{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IMinuit\n", "\n", "Minuit - программа численной минимизации функций многих переменных, широко применяемая в физике элементарных частиц. Есть два питонских интерфейса, PyMinuit и IMinuit (он особенно удобен в ipython)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from iminuit import Minuit\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Простой пример\n", "\n", "Определим квадратичную функцию от двух параметров." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def f(a,b):\n", " return 10*a**2+10*b**2-16*a*b+12*a-24*b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создадим объект класса `Minuit`. `a` и `b` - грубые догадки, около чего надо искать минимум; `error_a` и `error_b` - оценки точности этих догадок (в начале минимизации программа будет делать шаги порядка этих величин, потом они будут уменьшаться). Пределы изменения задавать не обязательно. Валичина `errordef` показывает, насколько функция должна быть выше своего минимума, чтобы это считалось отклонением на одну сигму; поскольку минимизируемая функция - это, как правило, $\\chi^2$, значение 1 по умолчанию вполне годится." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/ipykernel_launcher.py:2: InitialParamWarning: errordef is not given. Default to 1.\n", " \n" ] } ], "source": [ "m=Minuit(f,a=0,error_a=1,limit_a=(-10,10),\n", " b=0,error_b=1,limit_b=(-10,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Наиболее популярный метод минимизации - `migrad`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FCN = -17.999997281186666TOTAL NCALL = 36NCALLS = 36
EDM = 2.7187527367825896e-06GOAL EDM = 1e-05\n", " UP = 1.0
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ValidValid ParamAccurate CovarPosDefMade PosDef
TrueTrueTrueTrueFalse
Hesse FailHasCovAbove EDMReach calllim
FalseTrueFalseFalse
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+NameValueParab ErrorMinos Error-Minos Error+Limit-Limit+FIXED
1a0.9998230.52678800-10.010.0
2b1.999350.52677600-10.010.0
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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "({'fval': -17.999997281186666, 'edm': 2.7187527367825896e-06, 'nfcn': 36, 'up': 1.0, 'is_valid': True, 'has_valid_parameters': True, 'has_accurate_covar': True, 'has_posdef_covar': True, 'has_made_posdef_covar': False, 'hesse_failed': False, 'has_covariance': True, 'is_above_max_edm': False, 'has_reached_call_limit': False},\n", " [{'number': 0, 'name': 'a', 'value': 0.9998227792225478, 'error': 0.5267876810263843, 'is_const': False, 'is_fixed': False, 'has_limits': True, 'has_lower_limit': True, 'has_upper_limit': True, 'lower_limit': -10.0, 'upper_limit': 10.0},\n", " {'number': 1, 'name': 'b', 'value': 1.9993477581584944, 'error': 0.5267762744877214, 'is_const': False, 'is_fixed': False, 'has_limits': True, 'has_lower_limit': True, 'has_upper_limit': True, 'lower_limit': -10.0, 'upper_limit': 10.0}])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.migrad()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Значения параметров." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'a': 0.9998227792225478, 'b': 1.9993477581584944}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Значение функции в точке минимума." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-17.999997281186666" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.fval" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ошибки параметров." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'a': 0.5267876810263843, 'b': 0.5267762744877214}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.errors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если, скажем, $a$ - наш окончательный физический результат, то мы напишем в статье $a=1\\pm0.5$. На самом деле у нас есть больше информации, поскольку ошибки $a$ и $b$ сильно скоррелированы. Матрица корреляции ошибок:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((0.27776493841711364, 0.22220727646667665),\n", " (0.22220727646667665, 0.27776101882046583))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.matrix()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Минимизация квадратичной формы сводится к решению системы линейных уравнений, а матрица корреляции ошибок - обратная матрица этой системы. В таком простом случае не имеет смысла использовать инструмент минимизации произвольных функций, такой, как Minuit." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.27777778, 0.22222222],\n", " [ 0.22222222, 0.27777778]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M=array([[10.,-8.],[-8.,10.]])\n", "M=inv(M)\n", "M" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.],\n", " [ 2.]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M@array([[-6],[12]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Нарисуем контуры, соответствующие отклонению на 1, 2 и 3 сигмы от оптимальной точки." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([-1.03835166, -0.997266 , -0.95618035, -0.9150947 , -0.87400905,\n", " -0.8329234 , -0.79183775, -0.7507521 , -0.70966645, -0.6685808 ,\n", " -0.62749514, -0.58640949, -0.54532384, -0.50423819, -0.46315254,\n", " -0.42206689, -0.38098124, -0.33989559, -0.29880994, -0.25772428,\n", " -0.21663863, -0.17555298, -0.13446733, -0.09338168, -0.05229603,\n", " -0.01121038, 0.02987527, 0.07096092, 0.11204657, 0.15313223,\n", " 0.19421788, 0.23530353, 0.27638918, 0.31747483, 0.35856048,\n", " 0.39964613, 0.44073178, 0.48181743, 0.52290309, 0.56398874,\n", " 0.60507439, 0.64616004, 0.68724569, 0.72833134, 0.76941699,\n", " 0.81050264, 0.85158829, 0.89267395, 0.9337596 , 0.97484525,\n", " 1.0159309 , 1.05701655, 1.0981022 , 1.13918785, 1.1802735 ,\n", " 1.22135915, 1.26244481, 1.30353046, 1.34461611, 1.38570176,\n", " 1.42678741, 1.46787306, 1.50895871, 1.55004436, 1.59113001,\n", " 1.63221567, 1.67330132, 1.71438697, 1.75547262, 1.79655827,\n", " 1.83764392, 1.87872957, 1.91981522, 1.96090087, 2.00198652,\n", " 2.04307218, 2.08415783, 2.12524348, 2.16632913, 2.20741478,\n", " 2.24850043, 2.28958608, 2.33067173, 2.37175738, 2.41284304,\n", " 2.45392869, 2.49501434, 2.53609999, 2.57718564, 2.61827129,\n", " 2.65935694, 2.70044259, 2.74152824, 2.7826139 , 2.82369955,\n", " 2.8647852 , 2.90587085, 2.9469565 , 2.98804215, 3.0291278 ]),\n", " array([ -3.83516801e-02, 2.73397445e-03, 4.38196290e-02,\n", " 8.49052835e-02, 1.25990938e-01, 1.67076592e-01,\n", " 2.08162247e-01, 2.49247901e-01, 2.90333556e-01,\n", " 3.31419210e-01, 3.72504865e-01, 4.13590519e-01,\n", " 4.54676174e-01, 4.95761828e-01, 5.36847483e-01,\n", " 5.77933138e-01, 6.19018792e-01, 6.60104447e-01,\n", " 7.01190101e-01, 7.42275756e-01, 7.83361410e-01,\n", " 8.24447065e-01, 8.65532719e-01, 9.06618374e-01,\n", " 9.47704028e-01, 9.88789683e-01, 1.02987534e+00,\n", " 1.07096099e+00, 1.11204665e+00, 1.15313230e+00,\n", " 1.19421796e+00, 1.23530361e+00, 1.27638926e+00,\n", " 1.31747492e+00, 1.35856057e+00, 1.39964623e+00,\n", " 1.44073188e+00, 1.48181754e+00, 1.52290319e+00,\n", " 1.56398885e+00, 1.60507450e+00, 1.64616015e+00,\n", " 1.68724581e+00, 1.72833146e+00, 1.76941712e+00,\n", " 1.81050277e+00, 1.85158843e+00, 1.89267408e+00,\n", " 1.93375974e+00, 1.97484539e+00, 2.01593105e+00,\n", " 2.05701670e+00, 2.09810235e+00, 2.13918801e+00,\n", " 2.18027366e+00, 2.22135932e+00, 2.26244497e+00,\n", " 2.30353063e+00, 2.34461628e+00, 2.38570194e+00,\n", " 2.42678759e+00, 2.46787324e+00, 2.50895890e+00,\n", " 2.55004455e+00, 2.59113021e+00, 2.63221586e+00,\n", " 2.67330152e+00, 2.71438717e+00, 2.75547283e+00,\n", " 2.79655848e+00, 2.83764414e+00, 2.87872979e+00,\n", " 2.91981544e+00, 2.96090110e+00, 3.00198675e+00,\n", " 3.04307241e+00, 3.08415806e+00, 3.12524372e+00,\n", " 3.16632937e+00, 3.20741503e+00, 3.24850068e+00,\n", " 3.28958633e+00, 3.33067199e+00, 3.37175764e+00,\n", " 3.41284330e+00, 3.45392895e+00, 3.49501461e+00,\n", " 3.53610026e+00, 3.57718592e+00, 3.61827157e+00,\n", " 3.65935723e+00, 3.70044288e+00, 3.74152853e+00,\n", " 3.78261419e+00, 3.82369984e+00, 3.86478550e+00,\n", " 3.90587115e+00, 3.94695681e+00, 3.98804246e+00,\n", " 4.02912812e+00]),\n", " masked_array(data =\n", " [[-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " ..., \n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]],\n", " mask =\n", " [[ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " ..., \n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]],\n", " fill_value = 1e+20),\n", " )" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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h+PiZvzPLKcKzSxayNuYkH9zck86Vq5rWlohA5tdIxjjw7alPP7uADg6aqUSy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\nmjRzS5vF0c5zZxm/cT3Ljh8lxNeXJ1u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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mncontour('a','b',nsigma=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "То же в виде цветов." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j/gIp44XsPZAtd47JH058mfMC+d8dKm12/vASKWMu6jjpSpw5he1ci0ucWQU/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\nD/txmU/MijnWIs+Y5aaXvrQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a,b,g,r=m.mncontour_grid('a','b',nsigma=3)\n", "pcolormesh(a,b,g)\n", "colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Дайте мне 3 параметра, и я профитирую слона. С 4 параметрами он будет махать хоботом.\n", "\n", "Пусть у нас есть экспериментальные данные, и мы хотим профитировать их прямой." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fit(a,b,x):\n", " return a*x+b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Данные не настоящие, а сгенерированные. Все имеют ошибки 0.1." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x=linspace(0,1,11)\n", "dy=0.1*ones(11)\n", "y=x+dy*normal(size=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Функция $\\chi^2$." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def chi2(a,b):\n", " global x,y,dy\n", " return (((y-fit(a,b,x))/dy)**2).sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Минимизируем." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/ipykernel_launcher.py:1: InitialParamWarning: errordef is not given. Default to 1.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "m=Minuit(chi2,a=0,b=0,error_a=1,error_b=1)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FCN = 17.644418929505377TOTAL NCALL = 32NCALLS = 32
EDM = 4.646403811874004e-23GOAL EDM = 1e-05\n", " UP = 1.0
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1.28405979,\n", " 1.29149247, 1.29892514, 1.30635781, 1.31379049, 1.32122316,\n", " 1.32865584, 1.33608851, 1.34352119, 1.35095386, 1.35838653,\n", " 1.36581921, 1.37325188, 1.38068456, 1.38811723, 1.39554991,\n", " 1.40298258, 1.41041525, 1.41784793, 1.4252806 , 1.43271328]),\n", " array([-0.25190892, -0.24751169, -0.24311446, -0.23871723, -0.23432 ,\n", " -0.22992277, -0.22552554, -0.22112832, -0.21673109, -0.21233386,\n", " -0.20793663, -0.2035394 , -0.19914217, -0.19474494, -0.19034771,\n", " -0.18595048, -0.18155325, -0.17715602, -0.17275879, -0.16836156,\n", " -0.16396433, -0.1595671 , -0.15516987, -0.15077264, -0.14637542,\n", " -0.14197819, -0.13758096, -0.13318373, -0.1287865 , -0.12438927,\n", " -0.11999204, -0.11559481, -0.11119758, -0.10680035, -0.10240312,\n", " -0.09800589, -0.09360866, -0.08921143, -0.0848142 , -0.08041697,\n", " -0.07601975, -0.07162252, -0.06722529, -0.06282806, -0.05843083,\n", " -0.0540336 , -0.04963637, -0.04523914, -0.04084191, -0.03644468,\n", " -0.03204745, -0.02765022, -0.02325299, -0.01885576, -0.01445853,\n", " -0.0100613 , -0.00566407, -0.00126685, 0.00313038, 0.00752761,\n", " 0.01192484, 0.01632207, 0.0207193 , 0.02511653, 0.02951376,\n", " 0.03391099, 0.03830822, 0.04270545, 0.04710268, 0.05149991,\n", " 0.05589714, 0.06029437, 0.0646916 , 0.06908883, 0.07348605,\n", " 0.07788328, 0.08228051, 0.08667774, 0.09107497, 0.0954722 ,\n", " 0.09986943, 0.10426666, 0.10866389, 0.11306112, 0.11745835,\n", " 0.12185558, 0.12625281, 0.13065004, 0.13504727, 0.1394445 ,\n", " 0.14384173, 0.14823895, 0.15263618, 0.15703341, 0.16143064,\n", " 0.16582787, 0.1702251 , 0.17462233, 0.17901956, 0.18341679]),\n", " masked_array(data =\n", " [[-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " ..., \n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]],\n", " mask =\n", " [[ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " ..., \n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]],\n", " fill_value = 1e+20),\n", " )" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Xr6NcSCiDGl4NFL65659IwI3uvpWATmj6e+jpXmZPeyNf3rxDaQrsU9UDqpoL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\nlxJeUJxzzqXE/wfCwuvjeTeomgAAAABJRU5ErkJggg==\n"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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mncontour('a','b',nsigma=3)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/mot5rAoJpzpVdqF2Zkm1oZget7HFKPqt1pUwbGY2zAal9VVsdX6KdDHfapR6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\nHyk5/yHgqn7m4IwnVp1PhA9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a,b,g,r=m.mncontour_grid('a','b',nsigma=3)\n", "pcolormesh(a,b,g)\n", "colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Нарисуем на одном графике экспериментальные точки, наш фит (сплошная линия) и истинную теоретическую кривую (пунктир)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "errorbar(x,y,dy,fmt='ro')\n", "xt=linspace(0,1,101)\n", "plot(xt,fit(m.values['a'],m.values['b'],xt),'b-')\n", "plot(xt,fit(1,0,xt),'g--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Когда фитирующая функция есть линейная комбинация каких-то фиксированных функций с неизвестными коэффициентами, минимизация $\\chi^2$ сводится к решению системы линейных уравнений. Нет надобности использовать Minuit.\n", "\n", "## Резонанс без фона\n", "\n", "Пусть теперь наша фитирующая функция - Брейт-Вигнеровский резонанс (без фона), с двумя параметрами - положением и шириной (лучше бы ввести третий - высоту, но я не стал этого делать для простоты). Теперь $\\chi^2$ - сложная нелинейная функция параметров." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fit(x0,Gamma,x):\n", " return 1/((x-x0)**2+Gamma**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Вот наши экспериментальные данные (с ошибками 0.1)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x=linspace(-3,3,21)\n", "dy=0.1*ones(21)\n", "y=fit(0,1,x)+dy*normal(size=21)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Минимизируем $\\chi^2$." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def chi2(x0,Gamma):\n", " global x,y,dy\n", " return (((y-fit(x0,Gamma,x))/dy)**2).sum()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/ipykernel_launcher.py:1: InitialParamWarning: errordef is not given. Default to 1.\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "m=Minuit(chi2,x0=0,error_x0=1,Gamma=1,error_Gamma=1)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EDM = 1.5113944332374378e-07GOAL EDM = 1e-05\n", " UP = 1.0
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[ "m.migrad()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Gamma': 0.991722016714531, 'x0': -0.008990774854701301}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.values" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "35.747379402362355" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.fval" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'Gamma': 0.026670486505639225, 'x0': 0.06595389618462269}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.errors" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((0.004349916421931988, -2.3890408807357486e-05),\n", " (-2.3890408807357486e-05, 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-- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " ..., \n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]\n", " [-- -- -- ..., -- -- --]],\n", " mask =\n", " [[ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " ..., \n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]\n", " [ True True True ..., True True True]],\n", " fill_value = 1e+20),\n", " )" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OvYQWDZ07vlNdZSUeVsxYzaL/LWfxlOXk7cvH5RLa92nL2aO707R1Y5q0akRa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\njDEpsURijDEmJZZIjDHGpOT/AahUdilhe68IAAAAAElFTkS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.draw_mncontour('x0','Gamma',nsigma=3)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4UWUuiW3tvEv3hoZFjTI3wbbG7eneYFwD7T6WhUCevm6CJdbtMfcSW5c6LauZ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\nenSNmOq2e+VcQmnLJfnOOM1F1tJQXtf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/XQ/sv2uK6wlsRyRxCwQCgSVFLOA7x/G9nsCc\nsd+uB/bfNcX1BDKEhBIIBAJLimDggUAgsKSIBbwnRORxIvLhlJTrwyani+1ztYh8TES+kBJ7/dxe\nzLUP+lxP6vffROTbIvJfz/Yc+6BHorXDIvLe1P4JEbni7M9yNvS4ph8VkU+JyJaIvGAv5jgLelzP\nvxKRL6bvzJ0i8sS9mOcyIhbw/ng1cKeqXkmV96V1IwKngRep6vdSJef6LRG58CzOcRb0uR6Afwe8\n8KzNagb0TLT2UuBbqvoU4M3Am87uLGdDz2v6K+AlwP93dmc3O3pez6eBYykZ3m3Ar5/dWS4vYgHv\nj+cCt6TyLTjJt1T1K6r61VS+H3gAWBjf9W3ovB4AVb0T+LuzNakZ0SfRmr3O24AfX/DUxZ3XpKp/\noaqfBRZ7t4EKfa7nI6p6Ov334+RR2oEpiAW8P/6+qp4CSH//t2mdReRpwCHgz8/C3HaCma5nQdEn\n0dqkj6puAY8AF52V2e0Mfa5pmTDr9byUHkF9gQqxI4+BiPwxcInT9CszjnMp8HvAi3UPt7We1/Us\nMPokReudOG1BsGzz7cIsiev+GXCMKiFeoAdiATdQ1Z8otYnI34jIpap6Ki3QDxT6XQD8EfCrqvrx\nXZpqL8zjehYcnYnWTJ+TKa/P3wMeZnHR55qWCb2uR0R+gopYPF1V18/S3JYeIaH0x+1AvSXci4EP\nbO8gIoeA91HtTvQHZ3FuO0Hn9SwB7gauFJEnpff+OqrrsrDX+QLgT3Sxgx/6XNMyofN6ROSpwH8A\nnqOqy0gk9g6qGq8eLyrd9E7gq+nv41L9MeB3UvmfAZvAPeZ19V7PfafXk/7/p8CDwKNUbOon93ru\n267jp6iyZf458Cup7kaqxQDgCPAHwL3A/wKevNdznsM1/VD6LL4LfBP4wl7P+Qyv54+BvzHfmdv3\nes7L8opIzEAgEFhShIQSCAQCS4pYwAOBQGBJEQt4IBAILCliAQ8EAoElRSzggUAgsKSIBTwQCASW\nFLGABwKBwJIiFvBAIBBYUvz/vG9CPvbnRxAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x0,Gamma,g,r=m.mncontour_grid('x0','Gamma',nsigma=3)\n", "pcolormesh(x0,Gamma,g)\n", "colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь контуры постоянной высоты $\\chi^2$ - уже не симметричные эллипсы с центром в оптимальной точке, а какие-то сложные кривые. Ошибки положения и ширины резонанса довольно-таки независимы.\n", "\n", "Нарисуем на одном графике экспериментальные точки, наш фит (сплошная линия) и истинную теоретическую кривую (пунктир)." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Nb571N+iZl8qhtwbcwcymmxi1Pp6AhIS/f9FHFufyZLqWqw0tXQq9Dg4m/slI\nbi/dic+i51G0YNGsvyGrJQvAZyZ8qNx5onMtzKUsVg/1gcW5PJmeudtIQgL0ejqWbt2gzlU/vtpy\nG+v6Lb1+sYNPL4uq8k58eHEuT6blbhN79hiqPjSVBWE3cc+QxWys/wxtk5Kcu9XRh5dFVS6QycS5\nywGwuOPdOuHJQk6Vu4i0E5E9IrJPRIZm8vVBIvKbiOwUka9FJNz1UVVmjIHXJx2n1uhOxNbrT8Ni\nrZnzSmsK+OVgsSc981J5kWHi3JXS5XmqZU26l5hBreEPcvT8GYsD+qZsy11E/IH3gPZALaCHiNTK\nsNtPQKQxpi6wFBjn6qDqn44cgQY9PmFYbB24cS3Dm77N1mdXUapwqZwdyIeXRVUukm7iXNCJWCYu\n+4VGF17nd1YQMbYuG3+0eOlgOz2vwUnOnLk3BvYZY/YbYxKBRUDn9DsYY9YZY64N2m4BKrg2pkov\nJQUmT4ZatWD33iQiQquya8AORtz9XO5mnPrwsqjKPcJC/YmZ8G8m3RxDUMxL3NGiKC+/DKfjbPL4\nPi/gTBOUB46kex2bti0r0cAXeQmlsrZjZwpVot7l6fcn0awZ7F76APuGfk+NktXzdmAfXRZVudeA\nbnU5uGQADz0EYz7YQJmxlRj0wUzvf8KTF3Cm3DObo57pVRIR6QlEAuOz+HofEYkRkZjTp087n1Jx\n4QLcP2gjDaZGcqjmQCK7rufLLw033og+61R5tGLFYP58mDe5DAUv1GXCn30oObQ5K7f9aHU0W3Om\n3GOBiuleVwCOZdxJRO4EhgGdjDFXMzuQMWaGMSbSGBNZsmTJ3OT1fjlcaS45GcZOjaXMgO4sD72d\nkJJnmXn3In58YSl+ftdZG8YHxxiVZ+v1r2qcfWsd9zKPc6mH6Px5E5qMGPiPu3A9gg0m9TlT7luB\nqiJSWUQKAN2Blel3EJEGwHQcxX7K9TF9jzHw6adw880wdPRxkm5cSZ/qr3Lyld95oumDuuiX8kpB\nQcInw3ux68m91DgzhB+/qErVqjBteqrnLEKWi+cQe6Jsy90YkwwMANYAu4ElxphdIjJSRDql7TYe\nCAE+FpEdIrIyi8OpbBgDK9dcJOLhMdw79TmMgRVTbuHkkCNM7/4awYHB2R9EKQ9Xq0oRdk96g+/e\nHkBEBPSb8iGlXr+RnlPG81fCZWvD2WRSn1PLDxhjVgOrM2x7Nd3nd7o4l88xBj7/z0We+WASB8u9\nBVXPU68F6G3aAAANBUlEQVTOvfwwKIWCBfyB4lZHVMrlWrSA776DdxffzLBvI1lYcDCLXhtP94qD\nmfJYP4oGFc7/UDaZ1KczVC2WmgorVkCNjqvotK4SB6u8TJ2iLfjuka3sGPqJo9i9/Ikwysvk83iz\nCAzsXo+L761hZPj3FLrQgIWnX6TM8x2YNet/oyP5xiaT+rTcLXL5Mrwy6VdubP4zXbpAwqG6NAq9\nm+97xfDLS5/RonKk1RGVL7JwvNnPD155tDkX31vD6IhNVNg3gt69oVLVi7QY9Ryb9+5xewbANpP6\ntNzz2Z6EMnRJbkjYwDaMPnczFxq8wocfwp8/VSRm6GKaRzSyOqLyZR4w3uznB8N6NWPPmtasXQvl\nm25iU+IUmn9UgwpD2/Pul6tISc1iJUpXsMmkPl3yNx8kJsKqVfDSiin83mIjFD1GUGIFom/6P0a/\n0JsSFgwrKpUpDxpvFoE2bWB7m3Zs2H6YFz6cwVaZwsAf/sWL68J586YdPNYjjJAQN7x5VBTMnOn4\n3EtvKdYzdzf68ad47hnyEeXDE7jvPjh25i+qXizO7O3NuDjqANOihlKisF4oVR7EQ8ebb29Ymh/f\nfIUTQw7xWOGPCTlyP8/0CaNsWYh8diyvLf2YK0kJ2R/Ih+iZu4sdOJTM6A+/ZvkfH3KhzHIIvkTT\n9suZ37ULbdsOJrBt2soMfvpPrzzQmDGOMfb0QzPOjjfn5gz32o0CTn5v6RIFmPNCV4zpyqanYPbc\nJOYxk227/uS1n0Kp69+Vp1s9xKOtWvr8zG09c3eBw4fh7behUauj3PheWeYktuOvcp/SrOiDLO/8\nDd/P7kyHDhAYqBOPlIfzkvFmEcdtlHNmBXJh1B4Gl/2KsnGd+Tl5MU9sbEO5buMZMQJ27EwmKSUH\ny1/biJ4+5oIxsHnbX7yz6kvWHlnJudiSsOZt6jcoR9Mi3Xnk1jt4/LYOFAwoaHXUnPHSsUXlYp4+\n3pzhbL9oEX/G9mnLWNpy6Ng0Ri76nF8vRzJyJLy2aBX+XZ6ghv+/6NGwE0+3v8uae+ctoOXupIsX\nYd06mLhuPpvj3+dqmQ3gn0RAiTBuq/w4cybDTTcJMMnqqEr5rPByQcwe1A0GwYkTMOHjMizYcxe7\nQlbw8i/zePmngpS52pIRtRdzX4cw7LzElZZ7FpKS4IvvjvP+xvV8d2QDp+dPJiXJn8DOWwmqcZJW\noc/S95stdIwXAta9ZXVcpVQGZcrA2KebMJaFnD2fxDsrNrJs5yr+uLSDJx8N5Umg9EP/pvSNZ+hU\nuy3RbVoRUTKHD7rxYFruaa5ehZgYWLx+JyuOTiU2YB2muGPShH+pG+j9wgs80LYKTZpNJLhQ2j/b\nZ61y9ibXZv5dveqY+TdmjMeNZSplR8VvCGTkY3cwkjtITYVtfWDNGphyKI6dyR+zc88sRu+B4Eu1\naRrck6G3DqVZShAh/lesjp5rPlvusceS+HjDL3zxy2Z2nN1E3NqnSPyzOVQ5jl/3D6iYehsty0Tz\nyG2taV2jQbor77n8J8tq5h9owSuVj/z84JZbHB8vM4ULF99l7pptrNixjh1X1vHNr6f4ZiT4yUoK\n9W1IxZceoUWl5nRt0ow769Ym0N87atM7UubR+QupbI6JZ9dPIWz46Tj/CevK1WLbITABCkDgDWVp\ne39nejeDps3bUKLEeQJcfavi9Wb+abkrZZmwogE8160Jz3VrAgzl4kXHH9hfPTWLeZdKsKfIGvac\nfJ85K4FlwdQ9NoGulftwc4MESlc9QpNqVXL3eEs3s1W5G+N4aPSyzTFs2Pszv5zaQWzqT1wN/Rl2\nPAZfvEuliBIU7RZIzeAnaV2tMT1ubU610pXSrY/upn8SD5r5p5TKWtGicNddcFf5xbx5BJLmnOCL\nLQdY9uMmth7dSty+mgyfB6bSD/BYKySxKDdcrU+VwvW5pUJ9eja5h8a1S+Fv8W32XlnuxsDB2ATW\n7tjLpr272Xl8F2ePh3Bh1WDi4oBB90LRo0hoCMUS69Eg6FE6PNKOfgugRIlAYH3+h65UyTEUk9n2\n69FxeqUsFRgodLrtRjrddiPQE3DcPff1D9VZtH0WP13YRmzyDrYWnMXWY/FM6bCFoHOlKN96NUm1\n51ItrDaR4bVpWasmt9euSlCB/LlF2uvKfcoUGPh9N5JvWg5+aQ/ZLezHDcXupnv3wdSrB1RaTNM6\npahXyYP+XMrNzD8dp1fKIxUtCl3alqFL22ggGoBLl1P4cus+zlWM4PdfYc3xcxxI+plDicv4zz7D\n/+0DVvhTe00sIweX4b773JvR68q9alVo9ucdFA6uTaPwGrSsXZNba1QnKLBQur1aWJYvS9fKODra\nUdbh4dmfhes4vVLWyeEErpDC/nRtVR1aXdvSE+jJ0VNX+DLmdzb/sZtdJ/4g9IbSBAW5NmpmvK7c\n27aFtm37WR0jd3I680/H6ZXyeuVLBRHdoQHRNMjX9/WQMQuVKQ9doU8p5fm03D2ZTZ4Io1Sm8vlx\nfr7G64ZlfEpuxumVcgV3LximNwu4nZ65e7qoKGjaFFq2hIMH9Qdf2UNuH+enZ/tO03LPLf0hUyr3\ncnOzgIUP7/ZGWu65oT9kSuVNbm4W8ICHd3sTLffc0B8ypfImNzcL6K3BOeJUuYtIOxHZIyL7RGRo\nJl8vKCKL077+g4hEuDqoR9EfMqXyJjeP89Nbg3Mk23IXEX/gPaA9UAvoISK1MuwWDZw3xtwETADG\nujqoR9EfMqXyLqc3C+itwTnizJl7Y2CfMWa/MSYRWAR0zrBPZ2B+2udLgTbyv2UW7Ud/yJTKf17y\n8G5P4cx97uWBI+lexwJNstrHGJMsInFAceCMK0J6HL3/XClrePrDuz2IM+We2Rm4ycU+iEgfoA9A\nJW8fwtAfMqWUB3Om3GOBiuleVwCOZbFPrIgEAKHAuYwHMsbMAGYAREZG/qP8fYL+j0AplQ+cGXPf\nClQVkcoiUgDoDqzMsM9KoFfa512Bb4wxvlneSinlAbI9c08bQx8ArAH8gTnGmF0iMhKIMcasBGYD\n74vIPhxn7N3dGVoppdT1ObVwmDFmNbA6w7ZX032eAHRzbTSllFK5patCegMdp1dK5ZAuP6CUUjak\n5a6UUjak5a6UUjak5a6UUjakF1SVUtbRmwXcRs/clVLKhvTMXSnlXfRs3yla7nmhP2RKKQ+lwzJK\nKWVDWu5KKWVDWu5KKWVDWu5KKWVDWu5KKWVDWu5KKWVDWu5KKWVDWu5KKWVDWu5KKWVDYtVzrEXk\nNHDIkjfPWgngjNUhcsCb8mpW9/GmvN6UFTwzb7gxpmR2O1lW7p5IRGKMMZFW53CWN+XVrO7jTXm9\nKSt4X970dFhGKaVsSMtdKaVsSMv972ZYHSCHvCmvZnUfb8rrTVnB+/L+l465K6WUDemZu1JK2ZCW\newYiMkpEdorIDhH5SkTKWZ0pKyIyXkR+T8v7iYiEWZ3pekSkm4jsEpFUEfHIOxBEpJ2I7BGRfSIy\n1Oo81yMic0TklIj8anWW7IhIRRFZJyK7034GBlqd6XpEpJCI/CgiP6flfc3qTDmlwzIZiEhRY8zF\ntM+fAWoZY560OFamROQu4Bt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "errorbar(x,y,dy,fmt='ro')\n", "xt=linspace(-3.5,3.5,101)\n", "plot(xt,fit(m.values['x0'],m.values['Gamma'],xt),'b-')\n", "plot(xt,fit(0,1,xt),'g--')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python3.6", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }