcJgf/vmd049JH9pVA+c94EfGnuFBaszDMMmhg0EblXunk/TywH3m8H/uafVDDlRLChnxkohgGb6gomX6snfBzX/uTFtNZdGzvyNNy1cAZmnDk258HGUHvLNMxdyl8Mmhg0EblbXA/NnSoI+sNa4H/utlHLKMdDdlZrUMX1lt2l6y17yJ29ZTZow1oA0h7ayuWwViaG4dzSW0bZU1BBE4tbEnmU1VpGqYbsKv8TOGO86v3J5HCXvlfpUDfwv/UDw3ATLwBeehj43+/bqEHl3twlO4bac5PtIEQbitvz4RGs2rwHR058PKTnY+5S/iuooEnDniYiD0pVNTs23DVLLT+SONy15nPAga6BelBAZnqgEgO6omFq7Tz9kNvbW4FfXVOQ1c71OUJPdbyPF948aPs5xp9xOr573Wx8LGVaOUL6NmiPb+van5GhOAC4uuxM/J9Pn8vcpQLAoIlBE5H3GOU6JQtMkvZAPQf8zT3A+OnxvU/6IE3bDsRv+/MhoOX/pE7uzuP8JTuGkuukV1o8Evd94QKMO2NEXCA0rEgMCpAOf3QKD/z/8cHRmJGn4egQe5U09yyeiRsunoyJY0dm5PnI3Rg0MWgi8qbEXKc/vaHqNSUOgSULWHavV4sEn/poYJvvbKDsBqDr6fjhwE9MAEQR8NGBgW3DhqvZbdrzvtYKPHWL+TDcuq8A5y0EPnN3XuUv2ZGJXCcjpcUjcd1FpXjmDz0Z6T2y8nociis8DJoYNBF5l1GuU2Ivj1kPlFnv0//cDfTsiAZj0e3bmoHnvxu/7x+eGJyMnmoYbusq4Dff80ztpWzJ1Cy1XPvbi8/C35w/kWUECpidoOm03DSJiMiisuuAmV9QQ2YHXgd+2wBs/WF8j9KxaAXniRcMPC7Sr4KtGYvi858mB4DjvdHtutIAHb9UAZP+eUeNG/y80/5a9VRtXWWcV7V3G/CJicCiekD2e6L2UjYsml2KirJJQ16WJJc4DEd2MWgiIvfRFv09dwEwZpLqPVp700B5guPRIosHXh/o/Xn396p36ku/iA9sjLab7Tv6zMHPqy2Bsu4rKk9pUBmBTQUzDJeKtujv9E+egbnTxuH7z/3R6SYZ4jAcpYtBExG5W9l1KijZ/M+qmram6DTghR8AN65VgY9R7xNgvN1s31ivUkLPVtl1wJJfqVypPRsH9vdNY8BkYOLYkaj67HScPf4Trhmyc7LAJuUPVwVNQggfgCoAkFI2OtsaInIN/ZDdsQ+BYacDh0LA8/cP9P6MGKP21fcSAca9R0bbgPhepSeWxyd3/+FxlVw+92vAmbOAT55fkMNwduiH7Ia6hEm6tNCosfJT7FmiIXNVIrgQohLAfACH7AZNTAQnKkBG9ZSmlw/0PgHGM+1SlQswrP9UeMnd2ZCtmXaaxNIDHIqjVDw9e04IUQXAx6CJiCwxrdx9p2723Hd0s+fu0s2ee8C8XEC2Ko1TVmbaacGRvmeLM+LIiqwFTdHhs6UAlkgpKwzurwEQjt60HfhEn4NBExGlz6hkgW8aUHa9QZ2mTwJCxNdpYo9STlgpWGlWpylZIUwiu7ISNAkhAgDmAfABWCalnJtwfw0wkIskhCiHCq6q7TSeQRMRDZlR5W+rFcHZo+QYo6VRjCqCM0CiTMrq8Fw076jOIGg6DOBcKWVYt01KKUX05xoA4w2eMi5/iUETERER5UrOi1sKIfxQgU7Y4L5yKWWQs+GIiIjIyzJVcsBvsj0MNZxnSXRIrwKATwgRklK2Jtl3BIARuk1jrL4OERERkV3ZrtPUC6DE6s5SyiCAoMXd6wCsTKdRRERERHYVpd5lSCwHTGmoB1Csu0zJ4msRERFRgctUT1PIZLsvyX1DIqU8CeCkEOJ2ALcj+wEgERERFbCMBBpSyhCAcDQhPPE+q8Nt6b72w1LKMgCXZPN1iIiIqLClEzSZDbnVAyjXbkRLEzSn0ygiIiIit7EcNAkh/NFaS9UAAkKIhmhgBCBW1NInhKjU1pCzW9gyHUKI24UQXQC2Zfu1iIiIqHC5bu25dLG4JREREdmV8+KWbnLkSNL3S0RERBRjJ27Ip56myQDed7odRERE5ElTpJT7ku2QT0GTAHAWgKNQ1cHfh6rddNTJdpEtPG7exOPmTTxu3sTjlh1jAHwgUwRFeTM8F32j+wBAxU8AgKOpxifJPXjcvInHzZt43LyJxy1rLH2WLAhJREREZAGDJiIiIiIL8jVoOgng/ug1eQePmzfxuHkTj5s38bg5KG8SwYmIiIiyKV97moiIiIgyikETERERkQUMmoiIiIgsyJs6TZroosLh6E1fdCFhcpnocQKA6QCQuLgzj6P7CSHapJQVCdt43FxKCNEAoDt6s1dK2aq7j8fNhYQQVQB8UMdmOoB6KWVYdz+PW47lVSK4diLWfnGEEOUAliSekMlZQogGKWWt7nYTAL92AuZxdD8hRCWAFiml0G3jcXMhIYQPwPMArpJShoUQAQAd2rHjcXOn6HFp1oKk6HFcI6Vcorufxy3H8i1oOgzg3IRIXOr/sZOzon/4LVB/3OHotgCADgDTpZQhHkd3ix7DpQCaEoImHjcXin4p6db3QgghyqWUwejPPG4uZNKTG9vG4+aMvMlpEkL4obonwwb3lee+RZTEPAB+3e1Q9NrH4+gJSwGs02/gcXO1KgCtQgi/dix0AROPm3uFhRBt0S8p2rEK6X7mcXNA3gRNiD8J64WhxoTJBaSUYSnlOCllp26z9kceAo+jq0X/IQcN7uJxc6HoyRUAAlDHISSEaNKdWHnc3OtWqONzOJqPVq4beuNxc0g+BU1megGUON0ISqoOQLXRtyYdHkd38EkpQ6l3i+Fxc5Z2cg1LKTujx64Waog8GR43h0X/HzYAaAVQA2CJ1uuUBI9blhVC0MRfIBeLfoN6UkrZnGJXHkeHCSGq9DOuLOJxc4d27YfoydiXYhiHx81h0f+NoWji93SoY9KR4mE8blmWT0GT2bdfX5L7yEHRGVhxCargcXSlaLJ+e5JdeNzcyeyzD0P1QvG4uZAuZykIAFLKkJRyLlSeUyV43ByTN3WaorOuwkIIf+LwgfaLR+6hS0htjt72ASjhcXStEgABXe/EdCA27TkkpWzlcXOf6N+TliuozyP0AWjn35tr+TFQf0mvCeD5zkn51NMEAPUYSCrWejJSDftQjkV7LQIAOqMzevxQM3x6o7vwOLqMlDIopWzULhj4592oG7LjcXOnWgDLtBvR4xLUTcbgcXOZaOATMMhhmsu/N2flVZ0mYOCbb/TmfH0RRXJe9J/A2zCY4WFQKJHH0YWi/5yXAagE0AigTTeFncfNhXSVpQFgfOJx4XFzn+j/yjoAhzAwKy5W7DK6D49bjuVd0ERERESUDfk2PEdERESUFQyaiIiIiCxg0ERERERkAYMmIiIiIgsYNBERERFZwKCJiIiIyAIGTUREREQWMGgiooImhPBZWD2eiIhBExEVvDqotb6IiJJi0EREhS6gW4eNiMgUgyYiKlhCiHIAbU63g4i8gUETERWyJQBaU+5FRAQGTURU2PxSylDq3YiIgNOcbgARUSpCiACAeQCmA9gOIAigKnp3WErZnMZzVgJoSXLffADdAELRS6+UMmy78USUN9jTRESuFi0HUC6lbJZS1gJYA6BOStkY3aU2zadeBmCdwetVAaiQUtZGgzEfVPA0L83XIaI8wZ4mInK7Kl2ApOmOXncCqE7zeX2JPUdCCD+ABgDn6jaHAUBKGUzzdYgoTzBoIiK3iyVqR4MaH6I9RImBTPT+SqjhtPkAmoxylqK9SU0Gr9UEIJgQTFVABWdEVOAYNBGRqyUEPeUAQklyi1qklHMBQAgRBPA8gLkG+y2RUlYYbC+HmlGnF4DKoSKiAsecJiLykgoklAjQlkCJJovHRAMrX7T3KXH/cOIT6/ZL7FViLSciAsCgiYhcLjqUpqmEmj0Xu0/X62SWqB1IuG02NAcgvmcrWvwSUsqgECKQGJgRUWFh0ERErhUNmBqiP1dCN0xmsMiuD0BvwrYwgJKEbRVGSd3RYCmkBUbR56+Gyo8C1Aw+5jYRFTDmNBGRmwUBNEeDp3aoIKZWCAEAJQn1mcIYHCD5oAukokNwyYpZLgFQLYToAAAp5RIhREv09RkwERU4IaV0ug1EREMW7SFaoyWCR7cdBjBXG3ITQjQAeJI9RkSUDg7PEVFeiAZCPu12dHgtlDD7LsCAiYjSxeE5IsonS6K9Sduh6jTFygdEe6IYMBFR2jg8R0QFQQjRBKCBC/QSUbo4PEdEhaKEARMRDQV7moiIiIgsYE8TERERkQUMmoiIiIgsYNBEREREZAGDJiIiIiILGDQRERERWcCgiYiIiMgCBk1EREREFjBoIiIiIrKAQRMRERGRBf8PtzmB5N9cKOUAAAAASUVORK5CYII=\n", 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" ] @@ -256,7 +268,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -287,7 +299,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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s+w075541s8Qchw6gCZTaXRekGeaWtZ36xbuflPzO7K7izDIBjW3OQcrMhiQNOec2Fbhkt6RVWdcPOudekbRdmgla+f7za9zMDnj37c0KUwmla6aG5jp2AI3Nz3JdkDYFBzbdGaqrOLNMQOOqaI2Uc65L6aW8RJ5zfWY26GOZb0RXlwXl3Y8QBTSxUrNMkv/luiDNMDNdxXccGpKTZt2bruJAc6p0sXlXgeMJpZcCSzKzIefcpszMlKRrarCyOecWSsred9zq53sA1Idyv7suu8llMZnrMrvrcsewvEjdE4DGVatdexeUp56qkKwde3527u2WtCfMoABEm99ZpiC76/76L7oDN8Ok7glARq36SPkOUSHskxTL+llRwe8CUCVBejgF2V0Xthlmpu7p63fdrHtWLyVEAU2q0kFqpMDxeJFzc2Jmk2aWyvxIuliJ7wFQXUGKwrN3zRWTuY5mmADCqujSnpmNOOcSzrkuMxvJOTdYye8G0FiCvLvu/juWB95dx3IdgDDKGaQKLdftk9QnKdNHalPm1wCQUWonXpB314XdXUebAgBBlaMhZ5ekTZIekpR5qfCbmQJxrxfUzqw+U71BmnECaHx+duIFeXfdhq90sLsOQFU4s3wT343DOdcmKZlMJtXW1lbr4QDIUWgnXmauKFOjNJaa0PP/ctbXu+va267WOvnpOQUAuVKplGKxmCTFvJrrvAhSACrGz+tZNv7olyVbDxz95tdmds35mb0CgLnyG6Rq1UcKQIMr9+tZ/nbLXZIoCgcQLQQpAGVXidezZKMoHEBU1KohJ4AGFaRxZtDXswBA1BCkAAQyNW06PjyuF94+q+PD45qanh2ZgizXfef+L2vZogVFr819PQsARAlLewB881P3FGS5LvN6lh2HhiTl7/eU7/UsABAVzEgB8CVT95QdoqSrdU8vnRyV5H8ZLnMdr2cBUM9ofwCgpCBtCiQFbmkg0e8JQLT4bX/AjBSAstY9ZZbrnK4uz2VkjuVbrsvsxPv6XTfrntVLCVEA6gI1UkCTK3fdkyRezwKgaRCkgAblZ6nMb7+nMG0KaJwJoBkQpIAG5GeWqVS/Jynd7+nOFXF95/4v67V//aRk3VNumwIaZwJodNRIAQ1kLDWhgdeG9Xie3XWjyQk9fmhIA68Nayw1UZW6JwBodAQpoIH89Ncfae+x00Wv2XvstH76649C1z3RpgAArmJpD6gjpeqeujv8tfjo7mjT4P/9N1/XUvcEAIURpICIG0tNaOzipH71/nkNvD4yazlu2aIF2nZvl9bftkztrQt1/tKkr3uevzRJ3RMAlAFBCoi4wyfO6OlX38t77vylKzNLed+674v67aeXfd3z7Y8T+sY9t/J6FgCYI2qkgIjb2tvp68W+W3s7A78EmLonAJgbZqSAiPtw/LKv3XUfjl/WPauXBp5lou4JAMIjSAE1VqqAfOziRJHffVXmujBdxal7AoBwCFJADflpnNne2lLot8+SfR2zTABQHQQpoAbGUhN6/l/O5u35lGmc+b0Nt+vBP75Z61YtUUesReeSE3m7kDulZ5vWrVoy6zizTABQeRSbAzUQpHHm+KVJPbr+1rwhSkrXQT26/laN+2x9AAAoH2akgAooZ+PMYu0PMvYeO63fX5nSt/u/NKdxAwCCIUgBZean7unK1LSve12ZmtbDX12p/u4bJaUD2ju/S+rTy59r8Q3X6Y6bYjMBrb11YZn/TQAApRCkAJ9K7q4LUPcUpIC8vS39k3FnZ3zO/y4AgPIgSAE++Jll+umvP9Lf/fz9ovfZe+y0Lk7+P/3Xvi+FKiAHAEQLxeZACS+dHNWOQ0OzQpQknUtOaMehIb10clRSsLonCsgBoDEQpIAiRhOf6YnnT+YNPOb9PPH8SY0mPgtU93T4xBlfu/YOnzgTeMwAgOphaQ9NrVTd0w9eftfX61l+8PK76vujG31954L58yggB4AGQZBC0/JT93RXZ1z/OHS25L3u6ozr1GjK1/eeGk1pw1c6KCAHgAbA0h6akt+6p9vaW33d77b2Vn3jT27R9zbcXvS67224Xd/4k1vCDRoAEDkEKTSdIHVPty69QcsWLSh6v2WLFujWpTeova1F2/50tf7+kR51xGa3N+iItejvH+nRtj9dPWsmCgBQ31jaQ9MJUve0YvENvq792Zsfz3QV54XBANA8CFJoOkHqnv7DHcvV332jfvX+eQ28PjIrVC1btEDb7u3S+tuWXVMUzguDAaA5EKTQcErtxAtS95TpKr7m5pj+871dzDIBAGYhSKGh+NmJl6l7KrZkl6l7ysYsEwAgF8XmaAhjqQkNvDasx/PsxMu8527gtWGNpSb0szc/9l33BABAMcxIoSEEec/dN/7kllB1TwAA5CJIoSEEec8ddU8AgHIhSKEhBHnPXTbqngAAc0GQQl0otROvvdVfk0u/1wEA4EddBCnn3Kbsz2b2XK3GguoZS01o7OKkr1qmsDvxAACYi8gHKedcXFKXmR3wPh+URJBqAodPnNHTr76X99z5S1e099hpSdK37vvizLFicjuQAwAwV5EPUmaWcM5td84NmtmQpEStx4Tq2NrbqcMnPio5y7S1t1Pz5zl24gEAqq4sQcqbNdoiabOZ9ec5v1NXA1A8M7sUwC5Jv3HODUm6bw5DRR35cPyyr1mmD8cv657VS9mJBwCoujkHKedcj6S1kuKSluQ5v1OSzGzA+9znnDtoZtsDfE2vpLsl7Zf0qvdrNLixixOlL8pzHTvxAADVMucg5S23DeUWhGfZLWlV1vWDzrlXJG2XZoJWvr/1xs3sgHffV7zv6XfOHXTO9ZnZ4FzHjtpiJx4AoN5VtEbKOdel9FJeIs+5PjMb9LHMt0TSSNbnVyRdKPKdCyVlF8L4e0MtqiLITrx1q5aoI9aic8kJWZ57OUnLY+kABgBALVS62LyrwPGE0kuBJZnZgHNup3NurXfogjc7VchuSXt8jxBVFWQn3rf7v6Q9G7u149CQnDQrTGXmrfZs7Kb+CQBQM7V6afEF5amnKsTMDpjZgPdTqvXBPkmxrJ8V4YeJctva26llixYUvSazE28sNaEVi2/Q7g23a2nO71m6aIF2b7hdKxbfoLGUv1oqAADKrVbtDyq2FmNmk5ImM5+dY7YiSoLsxPv1yHig2SsAAKqt0kFqpMDxeJFzaGBBduI9/NWV6u++seS19IYCANRKRYOUmY045xLOuS4zG8k5x667BlTOnXjtbekfAACiqpxBqtBy3T5JfZIyfaQ2ZX6NxvLSyVE9efSURpNXZ506Yi3as7FbD6zpkCR24gEAGsqci82dc11eL6jtknqcc/uze0p57Q3izrlN3vHegM04EXFjqQkNvDasxw8NzQpRkjSanNDjh4Y08NqwxlITGr80qUfX35o3REnpnXmPrr9V45cmC1wBAEB0OLNCf6U1Budcm6RkMplUW1tbrYfTkP725Xf1dz9/v+R1/+XPbtM85woWkGejgBwAUEupVEqxWEySYmaWKnRd5F9ajOjr7vAXULs72nT3LYtnCsinpk3v/C6pTy9/rsU3XKc7borN1FNRQA4AqAcEKczZlalp39flFpDf2Rmv0KgAAKi8WjXkRAPhnXgAgGZFkMKcZXbiFWp96pTevcdOPABAoyFIoaSpadPx4XG98PZZHR8e19T07A0K7MQDADQraqSQ11hqQmMXJ/Wr989r4PWRWa91WbZogbbd26X1ty1Te+vCoi8izth77LR+f2WKnXgAgIZC+wPk9T9e+VffbQoe/upKjV1MzzaV2olHp3IAQD2g/QHmZGtvpw6f+KjoC4aXLVqgrb2d7MQDADQtaqSQ14fjl4uGKEk6f+mKPhy/XKURAQAQPQQp5DV2caL0RQGuAwCgERGkkBe9oQAAKI0ghbzoDQUAQGkEKeRFbygAAEojSCGvwyfOaO+x00Wv2XvstA6fOFOlEQEAED20P0BeD391pfq7b5RUujcUAADNiiCFvOgNBQBAaSztAQAAhESQAgAACImlvSY1NW1644MLGrs4ofbWdBuDTN0TAADwhyDVRMZSExq7OKlfvX9eA6+PzHoFzLJFC7Tt3i6tv20ZLxcGAMAnglQTOXzijJ5+9b28585fujLT7uBb931R3+7/UjWHBgBAXaJGqols7e3UskULil6zbNECbe3trNKIAACobwSpJvLh+OVZy3n5nL90RR+OX67SiAAAqG8EqSYydnGirNcBANDsCFJNpL3VXwG53+sAAGh2BKkmsm7VEnXEWlSoyYGT1BFLt0IAAAClEaSayPilST26/lZZgfMm6dH1t2r80mQ1hwUAQN0iSDWRwyfOzLQ4KGTvsdM6fOJMlUYEAEB9o49UE3n4qyvV332jpHRn83d+l9Snlz/X4huu0x03xWY6m7e3LqzlMAEAqBsEqSbS3tYyq2P5nZ3x2g0GAIAGwNIeAABASAQpAACAkAhSAAAAIRGkAAAAQiJIAQAAhESQAgAACIkgBQAAEBJBCgAAICSCFAAAQEgEKQAAgJAIUgAAACERpAAAAEKK1EuLnXNxSdskycwOlDoOAABQS1GbkeqTtDTAcQAAgJqJVJAys+ckDfs9DgAAUEuBlva8JbYtkjabWX+e8zslJbyPcZbhqmtq2vTGBxc0dnFC7a0tWrdqiebPc7UeFgAADct3kHLO9UhaKykuaUme8zslycwGvM99zrmDZra9PENFPmOpCY1dnNSv3j+vgddHdP7SlZlzyxYt0LZ7u7T+tmVqb12o9raWGo4UAIDG4ztImdmQpCHn3KYCl+yWtCrr+kHn3CuStkszQStfndM4M1fhHT5xRk+/+l7ec+cvXdHeY6clSd+674v6dv+Xqjk0AAAaXll27TnnupReykvkOddnZoPVCkvOuYWSFmYdaq3G99bK1t5OHT7x0ayZqFzLFi3Q1t7OKo4KAIDmUK5i864CxxNKLwX64pzrk9QvqT975qvQ8QJ2S0pm/fzW7/fXow/HLxcNUVJ6ZurD8ctVGhEAAM2j0n2kLihPPVUhZjYoadDv8QL2Sfph1udWNXCYGrs4UdbrAACAf5UOUr5DVLmY2aSkycxn5xp711p7q78Ccr/XAQAA/8q1tDdS4Hi8yDmUwbpVS9QRa1GhuOgkdcTSrRAAAEB5lSVImdmIpIRXdJ57zu+SHEIYvzSpR9ffKitw3iQ9uv5WjV+aLHAFAAAIK0yQKjS1sU/pV7lIkryi8IEwg4J/h0+cmWlxUMjeY6d1+MSZKo0IAIDm4cwKzWXkXJiebdok6SFJPZIOSHrTe31L5pqdurqU12tmu8o73OCcc22SkslkUm1tbbUeTtllGnJK6c7m7/wuqU8vf67FN1ynO26KzXQ2pyEnAAD+pVIpxWIxSYqZWarQdb6DVL1q9CAFAADKz2+QitRLiwEAAOoJQQoAACAkghQAAEBIBCkAAICQCFIAAAAhEaQAAABCIkgBAACERJACAAAIiSAFAAAQEkEKAAAgJIIUAABASAQpAACAkAhSAAAAIRGkAAAAQiJIAQAAhESQAgAACIkgBQAAEBJBCgAAICSCFAAAQEgEKQAAgJAIUgAAACERpAAAAEIiSAEAAIREkAIAAAiJIAUAABASQQoAACAkghQAAEBIBCkAAICQCFIAAAAhEaQAAABCIkgBAACERJACAAAIiSAFAAAQEkEKAAAgJIIUAABASAQpAACAkAhSAAAAIRGkAAAAQiJIAQAAhPSFWg8AxU1Nm9744ILGLk6ovbVF61Yt0fx5rtbDAgAAIkhF2ksnR/Xk0VMaTU7MHOuItWjPxm49sKajhiMDAABSxJb2nHNx59xO59zOAuc3Oef6qj2uahtLTWjgtWE9fmhoVoiSpNHkhB4/NKSB14Y1lpoocAcAAFANkQpSkvokLc13wjkXl7RbUryK46mJn/76I+09drroNXuPndZPf/1RlUYEAADyiVSQMrPnJA0XOL1F0j9UcTg1093RVtbrAABAZQSqkfJmhbZI2mxm/XnO75SU8D7GzezAXAfo3bdH0qCkTeW4X9RdmZou63UAAKAyfM9IeWFmi9JLa0vynN8pSWY2YGYDkoaccwfLNM4uMxsp070ir721pazXAQCAyvA9I2VmQ0qHo0KzQrslrcq6ftA594qk7dJM0MpX/zRebObK+30j3vf2SlrtnBvxxtOQ1q1aoo5Yi84lJ2R5zjtJy2PpVggAAKB2ytL+wDnXpfRSXiLPuT4zGwy7zJf9+5xzvZLebOQQJUnz5znt2ditHYeG5KRZYSrTQWrPxm76SQEAUGPlKjbvKnA8oQC77LzWBv2S+nNnvrxzfZIe8oJboXssdM61ZX4ktfr9/qgYS01oxeIbtHvD7Vq6aMGsc0sXLdDuDbdrxeIbaH8AAECNVboh5wXlqacqxMwGlS4qL3Tubh+32S1pj9/vjKLDJ87o6Vffy3vu/KUrM60RvnXfF/Xt/i9Vc2gAACBLpYNULYp49kn6YdbnVkm/rcE4Qnv4qyvV331jyevaWxdWYTQAAKCQcgWpQjvq4kXOVYSZTUqazHx2rv7qiNrbWtTexo48AACiriw1Ul5rgkS+2iVvSQ4AAKDhhAlShZbr9ildDC4p/V48SQNhBlVJU9Om48PjeuHtszo+PK6p6XwNBgAAAEpzZv6ChDfbtEnSQ5J6JB1QuhXBc1nX7NTVpbxeM9tV3uEG5+3cSyaTSf3qzO/15NFTs14E3BFr0Z6N3XpgTUftBgkAACIllUopFotJUszMUoWu8x2k6lUmSD13/F195/n3rmlwmamgeuaRHsIUAACQ5D9IReqlxZX0N8dO5+0Snjn25NFTLPMBAIBAmiZI/VtqsuA5kzSanNCRt85Ub0AAAKDuNU2Q8uMnr39Q6yEAAIA6QpDK8ti9q0pfBAAA4Kl0Z/PIuLFtoc5PKm+dlJO0PNaizWtXVmUsU9OmNz64oLGLE2pvbdG6VUt4ATEAAHWoaYLUdzfcru88/56cZoepTHzZs7G7KmHmpZOjtGAAAKBBNM3SXn/3cj3zSI+Wx2a/emV5rKVqrQ9eOjmqHYeGZoUoSTqXnNCOQ0N66eRoxccAAADKp2n6SCWTSbW1tdVsWW1q2vS1/T+/JkTNjFPpUPfLXX/GMh8AADXmt49U0yztZcyf53TP6qW+ri1n6Dry1pmCIUqa3YJh67pbQn0HAACorqYLUn6Vu5bJb2uFn7z+AUEKAIA60TQ1UkFUopbJb2sFWjAAAFA/CFI5pqZNTx49VfbXyWxeu1IdsRYVWhh0Ss94VasFAwAAmDuCVI4gtUzZpqZNx4fH9cLbZ3V8ePyaoDV/ntOejd2SdE2YqnYLBgAAUB7USOUIU8vkt57qgTUdeuaRnmuuXU4fKQAA6lLTtT8o5WdvfKTv/tPJktf9zV+u0dZ1t8zUU+X+XzEzr5SvRxWdzQEAiDa/7Q9Y2ssRpJYpbD1VpgXD1++6WfesXkqIAgCgThGkcgSpZQpbTwUAABoDQSqPTC1TqdfJBKmnAgAAjYdi8wIeWNOh/u7lRWuZHrt3la96KnpDAQDQmAhSRZR6nczmtSv19Kvv61xyIm+dVOb9efSGAgCgMbG0Nwf0hgIAoLkRpObIbz0VAABoPPSRKhN6QwEA0Dj89pGiRqpMStVTAQCAxsPSHgAAQEgEKQAAgJAIUgAAACERpAAAAEIiSAEAAIREkAIAAAiJIAUAABBS0/SRSqUK9tICAACYxW9uaIbO5jdL+m2txwEAAOrSCjM7W+hkMwQpJ+kmSRezDrcqHa5W5BxHtPHc6hPPrT7x3OoTz628WiX9zoqEpYZf2vP+5WclyXS2kiRdLPb+HEQLz60+8dzqE8+tPvHcyq7k/w0pNgcAAAiJIAUAABBSswapSUlPev9E/eC51SeeW33iudUnnluVNXyxOQAAQKU064wUAADAnBGkAAAAQiJIAQAAhESQAgAACKnhG3Lmcs7tlJTwPsbN7EANh4M8nHNxSVskbTaz/jzneYYR5T0bSVotSWa2Pc/5hPeRZxcBWf97k9LPrUvSY2aWyLqG5xZxzrlXcv+85LlVR1PNSGX+kDezATMbkDTknDtY42Ehi3OuR+k/1OOSluQ5zzOMKOfcfjM74P1s9469knWeZxdN+yUNes9ll6QLko5kTvLcos85t0lSX84xnluVNFX7A+fcp5JW5fyXlpmZK/y7UAveHwy7zezunOM8wwjyZjWOKD2LmPCO9Uj6jaTVZjbCs4smL+y+kpmt8P4C3m1mi73PPLcIy5pRPJj9THhu1dM0M1LOuS6lpzYTec71Xfs7EDU8w8hbq/SyUMaI9884zy66zKw/Z8mnV9KgxP/m6sQWSc9mH+C5VVcz1Uh1FTieUHoZCdHHM4wo7w/sxTmHM39gjygdsvJJiGcXGd5McFzSZu8Q/5uLMC8UDeY5xXOromYKUoVcUJ5aHNQVnmE07Za03cwSWW+kz8Wzi4Cs5aG4pCP5ZjJy8NyiIe4tm8d9Xs9zq4CmWdorgv+nqn88w4hxzu2X9A9ekWsxPLsIMLOEV5ScqZP6tMRfzjy3GnPObTOz5wL+Np5bBTRTkBopcDxe5ByihWdYB7zloeGcuhueXQQ55+LOuf05oWlQ6efSJ55bJHkbOd4qcgnPrYqaJkiZ2YikhFeEl3su3xozIoZnGH2ZQtbMTJT3F3UXzy6yuiTt1OyZirj3zwTPLbKWSOpzzu30dlnul9I7Lp1zm3hu1dU0QcqzT1m9Nrz/ci619IDaKDQFzTOMKO+/knuU7lfT5f0hvk3pugyJZxc5ZjYk6YD3F2/GQ5KGsv7C5blFjJkNZvVsOyDpoHf8QNZyH8+tSpqqj5Q00yMl84dGr9eADhHh/eW7Sek/zHskHZD0ZnYtAM8weryloQ+UZ0dQTm8bnl3EeM9uW9ah1ZJ25elsznOLIC8gPaT0n5sHlO4JlmlfwXOrgqYLUgAAAOXSbEt7AAAAZUOQAgAACIkgBQAAEBJBCgAAICSCFAAAQEgEKQAAgJAIUgAAACERpAAgD+/1NvFajwNAtBGkACC/3Uq/iw4ACiJIAUB+Pd676ACgIIIUAORwzvVJeqXW4wAQfQQpALjWZknPlbwKQNMjSAHAtbrMbKTWgwAQfV+o9QAAICznXI+ktZJWS3pT0qCkbd7phJkNhLjnJklHipzrlTQsacT7uWBmicCDB9AQmJECUJe81gR9ZjZgZrsk/UTSbjM74F2yK+StH5L0bJ7v2yap38x2eQEtrnSgWhvyewA0AGakANSrbVmhKWPY++eQpO0h7xvPnWFyznVJ2i9pVdbhhCSZ2WDI7wHQAAhSAOrVTDG4F3Ti8maScsONd36T0ktxvZIO5quB8madDub5roOSBnMCVr/SgQ1AEyNIAahLOUGoT9JIkVqlI2Z2tyQ55wYlvSrp7jzXbTaz/jzH+5TeyZetR+maLABNjBopAI2gXzntCjKvd/EK0md4YSvuzVLlXp/IvXHWdbmzT/SaAkCQAlCfvGW4jE1K79qbOZc1O1WoGLwn53OhZT1Js2fAvIadMrNB51xPblgD0DwIUgDqjhei9nu/3qSsJbY8LxqOS7qQcywhaUnOsf58heNegBrJhCXv/tuVrreS0jsHqZUCmhQ1UgDq0aCkAS9QvaV0sNnlnJOkJTn9oxK6NjTFlRWuvOW7Yg04N0va7pz7jSSZ2Wbn3BHv+wlRQBNzZlbrMQBAxXgzST/JFJt7xz6VdHdmuc45t1/SPzCzBCAolvYANDQvHMUzn72luZGcXX89hCgAYbC0B6AZbPZmnd5Uuo/UTCsDb8aKEAUgFJb2ADQ159xBSft5STGAMFjaA9DslhCiAITFjBQAAEBIzEgBAACERJACAAAIiSAFAAAQEkEKAAAgJIIUAABASAQpAACAkAhSAAAAIRGkAAAAQvr/dFOruH2oGGEAAAAASUVORK5CYII=\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -364,10 +376,18 @@ "symmetrised_correlator.show([5, 20], comp=[first_derivative, second_derivative], y_range=[-500, 1300])" ] }, + { + "cell_type": "markdown", + "id": "7fcbcac4", + "metadata": {}, + "source": [ + "There is a range of addtional methods of the `Corr` class which can be found in the documentation." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "ff177781", + "id": "2fbe1263", "metadata": {}, "outputs": [], "source": [] @@ -375,7 +395,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -389,7 +409,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/03_pcac_example.ipynb b/examples/03_pcac_example.ipynb index 595a1be5..9b163317 100644 --- a/examples/03_pcac_example.ipynb +++ b/examples/03_pcac_example.ipynb @@ -25,34 +25,60 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Primary observables" + "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can load data from preprocessed pickle files which contain a list of `pyerror` `Obs`:" + "## Loading data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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 parameters `padding_front` and `padding_back` 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." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "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 11:27:27 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n", + "\n", + "Description: SF correlation function f_A on a test ensemble\n", + "Data has been written using pyerrors 2.0.0.\n", + "Format version 0.1\n", + "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", + "\n", + "Description: SF correlation function f_P on a test ensemble\n" + ] + } + ], "source": [ "p_obs_names = [r'f_A', r'f_P']\n", "\n", "p_obs = {}\n", "for i, item in enumerate(p_obs_names):\n", - " p_obs[item] = pe.load_object('./data/B1k2_' + item + '.p') " + " tmp_data = pe.input.json.load_json(\"./data/\" + item)\n", + " p_obs[item] = pe.Corr(tmp_data, padding_front=1, padding_back=1)\n", + " p_obs[item].tag = item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now use the `pyerrors` function `plot_corrs` to have a quick look at the data we just read in " + "We can now use the method `Corr.show` to have a quick look at the data we just read in " ] }, { @@ -62,7 +88,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -74,23 +100,26 @@ } ], "source": [ - "pe.plot_corrs([p_obs['f_A'], p_obs['f_P']])" + "p_obs['f_A'].show(comp=p_obs['f_P'], y_range=[-0.8, 8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Secondary observables" + "## Constructing the PCAC mass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "One way of generating secondary observables is to write the desired math operations as for standard floats. `pyerrors` currently supports the basic arithmetic operations as well as numpy's basic trigonometric functions.\n", + "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 start by looking at the unimproved pcac mass $am=\\tilde{\\partial}_0 f_\\mathrm{A}/2 f_\\mathrm{P}$" + "We now need to obtain the first derivative of f_A and the second derivative of f_P" ] }, { @@ -99,16 +128,8 @@ "metadata": {}, "outputs": [], "source": [ - "uimpr_mass = []\n", - "for i in range(1, len(p_obs['f_A']) - 1):\n", - " uimpr_mass.append((p_obs['f_A'][i + 1] - p_obs['f_A'][i - 1]) / 2 / (2 * p_obs['f_P'][i]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more complicated secondary obsevables or secondary observables we use over and over again it is often useful to define a dedicated function for it. Here is an example for the improved pcac mass" + "first_deriv_fA = p_obs['f_A'].deriv()\n", + "first_deriv_fA.tag = r\"First derivative of f_A\"" ] }, { @@ -117,15 +138,15 @@ "metadata": {}, "outputs": [], "source": [ - "def pcac_mass(data, ca=0, **kwargs):\n", - " return ((data[1] - data[0]) / 2. + ca * (data[2] - 2 * data[3] + data[4])) / 2. / data[3]" + "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": [ - "Now we can construct the derived observable `pcac_mass` from the primary ones. Note the additional argument `ca` with which we can provide a value for the $\\mathrm{O}(a)$ improvement coefficient of the axial vector current." + "We can use these to obtain the unimproved PCAC mass:" ] }, { @@ -134,17 +155,15 @@ "metadata": {}, "outputs": [], "source": [ - "impr_mass = []\n", - "for i in range(1, len(p_obs['f_A']) - 1):\n", - " impr_mass.append(pcac_mass([p_obs['f_A'][i - 1], p_obs['f_A'][i + 1], p_obs['f_P'][i - 1],\n", - " p_obs['f_P'][i], p_obs['f_P'][i + 1]], ca=-0.03888694628624465))" + "am_pcac = first_deriv_fA / 2 / p_obs['f_P']\n", + "am_pcac.tag = \"Unimproved PCAC mass\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To calculate the error of an observable we use the `gamma_method`. Let us have a look at the docstring" + "And with the inclusion of the improvement coefficient $c_\\mathrm{A}$ also the $\\mathrm{O}(a)$ improved PCAC mass:" ] }, { @@ -153,32 +172,52 @@ "metadata": {}, "outputs": [], "source": [ - "?pe.Obs.gamma_method" + "cA = -0.03888694628624465\n", + "am_pcac_impr = (first_deriv_fA + cA * second_deriv_fP) / 2 / p_obs['f_P']\n", + "am_pcac_impr.tag = \"Improved PCAC mass\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can apply the `gamma_method` to the pcac mass on every time slice for both the unimproved and the improved mass." + "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": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "masses = [uimpr_mass, impr_mass]\n", - "for i, item in enumerate(masses):\n", - " [o.gamma_method() for o in item]" + "am_pcac_impr.show(comp=am_pcac)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now have a look at the result by plotting the two lists of `Obs`" + "## Plateau values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now construct a plateau as a derived observable from the masses." ] }, { @@ -187,122 +226,41 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "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": [ - "pe.plot_corrs([impr_mass, uimpr_mass], xrange=[0.5, 18.5], label=['Improved pcac mass', 'Unimproved pcac mass'])" + "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": [ - "## Tertiary observables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now construct a plateau as (tertiary) derived observable from the masses. At this point the distinction between primary and secondary observables becomes blurred. We can again and again resample objects into new observables which allows us to modulize the analysis. Note that `np.mean` and similar functions can be applied to the `Obs` as if they were real numbers." + "We can now plot the data with the two plateaus" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.79208242e-03 +/- 2.09091228e-04 +/- 1.90500140e-05 (4.363%)\n", - " t_int\t 1.09826949e+00 +/- 1.84087104e-01 S = 2.00\n" - ] - } - ], - "source": [ - "pcac_plateau = np.mean(impr_mass[6:15])\n", - "pcac_plateau.gamma_method()\n", - "pcac_plateau.print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also use a weighted average with given `plateau_range` (passed to the function as kwarg)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def weighted_plateau(data, **kwargs):\n", - " if 'plateau_range' in kwargs:\n", - " plateau_range = kwargs.get('plateau_range')\n", - " else:\n", - " raise Exception('No range given.')\n", - " \n", - " num = 0\n", - " den = 0\n", - " for i in range(plateau_range[0], plateau_range[1]):\n", - " if data[i].dvalue == 0.0:\n", - " raise Exception('Run gamma_method for input first')\n", - " num += 1 / data[i].dvalue * data[i]\n", - " den += 1 / data[i].dvalue\n", - " return num / den" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.78698515e-03 +/- 2.04149923e-04 +/- 1.85998184e-05 (4.265%)\n", - " t_int\t 1.06605715e+00 +/- 1.79069383e-01 S = 2.00\n" - ] - } - ], - "source": [ - "w_pcac_plateau = weighted_plateau(impr_mass, plateau_range=[6, 15])\n", - "w_pcac_plateau.gamma_method()\n", - "w_pcac_plateau.print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case the two variants of the plateau are almost identical\n", - "\n", - "We can now plot the data with the two plateaus" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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uwqyiRRA7Hguc0RwAPB7IHY9DFi/G2ZKr4qrzuEdi86/2RG2R+IeX9+ILi2bF1SKR+/sWbB59B/fhAQhISL8RGAIeAAKbRx/D2O/X4twXHzRc7qenVcPHrSzNNVxusJS+z7KpXJPeZ2adN7G8GmLRet+27bODmPGb7+L8V38Kz5xlvv1yVilkCl9vSh+j728urB3Fa6+9hpkzZ6a7GpRFLqpHcBWA3bt3Y+8nn6WkTDH/h5id81vc3P8sXp/9DfSXfgnygAQO/DruslaefgUrP3s15nF75nwNe+bdFlfZlxb+Ob50oBXyxTsh1j3oa6mTOx4HDryG3y2swdHfbIurzJ6hHJwaivwZlABODo7in9teQ3nhuOFy88fmY0b5zagZOoK2U/PRPzbxI65MG8Mdl5yEKLwZvzldjJFfG3+de4ZyAMT+zjj4Rxd+/Ynx+voT0oPZvb/FVQBOvPdL7Dt0GlKkZviuGe/fVJdr1vvM7PMGALNHTuIWAG/vP4n+fP8gLsExdZRxzp07Z+g4Bl9R5ObmoqioKN3VoCxybkTrtpmRPwMzU/jeGRy9EugHBouvRGGxknA5p/L/HGrZxDixWeeP4Vr3P+MD+9/i7IyFvv2j0xUU5cZX/8GiP4NrxkxUfPocZvi11I3kX4J9yx7AYMn1iPcVGRsxNqh+bPpMFBXF00pdhHEswudLgavsEr89AbxwELhzGfCl+bmwiSUYB5DrvRn1+UIJ5QigRumtnJ0HfH7BTNgSaFW/pG8nKo4+hxkjWuvlzf3P4vz517Hv0rtxquT6GLljkB7M6tcGlS+WJ3C28EogRUFdKj8XZrzPzD5vulk5s7T7WbMwPou/LZPR2NiYoeMYfEWRn5/Pli+K6vzgGVw82+fbnnb+lO9+bGDiZ3v6rBLMKJqb0GN4pMT+iwX41fgaHL5YgGtnzEj8B2DmTIxhIsgaH9LG+4zPLsdYYblvv7F2gFBDM/8M7y1ch+nu1/GlI/+C3y76Di7abwZETkLllRaOA4h9xWFpYT5mzkx8/NsV88aBgyO4Yl4+CmYlN47uGxVj+KfdoxHT774yDwWz4v/qnXv6Xaw4+BPg8luAdT/wtfjk73gMjgM/wd4V9Tgzb01CdZ57+l0scz+DfO/7d/2ppzEy+CoO2u9JuEx/Y3naZyE3Lzcl36mpfp8B5p03f/nj2uctPz8fY/xtmZTY8kXkRx3xQB2N3Yqi5Ako+XH8t//xNmwcag/Z/Zen/ifgd8HVbwqrgeu+Ybxcr10nx/Dc/gvoH50H4LtADzD76HncfWUuVpVl6MdX5OCzmVog99nMchSJJIKiEhtm5wn0Rzl3JfkCV5Ska77oUKvKpuG/XgPveZuod0m+wF3LEzxvchzL3M8Al98SONZp0WqIr78A+eKdWOb+V5yZez0Q5+s99/S7WLG3KSSoy9vxGFbsbUoqqDNVCt9ngEnnjSgCvptoSuj+5DRch0/HPM6xZB5uWh7HQPPLNuDls7G7e6bPin9Rh10nw/8n3j8q8U+7R/Ffr8Gk/0HIv9CPby89h8c+Urx7/Fv8tB/Iv/lcP/IvzMSFvEQWzjDHqrJpcFySgw8OHsayT1/EwcVfx7XLliTcYqmo+7RWqXU/CDu/lVj3IPKfroSi7oM6+yrjBZsY1GWjVJ83okgm9zc3kdfdOW/gv+dtjXncRzmbcBJ3GS53RtHchLsTo/FIief2R5/m4PmPLsBxSc6k/mFYcPw1fPHwi1g2fTUeufhNnMDEZf7z8Rk2T/93bPjkfXwiv45Plt6ZxpqGsgmBywouYGPOu3i54K+SOk+5F/q1P2LMb+U7ziDTgjo/Hinx8XAufjW+BgeHc3GtlBn9nk3leSOKhMEXTQl9izdgV9kNvu2zZw5j/eEnsX3J/Zg1d4lv/4XcSGuvW+tPfZ6oXW0A0Dci8ac+D66cM3lbJI4vuAVn5l6PuQB+Ii/inVOn8VKPxO3lAmsvGUeOuBO7cGfGnDez+J5fjPmt4n0dzArqdFnZbU5kAb77aUq4kFcS0C01OKxdLj6Qfymk30DzTDFgYHxaPMdZKZUtHcHnbY4cx9GeEcwpzcf5oskbdAZTlQqMzLgEeVHmtxqdUQZVqYirXLOCOsD8bvNsa1Ej8sfgiygDFecZ+xExepxV2NJhEpGDg/Z7sGJvU8T5rQ6uqI97XJZZQZ3Z3eZ8n1G2y5xLhIjIR7/KL5pMu8pPb+kI7i7VWzp2nTQ2/w2Fd2beGuxdUY/RIx8AT1cCjZcCT1di9MjuxK9I9AZ1OPAa5It3Akd2AqNDwJGd2vaB13DQ/n/FHdTF020eLzPeZ7mjfSgY6vHdikeOAgCKR44G7M8d7YtREpEx/Bchin//93+POSdNaWkpbr/99oB9L730Enp7Yy/hsmrVKlx33XW+7QsXLuCZZ54xVLe/+Iu/QFnZxEKsPT096Orqiplv+vTp+Ju/+ZuAfdu3b8dHH30UM6/dbkdlZeASmc8++yzOnj0bM++Xv/xlXHnlxLiSvr4+tLeHTtEQzt13342CggLf9ocffoh33303Zr7Zs2fjjjvuCNj361//GkePHsWFs3347WcjOOj6P8gNcyXi1VdfjTVrAn/MWlpaDNX3z//8z7Fo0SLf9pEjR/Af//EfhvLW1mpLlNqEwN1X5uKfdo9419HxC8Sk9qMz99Pt2NJ6BABw6aWX4tZbbw0oq62tDf390cfq5F5Q8X8XjWHGxNsQw8PDeO655wzVt7q6GiUlJX4tHfqiP0GkROuuPuw69ksISMyaNQvf+Ebg1BudnZ1wu91RH29gVOKstANrbgrY/7Of/QwXL16MWV+n04ny8olu5pMnT+JXv/oVBkYl+o+Poe3AtIitiffccw9ycyfmbuvu7sauXbsCjskZH0HO+MS8ZBfPD+AILmDlkqOYNTQRwLyw7fc43jccs77B3xHHi6/DP+7+A5aJT7B89A/4KO/zOCg/B7n7QwAfBuSN5ztidcG1+Nb4O5hzYGJm+NEZZTjoDeri/Y44e3YIwPSYx//y1f/A2yPHARj7jpAQcC38SyBnZuBnws/zH13AtFP78N7vjX9HLDj+Gj53+EUAwNOuCzjwmQfaJ7Yp4PiB4isxUKzVMZHviNwLKspOjuD6spOYs3zifZjId4Tu3XffxYcffhjh6AmJfkcAwJo1a3D11Vf7thP5jtDt378fb7/9dsx8iX5HAMDy5cuxfv36gH3JfkcYcc899xg6DmDwFdW5c+fg8UT/zyzcDPgjIyMYHo79xTo6Gjoewkg+ACH1GhsbM5TX/8dDZ7S++vps/s6ePWsob/Csvx6Px/BzlTLwP9yLFy8aypuXlxey7/z58xgeHsbFc+ehjkicPXseF2RoWcmcm+DnavTcBFtVNg03Tu/BO+fKcHH6RPA5/eIw5p/8HfKGDkEvNdy5OXfuXMzHnX7xHEZnATP89kkp434fTrR0RGitEwIXps3CKU8hCs4dD3uIfm6iOXdBQk4PnXT17NmzuHAh9iLYkd6H5y5IeEbGce5sDnIuGusGGx0dDalv4dBBzBrq8W3nAxiebcP6w08ChyeO6+hdjuGRUkOPEWxo+Cx2nsvFKXUch5VcTJ8Z/p+feL4j3houwPaT1+KK6cexwrMfy277nm/SUiD+74jyoZ0AvhTz+Gmf/QnDUvjqF1z/4MccnrkAF6bNilpm34jE4XPT4/qO0C/sAIB9x7qw9+wx7OvzoKLEhpnTJ94PHuRh3FtuvN8RNkgsm3EKS0psmH3+E0B+wff6Jvododcj0e9vI98RAEKClkS+I3TJPFcj3xGA9n4Nlux3RKox+Ipi5syZMVu+8vPzw+7zb6mJJFxwYCQfANiCLgufNm2aobzTp4f+N2q0vjNmzAjZN2tW9C9C//r5s9lshp9r8OLm06dPN5Q33LmbMWMGCgoKcEFcgDIicGbWDOTOCi0rmXMT/FyNnht/uaN9yL3Qj6tFD8Y/eQ2f2YpQePE0hqbPwxzPIAQkkAuM5+RjPCc/7LmZOXNm2B+IgMe5MIa8oB4lIUTc70OjA/+nFZSgwDYY9n2jn5toxkclhAz9zM2aNSvsPxYhjx/hfTg+KmHLH8PMWdNQYHAcXV5eXkh9bTOWY2TO53zbwxc9eGekGFde4cSCgokX+uLp36PAQMtXpPdhrPev/tz8GXkf9pydh/yBvZgdNGlpvN8R88pXYc6pcXx2wYZILaF5Y8NQ8nPhycnz1S+4/sGPOTLT2FxuoyL03ISjf0f4X9hhUz6CrVhi4OwYbMXTkBfh/RDPd8TqgpP4VtkBzMkZBpALDLRhZOd23woCiXxH+Ncj0e9vI98RQOjvRiLfETqjzzXR7wgg/O9yst8RqSaCWxUIEEIUARhob2/H3Lmpn8OJ0m/wxAFs/NP/g5ev+P9QNP/ydFcnxOcOveDrBonmkyXxzW8VPNN/ybke3+vQN3OiqT3emf73fzaOpvdjLwNUvzo/qakxPhkYxz+8O4J/WJOPzxWn7mrHbCrXI6Vpk4Cm8nMR6WpH3X+9Ji/uwfHZ+D7zX0FA+K0gIHc8Bhx4LXNXEKCEnDlzBtXV1QBQLKUcjHQcW75oysmGS9T9u0EA4PjwOFo+vIDaq3MDWlDinQLgrSNj+FXPRBfCCnEBG/OAlg8vYK+c+FH7i/LpuP0y40tKZ+MyQNkom67yM2O5nmx7n6nnL2L1wegrCHzu4L/iYMEqKDNij5GjySOzPq1EJtt1cgwv7B/BZRdP4zCuQ++h03j+6FzceWV+Rv14Bc9v1ecZx145gr6Z+SgqTPw/8RsXTcO1pRP5x89MB44Af7VsOqrnTjTVK3FOYTFxgUDklo67ludmXJCbTbJxuSl9uZ7tRy7i3/ZdxLcqpmP9oukJvw+y7X12pGcPZo1GX0Gg4EAljvTsgbLy2vRUktIisz6pRCbadXIMH3+4A6/kPY8FuRNXox4XpXjkw7sArMu4H69UU/JtUPyGQwye036k5s0UKEqieyV3tA9/Nqsf+cvz8Iy7CJ9dmChrTu447rEP4guzRnFhdHZGrMEY3P16fNgTcK+Le6F1k1ix3JRZLcI2IbC0OAfARSwtTn45rGxaAPsLyiBwBjFXEPiCMojY14xP0MeExnIhNzM+bxQqc96lRCbySIkT+9/BU7lPAstuAb48Mfai7O3H8NTHT6LuIwHPJesz5r9m08lxzDmnXZk351wPLsryhBdP1i/VXwXgPiGwc/py9EJBKVRcLz5CzicS+CT+MWpmCe5+1bX+MTDAibf71SxmLzeVLd2ZetDxZ7OAddcBHxzpw/yTb+BE2U24dlEJcgSAocwJOqbr09jEWEFgepjpbqLxnxojmkz5vFGozPlUEZnowGcX8aDtOWDZLbDdGTj2wnbnC/C8cCe+f/B5vPzZF7F8bvp/bM029/S7WOZ+RltUGcCXjvwLRs685Lv6Kl7BY9ROD4+j1TtG7YMkxqiZJbj7NZJ4u1/NYuZyU9nUnRkcdNwAADkATr8LnJ44LlOCDrNWEAj+vM08dxQV+5/Aviu/j3MzL/Xtz5TPG4XKjE8UkcmK1H1YIHu1Fq8wYy9sX34QCz6uRJG6D5h7TVrqaBX/q6/gd/VV3o7HsGJvU0JXX5k1Rs0swd2vmc6s5aas6M5MJbMuRDGNSctCBX/edOdmXorhDFyrlkIx+IpiZGQE586dS3c1KAVmj3n/LY4x9mL22OmMPOcjI9J7P4Jz05P4EZQelPf8LOrVV+U9z+DTmVcBIvGxTimrr0XlmiVV9V2UL6HkAmqUOGl2HrAofwTnzhl/nAOqRH+MaZ76RiQ+PHEOlyuJ1z9Vr8M55AM5833bx20SeyVw3AZMy/ErdxxAEp/jVL7PPp31eYwuewAVnz6HGU9PrBAykn8J9i17AKdmfT6pugLANO+koiMjIziXk3nfX1NJuAlew2HwFcWFCxcwOBhxmg7KInOne7sSY429mJ6LMxl4zs+eswGYhbNnz2JQxr8enq707J8wY6Q36tVXM56uRP7JbvTOuiLt9bWqXLOksr5fWzANz36iN9f5BwRaoHDr/BEMD8W3ruHJgWkIXN8g0nHnUWZLfG3ObHs/pLrcwWnLcXDJI5jd+1vc3P8sXp/9DfSXfglS2IAUfN/kjGjD9c+ePYvB8cz7/ppKjMyiDzD4iuqWW24Ju3wQZSHPBpxr2Yr8tx8LHPMFAB4PPG8/jpGZl+KGqvsBW+Z1k+07MYSfHvgAa9euRcX8woTLmbb/InAEMVsA11xlx9iVt4Y/xoBU1ff00ChOD098mckz54ADf8KC5Q4snTuxgsG8glzMKwydcTzdUvU6AMCtAK776Aweff0gTg1NvCZlRfmoryyHc3n8E0KXHlbx4uHY6wLevO4GrF6ixF2+LpWvgxnlWvU++2TvPGDbs5h/w19izYovJFxOMNupPwKfAGvXroXnkqtSVi7Fz2iDDYOvKAoLC1FYmLovCkqzrz0K2fZNeF64E7YvT4y98Lz9OMTHr2HmHT8HipV01zKsWYPaf9+zZs1K7j0573PafYwWwBnzPgck8Tipqu///v0JPPnGxyH7G17+U8D2/Tddhr+rzLyVClJ23rxuX12Ijdd9Di91bcf0HY/i4rqHcLtzPXJsiXWNra8owPziAzg5MIJwQ/UFgLLifKyvuDThxwBS/zqkulyr3mf6Ej8zZsxI7W/LkBYgzpo5M6nPLSXP6KpBDL5o6qjYCHHHz4HXHwb8xl4IZYm2v2JjGitnkSVfBJTFwI7Hga8/H9ICiB1PAMoS7bgMcPcNi1FZcUnM40ozsNXLLDk2gWsumYZlOe/i4CXTkgqKcmwCm2+rwH3PuiCAgABML3XzbRVJPUY24PuMrMbgi6aWio0Qy2/FsTdbsPCdBhxb24iFX6nNuK7G3sER9A5NjIQ+2DsccK8rLcxDaVEcl+3ZcoCbfwy0fRN48S5g3fd9LYDY8QRwYBtwx88z5vUoLcqP7/mlmWnnzUQbVs7HU99w4JFX9uHEwMRg4bLifGy+rQIbVs6PkntyyLb3GWU/S4IvIUQdANW7qUgpm5PNE0+ZQohOKWVlpHSaYmw5OD/vagDQ7jMk0PD33Hufhu0GeWDr7oDthLpBKjZqAVZQCyCUJdr+qdACaBJTz5uJNqycj8qKspR1ZxJRdKYHX94gCVLKVu+2UwjRIqWsTTRPPGUKIaoAOFP8tIhMZXo3SMVGIAtaALNNNndfpbI7k4iis6LlqwHAUn1DStklhOgEEDH4MpDHUJlCCAVA+teYIIqTJd0gWdACmG3YfUWRjHskdp8aw97xNbh4agxLPZIB7hRmavAlhLBD6xJUw6Q5pZRd8eYB4I6jzDsAtAFoSfQ5EBERJWPbnhN+Y+q+C7x5Fo93v5nwmLrgsYX5Z4axDMDB08MYkQO+/Zk0tpACmd3yZY+wXwWgJJjHUJneQC0kuAtHCJEHwL8fgNfqEhFR0rbtOYH7nnWFTOVxcmAE9z3rwlPfcMQdgAWPLVwhDuHXecD9L+7GXr/gK+6xhUMntVsshWXajRKWrqsd+xB/d6CeRzVYpiKldHu7HmNpALA5zvoQEWW3oB/bPPXgxP3xgonj4vyxNeuqz2y7mnTcI/HIK/vCzqEmoU3n8cgr+1BZURZXF2Tw2MLTB3KB7cAPbr4C8y6fWPsy7rGFu54Btj8a+7j1DwE3NsRXNgVIV/CVyDisWHl86UKIGn0wvkGNAJ7w2y4EcDSO/ERZJ5VjULLtR5G8gn5sF+n3b90PvOV3XJw/tmZd9ZltV5PuPNQXMH1HMAngxMAIdh7qw5ryOYbLDR5bePCMNnnropIZWLawOOH6YtU9wBVfndg+cwD4xb3AX20B5vq9nmz1SprZwZc7wn4lSlqsPFHThRAOALuMVU8jpRwF4PvlEIKDIGlyS/UYlGz7USSvoB/bg6eHcf+Lu/Hk16/BsnlBLV9xMOuqz2y7mrT39Cnjx8URfJkmUgvn3MuBBddYXp3JzNTgy9vtpwoh7FJKd1Ba2PFYRvJES/eO9XJ47wGg3JunDoBbStmRoqdHWeTM8cNQTx/xbfcf3uu7P+h3nDJvEeYuWGJx7axlxhiUbPtRJK+gH9sROYC9cgAjc68CFiTegmLWVZ/ZdjVp6bEueH+CDBxXYXp9KHNY0e3YCG2eLX1Orir9b++2HUBV0CSpUfNES/cGaL7AztsSVmNkYleavD7+zU+x5siWkP2rXXWAa2L73UX3Yu63H7OwZtYyawxKtv0oZh2TxmaRua53VmP+/t04OeyJvHZmQQ6ud1ZbXTVKM9ODLyllsxCizhsgAcDqoMlQndDm52o2msdAmQB8Qdkm799NADojtbhRZggeOxRJvGOHLvvqd3Hw9F/GPm7eopjHZDOzxqCQyUwam0Xmyimej81/iehrZ/7l55FTnMQSTp5xzDj9IQBo9541nLMvC1gy4D6o1akjKK0Vga1aMfMYSfce0xEpjTJTpLFDweIdOzR3wZJJ351oRO9Q5MArkePIIiaNzSLzmbp25r6XgdcfxkL1UwDAwncagD1Paeu3cpmwjMaFtSmjBI8dOtg7jAe27sZPNl2DZaUTPzIcO5SY0kJjrYVGjyOLmDQ2i6yhr5259f1P8cOX9uAfb1+JTasXJzfD/b6XgbZvApdvAP76Z0DplUDvfmDH49p+rtOa0Rh8UUaJNHZoWWkBViZzCTUBAK5fWoL5xfk4OTASeQxKcT6uX8pVuYhSKccmcPWlCgDg6kuV5AIvzzjw+sNa4PX15wGbTdu/aLW2/eJdwOt/Dyy/lV2QGcqW7goQkXVybAKbb9Ouqgr+6veNQbmtgmvOEWWyw78D1E+BdQ9OBF46mw1Y931APawdRxmJwRfRFKOPQSkrDmxhLCvOT2iaCSKy2LB3/rDSK8On6/uHjc0zRtZjtyPRFKSPQXmpazum73gUF9c9hNud69niRZQNCrzjYnv3a12NwXr3Bx5HGYfBF9FU4jdfVA6AG/I+waKcd3Ek7xPknFQmjuN8UUSZa8kXAWWxNrjef8wXAHg8wI4nAGWJdhxlJAZfRFMJ54siyn62HG06ibZvaoPr133f72rHJ4AD27SrHTnYPmMx+KKMNe6R+PCoCgD48KiKK+cXsVssWZwvivxwQfQsVrFRC7Befxh4unJiv7KE00xkAQZflJECF34GfvjSHvz0zYPJT0o41XG+qKxkVpDEBdGzXMVGYPmtOPZmCxa+04Bjaxux8Cu1Cbd4Bb/P8s8MYxm0f9JG5IBvP4Px5DH4oswydBLbPjiE+/6jL/LCz39egg3XLmXrDE0ZZgVJXBB9ErDl4Py8qwFAu0+iqzH4fbZCHMKv84D7X9yNvX7BF4Px5DH4oowy/v4zeOT1hZAoQfBMVNrCzx488h8HUDn2n8j5Csck0dRgVpDEBdHJX8D7zDOOi7sOAH8AfnT9RUxfNbFmJIPx5DH4ooyys7QKJ3AgYrqEDScwFztLv4g1FtaLKJ0YJFEk4x6J3afGsHd8DS6eGsNSj0x4bKzvfeZdMxLeNSOv/cM/AId/xjUjU4jBF2WU3rGZKT2OiChdzL6gIXBs7HeBN8/i8e43kxsbyzUjLcHgizIKF34mosnCzAsatu05gfuedUUeG5vIahVcM9IyDL4oo3DhZyKaLMwaqzfukXjklX1hvyO1sbHAI6/sQ2VFWXxdkPqakX/9s8hrRj5dqR23dF1cdaZADL4oo+gLP9/3rAsCCPhy4cLPRJRNzBqrt/NQn28annAkgBMDI9h5qA9ryucYL5hrRlqGC2tTxuHCz0REkfUORQ68EjnOx3/NyLAFcs3IVGHLF2WkDSvno3L5PLzz0lOY9cd/w9mrvoW1t9+HnGl8yxLR1Gba2FiuGWkZtnxRZtr3MnL+6Vqs3/v3WGX7GOv3/j1y/ula7UocIqIpTB8bG2nwhQAwP5GxsfqakQe2aYPrj+wERoe0+xfv0vbf/CMOtk8BBl+UefRLnUtXAN/uAhqOafelK7T9DMCIaArTx8YCwVNRp2BsrL5mZO9ebXB946Xafe8+TjORQgy+KLMEX+q8aDWQVzBxqfPlG7RLnT3j6a4pEVHamDo2tmIj8L3dOLa2EQC0++99wMArhTiAhjILL3U2ldmTPhKRdTasnI/KijK81LUd03c8iovrHsLtzvWpuRo8hWtGUigGX5RZeKmzqcyc9JGIrJdjE7jmkmlYlvMuDl4yjdPwZAkGX5RZ/C91XrQ6NJ2XOifFrEkfiYjIOAZflFF6ZzugFC7C9B2PQXz9hZBLneWOx3GxcDHU2Q6Upq+aWYsLNBMRpR+DL8ooz71/DB999td4auhJyBfuhO3LD/oWdvW8/Tjw8Wv47oX7sfz9Y+wWI6KpaeikdvPKUw9O3B8vmDiusEy7JWDcI7H71Bj2jq/BxVNjWOqR7NJMISFluNWhUvwgQtQBUL2bipSyOdk8BtMBoBwApJS1cdS3CMDAwMAAioqKjGajFNAHhBcd+g3K3vsRcoeO+NIuFC7GyRsexuDSr3JAOBFNXW81AtsfjX3c+oeAGxviLn7bnhN45JV9AUsYzS/Ox+bbKrjCSAyDg4MoLi4GgGIp5WCk40wPvvQgSA+OhBBOANXRgqFYeQykN0kp6/3KawFgl1JWGqwzg69M4BkHXD8HXn0A+NpPAMc3ecUNEVFQy9fB08O4/8XdePLr12DZvORavrbtOYH7nnWFLNqtt3lxibfoMin46gewVEqp+u2TUsqI7Zex8kRLF0IoANqhBWOqN80BoBtAuZTSbaDODL4yxfHdQOt6oGY7sOCadNeGiCjj7Dk2gK/99B28+t21WLmwOOFyxj0Sa5vejLhot4A2j9g79V9hF2QERoMvUydZFULYoXUJqmHSnInkMVjmKgB2vyQ94FLiqD4REdGUsfNQX8TACwAkgBMDI9h5qM+6Sk1SZg+4t0fYryJyIBQrT9R0b1A2OyhND8rCtnoJIfIA+F9bXxjhMYiIiCal3qHIgVcix1Fk6VpeqA9AnCt+xswTLb0BQG241jK/9AG/29E460ZERJTVSguNXcRk9DiKLF3BV7yBl5E8YdOFEE0AtkopW6PkbQRQ7He7NIH6ERERZa3rl5ZgfnF+yGLdOgHtqsfrlybyE07+zA6+Ig1uV6KkxcpjuEwhRBWAnlhTW0gpR6WUg/oNwFC044mIiCabHJvA5tsqACAkANO3N99WwcH2KWDqmC8ppVsIoQoh7MFXGUopuxLNY6RMffC93uLlvQqyxMjVjpRGQZdQ48yBwHtdEpMHEhFReBtWzsdT33CEzPNVxnm+UsqKGe4boQ1414OgKv1v77YdQFVQ61TUPAbKdABwAOjwlg8AwWVQJtr1TPjJA39xb+B2gpMHEhFRdBtWzkdlRRle6tqO6TsexcV1D+F253q2eKWQ6cGXlLJZCFHnDZAAYHXQBKtOALUAmo3miZbubeF6A1o3ZFNwXVL41MgMq+4Brvhq7OPY6kVElFp+PQ85AG7I+wSLct7FkbxPkHNSmTiOPQ9Js2R5oWzDSVaJiChbpGqSVbOXLZoKjE6yyoW1iYiIsoi+Bq7uYO9wwL0u7jVwg3oeoi5bRElh8EVERJRFnnvvUzz5xsch+x/Yujtg+/6bLsPfVV5uvOCg7sQROYC9cgAjc68CFiTRokYhGHwRERFlkbtvWIzKiktiHldamBfzGEoPBl9ERERZpLQoP77uRMo46ZrhnoiIiGhKYvBFREREZCEGX0REREQW4pgvIiIiMk3w1BiRxD01RhZj8EVERESmiTQ1RrC4p8bIYgy+iIiIyDT/ZUUebiudmCfsSN95PPb6n/CDm6/AopIZvv3KvKkzNQaDLyIiIjLN3D89j7l+yxYtA3BjHoDtQQeufwhYMDWWLWLwRUREROYJWrboyMe7seit+3Hkxiex6LJrJo6bQssWMfgiIiIi89aMDFq2aPS0Vt6osgxYcE3iFc5iDL6IiIjIvDUjKQSDLyIiIuKakRZi8EVERERcM9JCnOGeiIiIyEIMvoiIiIgsxOCLiIiIyEIMvoiIiIgsxOCLiIiIyEIMvoiIiIgsxKkmKCHBMyFHEvdMyERERJMcgy9KSKSZkINxJmQiItKNeyR2nxrD3vE1uHhqDEs9Ejk2ke5qWU5IKdNdh4wjhCgCMDAwMICioqJ0VycjhVsD7IGtu/GTTddgWWmBbz9bvoiICAC27TmB//7yH7Fo+A8ohYpeKDhS8Hn8vxuvwoaV89NdvZQYHBxEcXExABRLKQcjHceWL0pIpJmQl5UWYOXC4jTUiIiIMtW2PSfwy+f/Be15z2NBbq9v//GLpfhvz98F3PWdSROAGcEB90RERGSacY/Ef/7yZ/hfuU+ibJkD+HYX0HAM+HYXypY58L9yn8R//vJnGPdMnZ44S1q+hBB1AFTvpiKlbE42T7LpREREZL6dPafxvfF/BS67BbY7XwBs3nafRathu/MFeF64E987+G/Y2XMv1lxWmta6WsX0li9vEAQpZauUshWASwjRkkyeZNOJiIjIGuOf/BYLZC9sX/7BROCls9lg+/KDWCBPYfyT36angmlgRbdjA4BWfUNK2QWgJsk8yaYTERGRBUqF6v3jyggHXBl43BRgavAlhLBD6/JTw6Q5E8mTbHqEx8wTQhTpNwCFUZ8YERERGVJuL9f+6N0f/gDvft9xU4DZLV/2CPtVAEqCeZJND6cBwIDf7WiE44iIiCgOOZ/7Es7PuhSetx8DPJ7ARI8Hnrcfx/lZlyLnc19KTwXTIF1XO/YBKElxnmTSGwEU+90ujbNuREREFI4tBzNubYT4+DV4XrgTOLITGB0CjuyE54U7IT5+DTNubQRsOemuqWXSNc9XvIGXkTwJp0spRwH4ZgwVYurNtktERGSaio0Qd/wceP1h4OlK326hLNH2V2xMY+WsZ3bLlzvCfiVKWqw8yaZTio17JD48qgIAPjyqTqm5WoiIyKCKjRDf241jaxsBAMfWNkJ874MpF3gBJgdfUko3ANU7CD44rSuRPMmmJ/I8KLJte05gbdOb+OFLewAAP3xpD9Y2vYlte06kuWZERJRxbDk4P+9qANDup1BXoz8rxnw1AvBdZSiEqILfNBBCCLs+L5fRPClIpxTYtucE7nvWhRMDIwH7Tw6M4L5nXQzAiIiIwjA9+PLOLK8IIaq8QdBqKWWt3yFOALXx5Ek2nZI37pF45JV9CNfBqO975JV97IIkIiIKYsmA+6ClfTqC0loRplUqWp5UpFNydh7qC2nx8icBnBgYwc5DfVhTPse6ihEREWU4LqxNCekdihx4JXIcERHRVMHgixJSWpif0uOIiIimCgZflJDr517A/AIbIs2IJgDML8jB9XMvWFktIiKijMfgixKS4/pXbB59HICEQOByEdq2xObRx5Dj+td0VI+IiChjpWuGe8p2q+7Bhiu+iqcOnscjb6s4MTwRgJUVTMfmLxdjw7LHgMKyNFaSiIgo8zD4osQUlgGFZdiwAKhcK/FS13ZM3/EoLq57CLc71yPHxiWaiIiIwmHwRUnLsQlcc8k0LMt5FwcvmcbAi4iIKAoGX0RERGSa3sER9A6N+rZP953HMgBH+s5j5NiAb39pYR5Ki6bGFfIMvoiIiMg0z733KZ5842Pf9gpxCDfmAY+9/ifsfW3iivj7b7oMf1d5eTqqaDkGX0RERGSa/7IiD7eVFvu289QC4C3gXzYUYFSZ2K/My0tH9dKCwRcRERGZZu6fnsfc7Y+G7F/01v2BO9Y/BCxosKhW6cXgi5LnGceM0x8CgHbvWQPYctJcKSIiygir7gGu+Grs46bQ1ERCSpnuOmQcIUQRgIGBgQEUFRWluzqZbd/LwOsPA+qnE/uUxcDNPwYqNqavXkRERBYbHBxEcXExABRLKQcjHccZ7ilx+14G2r4JlK4Avt0FNBzT7ktXaPv3vZzuGhIREWUctnyFwZav2HrVs1CevgHT56+A+PoLgM0vjvd4IF+8ExdP7IP67d+jVJmVvooSERFZhC1fZKrtXS8jd+gIxLofBAZeAGCzQax7ELlDn2J7F1u/iIiI/DH4ooTcvNjbYlp6ZfgDvPt9xxEREREABl+UoOJ5i7Q/eveHP8C733ccERERAWDwRYla8kXtqsYdjwMeT2CaxwPseAJQlmjHERERkQ+DL0qMLUebTuLANuDFu4AjO4HRIe3+xbu0/Tf/iPN9ERERBeHVjmHwasc4hJ3na4kWeHGeLyIimkKMXu3IGe4pORUbgeW3Aq6fA68+AHztJ4Djm2zxIiIiioDdjpQ8Ww6w4Frt7wXXMvAiIiKKgsEXERERkYUYfBERERFZiMEXERERkYVMH3AvhKgDoHo3FSllc7J5DKYDQDkASClrE6k7ERERUaqZGnzpQZCUstW77RRCtEQLhmLlMZDeJKWs9yuvRQjRKaWsNOdZTlFDJ7Wb7syBwHtdYZl2IyIiIgAmz/MlhOgHsFRKqfrtk1JKkWieaOlCCAVAO4BqPV0I4QDQDaBcSuk2WG/O8xXLW43A9kdjH7f+IeDGBvPrQ0RElGZpn+dLCGGH1iWohklzSim74s0DwB0jfReAVQDsAFzeJD3gUhJ5HhTBqnuAK74a+zi2ehEREQUws9vRHmG/isiBUKw8UdO9QdnsoDSn9z5iq5cQIg9Ant+uwkjHkhe7E4mIiBKSjqsd+wCUpDhPtPQGALXhWsuCjhnwux2Ns35EREREhhhu+RJCVAHYZODQRimlK0p6vIGXkTxh04UQTQC26oPzo2gE8ITfdiEYgBEREZEJDAdfUsoOAB1xlB2pm0+JkhYrj+EyvcFij4HAC1LKUQCjfnljZSEiIiJKiGndjt4rC1XvIPrgtJDB9kbyGC3TO/jefzoKJVweIiIiIquZPearERMD3vXWqFa/bbvfhKiG8hgo0wHAAcDlLd8OoAbauDAiIiKitDJ1ni/ANymq3iW4OmgC1BoA9VLKcqN5oqV75/k6hDBXU0abWyxMnTnPFxEREcXF6Dxfpgdf2YjBFxEREcXLaPDFhbWJiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIjioKpqRpVD2cf04EsIUSeEqPHe6lKRJ54yhRCdidadiIjIX319PRRFSUlZra2tcLvdKSmLsouQUppXuDcwklI2e7edAKqllLWJ5omnTCFEFYB2KaWIs95FAAYGBgZQVFQUT1YioimvubnZF6Coqoq6utj/d8fKEy29q6sLLS0tqKyshN1uR2dnJ1avXo2qqipD+VVVRVtbGwCgp6cHbrcbW7ZsCQmyWltb4XQ6YbfbDT+uv8rKSnR2BrYH1NbWoqWlJebrQ9lhcHAQxcXFAFAspRyMeKCU0rQbgH4AStA+mUweo2UCUADUxHq8CHUoAiAHBgYkEREZ19TUJJuamnzbnZ2dsqamJqk8sdLb29uloigSgLTb7bKlpSWu8mtqamRPT0/AttPpDCijp6cn5HnEetzgY72/RwE6OzsD6kbZbWBgQAKQAIpktDgjWmIyNwD2CEGRBOBMJE88ZXoDL8VI8AUgzxtw6beFDL6IiOKnKIrs7+8P2Bcu6IgnT6z09vb2kPR4ync6nQEBUFNTk1QUJeD4urq6gADNyOPq+vv7ZUtLS8TXweFwxCyDsoPR4Gua4ba0+Nkj7Fe9QVEieQyV6e2K7Ipau0ANADbHcTwRkWm6urpCuqfCWbx4Mf72b/82YN8///M/Y8uWLbjlllui5q2srITT6fRtj4yMYPPmzRHTjXC73VBVNeyYqK6urrDlxcpjt9vjLjPeOgW/1u+//35IuV1dXWhqaor6WJG0tbXhjjvuQG1t+BE3drsdLpcLDocjofIp+5gZfEXSB6AkwTyqwTIVKaVbCKEYLL8RwBN+24UAjsZZRyKilDh//ryhK+FKSkK/SoeHh3HDDTfEzH/+/PmQff55wqXHEmnwuKIoEesTK4/RMtva2lBSUoK+vj709PT4AqV469TR0QFVVdHe3h5Qx3CvdbTH1RkJECsrK9HV1cXgawoxHHx5B69vMnBoo5TSFSU93sDLSB5fuhCiRkrZGk/hUspRAKN+ZcRXOyKiFJoxY4ahK+oKCgrC7jOSd8aMGSH7/POFS0+UHpwkkifSc/EvUw9a9IHwra2tqK6uDgigYtVJH3Svqiqqq6sDHldVVV/Z/ow8rp43WjBcUlKCnp6eiOk0+RgOvqSUHQA64ig70vWzSpS0WHmipgshHAB2GaseEVFmcjqdcXf56YK7IY3Kz89PuFstlngDLyN5/NODAyO9iy9awBNcvqIoqKmpAaAFUbNnz8ahQ4egKArcbnfYIDDa4yqKgtbWVl+Z0djtdmzdujXmcTR5mDbPl5TSDUAVQoT8uyClDDseK1YeA2WWAHB65wGrA9AE+OYFC3/tLxERpUS41iEgcsuRkTxGyuzoCGwX0AMlt9sdM7+qqqivrw8I1JxOJ1RVRVdX9KHD0R7X5XJh1apVUfPr+vr6InZr0uRk9iSrjdCuUgTg67ps9du2h5kkNWqeaOneAK1ZvwFo8e5v9rbcERGRSex2u6+lKFiklrxYeWKl692E/ul6IKUHb9Hyu91uNDc3h3RBAhPBlN1uD8kf63H7+vrQ1dWF5uZmNDc3o76+HoA231hw0KaqKsrLy8O+PjQ5mRp8eQMgRQhR5Q2SVsvAyVCdAGrjyWOgTAC+oKzB+3eT9wpIIiIyUUNDQ0CLUUdHR0DXmx7sxJMnWrqiKKirqwto4WptbUVVVZUveIqW3+FwhOTfunUrHA6HL2AMF3zFelyn04m6ujrfTb/Ssa6uLmQS1kjdmjR5mTrDfbbiDPdERIlrbm72BSXvv/9+wFiyjo4O1NfXhwwwj5YnVrqqqmhtnegg+eyzz5LKr1+16B8QhZud3sjj6s9569at6OjoQF1dXcg0HtXV1WFn1KfsY3SGewZfYTD4IiIif83NzQGtYakU68pMyh5Ggy/TF9YmIiLKdnV1daaswdjc3Bxx8lWavBh8ERERGbBp06aQwfLJUFUVn332mSmtaZTZGHwREREZoA+UjzRrfrxaW1tNm1uNMhvHfIXBMV9EREQUL475IiIiIspADL6IiIiILMTgi4iIiMhChhfWJiKizNM7OILeodGYx5UW5qG0KN+CGhFRLAy+iIiy2HPvfYon3/g45nH333QZ/q7ycgtqRESxMPgiIspid9+wGJUVl/i2D/YO44Gtu/GTTddgWWmBb39pYV46qkdEYTD4IiLKYqVF+WG7E5eVFmDlwmJL6+JyudDV1YXGxkaUlJSgtrYWVVVVAYtPT0a1tbVoa2tDe3t71AlT3W43WlpafOtM6jPb9/T0oK+vD5s2bQpZdFtXX18PAJgzZw4URUFJSQmqqqpQX18fca6wrq4uuFwu1NXVxXwOiZRPSZBS8hZ0A1AEQA4MDEgiomwxNu6Rz/3+E7mk/lX53O8/kWPjnrTUw+FwyJqamrQ8dro4HA7Z2dlp6Fin0xn29bHb7bKpqSlgX3d3d9iye3p6ZFVVlbTb7REfJ1Z6suVTqIGBAQlAAiiSUeIMXu1IRDQJbNtzAmub3sQPX9oDAPjhS3uwtulNbNtzwvK6lJSUWP6Yk0Ftba2vBUpXXV2NpqamkBY1/5azSEpKSuB2u6POyJ9M+ZQ4Bl+TXO/gCPYcG4h56x0cSXdViShB2/acwH3PunBiIPBzfHJgBPc960pLAEbxUxQFgLbmIzDRFRipK9PpdEbs0u3o6EBTUxPsdnvEBcGTKZ+SwzFfkxyvhCKa3MY9Eo+8sg/hFoqTAASAR17Zh8qKMuTYhMW103R1dfl+6Lds2QK3242+vj50d3ejpaUFra2tKCkpwdatW9HQ0ACHwxGQr6SkBNXV1QAmFqPWxyHpx+gtNZ2dnQDgS9fHVwHamKu6ujp0dHSgsbERbrfbN07L7XajsrLSF6zY7XZfXrfbDbvdHjAeq7m52Tc2KlW6u7vhcDh8QVhHR0fMRbeDW8p0brcbiqKgqqrKF4gFS6Z8IPHzCmjnsbW1FXa7HZ2dnaitrQ1I19NUVfU9l5qamoj7s060PsmpesMkGvN1auC8/ONR1Xd7yXVULql/Vb7kOhqw/9TA+XRXlYgS8LuDZ+SS+ldj3n538IxldQo3pqmzs1Pa7faAsUV2u13W1dX5ttvb26XD4QjI197eLgHInp4e3766urqA8vV8nZ2dsru721dmVVVVwOP19PRIp9MZUB9//uOtqqqqZHt7e8Bz6u7u9j1+S0uLL62/v18CSHjMV39/v2xqapIOh0P29/f79gMIGQNmRH9/v69+PT09EoCv7v4SLd9foue1rq4u4Jza7Xbfc29vbw94fXt6emRLS0vE/ZmEY74IgHYl1MqFxb6bfum5fiWUfuPki0TZqXfI2JABo8eZRR9/5N/SEtyl5XA4QsYnKYoCh8MRcGxDQwNaW1t9xyqKApfLBafTCYfDgaamJt+Vl8GP19fX59vf19cHl8sV8FiA1mrU0dER0NJVXV2NlpYWqKqK5ubmgNYWvY7x2LVrF1pbW9Ha2oq2tjY4nU50d3f76pCMtrY23HHHHQC05+xwOLB169akyw0n0fPqdrvR1dUVkMd/u7293df9arfbsWrVqqj7sw27HYmIslhpobF/nIweZ6bgH2VFUVBeXh53OYqi+AIuvczgsnft2hV2vJLezeV0OlFTU4OWlha0tLSgq6vLF7B0dXVBUZSAYKCnp8cXMKQiQFq1alXM7jK73Y6enp6ox+hdov46Ozt9AYqutbU1pOsx0fLD1dOfkfPa3t4OAL7uw76+PvT19QEAqqqq0NLSgtmzZ8PhcGDTpk2oq6uDw+EIuz8bseWLiCiLXb+0BPOL8xFpNJcAML84H9cvndxXIAYHRMHBRzj6/FzAxBgpPa/dbofT6fTdmpqafOPJrLqas6qqKiAADCc4XVVVX1Ci39544w2oqhrQypdo+anicrlQXV2NtrY22O32sAFkd3c3Nm3a5JsbLdr+bMPgi4goi+XYBDbfVgEAIQGYvr35toq0DbY3g6qqUFU1alefPog+mNvtxurVqwFoLTYlJSXo6OgICKjCdZPpjxspzQz+FxWEo6pqSCDY1tYWMlGr3i0afNVjIuWngqqquOmmm9DQ0ICamhooiuILlt1uN1pbWwFo56Gurg7d3d3YunVrxP3ZiMHXFDLukfjwqAoA+PCoinFPuOujiCjbbFg5H099w4Gy4sCuxbLifDz1DQc2rJxvaX307qNYjLROAVorif+xjY2NqKmpidod5nA44HQ6AwILveXHPzipra3FvffeGzBmyel0YtWqVejo6AgoU2+l0a+6838ewXVMlfb2dtTX14dt4WptbQ0JtLq7u8OWs2nTJl8rXzLlGxHrdXC73SHBs/6e0V9H/9cXgO8Kx3D7sxHHfE0R2/acwCOv7PPNA/TDl/bgp28exObbKiz/Yiai1Nuwcj4qK8qw9f1P8cOX9uAfb1+JTasXW9ripQ9y139c9YHpbrfbN7VDc3Mz6urq0NzcjF27dgGYaIFqbGyEqqqor69HQ0ODrxvQ4XD4xlq5XC7MmTMnoNWmqanJV7b/ckZ6YKG3VPX09IQEJzU1Nejp6Qnptuzs7ER9fT36+vp8rT/6GC29u6ujoyOgjo2NjVAUJeL0DfpA/l27dvnqq7f8ROJwONDd3Y36+np0dnb6lv8BEDDeSZ/2weVyoby8PCRNHwdWXV0dsIyR0fLDcblcCZ1XveWqvr4elZWVACbO1aZNmwKm2tBfty1btviCx+D92UhIydaPYEKIIgADAwMDKCoqSnd1kqZPwBh8pvWv5HT8Z0xE5thzbABf++k7ePW7ay1f29EMelARqUWHKJMMDg6iuLgYAIqllIORjmO34yQXawJGQJuAkV2QRERE1mC34yS381BfyJIj/iSAEwMj2HmoD2vK51hXMSJKid7BEfQOjfq2D/YOB9zrSgvzOJ8fUYYwPfgSQtQBUL2bipQy5nWhsfIYKVMI0QRAn8CkT0rZEXzMVJAtEzASUWIiLSH2wNbdAdvZuISYPp7L5XL5xhQRTQamBl/eIAlSylbvtlMI0SKljLhUeqw8BtIVAG8AuElKqQohHAC6EXoV9pSQTRMwElH87r5hMSorLol5XGlhngW1SS19ji2iycbUAfdCiH4AS6WUqt8+KaWMGAjFymMgvQVAj39rmBDCKaU0PFPcZBpwP+6RWNv0Jk4OjIQd9yWgXY7+Tv1XJtU8QERERFZL+4B7IYQdWpegGiYt7L8ysfIYLLMGQIcQwq7viyfwmmym4gSMREREmczMqx0jzXymAlASzBM13RucAYDDe7xbCNESKdjTCSHyhBBF+g1AYbTjs02mTcBIREQ0laXjasc+APGuV6DnUWOk68GXKqV0AYAQoh7AIQCzo5TfAGBznHXKKpkwASMRmWDopHaLpbBMuxFR2hkOvoQQVQA2GTi0UQ98IkhkoahYeYLTd+l/eAfdKzHGfTUCeMJvuxDA0firmdlybAJXX6oAAK6+VGHgRTQZ7HoG2P5o7OPWPwTc2GB+fYgoJsPBl3eqhnima4i08qgSJS1WnkTTVUTusoSUchSAb6IcIRiUEFGWWHUPcMVXJ7bPHAB+cS/wV1uAuX5TS7DViyhjmDbmS0rpBqD6jcPyTwvbAhUrj8F0N0IDLQV+rWFERJNGYRmw4JqJmx5wzb08cL8FwZe+xt/s2bNRXl6O5ubmgEWW6+vrMXv2bFRXV/sWuaZQtbW1mD17dshi18Hcbjfq6+shhPC93s3NzaitrUV1dXXIwuD+6uvrUV9fj+bmZrS2tvqOra+vj5inq6sLzc0xp+pMqOwpR0pp2g1AHYAav+0qAC1+23YAdXHmiZVeBaApaLszznoXAZADAwNysvnjUVUuqX9V/vGomu6qEJEZjn0g5eYi7T5NHA6HrKmpCZtWV1cXd3l1dXWyqqoq2WplFYfDITs7Ow0d63Q6w77edrtdNjU1Bezr7u4OW3ZPT4+sqqqSdrs94uPESk+m7MliYGBAQls8pkhGiTNMXdtRanNtKUKIKu+YsdUycIJVJ4DaePIYSO8A0COEqPNOyLpaSllp3rPMbL2DI9hzbMB38196xH9/7yBnuCfKep5x4PgH2t/HP9C206CkJPIw3fLy8rjLq6ysxKZNRoYck7/a2tqQ1qbq6mo0NTWFTF5rt9tRWxtx/nMA2nl1u91wu8OP8Emm7KnG9KsdZeDSPx1Baa0AWuPJYzA9pMypajIvPUJEfva9DLz+MKB+qm2/+gDwzhPAzT8GKjamtWrJ4iz3iVEUBQCgqioURfEFYpFeT6fTCbs9/PDojo4ONDU1oaurCy0tLWhqagpIT6bsqYgLa09yk3npESLy2vcy0PZN4PINwF//DCi9EujdD+x4XNt/x88zMgDr6ury/Whv2bLF16ry2Wef+X7cXS4X6uvr4Xa70dPTEzFfX18furu70dLSgtbWVpSUlGDr1q1oaGiAw+EIyFdSUoLq6moAWmDi/3j6MXprTWdnJwD40pubm31BhNvtRl1dHTo6OtDY2Ai324329nY4nU643W5UVlbCbrejpaUFdrvdl9ftdsNut6Oqqsr3WjQ3N0NRlKithvHq7u6Gw+HwBWEdHR0xA9lI47LcbjcURUFVVZUvEPOXTNm6RM8roJ3H1tZW2O12dHZ2ora2NiBdT1NV1fdcampqIu43XbQ+yal6wyQe80VEk8z4mJT/Y6WUz22Scnw8KG1c2/8/rtKOs0ikMUhSStnS0hKw3dnZKe12e8A4IbvdLru7u33b3d3dIeOFIuXzH1PW3t4uHQ5HQL729nYJQPb09Pj21dXVBdRXz9fZ2Sm7u7t9ZVZVVQU8Xk9Pj3Q6nQH18ec/3qqqqkq2t7f7tp1Op+851tXVBbwu/f39EkDCY776+/tlU1OTdDgcsr+/37cfQMgYMCP6+/t99evp6ZEAAs5PMmUHS/S81tXVBZxTu93ue+7t7e0Br29PT49saWmJuD8ZGTHmi4iITHb4d1pX47oHAVvQV7rNBqz7PqAe1o7LQPo4Iv9WE711KJF8/hwOR0g5iqLA4XAEHNvQ0IDW1lbfsYqiwOVywel0wuFwoKmpCS6XC11dXSGP19fX59vf19cXcBWn3uLkdrvR0dER0NJVXV2NlpYWqKqK5ubmgNYWvY7x2LVrF1pbW9Ha2oq2tjY4nU50d3f76pCMtrY23HHHHQC05+xwOLB169akyw0n0fPqdrsDrg612+0B2+3t7b4rb+12O1atWhV1v9nY7UhElM2GT2n3pVeGT9f368elmf/UE7rgH1dFUdDX1xezrHD5EhnQryiKL+DSywwue9euXWHHLOndXE6nEzU1NWhpaUFLSwu6urp8AUtXVxcURQkIBnp6enwBQyoCpFWrVsXsLrPb7b6u20j0LlF/nZ2dIeettbU1oOsx0bIj1dOfkfPa3t4OAL7uw76+Pt97qKqqCi0tLZg9ezYcDgc2bdqEuro6OByOsPutwJYvIqJsVuAd09m7P3y6vr8g9tjPVDHScpXpggOicEFjsNraWrS1tQGYGCOl57Xb7XA6nb5bU1OTbzxZKsd5RVNVVRVz7rDgdFVVfUGJfnvjjTegqmpAK18iZaeSy+VCdXU12traYLfbwwaQ3d3d2LRpE1paWnzzlUXabzYGX0RE2WzJFwFlsTa43uMJTPN4gB1PAMoS7TiLVFZWZlXwpaoqVFWN2tWnD6IP5na7sXr1agBa0FlSUoKOjo6AgCpcN5n+uJHSzOB/UUE4qqqGBIJtbW0B3aXARLdoS0tLUmWniqqquOmmm9DQ0ICamhooiuILlt1uN1pbtQkQHA4H6urq0N3dja1bt0bcbwUGX0RE2cyWo00ncWAb8OJdwJGdwOiQdv/iXdr+m3+kHWeRqqoq2O1234+bLnhsUyRGWpmSyedyuQKObWxsRE1NTdQuMYfDAafTGRBc6C0//sFJbW0t7r333oAxS06nE6tWrQqZcV5vpdGvuvN/HsF1TJX29nbU19eHbeFqbW0NCbS6u7vDlrNp0yZfK1+iZRsV63Vwu90hwbPe5ai/jsHvRf0Kx3D7rcAxX0RE2a5iozadxOsPA0/7zSmtLEnbNBOdnZ1obm5GfX095syZA0ALUvy781wul2+KhubmZtTV1aG5uRm7du1CX18fSkpKYLfbQ46Jlg+YaIFqbGyEqqqor69HQ0OD77EdDodvrJXL5cKcOXMCWm6ampp8ZeuBJDARXOgtVT09PSHBSU1NDXp6ekK6LTs7O1FfX+97XvqxAHzdXR0dHQF1bGxshKIoEadw0Afy79q1y1dfveUnEofDge7ubtTX16OzsxNz5szxHe8/3kmf9sHlcqG8vDwkTR8HVl1djU2bNqGqqspw2ZEkel71lqv6+npUVmrvf/1cbdq0KWCqDf1127Jliy94DN5vBSG1qRXIjxCiCMDAwMAAioqK0l0dIiJjPOOA6+faBKtf+wng+KalLV7ZQA8qIrXoECVjcHAQxcXFAFAspRyMdBy7HYmIJgtbDrDgWu3vBdcy8CLKUOx2nOyGTmq3WArLtBsRZZfgz/iZA4H3On7GiTIGg6/JbtczwPZHYx+3/iHgxgbz60NEqRXpM/6LewO3+Rn3jedyuVy+MUVE6cAxX2FMqjFf4f4r/sW9wF9tAeb6LaTN/4qJshNbt4kyhtExX2z5muwifeHOvRxYcI3l1SGiFGNQRZR1OOCeiIiIyEIMvoiIiIgsxOBrKvGMA8c/0P4+/oG2TURERJZi8DVV7HsZ+J/XaJMvAtr9/7xG209ERESWYfA1Fex7GWj7JlC6Avh2F9BwTLsvXaHtZwBGRERkGU41EcakmmrCM661cJWuAL7+PGDzi7c9Hm3h3d59wPc+4GzYRERESeDyQqQ5/DtA/RRY92Bg4AVo2+u+D6iHteOIiIjIdAy+JrvhU9p96ZXh0/X9+nFERERkKgZfk13BJdp97/7w6fp+/TgiIiIyFYOvyW7JFwFlMbDjcW2Mlz+PB9jxBKAs0Y4jIiIi0zH4muxsOcDNPwYObNMG1x/ZCYwOafcv3qXtv/lHHGxPRERkEV7tGMakutpRt+9l4PWHtcH3OmWJFnhVbExfvYiIiCYJo1c7MvgKQw++jhw5MnmCL0CbdmL3C8BrDwG3PApccydbvIiIiFJkcHAQixYtAhh8xU8IsRDA0XTXg4iIiLLSpVLKY5ESGXyFIYQQABYAGDJweCG0QO1Sg8dTZuB5y048b9mJ5y078bwlphDAcRklwJpmYWWyhvcFixix+tPiNADAULQmRsosPG/ZiectO/G8ZSeet4TFfK14tSMRERGRhRh8EREREVmIwVfyRgE84r2n7MHzlp143rITz1t24nkzCQfcExEREVmILV9EREREFmLwRURERGQhBl9EREREFmLwRURERGQhTrKaBCFEHQDVu6lIKZvTWB0yQAjhBFALoBOAG0AlgPellB1prRgFEEIoAO4AUC2lrAyTzs9eBop23vjZy2zezxQAlAOAlLI2TLrq3eRnLkkMvhKkv1GllK3ebacQoiX4DUsZRwHgBFAF7QegiV/+mUUI4QCwCtq5KgmTzs9eBop13sDPXsYSQjRJKev9tluEEJ16AM3PXOpxqokECSH6ASyVUqp++6SUUkTORekmhKgC0OV/3igzec9Vg5TyuqD9/OxlsCjnjZ+9DORtrWyH1lqpevc5AHQDKJdSuvmZSz2O+UqAEMIOrdlVDZPmtL5GRFMDP3tEplgFwO637fbeK/zMmYPdjomxR9ivQmtap8x2hxCiD1rXSLl/cztlPH72shs/exnGG1TNDtqtB1VuaIFZOCr4mUsYg6/U0r9UKHO5AEBK6QYAIUSNEKJdSlmd3mpRkvjZy3z87GWPBgC1UkpViIg9i/zMJYHdjqnFN2KGk1K69S9/rzYAVd5xD5S9+NnLcPzsZQchRBOArfrg+ij4mUsCg6/EuCPsV6KkUQbwDvr18RvHEKk7izILP3tZip+9zOc9Rz1B00jwM2cCBl8J8P73pnoHIgandaWhSmSAflWP/3nz+6+bXyJZgJ+97MTPXubTB8/7TSehCCHs/MyZg8FX4hoxMShR/48hVjMtpZH3P+3moK6PGgAdvPw9I0Xq1uBnL7OFnDd+9jKbd2oJBwCXEMLuDbRqoI3rAviZSznO85UE78Rz+pfJal65k/m8/23X+O2aw/OWWbxf/FUANkH7QWhG0Ezo/OxlnljnjZ+9zOQ9L4cQ5spF/3m8+JlLLQZfRERERBZityMRERGRhRh8EREREVmIwRcRERGRhRh8EREREVmIwRcRERGRhRh8EREREVmIwRcRERGRhRh8ERGliHdJFiXd9SCizMbgi4godRrAhaKJKAYGX0REqeOQUrrSXQkiymwMvoiIUkAI4QTQme56EFHmY/BFRJQa1QA6Yh5FRFMegy8iotSwSynd6a4EEWW+aemuABGRlYQQDgCrAJQDeB9AF4Aab7IqpWxNoMwqAO1R0lYD6AHg9t76pJRq3JUnokmBLV9ENGV4p4FwSilbpZT1ALYAaJBSNnsPqU+w6E0A2sI8Xg2ASillvTeoU6AFYasSfBwimgTY8kVEU0mNX6Cl6/HeuwDUJliuEtySJYSwA2gCsNRvtwoAUsquBB+HiCYBBl9ENJX4BsR7gyMF3har4IDIm14FrZtwNYCWcGO6vK1bLWEeqwVAV1BQVgktyCOiKYzBFxFNGUHBkxOAO8rYq3Yp5XUAIIToAvAGgOvCHFctpawMs98J7QpIfw5oY8yIaArjmC8imqoqETQ1hL40kHdQvo83QFO8rWHBx6vBBfsdF9zKxbnAiIjBFxFNHd4uQl0VtKsdfWl+rWCRBsQ7grYjdTkCCGxp807CCilllxDCERzgEdHUweCLiKYEb+DV5P27Cn7df2EWw1YA9AXtUwGUBO2rDDd43ht0ufUAy1t+LbTxY4B2xSXHfhFNURzzRURTRReAVm8QtgtaMFQvhACAkqD5vVSEBloK/AIyb9ditElVqwHUCiG6AUBKWS2EaPc+PgMvoilMSCnTXQcioozibbHaog+49+7rB3Cd3pUohGgCsJUtWEQUL3Y7EhEF8QZUir7t7TZ0B10t6WDgRUSJYLcjEVF41d7WrfehzfPlmzbC2zLGwIuIEsJuRyKiOAkhWgA0cSFtIkoEux2JiOJXwsCLiBLFli8iIiIiC7Hli4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILMTgi4iIiMhCDL6IiIiILPT/A5G1TXe/49J0AAAAAElFTkSuQmCC\n", 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" ] @@ -314,212 +272,7 @@ } ], "source": [ - "pe.plot_corrs([impr_mass, uimpr_mass], plateau=[pcac_plateau, w_pcac_plateau], xrange=[0.5, 18.5],\n", - " label=['Improved pcac mass', 'Unimproved pcac mass'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Refined error analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two way of adjusting the value of S. One can either change the class variable `Obs.S_global`. The set value is then used for all following applications of the `gamma_method`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.79208242e-03 +/- 2.02509166e-04 +/- 2.05063968e-05 (4.226%)\n", - " t_int\t 1.03021214e+00 +/- 1.94552148e-01 S = 3.00\n" - ] - } - ], - "source": [ - "pe.Obs.S_global = 3.0\n", - "pcac_plateau.gamma_method()\n", - "pcac_plateau.print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively one can call the gamma_method with the keyword argument S. This value overwrites the global value only for the current application of the `gamma_method`." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.79208242e-03 +/- 2.04669865e-04 +/- 1.97135904e-05 (4.271%)\n", - " t_int\t 1.05231340e+00 +/- 1.88061498e-01 S = 2.50\n" - ] - } - ], - "source": [ - "pcac_plateau.gamma_method(S=2.5)\n", - "pcac_plateau.print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can have a look at the respective normalized autocorrelation function (rho) and the integrated autocorrelation time" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "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": [ - "pcac_plateau.plot_rho()\n", - "pcac_plateau.plot_tauint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Critical slowing down" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`pyerrors` also supports the critical slowing down analysis of arXiv:1009.5228" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.79208242e-03 +/- 2.28649024e-04 +/- 1.67571716e-05 (4.771%)\n", - " t_int\t 1.31333644e+00 +/- 5.19554793e-01 tau_exp = 10.00, N_sigma = 1\n" - ] - } - ], - "source": [ - "pcac_plateau.gamma_method(tau_exp=10, N_sigma=1)\n", - "pcac_plateau.print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The attached tail, which takes into account long range autocorrelations, is shown in the plots for rho and tauint" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pcac_plateau.plot_rho()\n", - "pcac_plateau.plot_tauint()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additional information on the ensembles and replicas can be printed with print level 2 (In this case there is only one ensemble with one replicum.)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result\t 4.79208242e-03 +/- 2.28649024e-04 +/- 1.67571716e-05 (4.771%)\n", - " t_int\t 1.31333644e+00 +/- 5.19554793e-01 tau_exp = 10.00, N_sigma = 1\n", - "1024 samples in 1 ensembles:\n", - " : ['B1k2r2']\n" - ] - } - ], - "source": [ - "pcac_plateau.print(2)" + "am_pcac_impr.show(comp=am_pcac, plateau=pcac_plateau)" ] }, { @@ -531,12 +284,14 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 12, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": 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t37UdT39sR6mTXz87OZMw6u6PRWhK+656HihVGBNCjAGYkFJOG5fGOtqtJGlSpm8iqlHsOTiFa68RAIBrrxHYc3CqFmGhKZ1l6uTqI0VS0OaBLDWhu1DrbhN1Pd98rmundAxZZa40vWULfaS56G3E1l5i+kPTdprb2ncdeXTNA2XlpWwz5SoTYofZzrWXEqVJmb5x6I3irbclfvkDE/iDry/LmnWocZvgLDy/uIQ9B7/T/bw14QRMH7NlYkwdZbWJGD+wOtpkyufOLy5h6uQs9hya6po6XWXeD/5hTaFpLgumaRxCrGovoe2yqeZMvX0XzWPe+rPNA20+m3Lc8f2M51qeNCnTNw4ziv+f/83p2qP41z0ZHD17sVseALB/1/ZknaIJwmZZxAxMsb4bZTjGxwx8dbXJkOdmlbv+roq6/WWaRplCkeveTRRWTNO4wmwvIe2ySf5ZLorksUj92eaBMg94Z9BXAEKIISHEqPoDUJp0k1fFufuum7v/P1GRb0qTsZmHUhLjY5aSMtXxug/Eh377a5h5Y9H7+1BTh5nnVO+QZS4uu6xS3TvE90R/V0U/a2VNyvTf8d3b1wbrMu/p/VJtGgHytZe63QtCKJLHoi4n5jxQZnmVrRmbcXw/7rmWJ03R9I8A+HTAvQsxv7iEDz/6PI6du4St69fgqwGClC7ZX3uNwFtvy67w0VYhLMUKtxe1V6GruLzlpw9Mp+YWcO9jL+C5T36wkOnRzPOhh3Zg1xNHkmgSfObiMjUWqe8dsrLX33Xr+DD2T74Xt9wQHmupaaa01JSpwfHd29UGTVPh/sn3FhqTY+rP7JfqHfLUfRvG0SJ5TO1yUmZ5lS2MTQPLTvVSylnt+zF1LVGaouk/A+Bz2ucRACcDnhXF1MlZHFPq5XOXMHVyFndMrPemMf3F9k9ux0e2byq105RtDkg10dVtKk1NyIRTpPy2bRjBptEhnJpbAACcvnC5sOnRzPPh751NNmn6Br66JucQzP4TMiHYJtjQem6iKS01ZfpxZt37s7u2A8AKYcs0FT5w4MXcZR9Tf3rb0ttkkbbfhnE0bx7LihNXRnmVKoxJKWeFENNY1krNGtesjvR50iR45gKABfVZCJH1mMowB4oqBLEyB/aqfRTapDEImXCKlN/w4AC+9PG7cO9jL+D0hctJJjUzzzvfvSHJpOmadFzPLWty3jo+jIUryzvWQsOi2LTfIROCPsiH+KZUHWy2TsrUSLjubRsLFXobUeQt+9A+3Q9Cdxm0QdgEqonAvw/AJAAVgHU3luOAofN5AsBOKeWB0DQaLof80PSVsn3zWNcRf+v4cJCfU9Vq5LKFpSp3KrZt8Aqp6zzlpws249cP4rlPfjBZe7LluWh7ddWbKViXOTkfemgHvvzKaRx4fjpK6+HSfsdOCGY9b1m3Bi+fmO2+qy/YrPnbJpJnkVTGBhE9D+a9fWOhan/m7tc841lon26Ds31VtGmRHYqQUpb/ECH2YFlLNQZgvR4JXwlKUsqbI9KoCPyTWA5XcQDAt3WBzpc+IL+jAC5cuHABo6Oj4S8aQBWNqMgzYv2Wtqxbg+PnL0U9q6qO9PKJWdz3+JHu56c/tqMnBq+Y8muKQBqTZ7Pentp9JwAEhXxIlVdzZyOAIBeBb06fwwMHXlyR9yxXBF8+VB9TfnjKPwnAqucMXTew4rdNre8mtMmQPFQxFpr3iA1DAeT3F2srTWg/Pubm5rB27VoAWCulnAtNV8nZlL5zITsC1AHL9740L2E5XphTwGrqWZRlq0yLNtRYp219U0Hos6pSG2etONu6uoopvxgTSJV+gipvtueZZkIlhCnK1grYdjaqIMtPfO1VbzvPo/12oepZN1kq/yRTG6b8mcrceq9TRNhqgoYnJA8x5uVtG0ZyjbtZ5nhbfqZOznbTZgnebR3jfDSh/ZRBJcIYqY4UDTXGafutt2WhZ5WJbzBt+urKhT64Atmr4hD/p7LLwjwpYerkLP7VoSnn8/R6W7iytEIDBJS/Bd8ss1/Y8eP4zWf+v27+9XZuTnbDgwP46ic+sOK7ohOizT/p2Mx8Vxum3zeFG4Atv2a7KyJspXJVKFKuoXkIXfi43rkMDaHqO/pmHFs5t3WMy6JKV5cqoTBWIVWsUqpoqPozdM1YqmfFllOecm3i6iomMKgr8raJ6dti83/ylUXRNms7KUE9x/Y8Pd9qMjNDPpQd1kUXBresW4Ofe/yF7rVrrxHYsm5N991sk50+gYf6v4Xkx/RPUlo3pSlR5VLEl86lxdS/++yu7d72snBlyRuU2tTw5KEKC0AMtnHXFvZFN2PmGYP0NKfmFvCute9wbsZJvSu4KVTtRx2CuVjJA4WxxLhWlaHHnLjuEUoVDdWcrGx+EnnfIXaQ9f3ed61pq6uQ9za30yuyBtrhwQEMXTfQNfWZv3eVRYqV9dGzq09K2L55LMp8XNcxR8pEeOL85e73b70tcfz8JYxfPxg02dl+k8ekNTw4gDsm1uOrRviLD//u17vlq+/ezLuwsOVX/V//NyT21lO77/QKzj7taJ58prYA+LBpRLOiteu7mL/88F25xiAzjSng+X4bM8Y1XatWlatLCHpZbRodwp/8wvty3YfCWEKyVpWKLN+dop2gioaqP0Ot8mw7vWLfIXaQ9f0+ZDdUU1ZXIe9tms90zZhroNWdi10Ds6ssUvibmROCqb3Zsm5NUNupa+DdtmHlUWT6Gaghk53tN0UECb0sXj4xu0LQPZZAw+t6J7MObe1FN0cfm5nH0HUDzn5VVJgqczEVo6F2aUTNPG4aHcKpC8tCvf6+Zmy5rF2wtr46fv1g8G9DaaLlwEdInZU11pvayvuf/Mtc96EwlpCsVaUiZvvyM1OnSo8tVgTbwFSkI8cOsts2jHRNIuZh4Vn3atLqKuS9zcEV8PuMZZlJzHv7hD9XnkIEb1vQTJfTc4pJoOhuYpsPmGkKVO+QNdnZfpNKkPAJinlxvZPtO9M/yTRH+/JStAzKWkzFaqh9bdRcdOjO9up9dXN86AI2ZtzKO8Y1zXLgI6vsytbybduwMpj22bmFjBR2KIwlJGtVGeLzYvpjhezgqhOXGSZvR841yKrwLEaYlqZpv3yE5tUcXGO0hsfPX4oamEPylOVv5gqa6UobKgCmdorOSms6yutlFLILTv9NqnbpExSLYHunrPe0maN9eUlRBmUspmI11Fnjm55H3/s2TRPVprEzq+zKLtvhwZXBtLeOD+NEjvtQGEtIzKoy6x7PTJ3CnoNTAJrROXX0ydA2MBXtyDGDrD4JHJuZX1VOdWu/Yh21i+Q1zzE8WWTlyTTFKOd2IG5XnUqb1XbKDJtQxL8rVhuXql0qX7KipNzxqW8u8FF337SRR0MdWl6+922iJqqJ9WNrp1llV0XZ6sG03zUssfE34u9RSdDXtlFm0NdQmupA2bTAg3nPdasin1XVoW+DSBXvPPPG4irn5FDfQVdaF1mBfFNrxo6evZgZOLipfTWUVPlv6u67WOp6j14pPx9lBSSv02fMpNFBX0k8TVUTuzQPISuoMjpEaDkVnXCaGj5DF2YU+rOqWN0eP3+p+/zXz608Aiirbsy0WWUU4geYt9/k9e9qmokpllT5b6ImRSe0D8e8R8oxLXX55clb1cGfY56RtSHLV3ZNb5sAhbFGEhOVuYo86B1my7o13dhiesylkPuFOpPnyWNMfJ7YCWd+0X74cxZlq8fnF5fw3/8fz+O04TBa1rN8OyZ1R/I9h6a6ZRRj5rxpffa5ijaByeZ0n/eUgTz+XU00McXgMzX3CmVoL5usEc2Tt7Lfp8k7aJsAhTEPUydncdtPvLNyVXXdJgNfHo6fv9SNuq/HXMrCjMIeY5qKzaONIh3ZdfhzFim1m7b6PHr2YncHDwBsHB3Co//Df5c8KGpWWQ8PDmD/5Hu7kfJjQizoZRR6rqIuMIW2gyL9yidQqnrxLS7qMI/H+q8demgH7n3sBZy6cBm7njjSKMEiBWVoL5uqEZ1fXMIzU6ei81b2+2xZt2ZVkNo8wY9j2nabTL/X1J2BJvNP//CbuOfR5zG/uFTZM20dIhY18dz3+BFv/ucXl/DyidlV1315UEINEK6BMbe9bxwdWmGaembqVHQZx5aT6shP7b6zG2qhLbjqU2kpAWBAAIce2tE1D7ruY6vvLExB2lbWKpArEK+ZU8LO8fOXotu+rx3o75vVXvKUjV4vu5444t1wkNUXU5H3eTZTcy+RZ9yq455FUfW/5+BUd2zQ8+Zr52W+j+5OsWl0CIce2gEA0W1VjRWhgliVfa8o1IxlUGSFkEcq92lwQu8XssLJG50+z+pk1bb3ye349Be/uyJ8x+/+P0fxpY/fFaRly8qjjzxRv2+5YQQbR4dwZm4h6vDnVFpOsz6VZk7XUi5J4O/eWMCPrRtOmpfQ+FEptIB56tRlNreZxX39Kk/ZhPSzqrUneZ/nMzW3gayxsQwf3NT3TKHF0ev/rbcl9k9u78apDNFwlxW7TXenODW3gOPnV57oYGurenmod2uaz25KKIxlkHeFkHeAd3WImPsVdTrO6pSxzpBmfm7/8fX48sN34c9eOolPPf0KgOUOeu9jL+C5T34wuLPZgon6yNM55xeXsOuJIzgzt4BNo0P4wj//6WAzlO95MQOva6LMEtz1+FN5B6ZVgrQnflRRJ9k8k4HLbG6+7/Hzl5znIRYRYLL6WdV+LrbnhfrK5TU1l0VoHwkdG8tw4k51z5B3CCkPs/71gOEh7byMMjLdKd619h3dfhCyQNo4MojB667F8Zm4sknZ96owd1IY8/Anv3QHbvuJH0uizYgZ2GwdwqUdcaUv6nScslO6nK4PPP/ait+dvnA5WEDyBRN1Eds5Td8LtaLTtXd5NIx5Bl7XRGkr16mTs/i1g9/BCS0y+xc8mqGYMrvlhpHMI1vykHfTiquMXd/bNKN5HdhD+llZ2obQPAEIXsRlnRlaJXofyQqWnXeRZVvwlqEVSmHNiBE4Xe2tLgd4s3996Vff382XK696eZy5uAhgEcDVuU8Pwuwqm1R9r6qNGhTGPChzVJ7JJ3XDjzUjZAlTdUwSZlRkXeMChGshXQOXOfDZdtmFvrPeAZUZzJa/EA2jqY3JM/C6JkqXM7vOMU0zlMf5VTmnhzrYx+CLkRaCq05t35sHN6tydzmwA9mmkZBFSxnahtDnud7ZlS5rp2oRYu6l95FjM/N44MCLzraRZ5Fl7o4GwoXW0HdKac2IEThd7c0mqKu5TT2jjA0ovnHXldct69ZgQCy7X+hsHR9eNVbYfFpDQl6kdPtJAYUxD/OLS/i5J8NWZyaphZ0yzAhVTxI6+uATW7a6YLp1fLhrilED36bRIXz+oR34Z3/0onW1FGqec/leuN7DNRGY2pgsTYyr88ccTaSj/Lxi6ts2kaQalNQgqAt3ijz39U0+roj/Zl3ZHNjz+BeWSWofVBsu4b5oGRTZ/azwmdZixlrb7uih6wZyadfyngYRu1BMtbhX9WtqHiGEdTGUqg3EzjXHz19aIYipneIAunOg6qcxZ6IqUrv9pIDCmIfv/yB8dWYjtbCTwozQlK2+sQOo6cwJIVb8qw98p+YW8I9/7xs49+ZV1XbsBO/zvYh5D9eA7AolML+4hIUrS93Dz11asKw8bx0fxr+57z0YunYgV6gLW77NMsmKCWbDFJpPRcZIK9J+XZpKYHV9A37n4qpJ7YMaQkqNQOy99LrSNSGutuHqG6HtJc+Em/VOsW4Kvv5dZLzMWrjpFgrzPcrSCmXlb5Vps7O5a35xaVU/NX1agWxrVqymsQorEoUxD7fcEL46q4KQRpFKbV4FocKqme/P7tq+YmWr3lef3M+9ubgqpk1s3kI7oO89XAOyLfL8tg0jK1arT+2+sytIhTphpxo0bDGB9PubJsvQIL6m0KyeEaIdTdV+bRovmwmnKf5TQHof1BBSagTy3Gt4cPncza/mNJ26jtravnlshWZdtbnYvhPyTrZNRnnrMu94mWXaNTVj+nuUoRUKyV+o+wGwsp/eckPYWbJFNMa290kx5lIY8xC7OqsqT75GUdYhynVi5htYPVEODw7gSx+/a8XgWzTKfwrNpmtQsQ0G5mp16LqB7uQTKoSkyLPaQarHBNIHw/fdOLbKFykriK9umtTfO6aOfL4hoWT5+Om+h4ce2oHv/+Bi1P118gzSrjR1OF+nFO6L3Gt4cKDbP0L9Fs1wCqaP4Fc/8QHrRB9rHne9k2+TUdl1GepTawo1Lp+u1FqhGPOtz/3A9Gk1x1DfGGF7/zy+4Xk3k9mgMJaBa3XWRGLU5lvHh7FwZQnzi0tBGrY6zZvm4HXLDSPWFef49YN47pMfXJFPpdrOY05L6e8X4ufiGqSrFqJN7ZXtlAXTjHDK8Lfy7XYyBbCQ2HJmvLMt4/kmsayJMNSXxpY/s78UOY7G1BRWZSoxSelqkfdeLtO2ry8cPbv6dAq9rouaNbPukyX0++qy6Nhja+Ou9qjnPXZDQF5izbeuna+232bNcTZhr4jG3VbPE2vzxdKnMBZI6gbpokhHzJpoTE2fywfONnmm3kWnExOw0bYyNn+bZ0u4mZ+8nTOm/sy8xmjRyiTkea46uXHdO/DwUy/jjGZ6BFb6Xx0/fymXRkv3DVl8K1807ayJMNSXRifvZgezrZjPNvunriFq8qLQR57xzWXa9vWFbRtW7j4fvDZsZ2QqNw6fwKHeP7QtxebB1sZjdtWa+Und3lx90OWnaisPV//yzXGAfcdskcWurZ6vXH4zV7lQGGsQRTuiq5GbHWrouoGuz5Wt8ZmN8/D3zpammfG9s20VEzuohHY0/Vl50uTVhpj4tGipj6hxDbShWhg9ryp/D//pX+NMRyOhBIqNI4PYMr6mG7Qxr0ZL14qcmVuIbof6+7qEmlBfGh3XJBKqgVNtRU+jMM1MKXc35p1k86Z1+XBlYZZliGl7eHDl7vPjM+nGihBsfSik/lLlwRxH8izo9PzeuO4d+OUP/AR+9j3vCj4lJSZ/rjy6ysP3Pq45Tv3fvJfrFI/Q99AXpUfPXsS7hmV2QgsUxiIp01yXoiOGaIZ8Ddm2m2/nuzck08z4tAEhE0/soOJS2fsGSd/ROb5yLducmCLUgi2shO1+tnaUpb0cum6gK4jpnLm4iI2jYsWGhFiGB1f7BMa0w1DzY6gvjY6tjcVo4PS24vNRTdW+imp+Y9Lq7c3lw5WFrSxdAoHeTmN3n6fWQJt9KKT+QsarvHmJNXPr+T1x/jI+9fQr+I0vvoJ//wv/ALffNJ58/otx3QD8J7C40tm+c53iEZNvXYN34zspjJXO/OISPvy7X+/uwvnqJz6QtEHadrAVxTfo++zw5m6+FP4qMYJhlho6xhRoTq5ZQlRIgNQ82pAipJiI8/jemOl8ux719984MggpBM5qmiy1ISEWfTIyfQKzfm8zgdjMj6amLMSXRuFrkwtXloPamuXlm3S3bx6z+qimal9F2lJMWl8YE/1InBBC3ERs40tWEFufU3sVvly299S14GozTQptaKyrjWnqBZaDsP6P//6/lOKuYstjqIbRdh9bXaZ0A3FZU8xg5qFQGItg6uRst6CPzcx7jySKxbeDTV3PMxC4GpqtY5oNSp88U/jMxQiGWWrovEFBbWZOl2YjNKZXqDYkD65diHlifGX53rjaWJYvk8J8//nFpdyaLP39zYHXVy+hjr26ZmzLujXJzMtqs8iWdWvwT37/G93xQkV692ngst4zVfsqItTFpHW1t40jg9j389tz5d2Ha3xxBbF1+cJW6cvlQmnBsxZNpW+sUvEcDcrQ/PvcJmI1jLZ0vu9i+5XPmrJ1fBgngt/6Kj0vjAkhxqSUs0XuoRrJwpW3E+Vq5X1NydrcwVZkIIhpaGVqdnz3T9VBQrD5B9js/lnPtE2mSjhybdO2ERMXTvnK/Oj1Q8l9bwD3cTA+XyZTo6TX5fDgQJAmy0esFidUo6p+6/NLicWnCbKdmJG1SAidYGIp0reKjCcqVMieQ1PR2pWQvpQ1fpn1HOMLW5YvV9azfBsW8vrghXL07MWu3xUAPPiBCXzlb84U8v1UFPG3LcOCFNuvzDA7ujXlXcMSG38jPg+VCGNCiN3axzEp5f6iaXzXhRA7ATyrfZ4GcLeUcjo276aJRjkib12/pnt2Zeh98vophQwEvsHKt9ozJ9Iyt8/nMTGm3sHq8g8w7f4hg5uuDbGpzUMOA4+JC6cEpw/99teCfG9C6lcJ/D5hQKX7q/96DnsO/Q3OdAbBEI1SqHlJmWXMILemRjCP349rB5v6fxnmv1NzC9g4OtT1ocs6pqXshZBJkb6l+kpWP7a1t6Hz/s1DMWEMbL/3xf0K9YW15SFl/WQJlr5Fk/7eeX3wQjHz8YkP/T184kN/rxR3lVBhN8uCVCb6mGQ7gkn1qbm5uVz3L10Y6whNXWFJCDEphNgnpdybN03APccA3Nb5/2weIUxhmmj+7X3vwS03XI9bbgjfYh7S+Hx+SlkDQeqYRmWG8Cj7/lmY5io9Dk3e1a8tnfq/715Zz7PV+9GzF4N8b1xtwlb+5kTlikH361/8Ls5cuIyNnUHw+PlLhbUFuh8mgO699XNFYwLDhpj/bAJjGeY/PWhs1saFLEGiVFNUJDHjjdnefGOZ676ufpIVO8u2WSXLF9bXb8rymw1pj6ZDeeg4kJUX3/u43rkMd5VQbZfPghSL3j6yxhef1nv/ru1J+mUVmrG9AO5WH6SUB4UQf9j5Pm+akHtOFzVPTp2cxX+zZWN38Lj2GoH/9elXVvicmOdmZfnc5PFTyhoI8ggRoX5AvYYqS1scmryrX1c636QTovWx1bv+rE2jQ/jSr77fWl8xK01dKP/jX7wdv/7F765qD/rhymfmFvCVV07jZ9/zrsLagqNnV8YPO2M5VzQ2LlmTzH93XH/VpzRk8rMJynl2L5YpuBUx2fnGMtd9XQsoXz5ck2eWL6zvninaSIy/k0/7GDoOuAhtV753TuXHrITlEG1XCg2l0sSrHcvKbcVXDj7TcYyFzEepwpgQYgzAhEUzNSaEuFVK+VJsGgDTsffMyz/9w2/i5k0/gkMP7cBXXjmNTz39CoCVuyVOzS3g7t/5Ov7Tr/z0itW8y+fGdPb+q/96Didm5ldpIQB7nC2TLK2Gq8P4/ICyHKSbtErPw/Dg6jg0Kmp8niOUXCts29Zrlx9Y6AQdukI3B2tX7BxTKD8zt+A1Iyk+9fQrePy57+PzD+3A372xkLs9bNuwesdW0XNFzfvHbnwIXTHb+oKvn+bx+4wRfFLGIfNRdEJ0lZHrvq4FVKhrR2ig2BTvlkXo/bPqMlZTFxpSKJS81hiVB33H6Pd/EK7tKqqh1POtUG4rvnIIMR0XpWzN2ITj+9nONZvglJXGhXnPjwohZjr/vz3DLDoEYEj7qttDXj93Cd//wUUceP617sUbx4dx5a23u/4g595cXLWat/nc2HwhfvH/+hbeelvi0//pu/gv/3pnlNN+llbDFznfHOD0mEYugauqwb4KzNW2XgaHHtoR3dlN84hr67XLDyzmeSEr9OHBZcHy3sdewKkLl7HriSPW+jIHGZcfjX64suLU3AIm/+Av8dwnP5i7HQwPLp8T+I1Xf4Bf/g9/jaXOxoqDv/xThYQ8/f765oysMAF63dlWzKFx2kxc2vGiTulZzwjxLY1dYIVOiHkWbq7YUbYFVIxrR+jkmcocWfT+IXUZqqmLCSkUSqwwZ1uEqh2jW8eHV/jyZeWliIZSz7eJK+Crasd5jnGLoWxhbNzx/YznWlaa2YB7TmPZTPkSAAghxoUQT0opH3SkfQTAp20Xblq/XDn6JPTbk+/FLTeM4O7f+XpXAMtazdt8Gb77txdWOJMf/t5ZfPQnbwQQ1tiztBpZu4WGB1efuwnYfWzmF5fwzNSpwj5CMcTY9POgBn4A3Ujdr5/LPvQ6C1/d2TQ1KQRc28R3/PwlnNbOjHQN6ObkYJsslNBkRtk/feGy1wQaMqkNDw7gR0eGsaT1hb97YyFZ21J9L8RkqdeduWLetmHEavoK6Qt56z1GOLBNsHodAKv7tu27FAsCc6dfljAUEjuqqGtH6OSZwhxZ9P4pNXSu8aiI0BmbPzMP+tx0bGYeT+2+c8V5tmWxbcPIKp8vhS3ga5UKiJ4MbWExVR4G8KQQYq/Dj+wzAD6nfR4BcHLjyCD+w/9yJ06ev7RCclertmf/xQeiBhxg9RETepiFne/e0P1dSGPP0mqERs7P8rHRJyFdW5Bl8vFp2EJW1T4NRSy+SUk3d5iHXucROH11Z04SRc0F6t2KnFZgM4W6Jrg7Jtbjyx+/C/c89sLy+ZOO+8YOYmWbh0Kfof9Gb3dmXZ2aW8CPXD+IH76xGLyaz1vvocKB+QxgZTv/7K7tq56p/h+SD0VW/7Xt9FPtxdUWQjVBde/GrgpTo1tEC+kz/9rC8ADZp07E1kXWXGW6cpSpmdRP8sg68sxsl89MncJHtm8qRSArWxibcXw/7rmWlSb6nlLKabEcvM5qGpVSLgDoisqd3+LMxUVM/sFfdivNPM5l/PrBVXGUzNWXz1Z/4vxl/OuffTfeOXTtqjO/Qhp7iFYjNn6WvqtF+Z/pMVXeelti/+R27Hz3Bq+ZxjUZh07SPg1FrBbGfKY5KenmDtP85DPbusiqO30QTCGEmAOG8n9Tvhkx9R/C8OAAhgY6wSCl/eiPWCEzlXkoK8RLTJ8yNbLbNox0F2XXXiPwwzcWo7bX6/VeRqwk/Rk2TTaAFYsOFWMvpv2F+DwePXtxlebhTMYCJ+/CoRfwBTvVF8K6xtD0Ty3iX2a6u5hHhZnP0++bNRbraUPmJiCfpjaU+cUlHD9/CX/+q+9fEWPRLE/12Vyc7Tk4hSe+9mopGrKyhbFpwBp4dUxdy5HGe72zAeB1ALcpJ//Od9FsHBnsmnnMXTgK3+DgstXrvjf/7ivfw9b1a/Dzt25elT5k4MnSapgd2necjW5a2DgyCAlhPej5I9s3ZU62ruuhk7RPQxFSzvq7mc/8/g/eWOWjoJdbbGiEGMqI7aaXlen/ptpcync4evbiipMobKvFPEJm0Yk2JAhmbJ9aZdqSV02pQL7t9WXHSnJplbdvHrP6Eca0P7MvmeUNYMWGIhNXW8izcAxdJOXRtNjiQpahrYkdu6ZOzq46o7aof5np7qJwPS+rrH2CnW9ucmlvUwnfvrJ27cL90seX+8efvXSyu4GvLBeda5LezaAjLE3D4gfm2vWYlSbwnt8ydltO+J7p4o//5zu7PmOpnByHBwewf/K9K36nonOXhdnZHjjwIu559HnMLy51f9M1LXSEzzMXF3F85lL3/4tvLeGp3XeuMoEB9rJxXXd9P7+4fIyMypManJ/+2A58/V/+Q+yf3O6ctGzl7MqLCk8CKfHU7jtx6KEdmDo5i29On1vxbBW1PeveNlSHvu/xIyvKeeaNRfzM//6fV32vPy8Pelntn3zvqt2Qed7Bh1meew5OrWpPep7K9LNQ7WbmjcUV7Tfve5rtUEcXQhWh44J+X9Pcefz8Jedv82BqlX/lZ36i23dsfoS29ufKg173m0aHVtxr6uQs7nn0+WX/Synxx794O7Z2fqssC1kxybZtGMGuJ46s6iMmrj6W93e+NDNvLEbfI5SYsUv9a3MjMcfUmDakp1cLb9/zAHf7MOcacyzKen/9uandFULGQbNv3vvYC5hfXFqxgS8rgHNeqvAZ2wdgEoAK0LobWjwwIcQEgJ1SygOhaXzXpZSzQohnsZJH4I9rZuWX/viv8MV/8Y+s0Y/zOtQCq3emparcImEsTNPCxtHlzaXKUds86DnEFKeu/+j1Q3hm6hR2vnsDxq8fDAq0qPJk27WmrunmI58WRuXlmalT2HNwCsDVFeDP/f43cMJxfqBZdiF+cipvtgGzzIjZunnKVhYp/bFs5Wl7n7JNSr5AjCoIZoxGI0tLYWq1NwZqtWymPVd9pHAYNrXKv/fcq/jz75wK3kVn0w4ozZ/Zrz/wW1/r+rwuXHl7xUQ8OjxoPfDc9rxYX7pUv7O1DzONbSOUymtRTVno2KVr6m2/13ehAnHa/OHBqzuvl91TBrouOfrzlNvKzBuLThcVU0tv+mOZ5W2+//bN4RsLYrWVIW1/24aVDv6nL1zG4e+dXbEISxXk1aR0YUxKeUAIsUdFzQew3ggzsRPLgtKB0DQB1/cLIfZ0Pt4M4FlD2Avi2Mz8qoCTWYOl2UCydqYB2dG5Q/DlS+XDFsZCoTdUNQDPX1nCXb/1tW64AXPbb9ZkOzw4gC3r1uAf/LvD3QFbhe/wmTR11bi5a82lNg/xBfrI9k144muvdtMuXHm7K4gByxpK0+Rmmk9CwhnYOr0p7OaJmB2CqyxSb9e3lWcZ7+PDXMUqHywVBBOIm5SyJm6l1VY7b88EmijN+/pCMsT62tnIEpaz2oJNO6CHL1H9/uUTsyt2gw9dd82qdp81RvgEVddJEID/JA3X70IFXzON6Wyeage0KsuQsUs3oynB65YbRlaN6aFmSxNdY2q65Jgx3rJ2EpuCYZbLhx5zzHxfF3kWLaFlrTv4uzYbmHlJMbZWspvSdxZlR0haJShlnV9Z9HoIW8eHV00wLq2Hb7L27UxLQUjYCfU810rV1lBfPjG7ItxAnqMnvvLKaWf4DoU5+Kl3AFZOslvHh/HqD96wvmeoL5D+jvoAAAADDgdNffIJGeRsZblK2I2MmB2DrSzK0FKl8HcrgtluTGfyb06fy6wvl7OuS7jcvnksWstou6+rPlI59vuE5ay2YNMO2MouVKvhm6xcgqrthAzbeBX6O9vzzcOe9bHE3MSht60UArOOqz5sfms2fyyFabYs2kb1/Okx3lw7ibM2d/jGzxi/NCD/oiVkHLRtzHO1oZCQLKH0ZGiLVBw0TBDzi6sPm9VXST/yzuvwwzevAFhuIH/20kn8/K2bS52kXM66rg7oa4zmtTydWuVJDWR/8PVXu9+b4Tv05/pU8eqMvz2HpvCpp18Jek8X+jvq5uLxd16HGa3uQiYf37PNsqxbcCmLIkJe0RWlMq8c/t7ZrglcLRZm3ljEw3/6193f2lwBbANpnh3MIfkMSZPasT9vm7NpB2xt3XX/GEuCS1A1A7y6nNJtv9MXWq7zducXl6yHPev33rbBvvHFlues3dymUJVVJ7bNKKY/lolefinaqG9Xob6TGFgOibRwZWmFgGTm3zV+5hGs8s5Niqw6sI3fyg3k5ROzXSF97tKVVXmfWJvPFZ/CmAeXBHzjunfgf7vvPfjZ97xrxWHJP3zzCq4RQEcRhE89/QoOvDC9yg8pJaaz7v7J7fjI9k0AsMq3KSQ+kH49T6f2+fH8n7/wk95jLvTGb65MAXQHXf09Q97DhTIX27SaMZNPKGVop1JTZowf8zmu0CcxPl42TbTajHJmzn+Yr2sSCDmLLza8Skjdm+bBIocg256rL5Ky4iHatANZ9zcJ1djb+pRrsjXfwTzfdcu6NSsOnrf5gQKrN2PEtA/b4tElcNq0RSGnQNj8S13+WLYd8nnGGj2NTRg0Td+n5hbwyqlZ7D30N90d+GrXvRmvcerk7HKMwoi61svD5urjEx59Y0dev0yb4kNF+AGuCvRXLr8ZXOY6FMYC0TvmifOX8amnX8EfvTCNQw/tWCF0vG2EXToWKOmbhApO5mCkBLHYSNuuBhrbqV1+PDetX4PbfzzcLGuuTM0jM3yCWKzzqi2kRejkU5XwUgUpnMdDcZn7i/p4bdswgmemTq3yz7Md5vuj1w9h4Brh9InUCfEV/fCjzy9Pjg4BIIuiq30fLg161u7GPIuH+UX3QcyuRU5IgFfXO5gmxGOGH6jNad+0cNjaR5bpzhcoW98YpV/LOhVFpTHb75Z1azB1cha/+XN/H0PXDqzyxzLbYpExySUMvu/GsRWm7xvXvQOfeOo7mOmcQnPm4iI2jgo8tftO3HLDCP7J73+jWxe/dvA7+O2OwBgTzNcXrsZcaOhKgM8/tAMnO4t40yfbrJPQQK6m4gMAlrT5Xgn0Vy4HF/UKKIwFondMhfJxMCP6vg2s2KGXR4UaIziF2ObV//XvQuKCxZLlxxODqZYPOTKjyHvETj5Z9VCllinEBJb1m9S+ML482Ca62Oeb99BdBtRk7fLPm19cwv1PHAn2iczK29TJ2a7m9pimCYgpkzJN2baJJMSPLlSrrqe1HcT8Kz/zE/jFn7op6p3M/uh6h+//4CKGrruqZdF3vG4ZX2lG/NHrh7yBvM3nh9SHT2gzr4WciqKn2TQ6hM//8k+tEGx0Yd9WdzELGpsJ1baY0f0Nla/ew3/6111BTKF23Y9fP7his8uJTkglV55c7xK6A91UArx//3NdxYi5ODJNrqGBXM10alOaHsevCBTGAlANVvddMgOG6ip9AIV2SmYN/Dan1xBfL98gkGpVbhvA8ppazDyFlGWsP0cRzHowj8aKOUg6LyGDb+gAXZVmxuWfZROufGFEzLbmMtnb0prah/XvHPRqxsooG1e9lGHKNoUUINyPDgjflarXgcIMr5G3H9gmQ1uQ4y/885/GPR0ztYDEzJtXT1IZEFe1Ga5A3joh9eET2mzXYv0SQ7R9CtemBBs+E2rWYubYzKUVbgAKvU3pm10UMYs8m4bQ1e+2bRhxWqhs5fXZXdvx6g/eiArkqteLbiZPdW4yhbEMbIOTebC2mjD0iiyyUzJr4M+67urwoXHBijQsZaLQP4euqG1+AbqDdkieYvw5imKuYHUfiSKxiWKER1MgVEch6YNEqMapKs2MngeX/1BoGBF9sjT7hc/0oP92QADn3lzsRqTPoyXRN4NsHR8OWiWXrYnUGR5cGZYDCPeTUv8Pyafp17T7rpuTRS63TYYLV5a676TnVwkKx2bm8Y9/7xs419Hg6GallOFlfEKbuqacv10+h677mYK07zxY16aE2HhqrsWMzVSso9pUlgIjBHN89e1AHx4cwOcf2oGfe/wb+OEbi7gGwNuOcnC5voTkS68XpWQo6tepoDCWgW8SyTJT5SVr4A9dXbnCG+iDgrmKKzIZzC8u4R/9zn/GifPLQsmN48O4xjgOI8bPy+WgnYXSshw9e3HVDp+UE55PeMgTm8j0tQl5Z3Pys/np+IKM2t6pLM1MSB7U80PDiJhpQ4VJ9duswLW2vLmuqc0gof2/TE2kiYqUntdPKqb9mIuhP3phOtk7mpOhGeR4y7o1+P4PLnbf09xEpHwEyw4vY1LEH1O1rSxri2tTQt54arbFjKl9/rf3vQd/+ML0ijYVo8AI6aOhC9h/9kcv4odvLHa1nxtHBrF/8n24/aZxqwAa6vriI6XVhcJYBr4B02Wm8p3/GErWpJh30izTSXvq5GxXEAOwIqBqqL1f/11ezUHe1U+ejqXXg88ckfUuNl+bWNW5riUwfWr0QIxVTUAKfZUculjx9buiuxb1e4T48IQS2yfL1EQCKzf5qIVCXj+pmHya5eBLmyK8iW1BpN7zlhtGut8px+6/e2OhdD9Ok6Ja0OHB7LiUNrcO37N9JlTX+aDmM3bduhm7bt284reuhZSpwAiZJ0P7lP6OSvt55uIihq67xusKUWQ8TD2XUhjLwDdg6hW7UTun7ViGs2KdVGka2TA6hHd04gCZPkAqL9s2uINt5tUc5Fn9xHQs1wSia+TUNZcJzXwXm69NrOpc1xL4fGqy3iMleQcsV78LvV9W3CebRjv0gOqUlKWJdIWYyesnVSSfrrSpJjObNlW9p+34tR9bN5zrPVSe87SRKrSgrj7je7ZZN2oMyzrNxXxGaHxKc2xONU+G+hNmLYCKuIgUnUspjAXgGkxUxU6dnMWvabZ6RdnCTh7KckQ+evYibrlhpKuF2jg6hC9/fLnxm6vWjSODGLzuWhyf8QfbzKs5yLP6MTuWa7uzbwLJmlz0Y0x8A2Zezaq5snX51ORZoealyIBl63ch98t6N/MeSluXdxeaumfV2hYf+juaIWbKNIfGkHoyc41tWe4ZoRQ1NeYZy2KFP5cgHfPsrHoJsdqEKDAUKeo+ZuxLtThIPZdSGCvI8OByFGjdJLdx7TtwpmEDnyK1acRswF/QtAzf/8GyM+32zStNjmcuLgJYdqg11di2/OYJXhj7jubKyrXd2TdQ2Zzp75hYv0o4gMWPLlW9ZPnU6MJD0RVqSNRxMw5e0f4QMgBmvZvtHjGCgR77yFWfLqoKd2K+o81ErG+2qcN8nWIyM4Vilzk+hRYuhamxSOicItqjmGfnqRdbwPAsBUYep34foWOfi5idqPq70GesQYQMfE0ipWnEpWUwo2B/wQiOq7B1khQTVl7/nSyHbt/Zgds2rNz1tOfQVNdxVRcOFOb9yzBZhZguXPnx4dt0YQ60KftDyACY9W62e4ROQGbsI199mvgCWKbG9o76rq/5xaWgSPVlUnQyy1rk6OQRpMxxKEZIyTuG6eli85xK0I+tlzyBtn3nJKci5j18O1GznpFqzKYwlgDXwFdULZ5FE6K/u7QMZlwcW3Bcm2msyijwJsOD7sOVVd58ZwcOD64MH6Di25gmSP0Yk4UrS5hfXHK+Y1mC6fDgctiQe7Rjg2ICFNsmCt3Up1ACekoh0/Y+tuCpvtW3zVcmZOA+enZl7KONo0Pd8xFjhLgqXBh8E4Wtj9bhUlFkMgtd5ADxR+64xqGsNpJnR7Se1vRl1McN31iRetwM6WOKvBrDPJrC2LEw9Blmf7CFfUmRHx8UxnJiU/G7dseVIVTUKbTouLQMtrg4w4MD3vPuVPRnV8euQvj0Dbi6Gtt1dqAe6PAm7b3N7f5qwPaZB8us4/nFJRz+3tnM8xtdZJn6FFWY6l3lFLv6Dhm49ffeNDqEL2l+kTFCXMoYVy58/WXLujUrAqCqSPV1Etu/XYscW5vz9Wtb+3EJGL42kndHtMJmZdAXFb6xItbEFotvLIoVdFM/PwU2P+OY/Oja/7xQGMtBiIq/bBVzFSps3e/HZ2ayaRlccXFcg5ktmKDesfN0xryDgJ5HvQxC1NiuQd98b6VNAfxn1JUxwLrKOuY4jyxTn03zWZYw7Sun1KZfV/1GC3Elx7jK6i/Hz19aEQD1tybDBfEyyNO/bYscX/tytQWXljfWbypkMeLrA7ZnDg8OZI4VeU1sMWT1sRhBN087K2ssVMSaZs386O4Hf/o/vS9XHiiM5SBExR/rXxC7k8s86Dbl/c00oQcL6yjNRCh647ZFf87qjKFmBte7Zg0kpr+bT4sUq2Fx1V8KB2cbZlnnOTcQiDP1lbmyLaucXOQR8PL44RQRXM3+Yu4OjtUElE0qU1eeCdolBBXZBORajPj6gOuZtvyZvmWxJrYUZaQTI+imqqPUxPRrc3Gln76iNq7FQmEsBy4znE5MZ3atzLImtawAjr77Z5n+zAnbljYlZmczw0r4OqMtlIHKb1a+fQOkXgZmeICik1dI+8gzIYSgl2WqcwP1PJc5KLueWWbw1FQo7WFWPrMm7RBBzaxjc3dwyjJLofE0BZksX8qU2Moir3+Sr0xD+oCt/9g0gC7fsrIE67ztJVaI8sVw9C30qu77en7M01duuSGfoEhhLAc+M5z5u5AJx2ywvqNzTKfVrACOtvuHmP7Mwdw0Gyqq2sHju26WyQMHXgyOvO8bIM1yM/0Cim7OCGkfqc1s6p6+naNlDG6xmuKyHHXrJFQ7mLV4CrlHVh2r3xQts5Qaz8/u2o65y4v49S/+v5UHzTZdE/IKw74yLaLd0e9rRrhXvmVV+NNmucKYc2KMEBeiOfTFFKxjw5ft9JUrl9/MdT8KYzmJNcOZ2HZ/6RoxvbOpw5+3bXBHq8/Kq61DhPoBuHzGqtjBk3XdNNkqQiPv+8rTVm5ql2wTNk8UYXjQvnO0rHcLHZR7oWxdhGoHfW0yRsPoquM63smHXuf6poIyNfE+bOOvGuurCPyatRhxmVWrLCebW4jLjzo0b3naUtm+ZKHo73jlsv+3LiiM1YCrQ9s0MrYjHfKoa20dIsYPwHYyfRkdIUYrYpon//gXb8evf/G7Kw6tzbpHiEauKsf6qrG9e54DumOel2pwbUJYl1hCF1K+Nhm7GCvbhJvCl0evc31Twfp3DmLLujWpshqM6Yai4gUC8O72DiFEu5Ql7NVtlnftPs0TKkXvx3naUkpfsrrHFApjNZA14eidzXWkQwp1bdFOndrPI/YdTPPk6PBgrkCCsatKm1m5zHhyscQMKua7V+0MbxLy/LZqz2L6m6tN5umzZWpNUggGNpeIAQGce3MRu544Unn9Dg+ujhc4dXIW/+rQVHe8AYBrrxHJhcXQxUidZnmXj3OWH7WJrR/naUuuUxdiaMKYQmGsBkImHNXZQo90SLUbKQY1EIfEwQkh5B2yVlJFB6kQQcbnvFm3YFB0UKl71R3y/DZrJmPbp609Ns0/rmh+zP50+HtnvadgVIEZL1DlReett6U11mAR6l4MheAad0P8qHVc/Ti0rm1jXV6aMKZQGKuB2BVyyG/r6sTDg9lxcELJeof5xSV8+NHnl6PXd3wSUgoOMYKMmoDKNOvlIcWgUvdkn/X8NkxYKWjCar0q9Dov288tND/mDsaQDU2pn9vE+nblMdaPWu/Hm0aHgrSMRY6LCs1LXW2OwliFmKvc0Ibj+q1vE4Byqozxv0phrizSkLMGoqmTs12hT5kO7phYn8ynKE/ntp1VWafvQaq6qNt/wkcbJqwUlLlab1L9mnmps359Y3TWhqZU1L0YCiFFHocHl49ku/exF3DqwuVMk7TvuKiiAlQTxhQKYxWRepWbtQkgNuhpU0xbKTp53veJFWTmF1efVQnA+uyqJr8UddEGjUzKCSu0bqoWYMparecJMl1W+bjyUodAEhNaIaVpsp85fv4STmsBU30LDnNxkjqkR91CcCXCmBBit/ZxTEq5v2iaoterJvUqN+t+WddTqHvzavpC72eyffNY10l06/iwM7hhEf+5mJg4+s4qdValeqb+7G0bRioVborWRRP8J6oiVDCpQ0BNvVpX/WvhylLwjtWpk7P4lwencHzm0qpj3/T+CtgXIVk0qa01KS9l0CRtqCJmwdGEkB5lUrow1hGKusKQEGJSCLFPSrk3b5qi1+sg9So3636+6ynUvVVp+nSUk2gK/zlfpOeQ7di+czTNZ5d9iG9qmuA/URWhE3BdE3WqycYMA5MVEFn/veLYuUv4s5dO4udv3QxgpfD12V3bc5VPk9pak/KSmqZqu8vwn24rQkqZ/asiDxDiNQB3Symnte/OSynX5U1T9HpAnkcBXLhw4QJGR0eD3zWL1CsT/RBrV1BW2/NePjGL+x4/0v389Md2dDVkoXmz3aPIpJH6fr6yLjowmXk1z9E0NQZZh8rneYeyyGpTbSXk/NGmacZSYrbZrIDI5u91lPClQj+o+6nQD7Hl0ySNTZPykpKQ8bVX3z2WouUwNzeHtWvXAsBaKeVcaLpSNWNCiDEAE7pQ1GFMCHGrlPKl2DQApotctz2zKlKrVIcHB7wmMNfzUqh7q9b0xeJ7n6JaDjOv5jma+rNfPjHrPcS3qJCQEpvGtBcGZ19Zhq62274qNzeaZIUe0H+vR8UHrprh9T6wffNY7vKp29SU2t2iiYTsVM8z3vSaAFfnoqtsM+WE4/vZzjWbYJSVxkXo9dqEMR95G3UewSLvxJK1e7MIVU52RQW/mLyaz9L93EIPKc/y+UtVVuYz733she7k3TZNkE5WWYZOwG2dqG0bTbL8IdXvN44M4t/c999i6LprVp1uYesDbSuftms8Q8kas/LMI00ou9TjYJ1+g2ULY+OO72c817LSzBa8vgohxBCAIe2rSp0FijTqvIJF7MQSsnuzaKeoarJLIfjFTOAhB5ybHV+vV/N0g7IGQTPuz6nAXU4x1LGS7mVfoBD0dqY2mvh2A+q/P3NxEb/0f38bN61fgy88tGOV6bptwpdJrzvt6/jGrDx9pO6ySzEOmuNRnWMFQ1ss8wiAT9f1cLNRPzN1apXpy0VVGiVfx9M7xabRIXzp43c1fut3lVoO27PMA87Njq/q1Xa6QVmDoN6WzJMFUgxKda2k225iLErsBKP/XqFCCfSaoNLvgroiTx+pu+yKjoOu8aiusaJsYWzG8f2451pWmqLXbXwGwOe0zyMATjp+mxy9UV97jcCeg1N44muvBk9WVQgWvo5nrrzvfewFPPfJD7Z+0itLi2PubHtq951WH57hQfvpBmUOgnpbSj0o1bmSbquJMQWxE4y5EPDtumw7/S6o68T2kbrLrug46BqP6horyhbGpoFlp3wp5az2/Zi6liNN0eurkFIuAFhQn4UQ7jcqAdWon5k6FXQmWx2mHl/H27ZhZNmsNbdchKcvXG69uj+PFie0XvRB4NjMPIauG4gK01HVIJh6UKp7Jd3P5Jlo75hYj6/2gaDSz4J6Ueosu6LjYNPGo7pCW0gppVPiyUpT9HpAnksJbZFFiADQBKdJGzNvLCZx+G7K7pzYUBtlnngw88YiDn/vLHa+e0Pjzb9Z9NK7NI2m9B1C2kIZfaaRoS067AMwCUAFYN0NoBt8VQgxAWCnlPJAaJoE10snTyWHSPopTD1lNMDx6wfx3Cc/WOi+RQXNlO8Vu2qKqZeYFZ3a2dY04TsLW1209V3aQBv9NslVekGQbuM7NEkrWrowJqU8IITYo6LiA1hvRMLfiWVB6UBomqLXy6aIUJHVOGKFBLODlKlZK9qwiwiaqd8rVgUeWy+hZVX3jqU8uOqije/SFnrVb7MfaKq1I4ZeeIe6qWQ3pe9cyI5G7IDle+9ZkkWvl0mZk06IkKBHUTc1EWXmrejKqIgNv4z3ihEuy/LjappfQwiuumjju7SFXvTbjKWNmhmgnQsuE9s7xJ7q0u8wtEUJlD3p+IQE01yhBueyd+KlWBkVEWiaMNGXofKue8dSHlx10cZ3aQvDgwP40sfvWuG32U/Cbps1M00Yu4pivsOWdWtaWx91UboDfxtJ4cBf1yrNdDzXj0BRHaKMvKU+WzIPbV0Z9yKsi3poarmXna+qxp8yw900sd5Mss781TVidc8HddFkB/6+pC7HQHOFcsgSNbuMvDVhdVdVmZd14HvTB+IYmuQY21bybgJqWrlXobWqYvxpsr9tFWS9v/4OTZgP2gaFsR7DZgqqYldVv5igzICt+yffm3nocuj9qM4nil5qF1X4RFUx/vSCb1cRytoxTpa5pu4MkPSoFUrVHaCu51aJGbD1gQMv4p5Hn8f84lLh+6kBjhDbEWl521jdKC0JgFK1JGWPP1W9h8784hJePjHbiLqPff9+mA9SQp8xC3UFfSXNR9dY6OT1ieglDQhJh94urr1G4K23ZavbR9tN8foOddPto8xnNm1saHs9ZpHi/fL6jFEYs0BhjPiYX1xadW5fkYGy1wc4F/3w3kXecX5xacURaUB/OUI3hbqEoio2JdTRB5va71PVMx34W0xTG2dbKbs8U5/b1wbn3dQ0cdWfmqLvODw4gI9s34QnvvZqzzhCt3Gsi/UVS/WORQN8Z1FHH2xyv6/bJ5DCWM00uXG2kSrLsx+FqFTUPfBVQYp37CVH6NjzW5vyzjFCUcrxJ/bYtNjn1tEHm9zvbbHSXj4xW1kbpDBWM01onE0a+IrShPJsA3XXeT9sfY+dxF310StCv9k3p07O4o6J9at+17QFaoxQlHr8SX1smt7O6uiDTe73ej3bTq8puw1SGKuZuhtn0wa+otRdnnmpUjhqQp33ksbHReg7NqE+qmDbhhFsHR/GsZl5AMCeQ1P4quVdm7igChWK6hp/Qp5ra2dV98Gm93tVz9+cPld5G6QwVjN1N84mDnxFqLs881D1ZNyUOu8VjY+PkHdsSn2UzfDgAPZPvhcPHHgRAHDM8a5tXVAB9Y0/Ic91tbOyTypQz1b5anq/n19cwp6D3+l+3lpRG6Qw1gDqbJxtHvhcNL2zm1Q9GfdinbeZfqqP7ZvHMt+1jQsqnbrGn6znVtXOzMDYECLJrvOqOHr2Yld7CwD7d22vJM8MbWGhV0NbuExhdfsP9TupNGMx9dhvdd709216/hQp8tmWd+1Fqih7MySHThtCsxQdjxlnLCFKGDvzdzM4PS96YtDoF7+UtlJ0kGT9umHZpIHlSEJou2YMKDYeM85YCUw+cQQn3hStakQu+sUvpa0UNW2wft2wbNLAciQhmGZmAJVpQlNp/uowNfNsSg/KbtwLZwbWca4aKUbMuXSsXzcsmzRsWbcG71r7DgDpyrFJZy+SdOjnUlZ1RqXSyN33+JFC5wXXBTVjHraOD+PEm70xgLfdKbbfiDUJsX79fHbXdgDLDuQsm3jmF5ew64kjOH3hMjaNDuHQQzsKlyPNniQlbdfcUhjzcPChHT3jMwa0b5dhP5NnYGH9rsY24ZN49PZ4am4Bx89fwvj1g8nu2cbJs8304iaKtu9KpjDmYXhwAO/7kd7ZTUnaQ9sHlqbACT8NZbRHtvF66FWNZNutAxTGCGkgbR9YmgIn/DRktcc8mha28Xro5QVKm60DDG1hoY1xxnpR7UxICtg3yqVXNS29CuurXBjaoo9h5yLETZtXy22glzUteWi68E+NZDOhMNYDcDAkhNTFlnVrcO01Am+9LXHtNQJb1q2pO0u10ZaFMRcozYNxxnoAxlEiRWG8J5KX4+cv4a23l91d3npb4vj5SzXnqD5sC2NCQuhpzZgQYkxKOVt3PsqGamdShLas5kkz4SaJq7AsSF5Kd+AXQuzWPo5JKfcXTeO7LoTYCeBZ7fo0gLullNMReW6dAz8heTEP9m3DYb6kWTTdT6pKWBb9TV4H/lLNlB2haUxKeUBKeQDAtBBiX5E0AfccA3Bb5+9mKeXNMYIYIW0hlWmRZu566QUTcVVH3rQBlgXJQ6maMSHEazC0UkKI81LKdXnTBFyfBHC4iHmSmjHSdFKbFrmarweaiHsL9iPSOM2YEGIMwIRFKzUmhLg1T5o89ySkF0ntKMzVfHXomjA6fPcObT+omtRLmWbKCcf3s55rWWlC7/lRIcRk589rFgUAIcSQEGJU/QGgncZCL5hTegWaFtuJOWFvWbeG9dgjULAmRShzN+W44/sZz7WsNLMB95wGMC2lfAkAhBDjQognpZQPevL6CIBPe673PFnqdZpTmgV30LYTc8I+fv4S67FH4E7KYvS7ibfnQlsoIUzjMIAnhRB7PX5knwHwOe3zCICTJWSvkYQIWgws2zwYuLF92CZs1mNvwAVSfrjYjxDGOrsY7w746d6OT9eM4/q451pWmuh7SimnhRDAshnTFNTUbxYALKjPnd/3DSGCFld9hBSHE3Zv0wbBuokaKC72I4SxThiJAxH3ngasgVfH1LUcabzXOw7+rwO4TTn5d74jHkIELU4izaaJAyyx04YJm/QmTdVAcbFfoplSSjkrhJiGxdfLYkoMTuO73hG8vmXstpzwPZOEC1qcRJpJUwdYQkizaKoGiov98s+m3AdgUn3omDr3ap8njGj6mWl81zvaMj36PrDsnL8XxAtDG7QX7uIibYM7s+tB34W9aXSoUYe69/scVMVxSHuwrMUaA7BeSqkLY7ux7GN2c2iaiOsAcDOAb3dMrDF5ZtBX0hqoGSNtgu21XmbeWMS9j72A0xcus/wTM7+4hG+/+rd4/9/fCkQGfS19N6XvLEqXH1rW+ZVFr5PmQx+ocKjiJ22ibFNZHWNHm8ar4+cv4fSFywCaZapsO2qR8dqpH+ZK33OhLUj74co5HvrzkbZQprN2HWNH28YrOsuXg77IyAOFMdI4mupkSggpTpma3DrGjraNV9Skl4MScl87lU8gK9uBn5BoeNQPIb1NWc7adYwdbRyv+t1ZvgyUkPsnv3RHrvSlO/C3ETrw10+bfDAIIc2BPmOkTubm5rB27Vog0oGfwpgFCmOEENIflCFIUTjrX/IKY/QZI4QQ0peU4XzfNod+0gzoM5YABjAkhJByKWOcLSNgMoMwkzxQM1YQroIIIaRcyhpnywjzwNARJA8UxgrStm3NhBDSNsoaZ8sI88DQESQPNFMWpI3bmgkhpE2UOc6WEeaBoSNILNxNaSF2NyV3zhBCSLlwnCVtgLspa4RH0RBCSLlwnCW9DM2UhBBCCCE1QmGMEEIIIaRGKIwRQgghhNQIhTFCCCGEkBqhMEYIIYQQUiMUxgghhBBCaoTCWAPg2ZaE9DccAwjpbxhnrGZ4tiUh/Q3HAEIINWM1YztzjRDSP3AMIIRQGKsZnm1JSH/DMYAQwrMpLcSeTVkUnrlGSH/DMYCQ3oBnU7YYnrlGSH/DMYCQ/qZ0YUwIsVv7OCal3J8ijRBiJ4AHpZT3p3gmIYQQQkgdlOoz1hGKxqSUB6SUBwBMCyH2FUkjhLi18/l+ABMpnkkIIYQQUhel+owJIV4DcLeUclr77ryUcl3RNEKISQCPSClvK/pMSx4q9RkjhBBCSPvJ6zNWmmZMCDEGYEIXijqMCSFuTZUmZXpCCCGEkKop00y5yoTYYdZzLU+alOkJIYQQQiqlTGFs3PH9jOdanjQp0xNCSN/AY5gIaQYMbQFACDEEYEj7ilEXCSE9DY9hIqQ5BAtjnV2Kdwf8dG/HZ2vGcX3ccy1PmhTpHwHw6YD7E0JIT2A7homxzgiph2BhrBMm4kDEvaeBZad6KeWs9v2YupYoTYr0nwHwOe3zCICTAc8jhJBWoo5hUpoxHsNESH2UZqaUUs4KIaaxrJWaNa69lCpNivRSygUAC+qzECLrUYQQ0mqGBwfw5YfvSn4ME492IiSesg8K3wdgUn3omDr3ap8njGj5mWk0XA75oekJIaSvUccwpRTE7nn0edz3+BHc8+jz3BhASCClCmMd0yaEELuFEHsA3GwcTbQThqCUlUaLwL8XwK1CiCd1gS7gmYQEw91mhIRj80MjhGRTagT+tsII/ATgbjNCYmGfIf1O3gj8DG1BiAPuNiMkjrL80Ajpdcr2GSOktajdZgC424yQQFL7oRHSD9BMaaFNZkruXCoXli8hhJBQaKbsQ+ifUT5qlU8IIYSUBc2ULYY7lwghhJD2Q2GsxdCniRBCCGk/9BmzQJ8xQgjxw7GHkNXQZ6xPoU8TIaRq6K9KSFpopiSEEBIF/VUJSQuFMUIIIVHQX5WQtNBnzEKbfMYIIaQO6DNGyGry+oxRM0YIISQaRtonbWB+cQkvn5jF/OJS3VnxQgd+QgghhPQcbdpoQs0YIYQQQhpNHg1XmzaaUDNGCCGEkMaSV8OlNpqodE3eaEJhjBBCCCGNxabhComvOTw4gC8/fFcrNprQTEkIIYSQxlIklEpbNpowtIUFhrYghBBCmkNbQqnwOCRCCCGE9CS9fvQfzZQ9TFviqxBCCCH9DDVjPUqb4qsQQgghTaQq8yiFsR4l7+4TQgghhFSr1KCZskfhQb6EEEJIfqoMGkvNWI/SpvgqhBBCSNOoMmgsQ1tYYGgLQgghhMT6jOUNbdHTZkohxFjdeSCEEEJIO6kqaGzpZkohxG7t45iUcn+KNEKInQAelFLeb/n+We3zNIC7pZTT0ZknhBBCCCmZUoWxjlDVFaaEEJNCiH1Syr150wghbgXwAIAxABOWW4wBuK3z/1kKYYQQQghpMqX6jAkhXoOhlRJCnJdSriuaRggxCeARKeVtlu8PSylnC+SbPmOEEEIIiaJxPmMdf60Ji2ZqrKPdSpKGEEIIIaTNlOnAbzMhAsCs51qeNDY+2jFvTgoh9kWkI4QQQgiplDJ9xsYd3894ruVJYzINYFpK+RIACCHGhRBPSikfdCUQQgwBGNK+YoRUQgghhFRCz4W2kFK+pASxDocB7M4Ic/EIgAva38nyckgIIYQQcpVgzVhnl+PdAT/d2/H5mnFcH/dcy5PGi5RyWggBLJs5X3L87DMAPqd9HgEFMkIIIYRUQLAwJqU8AOBAxL2ngWWnfGNn45i6lihNl47263UAt6lNACGBX6WUCwAWtPtkJSGEEEIISUJpZsqOMDUNi6+XYUYslMbCt4zdmBOR6QkhhBBCKqNsn7F9ACbVh46pc6/2ecKItp+ZRsMmsM1Ci77f4RFHekIIIYSQ2in9oHAhxB4sh6YYA7Bej76vBC0p5c0RaVQE/kksa70OAPh2x4yqpweAm81rgXlm0FdCCCGERJE36GvpwlgboTBGCCGEkFgaF4GfEEIIIYRkQ2GMEEIIIaRGKIwRQgghhNQIhTFCCCGEkBqhMEYIIYQQUiMUxgghhBBCaoTCGCGEEEJIjVAYI4SQCplfXMLLJ2Yxv7hUd1YIIQ0h+KBwQgghxZhfXMI9jz6P189dwk3r1+DLD9+F4cGBurNFCKkZasYIIaQijp69iNfPXQIAvH7uEo6evVhzjgghTYDCGCGEVMS2DSO4af0aAMBN69dg24aRmnNECGkCPJvSAs+mJISUxfziEo6evYhtG0ZooiSkx8h7NiV9xgghpEKGBwfwvhvH6s4GIaRB0ExJCCGEEFIjFMYIIYQQQmqEwhghhBBCSI1QGCOEEEIIqREKY4QQQgghNUJhjBBCCCGkRiiMEUIIIYTUCIUxQgghhJAaoTBGCCGEEFIjFMYIIYQQQmqEwhghhBBCSI1QGCOEEEIIqREKY4QQQgghNXJt2Q8QQuzWPo5JKfcXTSOE2NP57+0ApqWUe4s+kxBCCCGkDkrVjHWEojEp5QEp5QEA00KIfUXSCCH2SSn3d/7uBzAhhPh8kWcSQgghhNSFkFKWd3MhXgNwt5RyWvvuvJRyXZ40QogxAH8B4ENSytnOtVsBfBvAzVLK6TzPtORhFMCFCxcuYHR0NDQZIYQQ0mjmF5dw9OxFbNswguHBgbqz03PMzc1h7dq1ALBWSjkXmq40zVhHcJrQhaIOYx0BKm+aic6fQv12Is8zCSGEkH5gfnEJ9zz6PO57/AjuefR5zC8u1Z0l0qFMM+WE4/tZzzVvGinlrJRynZTyJUua6ZzPJIQQQnqeo2cv4vVzlwAAr5+7hKNnL9acI6IoUxgbd3w/47mWJ82DAA53tGF50kMIMSSEGFV/AEZcvyWEEELayLYNI7hp/RoAwE3r12DbBk51TaH03ZRl0jE97gRwW8FbPQLg08VzRAghhDST4cEBfPnhu+gz1kCChbHOLsW7A366t6OlmnFcH/dci02zD8Btypk/5zMB4DMAPqd9HgFw0vN7QgghpHUMDw7gfTeO1Z0NYhAsjHXCRByIuPc0sOyUrwlLADCGq073udMIIZ4E8KDxuzzPhJRyAcCCdm/XTwkhhBBCklKaz1hHGLL6cRkO+NFpOlq6fWrXpBBiQghxa55nEkIIIYTUSdnHIe0DMKk+dISovdrnCSNafkiaSSxruiaEEDs7n/fiqubLm54QQgghpEmUGvQV6B5dNItlAWq9fnSREpSklDeHpOnEETtve46Usmtb9D0zMM8M+koIIYSQKPIGfS1dGGsjFMYIIYQQEkvjIvATQgghhJBsKIwRQgghhNQIhTFCCCGEkBqhMEYIIYQQUiMUxgghhBBCaqTVZ1OWzdxc8EYIQgghhPQ5eeUGhrawIIT4cQCv150PQgghhLSSzVLKvw39MTVjdtSh4psBXKwzI2QV6hB31k3zYN00F9ZNc2HdNJs89TMC4FTMQyiM+bkYE7SNlI92iDvrpmGwbpoL66a5sG6aTc76ia5HOvATQgghhNQIhTFCCCGEkBqhMGZnAcBvdv4lzYJ101xYN82FddNcWDfNppL64W5KQgghhJAaoWaMEEIIIaRGKIwRQgghhNQIQ1sQQqIQQjwrpbzb+G639nFMSrk/5johhJSJEGIngAellPdbrhUav1KMb/QZM+CkUR9CiD2d/94OYFpKude4zgm/ZoQQkwA+L6UU2ne7oZV35ze3q/rLuk6K0+k7s52PM1LKg9o19pua0Mp2DMB6AJ+RUs5argOsm1IQQtwK4AEs18FPSilvM64XGr+SjW9SSv51/gDsBrBH+zwJYF/d+eqHP7OcAXwey5N+UN2w7iqpozEAe5aHjRXfvwZgwvjufOh1/hWul2dV+QK4Va8f9pta62VPZ5JWn8cAPMm6qa0+JgF82/J9ofEr1fhGzZiGEOI1AHdLKae1785LKdfVmK2eRwgxBuAvAHxIdlaNndXMtwHcLKWczqob1l35dFaA/xHLA43ofDemf9Z+KwHcBmDad11K+VIVee9VOnVys9RW4UKIW1W5st/Uh8Oc3/2OdVMtHY3VI1LTjBUdv7Kux4xvdODv0KmUCb3hdxjrCAakXCY6fwpVDxNZdcO6K59OOX7LcmnC8h2wbDIz69R2nRRjH5Y1Y100QWwM7Dd1Mq65XqyAddMYio5fycY3CmNX4aRRE1LKWSnlOmMVocp8Gpzwm8BPOlZ5447fz3SuZV0nOelM2GNYnqB3d/72aT9hv6mXvQD2CSGeFUKMdermwc411k0zKDp+JRvfKIxdhZNGs3gQwOHOypATfo0IISallAfqzgdZhZqUx6WUBzp19KwQ4vPqe0c69psKkFIeBnA3gJ0AzgP4K03TxbohK6AwRhpHRw2/E8CqLcikWjral1nPT2Yc3493rmVdJ/lRk3LXfNwRACaFENSe1EynDm4FsA7AAQCfN3ZHkvopOn4lG98YZ+wqnDSawz4sOz/Odj5zwq+PjwK4WfNTuRnohlKYBnC483lMqy9g2Xw23fnzXSf5mTb+VcxiWQhwlS/7TTXsk1djWj3Y0Vg+K4Q4DI5pTSFrfCp6PRgKY1fhpNEAhBBPYjkw36z2dWUdgqzENE92Vvu7pRbvSAihTMmzRtqXQq6TfHR2GQPL5kq9LMc6/7Lf1ERn8bKiDKWUh4UQ+7Gs9f+Pnd+xbmpESjlbdPxKNb7RTNmh0+Ct/kmcNKqho8Lfp/wqhBATnW36s/DUDeuuUsYs3+3DcgwfAN163BtxneTnJdh9iDL7BftNLbyG5YDWs2DdVI3L167o+JVkfGOcMQ1LJN0Vn0l5dGLA6Cv8MSw7v+7trF68dcO6K59Omd6P5ZX9QSwHsFRmShUBfgzAern69ATvdZIPsXzEy/1Sygc7n3djOTbV/dpn9psaEEI8i+W6mdW+e9KoK9ZNyWgR+NUccwDLwV8PaL8pNH6lGN8ojBlw0qgeFXjPdk2uPHaHEz4hBirwq/pcx0RCVtMZ1x7pfDwH+3FIrBsCgMIYIYQQQkit0GeMEEIIIaRGKIwRQgghhNQIhTFCCCGEkBqhMEYIIYQQUiMUxgghhBBCaoTCGCGEEEJIjVAYI4QQQgipEQpjhBBCCCE1QmGMEEIIIaRGKIwRQgghhNQIhTFCCCGEkBr5/wFOPFb2LAkOXAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -555,16 +310,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If everything is satisfactory, dump the `Obs` in a pickle file for future use. The `Obs` `pcac_plateau` conatains all relevant information for any follow up analyses." + "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": 22, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "pcac_plateau.dump('B1k2_pcac_plateau')" + "pcac_plateau.tag = \"O(a) improved PCAC mass extracted on the test ensemble\"\n", + "pe.input.json.dump_to_json(pcac_plateau, \"pcac_plateau_test_ensemble\")" ] }, { @@ -577,7 +333,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -591,7 +347,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/04_fit_example.ipynb b/examples/04_fit_example.ipynb index c6b1281e..3fe8fabc 100644 --- a/examples/04_fit_example.ipynb +++ b/examples/04_fit_example.ipynb @@ -32,14 +32,21 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "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 11:27:34 +0100 on host XPS139305, Linux-5.11.0-44-generic-x86_64-with-glibc2.29\n", + "\n", + "Description: SF correlation function f_P on a test ensemble\n" + ] + } + ], "source": [ - "p_obs = {}\n", - "p_obs['f_P'] = pe.load_object('./data/B1k2_f_P.p')\n", - "\n", - "# f_A can be accesed via p_obs['f_A']\n", - "\n", - "[o.gamma_method() for o in p_obs['f_P']];" + "fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding_front=1, padding_back=1)" ] }, { @@ -80,12 +87,19 @@ "Fit with 2 parameters\n", "Method: Levenberg-Marquardt\n", "`xtol` termination condition is satisfied.\n", - "chisquare/d.o.f.: 0.00287692704517733\n" + "chisquare/d.o.f.: 0.0023324250917749687\n", + "\n", + " Goodness of fit:\n", + "χ²/d.o.f. = 0.002332\n", + "Fit parameters:\n", + "0\t 0.2036(92)\n", + "1\t 16.3(1.3)\n", + "\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -94,25 +108,14 @@ "needs_background": "light" }, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mass, Matrix element:\n", - "[Obs[0.2102(63)], Obs[14.24(66)]]\n" - ] } ], "source": [ - "# Specify fit range for single exponential fit\n", - "start_se = 8\n", - "stop_se = 19\n", + "start_fit = 9\n", + "stop_fit = 18\n", "\n", - "a = pe.fits.standard_fit(np.arange(start_se, stop_se), p_obs['f_P'][start_se:stop_se], func_exp, resplot=True)\n", - "[o.gamma_method() for o in a]\n", - "print('Mass, Matrix element:')\n", - "print(a)" + "fit_result = fP.fit(func_exp, [start_fit, stop_fit], resplot=True)\n", + "print(\"\\n\", fit_result)" ] }, { @@ -131,15 +134,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Covariance: 0.003465486601483565\n", - "Normalized covariance: 0.8360758153764554\n" + "Covariance: 0.009831165592706342\n", + "Normalized covariance: 0.8384671239654656\n" ] } ], "source": [ - "cov_01 = pe.fits.covariance(a[0], a[1])\n", + "cov_01 = pe.fits.covariance(fit_result[0], fit_result[1])\n", "print('Covariance: ', cov_01)\n", - "print('Normalized covariance: ', cov_01 / a[0].dvalue / a[1].dvalue)" + "print('Normalized covariance: ', cov_01 / fit_result[0].dvalue / fit_result[1].dvalue)" ] }, { @@ -162,10 +165,8 @@ "metadata": {}, "outputs": [], "source": [ - "m_eff_f_P = []\n", - "for i in range(len(p_obs['f_P']) - 1):\n", - " m_eff_f_P.append(np.log(p_obs['f_P'][i] / p_obs['f_P'][i+1]))\n", - " m_eff_f_P[i].gamma_method()" + "m_eff_fP = fP.m_eff()\n", + "m_eff_fP.tag = r\"Effective mass of f_P\"" ] }, { @@ -184,20 +185,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Effective mass:\n", - "Obs[0.2114(52)]\n", - "Fitted mass:\n", - "Obs[0.2102(63)]\n" + "Fit with 1 parameters\n", + "Method: Levenberg-Marquardt\n", + "`ftol` termination condition is satisfied.\n", + "chisquare/d.o.f.: 0.13241808096937788\n", + "\n", + "Effective mass:\t 0.2057(68)\n", + "Fitted mass:\t 0.2036(92)\n" ] } ], "source": [ - "m_eff_plateau = np.mean(m_eff_f_P[start_se: stop_se]) # Plateau from 8 to 16\n", + "m_eff_plateau = m_eff_fP.plateau([start_fit, stop_fit])\n", "m_eff_plateau.gamma_method()\n", - "print('Effective mass:')\n", - "m_eff_plateau.print(0)\n", - "print('Fitted mass:')\n", - "a[0].print(0)" + "print()\n", + "print('Effective mass:\\t', m_eff_plateau)\n", + "print('Fitted mass:\\t', fit_result[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now visualize the effective mass compared to the result of the fit" ] }, { @@ -207,7 +217,7 @@ "outputs": [ { "data": { - "image/png": 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XLn760SvFigfhQnMjhMrQ0JD+8i//UkNDQ36HAgAIsJDMk9Ap6XSWf53+hYaVjpoEAACAlalH0lfc398o6UuSPiDpR+46ahGwaCQJAAAAK5DblOiKJLU++YK3+kcXP/1obzHjMMa0SeqSdEBSTNKRhM1RSfsk9Vlrd6Qc0ynpvKQhSbvcfc9L2mWt3bvEMXZLalvq84YZSQIAAAi8gfikBkadfgTnBsaSfkpSS32VWiLVvsRW6qy1fZI6jTE7JZ2y1nal7NJpjDmRsu60pHuttTFpoRDfba094v6+aMaYA9baIymrvy9p8E7OW2pIEhAq5eXlampqUnl5ud+hAACW0Je+e0lPv3g2ad3Hn31l4fcnHt6qT+y+L+/zhek+0frkC1slfcxd/Fjrky8cuvjpR89mO2aZZOsQeMz7xRjTIadmIZawPSbplCSlSTIKtVvJtRmy1h6/w3OWHGOt9TuGO2KMiUgaGRkZUSQS8TscAACwDBJrEtIptCYhHo+roaFBkhqstfE7j9AfrU++8GFJfyrJSiqXNCfJSPrIxU8/+kwxY3FrC/qstZ3uclRSk7W2zxjT7m6LuUlCl7V2d8KxpyV91Fq76KZS7uM9JakjsWkTFoeaBAAAkNVSF9AXoyVSTXOiFG4Nwp8qebRKr4rkC61PvvCti59+9FzxI1uw0/3Z5xX+3QShU9LOlGZFbZKeMsb0STrkJhPdcpoJtUmKJTYhMsYclFP7MCQt1BR0uPu2JZz7kLvu8+5+O9zjD8jpR9Emaa+19ribyLwoqc9d1+fumzGORO5z8x73o+7+TZJ2WGs73ccckrTffY69Cce2SWp3F3dJOmGtPZmw3TvWe61ibtOstOvTxVcokgSEyrVr1/Rnf/Zn+uAHP6h169b5HQ4AhEK6pj6JCm3qEwQDAwN+h7AU/oukeaUf0t5K+oicb9aLqcMtVEfldGTenbjRWnvSGCM5nYgXmhW5Bewer2Ds1kp0JywfM8b0ucf3SDrvFYbdbW3W2sPGmFjquSX1GmO65IwG5cVxxE1IerymSNbaXmPMIWvt4YS4MsaR+sTd2LzHafLOa4w5b4zp9mJyn//nJSXWdvTISQwOSzruHrPDTZb2uOf3ztcmaU+m9RnfmQKRJCBU5ufndfPmTc3Pz/sdCgCExgcevEe773e+eDk3MKaPP/uKPrf/LdrSUifJqUlYaUJyn2hV9jmvWosTRpKTCYXh1M7KeXG/0d+ZUhA/IacDdJ+kA9Zak7CtS9n7Q0hO7UASt1DfZIxpT/hWP5ZPHJJuSxJcQ3KSlMTtqY/dKyeJStSZ8hz65NSMeH0p9hpjjlprY27zrZNya0HSrF8SJAkAACCrdE19trTU6YG7G3yKCK6LylyT4G33k1eQleTUFqT7Bj6NnZKGvG/KXVE5TX7alVCQlxZGV1qsI3IK6J1ubcbRPOPIJpZm+XzKuqbEBbeA32aM2ScnWWjy9nGbQnVKGjbG9Ep61q1x6M2wfkmQJAAAAKxM/0vSwQzbjKQvFDGW27ijFyV2RG7LsGum428bkSilwJ6T2wwpWxLRo1uzU6fWAGSMI4dctRq3cftYLMwPYYzZnxLDbrc5UYekLmNMs7W2K9P6Qh8/nWxVVAAAAAgod5jTj8ipTZhzV8+5yx/xudNyEm/Y0zx3P6U0CYVbGE7XVMfblk57hvWSFmoh+lI6AOcTx5JxR2XqTpnoLepuazfGHDDGRK21fW4/jB1y+n6kXb9UcZEkIFSam5v1kY98RM3NzX6HAgAIsKamptw7rQDuMKfbJP25u+rPJW0r9vCnrmwvarfyTBLc/gHH3YK7pIWCdLtbqD/ifvOeuM0ruPd5v7uFee8xo1keskdOIT2pxiBbHDmeQurrkPrYqctNCedOPUebbnUCT9SXZf2SYJ4EAACQtx++NqL3/eG39NVfe+eK7pMQlnkSPK1PvtAup9nMjouffnTRcw0shlsY75JTYI0peSIzrylM1FprEoYJbZd02Frb5Y6GdFBOLcFCu3p3/aB7zqHEQnwe2yR3BCS3A3K3G8cRbx6HhP2jkp7K1Ewn22Ol7Jf0OO5r8lTCczskpxCftI87gtFBSZvldIyOufv1KGESOiUPdXpE0r5061MmqVs0kgSESjwe18svv6y3v/3tfB4AYBmEJUno7+/Xpk2bJJIEIC06LiNUxsfH9fLLL2v79u0kCQCAjG7evOl3CHes9ckXNkja4C6+0fvZ+uQL3i5XLn760StFDwyhQJIAAACwMnVK+u2UdV9K+P13JH2yaNEgVEgSAAAAVqYeSV/Jsp1aBCwaSQIAAMAK5DYlIhHAsmAIVITK6tWrtWvXLq1evdrvUAAAAVZTU+N3CECgUZOAUGloaNCjjz7qdxgAgIBzhz8FkAE1CQiVmZkZXblyRTMzM36HAgAIMO4TQHYkCQiVGzduqKenRzdu3PA7FAAInQs3xvXMdy5Kkp75zkVduDHub0B3YHBw0O8QgEAjSQAAADkdPXVZD3/mG3q+t1+S9Hxvvx7+zDd07NRlnyMDsBwK7pNgjDmQsBj1ps7OccxB99ddkvpSp73OtR0AAPjnwo1xPfncGc3bW+u837ueO6NdrU1qXVPrT3AAlkVBNQlughC11h6x1h6R1GeM6c5xTLe19rD7b6+kNmPMsXy3AwAAfx09dVnGmLTbjDF6ltoEIHQKbW7UJem4t2CtPS7pQKadjTFRSR3uT88hSXuMMW25thcYGyBjjKqqqjLezAAAhesfnpC1Nu02a636hyeKHNGd4z4BZJd3cyO3IN9mre1L2RQ1xrRba3szHNrm/vO29yWsH8qxPfWxgKzWr1+vp556yu8wsEgD8UkNjE5l3N5SX6WWSHURIwIgSRsba5xCdZpEwRijjY0rb86BdevW+R0CEGiF9EnI9M1+TMmF/AXW2pikxgzn6cu1vYDYAITAl757SU+/eDbj9ice3qpP7L6viBEBkKR9Ozep5x/Pp91mrdX+nZuKHBGA5VZIktCUYf1Qlm3pdEo6maZGIq/txpgqSVUJq+oLeGyE3PXr13X06FHt27dPa9eu9TscFOgDD96j3fc73+6dGxjTx599RZ/b/xZtaamT5NQkACi+e9fUqvux7ep67owkp9Nymdtap/ux7Suy0zJDZQPZFXXGZWNMu6QOSTsWs931lKTfXvroEAazs7O6fv26Zmdn/Q4Fi9ASqb6tOdGWljo9cDczowJ+27tzk3a1NumPXjqn46f79f72jfrYu7esyARBEvcJIIdCOi4PZVjflGVbqm5JO9xmRovZLjkdmxsS/m3M87EBAMAdaF1Tqw+9o1WS9KF3tK7YBAFAboXUJPRJTgfmlEJ8VHn0HzDG9EjqzJQA5NrusdZOSVro2cjoBAAAAMDSyrsmwS289ylN/4MsIxtJWphfodvrZ+AOf9qe73YAAAAAxVPoPAndkvZ4C27hvithuS1lRmYZY/bIqW1oM8Z0uMtdulUzkXU7UIjGxkY9/vjjamxMHTQLAIBbotGo3yEAgVZQx2Vr7RFjzEFv5mVJzdbaroRdOuQU8I9IC3MrpJ092VrbmWt7IbEBklRdXa1t27b5HQYAIOCqq5lzBcim4NGNrLWHs2w7IjdBcJdjkjJ2Gsi1HSjU2NiYfvCDH+itb32r6urq/A5nRWEiMwevA4KGz+TyGBsb8zsEINCKOgQqsNxGR0f14osvasuWLSQJBWIiMwevg4OCaXDwmVweJAlAdiQJACQxkZknCK9DEAroFEyDIwifSQClhyQBgCQmMvME4XUIQgGdgmlwBOEzCaD0kCQAQMAEoYAehIJpEGpUAKBUhSZJGIsPq0xzfocBn81OT6qt9R7NTk9qLD7sdzgr1s3xMfdnXGPxeWIocgyrJbXWezE4j3tX7bxa670YJjQWnyhaPH69Dl/8p0v6n9/qz7j9v75zo371XfcULZ4gKOW/i6U2Oz3pdwhAoIUmSXj1219W3eoav8NAANzfLF07c1LX/A5kBbs4VilprS6eflFldTPEQAy+xNA+XabDP1kuSeq/WaE/ONuoX986rI2rZyVJjVNX9aN//F7R4gmCUv48LLWxm8VLtIGVKDRJgqlYpYrVtM8sdXNz85qYnFRNdbXKywudKxCe8lnntSuvrlPFan++KSQGYli7WlrrxRB3YnhDU7U2R1but9d3yq/3YmjKaHjKGbH8ypz3s17ls1aS1Fhl1VRlixbPUpifXLkJDlAMoUkSyiurVVld63cY8FnsxpCOfvnvte/nf05r1zT5HU7ehiadf5k0VTv/iqXCjaViVY0qfWryTQzEEIQY+Nt0nPx36a9eTV73+z+8FcAv3Sd9YIXNYxkff93vEIBAC02SAKxkf5fmBpxoJd6AgTDgb9Pxc2+QHlyXeXsxEyUAxUGSAARA4g348pj0ez+Q/ttbpU3ufHDcgAF/8LfpKHaNCQD/kSQAAZDuBrypTtoS9SUcAC7+NgGUKnp2AgAAAEhCkoBQWdPcqM4P/ietaW70OxQAWFKvjUl/e8H5/W8vOMtYvOZGRkQEsqG5EULFGKOKinK/wwCAJXXikvT0P0vGXf6HfunFfumJt0i7N/kZ2cpljMm9E1DCqElAqMRG4vqbr51UbCTudygAsCReG3MSBCvJmxlhXs7y069Ir4/7FtqKNhKnKgbIhpoEhMrMzKxeu3JNMzOzeR8TtHHQcXuzin1bpbvr/I0J8MuJy5Ixkk0zV5kx0t9fkj70E8WPa6Wbmc3/PgGUIpIElDzGQQ8WmlUAya7dTJ8gSJKssx0AlhpJAkoe46AHR2KzCq9M5DWvePoV6U1N0l1MrI4Ss2515poEGWc7ACw1+iSg5DVVO2Oeb4neSgy8cdC3REkSislrVpGO16wCKDW7N2WuSbBW+pl7ihsPgNJAkoBQqatbrXe/80HV1fHV2kpEswrgdnfXOc3tjG7dtMvkLD/xFmrXFquulvsEkA1JAkKlprpa92/boppqvv5fibxmFWnRrKJkMT+AU5tw5D3SezY6y+/Z6CzTT2fxqqtW+R0CEGgkCQiViclJ/euPz2liMstwRQgsmlUEj98F9BOXpM6XnA7skvOz8yWnaVqpuatW+g/3Or//h3upQbhTk1PTfocABFpoOi6v//cvKzqU/Yo5U92soU0/k7Su6fLfq3JyMOf5x5oe0HjzmxeWzdy0Wvqeyyu2oY0dmqlZu7BcNXpJ0avfznncvKnQ9S17k9bVX/ueVsfP5zx2sm6TRja8M2ndmgt/o/LZiZzHxlt2aaJhy8Jy+dSI1lz6Ws7jJOl6689rvvLW172rh3+k+hs/yHnc7KqIBt/waNK66Gsvqerm1ZzHjke3aWxtuyRpbOymXvrWd/Xg5D9p9eqanMcO3/WQpms3LCzXTl7RkxXf0P2Xpcbr2Y+9tvXxpOW6672qjf0452NOrV6v2N3vTlrX/O8vqGLamdth1aT0ZIXSxjC65q262fjGheWymZtae/HLOR9Tkm7c84jmqm7NMFozck6Rge/ftt+l6Tq9PLhZP2Fq9fKZcd3V8ppWv3F30j4NV76l6rHcpbSbkc0aXfe2pHVrzx1TmU0/9OA6Sf99/b361NUHJUnzMiqT04n5N+6P662vv5DzMSVpoO0x2fJb3xLWDv6L6oZ+mPO4dNeI/1z+97r/8mDOz8NyXSOyfR6W+xrxldi9+h9XH5RkZeQU0P+h3+i/3Teg/2/buoX9lusacWm6Tn/Q9z5ZmbQd2d8+/X+1ef5CzsdMvEZ41p3967zi9a4RXrJUqwm9fOaCNjWf1z2rsmdMy3GNyPZ5kIpzjUg1V1GjG/f+QtK65bpGJIqt/ylN1d/65qBy4rqa+k/mPE5yrhFj47RfBLIJTZJQPjuhspns+5RV3J5ElM1Oqmwm90w0Zv72bxzyOU6SZOeTFo2dze8xyypvf8y5/OItm5u6bZ3zGuXzXJMvzsbO5/9clfJc52eyHjs15/ybnK7UuVjytrbRSa2ZGVdVjgmUy9K8NxVzEyqbmU+zdzJj55LPZecU0bgq55Tz85Qujvzem9trOcoS3pvKOSni/kyNwcynBpX/e2NSP4fzt38Ovxzfqk9d/2lJTsH8xyPS0ZGf0K/XJjdrKJubWvRzLZ+9meZ53PLztT/UW++5pM9P7dYL1xr1no3S/vukN5gJlV1c3KxRJt/3Js01olaTqpwbz/l5WK5rRNbPwzJeIy5NR/Q/rr5N8zLyBqS17v+/92qLtm689U32cl0j/nboJ9z04PY2aMZIX72xVp+oz538pbtG5P93M5c0LO+8qnV05I06OvJG/fe139R/jJzN6zxeHHd6jcj2eZCW/xqRr+W8RizEl5pIFPQ5BJBLaJKEuYoazVdmr0mYr7i9nfp8RXXO4yTJlt3edjGf4yRJJrlVlzUVeR07b25/e+bL84t3vrzqtnVzFbm/WZckW5b8uNaU5f9cU1qw2bLKrMe+Gpf+bVi6bmv0hb7kbfvKq/VoU63e1Jz9EefTvDez5TWar8z9fK1JzkDmTbniqtVMuTR/e/krZxz5vTfpPoc1mndvijNzUlxKG4O9rVCY/3tjUz+HZcmfw0vTdfrU9Xe5hUJ3H/f/1OFH58urFv1c5ypW5/yWcGPlnPasHdUL1xpvNauYKORzmMzm+94kXCO8b47Pze9SLD6sfTm+OV6Oa8Sl6TodjW/WP8/V6kx8/LYYlvMa8TfDD6QpmktOUdkmTeC1XNeI1+Yalakfu6x0ZaYuv+d6B+/N5YlVKcPyGvdvxOr/v/4u/WR9XJty1CgkxnGn14hs1wdpea8RmaS7tyz3NUJy/k6SFPQ5BJCLsRmHElkZjDERSSPf/PIX1di8Nuf+CJbE2Y4zzVFQyBCk128M6eiX/077fv7ntHZNU8HxnItJT3xTevqnneFP/eBXDM/8m/TceWk+zSWhzEiPbS7urK5+vhfJ3xxroclTMSd08zuG7tPSN19X2kJ6maR33iV17VjeGILwmQxCDImCcI0Ki7Nnz+oXP/wJSWqw1sb9jgcImtDUJPzV+UrVXMu+T0uN9L57k9d99YI0kLuZvt661vnnmZ6T/jJ301JJ0vtapZaEUVkuxKWX+nMfV1km/ec3Jq/79uvSj2O5j22N3BoFw/PsWWk8jyY0P7VB2tZ4a3l4SvrfuZs4S5L2b5VqE77I+uGg9L0c74skNVZJb3ZrDLw5Cr5+Sfqn13Mf+0Cz9Da3eXRlZYXu3rBOxy/Xqmog97HvvccZXtAz4DZR/coFKXr7F61J/sv9ycvfu+Y831zurnMeN9H/Pu+8zpIUm8ocw9vWOc/XMz7jvK/5+MXNzuvs+fGw9O0rt5a/ey19QUjSbcOP/kO/dDGPW+q2qPRTdyWv+4sfSXm0BNPmhuTlgZvSVy/mPk6SfnmbtCqhkugH151/ubTUOH/nmSZ0+9wr0r8NSXVpvsFdymvEd69Kn/tn5/dsMSznNSLXcLOJI00t1zVidDrzZ9JaqaZC+l//mvsxE68RnnyOk6RLo5k7089b6eWrmWNcjmtEtuuDtLzXiExqK533NVExrhHv3ijdG7m1XOg1orIiNEUgYFmE5i/k5oxRriaME2lGO5uYy6/gPD13+7p8jpOkuZQbyOx8fseuStMWfzLPeKfS1NSOz+R37GzKxXne5v9cU2+WM3k+13T9DiZn83yuCe9NtCGiX3ikQ3/4z9JsPs81JV7vvZqYdQpghZjK872ZTPPe3Jy99f70uTfWHw1LbZHkAlXqjfNO3pvUz2FlmdeQJI2U4Uen8nxvJtP83dycTf/3dFu8Kc91roDnmmo6z/dmYtWtCd3SFQyNpFdjTsEm3WOkWuw14ttXMr8XiTEs5zWipSZzsmGVPNLUcl0jyoz0QJP0w6Fbr0dijcrNmVuJfTZTd/DeNFZn/zxUluV/rqW4Rky42zJdo5bzGlGIYlwjUu9VhV4jGiJ1uXcCSlhokoTVlVY1OdqQ16S5odaUJxfCMkl3M87nOEkqT2nYW1GW37HpbgDVecZbleadzTfeipTHLTP5H1uW8lwr83yuq9PEW12R53NNeG+stZqbm1dtpTfVUHYVKbt471VNno+dGkc+x1Snea6rK6QfT0inr9+K+vVx6bVxacda51tf6fbPxJ28N6mfw60NTi1XOqnDj1bl+fpUp/m7WV2RXwJWlrJPeQHPNdWqPN+bmnKpbyTLMKxyCmHpzrWU14jYVIZkLSWG5bxG1FY6nz3vM5kYz/6tycNvLuc1Yluj881634h0dkQLHdnvqnVqGwu9Rnjyjfcta5ymX+lYSfdF8z/XnV4jpipvJQGZrlHLeY3IJN0+xbhGpN6rCr1GrPTm1sByo08CAmMp2tqu1D4Jr405Y79n+ub4yHuKMyb6icvO0JJ+tsX3lHLfjCDE4Hl9XHr2Velkv9SRUEAvNl/7qPB3EUr0SQCyYzI1IAC8Ji7pGCP9fYZvMpcas7oGY0K3IMTgYQIv/i4AlCaSBCAArt3MXChM7TS83Eq9UHh3nfMNsdGtC6TXeO2JtxTn9QhCDEhW6n8XAEpPaPokACvZutWZO0emdhrG8tu9yZkXwmtm8x4fmtkEIQYEQ+pQ0Yk/pcKHigaAfJAkAC5v8izJ+blva/LwqMtp9ybp+Ln024rdvAQO75vjk/3+fXMchBjgv7/7d+mvXk1e93s/uPX7L90nfWBbcWMCEH4kCQiVpsYGffA//aJqqnNMcpAiceIqyRnj+8X+4nVM9JqXZOocSeEQKF0/9wbpwXWZt1OLsDiNDZHcOwEljCQBoVJeXq662sLa5rw2lnnyrKdfcZp8FKOQTvMSAOnQnGh5lJfTLRPIhr8QhMpIfEz/58VvaiQ+lntnV1BGFpLoHAkAxTI6Nu53CECgkSQgVKanp3X+4iVNT0/nfUyQRhYCABTH1PQip5MGSgRJAkqeN7JQWowsBAAASlBo+iSUzc+obHbC7zCwSK+NG321r0JShb7aN6v9m2d1d23hs4GXzU3KzM+qbG4y78/De+8yOn7O6+icmC1YWSv97F1TKpst3szkZXNGUrX7HPyZEZ0YiIEYghkDlk7ZPDUJQDbhSRLmplU+k387dATH11+v0mf/tX6heP7ia+U6+Vq5fuP+Uf3MXVMFnatsZlxl89MqmxlX+UxlXsfcs0r6jfun9Zl/rZeR1byMymRlJf3G/aPatGpKKuK9pGy2QlK1ymYnVD4zW7wHJgZiIIbAx4ClQ5IAZBeaJGFowztlmqJ+h4ECXYlP6bMvnk0ZWchJFz7zbxFteuNWrY/kP5zpTOO4HviZuzWz5U0ars2/1++ujdLn3jil589c1z+ei+mntzTq/dvXan2kSsMFPJ+lEB+ckHRe8XU7NdxcU+RHJwZiIIYgx4ClM2Ve9zsEINBCkyTMVdZpvrJIM19hyfzD+SEZ3UoQEhlJL54f1+Nva877fNXROm1/uzOg+HyOfVO1NNfpvW+u1j+ei+m9b96klubags+xFGyFcX+u1nylP8MbEQMxEEMwY8DSWRVp8TsEINBCkyRgZbo+NpU2QZCcxOH6WGHNjaamJnX1tctaf/cmVVUxsDiwkg3fnFbsptMk5LXYRNJPSYqurlTj6lWhjwHLY3pq0u8QgEAjSYCv1tZVZa1JWFtX2MzJoyMxnfjb5/ULj39QVS3rlyJEAD558d+u6bne15LW/fFL5xZ+f6z9bu3ZsbxTogchBiyPsdG43yEAgUaSUMISvyFLpxjfkD20ba3+9kz6dqFW0ru3UR0MlKqHf2KddryhKeP26Or8BidY6TEAgB9IEkpYum/IEhXjG7INDTXqfFebev6pT0bSvL01Z0Hnu9q0voEmQ6WEph1I1Lh6le/vdxBiAAA/hCZJmJ6d08T0nN9hrCg/tXmNHrirQZJ0ZWRCR755QQd++l5taHBG7WioqSzKa/q21ma9oalWXz3zur59flDvaGvW+7bfpXWR6oIff3JmXrNzVpMz84uKfWpmbuGnX5+nUo7h//zwqr7yz8k1S4lNO/7jT96lX3jL3UWLp5Tfi6DFACy16Vk+y0A2oUkSZubmNTbFmMeFqCg3aq5zviG7Oe2M+d1QU7mwTlLRXtPaqnK9Y3OzkyRsblZtVfmiHnty1qo6EtXkrF3U8d7rcHN61rfPUynHsKu1UdvWZR6lLFJTWdR4Svm9GJmYUXzCebxrcaeD54UbYwvxRGoq1VBDUxusXFZlfocABFpokoR3bWtRJBLxO4wV619fdzpw7bq3Wfff5c/ruDQxrNMv/tT9PsdwZ4ghOILwOvgVwx+/dE7/8xvnk9b9+f+9tPD7f31os36xfWPR4gGWWvxu5roAsglNklBXVaG6qtA8naJbvap84adfryMxEEPQBOF18CuGD7+jVY++eUPG7S31VSX92cDKN8/nF8iKvxCEytWrV/XFL35RH/7wh7V+PUOgFmIgPqmBUWdeinMDY0k/JadQ2BKhI3mpaIlU834j1K5du+Z3CECgkSQgVKy1mpqakrWZpmhDJl/67iU9/eLZpHUff/aVhd+feHirPrH7viJHVZpI2IDlx30CyI4kAQiAIBQKP/DgPdp9/7qM21vqC5vYDotHwgYA8BtJAhAAQSgU0rzEQcIGAABJAhAIFAqDg4QNAIBFJAnGmAMJi1Fr7eE8jjno/rpLUp+1tutOzwmks2bNGnV2dmrNmjV+h1IQCoXBQcIGlIbm5ma/QwACraAkwS3MLxTijTF7jDHdqYX+lGOSthtjjhljjllr9y72nEAmlZWV2rAh87CNQC4kbEBpqKxkMkAgm0KnG+ySdNxbsNYel3Qg087GmKikDven55CkPcaYtsWcE8hmZGREL7zwgkZGRvI+ZiA+qR++NqIfvjaS1AbdWzfgzjYLAAiPQu4TQCnKuybBLei3WWv7UjZFjTHt1treDIe2uf+87d7xbcaYoUWeE0jr5s2b+v73v6/29nY1NDTkdUwQ2qADAIprYmLC7xCAQCukuVFbhvUxJScBC6y1MUmNGc7Tt5hzYulduDGuZ75zUZL0zHcu6lffvUX3rqn1N6giog06AABAskKShKYM64eybEunU9JJa22fMaaj0HMaY6okJZba6gt4bKQ4euqynnzuzMLy8739er63X92PbdfenZuW/fGDMNwkbdABAACSFXUIVGNMu6QOSTvu4DRPSfrtpYmotF24Ma4nnzuj+YRJJ73fu547o12tTWpd5hoFmvoAAAAETyFJwlCG9U1ZtqXqlrTDbYa02HMekvTZhOV6Sf15Pj4SHD11WcYYKc3U9MYYPXvqsrp+9o3LGsNSN/Wpra3V29/+dtXWlk5zKQBA4VavXu13CECgFZIk9ElOB+aEQr4kRXWrM3JGxpgeSZ0pxxZ8TmvtlKSphPPmEzvS6B+ekE2TIEiStVb9w8vfqWupm/pEIhG9973vXbLzAQDCKRKJ+B0CEGh5D4HqFuL7lKavQK5RiNy5ELq9UYyMMW3u6EWLPifu3MbGmoxJljFGGxtrihzRnZuentbly5c1PT3tdygAgADjPgFkV+g8Cd2S9ngLbuE/caK0tpTZk2WM2SOnZqDNGNPhLnfpVk1B1nNi+ezbuSlrTcL+InRcXmqDg4P6whe+oMHBQb9DAQAE2NBQvi2lgdJUUMdla+0RY8xBb5ZkSc0pMyN3yCngH5EW5lY4luFcnXmeE8vk3jW16n5su7rc0Y3mrVTmVix0P7Z92TstAwAAIJgKHt3IWns4y7YjchMEdzkmKWengWznxPLau3OTdrU26Y9eOqfjp/v1/vaN+ti7t5AgAAAAlLBCmxshhFrX1OpD72iVJH3oHa0kCAAAACWOJAGhUlZWptWrV6usjI82ACAz7hNAdkWdTA1YbuvWrdPBgwf9DgMAEHAtLS1+hwAEGmk0AAAAgCQkCQiVgYEB/cEf/IEGBgb8DgUAEGDXr1/3OwQg0EgSECpzc3MaGhrS3Nyc36EAAAKM+wSQHUkCAAAAgCQkCQAAAACSkCQAAAAASEKSgFBpamrSL//yL6upqcnvUAAAAdbY2Oh3CECgMU8CQqWqqkpbtmzxOwwAQMBVVVX5HQIQaCQJJWwgPqmB0SlJ0rmBsaSfktRSX6WWSLUvsS3W6OioTp8+rR07dqi+vt7vcAAAATU6Oup3CECgkSSUsC9995KefvFs0rqPP/vKwu9PPLxVn9h9X5GjujNjY2P6xje+oW3btpEkAAAyGh8f9zsEINBIEkrYBx68R7vvX5dxe0s9VbEAAACliCShhLVEqldccyIAAAAsP0Y3AgAAAJCEJAGhUl1dre3bt6u6mhoSAEBmjG4EZGestX7HcEeMMRFJIyMjI4pEIn6Hk7fEkYXSWYkjCwEAsFLE43E1NDRIUoO1Nu53PEDQ0CfBJ+lGFkq0EkcWCoLZ2VnF43FFIhFVVPDxBgCkNzs763cIQKBRivJJ4shC5wbG9PFnX9Hn9r9FW1rqJDGy0GJdv35dPT096uzs1IYNG/wOBwAQUDdu3PA7BCDQSBJ8km5koS0tdXrg7gafIgIAAAAcdFwGAAAAkIQkAQAAAEASkgQAAAAASeiTgFDZsGGDPvnJT/odBgAg4NavX+93CECgUZMAAAAAIAlJAkLlxo0b+tM//VOGtgMAZDU4OOh3CECgkSQgVGZmZtTf36+ZmRm/QwEABBj3CSA7kgQAAAAASUgSAAAAACQhSfDZhRvjeuY7FyVJz3znoi7cGPc3IAAAAJQ8Y631O4Y7YoyJSBoZGRlRJBLxO5yCHD11WU8+d0aSNG+lMuOs735su/bu3ORjZCvXxMSEzp49q61bt6qmpsbvcAAAAXXt2jVvGNQGa23c73iAoKEmwScXbozryefOaN46CYKkhd+7njuji9QoLEpNTY22b99OggAAyIr7BJAdSYJPjp66LGNM2m3GGD176nKRIwqH8fFxfe9739P4OEkWACAz7hNAdiQJPukfnlCmpl7WWvUPTxQ5onCIx+P62te+pnicmmMAQGajo6N+hwAEGkmCTzY21mStSdjYSDUoAAAA/EGS4JN9OzdlrUnYT8dlAAAA+IQkwSf3rqlV92PbVWZujWrk/d792Ha1rqn1N0AAAACUrAq/Ayhle3du0q7WJv3RS+d0/HS/3t++UR979xYShDuwatUqbd68WatWrfI7FABAgHGfALJjnoQA+OFrI3rfH35LX/21d+qBuxv8DgcAgNCLx+NqaGiQmCcBSIvmRgiV+fl5TU1NaX5+3u9QAAABxn0CyI4kAaFy7do1HTp0SNeuXfM7FABAgA0MDPgdAhBoJAkAAAAAkpAkAAAAAEhCkgAAAAAgCUkCAAAAgCTMk4BQaWlp0W/+5m+qurra71AAAAG2du1av0MAAo0kAaFSXl6u2lomowMAZFdeXu53CECg0dwIoTI0NKS//uu/1tDQkN+hAAACjPsEkB01CT4ZiE9qYHRKknRuYCzppyS11FepJUKTmUJNTU3pxz/+sR566CG/QwEABNj09LTfIQCBRpLgky9995KefvFs0rqPP/vKwu9PPLxVn9h9X5GjAgAAAEgSfPOBB+/R7vvXZdzeUl9VxGgAAACAW0gSfNISqaY5EQAAAAKJjssIlfr6er33ve9VfX2936EAAAKsrq7O7xCAQDPWWr9juCPGmIikkZGREUUiEb/DAQAAK0A8HldDQ4MkNVhr437HAwRNwc2NjDEHEhaj1trDeR7XIanTWrs3yzmjkpolHbLWxgqNDZiYmFBfX5/a2tpUU1PjdzgAgICamJjwOwQg0ApqbuQW5qPW2iPW2iOS+owx3TmOaXf32SupLc32g5KOuuc8LOmQpKznBDKJxWI6duyYYrGY36EAAAJsZGTE7xCAQCu0T0KXpOPegrX2uKQDmXeXrLW91touSScy7LI7sdbA/f22ZAIAAABAceSdJBhjopLarLV9KZuixpj2O4ihya1NAAAAABAAhdQkZPp2P5ZlWz66JHUbY04YY6Ju06TOOzgfAAAAgDtQSMflpgzrh7Jsy8lae9IYs1tOc6RhSXvT1FYsMMZUSUqcaYyxLrGgoqJCGzZsUEUFU4AAADLjPgFk5/s8CcaYNkntkholHZF0LGUEpVRPSRpJ+Ne/7EFixVi7dq06Ozu1du1av0MBAATYmjVr/A4BCLRCkoShDOubsmzLR7e19rC1Nmat7ZS0W1KPmzykc0hSQ8K/jXfw2AAAAABSFJIk9EkLHZgTRb1thXI7PCcda609KemwpI50x1hrp6y1ce+fpNHFPDbC6cqVK/rUpz6lK1eu+B0KACDArl696ncIQKDlnSS4Q5P2KU3/A2tt7xLGJEnntcjEA5ibm/M7BAAAgBWt0D4J3ZL2eAtu34GuhOW2LP0JMiUX7WlqJ3a4NQoAAAAAiqygrv3W2iPGmIPezMuSmt2J0jwdcpKGI94Kt0nRfjnJRZsxpkfSaXfGZsmZifkpY4wkDUpqVkLiAQAAAKC4Ch7/y1p7OMu2I0pIENx1vZJ6laHg7zZjIikAAAAAAsJYa/2O4Y4YYyKSRkZGRhSJRPwOBz6bmZnR8PCwGhsbVVlZ6Xc4AICAGhwc9IZBbXAHQgGQgJlEECqVlZVqaWnxOwwAQMDxRRKQne+TqQFLKRaL6Stf+YpisZjfoQAAAoz7BJAdSQJCZWJiQr29vZqYmPA7FABAgE1OTvodAhBoJAkAAAAAkpAkAAAAAEhCkgAAAAAgCUkCQqW2tlbvfOc7VVtb63coAIAAW716td8hAIHGPAkAAKDkxONxNTQ0SMyTAKRFTQJCZWpqShcvXtTU1JTfoQAAAoz7BJAdSQJCZWhoSM8884yGhob8DgUAEGDDw8N+hwAEGkkCAAAAgCQkCQAAAACSkCQAAAAASEKSgFApKytTJBJRWRkfbQBAZtwngOwYAhUAAJQchkAFsiONBgAAAJCEJAGhcu3aNX32s5/VtWvX/A4FABBgAwMDfocABBpJAkJlfn5e8Xhc8/PzfocCAAgw7hNAdiQJAAAAAJKQJAAAAABIQpIAAAAAIAlJAkKlqalJH/rQh9TU1OR3KACAAGtsbPQ7BCDQKvwOAFhKVVVVam1t9TsMAEDAVVVV+R0CEGjUJCBU4vG4Tp48qXiceXEAAJlxnwCyI0lAqIyPj+tb3/qWxsfH/Q4FABBgN2/e9DsEINBIEgAAAAAkIUkAAAAAkIQkAQAAAECSkhzdaCA+qYHRqYzbW+qr1BKpLmJEWCo1NTVqb29XTU2N36EAAAKsupr7PJCNsdb6HcMdMcZEJI2MjIwoEonkdczvn3hVT794NuP2Jx7eqk/svm+JIgQAAEETj8fV0NAgSQ3WWoY6AlKUZE3CBx68R7vvXydJOjcwpo8/+4o+t/8t2tJSJ8mpScDKNDMzo+HhYTU2NqqystLvcAAAATUzM+N3CECglWSS0BKpvq050ZaWOj1wd4NPEWGp3LhxQz09Pers7NSGDRv8DgcAEFCDg4N+hwAEGh2XAQAAACQhSQAAAACQhCQBAAAAQBKSBIROeXm53yEAAACsaCXZcRnhtWHDBv3Wb/2W32EAAAJu/fr1focABBo1CQAAAACSkCQgVK5fv66enh5dv37d71AAAAF248YNv0MAAo0kAaEyOzurK1euaHZ21u9QAAABxn0CyK6kk4QLN8b1zHcuSpKe+c5FXbgx7m9AAAAAQACUbJJw9NRlPfyZb+j53n5J0vO9/Xr4M9/QsVOXfY4MAAAA8FdJJgkXbozryefOaN5K89ZZ5/3e9dwZXaRGAQAAACWsJJOEo6cuyxiTdpsxRs9Sm7BiRaNR7d27V9Fo1O9QAAAB1tDQ4HcIQKCV5DwJ/cMTstam3WatVf/wRJEjwlKpqanRm970Jr/DAAAEXE1Njd8hAIFWkjUJGxtrstYkbGzkwrFSjY2N6eWXX9bY2JjfoQAAAoz7BJBdSSYJ+3ZuylqTsH/npiJHhKUyOjqqr3/96xodHfU7FABAgJEkANmVZJJw75padT+2XWVGKnMrFLzfux/brtY1tf4GCAAAAPioJPskSNLenZu0q7VJf/TSOR0/3a/3t2/Ux969hQQBAAAAJa8kaxI8rWtq9aF3tEqSPvSOVhIEAAAAQCWeJCB8qqqqtG3bNlVVVfkdCgAgwFatWuV3CECglWxzI4RTU1OTHn/8cb/DAAAEXFNTk98hAIFGTQJCZW5uTuPj45qbm/M7FABAgHGfALIjSUCoDAwM6Hd/93c1MDDgdygAgAC7fv263yEAgVZwcyNjzIGExai19nCex3VI6rTW7s2w/aCkmLs4ZK09XmhsAAAAAO5cQUmCmyAsJAbGmD3GmG5rbVeWY9ol7ZcUldSWYZ8TchKIPnf/05LST4kMAAAAYFkV2tyoS9LCN/zut/0HMu8uWWt73STiRLrtbuLRa63t8/aXtKPAuAAAAAAskbyTBGNMVFKbV5hPEHW//V+sbqUkEG6iAAAAAMAHhTQ3SttUSE4/gjZJBRfs3cQjKifR8GokNmdrvrQUBuKTGhidkiSdGxhL+ilJLfVVaolUL2cIWCbr1q3TU089pcrKSr9DAQAEWEtLi98hAIFWSJKQaUDhoSzbcvESjyZr7RHJ6eBsjDmWqYPzUvjSdy/p6RfPJq37+LOvLPz+xMNb9Ynd9y3Xw2MZlZWVMZEaACCnsjIGeASy8XsyNS+5OOWtsNaeNMacMMaka9okY0yVpMRSYH2hD/qBB+/R7vvXZdzeUk8hc6UaHBzU1772NT3yyCNqbm72OxwAQEANDQ35HQIQaIUkCZn+mpqybMulL+WnJyapPc16SXpK0m8v8vEkSS2RapoThdT09LTOnz+v6elpv0MBAAQY9wkgu0Lq2vqkhX4EiaJKX5jPKaGmILW/Q+pjJDokqSHh38bFPDYAAACA9PKuSbDWxowxfXJqDmIp2+5kNKJepe/TkPac1topSVPesjFMpwAAAAAspUJ77XRL2uMtuCMSdSUst6XMyJwoU+fmLkkLnZTd44+n648AAAAAYPkV1HHZWnvEGHPQm3lZUnPKcKUdcgr9R7wVCTMu75HUZozpkXTaG83I7ajcZozpTnicZRvZCOEWiUT0yCOPKBKJ+B0KACDA6usLHvcEKCnGWut3DHfEGBORNDIyMkLBEAAA5CUej6uhoUGSGqy1cb/jAYKGQYIRKhMTEzpz5owmJib8DgUAEGDcJ4DsSBIQKrFYTM8//7xisZjfoQAAAmxkZMTvEIBAI0kAAAAAkIQkAQAAAEASkgQAAAAASUgSECqVlZXauHGjKisr/Q4FABBg3CeA7BgCFQAAlByGQAWyoyYBAAAAQBKSBITKlStX9MlPflJXrlzxOxQAQIBdvXrV7xCAQCNJAAAAAJCEJAEAAABAEpIEAAAAAElIEgAAAAAkYQhUhMrs7Kzi8bgikYgqKir8DgcAEFBDQ0Nqbm6WGAIVSItSFEKloqJCTU1NfocBAAg4vkgCsqO5EUJleHhYzz//vIaHh/0OBQAQYNwngOxIEhAqk5OTOnPmjCYnJ/0OBQAQYFNTU36HAAQaSQIAAACAJCQJAAAAAJKEptdOPM7ABJBGR0d18uRJ/dIv/ZJqa2v9DgcAEFBjY2N+hwAEWhiGQL1bUr/fcQAAgBVpo7X2Nb+DAIImDEmCkXSXpNFFnqJeTpKx8Q7OEQa8Dg5eBwevg4PXwcHr4OB1cITpdaiX9Lpd6YUhYBms+OZG7h/2or8BcHIMSdJoKU+mwuvg4HVw8Do4eB0cvA4OXgdHyF6HlR4/sGzouAwAAAAgCUkCAAAAgCQkCdKUpN9xf5YyXgcHr4OD18HB6+DgdXDwOjh4HYASsOI7LgMAAABYWtQkAAAAAEhCkgAAAAAgyYofAnWpGGNOWGt3+x0HUGzGmA5JndbavWm2HUhYjFprDxcvsuLK8TocdH/dJanPWttV1OCKKNvrkLJfqK+ZuV4H9zMRcxeHrLXHixVbMeV5fYhKapZ0yFobK150AJYTSYIkY8weSR1+x+GnUrnhZVNqNzxjTLuk/XKeb1ua7QeUkBgYY/YYY7rDVkDO43VIes7GmGPGmGO5CtErTa7XIWXf0F4z83kdjDEn5BSc+9z9T0sy6fZdqfL4uzgo6Yh3jTTGRCV1S+osWpAAllXJJwnuhS3rDTHsSuGGl0sp3vCstb2Set0C3840u3RJ2p2w/3FjzOfd9aGR7XVwPwcdxphoQsJ4SNJpY0ybtbavqMEuozw+D5LCf83M9Tq4yXOv995ba3uNMTuKHOayy+PzsDuxZtFaGzPGhPZzAZQi+iRI+yQd8TsIv6S74UkK3Q0vD7sTaw3c30v2hucVBNMUgqNuIllK2pT8WehLWF+KSvqaKefLgxOJK9zrZqlpSmiGByCESjpJcAs7p/yOw2fc8Bzc8JJlKgDHsmwLHWttzFrbmPI34T3/0NQi5KvUr5lu8hyVkywfcP91+xuVb7okdRtjThhjou7rENqaV6AUlXSSIGlniRaIJXHDS8ENL1lThvVDWbaVik5JJ8PU1KgAJX3N1K0Esclae8Rae0TSCWPMMT+D8oO19qSc5ogdkoYlfb9E/yaA0CrZJMEYs8e9wJcybngubnjIh/tNeoekUHVazgfXTEm3EuSF2hT32rGn1Nrju8+3XVKjnOZnx1JGQwOwwpVkkuB+gx7zOYwg4Ibn4oZ3m6EM65uybCsF3ZJ2hHnUq3S4Zi7oS/npicm5fpSSbmvtYbdJXqecL1l6Su3eAYRZqY5utE/S5oQOmJulhRFu+kpo+M9cN7xS+ia9O2FIy063NuWEMaZUm5X0SU7hMKVAHFVpfS4WGGN65IwCFvM7Fh9wzZTkjgAnObWwic2uor4E5BP3c5B0HbDWnjTGHJZT01bqNU5AKJRkkpBaZe5+83EgzBNFpcMNz8EN73bucIZ9cmoOYinbSq5Nulur1O0ljO41I1oqrwXXzCS9St8vpyQ+CzmcV4l+iQCEUUk2N0oj6ncAPuKGl1mp3PAydUTulrTHW3ALyqGaIyFF2tfBHSc+KqnNGNPhLncpvJ+NfDqmR5c7iADI9Dp0KaFPivt3cTzENY63vQ5uctzuNkNLtMNtsgogBIy11u8YfOVe4PfK+cb4uKSeUrrIGWM6JO1125R6r8fusM0mm4s7odzexKYkxpge73UJo4QZVffIqU06Iul04rfGCTNxRyU1h222ZSn76+AWgobTHWetDdWEg/l8Htz9Qn3NzPPv4oDcJleSVGp/F+72qKSn3N0HVQKz1AOlpuSTBJTGDS8XbngAAAC3kCQAAAAASEKfBAAAAABJSBIAAAAAJCFJAAAAAJCEJAEAAABAEpIEAAAAAElIEgAAAAAkIUkAUJKMMW3GmDa/4wAAIIhIEgCUqpKbNBAAgHyRJAAoVW3W2j6/gwAAIIhIEgCUHGNMu6Rev+MAACCoSBIAlKJOST1+BwEAQFCRJAAoRTQ1AgAgC5IEACWFpkYAAORW4XcAAJCJW6Bvc/9J0nFJe7zt1trDizhtp6TuDI+3R9IuSYOS+iTtl3TIWktSAQAoKSQJAALJncOgzVp73F0elrTZWttpjOmRtFPSYpKEtE2NjDEHJO211u5OWN4j6aOLfQ4AAKxUJAkAgqrDWnskYTkq6YT7+6LmOMjU1MhNSHokNSas7pMUs9bGFvNYAACsZCQJAILqqPdLwszIJyUpteDubt8jp2DfJul4ho7JmZoa9bjHJJ633Xs8AABKDUkCgEBKKbB3SOrN8q3+MWvtDkkyxkQlvShpR5r9Mo1q1CEngUi0W7dqLgAAKCmMbgRgJdgt6VS6DW4Toqi37CYS0YTaB2+/DmVuaqQ05+8QNQkAgBJFkgAgkFIK+R2STids25OwbaekWMrhMTnNhRLtVfYJ1BZqGNyEQtbaXmNMe2rCAQBA2JEkAAgcNwk4b4yJur8Puf+85kRNCbtHvW0pmlKW0zY1ctd5fRm883fqVuLRwcRrAIBSQ58EAEHUK+mIpH1yEoDdkrqMMU2SlDLqUUy3JwRSQuKQqalRgr2SOo0x5+WMaLTXGHPMGHMwx3EAAISSsdb6HQMALJrbJ+GYtXZzwrrzknZ7NQDuvArd1AgAAJAfmhsBWNFSZ0N2mwvFUhKCTKMaAQCANGhuBCAM9hpjuiV9X9IuOc2HJC00NWIoUwAACkBzIwChRlMjAAAKR3MjAGHXRIIAAEBhqEkAAAAAkISaBAAAAABJSBIAAAAAJCFJAAAAAJCEJAEAAABAEpIEAAAAAElIEgAAAAAkIUkAAAAAkIQkAQAAAECS/wfh/3UcKa5jNwAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -219,87 +229,7 @@ } ], "source": [ - "pe.plot_corrs([m_eff_f_P], plateau=[a[0], m_eff_plateau], xrange=[3.5, 19.5], prange=[start_se, stop_se], label=['Effective mass'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting two exponentials" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also fit the data with two exponentials where the second term describes the cutoff effects imposed by the boundary." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def func_2exp(a, x):\n", - " y = a[1] * anp.exp(-a[0] * x) + a[3] * anp.exp(-a[2] * x)\n", - " return y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can trigger the computation of $\\chi^2/\\chi^2_\\text{exp}$ with the kwarg `expected_chisquare` which takes into account correlations in the data and non-linearities in the fit function and should give a more reliable measure for goodness of fit than $\\chi^2/\\text{d.o.f.}$." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit with 4 parameters\n", - "Method: Levenberg-Marquardt\n", - "`xtol` termination condition is satisfied.\n", - "chisquare/d.o.f.: 0.05399877210985092\n", - "chisquare/expected_chisquare: 0.7915235152330492\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": [ - "Fit result:\n", - "[Obs[0.2146(65)], Obs[15.15(88)], Obs[0.623(60)], Obs[-9.64(74)]]\n" - ] - } - ], - "source": [ - "# Specify fit range for double exponential fit\n", - "start_de = 2\n", - "stop_de = 21\n", - "\n", - "a = pe.fits.standard_fit(np.arange(start_de, stop_de), p_obs['f_P'][start_de:stop_de], func_2exp, initial_guess=[0.21, 14.0, 0.6, -10], resplot=True, expected_chisquare=True)\n", - "[o.gamma_method() for o in a]\n", - "print('Fit result:')\n", - "print(a)" + "m_eff_fP.show(plateau=fit_result[0])" ] }, { @@ -318,18 +248,18 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(Obs[-0.08(35)], Obs[0.19(25)])\n", - "(Obs[1.85(35)], Obs[0.34(25)])\n", - "(Obs[4.01(35)], Obs[-1.39(25)])\n", - "(Obs[6.10(35)], Obs[-1.30(25)])\n", - "(Obs[8.08(35)], Obs[-0.37(25)])\n" + "(Obs[0.57(35)], Obs[0.49(25)])\n", + "(Obs[2.53(35)], Obs[0.56(25)])\n", + "(Obs[4.17(35)], Obs[-1.52(25)])\n", + "(Obs[5.97(35)], Obs[-1.40(25)])\n", + "(Obs[7.82(35)], Obs[-0.58(25)])\n" ] } ], @@ -353,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -381,18 +311,16 @@ "Fit with 3 parameters\n", "Method: ODR\n", "Sum of squares convergence\n", - "Residual variance: 0.5988333933914471\n", - "Parameter 1 : Obs[-0.01(29)]\n", - "Parameter 2 : Obs[-0.165(55)]\n", - "Parameter 3 : Obs[0.89(23)]\n" + "Residual variance: 0.4144435658518591\n", + "Parameter 1 : 0.26(28)\n", + "Parameter 2 : -0.228(53)\n", + "Parameter 3 : 0.98(22)\n" ] } ], "source": [ "beta = pe.fits.odr_fit(ox, oy, func)\n", "\n", - "pe.Obs.e_tag_global = 1 # Makes sure that the different samples with name length 1 are treated as ensembles and not as replica\n", - "\n", "for i, item in enumerate(beta):\n", " item.gamma_method()\n", " print('Parameter', i + 1, ':', item)" @@ -407,12 +335,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -441,68 +369,50 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Parameter 0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Parameter 0\n", "\n", - "Parameter 1\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Parameter 1\n", "\n", - "Parameter 2\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Parameter 2\n", "\n" ] + }, + { + "data": { + "image/png": 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WzmRS/QC86in25EgHIWdgIaFseBbAidIhiFzxyMbBOxdNAQANHHhwnvsE6UwmNxPAL6RDkDOwkFBGGb7AtQAul85BBAAjNr9Zo1K7SX8yTC2vLFSDpDNZwB1cn4SygYWEMsbwBYaA80bIJNzx8Lrjdn84BQASwL7HL3SdKhzJKhSAZ7kJH2UaCwll0nMAekmHIAKAkzb9oa7563cnqDWNXVSBZB6LGQZeuqEMYyGhjDB8gesBnC2dgwgA3LGmtQP3lJwGADEXdhTPdU2TzmRBP/AUe6ZIhyD7YiGhtDN8gcEAHpbOQdTs5I2vHWj++pUzXDtibsWVgtvPBeAFT7GH/+8oI1hIKBN+DaCndAgiAMiJNq4ZsHflRABoykPp26cpjo503GgA90iHIHtiIaG0MnyBywFcIJ2DqNnIja/Emr9+8gJXhEvEd5rPU+zxSIcg+2EhobQxfIE+AJ6QzkHULCfa8OmxlavHA0B1T3y08iTeWZMGuUheuuEGfJRWLCSUTj8HcKx0CKJmo9e/DADQQPzBee7ewnHsZBKAH0mHIHthIaG0MHyBMQBukM5B1Cw3Ure6b/WacQCwrT+WbRuguFpwev3UU+zhwnKUNiwklC6PAOAQLpnG6HXFbgDQQONDl7iHS+exoe4AHpAOQfbBQkKdZvgCXgDcqpxMIy8c+viYmnWnAMDKEWpFdYEaKJ3Jpq7yFHsmSYcge2AhoU4xfIEccM0RMpkx617sAgAJoPqp810TpPPYmALwmHQIsgcWEuqs7wEYKR2CqFn+gZqPetduGgMAf5+k1jZ1Udy+IIPytB74rwcHXCidg6yPhYQ6LHWb70+lcxC1NKb0hR4AEHOh7OU5XCI+U5TWVdeE9i9eHtwx5KzGpodQVJAjnYmsjYWEOuOnAPpIhyBq1uVA9YrC/VtHAcDLc1y74m6VK53JdrRumt3YuGhJWXnej/fVzsoBcgCMAPAd6WhkbSwk1CGGLzAEwI3SOYgO0lqPWftCAQA05mHtO5M5OpJWWusRkciSf5Tvqn2qomp2T60PvRT2Pygq6C6SjWyBQ2zUUXcC4CZbZBpdD1QtL6gLTgWAJ77iih3tz1Pb9Y7HVz9RUdn11HDk9CP8sQEAbgXwy+ykIrvhCAm1m+ELDAJwrXQOooO0Toxd+3xfAKjshRWrRrjGSUeyg7yE3vLLyuqPFm/fOf7UcKQtk9fvQFEBV8SlDmEhoY64A0C+dAiiZt0aK0p61u84MbVE/DHSeaxOaV15XW3ogxVlO4yv1DdMbsdbewH4bqZykb2xkFC7GL5AfwDXS+cgOkjr+NjS5wcCwJaBWFrWX3FV1o7SunFuQ+OiZWXlXX9QE5rp7tjqyzejqIA/sFC7sZBQe/0YQFfpEETNujfuLunRsOsEDTQ8fLH7JOk8lqR1YmQ48uG7O3btf3Rv1ezuWvfoxNEGALgyXdHIOVhIqM0MX6AvOBxLZqJ1fOza5wYDwIqT1cp9vVR/6UhWc0ws/vEruyo2/3HXnhkD4vEBaTrsbSgqUGk6FjkE77Kh9vghkhtqEZlCj/ryZd0bK2YkFCoXnO+aKJ3HSvITiU0/q9q339vQmIn/b6MAeAG8nYFjk01xhITaxPAFugO4SToH0UFaR8eWPj8UAN6erNYdyFOduczgGC6tK66vCX24vKx8WIbKSLPbM3hssiEWEmqrbwAokA5B1KxnXVlJt6bK46NubHvlTNd06Tymp3XDOfUN/1lWVt7j5trQjA5OWG2PWSgqOC3D5yAbYSGhtvqedACig7SOjC19YRgA/Haua2/CpXj5uTVax8eEwx+8t2NX/cOV1Wd00zqbl11/nMVzkcWxkNBRGb7A6QC40BSZRq/920q6Hqg+riEfa96d4Joinces+sViK1/bVbH1tV0VM/vH4xITfi9GUcEwgfOSBfGnCmoLjo6QeWgdHlv6wggAePSr/JnqcLokEht+XrWv/tyGxknCUdxIToa/WTgHWQC/m+mIDF/gWADzpHMQNSsMbS7pEq4ZWFGA5Z8Nc3mk85iJS+vdN9XUfri8rHzEuZmdsNoe30RRQTfpEGR+LCR0NNeBm+iRWWjdNKb0xVEaiM2/1H2sdBzT0Lr+vPqGRSVl5QU31u6f4TLXZ3sPABdJhyDzM9NfWjIZwxdwAbhBOgdRs941G1bkR0LHbjwOS8v7qROk84jTOu45EP7g3zt2Ns2vrJ7dVWuzjkRcJR2AzI+FhI7kPABDpEMQAQC0bhi97rejNVD/8MXuUdJxpPWPxT764649wVd2V8zsF0/0k85zFHNRVJCuVWDJplhI6Ei+IR2AqFmffaUr86N1/ZaOUh+Heiiz/wOcMV0TiXWPVVSufm/HrskjI1GrbCToBvB16RBkbrzLhg4rtTLrV6RzEAEAtK4bvb7Yk1CoWHieS/rOERFurXd9vya07drQ/ukKsOI+MVcBeEQ6BJkXR0ioNRcAMOv1aHKYvlWfrcqLNvT561S1MZynnLWfktb7v1JXv2h52Y4+14X2n27RMgIAp6KogHdFUatYSKg1X5MOQAQA0Do0asPvxkXc2PL6LActEa91bPyBA4v/s31n5JdV+2bna3SRjpQGnNxKrWIhof9i+AIFAM6VzkEEAMdWrv4kN9ZY+MKXXNUJl8r0/iumMCgaW/Hmzj07Xtq9d9YxiURf6Txp9HUUFfDfHTosziGhw7kIQL50CCJoXXvyhldOreuCT/99qsv2G7V1TyRK5++tis5uOmDX/9bjAMwB8J50EDIfFhI6nCukAxABQP+9H32SG28644HLXLb+rHJrXf6Dmtrt3wzVTbPwHJG2ugosJHQYHDqjLzB8gX5I/gRDJEvrfSdvfG3S7t4o+dxwjZGOkxFahy6uq//PiuCOft8K1Vn17pn2ugBFBY649Ebtw0JCh7oAHDkjExi4p2SNOx7Of3Cee6B0lrTTOjqp6cDixdt3xn9Wte+MPGddIu0NYLJ0CDIf/sNDh+JkVpKnE5UnbfrDpPXHY9nOvmqWdJx0GhyNliyoqBw4LBqz1X9XO50DoEQ6BJkLR0jooNTeNXOlcxAN2r2k1JWIxB+5yD1aOku6dE8k1v5mz97P3infPXVYNDZUOo+wc6QDkPmwkFBLk5EcTiWSoxN7R2x+87QPxqjVoe7K8re85mi9/c7qmmUlZeVjZjQdOEU6j0mchqKCQukQZC4sJNQSf2ohcYN3Ll4HHa195svWvs1XaV172f66RcuDOwZcub9umnQek3GDo7F0CBYSaomFhGTpxO4Tt/x56p+nqy2RXNVVOk6HaB2Z0nRg0Qfbd+Le6prZeUCedCST4ucNfQEntRIAwPAFCgFMkc5Bznb8jn9virpjA9+Y4bbkiMKQaHTZgj2Vxxmx2GzpLBbAQkJfwBESanYWksOoRCJUIr5z+La3pj57rqvWakvE94wnPnt2d8XngfLd04xYbIh0Hos4HkUFo6RDkHlwhISafUk6ADnbkB3vba3vEt+72JNjmTUqcrQuu7O6ZvfX6uqnSmexqHMArJMOQebAERJq5uQ1EUiYSsR3GMG3p/zqYrclFghTWu+7IlS3eEVwxyCWkU7hZRs6iIWEmnf3PVk6BzmXUfZO2Z7eiZXrhihzrzuidXh6Y9OiD7aXu+/eVzMrF8iVjmRxp6OowAnL5VMb8JINAcn1R/ihQCJUIlY2tOzvE269wV0tnaVVWusTorFlT1VUDhnCCavp1BPACAAbpYOQPBYSAgBLr/dA1nZCMFBeOhTBPX2UKf+hL4jHP310b1Xu5APh6dJZbGoCWEgILCSUxEJCIlQiunXI9ndHXfcDl+lG6HK13nZ39b7KeXUN/P7IrAkAXpMOQfJYSAhgISEhw7e+tWfRKdhR3808oyNK66qr9teV/nBf7fQc4ATpPA4wQToAmQMLicMZvsBgAPbb3p1MzxWPbB608/0hd13hMsd+NVofmNV0YLl/b9X4nlrzrrPsGS8dgMyBd9kQR0dIxIlb/lz1xkxVFs1RXUSDaK1PjESWvFO+q3pBReXsnlr3Es3jPH1QVGBIhyB5HCEhFhLKOlc8vOGYysWFf77afZJkjt7x+OrHKiq7TAhHTpfMQZgAICgdgmSxkNBY6QDkPCM2vxl6+jyXWyslMkqbl9Bb7q3eV/3Vek5YNYkJAP4kHYJksZCQ6E+o5DzueHhd9/1L3EtH50zM9rmV1pXfCtWtv6WmdrobGJ7t81OrOLGVWEiczPAFcsC7CCjLRmx8ff/DF7sLsnpSrZvObGxacX9l9YQeWs/M6rmpLVhIiJNaHe4EsJRSFrljTWvj8eWxjYPVyKycUOvESeHIkn/u2FX7xN6q2T207pmV81J79UdRgTnutiIx/MfI2UZIByBnGbHptfoH5rmHZuNcfeLxVU9WVHY/hRNWrWIIgCrpECSHhcTZOH+EsiYn2vhZZbePD1T0dg/O5HnyE4nNRVX7as9vaJyUyfNQ2g0BsEo6BMlhIXE2FhLKmuGbX6279wrXuEwd36X13mtD+zfcVBOa7gbcmToPZczx0gFIFguJs/GSDWVFbrT+k9KBq2P1Xd2FaT+41g1nNzat/EVl9aRunLBqZUOkA5AsFhJnYyGhrBi69ZX9vitdU9N6UK0ToyORpU9UVI3oH4+bZi8c6jCOkDgcC4lDGb6AC0BGr+UTAUBuZP/qD05ekxPLceWn65h9Y/GPn6yo7DU2EpmRrmOSOI6QOBwLiXP1A6+zUxYM2vFK9ZPfUGel41hdEomN91Xtq/tyQ2PWF1WjjOMIicNxHRLnGiAdgOwvLxxa+ZdJa/tAKdWZ47i03vPdmtCHy8vKT2QZsa2BKCrgD8kOxkLiXAOlA5D99ar6fdXyka6Or8Kpdf2X6xsWlZSV9/pebWiGi59ZduYGMEg6BMlhG3WuY6UDkL3lhWtWvDp9/TCgA4MjWsfHhiNLn9hbObJfPMEJq84xBMB26RAkg4XEubhMM2VWw++qtgxS7d5N99hY7KOnKir7jIpEeQuv83CivYOxkDjXMdIByL7ywtUlr8zYNL49oyNdE4n191dWN81tbJqcwWhkbtnddJFMhYXEuVhIKDO01jXqdzVVBapN6464tN79/ZrQ1utC+6erDl3fIRvpKh2A5LCQOBcLCWVETrR62ZtTtkw/arfQuu6C+sZV/1O977QuWnMDPAJYSByNhcS5ekgHIBvSOrGtR3FjYxfV+tC71rFx4cjSx/ZWjurLCav0RSwkDsZC4lx50gHIflS8csk/Ti2b1droyMBYbMVTeyr7nRSNzspyNLKGbtIBSA4LiXOxkFB6aR1f3f+3iLnVf/3d6pZIlM6vrI6c0djU7rtuyFE4QuJgLCTOxUJCaRVDxYclI8q/MDri1rr8lpra7d8K1U3jhFVqAxYSB2Mhca5c6QBkI1rHFp3wYs+DS8RrHfpqfcMn91Tvm5KvubYEtRkv2TgYC4lzcYSE0qY+d9eSTQP3zIbW0YkHwsse2Vs1pk+CE1ap3ThC4mAsJM7FERJKC60S8X+c/MLg46Kx5Qsq9vYfHo1xwip1FAuJg7GQOBdHSCgt9hUurvtZY2nTzOoDU6SzkOWxkDgYd850Lo6QUFp8FDtl1rhGFyesUjrwh2QHYyEhok4JoUfhlPCC4Xt14UrpLGR5DdIBSA4LiXM1SQcg+2hCfrfTw0+csjUxYJl0FrK0RukAJIeFxLn4jU9pFUVO3lmRh6esTgxfLJ2FLIsjJA7GQuJc/MantNNwuS6K/HzWe/EJi6SzkCXxc8nBWEiciyMklDHXRX88+5XYHJYSai9+LjkYC4lz8RufMuru2HWzH41e8qHWiEtnIcvgCImDsZA4F7/xKeMej18y4yexb3+kNcLSWcgS+LnkYCwkzsUREsqKV+Jzp94YvbVUa9RLZyHTYyFxMBYS52Ihoaz5R+K08V+L3LM9odU+6SxkavxccjAWEucKSQcgZ1muR4/2Ru6viWnXbuksZFocIXEwFhLn4j8KlHXr9NDhZ0YeSUR0zjbpLGRKLCQOxkLiXLukA5Az7dDHHjc9/ESPRp2/XjoLmc5e6QAkh4XEuXZKByDnqkJhv9PCCwbV6B6fSmchUynvzJuVUnekHk8rpZ5OVyjKDhYS5+IICYmqR7de08JPnrRL91khnYVMQaMTn0tKqfla6wdTjxtSr72btnSUcSwkzrUHQEI6BDnbAeR3nRl+fMKGxOAl0llI3F4UhaIdeaNSqhDAhNSvzZ4GMFcpNSwN2SgLWEgcKuj3xgBUSucgisOdc05k/vSSxCguNe9snb2MPAlAy/KxNfVrYSePS1nCQuJsnEdCJqHU1yL3zn47PuU/0klIzI6OvlFrXau17q21XtXi5bmpX7ce7j1kPiwkzsZ5JGQq34/+4IwXYucu0hpaOgtlXbpvBb8LwA1a69o0H5cyhIXE2bZLByA61H2xq2f7Y1cs1Rox6SyUVVvSdSCl1HwAr2utn0nXMSnzWEicbZ10AKLDeTp+wem3x25YpTWapLNQ1qTl0opSah6ALVrrB9NxPMoeFhJnK5UOQNSaN+KzT/t29PaNWnObA4fo9AiJUmouADSPjCilCnmXjXWwkDgbCwmZ2vuJ8eMuiRTtTmjFO8LsLQEg2JkDKKUmAJgAYJVSaliqiFwPgBs6WoTSmnPHnMzwBaoB9JHOQXQkI1R58J08X06OSgyWzkIZsQ1FoQ6PZKTWH9mGw9ziq7VWHY9F2cQREuIoCZneJj3YmBV+zB3WuWmb+Eimsurof6R1LW77VYc+0hWQMo+FhFhIyBJ2oe/AqeEne9frLvw7az8rpQOQPBYS4oc7WUYNevWZEl4wpEr36tRP1GQ6LCTEQkJYKx2AqD0a0LXH9PCTY7Yn+pVIZ6G0+Vg6AMljIaHPpAMQtVcEuflnRB6d/HnC+EA6C3XaVhSFaqRDkDwWEocL+r17kcYVEomyJQGX+/zI/TMXxz3clM/aeLmGALCQUNJS6QBEHXV19K7Zb8ZnspRYFwsJAWAhoaQl0gGIOuO26Hdn/yZ2wWKtkZDOQu3GQkIAWEgoiYWELG9+7IpZ98WuWq41ItJZqM00OrkGCdkHCwkByTttaqVDEHXWi/EvT7s5evMardEgnYXaZDOKQtyriACwkBCAoN+rASyTzkGUDm8npk28KnrX1oRmybaAj6QDkHmwkFAzTmwl2/gw4fF8NfLzyrhWFdJZ6IjelQ5A5sFCQs04j4Rs5TM9fMTcyEPhqHaXSWehw9IA/i4dgsyDhYSalQA4IB2CKJ226UFDZoQf79qk8zZJZ6H/8gmKQnukQ5B5sJAQACDo9zYBeF86B1G6VaDPsVPCTx0b0t3WSGehL3hHOgCZCwsJtfS2dACiTNiPHgVTwwuG79WFXPPCPFhI6AtYSKilgHQAokxpQn636eEnxm1NDOQEbnm14J19dAgWEjoo6PeWAfhcOgdRpsSQk3tW5KGpqxInLpbO4nDvoigUlw5B5sJCQofiZRuyNQ2X6+LIfbPejU/k/jdyeLmG/gsLCR2Kl23IEb4TvW3272JnLdIaWjqLw/B2XzosFhI61DIA1dIhiLLhnti1sx+LXbJEa/DyQfZ8iqLQbukQZD4sJPQFQb83Dg6nkoM8Hr9kxt2xa1dqjbB0Fof4X+kAZE4sJHQ4b0gHIMqmV+NnTbkxemup1qiTzuIAr0oHIHNiIaHD+V/wsg05zD8Sp42/PHLvjoRW/LufOatQFOKdfHRYLCT0X4J+bxTAa9I5iLJthR412hu5vzamXZzjkBnF0gHIvFhIqDUvSwcgkrBODx1+ZuSRRETnbJPOYjNRAK9IhyDzYiGhwwr6vcsBbJTOQSRhhz72uGnhJ3s26vz10llsJICiUJV0CDIvFhI6kt9JByCSUo2CvqeFFwyq0T0+lc5iE7xcQ0fEQkJH8jLARaPIuerRrdfU8FMn79THrJDOYnFV4KKLdBQsJNSqoN8bBPChdA4iSWHkdZkVfmzChsTgJdJZLOxVFIWi0iHI3FhI6Gg4zEqOF4c755zI/OkliVHc/6Zj+DlCR8VCQkfzKoAa6RBE8pT6WuTe2X+LT/2PdBKL+RxFoY+lQ5D5sZDQEQX93kYAL0jnIDKLm6O3nPF87MvclK/tXpQOQNbAQkJtsQBAQjoEkVn8PHbVbH/siqVaIyadxeRCAJ6TDkHWoLRmybcrpdQdAGpTTwu11g929FiGL/AWgAvSkYvILi5xLf7o4dyFY5VCV+ksJjUfRSGfdAiyBo6Q2FSqjEBr/YzW+hkAq5RST3fikI+mJxmRfbyZmDX529HbN2qNkHQWEwoDeEw6BFkHR0hsSilVA+AErXVti9e01lp19JiGL7AKwPg0xCOylfFq04Y384r6uJTuJ53FRJ5DUeg70iHIOjhCYkNKqWFIXqKpPczvze3EoX/VifcS2dZqPeLkcyLzG2LaVS6dxSQSAB6SDkHWwkJiT8Naeb0WQGEnjvs6AH7gEh3GJj3YmBV+zH1A526RzmICf0FRiHthUbuwkDjLPgB9OvrmoN8bA9DhibFEdrcLfQdOCz/Zu053XSudRdh86QBkPSwkztLhMtLCMwB2pOE4RLZUg159poafGlqle62SziJkEYpC3PuH2o2FxJ62tvJ64RF+r02Cfm8YwC86cwwiu2tA1x7Tw0+O2Z44tkQ6iwCOjlCHsJDYkNZ6K4Da1OTWQ3/vvTSc4kUA29JwHCLbiiA3/4zII5M/TxgfSGfJos9QFHpHOgRZEwuJfT0A4OAdNUqpeUhebum0oN8bBXBfOo5FZGcJuNznR+6fuTjuccqmfPdKByDrYiGxqdSqrIVKqXmpMjJZa31DGk/xMoANaTwekW1dHb1r9h9js+xeSt5HUegt6RBkXVwYjTrM8AWuAPCKdA4iq7g957UPvud+63SlbPfDYALAJBSFVksHIeuy2zcFZdfrAD6XDkFkFQ/FvjbzvthVy7VGRDpLmr3EMkKdxUJCHRb0exMA7pDOQWQlL8a/PO3m6M1rtEaDdJY0aQDwE+kQZH0sJNQpQb/3HQB/k85BZCVvJ6ZNvDJ697aEPrgbt5U9hKLQLukQZH0sJJQOtwI4IB2CyEqWJMaOvTDyi8q4VhXSWTphJ7hnDaUJCwl1WtDv3Qp+KBG12xo9bMRZkYcjUe0uk87SQT9BUahROgTZAwsJpcsDAKz6oUokJqgHHj8j/HjXJp1ntc3oVgF4SToE2QcLCaVF0O9tAvAj6RxEVlSBPsdOCT/VP6S7rZHO0g63oSjEdSMobVhIKG2Cfu+fALwrnYPIivajR8HU8ILhFbpwpXSWNngZRaH/SIcge2EhoXS7BUBUOgSRFTUhv9vp4SfGbUkMXCqd5Qh2Ifl9TpRWLCSUVkG/dz24GzBRh8WQkzs38tDUVYkTF0tnacX1KArVSocg+2EhoUy4H4AVhp2JTEnD5bo4ct+sf8Qnmm3/m2IUhQLSIcieWEgo7YJ+bwzA1eDaJESdckP0ttkvx+Yu0hpmmDy6E8APpEOQfbGQUEYE/d51AO6RzkFkdffGvj370di8JVojLhzlOygKhYQzkI2xkFAmPQrgA+kQRFb3RPziGXfHrl2pNcJCEV5AUegdoXOTQ7CQUMakNt/7JoB64ShElvdq/KwpN0R/WKo16rJ86h3gGkOUBSwklFGpZeVvl85BZAf/TEwef3nk3h0JraqzeNrreKmGskFpbYa5UmR3hi8QAHCedA4iOxiptm99O+/urjkqMTDDp3oaRaEbM3wOIgAcIaHsuQrc64YoLdbrIcPOjDySiOicbRk8zUfgXTWURRwhoawxfIFJAD4EkC+dhcgOjkGoanH+rZXdVXhUmg9dCWAiikI70nxcolZxhISyJuj3rgSXnCZKm2oU9J0SXnBcje7xSRoPGwdwOcsIZRsLCWVV0O99BsBvpXMQ2UU9uvWaGn5q5E59zIo0HdKHotD7aToWUZuxkJCE7wH4RDoEkV2EkddlVvixCRsSg5d08lB/QFHo4bSEImonFhLKuqDf2wTgEgC1wlGIbCMOd845kfnTl8VHdXT/m88BfDudmYjag4WERKTWJ7kKQEI6C5F9KHVF9N7Zb8Wn/aedbwwBuAhFoYYMhCJqExYSEhP0e98GV4AkSrtbojef8VzsvMVt3JRPA7gSRaHNmc5FdCQsJCQq6Pc+juSeN0SURr+IXTnrgdjXl2qN2FH+aBGKQm9nJRTREbCQkBncBuCP0iGI7OaZ+Pmn/zh642qt0dTKH3keRaH7shqKqBUsJCQu6PdqJOeTfCidhchu3kzMmvzt6O0btcah+9H8DcANEpmIDocrtZJpGL5AHwBLAIyUzkJkN+PVpg1v5BX1cSvdD8BSAHNRFGpt5IQo61hIyFQMX8AAUAKgv3AUItsZrnaWvZV377bu6sAlKArtk85D1BILCZmO4QtMBPBvAL2ksxDZTBDAzKDfWy4dhOhQnENCphP0ez8G8CUA+6WzENnIbgBzWUbIrFhIyJSCfu9yAOcAqJPOQmQD1QDODvq9W6SDELWGhYRMK+j3loClhKizqgF8Kej3rpUOQnQkLCRkakG/dxmAc8FSQtQRewDMDvq9q6SDEB0NCwmZXtDvXQrgywDqpbMQWch2JCewcmSELIGFhCwh6PcuQXKkhKWE6Og2I1lGuD8NWQYLCVlGqpScCWCvdBYiE1uLZBnZLh2EqD1YSMhSgn7vSgDTAWySzkJkQh8jOWdkj3QQovZiISHLSd26OB3AcuksRCbyAYCzgn5vtXQQoo5gISFLCvq9VQDmILlBGJHTFSO56NmhG+gRWQaXjidLM3wBN4AF4K6l5EwawN1Bv9cvHYSos1hIyBYMX+AeAD+XzkGURQ0Argr6vX+WDkKUDiwkZBuGL3AZgBcAdJfOQpRhOwFcEPR7V0sHIUoXFhKyFcMXOAXAnwEMk85ClCErAVwY9Ht3SQchSidOaiVbCfq9nwGYDOCf0lmIMuAVALNYRsiOOEJCtmT4Ai4ARQDuAaBk0xB1WhOAW4J+73PSQYgyhYWEbM3wBc4F8DsAx0hnIeqgUgCXcU8asjtesiFbC/q9fwcwHsBS6SxEHfA8gMksI+QEHCEhR0itV3IngJ8CyBOOQ3Q0dQBuCPq9r0oHIcoWFhJyFMMXGAfgZQAe6SxErVgF4HLu1EtOw0s25ChBv/dTAJMAPAggIRyHqKUIgP8BMJVlhJyIIyTkWIYvcDqSe4AMl85CjrcMwHVBv7dUOgiRFI6QkGMF/d4lAMYBWIjkniBE2dYA4FYAM1hGyOk4QkIEwPAFpiO5Sd+pwlHIOd4FcH3Q7w1KByEyAxYSopTUnTg3AvgFgELZNGRjNQBuC/q9L0oHITITFhKiQxi+QD8A8wF8E1zlldInBuA3AH4W9HurpcMQmQ0LCVErDF9gKpKXcSZIZyHLexvAj4N+7wbpIERmxUJCdASpPXGuR3JfnP6yaciCPkPy8sx70kGIzI6FhKgNDF+gG4CbAdwBoI9wHDK/CgD3Ang+6PdyvRuiNmAhIWoHwxfoBeBHAH4IoJdwHDKfGgCPA3gk6PfWSYchshIWEqIOMHyBPkiOltwMoJtwHJJXCeBXAH7NIkLUMSwkRJ1g+AIDANwF4DqwmDjRLgAPAXgm6Pc2SochsjIWEqI0SI2Y3ADg+wAGCcehzCtD8tbwF4J+b1g6DJEdsJAQpZHhC+QCuAzJOSYTheNQ+pUA+DWAV4N+b0w6DJGdsJAQZYjhC8xEsphcCO4bZWUNAH4P4DdBv/cT4SxEtsVCQpRhhi8wDMnLOVeCl3OspBTJlVVfCvq9+6XDENkdCwlRlqT2ypkL4GoAFwHoKpuIDuMAgL8gORqyWDgLkaOwkBAJMHyBngAuBXANgJngnjmS4gDeR/KyzJ84GkIkg4WESJjhC5yA5OWciwCMF47jFAkAHwJ4A8Afg37vHuE8RI7HQkJkIoYvMBjA+QAuADAHQBfZRLYSBvABgD8hORJSIZyHiFpgISEyqdT+OXORLCfnAxggm8iSPgfwLoB/AljMxcuIzIuFhMgCDF9AIXk5ZxaAGQBOBwvK4VQAeA/JAvJe0O/dJZyHiNqIhYTIogxfYDiS5aT5MVI2UdZFAHwKYCWAjwCsAFAa9Hv5oUZkQSwkRDZh+AJ9AUwBMC71OAXACABuyVxpEgewDsni0fz4LOj3RkRTEVHasJAQ2ZjhC3QFcDKSoyejUo8RAAYD6CsY7XASALYD2ARg8yG/bmX5ILI3FhIihzJ8gXwkV449rsVjcOrXgQB6AOieejR/ndPO02gANQCqAFQe4ddtYOkgcjQWEiJqM8MXyMP/l5RuSI5qRAHEUr+2/DrGDeiIqK1YSIiIiEgcdyAlIiIicSwkREREJI6FhIiIiMSxkBAREZE4FhIiIiISx0JCRERE4lhIiIiISBwLCREREYljISEiIiJxLCREREQkjoWEiIiIxLGQEBERkTgWEiIiIhLHQkJERETiWEiIiIhIHAsJERERiWMhISIiInEsJERERCSOhYSIiIjEsZAQERGROBYSIiIiEsdCQkREROJYSIiIiEgcCwkRERGJYyEhIiIicf8Hv/Sia8EpwOcAAAAASUVORK5CYII=\n", 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -512,244 +422,6 @@ " print()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting with priors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When extracting energy levels and matrix elements from correlation functions one is interested in using as much data is possible in order to decrease the final error estimate and also have better control over systematic effects from higher states. This can in principle be achieved by fitting a tower of exponentials to the data. However, in practice it can be very difficult to fit a function with 6 or more parameters to noisy data. One way around this is to cnostrain the fit parameters with Bayesian priors. The principle idea is that any parameter which is determined by the data is almost independent of the priors while the additional parameters which would let a standard fit collapse are essentially constrained by the priors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first generate fake data as a tower of three exponentials with noise which increases with temporal separation." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m1 = 0.18\n", - "m2 = 0.5\n", - "m3 = 0.8\n", - "\n", - "A1 = 180\n", - "A2 = 300\n", - "A3 = 500\n", - "\n", - "px = []\n", - "py = []\n", - "for i in range(40):\n", - " px.append(i)\n", - " val = (A1 * np.exp(-m1 * i) + A2 * np.exp(-m2 * i) + A3 * np.exp(-m3 * i))\n", - " err = 0.03 * np.sqrt(i + 1)\n", - " tmp = pe.pseudo_Obs(val * (1 + err * np.random.normal()), val * err, 'e1')\n", - " py.append(tmp)\n", - " \n", - "[o.gamma_method() for o in py];\n", - "\n", - "pe.plot_corrs([py], logscale=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As fit function we choose the sum of three exponentials" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def func_3exp(a, x):\n", - " y = a[1] * anp.exp(-a[0] * x) + a[3] * anp.exp(-a[2] * x) + a[5] * anp.exp(-a[4] * x)\n", - " return y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can specify the priors in a string format or alternatively input `Obs` from a previous analysis. It is important to choose the priors wide enough, otherwise they can influence the final result." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "priors = ['0.2(4)', '200(500)', \n", - " '0.6(1.2)', '300(550)',\n", - " '0.9(1.8)', '400(700)']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is important to chose a sufficiently large value of `Obs.e_tag_global`, as every prior is given an ensemble id." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "pe.Obs.e_tag_global = 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The fit can then be performed by calling `prior_fit` which in comparison to the standard fit requires the priors as additional input." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit with 6 parameters\n", - "Method: migrad\n", - "chisquare/d.o.f.: 1.100354109100944\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": [ - "[Obs[0.1795(32)], Obs[186(14)], Obs[0.578(70)], Obs[597(170)], Obs[1.42(83)], Obs[239(173)]]\n" - ] - } - ], - "source": [ - "beta_p = pe.fits.prior_fit(px, py, func_3exp, priors, resplot=True)\n", - "[o.gamma_method() for o in beta_p]\n", - "print(beta_p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now observe how far the individual fit parameters are constrained by the data or the priors" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "[o.plot_piechart() for o in beta_p];" - ] - }, { "cell_type": "code", "execution_count": null, @@ -760,7 +432,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -774,7 +446,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/05_matrix_operations.ipynb b/examples/05_matrix_operations.ipynb index d5e1da95..926d1734 100644 --- a/examples/05_matrix_operations.ipynb +++ b/examples/05_matrix_operations.ipynb @@ -51,7 +51,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The standard matrix product can be performed with @" + "The standard matrix product can be performed with `@`" ] }, { @@ -101,13 +101,38 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Mathematical functions work elementwise" + "For large matrices overloading the standard operator `@` can become inefficient as pyerrors has to perform a large number of elementary opeations. For these situations pyerrors provides the function `linalg.matmul` which optimizes the required automatic differentiation. The function can take an arbitray number of operands." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[Obs[78.12099999999998] Obs[-22.909999999999997]]\n", + " [Obs[-22.909999999999997] Obs[7.1]]]\n" + ] + } + ], + "source": [ + "print(pe.linalg.matmul(matrix, matrix, matrix)) # Equivalent to matrix @ matrix @ matrix but faster for large matrices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mathematical functions work elementwise" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -126,12 +151,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For a vector of `Obs`, we again use np.asarray to end up with the correct object" + "For a vector of `Obs`, we again use `np.asarray` to end up with the correct object" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -160,14 +185,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[Obs[7.2(1.7)] Obs[-1.00(45)]]\n" + "[Obs[7.2(1.7)] Obs[-1.00(46)]]\n" ] } ], @@ -182,40 +207,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Matrix to scalar operations\n", - "If we want to apply a numpy matrix function with a scalar return value we can use `scalar_mat_op`. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "det \t Obs[3.10(28)]\n", - "trace \t Obs[5.10(20)]\n", - "norm \t Obs[4.45(19)]\n" - ] - } - ], - "source": [ - "import autograd.numpy as anp # Thinly-wrapped numpy\n", - "funcs = [anp.linalg.det, anp.trace, anp.linalg.norm]\n", - "\n", - "for i, func in enumerate(funcs):\n", - " res = pe.linalg.scalar_mat_op(func, matrix)\n", - " res.gamma_method()\n", - " print(func.__name__, '\\t', res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For matrix operations which are not supported by autograd we can use numerical differentiation" + "`pyerrors` provides the user with wrappers to the `numpy.linalg` functions which work on `Obs` valued matrices. We can for example calculate the determinant of the matrix via" ] }, { @@ -227,26 +219,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "cond \t Obs[6.23(58)]\n", - "expm_cond \t Obs[4.45(19)]\n" + "3.10(28)\n" ] } ], "source": [ - "funcs = [np.linalg.cond, scipy.linalg.expm_cond]\n", - "\n", - "for i, func in enumerate(funcs):\n", - " res = pe.linalg.scalar_mat_op(func, matrix, num_grad=True)\n", - " res.gamma_method()\n", - " print(func.__name__, ' \\t', res)" + "det = pe.linalg.det(matrix)\n", + "det.gamma_method()\n", + "print(det)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Matrix to matrix operations\n", - "For matrix operations with a matrix as return value we can use another wrapper `mat_mat_op`. Take as an example the cholesky decompostion. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation." + "The cholesky decomposition can be obtained as follows" ] }, { @@ -259,12 +246,12 @@ "output_type": "stream", "text": [ "[[Obs[2.025(49)] Obs[0.0]]\n", - " [Obs[-0.494(51)] Obs[0.870(29)]]]\n" + " [Obs[-0.494(50)] Obs[0.870(29)]]]\n" ] } ], "source": [ - "cholesky = pe.linalg.mat_mat_op(anp.linalg.cholesky, matrix)\n", + "cholesky = pe.linalg.cholesky(matrix)\n", "for (i, j), entry in np.ndenumerate(cholesky):\n", " entry.gamma_method()\n", "print(cholesky)" @@ -321,7 +308,7 @@ } ], "source": [ - "inv = pe.linalg.mat_mat_op(anp.linalg.inv, cholesky)\n", + "inv = pe.linalg.inv(cholesky)\n", "for (i, j), entry in np.ndenumerate(inv):\n", " entry.gamma_method()\n", "print(inv)\n", @@ -334,59 +321,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Matrix to matrix operations which are not supported by autograd can also be computed with numeric differentiation" + "## Eigenvalues and eigenvectors\n", + "We can also compute eigenvalues and eigenvectors of symmetric matrices with a special wrapper `eigh`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "orth\n", - "[[Obs[-0.9592(76)] Obs[0.283(26)]]\n", - " [Obs[0.283(26)] Obs[0.9592(76)]]]\n", - "expm\n", - "[[Obs[75(15)] Obs[-21.4(4.1)]]\n", - " [Obs[-21.4(4.1)] Obs[8.3(1.4)]]]\n", - "logm\n", - "[[Obs[1.334(57)] Obs[-0.496(61)]]\n", - " [Obs[-0.496(61)] Obs[-0.203(50)]]]\n", - "sinhm\n", - "[[Obs[37.3(7.4)] Obs[-10.8(2.1)]]\n", - " [Obs[-10.8(2.1)] Obs[3.94(68)]]]\n", - "sqrtm\n", - "[[Obs[1.996(51)] Obs[-0.341(37)]]\n", - " [Obs[-0.341(37)] Obs[0.940(14)]]]\n" - ] - } - ], - "source": [ - "funcs = [scipy.linalg.orth, scipy.linalg.expm, scipy.linalg.logm, scipy.linalg.sinhm, scipy.linalg.sqrtm]\n", - "\n", - "for i,func in enumerate(funcs):\n", - " res = pe.linalg.mat_mat_op(func, matrix, num_grad=True)\n", - " for (i, j), entry in np.ndenumerate(res):\n", - " entry.gamma_method()\n", - " print(func.__name__)\n", - " print(res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Eigenvalues and eigenvectors\n", - "We can also compute eigenvalues and eigenvectors of symmetric matrices with a special wrapper `eigh`" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -395,8 +337,8 @@ "Eigenvalues:\n", "[Obs[0.705(57)] Obs[4.39(19)]]\n", "Eigenvectors:\n", - "[[Obs[-0.283(26)] Obs[-0.9592(76)]]\n", - " [Obs[-0.9592(76)] Obs[0.283(26)]]]\n" + "[[Obs[-0.283(26)] Obs[-0.9592(75)]]\n", + " [Obs[-0.9592(75)] Obs[0.283(26)]]]\n" ] } ], @@ -421,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -451,7 +393,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -465,7 +407,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/examples/data/B1k2_f_A.p b/examples/data/B1k2_f_A.p deleted file mode 100644 index 48c52af6..00000000 Binary files a/examples/data/B1k2_f_A.p and /dev/null differ diff --git a/examples/data/B1k2_f_P.p b/examples/data/B1k2_f_P.p deleted file mode 100644 index 748b6b30..00000000 Binary files a/examples/data/B1k2_f_P.p and /dev/null differ diff --git a/examples/data/correlator_test.json.gz b/examples/data/correlator_test.json.gz new file mode 100644 index 00000000..95c714ea Binary files /dev/null and b/examples/data/correlator_test.json.gz differ diff --git a/examples/data/correlator_test.p b/examples/data/correlator_test.p deleted file mode 100644 index ecc7547b..00000000 Binary files a/examples/data/correlator_test.p and /dev/null differ diff --git a/examples/data/f_A.json.gz b/examples/data/f_A.json.gz new file mode 100644 index 00000000..be30ce31 Binary files /dev/null and b/examples/data/f_A.json.gz differ diff --git a/examples/data/f_P.json.gz b/examples/data/f_P.json.gz new file mode 100644 index 00000000..60f31ad0 Binary files /dev/null and b/examples/data/f_P.json.gz differ diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py index 9c427818..856b5178 100644 --- a/pyerrors/__init__.py +++ b/pyerrors/__init__.py @@ -193,6 +193,56 @@ Make sure to check the autocorrelation time with e.g. `pyerrors.obs.Obs.plot_rho For the full API see `pyerrors.obs.Obs`. # Correlators +When one is not interested in single observables but correlation functions, `pyerrors` offers the `Corr` class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a `Corr` objects one needs to arrange the data as a list of `Obs´ +```python +my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3]) +print(my_corr) +> x0/a Corr(x0/a) +> ------------------ +> 0 0.7957(80) +> 1 0.5156(51) +> 2 0.3227(33) +> 3 0.2041(21) +``` +In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced. +```python +my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding_front=1, padding_back=1) +print(my_corr) +> x0/a Corr(x0/a) +> ------------------ +> 0 +> 1 0.7957(80) +> 2 0.5156(51) +> 3 0.3227(33) +> 4 0.2041(21) +> 5 +``` +The individual entries of a correlator can be accessed via slicing +```python +print(my_corr[3]) +> 0.3227(33) +``` +Error propagation with the `Corr` class works very similar to `Obs` objects. Mathematical operations are overloaded and `Corr` objects can be computed together with other `Corr` objects, `Obs` objects or real numbers and integers. +```python +my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr +``` + +`pyerrors` provides the user with a set of regularly used methods for the manipulation of correlator objects: +- `Corr.gamma_method` applies the gamma method to all entries of the correlator. +- `Corr.m_eff` to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available. +- `Corr.deriv` returns the first derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available. +- `Corr.second_deriv` returns the second derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available. +- `Corr.symmetric` symmetrizes parity even correlations functions, assuming periodic boundary conditions. +- `Corr.anti_symmetric` anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions. +- `Corr.T_symmetry` averages a correlator with its time symmetry partner, assuming fixed boundary conditions. +- `Corr.plateau` extracts a plateau value from the correlator in a given range. +- `Corr.roll` periodically shifts the correlator. +- `Corr.reverse` reverses the time ordering of the correlator. +- `Corr.correlate` constructs a disconnected correlation function from the correlator and another `Corr` or `Obs` object. +- `Corr.reweight` reweights the correlator. + +`pyerrors` can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see `pyerrors.correlators.Corr.GEVP). + For the full API see `pyerrors.correlators.Corr`. # Complex observables diff --git a/pyerrors/correlators.py b/pyerrors/correlators.py index c7427cd3..b78b13d6 100644 --- a/pyerrors/correlators.py +++ b/pyerrors/correlators.py @@ -74,7 +74,7 @@ class Corr: @property def reweighted(self): - bool_array = np.array([list(map(lambda x: x.reweighted, o)) for o in list(filter(None.__ne__, self.content))]) + bool_array = np.array([list(map(lambda x: x.reweighted, o)) for o in [x for x in self.content if x is not None]]) if np.all(bool_array == 1): return True elif np.all(bool_array == 0): @@ -614,7 +614,7 @@ class Corr: if self.N != 1: raise Exception("Correlator must be projected before plotting") if x_range is None: - x_range = [0, self.T] + x_range = [0, self.T - 1] fig = plt.figure() ax1 = fig.add_subplot(111) diff --git a/pyerrors/fits.py b/pyerrors/fits.py index d446e72e..cad4c0d8 100644 --- a/pyerrors/fits.py +++ b/pyerrors/fits.py @@ -1,3 +1,4 @@ +import gc from collections.abc import Sequence import warnings import numpy as np @@ -7,6 +8,7 @@ import scipy.stats import matplotlib.pyplot as plt from matplotlib import gridspec from scipy.odr import ODR, Model, RealData +from scipy.stats import chi2 import iminuit from autograd import jacobian from autograd import elementwise_grad as egrad @@ -45,6 +47,8 @@ class Fit_result(Sequence): my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n' if hasattr(self, 'chisquare_by_expected_chisquare'): my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n' + if hasattr(self, 'p_value'): + my_str += 'p-value = ' + f'{self.p_value:2.4f}' + '\n' my_str += 'Fit parameters:\n' for i_par, par in enumerate(self.fit_parameters): my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n' @@ -306,6 +310,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs): output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) output.dof = x.shape[-1] - n_parms + output.p_value = 1 - chi2.cdf(output.odr_chisquare, output.dof) return output @@ -619,6 +624,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs): output.chisquare = chisqfunc(fit_result.x) output.dof = x.shape[-1] - n_parms + output.p_value = 1 - chi2.cdf(output.chisquare, output.dof) if kwargs.get('resplot') is True: residual_plot(x, y, func, result) @@ -703,7 +709,7 @@ def residual_plot(x, y, func, fit_res): ax1.plot(x, residuals, 'ko', ls='none', markersize=5) ax1.tick_params(direction='out') ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True) - ax1.axhline(y=0.0, ls='--', color='k') + ax1.axhline(y=0.0, ls='--', color='k', marker=" ") ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k') ax1.set_xlim([xstart, xstop]) ax1.set_ylabel('Residuals') @@ -740,3 +746,43 @@ def error_band(x, func, beta): err = np.array(err) return err + + +def ks_test(objects=None): + """Performs a Kolmogorov–Smirnov test for the p-values of all fit object. + + Parameters + ---------- + objects : list + List of fit results to include in the analysis (optional). + """ + + if objects is None: + obs_list = [] + for obj in gc.get_objects(): + if isinstance(obj, Fit_result): + obs_list.append(obj) + else: + obs_list = objects + + p_values = [o.p_value for o in obs_list] + + bins = len(p_values) + x = np.arange(0, 1.001, 0.001) + plt.plot(x, x, 'k', zorder=1) + plt.xlim(0, 1) + plt.ylim(0, 1) + plt.xlabel('p-value') + plt.ylabel('Cumulative probability') + plt.title(str(bins) + ' p-values') + + n = np.arange(1, bins + 1) / np.float64(bins) + Xs = np.sort(p_values) + plt.step(Xs, n) + diffs = n - Xs + loc_max_diff = np.argmax(np.abs(diffs)) + loc = Xs[loc_max_diff] + plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0)) + plt.draw() + + print(scipy.stats.kstest(p_values, 'uniform')) diff --git a/pyerrors/input/hadrons.py b/pyerrors/input/hadrons.py index efe4feb1..92e4bc40 100644 --- a/pyerrors/input/hadrons.py +++ b/pyerrors/input/hadrons.py @@ -58,16 +58,13 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson', idl=No meson : str label of the meson to be extracted, standard value meson_0 which corresponds to the pseudoscalar pseudoscalar two-point function. - tree : str - Label of the upmost directory in the hdf5 file, default 'meson' - for outputs of the Meson module. Can be altered to read input - from other modules with similar structures. idl : range If specified only configurations in the given range are read in. """ files, idx = _get_files(path, filestem, idl) + tree = meson.rsplit('_')[0] corr_data = [] infos = [] for hd5_file in files: diff --git a/pyerrors/input/openQCD.py b/pyerrors/input/openQCD.py index 5e1c8d49..132be92e 100644 --- a/pyerrors/input/openQCD.py +++ b/pyerrors/input/openQCD.py @@ -1,6 +1,3 @@ -#!/usr/bin/env python -# coding: utf-8 - import os import fnmatch import re @@ -39,13 +36,14 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): if not ls: raise Exception('Error, directory not found') - - # Exclude files with different names - for exc in ls: - if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): - ls = list(set(ls) - set([exc])) - if len(ls) > 1: - ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) + if 'files' in kwargs: + ls = kwargs.get('files') + else: + for exc in ls: + if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'): + ls = list(set(ls) - set([exc])) + if len(ls) > 1: + ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) replica = len(ls) if 'r_start' in kwargs: @@ -64,9 +62,9 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): else: r_stop = [None] * replica - print('Read reweighting factors from', prefix[:-1], ',', replica, 'replica', end='') + print('Read reweighting factors from', prefix[:-1], ',', + replica, 'replica', end='') - # Adjust replica names to new bookmarking system if names is None: rep_names = [] for entry in ls: @@ -85,7 +83,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): tmp_array = [] with open(path + '/' + ls[rep], 'rb') as fp: - # header t = fp.read(4) # number of reweighting factors if rep == 0: nrw = struct.unpack('i', t)[0] @@ -94,7 +91,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): for k in range(nrw): deltas.append([]) else: - if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): # little weird if-clause due to the /2 operation needed. + if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')): raise Exception('Error: different number of reweighting factors for replicum', rep) for k in range(nrw): @@ -106,7 +103,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): for i in range(nrw): t = fp.read(4) nfct.append(struct.unpack('i', t)[0]) - # print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting else: for i in range(nrw): nfct.append(1) @@ -119,7 +115,6 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): if not struct.unpack('i', fp.read(4))[0] == 0: print('something is wrong!') - # body while 0 < 1: t = fp.read(4) if len(t) < 4: @@ -135,8 +130,11 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): for j in range(tmpd['n'][0]): tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j]))) if print_err: - print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw[j]))), np.std(np.exp(-np.asarray(tmp_rw[j])))) - print('Sources:', np.exp(-np.asarray(tmp_rw[j]))) + print(config_no, i, j, + np.mean(np.exp(-np.asarray(tmp_rw[j]))), + np.std(np.exp(-np.asarray(tmp_rw[j])))) + print('Sources:', + np.exp(-np.asarray(tmp_rw[j]))) print('Partial factor:', tmp_nfct) elif version == '1.6' or version == '1.4': tmp_nfct = 1.0 @@ -146,7 +144,9 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): tmp_rw = struct.unpack('d' * nsrc[i], t) tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw))) if print_err: - print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw))), np.std(np.exp(-np.asarray(tmp_rw)))) + print(config_no, i, j, + np.mean(np.exp(-np.asarray(tmp_rw))), + np.std(np.exp(-np.asarray(tmp_rw)))) print('Sources:', np.exp(-np.asarray(tmp_rw))) print('Partial factor:', tmp_nfct) tmp_array[i].append(tmp_nfct) @@ -165,11 +165,14 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs): return result -def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs): +def extract_t0(path, prefix, dtr_read, xmin, + spatial_extent, fit_range=5, **kwargs): """Extract t0 from given .ms.dat files. Returns t0 as Obs. - It is assumed that all boundary effects have sufficiently decayed at x0=xmin. - The data around the zero crossing of t^2 - 0.3 is fitted with a linear function + It is assumed that all boundary effects have + sufficiently decayed at x0=xmin. + The data around the zero crossing of t^2 - 0.3 + is fitted with a linear function from which the exact root is extracted. Only works with openQCD v 1.2. @@ -180,14 +183,17 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar prefix : str Ensemble prefix dtr_read : int - Determines how many trajectories should be skipped when reading the ms.dat files. + Determines how many trajectories should be skipped + when reading the ms.dat files. Corresponds to dtr_cnfg / dtr_ms in the openQCD input file. xmin : int - First timeslice where the boundary effects have sufficiently decayed. + First timeslice where the boundary + effects have sufficiently decayed. spatial_extent : int spatial extent of the lattice, required for normalization. fit_range : int - Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5) + Number of data points left and right of the zero + crossing to be included in the linear fit. (Default: 5) r_start : list list which contains the first config to be read for each replicum. r_stop: list @@ -204,7 +210,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar if not ls: raise Exception('Error, directory not found') - # Exclude files with different names for exc in ls: if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'): ls = list(set(ls) - set([exc])) @@ -216,7 +221,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar r_start = kwargs.get('r_start') if len(r_start) != replica: raise Exception('r_start does not match number of replicas') - # Adjust Configuration numbering to python index r_start = [o - 1 if o else None for o in r_start] else: r_start = [None] * replica @@ -235,7 +239,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar for rep in range(replica): with open(path + '/' + ls[rep], 'rb') as fp: - # Read header t = fp.read(12) header = struct.unpack('iii', t) if rep == 0: @@ -254,7 +257,6 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar Ysl = [] - # Read body while 0 < 1: t = fp.read(4) if(len(t) < 4): @@ -273,7 +275,9 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar Ysum.append([]) for i, item in enumerate(Ysl): - Ysum[-1].append([np.mean(item[current + xmin:current + tmax - xmin]) for current in range(0, len(item), tmax)]) + Ysum[-1].append([np.mean(item[current + xmin: + current + tmax - xmin]) + for current in range(0, len(item), tmax)]) t2E_dict = {} for n in range(nn + 1): @@ -286,10 +290,13 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar new_obs = Obs(samples, [(w.split('.'))[0] for w in ls]) t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3 - zero_crossing = np.argmax(np.array([o.value for o in t2E_dict.values()]) > 0.0) + zero_crossing = np.argmax(np.array( + [o.value for o in t2E_dict.values()]) > 0.0) - x = list(t2E_dict.keys())[zero_crossing - fit_range: zero_crossing + fit_range] - y = list(t2E_dict.values())[zero_crossing - fit_range: zero_crossing + fit_range] + x = list(t2E_dict.keys())[zero_crossing - fit_range: + zero_crossing + fit_range] + y = list(t2E_dict.values())[zero_crossing - fit_range: + zero_crossing + fit_range] [o.gamma_method() for o in y] fit_result = fit_lin(x, y) @@ -313,12 +320,6 @@ def _parse_array_openQCD2(d, n, size, wa, quadrupel=False): return arr -# mimic the read_array routine of openQCD-2.0. -# fp is the opened file handle -# returns the dict array -# at this point we only parse a 2d array -# d = 2 -# n = [nfct[irw], 2*nsrc[irw]] def _read_array_openQCD2(fp): t = fp.read(4) d = struct.unpack('i', t)[0] @@ -343,3 +344,232 @@ def _read_array_openQCD2(fp): arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True) return {'d': d, 'n': n, 'size': size, 'arr': arr} + + +def read_qtop(path, prefix, c, dtr_cnfg=1, version="1.2", **kwargs): + """Read qtop format from given folder structure. + + Parameters + ---------- + path: + path of the measurement files + prefix: + prefix of the measurement files, e.g. _id0_r0.ms.dat + c: double + Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L + dtr_cnfg: int + (optional) parameter that specifies the number of trajectories + between two configs. + if it is not set, the distance between two measurements + in the file is assumed to be + the distance between two configurations. + steps: int + (optional) (maybe only necessary for openQCD2.0) + nt step size, guessed if not given + version: str + version string of the openQCD (sfqcd) version used to create + the ensemble + L: int + spatial length of the lattice in L/a. + HAS to be set if version != sfqcd, since openQCD does not provide + this in the header + r_start: list + offset of the first ensemble, making it easier to match + later on with other Obs + r_stop: list + last configurations that need to be read (per replicum) + files: list + specify the exact files that need to be read + from path, practical if e.g. only one replicum is needed + names: list + Alternative labeling for replicas/ensembles. + Has to have the appropriate length + """ + known_versions = ["1.0", "1.2", "1.4", "1.6", "2.0", "sfqcd"] + + if version not in known_versions: + raise Exception("Unknown openQCD version.") + if "steps" in kwargs: + steps = kwargs.get("steps") + if version == "sfqcd": + if "L" in kwargs: + supposed_L = kwargs.get("L") + else: + if "L" not in kwargs: + raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.") + else: + L = kwargs.get("L") + r_start = 1 + if "r_start" in kwargs: + r_start = kwargs.get("r_start") + if "r_stop" in kwargs: + r_stop = kwargs.get("r_stop") + if "files" in kwargs: + files = kwargs.get("files") + else: + found = [] + files = [] + for (dirpath, dirnames, filenames) in os.walk(path + "/"): + # print(filenames) + found.extend(filenames) + break + for f in found: + if fnmatch.fnmatch(f, prefix + "*" + ".ms.dat"): + files.append(f) + print(files) + rep_names = [] + + deltas = [] + idl = [] + for rep, file in enumerate(files): + with open(path + "/" + file, "rb") as fp: + t = fp.read(12) + header = struct.unpack(' 1: ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) - replica = len(ls) - print('Read', part, 'part of', name, 'from', prefix, ',', replica, 'replica') + + if not appended: + replica = len(ls) + else: + replica = len([file.split(".")[-1] for file in ls]) // len(set([file.split(".")[-1] for file in ls])) + print('Read', part, 'part of', name, 'from', prefix[:-1], + ',', replica, 'replica') if 'names' in kwargs: new_names = kwargs.get('names') + if len(new_names) != len(set(new_names)): + raise Exception("names are not unique!") if len(new_names) != replica: raise Exception('Names does not have the required length', replica) else: - # Adjust replica names to new bookmarking system new_names = [] - for entry in ls: - idx = entry.index('r') - new_names.append(entry[:idx] + '|' + entry[idx:]) + if not appended: + for entry in ls: + try: + idx = entry.index('r') + except Exception: + raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") - print(replica, 'replica') - for i, item in enumerate(ls): - print(item) - sub_ls = [] - for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): - sub_ls.extend(dirnames) - break - for exc in sub_ls: - if fnmatch.fnmatch(exc, 'cfg*'): - sub_ls = list(set(sub_ls) - set(exc)) - sub_ls.sort(key=lambda x: int(x[3:])) - no_cfg = len(sub_ls) - print(no_cfg, 'configurations') + if 'ens_name' in kwargs: + new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) + else: + new_names.append(entry[:idx] + '|' + entry[idx:]) + else: - if i == 0: - with open(path + '/' + item + '/' + sub_ls[0] + '/' + name) as fp: - for k, line in enumerate(fp): - if read == 1 and not line.strip() and k > start + 1: - break - if read == 1 and k >= start: - T += 1 - if '[correlator]' in line: - read = 1 - start = k + 7 + b2b - T -= b2b + for exc in ls: + if not fnmatch.fnmatch(exc, prefix + '*.' + name): + ls = list(set(ls) - set([exc])) + ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) + for entry in ls: + myentry = entry[:-len(name) - 1] + try: + idx = myentry.index('r') + except Exception: + raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.") - deltas = [] - for j in range(T): - deltas.append([]) - - sublength = len(sub_ls) - for j in range(T): - deltas[j].append(np.zeros(sublength)) - - for cnfg, subitem in enumerate(sub_ls): - with open(path + '/' + item + '/' + subitem + '/' + name) as fp: - for k, line in enumerate(fp): - if(k >= start and k < start + T): - floats = list(map(float, line.split())) - deltas[k - start][i][cnfg] = floats[1 + im - single] - - result = [] - for t in range(T): - result.append(Obs(deltas[t], new_names)) - - return result - - -def read_sfcf_c(path, prefix, name, quarks='.*', noffset=0, wf=0, wf2=0, **kwargs): - """Read sfcf c format from given folder structure. - - Parameters - ---------- - quarks -- Label of the quarks used in the sfcf input file - noffset -- Offset of the source (only relevant when wavefunctions are used) - wf -- ID of wave function - wf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions) - im -- if True, read imaginary instead of real part of the correlation function. - b2b -- if True, read a time-dependent boundary-to-boundary correlation function - names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length - ens_name : str - replaces the name of the ensemble - """ - - if kwargs.get('im'): - im = 1 - part = 'imaginary' - else: - im = 0 - part = 'real' - - if kwargs.get('b2b'): - b2b = 1 - else: - b2b = 0 - - T = 0 - ls = [] - for (dirpath, dirnames, filenames) in os.walk(path): - ls.extend(dirnames) - break - if not ls: - raise Exception('Error, directory not found') - # Exclude folders with different names - for exc in ls: - if not fnmatch.fnmatch(exc, prefix + '*'): - ls = list(set(ls) - set([exc])) - if len(ls) > 1: - ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) # New version, to cope with ids, etc. - replica = len(ls) - if 'names' in kwargs: - new_names = kwargs.get('names') - if len(new_names) != replica: - raise Exception('Names does not have the required length', replica) - else: - # Adjust replica names to new bookmarking system - new_names = [] - for entry in ls: - idx = entry.index('r') - if 'ens_name' in kwargs: - new_names.append(kwargs.get('ens_name') + '|' + entry[idx:]) + if 'ens_name' in kwargs: + new_names.append(kwargs.get('ens_name') + '|' + myentry[idx:]) + else: + new_names.append(myentry[:idx] + '|' + myentry[idx:]) + idl = [] + if not appended: + for i, item in enumerate(ls): + sub_ls = [] + if "files" in kwargs: + sub_ls = kwargs.get("files") + sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) else: - new_names.append(entry[:idx] + '|' + entry[idx:]) + for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): + if compact: + sub_ls.extend(filenames) + else: + sub_ls.extend(dirnames) + break - print('Read', part, 'part of', name, 'from', prefix[:-1], ',', replica, 'replica') - for i, item in enumerate(ls): - sub_ls = [] - for (dirpath, dirnames, filenames) in os.walk(path + '/' + item): - sub_ls.extend(filenames) - break - for exc in sub_ls: - if not fnmatch.fnmatch(exc, prefix + '*'): - sub_ls = list(set(sub_ls) - set([exc])) - sub_ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) - - first_cfg = int(re.findall(r'\d+', sub_ls[0])[-1]) - - last_cfg = len(sub_ls) + first_cfg - 1 - - for cfg in range(1, len(sub_ls)): - if int(re.findall(r'\d+', sub_ls[cfg])[-1]) != first_cfg + cfg: - last_cfg = cfg + first_cfg - 1 - break - - no_cfg = last_cfg - first_cfg + 1 - print(item, ':', no_cfg, 'evenly spaced configurations (', first_cfg, '-', last_cfg, ') ,', len(sub_ls) - no_cfg, 'configs omitted\n') - - if i == 0: - pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) - if b2b: - pattern += '\nwf_2 ' + str(wf2) - - with open(path + '/' + item + '/' + sub_ls[0], 'r') as file: - content = file.read() - match = re.search(pattern, content) - if match: - start_read = content.count('\n', 0, match.start()) + 5 + b2b - end_match = re.search(r'\n\s*\n', content[match.start():]) - T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b - assert T > 0 - print(T, 'entries, starting to read in line', start_read) + for exc in sub_ls: + if compact: + if not fnmatch.fnmatch(exc, prefix + '*'): + sub_ls = list(set(sub_ls) - set([exc])) + sub_ls.sort(key=lambda x: + int(re.findall(r'\d+', x)[-1])) + else: + if not fnmatch.fnmatch(exc, 'cfg*'): + sub_ls = list(set(sub_ls) - set([exc])) + sub_ls.sort(key=lambda x: int(x[3:])) + rep_idl = [] + no_cfg = len(sub_ls) + for cfg in sub_ls: + try: + if compact: + rep_idl.append(int(cfg.split("n")[-1])) + else: + rep_idl.append(int(cfg[3:])) + except Exception: + raise Exception("Couldn't parse idl from directroy, problem with file " + cfg) + rep_idl.sort() + print(item, ':', no_cfg, ' configurations') + idl.append(rep_idl) + if i == 0: + if compact: + pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) + if b2b: + pattern += '\nwf_2 ' + str(wf2) + with open(path + '/' + item + '/' + sub_ls[0], 'r') as file: + content = file.read() + match = re.search(pattern, content) + if match: + # the start and end point of the correlator + # in question is extracted for later use in + # the other files + start_read = content.count('\n', 0, match.start()) + 5 + b2b + end_match = re.search(r'\n\s*\n', content[match.start():]) + T = content[match.start():].count('\n', 0, end_match.start()) - 4 - b2b + assert T > 0 + print(T, 'entries, starting to read in line', start_read) + else: + raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') else: - raise Exception('Correlator with pattern\n' + pattern + '\nnot found.') + # this part does the same as above, + # but for non-compactified versions of the files + with open(path + '/' + item + '/' + sub_ls[0] + '/' + name) as fp: + for k, line in enumerate(fp): + if version == "0.0": + # check if this is really the right file + # by matching pattern similar to above + pattern = "# " + name + " : offset " + str(noffset) + ", wf " + str(wf) + # if b2b, a second wf is needed + if b2b: + pattern += ", wf_2 " + str(wf2) + qs = quarks.split(" ") + pattern += " : " + qs[0] + " - " + qs[1] + if read == 1 and not line.strip() and k > start + 1: + break + if read == 1 and k >= start: + T += 1 - deltas = [] - for j in range(T): - deltas.append([]) + if version == "0.0": + if pattern in line: + # print(line) + read = 1 + start = k + 1 + else: + if '[correlator]' in line: + read = 1 + start = k + 7 + b2b + T -= b2b + print(str(T) + " entries found.") + deltas = [] + for j in range(T): + deltas.append([]) - sublength = no_cfg - for j in range(T): - deltas[j].append(np.zeros(sublength)) + for t in range(T): + deltas[t].append(np.zeros(no_cfg)) + # we iterate through all measurement files in the path given... + if compact: + for cfg in range(no_cfg): + with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: + lines = fp.readlines() + if(start_read + T > len(lines)): + raise Exception("EOF before end of correlator data! Maybe " + path + '/' + item + '/' + sub_ls[cfg] + " is corrupted?") + for k in range(start_read - 6, start_read + T): + if k == start_read - 5 - b2b: + if lines[k].strip() != 'name ' + name: + raise Exception('Wrong format', + sub_ls[cfg]) + if(k >= start_read and k < start_read + T): + floats = list(map(float, lines[k].split())) + deltas[k - start_read][i][cfg] = floats[-2:][im] + else: + for cnfg, subitem in enumerate(sub_ls): + with open(path + '/' + item + '/' + subitem + '/' + name) as fp: + for k, line in enumerate(fp): + if(k >= start and k < start + T): + floats = list(map(float, line.split())) + if version == "0.0": + deltas[k - start][i][cnfg] = floats[im] + else: + deltas[k - start][i][cnfg] = floats[1 + im - single] - for cfg in range(no_cfg): - with open(path + '/' + item + '/' + sub_ls[cfg]) as fp: - for k, line in enumerate(fp): - if k == start_read - 5 - b2b: - if line.strip() != 'name ' + name: - raise Exception('Wrong format', sub_ls[cfg]) - if(k >= start_read and k < start_read + T): - floats = list(map(float, line.split())) - deltas[k - start_read][i][cfg] = floats[-2:][im] + else: + if "files" in kwargs: + ls = kwargs.get("files") + else: + for exc in ls: + if not fnmatch.fnmatch(exc, prefix + '*.' + name): + ls = list(set(ls) - set([exc])) + ls.sort(key=lambda x: int(re.findall(r'\d+', x)[-1])) + pattern = 'name ' + name + '\nquarks ' + quarks + '\noffset ' + str(noffset) + '\nwf ' + str(wf) + if b2b: + pattern += '\nwf_2 ' + str(wf2) + for rep, file in enumerate(ls): + rep_idl = [] + with open(path + '/' + file, 'r') as fp: + content = fp.readlines() + data_starts = [] + for linenumber, line in enumerate(content): + if "[run]" in line: + data_starts.append(linenumber) + if len(set([data_starts[i] - data_starts[i - 1] for i in + range(1, len(data_starts))])) > 1: + raise Exception("Irregularities in file structure found, not all runs have the same output length") + chunk = content[:data_starts[1]] + for linenumber, line in enumerate(chunk): + if line.startswith("gauge_name"): + gauge_line = linenumber + elif line.startswith("[correlator]"): + corr_line = linenumber + found_pat = "" + for li in chunk[corr_line + 1:corr_line + 6 + b2b]: + found_pat += li + if re.search(pattern, found_pat): + start_read = corr_line + 7 + b2b + T = len(chunk) - 1 - start_read + if rep == 0: + deltas = [] + for t in range(T): + deltas.append([]) + for t in range(T): + deltas[t].append(np.zeros(len(data_starts))) + for cnfg in range(len(data_starts)): + start = data_starts[cnfg] + stop = start + data_starts[1] + chunk = content[start:stop] + try: + rep_idl.append(int(chunk[gauge_line].split("n")[-1])) + except Exception: + raise Exception("Couldn't parse idl from directroy, problem with chunk around line " + gauge_line) + found_pat = "" + for li in chunk[corr_line + 1:corr_line + 6 + b2b]: + found_pat += li + if re.search(pattern, found_pat): + for t, line in enumerate(chunk[start_read:start_read + T]): + floats = list(map(float, line.split())) + deltas[t][rep][cnfg] = floats[-2:][im] + idl.append(rep_idl) + + if "check_configs" in kwargs: + print("Checking for missing configs...") + che = kwargs.get("check_configs") + if not (len(che) == len(idl)): + raise Exception("check_configs has to be the same length as replica!") + for r in range(len(idl)): + print("checking " + new_names[r]) + utils.check_idl(idl[r], che[r]) + print("Done") result = [] for t in range(T): - result.append(Obs(deltas[t], new_names)) - return result - - -def read_qtop(path, prefix, **kwargs): - """Read qtop format from given folder structure. - - Parameters - ---------- - target -- specifies the topological sector to be reweighted to (default 0) - full -- if true read the charge instead of the reweighting factor. - """ - - if 'target' in kwargs: - target = kwargs.get('target') - else: - target = 0 - - if kwargs.get('full'): - full = 1 - else: - full = 0 - - ls = [] - for (dirpath, dirnames, filenames) in os.walk(path): - ls.extend(filenames) - break - - if not ls: - raise Exception('Error, directory not found') - - # Exclude files with different names - for exc in ls: - if not fnmatch.fnmatch(exc, prefix + '*'): - ls = list(set(ls) - set([exc])) - if len(ls) > 1: - ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0])) # New version, to cope with ids, etc. - replica = len(ls) - print('Read Q_top from', prefix[:-1], ',', replica, 'replica') - - deltas = [] - - for rep in range(replica): - tmp = [] - with open(path + '/' + ls[rep]) as fp: - for k, line in enumerate(fp): - floats = list(map(float, line.split())) - if full == 1: - tmp.append(floats[1]) - else: - if int(floats[1]) == target: - tmp.append(1.0) - else: - tmp.append(0.0) - - deltas.append(np.array(tmp)) - - rep_names = [] - for entry in ls: - truncated_entry = entry.split('.')[0] - idx = truncated_entry.index('r') - rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:]) - - result = Obs(deltas, rep_names) - + result.append(Obs(deltas[t], new_names, idl=idl)) return result diff --git a/pyerrors/input/utils.py b/pyerrors/input/utils.py new file mode 100644 index 00000000..4e50c15c --- /dev/null +++ b/pyerrors/input/utils.py @@ -0,0 +1,24 @@ +"""Utilities for the input""" + + +def check_idl(idl, che): + """Checks if list of configurations is contained in an idl + + Parameters + ---------- + idl : range or list + idl of the current replicum + che : list + list of configurations to be checked against + """ + missing = [] + for c in che: + if c not in idl: + missing.append(c) + # print missing configurations such that it can directly be parsed to slurm terminal + if not (len(missing) == 0): + print(len(missing), "configs missing") + miss_str = str(missing[0]) + for i in missing[1:]: + miss_str += "," + str(i) + print(miss_str) diff --git a/pyerrors/obs.py b/pyerrors/obs.py index e19cc617..9d49c59e 100644 --- a/pyerrors/obs.py +++ b/pyerrors/obs.py @@ -4,6 +4,7 @@ import numpy as np import autograd.numpy as anp # Thinly-wrapped numpy from autograd import jacobian import matplotlib.pyplot as plt +from scipy.stats import skew, skewtest, kurtosis, kurtosistest import numdifftools as nd from itertools import groupby from .covobs import Covobs @@ -564,7 +565,7 @@ class Obs: y = np.concatenate(tmp, axis=0) plt.errorbar(x, y, fmt='.', markersize=3) plt.xlim(-0.5, e_N - 0.5) - plt.title(e_name) + plt.title(e_name + f', skew: {skew(y):.3f} (p={skewtest(y).pvalue:.3f}), kurtosis: {kurtosis(y):.3f} (p={kurtosistest(y).pvalue:.3f})') plt.draw() def plot_piechart(self): diff --git a/tests/fits_test.py b/tests/fits_test.py index 8a1759cb..48012edb 100644 --- a/tests/fits_test.py +++ b/tests/fits_test.py @@ -309,6 +309,25 @@ def test_error_band(): pe.fits.error_band(x, f, fitp.fit_parameters) +def test_ks_test(): + def f(a, x): + y = a[0] + a[1] * x + return y + + fit_res = [] + + for i in range(20): + data = [] + for j in range(10): + data.append(pe.pseudo_Obs(j + np.random.normal(0.0, 0.25), 0.25, 'test')) + my_corr = pe.Corr(data) + + fit_res.append(my_corr.fit(f, silent=True)) + + pe.fits.ks_test() + pe.fits.ks_test(fit_res) + + def fit_general(x, y, func, silent=False, **kwargs): """Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.