diff --git a/examples/03_pcac_example.ipynb b/examples/03_pcac_example.ipynb index 9b163317..57cc1643 100644 --- a/examples/03_pcac_example.ipynb +++ b/examples/03_pcac_example.ipynb @@ -39,7 +39,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can load data from preprocessed files which contains lists of `pyerror` `Obs` and convert them to `Corr` objects as explained in the previous example. We use the parameters `padding_front` and `padding_back` to keep track of the fixed boundary conditions at both temporal ends of the lattice. This allows us to specify absolut temporal positions without having to keep track of any shifts in the data." + "We can load data from preprocessed files which contains lists of `pyerror` `Obs` and convert them to `Corr` objects as explained in the previous example. We use the parameter `padding` to keep track of the fixed boundary conditions at both temporal ends of the lattice. This allows us to specify absolut temporal positions without having to keep track of any shifts in the data." ] }, { @@ -70,7 +70,7 @@ "p_obs = {}\n", "for i, item in enumerate(p_obs_names):\n", " tmp_data = pe.input.json.load_json(\"./data/\" + item)\n", - " p_obs[item] = pe.Corr(tmp_data, padding_front=1, padding_back=1)\n", + " p_obs[item] = pe.Corr(tmp_data, padding=[1, 1])\n", " p_obs[item].tag = item" ] }, @@ -291,7 +291,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/examples/04_fit_example.ipynb b/examples/04_fit_example.ipynb index 3fe8fabc..06bfc29d 100644 --- a/examples/04_fit_example.ipynb +++ b/examples/04_fit_example.ipynb @@ -46,7 +46,7 @@ } ], "source": [ - "fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding_front=1, padding_back=1)" + "fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding=[1, 1])" ] }, { @@ -91,6 +91,7 @@ "\n", " Goodness of fit:\n", "χ²/d.o.f. = 0.002332\n", + "p-value = 1.0000\n", "Fit parameters:\n", "0\t 0.2036(92)\n", "1\t 16.3(1.3)\n", @@ -255,11 +256,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "(Obs[0.57(35)], Obs[0.49(25)])\n", - "(Obs[2.53(35)], Obs[0.56(25)])\n", - "(Obs[4.17(35)], Obs[-1.52(25)])\n", - "(Obs[5.97(35)], Obs[-1.40(25)])\n", - "(Obs[7.82(35)], Obs[-0.58(25)])\n" + "(Obs[0.53(35)], Obs[0.38(25)])\n", + "(Obs[1.73(35)], Obs[0.59(25)])\n", + "(Obs[3.92(35)], Obs[-1.23(25)])\n", + "(Obs[5.73(35)], Obs[-1.18(25)])\n", + "(Obs[7.74(35)], Obs[-0.40(25)])\n" ] } ], @@ -311,10 +312,10 @@ "Fit with 3 parameters\n", "Method: ODR\n", "Sum of squares convergence\n", - "Residual variance: 0.4144435658518591\n", - "Parameter 1 : 0.26(28)\n", - "Parameter 2 : -0.228(53)\n", - "Parameter 3 : 0.98(22)\n" + "Residual variance: 0.08780824312692749\n", + "Parameter 1 : 0.06(25)\n", + "Parameter 2 : -0.160(47)\n", + "Parameter 3 : 0.80(18)\n" ] } ], @@ -340,7 +341,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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8wNjkFp/kVt7u+ilYSKgTWEios66XDkBkOzpQkbbr+dILK9fPkY4Shpklaemz03eWfCgdhMyNhYTOq20x63TpHES2oXUwxbdv9VTPIzNMtJU3HD8DwEJC58RFrdQZHB0hihEj0LJ32ta/br+k+P6FiYHmvtJ5IuTakrT0QdIhyNw4QhIH2p6MWdP201St9d2RurbT5e4F4DuRuh4RnYXWzYOrN66buPOZuYYOOKTjRJgDoc+RB6WDkHmxkFhc+2O625+QqZS6Uin1mNb6xxG6xbUAUiJ0LSI6g8TW+i0zih/o26e+fJF0lij6PlhI6ByU1lo6A4VBKXUCwGitdU2H97TWOiLbAp0u92rwQXp0Hr+s6bE7CWq8dA7L0do36tDbW8cceM1KW3nDkZG+s2SbdAgyJ64hsTCl1BiEpmhqzvBrV4Z7fafLPQEsI0RR0bPxyPq56/7QNPbAawvivYxoQJ/sgS3/WGTwpGc6K07ZWNuYs7xfAyA1Atf/YQSuQUQd6WDlhN3/PDi8Yu1l0lGirSURe9elqbKlC4xx1alqKoDU1xZn3OPJ9nBonk7DQhKfjgMYEM4FnC63Ap/USRQ5Wut+tQdWT9taMC0x0DRbOk60BBQqtznVriULjGF7hqvxAMZ1+OVRAOYA+EAmHZkZC0l8CquMtJkJYEQErkNke0awdV+G57H6gSdKLpfOEg0aqD04GFuXzzN6b5igpmqlhp7jt18HFhI6AxYSa9t/lvdTz/FrnfW1ML+fiLRuGXxk0wcTSxbH3VZeDbQe7YfNr88yAiunq+mtiaqz682+mbE441eebE8gqgHJclhILExrvV8pVaOUGqO13n/Kr60M8/JfDfP7iWwtsbVh6/TiB3r3rS9bJJ0lkuqT4Vk5TdW8dpkx6WQv1Z1TZIcAmAdgVYSjkcWxkFjfnQCuBNB+Dsk17T/urrbdNenhRyOyIa1rR5au3DJ2/ytxs5W3NQEHPpygDi6db4ypGKgyInDJz4OFhE7BQmJxWuu7lVI3tRURALg0AoeifTXM7yeypR6Nxz6cUXz/yB7NJ6z4VN7PCCpU7xipSpbONwaXjFTpAEZH8PJXA7glgtejOMBCEgdOOSp+WQQumRWBaxDZhw5Wjd+z5MCI8tWW3sqrgbrSQdjy0lyjx/o0NS1oqIVRutXUjMUZQz3ZnsooXZ8siIWEPsPpcqcitC2PiM5Ha93vpHf11C0FU5MCjZYsIxrwn+iDYvelRsu/L1HTWpLUvBjcVgH4HIDFMbgXWQQLCZ3qKvD/F0TnpYKt+zO2PXHyguPbLbmVt8GB7e9OUcdemWtM9PVWMwUiXA0WEuqAf/HQqXi0M9G5aN0y6GjxB5N2PD3H0P5k6Thd4Tdw8KPxyrtkgeEsu0BNEo5zVcbiDMOT7QkK5yCTYCGhT7Sdznq1dA4is0r0N3imFz/Us29d6SLpLJ0VBI7uugg7ls43Bm5zGpMQOi3VDAYidADjBukgZA4sJNTRJADnOmGRyJ60PnnR4aLN4/a9PF9Bm/6hpBpoKBuI4lfmGElrJqnpQUOZdVrparCQUBsWEurIkovyiKIpuen4hhmb77+oZ/Nxs/6lDgDQQKCmN4rfnGk0vTVTTW1yqLnSmTrhagC3SYcgc2AhoY7i9oFfRF2mg9Xj9y7dN6Jslal3nTUmoeT9DFX98lwj/URfdYl0ni6albE4o7cn21MvHYTksZBQRxwhIdJa9z15aM20rQ9PSfI3mrKM+A2Ubhqn9i1ZYIw8NFilw7onKycAmA5gjXQQksdCQgAAp8vdF8BE6RxEklTQf2Dy9r/5Bh3zmO6k1SBwfM9wbFs23+i/ZYyRAeAi6UwRcilYSAgsJPSpmQBMv1iPKCq0br3g6Ja1k3c8NcfQ/kgekR4WDTRV9sfmV+YYCasmq+mBBNMuTg3HpdIByBxYSKgd14+QLSX4G7dNL34wuZ9JtvJqIFjbC1v+fYlR756ppjT2UKacNoogFhICwEJCn2IhIXvRum5E2XsbL967fIEZtvI2JWH3mkmqYvlcY/yxFDVdOk8MjctYnNHfk+05IR2EZLGQUDsWErKN5KYTG2YU3zeiZ9PxaD08rlMCCmXFY9XeJQuM4QeGqvEAxkvmETQTwArpECSLhYTgdLlHAhgmnYMo6nTwyMV7l++9qOw9sWkQDfj2DcPWZfOMfpvGqSlQarhUFhO5FCwktsdCQgBHR8gG+p48tHraloczkvwNMS8jGmiuTsXm12Yb6t0papo/UZluF48wriMhFhICAMySDkAULSro907e/mTNoGNbY1oCNKDremLLiunq5L9mGVPqeyqe83N2LCTEQkIArHuoEtHZae0feMyzdvKOJ2cnBP3OWN22ORF71qWr8mXzjXHVqWparO5rccMzFmcM9GR7jkkHITksJAQAY6QDEEVSgr9x+/QtDyf1O3kwJotWAwoVHqfavWSBMWzvcDUewMWxuG+cGQuAhcTGWEhszulyKwBO6RxEEaF13fCy9zeO37ss6lt5NVDrHYKty+cZvT8ar6ZqpbgwPDxjwCf/2hoLCQ0D0FM6BFG4kptrPpqx+b4LezYdi9qoiAZaj/bD5n/NNgLvTFPTWxPV/Gjdy4bGSgcgWSwkxOkasjYdPDJu38t7Rh4umhuVywO6vge2vTNNnXhttjH5ZC/FReDRwUJicywkxEJCltXn5OE107c8NCnJXx/xMtKagP3r09ShpfONsZUDVEakr0+n4WeRzbGQED8EyHJU0H9w0o6njg0+WhzRKZOgQvX2kapk6QJjyM6LVBr45yOWOEJicywkxA9csg6t/QOPb18zefvfZicEW0dF5JJAXekgbHlprtFzfZqaGjSU6HHyNjY8Y3FGsifb0ywdhGSwkBALCVlCgr9px7Stf01IqT2wKNxracB/vC82uy81Wt+eoaa1JKl5EYhI4VEARgPYKR2EZLCQEAsJmZvW9cPLV388fs+S+Qo6IZxL1Sdj27tT1PFX5xgTfb0VTwc1n7FgIbEtFhIbc7rcPQEMlc5BdDaO5pqPZxTfP7RX49FuT6O0GvB+NF4dXLrAcJZdoCZHMh9F3EjpACSHhcTeRiA0TEpkLjp4dOz+V3eNKl3ZramUoMKRnSNQsnSBMXD7KGMSePifVQyQDkByWEjsLVU6ANGpeteVrZm+5cGJjtb6LpURDTSUDUTxy3MNx9qJalrQUJdHKyNFTap0AJLDQmJvfaUDELVTQf+hiSWLjww5sqnTW3k1EKjpjeI3LjWa/n2JmtrkUFE5HI1ipr90AJLDQmJvLCQkT+vAgBMlqzO2PTErIdjSqTUEjQ7seH+yOvrSPCO9po+6JNoRKWZYSGyMhcTe+kkHIHtLCDSXTN3ysErtxFZev4HSjePU/iULjJGlg9XEGMSj2EuVDkByWEjsjSMkJEPrhgsrPvhowu4XzrmVNwgc3zMc25bONwZsHWNMBnBRDFNS7HGExMZYSOyNIyQUc46W2o0zNt83uFfjkTNu5dVAY8UAFL9ymZG4erKaFkjg4lQbYSGxMRYSe+MICcWODh4bc+BfO52H3j5t94wGgrW9sOWtS4yGNy5VUxqT1RyJiCQuVToAyWEhsTeOkFBM9K4vXzu9+ME0R2vdZ8pIUxJ2rZ6kKl+aZ0w41k9Nl8pHppGSsTjD8GR7gtJBKPZYSOyNIyQUVSoYODyxZHHVkCMbPykiAQOHN49Re5cuMEYcGKomAJggGJHMRQHoA6BWOgjFHguJvXGEhKJD60D/EzvXTNn2+KUJwZYRGqjZNwyeZfONlE1jVQaUGiEdkUyLp0fbFAuJvXGEhCLOCDTvnLa1QKf49l1WlYrNr11mqPemqOn+BLVAOhtZgpYOQDJYSOyN//tT5GjdOKxy3fphh1/o985U3fj6rIQL63uqy6RjkeWwkNgU/0Kyt2bpABQf6ozq/YHAUweXZ5ZPOpJqXCidhyyNhcSmWEjsjYWEwjZLleyY4bx3wM2DB2ZAqZ7SecjyWEhsypAOQKJYSCgsSfC3POPIT/pCQ8OsB6uP7oLW9dKZyPJYSGyKhcTeWqQDkLU9nPTQuh6q9WIAyGxonPZo1ZF90JpbNikcPIPEplhI7I0jJNRtM9TunZ8zPv7MQWfzG5umPFlZXQqtfVK5yPI4QmJTLCT2xkJC3ZIIf+tzjj8bSp2+Dm1WU/OkZyuqKpTWxyWykeWxkNgUC4m9sZBQtzyYVLC2p2oZf7Zfn9bckvZ8edUxpfWRWOaiuMApG5tiIbE3FhLqsmlq764vGB+e9oC8U01uabl4SXllraF1VSxyUVxo8GR7/NIhSAYLib2xkFCXJCDgf95xB5RCUmd+f1pL69iXyyqaErQuj3Y2igvHpAOQHBYSe2MhoS65P+mRNb1Uc5cehjem1T/q1cMVwQStD0crF8UNrjuyMRYSe2uSDkDWMUXt2/MlY93c7nzvKL9/hPtwuZGo9cFI56K4whESG2MhsTcuOKROSUDA/4LjDr9ScHT3GsP9gQvfKi3v4dB6fySzUVxhIbExFhJ7K5MOQNZwT9Jja3qrpvRwrzMkEBjy79KyvsnB4J5I5KK4w0JiYywk9sY5fTqvSerA3q8Za7o1VXMmFwSCg1aUlg/sGQzujNQ1KW6wkNgYC4m9cYSEzslAMPCi4/aWcKZqzqR/MDhgRWn50N7B4PZIXpcsj4XExlhIbMybn1UPgEd801nlJz6+uo9qmhiNa6cEg6krDpVd1DcQ3BqN65MlsZDYGAsJcdqGzihdHdx3bcKqOdG8R1+t+60oLRuTGggUR/M+ZBksJDbGQkKctqHTGAgGljhua1IKydG+V2+t+6woLZ8w0B/YGO17kekdkg5AclhIiCMkdJo7Ep9c01c1TorV/Xpo3fPt0rLJQ/z+j2J1TzIdDWCvdAiSw0JCHCGhz5igDh24LuHd2bG+rwNIfqO0fOqFrf4PY31vMoUyT7anUToEyWEhIRYS+oRCMLjEcVudUughcX8H4HAfLr9kVGvrOon7kyieTWNzLCTEKRv6xO2JT61OUQ0ZkhkSgcRXD1fMGtfSslYyB8UcC4nNsZCQVzoAmcPF6rD3uwnvzJLOAQAJQMLysso56c0tq6WzUMywkNgcCwntAp/6a3sKweBSx59qlUJP6SztDMB4sbxy/rSm5lXSWSgmWEhsjoXE5rz5WX4APC3T5m5NfGZNqqqfIp3jVApQz1ZUXT6rsel96SwUdSwkNsdCQgBQLB2A5IxR5QezE96eKZ3jXJ6srF54eUPje9I5KGqCAPZJhyBZLCQEsJDYmNbLHXknlEIv6STnU1B1ZNFV9Q3vSeegqCj1ZHs4dWxzLCQEAJulA5CMPyT+Y3V/VTdNOkdn3Vd9dNGXTta9J52DIm6bdACSx0JCALAFoVMSyUacqqL0hwlvXiKdo6v+fPT4omtrT3JNSXzhCb3EQkKANz/rJID90jkolrRe7sg7qhR6Syfpjj8eO7Hw+77aVdCaRTo+fBypCymlVkTqWhRbLCTUrlg6AMXOLYkvrBmoTk6XzhGO3x6vufxHvto10DoonYXCFpFCopS6BsCVkbgWxR4LCbUrlg5AsTFSVR2+IeF1S5eRdj8/4VvwsxO+D6B1QDoLdVupJ9tTFe5FlFKpAAaEH4eksJBQu2LpABQbLzlurVYKfaRzRMoNvtr5vz1e8yG09ktnoW6J1MMUvwlgSYSuRQJYSKgdd9rYwG8TX1x9gaqdIZ0j0r5fe3LuH46d+Bhat0pnoS4L+5lFSqkrAayMQBYSxEJCAABvflYZuLA1ro1QR8pvTHh1qnSOaPnWybrLbj96fDO05nkW1vJBBK6RqrXm55fFsZBQR1ydHsdectxarhT6SeeIpq/W1c+6+8ixbdC6UToLdUojwhydVUrdoLVeFqE8JIiFhDp6WzoARcevEpeuGaxqTH08fKR8vr7hkgeqj+6E1vXSWei8PvJke7o9zaaUmoEIbhkmWYnSAchUigAEACRIB6HIGY4jFT9PeDlDOkcsXdHQOP2RqiNbbxwyaDSU6iudh87q3TC/fwCAGW1rSABgLAAopW4CsJ8jJ9aieK4QdeR0udcBuEw6B0XO+uTcj4eqE7YYHTnVhz2St18/dPAIKJUinYXOaJYn2xOxU1rbRkw2aq1VpK5JscMpGzoVp23iyM8SXlpr1zICALObmictrqgqV1ofl85Cp6lCZE9ovQbALW0/vqvDqAlZBEdI6DOcLvc8AGukc1D4huFY5QfJP+upFGw/OuBxOHZ/98Ih/bVSg6Sz0CcWe7I9/yUdgsyDIyR0qg8B1EqHoPAtT771EMtISEZLy/gl5ZW1htZhnwhKEeOWDkDmwkJCn+HNz/Ij/IVmJOwnCa+uvVAdnyWdw0zSWlrHvlRW0WhoXSGdheAHp4fpFCwkdCb8oLCwIThe/dvEFydJ5zCjsa1+52uHK/wJWh+WzmJzaz3ZHp90CDIXFhI6ExYSC1uenHfAUEiVzmFWo/z+i14/XK4StT4oncXGOF1Dp2EhodN487P2AtglnYO67oaE1z8YoY7Ols5hdiP8geFvlpYnO3jcuBQWEjoNCwmdzYvSAahrBuPEEVfiC2nSOaxiaCAw9K3Ssr7JweAe6Sw24/Vke3ZIhyDzYSGhs3lBOgB1zTLHn/YZSg+QzmElgwLBQW+Xlg/oEQxyRDB2lkoHIHNiIaEz8uZn7QRQLJ2DOucHCW+uG2lU84TdbhgQDA5cWVo+pHcwyH+1x8Zi6QBkTiwkdC7/lA5A5zcQvqN/SPzHeOkcVpYSDKauOFQ2om8g6JHOEuc2ebI926VDkDmxkNC5/BMAj/I1uWWOvD2G0gOlc1hdX637rSgtG50SCBRLZ4ljz0gHIPNiIaGz8uZnHQSwWjoHnd33E/69brRRNUc6R7zorXWfFaXl4wcEApuks8QhP4DnpUOQebGQ0Pk8JR2AzmwAfMfyEp8ZJ50j3vTUutfbpWUTB/v9EXsKLQEA3vRke45IhyDzYiGh81kGoF46BJ1uqeO23YbSfFhcFCRr9HiztHzqha3+D6WzxBFO19A5sZDQOXnzs+oQKiVkIt9JWLl+rFHBqZoocgAO9+HyS0a1tq6TzhIHTgD4l3QIMjcWEuoMTtuYSCpOnrg98amx0jnsIBFIfPVwxayxLS1rpbNY3IuebE+zdAgyNxYS6oxVAHZLh6CQpY7bShI4VRMzCUDCS2WVc9KbW7jAu/v4jxo6LxYSOi9vfpYGcL90DgK+mfDuhouNsrnSOezGAIwXyyvnT21qXiWdxYLWebI9G6RDkPmxkFBnPQ2AK+QFpaCu5s7Ev42SzmFXClD/qKi6/NLGpvels1jMvdIByBpYSKhTvPlZTQAKpHPY2YuO27cnKD1EOofd/b2yeuH8hsb3pHNYxD4AL0uHIGtgIaGuKADQKB3Cjr5hrPoozSidJ52DQh6tOrLoyvoGjpSc3wOebE9QOgRZAwsJdZo3P+soQlM3FEP9UOe7O+nxi6Rz0GfdX310YVZd/XvSOUzsBLiYlbqAhYS66j4A/BdPDL3guMOToIJDpXPQ6fKPHFt0Te1JjpScWaEn28NDFanTWEioS7z5WXsBvCKdwy6+Yqz9eJJxcL50Djq7W4+dWPifvtr3oTUfRPmpFgAPS4cga2Ehoe64RzqAHfRBQ+29SY8Ol85B53fz8ZqFP/LVrmEp+cTznmxPhXQIshYWEuoyb37WegA8uTLKXnDcsTVRBYdJ56DO+fkJ34Kf1vjWQmtOaYamdom6hIWEuusv0gHi2ReNdRszjAOcqrGYH9fUzv+f4zXrobVfOougZZ5sj0c6BFkPCwl116sANkuHiEe90XjygaQCLmK1qP+qPTn398dOfAytW6WzCPAD+J10CLImFhLqlrbj5P9HOkc8es7x5+JEFeTaEQu77mTdZbcdPb4ZWtvtgXJPerI9e6RDkDWxkFC3efOz3gXgls4RT642Ptw0zdi3QDoHhe9rdfWz7jpyzAOt7XKYYAOAP0mHIOtiIaFw/RZAQDpEPOiNxrqHk/46WDoHRc4X6htm3l99dCe0bpDOEgMPcmcNhYOFhMLizc8qAfA36Rzx4BlH/qYkFRghnYMi68qGxukFVUf2QOuT0lmi6DiAu6RDkLWxkFAk3Aognj9so+4/jI82z1B7OFUTpy5vbJr6RGX1QWjtk84SJXd6sj3x+t9GMcJCQmHz5mdVAbhbOodV9UJTfUHSQxcoBSWdhaLnsqbmyU9XVJcrrU9IZ4mwUvBUVooAFhKKlHsBlEmHsKKnHXdtTFIBPjzPBi5pbk5/rrzqiNL6qHSWCLrVk+2x224iigIWEooIb35WI4A/SOewmkxj05ZL1S5O1dhIRkvL+BfLK32G1tXSWSJgHfgEcIoQFhKKpGcAFEuHsIqeaG54LOn+VE7V2E96S+vY5WWV9YbWVt6V0grgBk+2h8/voYhgIaGI8eZnBQH8AgA/oDrh70n3fJykAqOkc5CMca2to189XNGaoPVh6Szd9BdPtmebdAiKHywkFFHe/KxVAB6VzmF2i4zirZcZO/isGptz+v0jXz9crhK1PiidpYv2ArhNOgTFFxYSioabARyQDmFWPdDc+HjSff2U4p8/Akb4A8PfKC13JGltpT8zOZ5sT5N0CIov/ECkiPPmZ9UB+CE4dXNGTyTdu8Gh/E7pHGQewwKBYf8uLeuTHAzulc7SCc96sj3vSIeg+MNCQlHR9pybx6RzmM18w+OZb2zjrho6zaBAcNDbpeX9ewSDu6SznMMxAL+WDkHxiYWEoum3AKw2Nx41yWhpejLpnj6cqqGzGRAMDlxZWj6kVzC4QzrLWfzGk+2JpzNUyET4wUhR02HqhgA8lnTfh8nKP1o6B5lbSjCYuqK0bHifQNAjneUUKz3ZnqelQ1D8YiGhqPLmZ70DTt1grrFt+0JjK3fVUKf0C+qUlaVlo1MCgS3SWdocAfB96RAU31hIKBZsPXXjQGvzU0n39FAKCdJZyDp6a91nRWn5xQMCgU3CUTSAbE+2x8qHuJEFsJBQ1Hnzs04CuF46h5RHkx5Yn6xax0rnIOvpqXWvt0vLJg7y+z8WjHG/J9vzpuD9ySZYSCgmvPlZKxF6AJ+tzFIlOzKNzZyqoW5L1ujxVmn5lGF+/waB228EcIvAfcmGWEgolm4G8J50iFhJgr/lGUe+g1M1FC4H4HCXlk+/qLV1XQxvexLAdZ5sT0sM70k2xkJCMePNzwoA+BYAqz67o0sKkh78oIdqHSedg+JDEpD0r8MVs8a2tK6N0S1/4sn2WOGgNooTLCQUU978rGoA1wCI6391zVS7Sq4yNnKqhiIqAUh4qaxiTlpzy5oo32qxJ9vzXJTvQfQZLCQUc978rA8ReipwXEqEv/VZx52JSiFROgvFHwMwlpRXzstoal4VpVvsAvDTKF2b6KxYSEiENz+rEMBT0jmi4aGkv67tqVouls5B8UsB6vmKqstnNja9H+FLnwDwZU+2py7C1yU6LxYSknQjQqv448Z0tWfX540N86RzkD08VVm9cF5D43sRulwrgGs82Z7dEboeUZewkJAYb35WE4BvIPTALstLhL/1OccdSikkSWch+yisOrLoivqGSIyU3OjJ9hRF4DpE3cJCQqK8+VkHAXwbQFA6S7juT3pkbS/VMl46B9nPA9VHF36hrj6cUnKvJ9vzt4gFIuoGFhIS583PWgGLP9J8qtq7+4vGek7VkJi7jhxb+I3auu6UktcA3BTpPERdxUJCpuDNz3oQwJ+lc3RHAgL+5x13BDhVQ9Lyjh1f+F1f7fvQWnfyW4oBfMeT7bH8CCVZHwsJmYY3P+v3sOCTgf+SVLimt2pOl85BBACu4zULr/fVrulEKakA8CVPtqc+FrmIzoeFhMzmRgBLpEN01iR1YO9XjbVzpXMQdfSLE74FuTW+tdD6bCMf9QC+4sn22OLUZLIGFhIyFW9+VhDA9wC8LZ3lfAwEAy86bm9RCg7pLESnyqmpnf/rEzXroHXglF9qRqiMfCSRi+hsWEjIdLz5WS0Avg7gQ+ks53J30uOr+6imidI5iM7mv30n591y7MQGaN3a9pYfwLWebM87krmIzoSFhEzJm59VD+ALALZLZzmTdHVw3zeMVXOkcxCdz3dO1s3509Hjm6F1I4DvebI9/5LORHQmqvOLsYliz+lyXwhgLQCncJRPGAgGtiT/aGdf1ThJOgtRJ+m1PXt8d97NVS9IByE6G46QkKl587PKAfwHgCrpLO3uTPzbGpYRshAN4HqWETI7FhIyPW9+1h4AlwM4KJ1lgjp04JsJ782WzkHUSRrA9cjz/V06CNH5sJCQJXjzs3YDmA+gRCqDQjC41HFbnVLoIZWBqAtYRshSWEjIMrz5WYcBLACwQeL+/5f499X9VEOGxL2JuigI4IcsI2QlLCRkKd78rGMArgCwMpb3vVgd9n4noWhWLO9J1E0NAL6OPN9T0kGIuoKFhCzHm59VByALwLJY3C80VfOnWqXQMxb3IwpDFYCFyPO9Kh2EqKtYSMiS2g5P+xaAJ6J9r7zExWtSVf2UaN+HKEw7AMxGnu9j6SBE3cFzSMjynC73nQBc0bj2GFV+8B3HbwYphV7RuD5RhLwD4BvI8/mkgxB1F0dIyPK8+Vm3APgNQrsKIkjr5Y68GpYRMrmnAXyeZYSsjoWE4oI3P+teAF8CUBOpa/4x8dnV/VXd1EhdjygK/og8338jz9d6/t9KZG6csqG44nS5xwJ4CUBYaz5Gq/JDRY7fDFQKvSOTjCiiWgD8AHm+56SDEEUKR0gornjzs/YBmAMgjA9qrZc5/nSMZYRM6jiAq1hGKN5whITiltPl/hmAewEkdeX7fpf43KobEt2XRycVUVg2ArgOeb690kGIIo2FhOKa0+WeB2ApgGGd+f2jVOXh9xy/TlUKfaKbjKhLNELl+ndcL0LxioWE4p7T5R4GYAlCz8I5p4+TczZdoGpnRD8VUadVAshGnu9t6SBE0cQ1JBT3vPlZFQAyATx0rt93U+I/V7OMkMm8AWAKywjZAUdIyFacLvdXATwOYFDH9y9S1WWrHL/spxT6igQj+qxmADcDeAh5Pn5Iky2wkJDtOF3uQQAeA/C19vc2JP9k42Dlu0QuFdEndgL4NvJ8xdJBiGKJhYRsy+lyfw/AQ79OXLLt54mvnHd9CVEMPAHgl8jzNUgHIYo1FhKyNafLPWJX8vfvS1b+a6WzkK0dAXAj8nwxeYI1kRmxkBABQF7KdwA8gFPWlhBFWQDAowD+F3m+GuEsRKJYSIja5aUMBHA/gO9JRyFb+ABALteKEIWwkBCdKi/lcwAKATiFk1B8qkZoB81i7qAh+hQLCdGZ5KUkA/gZgN8B6C+chuJDAMAjCD2ht0Y4C5HpsJAQnUteSn8AtyBUTnoIpyHrWovQ9MwW6SBEZsVCQtQZeSkjAdwO4D/BE46p86oB3ATgGU7PEJ0bCwlRV+SlTAFwF4CrpaOQqR1HaNfWQ8jz+YSzEFkCCwlRd+SlXAHgbgB89g11VA3gPgCPIM93UjoMkZWwkBB1V16KAnAdgDsAjBZOQ7LKAdwD4HGeskrUPSwkROHKS0kEcA2AXwKYLRuGYuwQQlN4TyLP1ywdhsjKWEiIIikvZTZCxeQaAImyYSiK9gHIR+gskVbpMETxgIWEKBryUoYDyAVwA4CBwmkocrYC+AuA55HnC0iHIYonLCRE0ZSX0hOho+h/AWCicBrqHh+AFxCalvlYOgxRvGIhIYqVvJSrEJrO+TwAJRuGzkMDeA/A3wEsR56vUTYOUfxjISGKtbyUUQC+2faaKZyGPuswgKcBPIU8337hLES2wkJCJCkvZQw+LSfThdPYVQuA1wA8CeBt5PmCwnmIbImFhMgs8lIuxqflZIpwmnhXC2AlgDcBvII831HhPES2x0JCZEZ5KWn4tJxMEk4TL3YAeKPttYbbdYnMhYWEyOzyUsYBWAjg8ravo2QDWUYDgCK0l5A830HhPER0DiwkRFYTevJwe0G5HMB42UCmEQBQgk9LyHs8PZXIOlhIiKwuL2UoPi0nCxGa4on3bcWtALYD2NT22ghgC7fnElkXCwlRvMlLSUXoELYJANI6fB0Lax5n3wzAg0+LxyYAHo5+EMUXFhIiuwg9BHAsTi8qEyB/vL0PgBfAwQ4vL4C9AHZwASpR/GMhISIgL6UXgP4dXgNO+fmZXgqhqZOOr5YzvNf+fjOAIwAqAVS1fQ298nx1MfivJCITYyEhIiIicYZ0ACIiIiIWEiIiIhLHQkJERETiWEiIiIhIHAsJERERiWMhISIiInEsJERERCTOisdIE1EcU0rd1PbDsQCgtf6xYBwiihEejEZEpqGUuktrfXOHnz8GYIzW+irBWEQUA5yyISJTUEqlApjR9rXdYwCuVEqNEQlFRDHDQkJEZjITQMfysb/ta2rsoxBRLHENCRGZgta6BqGH9nV0ZdvX/SCiuMYREiIys1sA/LitrBBRHOOiViIyJaXUXQCOaa3vls5CRNHHQkJEpqOUugbAAK3149JZiCg2OGVDRKailLoSANrLiFIqlbtsiOIfCwkRmYZSagaAGQA2KaXGtBWRGwAcl01GRNHGKRsiMoW280cO4AxbfLXWKtZ5iCi2WEiIiIhIHKdsiIiISBwLCREREYljISEiIiJxLCREREQkjoWEiIiIxLGQEBERkTgWEiIiIhLHQkJERETiWEiIiIhIHAsJERERiWMhISIiInH/H3QEbH4G77DzAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py index 856b5178..a8ee7aaa 100644 --- a/pyerrors/__init__.py +++ b/pyerrors/__init__.py @@ -206,7 +206,7 @@ print(my_corr) ``` In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced. ```python -my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding_front=1, padding_back=1) +my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1]) print(my_corr) > x0/a Corr(x0/a) > ------------------ diff --git a/pyerrors/correlators.py b/pyerrors/correlators.py index 14621fc1..900e004e 100644 --- a/pyerrors/correlators.py +++ b/pyerrors/correlators.py @@ -19,46 +19,55 @@ class Corr: to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices. The correlator can have two types of content: An Obs at every timeslice OR a GEVP - smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported. + smearing matrix at every timeslice. Other dependency (eg. spatial) are not supported. """ - def __init__(self, data_input, padding_front=0, padding_back=0, prange=None): - # All data_input should be a list of things at different timeslices. This needs to be verified + def __init__(self, data_input, padding=[0, 0], prange=None): + """ Initialize a Corr object. + + Parameters + ---------- + data_input : list + list of Obs or list of arrays of Obs. + padding : list, optional + List with two entries where the first labels the padding + at the front of the correlator and the second the padding + at the back. + prange : list, optional + List containing the first and last timeslice of the plateau + region indentified for this correlator. + """ if not isinstance(data_input, list): raise TypeError('Corr__init__ expects a list of timeslices.') + # data_input can have multiple shapes. The simplest one is a list of Obs. # We check, if this is the case if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]): - self.content = [np.asarray([item]) for item in data_input] - # Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices. - self.N = 1 # number of smearings - # data_input in the form [np.array(Obs,NxN)] + + self.content = [np.asarray([item]) for item in data_input] + self.N = 1 + elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]): self.content = data_input noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements self.N = noNull[0].shape[0] - # The checks are now identical to the case above if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]: raise Exception("Smearing matrices are not NxN") if (not all([item.shape == noNull[0].shape for item in noNull])): raise Exception("Items in data_input are not of identical shape." + str(noNull)) - else: # In case its a list of something else. + else: raise Exception("data_input contains item of wrong type") self.tag = None - # We now apply some padding to our list. In case that our list represents a correlator of length T but is not defined at every value. # An undefined timeslice is represented by the None object - self.content = [None] * padding_front + self.content + [None] * padding_back - self.T = len(self.content) # for convenience: will be used a lot + self.content = [None] * padding[0] + self.content + [None] * padding[1] + self.T = len(self.content) - # The attribute "range" [start,end] marks a range of two timeslices. - # This is useful for keeping track of plateaus and fitranges. - # The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant. self.prange = prange self.gamma_method() @@ -406,7 +415,7 @@ class Corr: newcontent.append(self.content[t + 1] - self.content[t]) if(all([x is None for x in newcontent])): raise Exception("Derivative is undefined at all timeslices") - return Corr(newcontent, padding_back=1) + return Corr(newcontent, padding=[0, 1]) if symmetric: newcontent = [] for t in range(1, self.T - 1): @@ -416,7 +425,7 @@ class Corr: newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1])) if(all([x is None for x in newcontent])): raise Exception('Derivative is undefined at all timeslices') - return Corr(newcontent, padding_back=1, padding_front=1) + return Corr(newcontent, padding=[1, 1]) def second_deriv(self): """Return the second derivative of the correlator with respect to x0.""" @@ -428,7 +437,7 @@ class Corr: newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1])) if(all([x is None for x in newcontent])): raise Exception("Derivative is undefined at all timeslices") - return Corr(newcontent, padding_back=1, padding_front=1) + return Corr(newcontent, padding=[1, 1]) def m_eff(self, variant='log', guess=1.0): """Returns the effective mass of the correlator as correlator object @@ -456,7 +465,7 @@ class Corr: if(all([x is None for x in newcontent])): raise Exception('m_eff is undefined at all timeslices') - return np.log(Corr(newcontent, padding_back=1)) + return np.log(Corr(newcontent, padding=[0, 1])) elif variant in ['periodic', 'cosh', 'sinh']: if variant in ['periodic', 'cosh']: @@ -479,7 +488,7 @@ class Corr: if(all([x is None for x in newcontent])): raise Exception('m_eff is undefined at all timeslices') - return Corr(newcontent, padding_back=1) + return Corr(newcontent, padding=[0, 1]) elif variant == 'arccosh': newcontent = [] @@ -490,7 +499,7 @@ class Corr: newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t])) if(all([x is None for x in newcontent])): raise Exception("m_eff is undefined at all timeslices") - return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1)) + return np.arccosh(Corr(newcontent, padding=[1, 1])) else: raise Exception('Unknown variant.') diff --git a/pyerrors/input/json.py b/pyerrors/input/json.py index 131dccf3..816be57e 100644 --- a/pyerrors/input/json.py +++ b/pyerrors/input/json.py @@ -8,6 +8,7 @@ import platform import warnings from ..obs import Obs from ..covobs import Covobs +from ..correlators import Corr from .. import version as pyerrorsversion @@ -19,7 +20,7 @@ def create_json_string(ol, description='', indent=1): ---------- ol : list List of objects that will be exported. At the moments, these objects can be - either of: Obs, list, numpy.ndarray. + either of: Obs, list, numpy.ndarray, Corr. All Obs inside a structure have to be defined on the same set of configurations. description : str Optional string that describes the contents of the json file. @@ -173,6 +174,18 @@ def create_json_string(ol, description='', indent=1): d['cdata'] = cdata return d + def write_Corr_to_dict(my_corr): + front_padding = next(i for i, j in enumerate(my_corr.content) if np.all(j)) + back_padding_start = front_padding + next((i for i, j in enumerate(my_corr.content[front_padding:]) if not np.all(j)), my_corr.T) + dat = write_Array_to_dict(np.array(my_corr.content[front_padding:back_padding_start])) + dat['type'] = 'Corr' + corr_meta_data = str(front_padding) + '|' + str(my_corr.T - back_padding_start) + '|' + str(my_corr.tag) + if 'tag' in dat.keys(): + dat['tag'].append(corr_meta_data) + else: + dat['tag'] = [corr_meta_data] + return dat + if not isinstance(ol, list): ol = [ol] @@ -193,6 +206,10 @@ def create_json_string(ol, description='', indent=1): d['obsdata'].append(write_List_to_dict(io)) elif isinstance(io, np.ndarray): d['obsdata'].append(write_Array_to_dict(io)) + elif isinstance(io, Corr): + d['obsdata'].append(write_Corr_to_dict(io)) + else: + raise Exception("Unkown datatype.") jsonstring = json.dumps(d, indent=indent, cls=my_encoder, ensure_ascii=False) @@ -222,7 +239,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True): ---------- ol : list List of objects that will be exported. At the moments, these objects can be - either of: Obs, list, numpy.ndarray. + either of: Obs, list, numpy.ndarray, Corr. All Obs inside a structure have to be defined on the same set of configurations. fname : str Filename of the output file. @@ -255,7 +272,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True): def import_json_string(json_string, verbose=True, full_output=False): """Reconstruct a list of Obs or structures containing Obs from a json string. - The following structures are supported: Obs, list, numpy.ndarray + The following structures are supported: Obs, list, numpy.ndarray, Corr If the list contains only one element, it is unpacked from the list. Parameters @@ -374,6 +391,22 @@ def import_json_string(json_string, verbose=True, full_output=False): ret[-1].tag = taglist[i] return np.reshape(ret, layout) + def get_Corr_from_dict(o): + taglist = o.get('tag') + corr_meta_data = taglist[-1].split('|') + padding_front = int(corr_meta_data[0]) + padding_back = int(corr_meta_data[1]) + corr_tag = corr_meta_data[2] + tmp_o = o + tmp_o['tag'] = taglist[:-1] + if len(tmp_o['tag']) == 0: + del tmp_o['tag'] + dat = get_Array_from_dict(tmp_o) + my_corr = Corr(list(dat), padding=[padding_front, padding_back]) + if corr_tag != 'None': + my_corr.tag = corr_tag + return my_corr + json_dict = json.loads(json_string) prog = json_dict.get('program', '') @@ -400,6 +433,10 @@ def import_json_string(json_string, verbose=True, full_output=False): ol.append(get_List_from_dict(io)) elif io['type'] == 'Array': ol.append(get_Array_from_dict(io)) + elif io['type'] == 'Corr': + ol.append(get_Corr_from_dict(io)) + else: + raise Exception("Unkown datatype.") if full_output: retd = {} @@ -422,7 +459,7 @@ def import_json_string(json_string, verbose=True, full_output=False): def load_json(fname, verbose=True, gz=True, full_output=False): """Import a list of Obs or structures containing Obs from a .json.gz file. - The following structures are supported: Obs, list, numpy.ndarray + The following structures are supported: Obs, list, numpy.ndarray, Corr If the list contains only one element, it is unpacked from the list. Parameters diff --git a/tests/correlators_test.py b/tests/correlators_test.py index f4f5794a..ff3445e6 100644 --- a/tests/correlators_test.py +++ b/tests/correlators_test.py @@ -115,7 +115,7 @@ def test_plateau(): def test_padded_correlator(): my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)] - my_corr = pe.Corr(my_list, padding_front=7, padding_back=3) + my_corr = pe.Corr(my_list, padding=[7, 3]) my_corr.reweighted [o for o in my_corr] diff --git a/tests/io_test.py b/tests/io_test.py index 92781785..31211db4 100644 --- a/tests/io_test.py +++ b/tests/io_test.py @@ -89,3 +89,36 @@ def test_json_string_reconstruction(): assert reconstructed_string == json_string assert my_obs == reconstructed_obs2 + + +def test_json_corr_io(): + my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)] + rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list) + + for obs_list in [my_list, rw_list]: + for tag in [None, "test"]: + obs_list[3].tag = tag + for fp in [0, 2]: + for bp in [0, 7]: + for corr_tag in [None, 'my_Corr_tag']: + my_corr = pe.Corr(obs_list, padding=[fp, bp]) + my_corr.tag = corr_tag + pe.input.json.dump_to_json(my_corr, 'corr') + recover = pe.input.json.load_json('corr') + assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]]) + assert my_corr.tag == recover.tag + assert my_corr.reweighted == recover.reweighted + + +def test_json_corr_2d_io(): + obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(8)] + + for tag in [None, "test"]: + obs_list[3][0, 1].tag = tag + for padding in [0, 1]: + my_corr = pe.Corr(obs_list, padding=[padding, padding]) + my_corr.tag = tag + pe.input.json.dump_to_json(my_corr, 'corr') + recover = pe.input.json.load_json('corr') + assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]]) + assert my_corr.tag == recover.tag diff --git a/tests/obs_test.py b/tests/obs_test.py index a5e72ec9..210ac67c 100644 --- a/tests/obs_test.py +++ b/tests/obs_test.py @@ -615,7 +615,7 @@ def test_covariance_symmetry(): cov_ab = pe.covariance(test_obs1, a) cov_ba = pe.covariance(a, test_obs1) assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps - assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps) + assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps) def test_empty_obs():