diff --git a/CHANGELOG.rst b/CHANGELOG.rst new file mode 100644 index 0000000..0ad2aeb --- /dev/null +++ b/CHANGELOG.rst @@ -0,0 +1,5 @@ +Version 0.1.0 + * Bugfix for weighted samples + +Version 0.0.0 + * First release diff --git a/demo.ipynb b/demo.ipynb index fb42dd5..b433761 100644 --- a/demo.ipynb +++ b/demo.ipynb @@ -4,7 +4,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Version 0.1.0\n" + ] + } + ], "source": [ "from __future__ import division, print_function\n", "import numpy as np\n", @@ -12,7 +20,9 @@ "import posterior_agreement\n", "\n", "from matplotlib import pyplot as plt\n", - "%matplotlib inline" + "%matplotlib inline\n", + "\n", + "print(\"Version\", posterior_agreement.__version__)" ] }, { @@ -115,7 +125,7 @@ }, { "data": { - "image/png": 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onuvuA9swJmYfXZT+SCLD6+86ikTVSjiSjXvago4geSqVohAysyn9N8zseFLsfhI5Gv2zjortLObR6InEaGjrCTqG5KFUisKPgRVm9l0z+y7wCnBrZmKJHK5ybzu16jpK2v2v1QYdQfJQ0kXB3e8B/gHYl/jzD4n7RLJiUaLrqE9dR0lZUqlFbJK6pLt/zGwscCEwKfF915gZ7n5zpsKJ9HN3ntxQTyhk2io7Sdub4kd0mhb5SQpS6T56jPh21xGgc9AfkYzbVN9ObXMXpeo7Slo05mzc2z7yhSKDpDJQPMPdF2QsicgR9Hcd9WrBWkrufaWGWz5yQdAxJI+k0lJ4xczemLEkIkewaEOD9joahWcTZ1iLJCuVlsJlwKfNbAfQS3yrC3f3uRlJJpKwvamDnfs7GVNqRLUrdEqaO8N09kQ4dpxmj0tyUvlJuSJjKUSO4JlN+wDo0+Z3o/Kn1bv4zGVvCDqG5IlUuo92ET+X+Tp3ryW+hbbO/ZOMe2bzPkpMh3aP1sNrtJW2JC9bJ6+JjEpLZ5g1ta0aTzgKVQ3tOrZUkpatk9dERmVZVSMxh6h+qY1a1OHZzRpwluRk5eQ1kdF6tmofIQOtVzs6d7+0I+gIkidSKQqHnrz2EvD9jKQSAXojUZZVNaGeo6O3urZVXUiSlKRmH1l8nfwLgE5ek6x5dUcL3X3RxCpm/UI7Gn1R59UdzVx6+tSgo0iOS6oouLub2aPufjE6eU2y5JlN+zBDJ6ylye3Lt6soyIhS6T561czenLEkIoO4O09v2qcGQhq9uqNZXUgyolSKwruJF4btZrbezDaY2fpMBZPitqm+nYb2HspKNaCQLn1R1ywkGZFWNEtOemZT/JdXWKuY0+r25dXMP09rTmV4WtEsOemZzft0wloGVOw+QExjNHIEWtEsOaehrYcNe9ooUVVIu5jDQ2t2Bx1DcphWNEvOebYqvgFeVMduZsSvX9wZdATJYVrRLDnnmU37KAmZfrgypLqxg+6w9iCXoWlFs+SUrnCEl6ubQVMnM8aBu15Sa0GGlnRRcPf7gK8B/wXUE1/R/OdMBZPi9OK2/YSjMY0nZNj9r+4KOoLkqBGnpJrZve7+STP7krv/DK1olgx6ZtM+SswIazwho+rbe2ju6OWECWODjiI5JpmWwsVmdirwGTObYmbHD/6T6YBSPKIx57mqRlzLmLPi9uXVQUeQHJRMUfglsAQ4m/iGeIP/rM5cNCk2a2pbae4MU1airqNseGTt3qAjSA5KZkXzJe5+rpn1uLsOepWMWbyxnpBBr1YxZ0VLV5g9rV1MnzI+6CiSQ1LpPtpyNN1HZrbAzLaYWbWZfX2Ix280s02JfZWeTbymFAl3Z8nGBtRGyK7bl28POoLkmGSKwq+Idx+dwyi7jxLrG24jvn/SecDHzOy8Qy57HZjn7nOBh4BbknluKQwb9rRR39ZDqbqOsuqJ9fVBR5AcM2JRcPefu/u5wG/c/Q3uftqgP8l2J10CVLv7DncPAw8AVx3yOsvcvStx81VgRgr/HZLnFidaCdoAL7vauvvY3dI18oVSNFJZp/AvR/E604HBG67UJe4bzmeBxUM9YGbXm9lqM1vd1NR0FJEkV7g7izfUUxIyzTsKwG3LtgUdQXLIiEXBzF5K/H3QzNoH/TloZu1Jvs5QfQJDvv/N7BPAPODWoR539zvdfZ67zysvL0/y5SWXbWvsoKa5S2cxB2Rp5b6gI0gOGXH2kbtflvh74lG8Th0wc9DtGcBh8+HMbD5wE/BOd+89iteTPLJ4QwMAES1YC0RrV58WssmAVPY+OhqrgDPN7DQzGwNcCywcfIGZXQTcAVzp7joeqogsqaynVF1HgbpbeyFJQjLbXNx4pMfd/ScjPYe7R8zsBmApUEJ80LrSzG4GVrv7QuLdRROAP1u8H2GXu1+ZxH+D5LEdTR1srj+oBWsBe6xiL19bcE7QMSQHJLN4rb/b6Gzgzfz1E/6HgBeSfSF3XwQsOuS+bw76en6yzyWF47GKeC+iuo6CtedANz3hKOPGlAQdRQKWzJTU77j7d4CpwJvc/Svu/hXgYjRtVI6Cu/NYxR7NOsoR96+sDTqC5IBUxhRmAeFBt8PA7LSmkaKyYU8bNc1dqOcoNzy4ui7oCJIDkuk+6ncvsNLMHiE+nfTDwD0ZSSVF4dHX9xIytE12jti67yCxWIxQKFvzTyQXJbNOwQDc/XvAZ4BW4ADwaXf//uBrRJIVjTmPr9urvY5ySMy1ZkGS6z5aZmb/amaz3H2Nu/8scdhOpZldbma/B67LcE4pMK/uaKapo5fSEn0qzSX3rNC4QrFLpvtoAfEWwh/N7A3EWwrjiE8tfQr4qbtXZC6iFKJHX48PMPdGYkFHkUHW1LYEHUEClsyK5h7gduB2MysjPgup290PZDqcFKaeviiLNzbgrrGEXBOOOmtqW7n41ClBR5GAJDOmcJ2Z7TezFuAuoEMFQY7G05v20dEboUxdRznp1y/ojIVilsy78j+B9xI/T2EX8P2MJpKC9+c1dZSVqOsoV724bX/QESRAyRSFdnd/3d0b3f0/iZ+NIDIq9W3dvLi1iVhMXUe5qjMcZUdTR9AxJCDJFIWTE2cYvN3MyoGyTIeSwvWXtXtwIKS5qDnt9mXVQUeQgCQz++hbwFzg48AbgQlmtghYB6x39z9mMJ8UEHfnz6t3Uxoy+tRSyGlPbdZ6hWKVzOyjOwffNrMZxIvEG4EPACoKkpQ1ta3UNHcxRvta5Lz27gh7WruYPmV80FEky1Ke/uHude6+yN1/6O6fzEQoKUwPramjJGTa1iJP3LZcs5CKkeYESlZ0hSM8vm6v1ibkkSfWHXY4ohQBFQXJisfX7aUzHNW2FnmkvSdCVX2yx7BLodA7VDLO3blnRS1lJUZYaxPyyo+e2hJ0BMkyFQXJuNd3H6Byrz5x5qMXtmohW7FRUZCM+8OK2vg0VA0w551wNMaSyvqgY0gWqShIRjV39PL4+r3ENMCct/7nWS1kKyYqCpJRD66uoy/qlGoJc97atLedrt5I0DEkS1QUJGOiMee+1+JdR1qbkL8c+OXzWrNQLFQUJGOe3byPutZuStRKyHt/eFUnshULFQXJmF+/uENbZBeI1q4+Xt/VGnQMyQIVBcmItbtaWVXTGu97kILw/57YHHQEyQIVBcmIu17cod1QC8zaXa10asC54KkoSNrVNneyRGcwFxwHfqwVzgVPRUHS7u6XdgKg4eXC88Cq3UFHkAxTUZC0au0M8+Cq3RgQUUOh4HSFozy5XiucC5mKgqTV71fU0BOJEdI01IL1wyUacC5kKgqSNl3hCL99uYYS7XNU0Ha1dLNZGxwWLBUFSZsHVu6mrbuPElMrodB945ENQUeQDFFRkLQIR2LcmZiGGo5qsVqhq9h9gMb2nqBjSAZkrSiY2QIz22Jm1Wb29SEef4eZrTWziJldk61ckh6PVeyhoa1HW1oUkX9Xa6EgZaUomFkJcBtwBXAe8DEzO++Qy3YBnwLuz0YmSZ9YzPnV89u1pUWRebaqkY7evqBjSJplq6VwCVDt7jvcPQw8AFw1+AJ3r3H39YB+q+SZpzbtY3tTp9YlFBl3+M7CyqBjSJplqyhMBwaveqlL3JcyM7vezFab2eqmpqa0hJPRc3d++fx2xpRoe+xi9Je1e+kOR4OOIWmUraIw1IfIUf0Gcfc73X2eu88rLy8/ylhytFZsb2bd7gNoR4viFHXnW49tDDqGpFG2ikIdMHPQ7RnA3iy9tmTQLxNjCdr4rng9tLZOJ7MVkGwVhVXAmWZ2mpmNAa4FFmbptSVDNtS18eK2/WolFLmYw02PaiZSochKUXD3CHADsBTYDDzo7pVmdrOZXQlgZm82szrgI8AdZqYRrBz3y+erKQ0ZUbUSit5jFXtp79ZMpEJQmq0XcvdFwKJD7vvmoK9XEe9Wkjywo6mDxRsaCJnO0ZF4a+H//nkdd/zzvKCjyFHSimYZlZ8/u42QmQqCDFi6aR+7mjuDjiFHSUVBUlbV0M5jFXsBRz1HMtjn/7Am6AhylFQUJGU/fmorJSG1EuRwm+oPsnxLY9Ax5CioKEhKXt/VytOb9uGgVoIM6csPVOgo1jymoiAp+dFTWygrMb3pZVgHuvv48VNbg44ho6SiIEl7uXo/L1c3465WghzZ7curaWjrDjqGjIKKgiSlLxrj5scrKSsxIqoIMoKYwz/9+jW1KPOQioIk5d4VtWzZ14HpVDVJ0o79nfxy+fagY0iKVBRkRA1tPfzk6a3xU9V0XoKk4NantlCrtQt5RUVBjsjduemRDXT3RdGhapIqd/joHa9qK5Q8oqIgR/RoxR6erWrEQOclyKg0tPfwhfvWBh1DkqSiIMPa3dLFfz5aSVlIg8tydJZUNnDPKzVBx5AkqCjIkCLRGP/nTxX09EXR2LKkw7cWVrJu94GgY8gIVBRkSD9YXMXq2lbM1G0k6eHA/7pjBfvaeoKOIkegoiCHeXzdXu56aSclIaNPBUHSqDcS4wM/f5FOndSWs1QU5G9s3XeQrz28njElIc0YkYxo7gxz1W0vE4lqenMuUlGQAW1dfXzu3jWJN6sKgmROdWMHn7xbK55zkYqCABCOxPjcH1ZT29yJYRpHkIxbsaOFz/9hjQpDjlFRENydrz+8nld3tFASMsJq1kuWLK3cx9ceWh90DBlERUH472e28ZfX91CqgWUJwJ/X1PHthZVBx5AEFYUid9eLO/jZs9so1QI1CdDvXqnh1qVVQccQoDToABIMd+f25du5dekWFQTJCbct206JGTe+7+ygoxQ1FYUi1NMX5aZHNvLw2jpKVBAkh/z8uWpau/u4+crztU17QFQUisz6ugPc+OA6qhs71EKQnHTvilp2NnVy13XzGFdWEnScoqMxhSLR0hnmG3/ZwFW/eJna5k7GlIRUECRnvVS9n3fcsozKPW1BRyk6KgoFLhKNcc+KGt79o+U8sGoXoRBEY65pp5LzGg/28sH/eYkfLd2i1c9ZpO6jAvbajma+tbCSqoaDlJUYY0qM3ohaB5JHDH6xrJpHK/Zw6zVzufT0qUEnKngqCgWovq2b7y+q4vF1e+PFoDSkYzQlL/Uvdt7T2s3Hfv0a7zhrKt+7+o3MPH58sMEKmIpCAWnr7uPuF3fw6xd30huJUmIkFqOpdSD5zQEDXti6n3feuozPvO00vjT/TCaOKws6WsFRUSgAbd19/OHVWu54fjvtPRFKQkZpSPsXSWEZ/NN810s7ue+1Xfzre87gM287TbOU0khFIU9FojFe3t7MQ2vqWLqxgXA0RmnIGFsaojcSIxp0QJEM6Z801xuJcsuSLfxq+XY+/67T+fhbTuW4Y9RyOFoqCnnC3alp7uKlbU28uG0/K7Y3c7A3QmnIiLkzptQIR1zTTKVo9P+od/RGuGXJFn769FauuXgG11w8k4tmTiYU0uK30VBRyFGxmLOtsYPVtS2srmll5c5m9hyIH2NYVhJfdNbfKgAIa1aRFKn+4hBz+OPK3fxx5W7GjylhzinHceGsyZx78kROL5/AG8onMGGsfuWNJGv/Qma2APgZUALc5e4/OOTxscA9wMVAM/BRd6/JVr4g9EaitHSGae4I09IZZndrF9v2dVDd2MG6ugMc7IkfWVgaMqKJIhCJxgZ2Mu3VjCKRAYNPCgxHYqyqaWFlTcvfXDNlfBlvKJ/AuSdN5LTyCZx6/HhmnTCemVPGc8wYjUtAloqCmZUAtwHvBeqAVWa20N03Dbrss0Cru59hZtcCPwQ+mo18qXKPd9NEY05fNEYk6vRGYhzs6eNgb4SDPRE6eiIc7OmjvaeP5s4wLYlf/M2dYfZ39NLSGaYrfHjPf8jAzIglikA4EhvoElIREEnOUN2opSHjYG+EtbWtrKltPezxKePLmDZxHFMnjmHy+DFMPqaMKePHMHl8GZOOKWPi2FImjCtlwthSJo4rZcLYMiaMK2V8WUlBdVVlq6VwCVDt7jsAzOwB4CpgcFG4Cvh24uuHgF+YmXkGjmW6d0UNP3t2G31Rx3ES/xv4e/B9f73ficXin0aio4hkHD4xtCRk9P8sRWNOzBNNYVcRkOTo1LLkDTfeFjIImdHW3UdrVx9b9qX+3MSEoc8AAAc8SURBVP0f5gb+Jv53/55+lrjd/zX21/vif//t1wx6rJ8Z9N9jBje+9yw+Mm9m6mFHkK2iMB3YPeh2HfCW4a5x94iZtQEnAPsHX2Rm1wPXA8yaNWtUYU494Vgmjx9DdWPHqL5/NPp/HMeWhigZ4lOFZtSl16nHHxt0hIw7YcIYzIzx6vZIq5g7PX2pfSDr/zAXb/tnp1C/tG1/XheFodpWh/7LJXMN7n4ncCfAvHnzRvWv/46zynnmxneO5ltFcsbcGZPZ8O33Bx1DCky2NsSrAwaXtBnA3uGuMbNS4DigBRERyZpsFYVVwJlmdpqZjQGuBRYecs1C4LrE19cAz2ViPEFERIaXle6jxBjBDcBS4lNSf+PulWZ2M7Da3RcCdwP3mlk18RbCtdnIJiIif5W1dQruvghYdMh93xz0dQ/wkWzlERGRw+mQHRERGaCiICIiA1QURERkgIqCiIgMsHye9WlmTUBthp5+Koesps5Rypleypleypl+6ch6qruXD/VAXheFTDKz1e4+L+gcI1HO9FLO9FLO9Mt0VnUfiYjIABUFEREZoKIwvDuDDpAk5Uwv5Uwv5Uy/jGbVmIKIiAxQS0FERAaoKIiIyAAVhSSY2VfNzM1satBZhmJm3zWz9WZWYWZPmdkpQWcaipndamZViayPmNnkoDMNxcw+YmaVZhYzs5ybpmhmC8xsi5lVm9nXg84zFDP7jZk1mtnGoLMciZnNNLNlZrY58f/5l4LONBQzG2dmK81sXSLndzL1WioKIzCzmcB7gV1BZzmCW919rrtfCDwBfHOkbwjI08Acd58LbAW+EXCe4WwE/gF4IegghzKzEuA24ArgPOBjZnZesKmG9DtgQdAhkhABvuLu5wJvBb6Qo/+evcDl7n4BcCGwwMzemokXUlEY2U+Br5Gtg1dHwd3bB908lhzN6u5PuXskcfNV4ifw5Rx33+zuW4LOMYxLgGp33+HuYeAB4KqAMx3G3V8gD05OdPd6d1+b+PogsJn4efE5xeP6D5UvS/zJyPtcReEIzOxKYI+7rws6y0jM7Htmthv4OLnbUhjsM8DioEPkoenA7kG368jBX2L5yMxmAxcBrwWbZGhmVmJmFUAj8LS7ZyRn1g7ZyVVm9gxw0hAP3QT8O/C+7CYa2pFyuvtj7n4TcJOZfQO4AfhWVgMmjJQzcc1NxJvt92Uz22DJ5MxRNsR9OdkyzCdmNgF4GPjyIS3vnOHuUeDCxFjcI2Y2x93TPmZT9EXB3ecPdb+ZvRE4DVhnZhDv6lhrZpe4e0MWIwLD5xzC/cCTBFQURsppZtcBHwTeE+QZ3Cn8e+aaOmDmoNszgL0BZSkIZlZGvCDc5+5/CTrPSNz9gJktJz5mk/aioO6jYbj7Bnef5u6z3X028Tfjm4IoCCMxszMH3bwSqAoqy5GY2QLg34Ar3b0r6Dx5ahVwppmdZmZjiJ9lvjDgTHnL4p/47gY2u/tPgs4zHDMr75+tZ2bHAPPJ0PtcRaEw/MDMNprZeuLdXTk5rQ74BTAReDoxffZXQQcaipl92MzqgEuBJ81sadCZ+iUG6m8AlhIfFH3Q3SuDTXU4M/sjsAI428zqzOyzQWcaxtuATwKXJ34mK8zsA0GHGsLJwLLEe3wV8TGFJzLxQtrmQkREBqilICIiA1QURERkgIqCiIgMUFEQEZEBKgoiIjJARUEEMLNvJ3bDvdnM5ifue3tiR8oKMzsmsctrpZndGnRekUwp+hXNIoO5++B9oz4O/MjdfwtgZp8Dyt29N5nnMrPSQRsAiuQFrVOQopXYg+mfiW8w1wSsAeYQ3358MnAL0Aa8QnzR3d8DG4D/Ap4DfgXMSjzdl939ZTP7NnAKMBvYT3xh1A+AdwFjgdvc/Q4zexfw7cQ1cxKv/Ql3dzN7M/Az4jve9gLvAbqGep50/5uIqKUgRcnMLia+RcRFxN8Ha4n/YgbA3e8ys8uAJ9z9ocT3dCTOrMDM7gd+6u4vmdks4iuMz018+8XAZe7ebWbXA23u/mYzGwu8bGZPJa67CDif+N5FLwNvM7OVwJ+Aj7r7KjObBHQDnx3qedx9Z6b+jaQ4qShIsXo78Ej/Hkxmlur+QfOB8xKbJQJMMrOJia8Xunt34uv3AXPN7JrE7eOAM4EwsNLd6xKvX0G8ddEG1Lv7KvjrWRlmNtzzqChIWqkoSDE7mr7TEHDpoF/+ACSKROfgu4B/dfelh1z3LuJdQ/2ixN+PNkyuIZ9HJN00+0iK1QvAhxOziiYCH0rx+58ivjEdAGZ24TDXLQX+JbE9M2Z2lpkde4TnrQJOSYwrYGYTzax0FM8jMipqKUhRcve1ZvYnoAKoBV5M8Sm+CNyW2LWylHiR+fwQ191FvFtobWKb5ibg6iPkCpvZR4H/SWyR3E28qyql5xEZLc0+EhGRAeo+EhGRASoKIiIyQEVBREQGqCiIiMgAFQURERmgoiAiIgNUFEREZMD/B18Ls2MEB1O+AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -214,7 +224,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "p-value 0.410, corresponding to 0.8 sigmas\n" + "p-value 0.411, corresponding to 0.8 sigmas\n" ] } ], @@ -243,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "p-value 0.674, corresponding to 0.4 sigmas\n" + "p-value 0.670, corresponding to 0.4 sigmas\n" ] } ], @@ -375,7 +385,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/posterior_agreement/VERSION.txt b/posterior_agreement/VERSION.txt index 77d6f4c..6e8bf73 100644 --- a/posterior_agreement/VERSION.txt +++ b/posterior_agreement/VERSION.txt @@ -1 +1 @@ -0.0.0 +0.1.0 diff --git a/posterior_agreement/posterior_agreement.py b/posterior_agreement/posterior_agreement.py index d0fd523..6603136 100644 --- a/posterior_agreement/posterior_agreement.py +++ b/posterior_agreement/posterior_agreement.py @@ -73,7 +73,7 @@ def __init__(self, chains, weights=None, nSamples=100000, nBins=100, randomSeed= self.diffDistWeight = None if weights is not None: - self.diffDistWeight = (weights[0][idx0]**2 + weights[1][idx1]**2)**.5 + self.diffDistWeight = weights[0][idx0] * weights[1][idx1] # Build KDE estimator self.setup_KDE()