From e7ea0df6606f077b22731592abefeea01c00db37 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Mon, 11 Oct 2021 17:12:57 +0100 Subject: [PATCH 1/2] README updated --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index eb288c4a..5806d7c4 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/he * implementation of the matrix-pencil-method [IEEE Trans. Acoust. 38, 814-824 (1990)](https://ieeexplore.ieee.org/document/56027) for the extraction of energy levels, especially suited for noisy data and excited states There exist similar implementations of gamma method error analysis suites in -- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors). +- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors) - [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl) - [Python 3](https://github.com/mbruno46/pyobs) @@ -33,15 +33,15 @@ The basic objects of a pyerrors analysis are instances of the class `Obs`. They import numpy as np import pyerrors as pe -observable1 = pe.Obs([samples1], ['ensemble1']) -observable1.gamma_method() -observable1.print() +obs1 = pe.Obs([samples1], ['ensemble1']) +obs1.gamma_method() +obs1.print() ``` Often one is interested in secondary observables which can be arbitrary functions of primary observables. `pyerrors` overloads most basic math operations and numpy functions such that the user can work with `Obs` objects as if they were floats ```python -observable3 = 12.0 / observable1 ** 2 - np.exp(-1.0 / observable2) -observable3.gamma_method() -observable3.print() +obs3 = 12.0 / obs1 ** 2 - np.exp(-1.0 / obs2) +obs3.gamma_method() +obs3.print() ``` More detailed examples can be found in the `/examples` folder: From 1d74e8c4f4f13b568a9dbd1ad5ce8666b664fff3 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Mon, 11 Oct 2021 18:31:02 +0100 Subject: [PATCH 2/2] Examples updated, minor bug fixes --- examples/01_basic_example.ipynb | 107 +-- examples/02_correlators.ipynb | 397 +++++++++ examples/02_pcac_example.ipynb | 591 ------------- examples/03_fit_example.ipynb | 774 ----------------- examples/03_pcac_example.ipynb | 599 ++++++++++++++ examples/04_fit_example.ipynb | 782 ++++++++++++++++++ examples/05_correlators.ipynb | 276 ------- ...tions.ipynb => 05_matrix_operations.ipynb} | 26 +- examples/base_style.mplstyle | 30 + examples/data/correlator_test.p | Bin 0 -> 94740 bytes pyerrors/correlators.py | 19 +- pyerrors/fits.py | 2 +- 12 files changed, 1886 insertions(+), 1717 deletions(-) create mode 100644 examples/02_correlators.ipynb delete mode 100644 examples/02_pcac_example.ipynb delete mode 100644 examples/03_fit_example.ipynb create mode 100644 examples/03_pcac_example.ipynb create mode 100644 examples/04_fit_example.ipynb delete mode 100644 examples/05_correlators.ipynb rename examples/{04_matrix_operations.ipynb => 05_matrix_operations.ipynb} (94%) create mode 100644 examples/base_style.mplstyle create mode 100644 examples/data/correlator_test.p diff --git a/examples/01_basic_example.ipynb b/examples/01_basic_example.ipynb index 794076eb..e529c865 100644 --- a/examples/01_basic_example.ipynb +++ b/examples/01_basic_example.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Import pyerrors, as well as autograd wrapped numpy and matplotlib. The sys statement is not necessary if pyerrors was installed via pip." + "Import pyerrors, as well as autograd wrapped numpy and matplotlib." ] }, { @@ -20,13 +20,21 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append('..')\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pyerrors as pe" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./base_style.mplstyle')\n", + "plt.rc('text', usetex=True)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -36,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -93,14 +101,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Obs[1.415(20)]\n" + "Obs[1.387(19)]\n" ] } ], @@ -118,15 +126,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Result\t 1.41522010e+00 +/- 2.03946273e-02 +/- 1.01973136e-03 (1.441%)\n", - " t_int\t 5.07378446e-01 +/- 4.51400871e-02 S = 2.00\n" + "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" ] } ], @@ -143,14 +151,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -187,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -205,15 +213,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Result\t 3.27194697e-01 +/- 1.96872835e+00 +/- 3.38140198e-01 (601.699%)\n", - " t_int\t 5.41336983e+00 +/- 1.59801329e+00 S = 2.00\n" + "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" ] } ], @@ -232,14 +240,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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sSpMD7dnvbutg3dYG1oQ/2Lj8vlfYtLOZTbua2byrmS31LQB84ebnGVqUz7DB+Qwpyu/8GlpWUsDkihKGFuUztGgQc37/LHecf2QY4LkU5AXhXjlvAQsvOnaf9d/x/Hq+eVL3O6ojKkd02z56WNE+bSUF3X8EDx6677wAh48fvk/bJXetYO7syXu13bfiXe7/92OAIADqW9rYWr+b4695jM9WjWVr/W621rfwzrZGlr6zHYCz5z9LTV0zI4oHMXpYUWe9f1m6gYllxbyvrJjhxYO6rUvSa3vDblZtrmNNGPBn3fgMqzfXYcDUUaVMHRWcHXPh7ElMLCvm4KFF5IZfIyvnLeBzR4xPV+lpkc4e/2x335LG9WeMbQ27eTEMp6/9z1LW1NTzzrZGxgwrYnL4g41xIwZzROUIDhpawKghhVSUFjL1Bwt54ruz91ne7c+9w1eOm7RP+7gRg5O7IWliZpQW5ncOAZ3azTnOC15awFPzTqCtvYOauhbe3dFE9Y4mFry0kX+uqeWPz6zlrdoGcnODsLjkruVMKi9hYlkxE8uKqWzrSOk2RdWu5lbWbK5j9eZ6Vm0KQr7qJw/T0trO1INKmToq+Hu46MQpTB1VSlnJoM7z2//4bP9Pf8w2GupJsr4O6bh7Zy/+239ewbJ126mpa2H6uGEAfPSDB/ONiiBw9oxbV85bwHnHTEzeRkRIXm5OZ2+/CrjozuXccPbhQPD/ZmvDbqp+8jAzJ47grS0N3PNiNW9vaeCdrfW0l43nX//vU4weWsTBQws5eFgRo4cWAlCzq5mykgJy9hyskB7Vt7SxZnMda2rqO3vxR/90MTuaWplSUcKUUaUcEvbi7//3WXv9gOmO59cza3JZ2mrPBOkKfgceMjMHbnT3+WmqI+l6M6TT0NLG029u5ZHXa3hsVU3nV9DDxw/jvGMmMnVUKbk5wZjjJ6aNTmX5EsPMKAuPE3QdGmhf9iKn/uZZvjf3WDbubGbjjibWb2vkube2AnD6L59kV1MbFUMKgh3DsGCHcNOTb1FeWkBFaWHwPKQgtRuVZnXNrbxRU8+azfWsqXkv4Lc3tjKpopgpFaVMCXvxf/rKUYwZVrTXzvPKB16LOywo8aUr+I9x9+rwPr6LzOx1d38idgYzuwC4AGD8+Owbf9ta38KClzcCMPOqxUwbN5TZh1Rw3jEzmVRezMRLH+CcmRPSXKX0Vm6OMaijrdvjG5XzFrDkByfT3NrO5l3NvLujmY07m7h3+btU72hi+fod1NS1UBs+AI79+SNUlBZSUVpAeWmwM7jrhfWUh9MVQwoYWZw5O4lVm+p4Z1tj8AjPpIkN+KkVpUwOA/7OC45izPD3xuABfv7gqqwdikyHtAS/u1eHzzVmdjcwg+BGL7HzzAfmA1RVVXnKi+yj3gzpNO1u56FXN3HPi9UsWbedE99fAcAzl57QOf4s2aswP5cJI4s7z/u+5K4VXPbxfW9dXTlvAX/80szOnUFNXTMAz6/dRk1dCzXhwfsdjcHZWCdc8xhDit47eD+0KPizvvHxN8Pp4DEkPLBfs6uZwQV5DM7P7ffQU21dC3XNrdQ1t4WPoJZrH1pFza6g5pq6FjbvCnZkc29fxvgRg4NHuP13XnAUY4fv3YP/+YOrGD9SAZ9sKQ9+MysGcty9Lnx9CvDjVNeRaPGGdNyd594OLmY646qHOXz8cD51+Bh+/W8fprggLzwVUaEve6ssK6ay7L2f6V9x/6tc89lpe83T2t7BlO8vZP4XqtjZ1Mqu5lZ2xZyeu6W+hTdr64P3mto62z/2q3/S2NJGY2s7heHZWzOvepi8nBzyc4PfTey5Jszsax6jpbWdlrYOmsNngFNveCL83UQ+pYV5lIan/ubmGNPGDaMi/FYyakghM69avM9lCP7z768q4NMoHT3+UcDd4YGYPOB2d38wDXUkVePuNu5+sZr/9/Q62jqCP5bF3zqOitLCNFcm2WJPOO85syvWj+59he9/7NB92ivnLeCF758EBL+RaGpt57DL/sE9c2fR1u60dXjnj+9O/+WT3HRuFQV5ORTm51KQl0NBXi5Tf7CQZT88udtlxzvFVwaWlAe/u78FTNvvjBnsJ39/lb8s20DVhBH88IxDmTV5JBMvfUChLwNKTo5RHP6OIt4B0knlustqNtJPFPvo+kWrqZy3oPNx/aLVQHDwau5ty4Dg6+79Xz+Gm86t4pgpZZ2nmSWrFmCvWvraLiLRovP4+6jrWP7rm3Zx4W1Lef7t7Zx/7EQWvLyRSw/gZ96xB4kr5y3Y5xZ5e9pir8cfr8betHe3vq633zuQdhEZeBT8B+Crf1zKknXbueAjE7nms9MYPCiPny58vVf/Nl5Q9hTayZCInUdP7X3ZkWnnIZIaCv4+qq1r4b/+EYR7VeVwrv/cdIoGxb/yY18DPtv0ZUfWl52HdhQi/afg70HXc/M/MqWMle/u4lOHjwHgy8fu/zrbUQn4ZErE0JWIvEfB34PY8fwpFSU4cNdXjmRyRSk3/fPtveZV4AwMff3WIBJFCv4ebGvYzQ/vXQnAt089hFMOHRX3DB317Ac2DSOJvEfBH8cTq2v57l9e4uPTDgbeu5SvAiG7aBhJokjB30Vzazs/e/B1Hly5ievOmsbRk8v4/ZPvDeuoZx9dPX1rWHT7k4B2CJIZFPzsexB3SkUJCy86lmGDdbcl2b+LT57KxeWNwcT06Z3t+oYgydCX4cl4FPwEf7hlJYP44b2vcM1np/HpD4/BzPSHKwdExxWyXzJ/AAkH/sPNS+LUHfngb2vv4Md/f5Vn3gxumPGZ/zW28z0N60gy6LhCag2UEO5vezJEOvh3Nbcy97ZlmBl/vfBoPnT5Q+kuSWQvUf/WMNDCOVuY+4C/xwlVVVW+ZMmShC7zna2NnHfrCxw1aSRDC/P51aNvdL6X6X8skgbLlwfPMWP8A0l3NwoC9mnrGqqx7XvEu41o1/ZErlP6x8yWunvVPu1RCv6uH67jp5Zzy5dmHPByRQZ68CdCvHBWaA9cCv7QAy9v5MLblnHLF4/g+EMqErJMkSgEv2SeeMEfqTH++1e8yxX3vwqg0BeRyIrMjVjuXV7Nf/z1JbbUBzd/1o1IRCSqItHj/+vSDfzswde5Z+4spo4qTXc5IiJplfXBf9eS9Vz30GpuP38mkysU+iIiWR38dz7/Dr9YvIbbzp+pm0aLiISyMvi7nl523/J3dXqZiEgoKw/uVm9vSncJIiIDVtb1+N+sreex1bX88bwZHDulPN3liIgMOFnV499S38IX//AC3z31EIW+iEgcWRP8Tbvb+fKtSzhz+mjOOmJcussRERmwsiL42zucb/7pRSpHDuYSHcQVEelRVozx//SB19jZ1Mov5xwe92boIiISyPjgP+m6x3mjph6AQ37wIDMnjuBPXzkqzVWJiAxcGR3867c1sr1hN/fOncW0ccPSXY6ISEbI2DH+1vYOvnHni3zt+EkKfRGRPsjY4L/2odUMK8rnS7MmprsUEZGMkpFDPY+vruWeF6tZ8I1jyMnRwVwRkb7IuOCv2dXMt/+8gl+efTgjSwrSXY6ISMbJqKGejg7n4ruWM2fGeI6aNDLd5YiIZKSMCv7fPv4mrW3ON06YnO5SREQyVsYM9Xzuxmd47u1tAEz+/kKdry8i0k9pCX4zOw34BZAL3OTuV+/v3yjkRUQSI+VDPWaWC/wG+ChwKDDHzA5NdR0iIlGVjjH+GcAb7v6Wu+8G7gTOTEMdIiKRlI7gHwOsj5neELaJiEgKDNizeszsAjNbYmZLamtr012OiEjWSEfwVwOxd0oZG7btxd3nu3uVu1eVl+tuWiIiiZKO4H8BmGJmE81sEHA2cF8a6hARiaSUn87p7m1m9nXgHwSnc97s7q+kug4RkahKy3n87v4A8EA61i0iEnUD9uCuiIgkh4JfRCRiFPwiIhGj4BcRiRgFv4hIxCj4RUQiRsEvIhIxCn4RkYhR8IuIRIyCX0QkYhT8IiIRo+AXEYkYBb+ISMQo+EVEIkbBLyISMQp+EZGIUfCLiESMgl9EJGIU/CIiEaPgFxGJGAW/iEjEKPhFRCJGwS8iEjEKfhGRiFHwi4hEjIJfRCRiFPwiIhGj4BcRiRgFv4hIxCj4RUQixtw93TXsl5nVAuvCyTJgSxrLSZUobGcUthG0ndkk07ZxgruXd23MiOCPZWZL3L0q3XUkWxS2MwrbCNrObJIt26ihHhGRiFHwi4hETCYG//x0F5AiUdjOKGwjaDuzSVZsY8aN8YuIyIHJxB6/iIgcAAW/iEjEZEzwm9lpZrbKzN4ws3npridRzOxmM6sxs5UxbSPMbJGZrQmfh6ezxkQws3Fm9qiZvWpmr5jZRWF71myrmRWa2fNmtiLcxivC9olm9lz42f2TmQ1Kd62JYGa5Zvaimf09nM667TSztWb2spktN7MlYVvGf2YzIvjNLBf4DfBR4FBgjpkdmt6qEuYW4LQubfOAxe4+BVgcTme6NuBb7n4ocCQwN/x/mE3b2gKc4O7TgOnAaWZ2JPAz4Hp3nwxsB85LX4kJdRHwWsx0tm7nbHefHnP+fsZ/ZjMi+IEZwBvu/pa77wbuBM5Mc00J4e5PANu6NJ8J3Bq+vhX4ZCprSgZ33+juy8LXdQSBMYYs2lYP1IeT+eHDgROAv4TtGb2Ne5jZWOBjwE3htJGF2xlHxn9mMyX4xwDrY6Y3hG3ZapS7bwxfbwJGpbOYRDOzSuBw4DmybFvD4Y/lQA2wCHgT2OHubeEs2fLZvQH4LtARTo8kO7fTgYfMbKmZXRC2ZfxnNi/dBUjP3N3NLGvOuTWzEuCvwDfdfVfQUQxkw7a6ezsw3cyGAXcD709vRYlnZmcANe6+1MyOT3M5yXaMu1ebWQWwyMxej30zUz+zmdLjrwbGxUyPDduy1WYzOxggfK5Jcz0JYWb5BKF/m7v/LWzOym119x3Ao8BRwDAz29PJyobP7izgE2a2lmDY9QTgF2TfduLu1eFzDcGOfAZZ8JnNlOB/AZgSnjUwCDgbuC/NNSXTfcC54etzgXvTWEtChGPA/w285u7XxbyVNdtqZuVhTx8zKwJOJjiW8SjwmXC2jN5GAHe/1N3Hunslwd/iI+5+Dlm2nWZWbGale14DpwAryYLPbMb8ctfMTicYV8wFbnb3K9NbUWKY2R3A8QSXe90MXAbcA9wFjCe4HPVZ7t71AHBGMbNjgCeBl3lvXPh7BOP8WbGtZvYhgoN9uQSdqrvc/cdm9j6CnvEI4EXg8+7ekr5KEycc6vm2u5+RbdsZbs/d4WQecLu7X2lmI8nwz2zGBL+IiCRGpgz1iIhIgij4RUQiRsEvIhIxCn4RkYhR8IuIRIyCX6QPzOx6M/tmzPQ/zOymmOlrzeyStBQn0ksKfpG+eQo4GsDMcgh+f3FYzPtHA0+noS6RXlPwi/TN0wSXYYAg8FcCdWY23MwKgA8Ay9JVnEhv6CJtIn3g7u+aWZuZjSfo3T9DcBXKo4CdwMvhpcNFBiwFv0jfPU0Q+kcD1xEE/9EEwf9UGusS6RUN9Yj03Z5x/g8SDPU8S9Dj1/i+ZAQFv0jfPQ2cAWxz9/bwAl3DCMJfwS8DnoJfpO9eJjib59kubTvdfUt6ShLpPV2dU0QkYtTjFxGJGAW/iEjEKPhFRCJGwS8iEjEKfhGRiFHwi4hEjIJfRCRi/j/rsirPrnndsgAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -274,22 +282,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Result\t 3.27194697e-01 +/- 2.14280114e+00 +/- 2.48970994e-01 (654.901%)\n", - " t_int\t 6.41297945e+00 +/- 2.18167829e+00 tau_exp = 20.00, N_sigma = 1\n" + "Result\t 3.27194697e-01 +/- 1.88231459e+00 +/- 2.01855751e-01 (575.289%)\n", + " t_int\t 5.86511391e+00 +/- 2.16269625e+00 tau_exp = 20.00, N_sigma = 1\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -320,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -328,16 +336,16 @@ "output_type": "stream", "text": [ "Binning analysis:\n", - "Result:\t 3.27194697e-01 +/- 1.81819841e+00 +/- 3.98347312e-01 (555.693%)\n", - "Result:\t 3.27194697e-01 +/- 1.66475180e+00 +/- 5.21149746e-01 (508.795%)\n", - "Result:\t 3.27194697e-01 +/- 1.41273466e+00 +/- 6.28627238e-01 (431.772%)\n" + "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", 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\n", 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" ] }, "metadata": { @@ -350,19 +358,19 @@ "output_type": "stream", "text": [ "Result from the automatic windowing procedure for comparison:\n", - "Result\t 3.27194697e-01 +/- 2.02097394e+00 +/- 3.22723658e-01 (617.667%)\n", - " t_int\t 5.70449936e+00 +/- 1.53928442e+00 S = 1.50\n", - "Result\t 3.27194697e-01 +/- 1.96872835e+00 +/- 3.38140198e-01 (601.699%)\n", - " t_int\t 5.41336983e+00 +/- 1.59801329e+00 S = 2.00\n", - "Result\t 3.27194697e-01 +/- 1.89700786e+00 +/- 3.67353992e-01 (579.780%)\n", - " t_int\t 5.02613753e+00 +/- 1.69573607e+00 S = 3.00\n" + "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", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -373,14 +381,14 @@ ], "source": [ "import pyerrors.jackknifing as jn\n", - "jack1 = jn.generate_jack(c_obs1, max_binsize=120)\n", - "jack2 = jn.generate_jack(c_obs2, max_binsize=120)\n", + "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", - "jack3.print(binsize=100)\n", "\n", "jack3.plot_tauint()\n", "\n", @@ -402,13 +410,6 @@ "source": [ "For this specific example the binned Jackknife procedure seems to underestimate the final error, the deduced intergrated autocorrelation time depends strongly on the chosen binsize. The automatic windowing procedure displayed for comparison gives more robust results for this example." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -427,7 +428,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.11" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/02_correlators.ipynb b/examples/02_correlators.ipynb new file mode 100644 index 00000000..db4dcdef --- /dev/null +++ b/examples/02_correlators.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c1065dd", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pyerrors as pe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "20f67709", + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./base_style.mplstyle')\n", + "plt.rc('text', usetex=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e5764fd0", + "metadata": {}, + "source": [ + "We can load data from preprocessed pickle files which contain a list of `pyerror` `Obs`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c49ff771", + "metadata": {}, + "outputs": [], + "source": [ + "correlator_data = pe.load_object('./data/correlator_test.p') " + ] + }, + { + "cell_type": "markdown", + "id": "ae93c7c2", + "metadata": {}, + "source": [ + "With this list a `Corr` object can be initialised" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "33a8fdec", + "metadata": {}, + "outputs": [], + "source": [ + "my_correlator = pe.correlators.Corr(correlator_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f954607", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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", + "\n" + ] + } + ], + "source": [ + "my_correlator.print([8, 14])" + ] + }, + { + "cell_type": "markdown", + "id": "b00d670b", + "metadata": {}, + "source": [ + "The `show` method can display the correlator" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b71529d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "my_correlator.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c659557e", + "metadata": {}, + "source": [ + "## Manipulating correlators" + ] + }, + { + "cell_type": "markdown", + "id": "416cf39a", + "metadata": {}, + "source": [ + "`Corr` objects can be shifted" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e8d65dd5", + "metadata": {}, + "outputs": [], + "source": [ + "shifted_correlator = my_correlator.roll(20)\n", + "shifted_correlator.tag = r'Correlator shifted by $x_0/a=20$'" + ] + }, + { + "cell_type": "markdown", + "id": "634dd613", + "metadata": {}, + "source": [ + "Or symmetrised" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "127a661d", + "metadata": {}, + "outputs": [], + "source": [ + "symmetrised_correlator = my_correlator.symmetric()\n", + "symmetrised_correlator.tag = 'Symmetrised correlator'" + ] + }, + { + "cell_type": "markdown", + "id": "3d733872", + "metadata": {}, + "source": [ + "And we can compare different `Corr` objects by passing `comp` to the `show` method" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8e264aed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "shifted_correlator.show(comp=symmetrised_correlator, logscale=True)" + ] + }, + { + "cell_type": "markdown", + "id": "232e88af", + "metadata": {}, + "source": [ + "## Effective mass" + ] + }, + { + "cell_type": "markdown", + "id": "83dc751c", + "metadata": {}, + "source": [ + "The effective mass of the correlator can be obtained by calling the `m_eff` method" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c686f7e0", + "metadata": {}, + "outputs": [], + "source": [ + "m_eff = symmetrised_correlator.m_eff()\n", + "m_eff.tag = 'Effective mass'" + ] + }, + { + "cell_type": "markdown", + "id": "4a9d13b2", + "metadata": {}, + "source": [ + "We can also use the priodicity of the lattice in order to obtain the cosh effective mass" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5acde8cf", + "metadata": {}, + "outputs": [], + "source": [ + "periodic_m_eff = symmetrised_correlator.m_eff('periodic')\n", + "periodic_m_eff.tag = 'Cosh effective mass'" + ] + }, + { + "cell_type": "markdown", + "id": "c658b000", + "metadata": {}, + "source": [ + "We can compare the two and see how the standard effective mass deviates form the plateau at the center of the lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1d6ea22a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "periodic_m_eff.show([4,47], comp=m_eff, ylabel=r'$am_\\mathrm{eff}$')" + ] + }, + { + "cell_type": "markdown", + "id": "e3762e68", + "metadata": {}, + "source": [ + "Arithmetic operations and mathematical functions are also overloaded for the `Corr` class. We can compute the difference between the two variants of the effective mass as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e56d164c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "difference_m_eff = np.abs(periodic_m_eff - m_eff)\n", + "difference_m_eff.show([0, 47], logscale=True)" + ] + }, + { + "cell_type": "markdown", + "id": "472ab97b", + "metadata": {}, + "source": [ + "## Derivatives" + ] + }, + { + "cell_type": "markdown", + "id": "d99414fe", + "metadata": {}, + "source": [ + "We can obtain derivatives of correlators in the following way" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "03007f8a", + "metadata": {}, + "outputs": [], + "source": [ + "first_derivative = symmetrised_correlator.deriv()\n", + "first_derivative.tag = 'First derivative'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c0311739", + "metadata": {}, + "outputs": [], + "source": [ + "second_derivative = symmetrised_correlator.second_deriv()\n", + "second_derivative.tag = 'Second derivative'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "165550d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "symmetrised_correlator.show([5, 20], comp=[first_derivative, second_derivative], y_range=[-500, 1300])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff177781", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/02_pcac_example.ipynb b/examples/02_pcac_example.ipynb deleted file mode 100644 index 62b4ee2f..00000000 --- a/examples/02_pcac_example.ipynb +++ /dev/null @@ -1,591 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append('..')\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pyerrors as pe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primary observables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can load data from preprocessed pickle files which contain a list of `pyerror` `Obs`:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "p_obs_names = ['f_A', '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') " - ] - }, - { - "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 " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pe.plot_corrs([p_obs['f_A'], p_obs['f_P']], label=p_obs_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Secondary observables" - ] - }, - { - "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", - "\n", - "We start by looking at the unimproved pcac mass $am=\\tilde{\\partial}_0 f_\\mathrm{A}/2 f_\\mathrm{P}$" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "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]" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "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))" - ] - }, - { - "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" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "?pe.Obs.gamma_method" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "masses = [uimpr_mass, impr_mass]\n", - "for i, item in enumerate(masses):\n", - " [o.gamma_method() for o in item]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now have a look at the result by plotting the two lists of `Obs`" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pe.plot_corrs([impr_mass, uimpr_mass], xrange=[0.5, 18.5], label=['Improved pcac mass', 'Unimproved pcac mass'])" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "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": 11, - "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": 12, - "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": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "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": 14, - "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": 15, - "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": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "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": 17, - "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": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "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": 19, - "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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Monte Carlo history of the observable can be accessed with `plot_history` to identify possible outliers or have a look at the shape of the distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pcac_plateau.plot_history()" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "pcac_plateau.dump('B1k2_pcac_plateau')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/03_fit_example.ipynb b/examples/03_fit_example.ipynb deleted file mode 100644 index e0de4040..00000000 --- a/examples/03_fit_example.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.append('..')\n", - "import pyerrors as pe\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Read data from the pcac example" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "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']];" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now define a custom fit function, in this case a single exponential. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import autograd.numpy as anp\n", - "def func_exp(a, x):\n", - " y = a[1] * anp.exp(-a[0] * x)\n", - " return y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fit single exponential to f_P. The kwarg `resplot` generates a figure which visualizes the fit with residuals." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit with 2 parameters\n", - "Method: Levenberg-Marquardt\n", - "`xtol` termination condition is satisfied.\n", - "chisquare/d.o.f.: 0.00287692704517733\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": [ - "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", - "\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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The covariance of the two fit parameters can be computed in the following way" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Covariance: 0.003465486601483565\n", - "Normalized covariance: 0.8360758153764549\n" - ] - } - ], - "source": [ - "cov_01 = pe.fits.covariance(a[0], a[1])\n", - "print('Covariance: ', cov_01)\n", - "print('Normalized covariance: ', cov_01 / a[0].dvalue / a[1].dvalue)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Effective mass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the effective mass for comparison" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "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()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the corresponding plateau and compare the two results" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective mass:\n", - "Obs[0.2114(52)]\n", - "Fitted mass:\n", - "Obs[0.2102(63)]\n" - ] - } - ], - "source": [ - "m_eff_plateau = np.mean(m_eff_f_P[start_se: stop_se]) # Plateau from 8 to 16\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)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "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": 9, - "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": 10, - "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: 0.7915235152326285\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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting with x-errors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first generate pseudo data" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(Obs[0.16(35)], Obs[0.15(25)])\n", - "(Obs[2.21(35)], Obs[0.88(25)])\n", - "(Obs[3.72(35)], Obs[-1.70(25)])\n", - "(Obs[6.10(35)], Obs[-1.58(25)])\n", - "(Obs[7.55(35)], Obs[-0.18(25)])\n" - ] - } - ], - "source": [ - "ox = []\n", - "oy = []\n", - "for i in range(0,10,2):\n", - " ox.append(pe.pseudo_Obs(i + 0.35 * np.random.normal(), 0.35, str(i)))\n", - " oy.append(pe.pseudo_Obs(np.sin(i) + 0.25 * np.random.normal() - 0.2 * i + 0.17, 0.25, str(i)))\n", - "\n", - "[o.gamma_method() for o in ox + oy]\n", - "[print(o) for o in zip(ox, oy)];" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And choose a function to fit" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def func(a, x):\n", - " y = a[0] + a[1] * x + a[2] * anp.sin(x)\n", - " return y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can then fit this function to the data and get the fit parameter as Obs with the function `odr_fit` which uses orthogonal distance regression." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit with 3 parameters\n", - "Method: ODR\n", - "Sum of squares convergence\n", - "Residual variance: 0.03576834451052203\n", - "Parameter 1 : Obs[0.02(40)]\n", - "Parameter 2 : Obs[-0.225(75)]\n", - "Parameter 3 : Obs[1.59(39)]\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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the visulization we determine the value of the fit function in a range of x values" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x_t = np.arange(min(ox).value - 1, max(ox).value + 1, 0.01)\n", - "y_t = func([o.value for o in beta], x_t)\n", - "\n", - "plt.errorbar([e.value for e in ox], [e.value for e in oy], xerr=[e.dvalue for e in ox], yerr=[e.dvalue for e in oy], marker='D', lw=1, ls='none', zorder=10)\n", - "plt.plot(x_t, y_t, '--')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also take a look at how much the inidividual ensembles contribute to the uncetainty of the fit parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter 0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "for i, item in enumerate(beta):\n", - " print('Parameter', i)\n", - " item.plot_piechart()\n", - " 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": 16, - "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": 17, - "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": 18, - "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": 19, - "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": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit with 6 parameters\n", - "Method: migrad\n", - "chisquare/d.o.f.: 1.0925587749193326\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[0.1861(27)], Obs[210(12)], Obs[0.701(60)], Obs[321(433)], Obs[0.711(51)], Obs[435(433)]]\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": 21, - "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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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "[o.plot_piechart() for o in beta_p];" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/03_pcac_example.ipynb b/examples/03_pcac_example.ipynb new file mode 100644 index 00000000..595a1be5 --- /dev/null +++ b/examples/03_pcac_example.ipynb @@ -0,0 +1,599 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pyerrors as pe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./base_style.mplstyle')\n", + "plt.rc('text', usetex=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Primary observables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can load data from preprocessed pickle files which contain a list of `pyerror` `Obs`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "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') " + ] + }, + { + "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 " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pe.plot_corrs([p_obs['f_A'], p_obs['f_P']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Secondary observables" + ] + }, + { + "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", + "\n", + "We start by looking at the unimproved pcac mass $am=\\tilde{\\partial}_0 f_\\mathrm{A}/2 f_\\mathrm{P}$" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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]" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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))" + ] + }, + { + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "?pe.Obs.gamma_method" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "masses = [uimpr_mass, impr_mass]\n", + "for i, item in enumerate(masses):\n", + " [o.gamma_method() for o in item]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now have a look at the result by plotting the two lists of `Obs`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pe.plot_corrs([impr_mass, uimpr_mass], xrange=[0.5, 18.5], label=['Improved pcac mass', 'Unimproved pcac mass'])" + ] + }, + { + "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." + ] + }, + { + "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "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", + "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": [ + "## 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", 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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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Monte Carlo history of the observable can be accessed with `plot_history` to identify possible outliers or have a look at the shape of the distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pcac_plateau.plot_history()" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "pcac_plateau.dump('B1k2_pcac_plateau')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/04_fit_example.ipynb b/examples/04_fit_example.ipynb new file mode 100644 index 00000000..c6b1281e --- /dev/null +++ b/examples/04_fit_example.ipynb @@ -0,0 +1,782 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pyerrors as pe\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./base_style.mplstyle')\n", + "plt.rc('text', usetex=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read data from the pcac example" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "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']];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now define a custom fit function, in this case a single exponential. __Here we need to use the autograd wrapped version of numpy__ (imported as anp) to use automatic differentiation." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import autograd.numpy as anp\n", + "def func_exp(a, x):\n", + " y = a[1] * anp.exp(-a[0] * x)\n", + " return y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fit single exponential to f_P. The kwarg `resplot` generates a figure which visualizes the fit with residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit with 2 parameters\n", + "Method: Levenberg-Marquardt\n", + "`xtol` termination condition is satisfied.\n", + "chisquare/d.o.f.: 0.00287692704517733\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": [ + "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", + "\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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The covariance of the two fit parameters can be computed in the following way" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance: 0.003465486601483565\n", + "Normalized covariance: 0.8360758153764554\n" + ] + } + ], + "source": [ + "cov_01 = pe.fits.covariance(a[0], a[1])\n", + "print('Covariance: ', cov_01)\n", + "print('Normalized covariance: ', cov_01 / a[0].dvalue / a[1].dvalue)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effective mass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the effective mass for comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the corresponding plateau and compare the two results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Effective mass:\n", + "Obs[0.2114(52)]\n", + "Fitted mass:\n", + "Obs[0.2102(63)]\n" + ] + } + ], + "source": [ + "m_eff_plateau = np.mean(m_eff_f_P[start_se: stop_se]) # Plateau from 8 to 16\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)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting with x-errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first generate pseudo data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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" + ] + } + ], + "source": [ + "ox = []\n", + "oy = []\n", + "for i in range(0,10,2):\n", + " ox.append(pe.pseudo_Obs(i + 0.35 * np.random.normal(), 0.35, str(i)))\n", + " oy.append(pe.pseudo_Obs(np.sin(i) + 0.25 * np.random.normal() - 0.2 * i + 0.17, 0.25, str(i)))\n", + "\n", + "[o.gamma_method() for o in ox + oy]\n", + "[print(o) for o in zip(ox, oy)];" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And choose a function to fit" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def func(a, x):\n", + " y = a[0] + a[1] * x + a[2] * anp.sin(x)\n", + " return y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then fit this function to the data and get the fit parameter as Obs with the function `odr_fit` which uses orthogonal distance regression." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the visulization we determine the value of the fit function in a range of x values" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x_t = np.arange(min(ox).value - 1, max(ox).value + 1, 0.01)\n", + "y_t = func([o.value for o in beta], x_t)\n", + "\n", + "plt.errorbar([e.value for e in ox], [e.value for e in oy], xerr=[e.dvalue for e in ox], yerr=[e.dvalue for e in oy], marker='D', lw=1, ls='none', zorder=10)\n", + "plt.plot(x_t, y_t, '--')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also take a look at how much the inidividual ensembles contribute to the uncetainty of the fit parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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": [ + "\n", + "Parameter 1\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Parameter 2\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "for i, item in enumerate(beta):\n", + " print('Parameter', i)\n", + " item.plot_piechart()\n", + " 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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eIOkGx/V22Q4CNAqFBG3Bcb13SfpvSXUN/aP1CqXq59iY1OrBet+j99DuvrHl2jtR3F7zkL4E2aJgpOSFtoMAjUAhQaw5rpdyXO9KSZ+QVPedGmi9fHmk6s8bk95Q346kvj+8duT+0+88xQyUh87paWi4+FojyXNc7422gwBLRSFBbDmut1zSdyW9xXYWzK/WOTapdG+6nq/vnBzeZ+SnfrMzlc4XTkvq+pFqOiR90XG9v7cdBFgKCgliyXG9tZJ+Iuki21lQn7Fi9XNsTEffunq+fv3gvjFfKu0+dZ1fUUdnY9O1hQ86rvcVx/UYKUQsUUgQO47rbVawBfxzLUfBAuTLw6urPZ9Kb6rrUL3e/t1rC526a6x8WqmxydrK6yX9OCzsQKxQSBArjuttk3S9pHMsR8ECFUpVz7EZM6nVvfN+se+XNgzs3XnXSaa/MnjOpoaHay/nK9hEjduCESsUEsSG43o7JV0rDseLHd/3/fFytXNsOg7U8/Xp8sS+dGVy5W92pkx+fDun4M5v6rbgs20HAepFIUEsOK53loJpmi22s2BRhnz5sxa1mtTKmtvJT7dm5P5+Xyrf5HRXKkqzRqI+WyT9LPx/B4g8Cgkiz3G9cyT9XFJdaw0QPRWVqxYPk9pQa/fW4/T237x8fJn2jVRO9xubrO1tlHS143rbbQcB5kMhQaQ5rneepJ9JYt+JGCtViiPVnjfpTXXd8rvp8C3b7t5iDlWGds2/3gQznSDpGsf1GF1EpFFIEFmO6z1Fwbk0621nwdIUK+P5as+nOjZXvfNmOlMpPbB8cqj3tzvS/tj4NtaPLI6joJSwIBiRRSFBJIVDzD+UxO2LbWC8nJ+s9rxJb5r3L8iV+Ucf8KXKb51u31eaE8oX7wxJ/+m4XrftIEA1FBJEjuN6PQo2PeO3uTZRKI9WW/sxaVLr5j17qOex2/2JZbp7SDtYP7J050j6keN6q2wHAWaikCBSHNfrUjAycprtLGicfGnYzH42fcAYM+/PoN7+3Sfee4J5pDR8zuZmZEugp0v6fnj0AhAZFBJEhuN6KUnfkPRU21nQWGOlodm36pquw/N+oe8fWT360LYbT09XCuPOzmZkS6jzJX3HcT2mwBAZFBJEyb9I+hPbIdB4+dJw18znTLq76kLX6ZZPDN4tSTds21DxlarrjhzU7UWSvhL+IgBYxx9ERILjeu+TdIntHGiOsdLwmpnPpdKbqkzjHG/94F35yQ7dM2B28LOqOV4t6bO2QwAShQQR4LjeKyVdbjsHmqdQGpl1om8q3Tdr1GSm3kO7N9y3WQfLQ6wfaaKLw18IAKsoJLDKcb3nSPqypHl/W0Y8+b5fmajkZ51jYzp6597szvcn1w/eteOm0zom8xOnsn6kuT7suN7zbIdAslFIYI3jemdK+g9JrPZvbwOa/bOmYlLr59w5tKNc2JeulFbcsK3Hl+a/GwdLkpb0LXZzhU38Tw4rHNc7QdKPJXVbjoImq/jlodnPpg4ak+6c6+vWDucOF9Pafyh9OofptUavpKsc15vzvwvQLBQStJzjemlJV0k6xXYWNF/Jn5x9jo1Zfmi+r+vt37My16eHysNPZP1I6zxN0sdth0AyUUhgw99KeobtEGiNycr4rBN9TWpd1cP2pus5fOv2321fNpGfOJn1I631Nsf1Xmc7BJKHQoKWChex/i/bOdA64+Wx4sznUumeObeBN5XifZ3F0Y2/3rbJlwwLnlvvc47r7bIdAslCIUHLOK63QdLXxJ+7RCmUZp9jYzr65lzIvHrs4EOllO4/2Mn6EUu6JH2Pg/jQSvzFgFb6oqSTbIdAa42VhmaNcKTSvRvn+pqew7eZB3r1QGnoHP682LNd0r87rscIFVqCQoKWcFzvL8W28ImULw3PumvDpDfOecrvpv49J+3ZtqxQmDyJQxbt+p+S/sZ2CCQDhQRN57je4yVdYTsH7BgrDa08/hlzyJjOVTW/wK/0r84fdH61rbfC+pFI+JDjen9oOwTaH4UETeW4Xpekb0laYTsL7MjPPMfGdD4y1/Urxgf2l1J66MEVO/kzEw0pBYfwzdptF2gkCgma7QpJZ9kOAXsK5dH10x+b1Jo5b/ndcGTv+IEe5UrDu05ubjIsQJ+C07iBpqGQoGkc13uJpLfazgF7fN8vTlbGjztYz6R6Zt0GPF3vod09N2/tHCtMbtne3HRYoNc4rvci2yHQvigkaArH9U6S9AXbOWCbP6AZByemOnprb03u+4Xuobt3XL9185z7lMCaKx3XWz//ZcDCUUjQLF+SxJxzwpWrnGOTSvd117q+ozS2z1fl8P7VO7qaGgyLdYKkT9gOgfZEIUHDOa73UkkvsJ0D9pX8ydGZz5l0T81bfruH9h95eKP2l4d2cc5RdL3ecb0LbYdA+6GQoKHCu2o+ZjsHomGyXBif8dSgSXV117p+U/+eVbc6nSPjxRO2NjcZlugzjuutnP8yoH4UEjSaK+lU2yEQDYXyWOn4Z5Y9XPNi3/d7Hrttx/VbT6g0ORaW7lQFh2QCDUMhQcM4rrdV0qW2cyA6CuWR48qFSa0erHVtqlK8J10uTOxbu3N104OhEd4dbnoINASFBI30cbEBGqbJl4bT0x+b9MbJWteuHjvwyCPrdU9pmPUjMdGh4K4bdtNFQ1BI0BDh1tIvtp0D0TJWHDruVN9Uurfm6b2b+m9J3XbK8qGJYp/T9GBolGdKepPtEGgPFBIsmeN6yyR90nYORM9Yafi423dNR9+aWtduOnzzqddtO5H9R+Lncsf15jy9GagHhQSN8NeSdtoOgejJl0eO26U1le7ZXPVCv/LIikL/ijvWs34khjZIusx2CMQfhQRL4rjeiZI+aDsHomm8NDJ9V88xk1rTW+26rsLh+w51657S0C6nNcnQYJc4rtdnOwTijUKCpfqIJH6rxSy+708U/clpUzQdNW/53Thwx+Ttp6wYnCxtYkFrPK2U9AHbIRBvFBIsmuN6z5b0Gts5EE1+cI7NUSa1cqDWtb39u3uv27ql3PxUaKK3hGdYAYtCIcFSXGE7AKKr7JcGpz82qQ2Fqhf6/uiaof0bb9uws+aCV8TCckl/YzsE4otCgkUJz7I4z3YORFepMpmf/tikN6WrXbesOLrvsXX+fcWRXdtakwxN9MZwg0RgwSgkWCwWsmJOE5Xjz7FJdfStqnZd9+A9I78/acVAsbSR4f74Wybpf9sOgXiikGDBHNe7QNLTbOdAtI2XR487x8ake6vehdHbv3v19VtPZv1I+3id43o7bIdA/FBIsBiMjmBe+dLI9IeTJrXuhFkX+X55/cAdm2/u2blu1muIq7SkD9kOgfihkGBBHNf7A0l/YDsHoi9fGpq2ZiR9wBgz6+dNqjJ59/DK8QOTI2dvb2U2NN0rHNc7y3YIxAuFBAvFKnrUZaw0fOwcG9N1uNo1a0YePHTnlq7HiuUNJ7YsGFohJenvbYdAvFBIUDfH9XZJeoHtHIiHfGn46CJWk15X9ZbfTYdv7riO9SPt6iWO6z3RdgjEB4UEC/Ee2wEQH/nS8NF1Ian0pqqH5m187Ja+3ZvOYP1IezKSLrUdAvFBIUFdHNfbIumVtnMgPgrl0aPn2KTSfStnvm4q5QOT6YEj42Nnc0dG+7rIcb0e2yEQDxQS1OudCvYYAObl+36+7JeOlhDT0TfrL6WuwqHc3hNX9pfK3RzK1r46Jb3BdgjEA4UE83Jcb7Wki23nQHz4qkw/t6ZiUuu3zLxm42O/L1/nnML6kfbHzw7UhUKCerxJUrftEIiPsl8aOvYoddCYdOfMa3oO71l/Y+8Z62c+j7ZzuuN6z7MdAtFHIUE9+A0HC1KsTBw7x8Ysf3TWBb4/pNL9E/nC2ae3Mhes4WcI5kUhwZzC2/bOtJ0D8TJRLkxMfW5S68Zmvt45OXT33Sd09ZfLa3tbmwyWsLgV86KQYD6vtR0A8VMojx5dG5JK98xaJ7J+8O6x6051WD+SHCxuxbwoJKjJcb2UpFfZzoH4mX6Ojeno65r5ek//npW/OYH1IwnzZtsBEG0UEszl+ZJmH4gGzCNfGuqY+jyV7j2+ePh+qWN8rz9WeMLOlgeDTTtY3Iq5UEgwF6ZrsChjpaEVU5+b9MbjbvlNlyf25Xo6BsqVNawpSB4Wt6ImCgmqclyvS9JFtnMgno6dY2MOGdO5avpra0fu77/+VKdkIxesY3EraqKQoJYXS1pjOwTiKV8eCc6nMZ2PzHxt4+GbO3514pkbWh4KUcDiVtREIUEtTNdg0cZLYxskyaTWjMx8bXn+Nn9k4qwzWp8KEcGZWKiKQoJZwiHVP7SdA/Hk+/5wReXlkmRSPcXpr5lK6YGDq4ujlcpqRkiS61zH9Vgsj1koJKjmlZI65r0KqKKiypGpz1MdvcdtGb8q/8gD15+6lf1Hks1IutB2CEQPhQTVMF2DRStXikenaVLpvu7pr20Y+H35ui2sH4EytgMgeigkOI7jeqdJeqrtHIiv6efYmHTPidNf6xy7tTJcPJP1I3iB43qzDlxEslFIMNOLbAdAvI1X8lPn2AyaVFf30Rd8/8hgx+GJSmUVO7RitaTn2A6BaKGQYKbn2w6AeBsvjVaCz5Y9PP355RNH7vnVKdtYP4IpTNvgOBQSHOW4XlrSs23nQLzly8OSJJNaPTj9+e7BfaPXnvz4jTYyIZIoJDgOhQTTnSdpre0QiLex0vAySTLpDcfd8ruscFtxqHTG4+ykQgSd5rge5xnhKAoJpuPgKyzZWHGoS5JS6b5jt477/kS+8mC5Ulm5zlowRBGjJDiKQoLpWD+CJZs6x8Z09B09eqCjVNj32xNP9u2lQkRRSHAUhQSSpPAWvGfazoH4K5RHuiUple7ZPPXc2pH7Dl978lmsH8FMz3Zcj2liSKKQ4JinSlppOwTizfd9f7w8tkHSmEmt6Z16vqNwx/hA+YwzLUZDNC2T9ELbIRANFBJMYboGjTDoy++QOo7d8uv7/nhxf8X3uzg9GtVwbhYkUUhwDAtasWQVlQclyaRWPjb1nPFL993as9FYC4WoY2doSKKQQJLjel2SnmY7B+KvFJ5jY1Lrp3Zr1aqxgwd+ftITeuylQsSd6bjeKtshYB+FBJL0DEnLbYdA/BUr43lJMuneoyMiywp7R/rNGWfZS4WIS0t6ku0QsI9CAon1I2iQ8XK+KEmpabf85sv3l+R38hsw5vIU2wFgH4UEEtvFo0EK5dGyJJn0pk2SJL/Sf8/K1DKroRAHFBJQSCBJYjgdDZEvDackTZrUuhMkacX4wL0/P/ls1o9gPhQSUEiSznG9XkkbbOdAexgrDS2T0geMSaUlqWPi3iOPpHdQeDGfUx3XY+O8hKOQgMPO0DBjpaEuma7DU49HSw9OyO9kwz3U4wm2A8AuCgnOsB0A7SNfGllj0usKkiTfLzzQUei0HAnxcbbtALCLQgJGSNAwhdJIdyq9yZekZaWxu6496cze+b4GCFFIEo5CAkZI0BC+71cmKvn1qXTfSklKTzz46MFlpz/edi7EBlM2CUchAYUEjTIgKWU6enskabj8cEH+shWWMyE+znJcj7+TEoz/+AnmuN5KSafYzoH2UPFLg5IqJrVhi3y/ciA1xPoRLMQqSdtsh4A9FJJk2ymJQ8/QEEW/OCKZh41Jd6YqxXtv6HVOsJ0JsXO67QCwh0KSbEzXoGGKlfFxmRX9kpQuPvrAQ8tPP9N2JsQOJTbBKCTJxh02aJjx8ljRpNaNStJQ5dCI1MGBjVgoCkmCddgOAKsYIUHD5EsjlVS6x0jSg4b1I1gUCkmCUUiSjUKChsmXhtOmo69TfuXgzd0bT7SdB7G02XYA2MOUTbKxoh0NM1YaWpZK965PlY7c+0DXdtaPYDEYIUkwCklChbf8rrKdA+0jXxruMumNW4b8IwNSmikbLAaFJMEoJMnFyZpoqEI5XzGmc1UuNbLMdhbEFoUkwSgkydVjOwDaS6FcnJDvj965opP1I1isFY7rddsOATsoJMlFIUHD+L5fnFSqqEp+7/2rT2X9CJaCUZKEopAkF4UEDeQPmNSGypBG+qU0UzZYCgpJQlFIkotCgoYp++XBVEffsvvSY/xMwVJRSBKKfUiSa73tAGgfJX9yzKQ2rdnbWVpjOwtij0KSUBSS5OIvDjTMRLkwblJrKg+uXH6O7SyIPQpJQlFIkos9SNAwE5WJ8eGOrryU4mcKlmq17QCwg/ne5OJ/ejTMRKUyuT894dvOASC+KCTJRSFBwxQqprR3WZn9R9AIxnYA2EEhSS4KCRomXymVDixfzWGNaAQKSUJRSJKLNSRomAeMyjKptO0caAsUkoSikCQXm1ehYW5Pd1Bw0SgUkoSikCTXuO0AaB+3dnSeYjsDgHijkCQXhQQNUZZSDy1bwfoRNAojJAnFngHJRSFBoxgZ/hJBw/BnKaEYIUmugu0AAFAFhSShKCTJxQgJACAyKCTJRSEBEEWMkCQUhSS5mLIBEEUUkoSikCQXIyQAoohCklAUkuSikACIoiHbAWAHhSS5mLIBEEWP2g4AOygkycUICYAoesR2ANhBIUkuCgmAKGKEJKEoJMnFlA2AKKKQJBSFJLkGbAcAgCqYskkoCkly5WwHAIAqGCFJKApJct1nOwAAzDCcy2ZY35ZQFJKEymUzY5IO284BANMwXZNgFJJkY5QEQJQwXZNgFJJko5AAiBJGSBKMQpJsOdsBAGAaRkgSjEKSbIyQAIgSCkmCUUiSjUICIEoO2g4AeygkyZazHQAAptlnOwDsoZAkW06SbzsEAIRutx0A9lBIEiyXzUyIVe0AouHhXDZzxHYI2EMhAetIAEQBoyMJRyEBhQRAFFBIEo5CgltsBwAAUUgSj0KCX9sOAACSbrMdAHZRSHCTpEnbIQAkWlEUksSjkCRceNT3Hts5ACTa7eFdf0gwCgkkpm0A2HWj7QCwj0ICSfqV7QAAEu0m2wFgH4UEEoUEgF0UElBIIOWymQOSHrSdA0AijYtbfiEKCY5hHQkAG3bnspmi7RCwj0KCKUzbALDhp7YDIBooJJjCCAkAG35kOwCigUKCKXsUzOUCQKsckvQ72yEQDRQSSJLCOVxWugNopZ/kshnfdghEA4UE0/3SdgAAicJ0DY6ikGC6H9oOACAxypL+03YIRAeFBNPdoGBOFwCa7YZcNnPEdghEB4UER+WymYqk/2c7B4BE+LHtAIgWCglm+r7tAAASgfUjOA6FBDNdIylvOwSAtnYwl83ssR0C0UIhwXFy2UxBLDQD0Fw/sR0A0UMhQTXfsx0AQFtjugazUEhQzffFrq0AmqMk6WrbIRA9FBLMkstmRsRvMACa44e5bGbIdghED4UEtXzLdgAAbekLtgMgmigkqOWHkkZthwDQVh4SC1pRA4UEVYV32/zAdg4AbeVLuWymbDsEoolCgrl8w3YAAG2jIulLtkMguigkmMtPJN1vOwSAtnB1Lpvh5wlqopCgpnBo9VO2cwBoCyxmxZwoJJjPFySN2Q4BINYOiXOyMA8KCeaUy2YGJX3Vdg4AsfbVXDZTtB0C0UYhQT3+RZJvOwSA2GK6BvOikGBeuWxmr6Sf2s4BIJZ+mctm7rIdAtFHIUG9Pmk7AIBY+jfbARAPFBLU66eS+C0HwEIMSrrKdgjEA4UEdcllM76CtSQAUK9PhLs+A/OikGAhvqLgNx4AmM8RSZ+wHQLxQSFB3XLZzJikL9rOASAWrshlM0O2QyA+KCRYqH+VxOFYAOYyIBbCY4EoJFiQ8CyK/2s7B4BI+2gumxmxHQLxQiHBYvyNpJLtEAAi6bCCkVRgQSgkWLBcNrNP0uds5wAQSf+cy2ZGbYdA/FBIsFh/J2nYdggAkXJInBCORaKQYFFy2Uy/pMtt5wAQKR/JZTN52yEQTxQSLMXHJT1kOwSASHhE0mdsh0B8UUiwaOEOjB+0nQNAJFzOrqxYCgoJluqrkm6xHQKAVQ9LutJ2CMQbhQRLkstmKpLeZzsHAKv+NpfNjNsOgXijkGDJctnM1QpOAwaQPDeIIyXQABQSNMr7JFVshwDQUmVJbwtPAweWhEKChshlM7cpOA0YQHJ8JpfN7LEdAu2BQoJG+qCkMdshALTEIwqOkQAagkKChsllMwckXWo7B4CWeE8um2G3ZjQMhQSN9llJV9sOAaCprs5lM9+wHQLthUKChgoXt71R0pDtLACaYlTSm22HQPuhkKDhctnMQ5LeZTsHgKZwc9nM/bZDoP1QSNAUuWzmK5K+bzsHgIb6pTivBk1CIUEzXSzpsO0QABpiXNJfsOcImoVCgqbJZTOHJL3Vdg4ADfG3uWxmn+0QaF8UEjRVLpv5niRW4wPx9jNJH7MdAu2NQoJWeLuC00ABxM8BSa/KZTNl20HQ3igkaLpcNnNE0l/YzgFgwYqSXh5OvwJNRSFBS+SymR9L+jfbOQAsyPty2cyvbIdAMlBI0ErvlnSn7RAA6vKdXDbzSdshkBwUErRMLpsZlfRisYsrEHV7Jb3JdggkC4UELZXLZu6W9GpJFdtZAFQ1Juml4S8QQMtQSNByuWzmR5I+aDsHgKrenMtm7rAdAsljfJ9N92CH43rfkfSntnMAOOpTuWzmHbZDIJkYIYFNfy7pVtshAEiSbpD0HtshkFyMkMAqx/VOkfQbSZttZwES7LCkc3PZzIO2gyC5GCGBVbls5gFJfywpbzsLkFB5SX9CGYFtFBJYl8tmbpL0GnHnDdBqk5IuymUz19sOAlBIEAm5bOY/JF1qOweQIGVJr8llMz+1HQSQKCSIkFw28zFJV9rOASTEW3LZzFW2QwBTKCSImrdL+qbtEECbe28um/mi7RDAdBQSREp4xPnrJH3DdhagTf1jOBoJRAq3/SKSHNdLS/qKgsWuABrj07ls5u22QwDVMEKCSApHSv5M0tdtZwHaxNcksQsrIosREkSa43opBSMlr7WdBYixHyg4MK9kOwhQC4UEkUcpAZbk55L+Ry6bGbcdBJgLUzaIvFw2U1EwffPvtrMAMXONpBdRRhAHFBLEQlhK3iDpq5ajAHHxNUkX5rKZEdtBgHpQSBAbYSn5cwXTNwBqy0p6fS6bKdoOAtSLNSSInXBNyRcUlBMAx1QkvSOXzXzGdhBgoSgkiC3H9T4o6e8kGdtZgAgoSHp1eC4UEDsUEsSa43oXKVhXssp2FsCixyT9cS6b+bXtIMBiUUgQe47r7ZL0fUmn2s4CWHCfpD/KZTP7bAcBloJFrYi9XDZzi6QnS7rOdhagxX4n6emUEbQDCgnaQi6b6Zd0viROMEVS/ETSc3PZzKO2gwCNwJQN2o7jen8l6aOS0pajAM1ypYK7adgKHm2DQoK25LjeCyV9W1K35ShAI41KujiXzXzTdhCg0SgkaFuO6+1UcKjYDttZgAa4TdKf5rKZu2wHAZqBNSRoW+EP7qdK+pHtLMASfVHSUykjaGeMkCARHNe7WNLHJK22nQVYgFFJl+SyGc5wQtujkCAxHNfbKun/SHqO7SxAHX4j6bW5bOYe20GAVmDKBomRy2buk/Q8SX+lYJttIIrKkv5e0rMoI0gSRkiQSI7r7ZD0ZUlPtxwFmG6/glERtoBH4jBCgkQKd7Z8tiRX0oTlOIAv6UuSzqGMIKkYIUHiOa53loID+s61nQWJdLOkt+eymettBwFsYoQEiZfLZn6v4PbgD0kq2k2DBDki6RJJ51FGAEZIgOM4rneupM9KeortLGhbFQXTM+/PZTOHbYcBooJCAszguJ6R9BpJH5Z0kuU4aC+/VTA9c6PtIEDUUEiAGhzXWynpfZIulbTSchzE22EFC6i/lMtm+KELVEEhAebhuN4WBaMlr5VkLMdBvJQVnMz7wVw2c8R2GCDKKCRAnRzXO0fSP0q60HIUxMO1kt6Zy2ZusR0EiAMKCbBAjus9W9I/SXqW7SyIpJ9K+nAum/mF7SBAnFBIgEVyXO9CBcVkl+0ssK4i6XuSsrlsZrftMEAcUUiAJQjvyHmxpHdJeq7dNLCgKOnfJV0e7v4LYJEoJECDOK53tqR3Snq1pC7LcdBcY5L+TdLHctnMQ7bDAO2AQgI0mON6GyW9WdLbJJ1sOQ4a64ikf5X0L7ls5jHbYYB2QiEBmsRxvQ5JL1EwasIC2Hh7QEERuTKXzYzaDgO0IwoJ0AKO6z1RQTF5laTlluOgPsOSvivpa5J+wYZmQHNRSIAWclxvk6S3SHqDpO1206CKkqSfKFio+oNcNjNuOQ+QGBQSwBLH9XZJukjSSyWdZTlO0t2ooIR8K5fN9NsOAyQRhQSIAMf1dupYOXmS5ThJkVMwHfO1XDZzl+UsQOJRSICIcVzvVAXl5CJJz5CUspuordyjYCfVb0u6jnUhQHRQSIAIc1xvs4I7dS5SsPFah9VA8TMs6WcKSshPc9nMfZbzAKiBQgLEhON6qyQ9WdLTJT0t/Ocmq6GiJy/pBgUH2/2XpBty2UzJbiQA9aCQADHmuN52BcVkqqScrWSNogxJul5BAblW0k25bKZoNxKAxaCQAG3Ecb2Vmj2K0ms1VGMckXRnlY/7c9lMxWYwAI1BIQHanON6PZJOq/KxXVKPxWjVPKzjC8cdku7MZTOPWk0FoOkoJECCOa7XJemkaR9bpv1ztaQVCnaWXTHj86l/dkoyVd66omA6ZVDB6MaReT4/LOnuXDYz1OB/RQAxQSEBsCSO6y3X8QVlRNIwt9QCWAgKCQAAsI4NlwAAgHUUEgAAYB2FBAAAWEchAdqcMeYCY8x3becAgLkkaUdHIFGMMedKeoWkbknb7KYBgLlRSIA25fv+bkm7jTEvk3Se7TwAMBembAAAgHWMkAAxZ4y5XNKNCqZlBn3f/7zlSACwYBQSIMaMMVdLutz3/WvCx981xuyfegwAccGUDRBT4aLV82aUj6slvcVSJABYNEZIgPg6T9JAuGh1SreC6RsAiBUKCRBzvu9fZTsDACwVUzZAfN2kKvuLGGPYcwRA7FBIgJgK9xm5yhhz8dRzxphuSefOuHRDK3MBwGIY3/dtZwCwBOFtv49JGpQ0MDWFM22n1pcpGEn5vKTfcVswgCiikAAAAOuYsgEAANZRSAAAgHUUEgAAYB2FBAAAWEchAQAA1lFIAACAdRQSAABgHYUEAABYRyEBAADWUUgAAIB1FBIAAGAdhQQAAFhHIQEAANb9f+Yw/lxq1I++AAAAAElFTkSuQmCC\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, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/05_correlators.ipynb b/examples/05_correlators.ipynb deleted file mode 100644 index 323a6a44..00000000 --- a/examples/05_correlators.ipynb +++ /dev/null @@ -1,276 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Correlator Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are often dealing with lists of observables defined at every time slice. For a more convenient analysis, those can be represented using the \"correlators.Corr\" class.\n", - "This is especially useful, if there is not one Obs per time slice, but a whole smearing matrix. We will load an example of such an object." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "64 4\n" - ] - } - ], - "source": [ - "import pyerrors as pe\n", - "import autograd.numpy as np\n", - "P5P5=pe.load_object(\"data/Example_Corr_P5P5.p\")\n", - "print(P5P5.T, P5P5.N)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we just printed out, are the only parameters a Corr has. T represents the number of time slices and N the rank of the NxN smearing matrix. \n", - "The content is accessible with P5P5.content and gives a list of np.arrays of obs. There is no formal difference between correlators, which contain a single observable per time slice \n", - "and those, which hold a smearing matrix. \n", - "To initialize a Corr, we only need to pass the content or in the case of N=1, we might pass a list of obs.\n", - "Lets run some code!" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "P5P5.symmetric().projected().show(logscale=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will go over this line the way it is executed, left to right. \n", - "The correlator has a method called **symmetric()**, which returns a time symmetrized version of itself. \n", - "We did not need to redo the error estimation. While a Corr has a **.gamma_method()** defined, which simply applies the Obs.gamma_method() to every Obs in the correlator, \n", - "this is done automatically, whenever a new corr is initiated. This is a little slow, but convenient. The next method called is **.projected()**. \n", - "This methods *projects* the smearing matrices $G(t)$ with a set of vectors, returning a single value at every $t$. $$v_l^T G(t) v_r$$ Since we did not pass an argument, it defaulted to $v_l=v_r=(1,0,...,0)$, giving us $G(t)[0,0]$ . The method returns another Corr, but this time with N=1. \n", - " The last method **.show()** just allows us to quickly inspect a correlator. \n", - "Let us now look at the GEVP. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-6.26306805e-04 1.64198098e-01 -5.99926044e-01 7.83024479e-01]\n" - ] - } - ], - "source": [ - "eigenvector=P5P5.symmetric().GEVP(t0=3,ts=6)\n", - "print(eigenvector)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**.GEVP()** Needs two time indices as arguments. It then solves:\n", - "\n", - "$$G(t_s)v=\\lambda G(t_0)v$$\n", - "\n", - "and returns the vector $v$ for the largest eigenvalue. It uses a *Scipy* method, which itself is based on a *LAPACK* method. \n", - "To really see the difference this makes, we can visualize the effective mass, which we also have a method for." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "P5P5.symmetric().projected().m_eff().show([0,32])\n", - "P5P5.symmetric().projected(eigenvector).m_eff().show([0,32])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plateau is definetely larger now. We can use it to extract the pseudoscalar mass. All we need is a plateau range. \n", - "Finding those can be time consuming, if we are dealing with many correlators and we need to zoom in to the plot, to see the range properly.\n", - "~We can call a GUI method to help us to visualize the range. Play around with the checkboxes at the bottom of the window to make the program adjust plot to your selected range.~" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# m_eff=P5P5.symmetric().projected(eigenvector).m_eff() # Our lines were getting a little long, so we just assign a new Corr. \n", - "# plateau_range=pe.correlators.GUI_range_finder(m_eff)\n", - "# m_p=m_eff.plateau(plateau_range)\n", - "# print(\"The pseudoscalar mass is: \",m_p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "~For fun you can compare your result with https://arxiv.org/abs/1912.09937v1 Table XV (the first value for $am_{H_{is}}$).~\n", - "Before we wrap up, we should look at Corrs and math operations. They can be multiplied by and added to other Corrs (of same N,T), or scaled by an Obs or float. \n", - "Usually the operation is just done for every value at the same time and smearing. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "64 4\n" - ] - } - ], - "source": [ - "new_correlator=0.5*P5P5+np.sqrt(P5P5)/np.sin(P5P5**2)+np.arcsinh(P5P5)\n", - "print(new_correlator.T, new_correlator.N)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a senseless but valid expression, which does exactly, what one would expect. It returns a Corr of the same shape as P5P5. \n", - " It is really important, that there is never any confusion about the appropriate time slices. Lets look at *m_eff* once again. \n", - " By default it is calculated as $$ m_{eff}(t)=\\ln(\\frac{Corr[t]}{Corr[t+1]})$$ \n", - " Therefore m_eff is only defined up to the second to last time slice. But the method appends a **None** object, so that m_eff.T=64. \n", - " If we add m_eff to P5P5.projected(), the resulting Corr would still have T=64, with the last item being **None**. \n", - "Another reason for a Corr being **None** at one time slice, is a division by zero or other undefined operation. \n", - "Even if a Corr is partially undefined, math operations still work, as long as T and N are identical.\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# print((m_eff+P5P5.projected()).T)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/04_matrix_operations.ipynb b/examples/05_matrix_operations.ipynb similarity index 94% rename from examples/04_matrix_operations.ipynb rename to examples/05_matrix_operations.ipynb index c68a1358..d5e1da95 100644 --- a/examples/04_matrix_operations.ipynb +++ b/examples/05_matrix_operations.ipynb @@ -6,8 +6,6 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "sys.path.append('..')\n", "import pyerrors as pe\n", "import numpy as np\n", "import scipy" @@ -169,7 +167,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Obs[7.2(1.7)] Obs[-1.00(47)]]\n" + "[Obs[7.2(1.7)] Obs[-1.00(45)]]\n" ] } ], @@ -229,7 +227,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "cond \t Obs[6.23(59)]\n", + "cond \t Obs[6.23(58)]\n", "expm_cond \t Obs[4.45(19)]\n" ] } @@ -261,7 +259,7 @@ "output_type": "stream", "text": [ "[[Obs[2.025(49)] Obs[0.0]]\n", - " [Obs[-0.494(50)] Obs[0.870(29)]]]\n" + " [Obs[-0.494(51)] Obs[0.870(29)]]]\n" ] } ], @@ -349,20 +347,20 @@ "output_type": "stream", "text": [ "orth\n", - "[[Obs[-0.9592(75)] Obs[0.283(25)]]\n", - " [Obs[0.283(25)] Obs[0.9592(75)]]]\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(4.2)]]\n", - " [Obs[-21.4(4.2)] Obs[8.3(1.4)]]]\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(69)]]]\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(15)]]]\n" + " [Obs[-0.341(37)] Obs[0.940(14)]]]\n" ] } ], @@ -397,8 +395,8 @@ "Eigenvalues:\n", "[Obs[0.705(57)] Obs[4.39(19)]]\n", "Eigenvectors:\n", - "[[Obs[-0.283(25)] Obs[-0.9592(75)]]\n", - " [Obs[-0.9592(75)] Obs[0.283(25)]]]\n" + "[[Obs[-0.283(26)] Obs[-0.9592(76)]]\n", + " [Obs[-0.9592(76)] Obs[0.283(26)]]]\n" ] } ], @@ -467,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.11" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/base_style.mplstyle b/examples/base_style.mplstyle new file mode 100644 index 00000000..48f4cf45 --- /dev/null +++ b/examples/base_style.mplstyle @@ -0,0 +1,30 @@ +# Base mplstyle, Fabian Joswig, 2021 +font.size: 12 + +lines.marker : o +lines.markersize : 6 +lines.linewidth : 1.0 +lines.linestyle : none + +font.family: serif + +axes.labelsize : 14 + +xtick.labelsize : 12 + +ytick.labelsize : 12 + +figure.dpi : 100 +figure.figsize : 6.4, 3.9555 # figure size in inches, golden ratio +figure.subplot.left : 0.125 # the left side of the subplots of the figure +figure.subplot.right : 0.95 # the right side of the subplots of the figure +figure.subplot.bottom 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Parameters @@ -371,7 +371,7 @@ 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] fig = plt.figure() ax1 = fig.add_subplot(111) @@ -382,12 +382,15 @@ class Corr: ax1.set_yscale('log') else: # we generate ylim instead of using autoscaling. - try: - y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) - ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) - except: - pass + if y_range is None: + try: + y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if (x is not None) and x[0].dvalue < 2 * np.abs(x[0].value)]) + ax1.set_ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)]) + except: + pass + else: + ax1.set_ylim(y_range) if comp: if isinstance(comp, Corr) or isinstance(comp, list): for corr in comp if isinstance(comp, list) else [comp]: diff --git a/pyerrors/fits.py b/pyerrors/fits.py index 0af94948..47687932 100644 --- a/pyerrors/fits.py +++ b/pyerrors/fits.py @@ -575,7 +575,7 @@ def residual_plot(x, y, func, fit_res): gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0) ax0 = plt.subplot(gs[0]) ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data') - ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10) + ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0) ax0.set_xticklabels([]) ax0.set_xlim([xstart, xstop]) ax0.set_xticklabels([])