From 1597800d13e433720e020215c6225e9c7229d6b9 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Wed, 31 Jan 2024 17:33:50 +0100 Subject: [PATCH] demonstrate use of keyword use_pdf --- doc/notebooks/cost_functions.ipynb | 5110 ++++++++++++++++++---------- 1 file changed, 3367 insertions(+), 1743 deletions(-) diff --git a/doc/notebooks/cost_functions.ipynb b/doc/notebooks/cost_functions.ipynb index 4123d810..ff57228b 100644 --- a/doc/notebooks/cost_functions.ipynb +++ b/doc/notebooks/cost_functions.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "id": "negative-concord", @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 76, "id": "lucky-canvas", "metadata": {}, "outputs": [], @@ -49,13 +49,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 77, "id": "destroyed-fusion", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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NRqOKRCLq7u5WOBwea/UBvCcWi6moqEiS1Nvbq1AoNME1AjAVjWb/fVLnoHR3d0uSSktLJUk7duxQMpnUokWLMmVOO+00zZkzR83NzZKk5uZmnXXWWZlwIklLlixRNBrVq6++OuL7xONxRaPRrAcAAJi6xhxQ0um0Vq1apY997GM688wzJUkdHR3y+/0qKSnJKlteXq6Ojo5MmcPDydDyoWUjWb16tSKRSOZRU1Mz1moDAIBJYMwBZcWKFXrllVf08MMP57I+I7rtttvU3d2debS2tub9PQEAwMQZU0C5+eab9fjjj2vz5s2qrq7OzK+oqFAikdChQ4eyynd2dqqioiJT5sireoamh8ocKRAIKBwOZz0A5I7jOJm/t23bljUNABNhVAHFGKObb75Zjz76qJ555hnNmzcva/l5550nn8+nTZs2Zebt2rVLu3fvVl1dnSSprq5OO3fuVFdXV6bM008/rXA4rPnz559MWwCMQVNTU9Z3b+nSpaqtrVVTU9ME1grAdDeqq3j+6Z/+SQ8++KAee+wxnXrqqZn5kUhEBQUFkqSbbrpJGzdu1Pr16xUOh/WlL31JkvTss89KGvyf2rnnnquqqirddddd6ujo0PXXX68bb7xR//qv/3pC9eAqHiA3mpqa1NjYqCN/BizLkiRt2LAha5gAADgZo9l/jyqgDP1oHen+++/X5z73OUmDA7V95Stf0UMPPaR4PK4lS5boxz/+cVb3zTvvvKObbrpJW7ZsUSgU0vLly3XnnXfK6/WeUD0IKMDJcxxHtbW12rNnz4jLLctSdXW1WlpaZNv2ONcOwFSUt4DiFgQU4ORt2bJFF1988XHLbd68WQsXLsx/hQBMeeM2DgqAyau9vT2n5QAglwgowDRVWVmZ03IAkEsEFGCaqq+vV3V19VHPLbMsSzU1Naqvrx/nmgEAAQWYtmzb1tq1ayUNPwF+aHrNmjWcIAtgQhBQgGmsoaFBGzZsUFVVVdb86upqLjEGMKG4igdA5jslSRs3btTixYs5cgIg57iKB8CoHB5GLrroIsIJgAlHQAEAAK5DQAEAAK5DQAEAAK5DQAEAAK5zYnfnAzClhUKhYXc0BoCJxBEUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUADhCLBaTZVmyLEuxWGyiqwNMSwQUAADgOgQUAADgOgQUAADgOgQUAADgOgQUADiC4ziZv7dt25Y1DWB8EFAA4DBNTU2aP39+Znrp0qWqra1VU1PTBNYKmH4IKADwnqamJjU2NqqtrS1rfltbmxobGwkpwDgioACABrt1Vq5cKWPMsGVD81atWkV3DzBOCCgAIGn79u3as2fPUZcbY9Ta2qrt27ePY62A6YuAAgCS2tvbc1oOwMkhoACApMrKypyWA3ByCCgAIKm+vl7V1dWyLGvE5ZZlqaamRvX19eNcM2B6IqAAgCTbtrV27VpJGhZShqbXrFkj27bHvW7AdERAAYD3NDQ0aMOGDaqqqsqaX11drQ0bNqihoWGCagZMP5YZ6Zo6l4tGo4pEIuru7lY4HJ7o6gCYYoZ+YyRp48aNWrx4MUdOgBwYzf6bIygAcITDw8hFF11EOAEmAAEFAAC4DgEFAAC4jneiKwAAbhMKhUYc8h7A+OEICgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CCgAAcB0CyhjFYjFZliXLshSLxSa6OgAATCkEFAAA4DoEFAAA4DoElDFyHCfz97Zt27KmAQDAySGgjEFTU5Pmz5+fmV66dKlqa2vV1NQ0gbUCAGDqIKCMUlNTkxobG9XW1pY1v62tTY2NjYQUAABygIAyCo7jaOXKlTLGDFs2NG/VqlV09wAAcJIIKKOwfft27dmz56jLjTFqbW3V9u3bx7FWAABMPaMOKNu2bdOnP/1pVVVVybIs/fa3v81abozR7bffrsrKShUUFGjRokV68803s8ocOHBA1113ncLhsEpKSnTDDTeot7f3pBoyHtrb23NaDgAAjGzUASUWi+mcc87RvffeO+Lyu+66S/fcc4/WrVun559/XqFQSEuWLNHAwECmzHXXXadXX31VTz/9tB5//HFt27ZNX/ziF8feinFSWVmZ03IAAGBklhnphIoTfbJl6dFHH9WVV14pafDoSVVVlb7yla/oq1/9qiSpu7tb5eXlWr9+va655hq9/vrrmj9/vl544QWdf/75kqQnn3xSS5cu1Z49e1RVVXXc941Go4pEIuru7lY4HB5r9UfNcRzV1taqra1txPNQLMtSdXW1WlpaZNv2uNULAIDJYDT775yeg9LS0qKOjg4tWrQoMy8SiWjBggVqbm6WJDU3N6ukpCQTTiRp0aJF8ng8ev7550d83Xg8rmg0mvWYCLZta+3atZIGw8jhhqbXrFlDOAEA4CTlNKB0dHRIksrLy7Pml5eXZ5Z1dHSorKwsa7nX61VpaWmmzJFWr16tSCSSedTU1OSy2qPS0NCgDRs2DDvSU11drQ0bNqihoWGCagYAwNQxKa7iue2229Td3Z15tLa2Tmh9Ghoa9Nprr2WmN27cqJaWFsIJAAA5ktOAUlFRIUnq7OzMmt/Z2ZlZVlFRoa6urqzlqVRKBw4cyJQ5UiAQUDgcznpMtMO7cS666CK6dQAAyKGcBpR58+apoqJCmzZtysyLRqN6/vnnVVdXJ0mqq6vToUOHtGPHjkyZZ555Rul0WgsWLMhldQAAwCTlHe0Tent79dZbb2WmW1pa9PLLL6u0tFRz5szRqlWr9C//8i/64Ac/qHnz5ulb3/qWqqqqMlf6nH766brsssv0hS98QevWrVMymdTNN9+sa6655oSu4HGLUCg04pU8AADg5I06oPz5z3/WxRdfnJm+5ZZbJEnLly/X+vXr9bWvfU2xWExf/OIXdejQIX384x/Xk08+qWAwmHnOL3/5S91888265JJL5PF4dNVVV+mee+7JQXMAAMBUcFLjoEyUiRoHBQAAjN2EjYMCAACQCwQUl4nFYrIsS5ZlKRaLTXR14DJsHwCmCwIKAABwHQKKyziOk/l727ZtWdMA2weA6YKA4iJNTU2aP39+Znrp0qWqra1VU1PTBNYKbsH2AWA6IaC4RFNTkxobG9XW1pY1v62tTY2NjeyEpjm2DwDTDZcZu4DjOKqtrdWePXtGXG5Zlqqrq9XS0sKQ+tMQ2weAqYLLjCeZ7du3H3XnI0nGGLW2tmr79u3jWCu4BdsHgOmIgOIC7e3tOS2HqYXtA8B0REBxgcrKypyWw9TC9gFgOiKguEB9fb2qq6tlWdaIyy3LUk1Njerr68e5ZnADtg8A0xEBxQVs29batWsladhOaGh6zZo1rj4BkhFO82cqbB8AMFoEFJdoaGjQhg0bVFVVlTW/urpaGzZsUENDwwTV7MQwgFh+TfbtAwBGi8uMXWaobZK0ceNGLV682PX/M25qatI///M/Z43RUV1drbVr17LjzLHJuH0AwBAuM57EDt/ZXHTRRTnd+eSjGybfA4jRdZQtn9sHALgJAWUayXU3jOM4WrlypUY6CDc0b9WqVSf1PnQdAcD0REBxmVAoJGOMjDEKhUI5e9183Mcl3wOIce+Z4fK1fQCA2xBQpoF8dcPkcwAx7j0DANMbAWWKy2c3TL4GEBuPriMAgLsRUKa4fHbD5GsAMe49AwAgoExx+eyGydcAYtx7BgBAQJni8n0fl3wMIMa9ZwAADNQ2xTmOo9raWrW1tY14TodlWaqurlZLS8tJjamRywHExqvOAIDxxUBtyBiv+7jkcgAx7j0DACCgTAPjcR+XXI/Pwb1nAGB6o4tnGpmM93GZjHWOxWIqKiqSJPX29jKgGgC8hy4ejGgy3sdlMtaZ4fkB4OQRUKaRyThM+mSrM8Pz41i4+SVw4ggomLZyvbNgeH4cD0fXgBNHQMG0lcudBcPz43g4ugaMDgEF01KudxYMz49j4egaMHoEFEw7+dhZMDw/joaja8DYEFAwreRrZ8Hw/Dgajq4BY0NAwbSSr51Fvu7sjMmPo2vA2BBQMK3ka2fB8Pw4Go6uAWNDQMG0ks+dBcPzYyQcXQPGhqHuMa2Mx52SHcfR9u3b1d7ersrKStXX13PkZJobOjFbUtZ2NxRaCLCYLhjqHjiK8eiKsW1bCxcu1LXXXquFCxcSTsDRNWAMCCiYdthZYCI0NDTotddey0xv3LhRLS0tbG/AUdDFg2mLrhgAGF+j2X97x6lOgOsMdcUAANyHLh4AAOA6BBQAAOA6BBQAAOA6BBQAAOA6BBQAwDFFo1FZliXLsvTEE09w52WMCwIKAEwBjuNoy5Yteuihh7Rly5achYimpibNnz8/M7106VLV1taqqakpJ68PHA0BBQAmuaamJs2dO1cXX3yx/u7v/k4XX3xxTkLE0BD9bW1tWfPb2trU2NhISEFeMVAbAExiQyHiyJ/yk73Pz9B9q/bs2TPi8lzctwrTD/fiAYBpwHEcrVy5csQbXw7NW7Vq1Zi6e7Zv337UcDL0+q2trdq+ffuoX3syy1dXGoYjoADAJJXPENHe3p7TclNBU1OTamtrc96VhpERUABgkspniKisrMxpufGW6yMdQ11pRwZCzsfJHwIKAExS+QwR9fX1qq6uzpzLciTLslRTU6P6+vpRv/aQfF55lMuThvPZlYajI6AAwCSVzxBh27bWrl2beZ0jX1eS1qxZM+YTZPPVXZKPK484H2diEFAAYJLKd4hoaGjQhg0b9L73vS9rfnV19ZivDpLy112SryMdnI8zMQgoADCJ5StEHP76f/3rX7V582Y9+OCD2rx5s1paWsb8upPxyiPOx5kY3omuAADg5DQ0NOiKK67Q9u3b1d7ersrKStXX1+dsfBLbtrVw4cKcvNZoQsRo3zNfRzqGutLa2tpGDFZDY8KczPk4+dLU1KSVK1dmfebV1dVau3btSYfXfCOgAMAUkMsQkU+T8cqjoa60xsZGWZaVFVJy0ZWWL0cbxG+oKy0XR9jyiS4eAMC4maxXHuW7K03KbVfMVLjyiKHuAQDjZmgI/eN1l4x1CP2howaSRjzScbJhwnGcvHSl5borZsuWLbr44ouPW27z5s3jeuSNoe4BAK40Wa88GjLUlXbttddq4cKFOQsnub6qaSpceURAAQCMq8l25VE+5asrZrJfeSTRxQMAmCD56i6ZTPLVFZPvrrSxGs3+m6t4AAATYrJceZRP+eqKmaxXHh2OLh4AACZIPrtixuPKo3yiiwcAgAkyHl0xbupKo4sHAIBJYDy6YiZrVxpdPAAATKDJ3hWTLxPaxXPvvffq+9//vjo6OnTOOefoRz/6kS644ILjPi+fXTz9CUev7DmktzqiCgV8et/MQs0MBbSvd0BOyujV1gNKmLQqwoUqCvrUN5DSwYG4fLatOTNCao/2q6I4oLKSQv1lX498lkceSdFESoU+W7KkmcUB+Wxbnd19MrJU4PHozXej+vgHKpRIO+qOJWTJ0qGBhKoihfJ6LRX6vbIt673XL9D/dkVV5LOVSqXVm3Bkez2aOzOk0pBf3f1JdR7qV088pQKfrcKArWQqLdvjUe9AUuFCn7y2R7bHUsBrK5VOq9DvVVHAq/6ko4Gko+7+hHweW9GBhGYUBuTzWooU+NUXT6mje0AyRl6vR4mEo7jjaGZxUDNDAR3oi6u0MKCO7n61d/fr9MqIYomUumMJVZQUKOCzlXLS2vtuTO/2J1U7q0he25LtsVQc8Gl/74B8tq1DfQk5TlqnlBWruz+pv+zvUc2MkAI+jwJeW5ak/qQjv+1RMp2W1+ORkVGhz6sDfXFFgn7tjw3I6/EoHPTJSIqnHHk9HnksqSJSoP29ce050KdkylHNzJBKiwLyez2yLUttB/sU8NkKB31qPRjTrKKgLEkd0X6VFgbUE09KRioLB2UkdXT3KzaQVMBnD/6vx0gBn0dFAZ+6BxKqmRHSQMqR4xh1RvsVLvBrRsivdNqovbtfKSetU2YXa8/BPr2656BqSotUFgkqnnTU0tkjY1kKB706/X0l2tc7oIO9icHPrMCnnr6k+pMp9SWcwXZFB7Qv2q8ZRUGdVTNDu9/tVdpIHsvS3oMxBby2bI+l0lBQb+07pFh/UocSjuZECpUwKUVjjpKelOy0VxVFPr3W1aN4Iq7+lFFf0pE3FddA2lbVjJCCQZ8O9vTrQG9cPqUV93gU8no0kHRkOY76HCmZTikxkJYjqbzYq56EpUQ8qZSkoE+SkQaSUlGhR04irX5HivZK3Smpokjye6SEI6WTUjQp1ZRKlbOLlHY8SjgJmbRH8URS0f6UbI9RpDCgksKgOnv6VBzwKRofUNv+tGaGLc0I+BQKF+r8mlnyez1KpY2S6bS6exIasIzePyMkI0tKG/U5KUWCAZWGApLHkt/j0d5oTPF4Sp2xAQVtn2YX+XVa1Qz5vJbiqbQ8llQ9I6TWgzHJSB6PlfmeGRmVhgIq8NuZbcxrexT02ToYiyueSsvv9aikwK+eeFJez+B31O/1aFZRQAdiCe0+ENMHZhcrmU7Ltiz1xlOKpxzNLg6qKODVwb6EfLZHfq9HRQGvbI+lRCqtzuiAnLRRuMCnAp+tg30JBX22Cny2ogNJhYM+JZy0nLSR7bEGfwsSjv63M6qSAr+qSwvl93qUSKW1vzcuxzE60BdXVaRQ78bi8tseeW2PDvbFVTuzSLIkJ23kpI1i8ZQkqSjglc/rUYHPVsJJy297lHDSkpG6egZUVhxUwkkrFk8pFPAqUuAb/E1OOirwDW6zTtqoP+nItqzBeqSNgj5bSSet4HtlJGXaIEnd/UnF4ikFfbb8Xo/8tifzOdnvrZ+hz0oarPfQc0KBwd/F3ngqa3qo/r3xlKL9STlpI7/XoxmFfh3sSyidNgr4bKWN0ayigGyPlWmHDvvt6k86mc/G9lhZXTFl5RU6/8KPqsDvU3/Syawb27Iyn5csybYsRQeSmlHol9/ryXxGQ+/VG0+pP+GoZyCpykiBZClrmaTMOhn6nPsTjvYc7FNJoV+lIX/ms8mFSdHF86tf/Uq33HKL1q1bpwULFmjNmjVasmSJdu3apbKysomqlhKOo7cPRPVS6yGFi3xKKCVjQnqrq08eK60/7T6klFKaU5LQjEKfugdS6ooNKCCv0sbotfYe1cws1BlWWi/tPqRwgU9ppXWwN6FIgVdGlt43UKDiAq/eaOuVsYxmFfr1fMtBnVFRpHf7UursGdz57D3UL8cYWTKaUehX0OvR6+09KvCktXNPt2aFvBpIGh0cSCjgteSzjWQVqivarzf29Sja4yhc4FW4wKv+pJHPNtrfm1R5OCC/z5LXY6k44Fd/wlFJyKeZRQF19yXVN5BU26G4SkJetR7oU+3skGQszZlRoP19cf1lX79SjqOigE/Rgbj64kZzZyWUTheq5d1+zZvpqGV/r15v71Vpoa39fQm1HUwoaRwVBbwaSDp6vb1Xe3ti8nok4zHyylZFJKA3u/pUUuhV28GYBpJG4UJbXb1xvfTOQXnSaXm8toqDtiTPewHMo/5ESgUBW4mkUVmRX2+/26e5M4J6a1+fCry2SiM+edKWogMphfwepdKWIoVetXfH9L9dUfX2O7I8Rl6PFPB7FfR5tPtATMUBr7ylBXqrq1e2jIwlvdnVo/fPctR+KC55LHm9ljzGqOXdqA72pBQK2PLalizLI69XqioO6p2DA5oZ8ml/b1Iyab3Z2aPKGQHZVkiJtNFf9/eoP5lWRSSgPe/26IW/HlQi5SillPoGUnqx7ZC8Hilc4FfNzKD+ur9Hnd0D8ngsVYSD6uhJqLc/oehASgknpTf39Wrvwbhmhvs0p9SvV9q75fFIAXtw+wn5bFmWR/PKEnrhnQPqi6fVPRBXdFZSvcmkDvU6StlJWSlb80oL9dLeg0okk0qmHA0kJaWkpKSunpQKAh4diiXU3Z+WMXrvfaR+R7JSUsKS0o4U1+Dh2v3dKfUbKTX4MvJLSr/3CHSnlZJk3lsmSX/tHf4djb0r7R/oVTotpVKSY0nJlDSQHnyPgkNxlYQS6ooaFQaSSiSkXiN17zfyWgmFDiYUtPwytuQ4RkmT1rt9CSWSacX6kkqkJUuWepNJlRb4VVlSKJM2Cgc9er2jV4mkUVu0VyG/X7NDQRUW+WQZKZFKK+1IJQW23uzqkceWfHovfBd4FU8aeWTkpP0K+Dx650BMhT6PSgp8ank3pkTKyO+1VD2jUO3dAwoFPLItj4L+we/wwd5+vd4eVWXYr0P9jgI+j97tHVC0LyW/bcnvtdQVHVBxwFbA71Wh35YtS6l0Wp3RfjlOWlKBbI9fXdEBzSj0yfb4ta8nrqDPo56BlFJOWj7bo0K/rYFUSm929ahqRlAVJUH55VEqnVZXdEDGpPWX/X2KBG3tPhBTod+jIp+tt9/tV3k4oFRaSjlpOU5a+3rjkgb/Y1bo88pnW4rFU/IEvYrFU7I9UuuBPoULvOodSGl/b1yziwIqDg7unmLxlAJej2xZShvzXtjwqLO7X6l0WiUFPvUmHM0o8Mm2BzsFhtogST39Ce3rjaukwKeg36vioDfzOdm2J1N28L89UtqYwef0xDWrOKCgz6Nof0L7D5seqn+0P6GO9/6DEfR7VeAfrFfSpFUa9Kk/ZVRS6JNleTLtGGqTJ+hVz0BSlpR5/8O7YpJOWgdiCfm8afUOJJV8b90EfJ7M5+WkpaDPo309cRUHvfLLk/mMht6rZyCp3v6E9nbHNSPkk5NW1jJLyqyToc854ThqPdAnS4P1H/psxtuEdfH88Ic/1Be+8AV9/vOf1/z587Vu3ToVFhbq5z//+URVCQAAuMSEBJREIqEdO3Zo0aJF/1cRj0eLFi1Sc3PzsPLxeFzRaDTrAQAApq4JCSj79++X4zgqLy/Pml9eXq6Ojo5h5VevXq1IJJJ51NTUjFdVAQDABJgUV/Hcdttt6u7uzjxaW1snukoAACCPJuQk2VmzZsm2bXV2dmbN7+zsVEVFxbDygUBAgUBgvKoHAAAm2IQcQfH7/TrvvPO0adOmzLx0Oq1Nmzaprq5uIqoEAABcZMIuM77lllu0fPlynX/++brgggu0Zs0axWIxff7zn5+oKkmS/Lat95eGZRLKGgfFyCMnZXTBnOQxx0EJeO3BcVAihfqbOeb/xkEpGT4OileezDgoKZPWjKIihQodRQoGx0GZVRQYNg7K6ZWWIqECnVXtDBsHpaokpNJCv2zLlsfY6gkPHwelovjY46AU+HwaKHQUKvDL57FVHPRnjYMS8Plky3vYOCgFWeOg2LZXpYUBOWlLQZ9X5ZEiFRWkNCOYPQ6KJ22poiSo6tLscVAsyyOfbSvk98lx0ppVVCif7dPfzDV63wmOg+KxbUWCflkeO2sclNLDxkEp8PlUGQnJcSwlU46qZ4QULvy/cVDmlIYU8Nkq9Pv0gbIizXhvHJQPlkmlhQEFfD7JDP5tJM2baVQWGnkcFJ/Pq0K/X2VhW45j9MHywUuGw4WD46DUzipWykmr0O9X9cxifaQ29X/joBQ6SiWVGQelKBhU7SwpEgxkxkGJBLPHQSn0+lQVGhwHJRIK6cxKkxkHpcC2s8ZBceamjzsOiseryTkOStHRx0E5szo8pnFQCgI+xeMpVcX8mXFQ5oSLssZBKQoG9cGy4hHHQSk5bByUuaWhzDgosjxZ46AEfN6scVD8tq0ZRQU6vTKtUCAgv29wHBS/bas05ChSGJDftlUWDmbGQfG8Nwqp1+NRebggMw7KULmgz5bftjW7ePC5xUErM9aGx7IU9Hr1wbJilRT45fV4Mq9VFg7KcYw8Ho9CgYDmlCozDopl2yrw+bLGQRkaAXVoHBSvx6NQwJv5V0aqKS1U0OuVp8Aj+735Q/U//G+PZSkUGPwtLI8UZMZBCQWHj4My9JziAr88Hk/WOChDn9PQOChDZYfeY+g5Q/UMF/gz9fIeMV+yMuOgBL1elUcKMuOgFBvz3m+ONaxNXo9HxUFf5j2PNPQcr8ejoqAvaxyUoc9raByU2cWBzDo68r2Kg77Bbcm2M885fNnQej38OX7bVk1poSKF/hHrNl4mdKC2f/u3f8sM1Hbuuefqnnvu0YIFC477PO7FAwDA5DOa/Tc3CwQAAONiNPvvSXEVDwAAmF4IKAAAwHUIKAAAwHUIKAAAwHUIKAAAwHUIKAAAwHUIKAAAwHUIKAAAwHUIKAAAwHUm7F48J2No8NtoNDrBNQEAACdqaL99IoPYT8qA0tPTI0mqqamZ4JoAAIDR6unpUSQSOWaZSXkvnnQ6rb1796q4uHjwzrE5FI1GVVNTo9bW1il5nx/aN/lN9TbSvslvqrdxqrdPyl8bjTHq6elRVVWVPJ5jn2UyKY+geDweVVdX5/U9wuHwlN3wJNo3FUz1NtK+yW+qt3Gqt0/KTxuPd+RkCCfJAgAA1yGgAAAA1yGgHCEQCOiOO+5QIBCY6KrkBe2b/KZ6G2nf5DfV2zjV2ye5o42T8iRZAAAwtXEEBQAAuA4BBQAAuA4BBQAAuA4BBQAAuM6UDyj33nuvamtrFQwGtWDBAv3pT386ZvlHHnlEp512moLBoM466yxt3Lgxa7kxRrfffrsqKytVUFCgRYsW6c0338xnE45rNG382c9+pvr6es2YMUMzZszQokWLhpX/3Oc+J8uysh6XXXZZvptxVKNp3/r164fVPRgMZpVx2zocTfsWLlw4rH2WZWnZsmWZMm5af9u2bdOnP/1pVVVVybIs/fa3vz3uc7Zs2aIPf/jDCgQC+sAHPqD169cPKzPa73U+jbaNTU1NuvTSSzV79myFw2HV1dXpqaeeyirz//7f/xu2Dk877bQ8tuLoRtu+LVu2jLiNdnR0ZJVzyzocbftG+n5ZlqUzzjgjU8ZN62/16tX6yEc+ouLiYpWVlenKK6/Url27jvs8N+wLp3RA+dWvfqVbbrlFd9xxh1588UWdc845WrJkibq6ukYs/+yzz+raa6/VDTfcoJdeeklXXnmlrrzySr3yyiuZMnfddZfuuecerVu3Ts8//7xCoZCWLFmigYGB8WpWltG2ccuWLbr22mu1efNmNTc3q6amRosXL1ZbW1tWucsuu0zt7e2Zx0MPPTQezRlmtO2TBkc+PLzu77zzTtZyN63D0bavqakpq22vvPKKbNvW3/7t32aVc8v6i8ViOuecc3TvvfeeUPmWlhYtW7ZMF198sV5++WWtWrVKN954Y9YOfCzbRD6Nto3btm3TpZdeqo0bN2rHjh26+OKL9elPf1ovvfRSVrkzzjgjax3+93//dz6qf1yjbd+QXbt2ZdW/rKwss8xN63C07Vu7dm1Wu1pbW1VaWjrsO+iW9bd161atWLFCzz33nJ5++mklk0ktXrxYsVjsqM9xzb7QTGEXXHCBWbFiRWbacRxTVVVlVq9ePWL5z3zmM2bZsmVZ8xYsWGD+4R/+wRhjTDqdNhUVFeb73/9+ZvmhQ4dMIBAwDz30UB5acHyjbeORUqmUKS4uNg888EBm3vLly80VV1yR66qOyWjbd//995tIJHLU13PbOjzZ9Xf33Xeb4uJi09vbm5nnpvV3OEnm0UcfPWaZr33ta+aMM87Imnf11VebJUuWZKZP9jPLpxNp40jmz59vvv3tb2em77jjDnPOOefkrmI5ciLt27x5s5FkDh48eNQybl2HY1l/jz76qLEsy/z1r3/NzHPr+jPGmK6uLiPJbN269ahl3LIvnLJHUBKJhHbs2KFFixZl5nk8Hi1atEjNzc0jPqe5uTmrvCQtWbIkU76lpUUdHR1ZZSKRiBYsWHDU18ynsbTxSH19fUomkyotLc2av2XLFpWVlenUU0/VTTfdpHfffTendT8RY21fb2+v5s6dq5qaGl1xxRV69dVXM8vctA5zsf7uu+8+XXPNNQqFQlnz3bD+xuJ438FcfGZuk06n1dPTM+w7+Oabb6qqqkqnnHKKrrvuOu3evXuCajg25557riorK3XppZfqj3/8Y2b+VFuH9913nxYtWqS5c+dmzXfr+uvu7pakYdvb4dyyL5yyAWX//v1yHEfl5eVZ88vLy4f1hQ7p6Og4Zvmhf0fzmvk0ljYe6etf/7qqqqqyNrTLLrtM//Ef/6FNmzbpe9/7nrZu3arLL79cjuPktP7HM5b2nXrqqfr5z3+uxx57TL/4xS+UTqf10Y9+VHv27JHkrnV4suvvT3/6k1555RXdeOONWfPdsv7G4mjfwWg0qv7+/pxs827zgx/8QL29vfrMZz6TmbdgwQKtX79eTz75pH7yk5+opaVF9fX16unpmcCanpjKykqtW7dOv/nNb/Sb3/xGNTU1WrhwoV588UVJufndcou9e/fqiSeeGPYddOv6S6fTWrVqlT72sY/pzDPPPGo5t+wLJ+XdjJEbd955px5++GFt2bIl60TSa665JvP3WWedpbPPPlvvf//7tWXLFl1yySUTUdUTVldXp7q6usz0Rz/6UZ1++un66U9/qu9+97sTWLPcu++++3TWWWfpggsuyJo/mdffdPPggw/q29/+th577LGsczQuv/zyzN9nn322FixYoLlz5+rXv/61brjhhomo6gk79dRTdeqpp2amP/rRj+rtt9/W3Xffrf/8z/+cwJrl3gMPPKCSkhJdeeWVWfPduv5WrFihV155ZcLOhxmtKXsEZdasWbJtW52dnVnzOzs7VVFRMeJzKioqjll+6N/RvGY+jaWNQ37wgx/ozjvv1O9//3udffbZxyx7yimnaNasWXrrrbdOus6jcTLtG+Lz+fQ3f/M3mbq7aR2eTPtisZgefvjhE/qxm6j1NxZH+w6Gw2EVFBTkZJtwi4cfflg33nijfv3rXw87nH6kkpISfehDH5oU63AkF1xwQabuU2UdGmP085//XNdff738fv8xy7ph/d188816/PHHtXnzZlVXVx+zrFv2hVM2oPj9fp133nnatGlTZl46ndamTZuy/od9uLq6uqzykvT0009nys+bN08VFRVZZaLRqJ5//vmjvmY+jaWN0uDZ19/97nf15JNP6vzzzz/u++zZs0fvvvuuKisrc1LvEzXW9h3OcRzt3LkzU3c3rcOTad8jjzyieDyuz372s8d9n4laf2NxvO9gLrYJN3jooYf0+c9/Xg899FDWJeJH09vbq7fffntSrMORvPzyy5m6T5V1uHXrVr311lsn9J+EiVx/xhjdfPPNevTRR/XMM89o3rx5x32Oa/aFOTvd1oUefvhhEwgEzPr1681rr71mvvjFL5qSkhLT0dFhjDHm+uuvN9/4xjcy5f/4xz8ar9drfvCDH5jXX3/d3HHHHcbn85mdO3dmytx5552mpKTEPPbYY+Z//ud/zBVXXGHmzZtn+vv7x719xoy+jXfeeafx+/1mw4YNpr29PfPo6ekxxhjT09NjvvrVr5rm5mbT0tJi/vCHP5gPf/jD5oMf/KAZGBhwffu+/e1vm6eeesq8/fbbZseOHeaaa64xwWDQvPrqq5kyblqHo23fkI9//OPm6quvHjbfbeuvp6fHvPTSS+all14ykswPf/hD89JLL5l33nnHGGPMN77xDXP99ddnyv/lL38xhYWF5tZbbzWvv/66uffee41t2+bJJ5/MlDneZzbeRtvGX/7yl8br9Zp777036zt46NChTJmvfOUrZsuWLaalpcX88Y9/NIsWLTKzZs0yXV1drm/f3XffbX7729+aN9980+zcudOsXLnSeDwe84c//CFTxk3rcLTtG/LZz37WLFiwYMTXdNP6u+mmm0wkEjFbtmzJ2t76+voyZdy6L5zSAcUYY370ox+ZOXPmGL/fby644ALz3HPPZZZ94hOfMMuXL88q/+tf/9p86EMfMn6/35xxxhnmv/7rv7KWp9Np861vfcuUl5ebQCBgLrnkErNr167xaMpRjaaNc+fONZKGPe644w5jjDF9fX1m8eLFZvbs2cbn85m5c+eaL3zhCxP242/M6Nq3atWqTNny8nKzdOlS8+KLL2a9ntvW4Wi30TfeeMNIMr///e+HvZbb1t/QJadHPobatHz5cvOJT3xi2HPOPfdc4/f7zSmnnGLuv//+Ya97rM9svI22jZ/4xCeOWd6YwUurKysrjd/vN+973/vM1Vdfbd56663xbdh7Rtu+733ve+b973+/CQaDprS01CxcuNA888wzw17XLetwLNvooUOHTEFBgfn3f//3EV/TTetvpLZJyvpeuXVfaL3XAAAAANeYsuegAACAyYuAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXIeAAgAAXOf/A0qbx+orJ1afAAAAAElFTkSuQmCC", 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" ] @@ -92,13 +92,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 78, "id": "b62cbb46", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1083,7 +1083,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1120,7 +1120,7 @@ "└───────┴─────────────────────────────────────────┘" ] }, - "execution_count": 15, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1149,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 80, "id": "8081f7f5", 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time = 0.3 sec │\n", + "│ EDM = 3.31e-05 (Goal: 0.0002) │ time = 0.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -2123,7 +2123,7 @@ "└───────┴─────────────────────────────────────────┘" ] }, - "execution_count": 16, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -2151,7 +2151,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 81, "id": "9da33a94", "metadata": {}, "outputs": [ @@ -2283,7 +2283,7 @@ "└───────┴────────────────────────────┘" ] }, - "execution_count": 17, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -2309,25 +2309,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 84, "id": "220d17c6", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.9998931767086212, 0.16217159778984394, 0.6799538081830168)\n", - "(0.9998386768107258, 0.15051842436022747, 0.6889258188275142)\n", - "(0.9984242446776961, 0.14035148868243807, 0.6972384519516458)\n", - "(0.995700712751885, 0.1129121042492769, 0.7891108969534214)\n", - "(0.9945860415151221, 0.09644664967345094, 0.8508773815570648)\n", - "(0.9936608100330468, 0.10083893484776896, 0.9609503937682806)\n", - "(0.9957186730752492, 0.09933118850725797, 0.9681455667879805)\n", - "(0.9945806067471119, 0.09860577681686127, 0.9707538004865539)\n", - "(0.994574578639614, 0.09863321444431876, 0.9715101254968863)\n" - ] - }, { "data": { "text/html": [ @@ -2341,7 +2326,7 @@ " \n", " \n", " EDM = 3.73e-05 (Goal: 0.0002) \n", - " time = 0.3 sec \n", + " time = 0.1 sec \n", " \n", " \n", " Valid Minimum \n", @@ -2432,7 +2417,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 147.6 │ Nfcn = 81, Ngrad = 9 │\n", - "│ EDM = 3.73e-05 (Goal: 0.0002) │ time = 0.3 sec │\n", + "│ EDM = 3.73e-05 (Goal: 0.0002) │ time = 0.1 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -2456,14 +2441,13 @@ "└───────┴────────────────────────────┘" ] }, - "execution_count": 18, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def grad(xy, *par):\n", - " print(par)\n", " return jacobi(lambda p: logpdf(xy, *p), par)[0].T\n", "\n", "c = cost.UnbinnedNLL((xdata, ydata), logpdf, log=True, grad=grad)\n", @@ -2472,16 +2456,6 @@ "m.migrad()" ] }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4f9704d9", - "metadata": {}, - "outputs": [], - "source": [ - "#c.covariance()" - ] - }, { "cell_type": "markdown", "id": "introductory-watershed", @@ -2656,7 +2630,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:12.602350\n", + " 2024-01-31T17:31:07.144075\n", " image/svg+xml\n", " \n", " \n", @@ -2821,18 +2795,18 @@ "L 62.624197 214.704628 \n", "L 43.992832 209.466929 \n", "z\n", - "\" clip-path=\"url(#p9b00d17435)\" style=\"fill: #1f77b4\"/>\n", + "\" clip-path=\"url(#pf68ddf17e9)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2878,7 +2852,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2945,7 +2919,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2961,7 +2935,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2989,7 +2963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3021,7 +2995,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3037,7 +3011,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3053,7 +3027,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3069,7 +3043,7 @@ " \n", " \n", " 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"m = Minuit(c, n=1, mu=1, sigma=2, tau=2)\n", + "m.limits[\"n\", \"sigma\", \"tau\"] = (0, None)\n", + "m.migrad()" + ] + }, + { + "cell_type": "markdown", + "id": "controlling-celebration", + "metadata": {}, + "source": [ + "### Binned Fit\n", + "\n", + "Binned fits are computationally more efficient and numerically more stable when samples are large. The caveat is that one has to choose an appropriate binning. The binning should be fine enough so that the essential information in the original is retained. Using large bins does not introduce a bias, but the parameters have a larger-than-minimal variance.\n", + "\n", + "In this case, 50 bins are fine enough to retain all information. Using many bins is safe, since the maximum-likelihood method correctly takes Poisson statistics into account, which works even if bins have zero entries. Using more bins than necessary just increases the computational cost.\n", + "\n", + "Instead of a pdf, you need to provide a cdf for a binned fit (which must be vectorized). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "robust-groove", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Migrad
FCN = 15.03 (χ²/ndof = 0.9) Nfcn = 270
EDM = 5.28e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.100 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.354) -0.24e-3 (-0.209)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.354) -0.001e-3 (-0.090) 1.43e-05 -0.045e-3 (-0.160)
tau -0.24e-3 (-0.209) -0.015e-3 (-0.050) -0.045e-3 (-0.160) 0.00564
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= 5.28e-06 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", + "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", + "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", + "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", + "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", + "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", + "│ 2 │ sigma │ 0.100 │ 0.004 │ │ │ 0.01 │ │ │\n", + "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", + "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", + "┌───────┬─────────────────────────────────────────┐\n", + "│ │ z mu sigma tau │\n", + "├───────┼─────────────────────────────────────────┤\n", + "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", + "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", + "│ sigma │ 0.020e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", + "│ tau │ -0.24e-3 -0.015e-3 -0.045e-3 0.00564 │\n", + "└───────┴─────────────────────────────────────────┘" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def cdf(xe, z, mu, sigma, tau):\n", + " return (z * truncnorm.cdf(xe, *xr, mu, sigma) + \n", + " (1-z) * truncexpon.cdf(xe, *xr, 0, tau))\n", + "\n", + "c = cost.BinnedNLL(n, xe, cdf)\n", + "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", + "m.limits[\"z\"] = (0, 1)\n", + "m.limits[\"sigma\", \"tau\"] = (0.01, None)\n", + "m.migrad()" + ] + }, + { + "cell_type": "markdown", + "id": "2dc873af-e615-498a-be72-3e66720c53e1", + "metadata": {}, + "source": [ + "iminuit also shows the chi-square goodness-of-fit test statistic when the data are binned. It is calculated for free in the binned case.\n", + "\n", + "Sometimes the cdf is expensive to calculate. In this case, you can approximate it via the cumulated sum of \"bin-width times pdf evaluated at center\". This approxmiation may lead to a bias. Using an accurate cdf avoids this bias.\n", + "\n", + "Here is the same example fitted with an approximate cdf." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "838d5fcc-e2b2-4eb6-9831-205ca2753810", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Migrad
FCN = 15.65 (χ²/ndof = 1.0) Nfcn = 189
EDM = 1.07e-05 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.104 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.353) -0.24e-3 (-0.208)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.353) -0.001e-3 (-0.090) 1.31e-05 -0.043e-3 (-0.159)
tau -0.24e-3 (-0.208) -0.015e-3 (-0.050) -0.043e-3 (-0.159) 0.00568
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= 1.07e-05 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", + "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", + "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", + "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", + "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", + "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", + "│ 2 │ sigma │ 0.104 │ 0.004 │ │ │ 0.01 │ │ │\n", + "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", + "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", + "┌───────┬─────────────────────────────────────────┐\n", + "│ │ z mu sigma tau │\n", + "├───────┼─────────────────────────────────────────┤\n", + "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", + "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", + "│ sigma │ 0.020e-3 -0.001e-3 1.31e-05 -0.043e-3 │\n", + "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", + "└───────┴─────────────────────────────────────────┘" + ] + }, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def logdensity(xy, n, mu, sigma, tau):\n", - " x, y = xy\n", - " return n, np.log(n) + norm.logpdf(x, mu, sigma) + expon.logpdf(y, 0, tau)\n", + "def pdf(x, z, mu, sigma, tau):\n", + " return z * truncnorm.pdf(x, *xr, mu, sigma) + (1 - z) * truncexpon.pdf(\n", + " x, *xr, 0, tau\n", + " )\n", "\n", - "c = cost.ExtendedUnbinnedNLL((xdata, ydata), logdensity, log=True)\n", - "m = Minuit(c, n=1, mu=1, sigma=2, tau=2)\n", - "m.limits[\"n\", \"sigma\", \"tau\"] = (0, None)\n", + "\n", + "def approximate_cdf(xe, z, mu, sigma, tau):\n", + " dx = np.diff(xe)\n", + " cx = xe[:-1] + 0.5 * dx\n", + " p = pdf(cx, z, mu, sigma, tau)\n", + " return np.append([0], np.cumsum(p * dx))\n", + "\n", + "\n", + "c = cost.BinnedNLL(n, xe, approximate_cdf)\n", + "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", + "m.limits[\"z\"] = (0, 1)\n", + "m.limits[\"sigma\", \"tau\"] = (0.01, None)\n", "m.migrad()" ] }, { "cell_type": "markdown", - "id": "controlling-celebration", + "id": "comparable-special", "metadata": {}, "source": [ - "### Binned Fit\n", - "\n", - "Binned fits are computationally more efficient and numerically more stable when samples are large. The caveat is that one has to choose an appropriate binning. The binning should be fine enough so that the essential information in the original is retained. Using large bins does not introduce a bias, but the parameters have a larger-than-minimal variance.\n", - "\n", - "In this case, 50 bins are fine enough to retain all information. Using many bins is safe, since the maximum-likelihood method correctly takes Poisson statistics into account, which works even if bins have zero entries. Using more bins than necessary just increases the computational cost.\n", + "The fitted values and the uncertainty estimates for $\\mu$ and $\\sigma$ are not identical to the unbinned fit, but very close. For practical purposes, the results are equivalent. This shows that the binning is fine enough to retain the essential information in the original data.\n", "\n", - "Instead of a pdf, you need to provide a cdf for a binned fit (which must be vectorized). " + "Since this approximation is useful in practice, the `BinnedNLL` computes it automatically if you pass the keyword `use_pdf=\"approximate\"`." ] }, { "cell_type": "code", "execution_count": null, - "id": "robust-groove", + "id": "c26df624", "metadata": {}, "outputs": [ { @@ -3759,11 +5442,11 @@ " Migrad \n", " \n", " \n", - " FCN = 15.03 (χ²/ndof = 0.9) \n", - " Nfcn = 270 \n", + " FCN = 15.65 (χ²/ndof = 1.0) \n", + " Nfcn = 189 \n", " \n", " \n", - " EDM = 5.28e-06 (Goal: 0.0002) \n", + " EDM = 1.06e-05 (Goal: 0.0002) \n", " \n", " \n", " \n", @@ -3815,7 +5498,7 @@ " \n", " 2 \n", " sigma \n", - " 0.100 \n", + " 0.104 \n", " 0.004 \n", " \n", " \n", @@ -3846,29 +5529,29 @@ " z \n", " 0.000235 \n", " -0.004e-3 (-0.067) \n", - " 0.020e-3 (0.354) \n", - " -0.24e-3 (-0.209) \n", + " 0.020e-3 (0.353) \n", + " -0.24e-3 (-0.208) \n", " \n", " \n", " mu \n", " -0.004e-3 (-0.067) \n", " 1.63e-05 \n", " -0.001e-3 (-0.090) \n", - " -0.015e-3 (-0.050) \n", + " -0.015e-3 (-0.050) \n", " \n", " \n", " sigma \n", - " 0.020e-3 (0.354) \n", + " 0.020e-3 (0.353) \n", " -0.001e-3 (-0.090) \n", - " 1.43e-05 \n", - " -0.045e-3 (-0.160) \n", + " 1.31e-05 \n", + " -0.043e-3 (-0.159) \n", " \n", " \n", " tau \n", - " -0.24e-3 (-0.209) \n", - " -0.015e-3 (-0.050) \n", - " -0.045e-3 (-0.160) \n", - " 0.00564 \n", + " -0.24e-3 (-0.208) \n", + " -0.015e-3 (-0.050) \n", + " -0.043e-3 (-0.159) \n", + " 0.00568 \n", " \n", "\n", "\n", " \n", " \n", - " 2023-11-22T17:19:12.873176\n", + " 2024-01-31T17:31:08.713342\n", " image/svg+xml\n", " \n", " \n", @@ -3911,59 +5594,59 @@ " \n", " \n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4009,7 +5692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4076,7 +5759,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4092,7 +5775,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4120,7 +5803,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4152,7 +5835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4168,7 +5851,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4184,7 +5867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4200,7 +5883,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4218,12 +5901,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4236,7 +5919,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4250,7 +5933,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4265,7 +5948,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4280,7 +5963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4295,7 +5978,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4310,7 +5993,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4359,7 +6042,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4374,7 +6057,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4411,68 +6094,68 @@ " \n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pb12da40fb3)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4530,7 +6213,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4540,8 +6223,8 @@ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 15.03 (χ²/ndof = 0.9) │ Nfcn = 270 │\n", - "│ EDM = 5.28e-06 (Goal: 0.0002) │ │\n", + "│ FCN = 15.65 (χ²/ndof = 1.0) │ Nfcn = 189 │\n", + "│ EDM = 1.06e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -4554,7 +6237,7 @@ "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.100 │ 0.004 │ │ │ 0.01 │ │ │\n", + "│ 2 │ sigma │ 0.104 │ 0.004 │ │ │ 0.01 │ │ │\n", "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬─────────────────────────────────────────┐\n", @@ -4562,22 +6245,18 @@ "├───────┼─────────────────────────────────────────┤\n", "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.045e-3 0.00564 │\n", + "│ sigma │ 0.020e-3 -0.001e-3 1.31e-05 -0.043e-3 │\n", + "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", "└───────┴─────────────────────────────────────────┘" ] }, - "execution_count": 12, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def cdf(xe, z, mu, sigma, tau):\n", - " return (z * truncnorm.cdf(xe, *xr, mu, sigma) + \n", - " (1-z) * truncexpon.cdf(xe, *xr, 0, tau))\n", - "\n", - "c = cost.BinnedNLL(n, xe, cdf)\n", + "c = cost.BinnedNLL(n, xe, pdf, use_pdf=\"approximate\")\n", "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", "m.limits[\"z\"] = (0, 1)\n", "m.limits[\"sigma\", \"tau\"] = (0.01, None)\n", @@ -4586,18 +6265,16 @@ }, { "cell_type": "markdown", - "id": "2dc873af-e615-498a-be72-3e66720c53e1", + "id": "275568f0", "metadata": {}, "source": [ - "Note that you can approximate the cdf as the cumulated sum of \"bin-width times pdf evaluated at center\", if the cdf is expensive to calculate, but this is an approxmiation and will lead to a bias. \n", - "Using the analytical cdf avoids this bias.\n", - "Here is an example of how to implement that:" + "Another option is to compute the cdf numerically with `use_pdf=\"numerical\"`, but this tends to be expensive and is only supported for 1D histograms." ] }, { "cell_type": "code", "execution_count": null, - "id": "838d5fcc-e2b2-4eb6-9831-205ca2753810", + "id": "5a6fe4cc", "metadata": {}, "outputs": [ { @@ -4608,12 +6285,12 @@ " Migrad \n", " \n", " \n", - " FCN = 15.65 (χ²/ndof = 1.0) \n", - " Nfcn = 189 \n", + " FCN = 15.03 (χ²/ndof = 0.9) \n", + " Nfcn = 270 \n", " \n", " \n", - " EDM = 1.07e-05 (Goal: 0.0002) \n", - " \n", + " EDM = 5.28e-06 (Goal: 0.0002) \n", + " time = 2.0 sec \n", " \n", " \n", " Valid Minimum \n", @@ -4664,7 +6341,7 @@ " \n", " 2 \n", " sigma \n", - " 0.104 \n", + " 0.100 \n", " 0.004 \n", " \n", " \n", @@ -4695,29 +6372,29 @@ " z \n", " 0.000235 \n", " -0.004e-3 (-0.067) \n", - " 0.020e-3 (0.353) \n", - " -0.24e-3 (-0.208) \n", + " 0.020e-3 (0.354) \n", + " -0.24e-3 (-0.209) \n", " \n", " \n", " mu \n", " -0.004e-3 (-0.067) \n", " 1.63e-05 \n", " -0.001e-3 (-0.090) \n", - " -0.015e-3 (-0.050) \n", + " -0.015e-3 (-0.050) \n", " \n", " \n", " sigma \n", - " 0.020e-3 (0.353) \n", + " 0.020e-3 (0.354) \n", " -0.001e-3 (-0.090) \n", - " 1.31e-05 \n", - " -0.043e-3 (-0.159) \n", + " 1.43e-05 \n", + " -0.045e-3 (-0.160) \n", " \n", " \n", " tau \n", - " -0.24e-3 (-0.208) \n", - " -0.015e-3 (-0.050) \n", - " -0.043e-3 (-0.159) \n", - " 0.00568 \n", + " -0.24e-3 (-0.209) \n", + " -0.015e-3 (-0.050) \n", + " -0.045e-3 (-0.160) \n", + " 0.00564 \n", " \n", "\n", "\n", " \n", " \n", - " 2023-11-22T17:19:13.016408\n", + " 2024-01-31T17:31:10.955181\n", " image/svg+xml\n", " \n", " \n", @@ -4760,59 +6437,59 @@ " \n", " \n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4858,7 +6535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4925,7 +6602,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4941,7 +6618,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4969,7 +6646,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5001,7 +6678,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5017,7 +6694,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5033,7 +6710,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5049,7 +6726,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5067,12 +6744,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5085,7 +6762,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5099,7 +6776,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5114,7 +6791,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5129,7 +6806,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5144,7 +6821,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5159,7 +6836,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5208,7 +6885,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5223,7 +6900,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5260,68 +6937,68 @@ " \n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p9a54e857b2)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5379,7 +7056,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5389,8 +7066,8 @@ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 15.65 (χ²/ndof = 1.0) │ Nfcn = 189 │\n", - "│ EDM = 1.07e-05 (Goal: 0.0002) │ │\n", + "│ FCN = 15.03 (χ²/ndof = 0.9) │ Nfcn = 270 │\n", + "│ EDM = 5.28e-06 (Goal: 0.0002) │ time = 2.0 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -5403,7 +7080,7 @@ "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.104 │ 0.004 │ │ │ 0.01 │ │ │\n", + "│ 2 │ sigma │ 0.100 │ 0.004 │ │ │ 0.01 │ │ │\n", "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌───────┬─────────────────────────────────────────┐\n", @@ -5411,57 +7088,24 @@ "├───────┼─────────────────────────────────────────┤\n", "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.31e-05 -0.043e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", + "│ sigma │ 0.020e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", + "│ tau │ -0.24e-3 -0.015e-3 -0.045e-3 0.00564 │\n", "└───────┴─────────────────────────────────────────┘" ] }, - "execution_count": 13, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import functools\n", - "\n", - "\n", - "def pdf(xe, z, mu, sigma, tau):\n", - " return z * truncnorm.pdf(xe, *xr, mu, sigma) + (1 - z) * truncexpon.pdf(\n", - " xe, *xr, 0, tau\n", - " )\n", - "\n", - "\n", - "def approximate_cdf(pdf):\n", - " @functools.wraps(pdf)\n", - " def _cdf(xe, *args, **kwargs):\n", - " return np.r_[\n", - " 0, np.cumsum(pdf((xe[1:] + xe[:-1]) / 2, *args, **kwargs) * np.diff(xe))\n", - " ]\n", - "\n", - " _cdf.__name__ += \"_cdf\"\n", - " _cdf.__qualname__ += \"_cdf\"\n", - " return _cdf\n", - "\n", - "\n", - "cdf = approximate_cdf(pdf)\n", - "\n", - "c = cost.BinnedNLL(n, xe, cdf)\n", + "c = cost.BinnedNLL(n, xe, pdf, use_pdf=\"numerical\")\n", "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", "m.limits[\"z\"] = (0, 1)\n", "m.limits[\"sigma\", \"tau\"] = (0.01, None)\n", "m.migrad()" ] }, - { - "cell_type": "markdown", - "id": "comparable-special", - "metadata": {}, - "source": [ - "The fitted values and the uncertainty estimates for $\\mu$ and $\\sigma$ are not identical to the unbinned fit, but very close. For practical purposes, the results are equivalent. This shows that the binning is fine enough to retain the essential information in the original data.\n", - "\n", - "Note that iminuit also shows the chi2/ndof goodness-of-fit estimator when the data are binned. It can be calculated for free in the binned case." - ] - }, { "cell_type": "markdown", "id": "c7a06b88", @@ -5488,7 +7132,7 @@ " Nfcn = 206 \n", " \n", " \n", - " EDM = 7.55e-05 (Goal: 0.0002) \n", + " EDM = 9.93e-06 (Goal: 0.0002) \n", " \n", " \n", " \n", @@ -5581,7 +7225,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.155387\n", + " 2024-01-31T17:31:11.174033\n", " image/svg+xml\n", " \n", " \n", @@ -5664,104 +7308,104 @@ "L 115.537273 268.321634 \n", "L 115.537273 268.321635 \n", "L 118.518291 268.321635 \n", - "L 118.518291 268.300658 \n", - "L 121.499309 268.300658 \n", - "L 121.499309 268.317536 \n", - "L 124.480328 268.317536 \n", - "L 124.480328 268.320834 \n", - "L 127.461346 268.320834 \n", + "L 118.518291 268.300654 \n", + "L 121.499309 268.300654 \n", + "L 121.499309 268.317534 \n", + "L 124.480328 268.317534 \n", + "L 124.480328 268.320833 \n", + "L 127.461346 268.320833 \n", "L 127.461346 268.321478 \n", "L 130.442364 268.321478 \n", "L 130.442364 268.321604 \n", "L 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5862,7 +7506,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5902,7 +7546,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5937,7 +7581,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5983,7 +7627,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6038,7 +7682,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6071,12 +7715,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6089,7 +7733,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6130,7 +7774,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6145,7 +7789,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6160,7 +7804,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6175,7 +7819,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6190,7 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1.5\"/>\n", + " \n", " \n", + "\" clip-path=\"url(#pa50ee3f7bd)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#pa50ee3f7bd)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#pa50ee3f7bd)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6448,7 +8092,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6459,7 +8103,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 25.22 (χ²/ndof = 0.3) │ Nfcn = 206 │\n", - "│ EDM = 7.55e-05 (Goal: 0.0002) │ │\n", + "│ EDM = 9.93e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -6483,7 +8127,7 @@ "└───────┴────────────────────────────┘" ] }, - "execution_count": 14, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -6538,7 +8182,7 @@ " Nfcn = 437 \n", " \n", " \n", - " EDM = 9.95e-07 (Goal: 0.0002) \n", + " EDM = 9.97e-07 (Goal: 0.0002) \n", " \n", " \n", " \n", @@ -6677,7 +8321,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.291750\n", + " 2024-01-31T17:31:11.443465\n", " image/svg+xml\n", " \n", " \n", @@ -6710,59 +8354,59 @@ " \n", " \n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6808,7 +8452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6875,7 +8519,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6891,7 +8535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6919,7 +8563,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6951,7 +8595,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6967,7 +8611,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6983,7 +8627,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6999,7 +8643,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7017,12 +8661,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7035,7 +8679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7049,7 +8693,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7064,7 +8708,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7079,7 +8723,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7094,7 +8738,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7109,7 +8753,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7158,7 +8802,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7173,7 +8817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7210,68 +8854,68 @@ " \n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p8ee39cb79b)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7329,7 +8973,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7340,7 +8984,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 15.03 (χ²/ndof = 1.0) │ Nfcn = 437 │\n", - "│ EDM = 9.95e-07 (Goal: 0.0002) │ │\n", + "│ EDM = 9.97e-07 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -7368,7 +9012,7 @@ "└───────┴────────────────────────────────────────────────────────┘" ] }, - "execution_count": 15, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -7407,10 +9051,10 @@ " \n", " \n", " FCN = 24.64 (χ²/ndof = 0.3) \n", - " Nfcn = 184 \n", + " Nfcn = 182 \n", " \n", " \n", - " EDM = 8.87e-08 (Goal: 0.0002) \n", + " EDM = 5.24e-08 (Goal: 0.0002) \n", " \n", " \n", " \n", @@ -7493,7 +9137,7 @@ " n \n", " 1e+03 \n", " 0 \n", - " 0e-6 \n", + " -0e-6 \n", " 0.0029 \n", " \n", " \n", @@ -7501,11 +9145,11 @@ " 0 \n", " 1.05e-05 \n", " 0e-6 \n", - " 0 \n", + " -0 \n", " \n", " \n", " sigma \n", - " 0e-6 \n", + " -0e-6 \n", " 0e-6 \n", " 5.68e-06 \n", " 0e-6 \n", @@ -7513,7 +9157,7 @@ " \n", " tau \n", " 0.0029 \n", - " 0 \n", + " -0 \n", " 0e-6 \n", " 0.00113 \n", " \n", @@ -7525,7 +9169,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.427437\n", + " 2024-01-31T17:31:11.678019\n", " image/svg+xml\n", " \n", " \n", @@ -7608,8 +9252,8 @@ "L 115.537273 268.321634 \n", "L 115.537273 268.321635 \n", "L 118.518291 268.321635 \n", - "L 118.518291 268.300658 \n", - "L 121.499309 268.300658 \n", + "L 118.518291 268.300659 \n", + "L 121.499309 268.300659 \n", "L 121.499309 268.317514 \n", "L 124.480328 268.317514 \n", "L 124.480328 268.320826 \n", @@ -7618,88 +9262,88 @@ "L 130.442364 268.321476 \n", "L 130.442364 268.321604 \n", "L 133.423383 268.321604 \n", - "L 133.423383 267.396486 \n", - "L 136.404401 267.396486 \n", - "L 136.404401 268.139897 \n", - "L 139.385419 268.139897 \n", - "L 139.385419 268.285934 \n", - "L 142.366438 268.285934 \n", + "L 133.423383 267.3965 \n", + "L 136.404401 267.3965 \n", + "L 136.404401 268.1399 \n", + "L 139.385419 268.1399 \n", + "L 139.385419 268.285935 \n", + "L 142.366438 268.285935 \n", "L 142.366438 268.314622 \n", "L 145.347456 268.314622 \n", "L 145.347456 268.320257 \n", "L 148.328475 268.320257 \n", - "L 148.328475 253.014242 \n", - "L 151.309493 253.014242 \n", - "L 151.309493 265.314625 \n", - "L 154.290511 265.314625 \n", - "L 154.290511 267.730933 \n", - "L 157.27153 267.730933 \n", + "L 148.328475 253.014332 \n", + "L 151.309493 253.014332 \n", + "L 151.309493 265.314644 \n", + "L 154.290511 265.314644 \n", + "L 154.290511 267.730937 \n", + "L 157.27153 267.730937 \n", "L 157.27153 268.205597 \n", "L 160.252548 268.205597 \n", "L 160.252548 268.29884 \n", "L 163.233566 268.29884 \n", - "L 163.233566 172.04008 \n", - "L 166.214585 172.04008 \n", - "L 166.214585 249.407927 \n", - "L 169.195603 249.407927 \n", - "L 169.195603 264.606195 \n", - "L 172.176621 264.606195 \n", - "L 172.176621 267.591768 \n", - "L 175.15764 267.591768 \n", + "L 163.233566 172.040184 \n", + "L 166.214585 172.040184 \n", + "L 166.214585 249.40795 \n", + "L 169.195603 249.40795 \n", + "L 169.195603 264.6062 \n", + "L 172.176621 264.6062 \n", + "L 172.176621 267.591769 \n", + "L 175.15764 267.591769 \n", "L 175.15764 268.178259 \n", "L 178.138658 268.178259 \n", - "L 178.138658 35.3336 \n", - "L 181.119677 35.3336 \n", - "L 181.119677 222.55308 \n", - "L 184.100695 222.55308 \n", - "L 184.100695 259.330784 \n", - "L 187.081713 259.330784 \n", - "L 187.081713 266.555457 \n", - "L 190.062732 266.555457 \n", + "L 178.138658 35.333571 \n", + "L 181.119677 35.333571 \n", + "L 181.119677 222.553082 \n", + "L 184.100695 222.553082 \n", + "L 184.100695 259.330786 \n", + "L 187.081713 259.330786 \n", + "L 187.081713 266.555458 \n", + "L 190.062732 266.555458 \n", "L 190.062732 267.974684 \n", "L 193.04375 267.974684 \n", - "L 193.04375 49.87584 \n", - "L 196.024768 49.87584 \n", - "L 196.024768 225.409782 \n", - "L 199.005787 225.409782 \n", - "L 199.005787 259.891959 \n", - "L 201.986805 259.891959 \n", - "L 201.986805 266.665696 \n", - "L 204.967823 266.665696 \n", + "L 193.04375 49.876311 \n", + "L 196.024768 49.876311 \n", + "L 196.024768 225.409881 \n", + "L 199.005787 225.409881 \n", + "L 199.005787 259.89198 \n", + "L 201.986805 259.89198 \n", + "L 201.986805 266.6657 \n", + "L 204.967823 266.6657 \n", "L 204.967823 267.99634 \n", "L 207.948842 267.99634 \n", - "L 207.948842 189.010474 \n", - "L 210.92986 189.010474 \n", - "L 210.92986 252.741619 \n", - "L 213.910879 252.741619 \n", - "L 213.910879 265.261071 \n", - "L 216.891897 265.261071 \n", - "L 216.891897 267.720413 \n", - "L 219.872915 267.720413 \n", - "L 219.872915 268.20353 \n", - "L 222.853934 268.20353 \n", - "L 222.853934 257.259086 \n", - "L 225.834952 257.259086 \n", - "L 225.834952 266.14849 \n", - "L 228.81597 266.14849 \n", - "L 228.81597 267.894739 \n", - "L 231.796989 267.894739 \n", - "L 231.796989 268.237775 \n", - "L 234.778007 268.237775 \n", - "L 234.778007 268.305161 \n", - "L 237.759025 268.305161 \n", - "L 237.759025 267.736108 \n", - "L 240.740044 267.736108 \n", - "L 240.740044 268.206613 \n", - "L 243.721062 268.206613 \n", - "L 243.721062 268.29904 \n", - "L 246.702081 268.29904 \n", - "L 246.702081 268.317196 \n", - "L 249.683099 268.317196 \n", + "L 207.948842 189.011103 \n", + "L 210.92986 189.011103 \n", + "L 210.92986 252.741745 \n", + "L 213.910879 252.741745 \n", + "L 213.910879 265.261096 \n", + "L 216.891897 265.261096 \n", + "L 216.891897 267.720418 \n", + "L 219.872915 267.720418 \n", + "L 219.872915 268.203531 \n", + "L 222.853934 268.203531 \n", + "L 222.853934 257.259278 \n", + "L 225.834952 257.259278 \n", + "L 225.834952 266.148528 \n", + "L 228.81597 266.148528 \n", + "L 228.81597 267.894746 \n", + "L 231.796989 267.894746 \n", + "L 231.796989 268.237776 \n", + "L 234.778007 268.237776 \n", + "L 234.778007 268.305162 \n", + "L 237.759025 268.305162 \n", + "L 237.759025 267.736126 \n", + "L 240.740044 267.736126 \n", + "L 240.740044 268.206617 \n", + "L 243.721062 268.206617 \n", + "L 243.721062 268.299041 \n", + "L 246.702081 268.299041 \n", + "L 246.702081 268.317197 \n", + "L 249.683099 268.317197 \n", "L 249.683099 268.320763 \n", "L 252.664117 268.320763 \n", - "L 252.664117 268.310028 \n", - "L 255.645136 268.310028 \n", + "L 252.664117 268.310029 \n", + "L 255.645136 268.310029 \n", "L 255.645136 268.319355 \n", "L 258.626154 268.319355 \n", "L 258.626154 268.321187 \n", @@ -7759,18 +9403,18 @@ "L 339.11365 268.321635 \n", "L 342.094668 268.321635 \n", "L 342.094668 268.321635 \n", - "\" clip-path=\"url(#p8cc75638b9)\" style=\"fill: #1f77b4\"/>\n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7806,7 +9450,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7846,7 +9490,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7881,7 +9525,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7927,7 +9571,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7982,7 +9626,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8015,12 +9659,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8033,7 +9677,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8074,7 +9718,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8089,7 +9733,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8104,7 +9748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8119,7 +9763,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8134,7 +9778,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8182,156 +9826,156 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", + " \n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#p2155079605)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8392,7 +10036,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8402,8 +10046,8 @@ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 24.64 (χ²/ndof = 0.3) │ Nfcn = 184 │\n", - "│ EDM = 8.87e-08 (Goal: 0.0002) │ │\n", + "│ FCN = 24.64 (χ²/ndof = 0.3) │ Nfcn = 182 │\n", + "│ EDM = 5.24e-08 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -8422,14 +10066,14 @@ "┌───────┬─────────────────────────────────────┐\n", "│ │ n mu sigma tau │\n", "├───────┼─────────────────────────────────────┤\n", - "│ n │ 1e+03 0 0e-6 0.0029 │\n", - "│ mu │ 0 1.05e-05 0e-6 0 │\n", - "│ sigma │ 0e-6 0e-6 5.68e-06 0e-6 │\n", - "│ tau │ 0.0029 0 0e-6 0.00113 │\n", + "│ n │ 1e+03 0 -0e-6 0.0029 │\n", + "│ mu │ 0 1.05e-05 0e-6 -0 │\n", + "│ sigma │ -0e-6 0e-6 5.68e-06 0e-6 │\n", + "│ tau │ 0.0029 -0 0e-6 0.00113 │\n", "└───────┴─────────────────────────────────────┘" ] }, - "execution_count": 16, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -8614,7 +10258,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.555043\n", + " 2024-01-31T17:31:11.961326\n", " image/svg+xml\n", " \n", " \n", @@ -8688,18 +10332,18 @@ "L 327.189576 236.397247 \n", "L 342.094668 236.397247 \n", "L 342.094668 268.321635 \n", - "\" clip-path=\"url(#pab747a6c6f)\" style=\"fill: #1f77b4\"/>\n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8745,7 +10389,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8812,7 +10456,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8828,7 +10472,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8856,7 +10500,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8888,7 +10532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8904,7 +10548,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8920,7 +10564,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8936,7 +10580,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8954,12 +10598,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8972,7 +10616,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8986,7 +10630,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9021,7 +10665,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9067,7 +10711,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9122,7 +10766,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9137,7 +10781,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9153,48 +10797,48 @@ " \n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc3f8d1737f)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9242,7 +10886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9281,7 +10925,7 @@ "└───────┴──────────────────────────────────────────────┘" ] }, - "execution_count": 17, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -9504,7 +11148,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.805893\n", + " 2024-01-31T17:31:12.521177\n", " image/svg+xml\n", " \n", " \n", @@ -9578,18 +11222,18 @@ "L 327.189576 259.356153 \n", "L 342.094668 259.356153 \n", "L 342.094668 268.321635 \n", - "\" clip-path=\"url(#pbe13740486)\" style=\"fill: #1f77b4\"/>\n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9635,7 +11279,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9702,7 +11346,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9718,7 +11362,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9746,7 +11390,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9778,7 +11422,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9794,7 +11438,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9810,7 +11454,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9826,7 +11470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9844,12 +11488,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9862,7 +11506,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9876,7 +11520,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9891,7 +11535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9906,7 +11550,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9921,7 +11565,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9936,7 +11580,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9985,7 +11629,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10000,7 +11644,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10037,68 +11681,68 @@ " \n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pca11fd55f9)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10156,7 +11800,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10195,7 +11839,7 @@ "└───────┴───────────────────────────────────────────────────┘" ] }, - "execution_count": 19, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -10373,7 +12017,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:13.943624\n", + " 2024-01-31T17:31:12.849460\n", " image/svg+xml\n", " \n", " \n", @@ -10447,18 +12091,18 @@ "L 327.189576 259.339744 \n", "L 342.094668 259.339744 \n", "L 342.094668 268.321635 \n", - "\" clip-path=\"url(#pa4d4cefba2)\" style=\"fill: #1f77b4\"/>\n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10504,7 +12148,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10571,7 +12215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10587,7 +12231,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10615,7 +12259,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " 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" \n", + " \n", " \n", " \n", " \n", @@ -10869,7 +12513,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10906,68 +12550,68 @@ " \n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p82a4a0acb7)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " 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"metadata": {}, "output_type": "execute_result" } @@ -11112,7 +12756,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -11135,7 +12779,7 @@ "\n", "plt.plot(x, yt, ls=\"--\", label=\"truth\")\n", "plt.errorbar(x, y, ye, fmt=\"ok\", label=\"data\")\n", - "plt.legend();" + "plt.legend(loc=\"upper left\");" ] }, { @@ -11229,7 +12873,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:14.177036\n", + " 2024-01-31T17:31:13.384447\n", " image/svg+xml\n", " \n", " \n", @@ -11264,12 +12908,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11314,7 +12958,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11355,7 +12999,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11391,7 +13035,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11438,7 +13082,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11494,7 +13138,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11527,12 +13171,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11547,7 +13191,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11589,7 +13233,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11604,7 +13248,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11619,7 +13263,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11669,64 +13313,64 @@ " \n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p010fa0d07e)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11808,7 +13452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11841,7 +13485,7 @@ "└───┴─────────────────┘" ] }, - "execution_count": 22, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -11867,16 +13511,6 @@ "id": "former-dominant", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", @@ -12076,7 +13710,7 @@ "└────┴──────────────────────┘" ] }, - "execution_count": 25, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -12232,7 +13866,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:14.805167\n", + " 2024-01-31T17:31:14.860788\n", " image/svg+xml\n", " \n", " \n", @@ -12267,12 +13901,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12317,7 +13951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12358,7 +13992,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12394,7 +14028,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12441,7 +14075,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12497,7 +14131,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12530,12 +14164,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12550,7 +14184,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12592,7 +14226,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12607,7 +14241,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12622,7 +14256,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12672,64 +14306,64 @@ " \n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#p23855264e5)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12811,7 +14445,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12844,7 +14478,7 @@ "└───┴─────────────────┘" ] }, - "execution_count": 27, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -12862,16 +14496,6 @@ "id": "available-organic", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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- " \n", + " \n", " \n", " \n", " \n", @@ -13379,7 +15003,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13429,64 +15053,64 @@ " \n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p38f8cc9298)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " 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+15225,7 @@ "└───┴─────────────────┘" ] }, - "execution_count": 29, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -13782,7 +15406,7 @@ " \n", " \n", " \n", - " 2023-11-22T17:19:15.942669\n", + " 2024-01-31T17:31:17.444846\n", " image/svg+xml\n", " \n", " \n", @@ -13817,12 +15441,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13867,7 +15491,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13908,7 +15532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13944,7 +15568,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13991,7 +15615,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14047,7 +15671,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14080,12 +15704,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14100,7 +15724,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14142,7 +15766,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14157,7 +15781,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14172,7 +15796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14222,61 +15846,61 @@ " \n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", " \n", " \n", + "\" clip-path=\"url(#pc04243655e)\" style=\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\"/>\n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14357,7 +15981,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14390,7 +16014,7 @@ "└───┴─────────────────┘" ] }, - "execution_count": 32, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" }