{
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib64/python3.4/site-packages/ipykernel/__main__.py:2: InitialParamWarning: errordef is not given. Default to 1.\n",
" from ipykernel import kernelapp as app\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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" FCN = -17.999997281186666 | \n",
" TOTAL NCALL = 36 | \n",
" NCALLS = 36 | \n",
"
\n",
" \n",
" EDM = 2.7187527367825896e-06 | \n",
" GOAL EDM = 1e-05 | \n",
" \n",
" UP = 1.0 | \n",
"
\n",
"
\n",
" \n",
" \n",
" \n",
" Valid | \n",
" Valid Param | \n",
" Accurate Covar | \n",
" PosDef | \n",
" Made PosDef | \n",
"
\n",
" \n",
" True | \n",
" True | \n",
" True | \n",
" True | \n",
" False | \n",
"
\n",
" \n",
" Hesse Fail | \n",
" HasCov | \n",
" Above EDM | \n",
" | \n",
" Reach calllim | \n",
"
\n",
" \n",
" False | \n",
" True | \n",
" False | \n",
" | \n",
" False | \n",
"
\n",
"
\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" + | \n",
" Name | \n",
" Value | \n",
" Parab Error | \n",
" Minos Error- | \n",
" Minos Error+ | \n",
" Limit- | \n",
" Limit+ | \n",
" FIXED | \n",
"
\n",
" \n",
" \n",
" 1 | \n",
" a | \n",
" 0.999823 | \n",
" 0.526788 | \n",
" 0 | \n",
" 0 | \n",
" -10.0 | \n",
" 10.0 | \n",
" | \n",
"
\n",
" \n",
" \n",
" 2 | \n",
" b | \n",
" 1.99935 | \n",
" 0.526776 | \n",
" 0 | \n",
" 0 | \n",
" -10.0 | \n",
" 10.0 | \n",
" | \n",
"
\n",
" \n",
"
\n",
" \n",
" \n",
" \n",
"
\n",
" "
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"
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"data": {
"text/plain": [
"({'fval': -17.999997281186666, 'up': 1.0, 'is_above_max_edm': False, 'has_valid_parameters': True, 'edm': 2.7187527367825896e-06, 'has_made_posdef_covar': False, 'has_covariance': True, 'has_posdef_covar': True, 'nfcn': 36, 'has_accurate_covar': True, 'hesse_failed': False, 'is_valid': True, 'has_reached_call_limit': False},\n",
" [{'is_const': False, 'number': 0, 'upper_limit': 10.0, 'lower_limit': -10.0, 'name': 'a', 'is_fixed': False, 'error': 0.5267876810263843, 'value': 0.9998227792225478, 'has_upper_limit': True, 'has_limits': True, 'has_lower_limit': True},\n",
" {'is_const': False, 'number': 1, 'upper_limit': 10.0, 'lower_limit': -10.0, 'name': 'b', 'is_fixed': False, 'error': 0.5267762744877214, 'value': 1.9993477581584944, 'has_upper_limit': True, 'has_limits': True, 'has_lower_limit': True}])"
]
},
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.],\n",
" [ 2.]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M.dot(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": {
"collapsed": false
},
"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": {
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Bhw4jRs4RTj7y4Ug9k07FXL58lyFDNlGxYjHCwiZQpkzuPUFwxYrTrFx5hqCg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\n",
"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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4lpFgBcO3I4lo3YvcMBp9nx8Ziap5NTKZYeepmfqRrTfZ4aALhs1CYOHmziXL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\njA+FF9RbyWYAwEsAjDvxvQL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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": false
},
"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": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib64/python3.4/site-packages/ipykernel/__main__.py:1: InitialParamWarning: errordef is not given. Default to 1.\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"m=Minuit(chi2,a=0,b=0,error_a=1,error_b=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" FCN = 7.341081582910074 | \n",
" TOTAL NCALL = 32 | \n",
" NCALLS = 32 | \n",
"
\n",
" \n",
" EDM = 6.20027421166483e-24 | \n",
" GOAL EDM = 1e-05 | \n",
" \n",
" UP = 1.0 | \n",
"
\n",
"
\n",
" \n",
" \n",
" \n",
" Valid | \n",
" Valid Param | \n",
" Accurate Covar | \n",
" PosDef | \n",
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" \n",
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" True | \n",
" True | \n",
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" \n",
" Hesse Fail | \n",
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" | \n",
" Reach calllim | \n",
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\n",
" \n",
" False | \n",
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"metadata": {},
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{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" + | \n",
" Name | \n",
" Value | \n",
" Parab Error | \n",
" Minos Error- | \n",
" Minos Error+ | \n",
" Limit- | \n",
" Limit+ | \n",
" FIXED | \n",
"
\n",
" \n",
" \n",
" 1 | \n",
" a | \n",
" 0.952919 | \n",
" 0.0953463 | \n",
" 0 | \n",
" 0 | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" 2 | \n",
" b | \n",
" -0.0578366 | \n",
" 0.0564076 | \n",
" 0 | \n",
" 0 | \n",
" | \n",
" | \n",
" | \n",
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"data": {
"text/plain": [
"({'fval': 7.341081582910074, 'up': 1.0, 'is_above_max_edm': False, 'has_valid_parameters': True, 'edm': 6.20027421166483e-24, 'has_made_posdef_covar': False, 'has_covariance': True, 'has_posdef_covar': True, 'nfcn': 32, 'has_accurate_covar': True, 'hesse_failed': False, 'is_valid': True, 'has_reached_call_limit': False},\n",
" [{'is_const': False, 'number': 0, 'upper_limit': 0.0, 'lower_limit': 0.0, 'name': 'a', 'is_fixed': False, 'error': 0.0953462587249722, 'value': 0.9529192501482413, 'has_upper_limit': False, 'has_limits': False, 'has_lower_limit': False},\n",
" {'is_const': False, 'number': 1, 'upper_limit': 0.0, 'lower_limit': 0.0, 'name': 'b', 'is_fixed': False, 'error': 0.05640760738050268, 'value': -0.0578365609047049, 'has_upper_limit': False, 'has_limits': False, 'has_lower_limit': False}])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.migrad()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'a': 0.9529192501482413, 'b': -0.0578365609047049}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.values"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.341081582910074"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.fval"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((0.009090909052849335, -0.004545454524510073),\n",
" (-0.004545454524510073, 0.003181818170392941))"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.matrix()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0.58416759, 0.59160027, 0.59903294, 0.60646562, 0.61389829,\n",
" 0.62133097, 0.62876364, 0.63619631, 0.64362899, 0.65106166,\n",
" 0.65849434, 0.66592701, 0.67335969, 0.68079236, 0.68822503,\n",
" 0.69565771, 0.70309038, 0.71052306, 0.71795573, 0.7253884 ,\n",
" 0.73282108, 0.74025375, 0.74768643, 0.7551191 , 0.76255178,\n",
" 0.76998445, 0.77741712, 0.7848498 , 0.79228247, 0.79971515,\n",
" 0.80714782, 0.8145805 , 0.82201317, 0.82944584, 0.83687852,\n",
" 0.84431119, 0.85174387, 0.85917654, 0.86660922, 0.87404189,\n",
" 0.88147456, 0.88890724, 0.89633991, 0.90377259, 0.91120526,\n",
" 0.91863794, 0.92607061, 0.93350328, 0.94093596, 0.94836863,\n",
" 0.95580131, 0.96323398, 0.97066666, 0.97809933, 0.985532 ,\n",
" 0.99296468, 1.00039735, 1.00783003, 1.0152627 , 1.02269538,\n",
" 1.03012805, 1.03756072, 1.0449934 , 1.05242607, 1.05985875,\n",
" 1.06729142, 1.0747241 , 1.08215677, 1.08958944, 1.09702212,\n",
" 1.10445479, 1.11188747, 1.11932014, 1.12675282, 1.13418549,\n",
" 1.14161816, 1.14905084, 1.15648351, 1.16391619, 1.17134886,\n",
" 1.17878154, 1.18621421, 1.19364688, 1.20107956, 1.20851223,\n",
" 1.21594491, 1.22337758, 1.23081026, 1.23824293, 1.2456756 ,\n",
" 1.25310828, 1.26054095, 1.26797363, 1.2754063 , 1.28283898,\n",
" 1.29027165, 1.29770432, 1.305137 , 1.31256967, 1.32000235]),\n",
" array([-0.27599298, -0.27159575, -0.26719852, -0.26280129, -0.25840407,\n",
" -0.25400684, -0.24960961, -0.24521238, -0.24081515, -0.23641792,\n",
" -0.23202069, -0.22762346, -0.22322623, -0.218829 , -0.21443177,\n",
" -0.21003454, -0.20563731, -0.20124008, -0.19684285, -0.19244562,\n",
" -0.18804839, -0.18365117, -0.17925394, -0.17485671, -0.17045948,\n",
" -0.16606225, -0.16166502, -0.15726779, -0.15287056, -0.14847333,\n",
" -0.1440761 , -0.13967887, -0.13528164, -0.13088441, -0.12648718,\n",
" -0.12208995, -0.11769272, -0.11329549, -0.10889827, -0.10450104,\n",
" -0.10010381, -0.09570658, -0.09130935, -0.08691212, -0.08251489,\n",
" -0.07811766, -0.07372043, -0.0693232 , -0.06492597, -0.06052874,\n",
" -0.05613151, -0.05173428, -0.04733705, -0.04293982, -0.0385426 ,\n",
" -0.03414537, -0.02974814, -0.02535091, -0.02095368, -0.01655645,\n",
" -0.01215922, -0.00776199, -0.00336476, 0.00103247, 0.0054297 ,\n",
" 0.00982693, 0.01422416, 0.01862139, 0.02301862, 0.02741585,\n",
" 0.03181308, 0.0362103 , 0.04060753, 0.04500476, 0.04940199,\n",
" 0.05379922, 0.05819645, 0.06259368, 0.06699091, 0.07138814,\n",
" 0.07578537, 0.0801826 , 0.08457983, 0.08897706, 0.09337429,\n",
" 0.09777152, 0.10216875, 0.10656598, 0.1109632 , 0.11536043,\n",
" 0.11975766, 0.12415489, 0.12855212, 0.13294935, 0.13734658,\n",
" 0.14174381, 0.14614104, 0.15053827, 0.1549355 , 0.15933273]),\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": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IT8/7mLu+ODvbsGvXYPz83GndejH//e9hNJpXn0R+HgccSCCBDDLYwTYe8IAq\nVCWVVOKIpQ99McOMElhhjnm+TrQzs7Sk259/0uLzz1ns7c3ZtWsLnLdkHOQQk/RK7kRFsap3bxzd\n3ek6Zw7mxYur1nYKKSxhMRWoQDd6YI65am0DaLWCoUM3cO9eBuvX99fpeQ5qiIm5y/Dhm9BotCxe\n3KvAhxVtYgPppGFJMbrRA4AV/IU7HjTElWyyiSaKKKJoSzuKk/9/2xunTrG6Tx/qDRhAu++/x8TM\nrEC5S7onh5ikArOtUYMRR46gycpiYYsW3I2NVa3tUpRiJKPJIgt/5pFMsmptA5iYKCxa1BMhBH5+\nm41qCWxuqlUrw/79vvTtW5emTRcwa9bxAuXckzfpTT960RsLLDhOzsqjhrgCcI97nCWSstgWqDgA\nVGjUiNEnTxJ/6hTLOncm/datArUnGZYsENIrsyhRgt5//YXr0KH4N2tG9O7dqrVtiSX9GEB9GjKP\nP4kmWrW2AczNTVm9uh9xccmMGbPV6IuEiYnChAlNCQ7246+/IujYcWmBNiS0xPLJXk2lKf1kMjqB\nBCI5g0DQlOaq5G5lZ8fgnTup0Lgx8xo35sapU6q0K+mfHGKS8iXu779Z99ZbdPn9d+r26aNq2zFE\ns5bVNKclLWj5ZHdSNaSkZNCt2wqqVi2Nv38PTE2N/zNSdraWn346xMyZx/j99y4MGFC/QO1d4yrL\nWYYLdbjHPRxwxAMPymKHQKj693127Vq2jRlDh59/xm3YMNXaldQjl7lKOnH/2jUsSpakWOnSqred\nTDIrWU4ZytCL3lii3jLV9PQsevZciY2NJQEBvYxqW44XOXHiOm+/vZ5WrZyZNesNihfP/1xNGmmE\nE0YlKuGAIxZYqF4cHrt59iyr3nyTKm3b0nnGDMwsjXPJ8etKzkFIOmFdqdJLi4MmMzNfBxOVpjQj\nGIUllsxjDrdQbyzbysqcLVveomRJC5o18yc6+o5qbeuSp2dFQkLeIS0ti1atFnH5cv7nakpQgua0\nwJnKWJBTIHVRHADK1a3LyOPHSY2PJ8DHh/vX83ZOhWQ4skBIOnX/+nX2f/0128aOzfPPmmNOL3rT\njGb4M4/znFMtr2LFzFi0qCfvvNOI5s0Xsn37JdXa1qWSJS1YsaIPgwY1oEmTBezZo5tT+zaynhgV\nTwQsZmPDgPXrqdW9O/M9Pbl84IBqbUu680pDTIqiFAPGAi0BARwC/hRCGM3dR3KIybgIIf6x9cKW\nd97B1Ny09xAoAAAgAElEQVScTv/7H6YWeR/SucpVVrECdzxoQ9snE65qCA6+woABaxk9uhFffdUa\nE5PCsWXE/v2xDBq0nokTm/LJJ81V3eoimijWsYYWtKI5LVTtXUTt2sXGoUNp+eWXNBk3Tm7RYWAF\nnoNQFGU1kAIse/Stt4AyQoh+qmVZQLJAGB8hBOfWr6dunz5E7dzJ6aVL6f3XX/luL5VUVrMSM8zo\nS/8nu5aqIT4+hf7912JvX4I1a/oVmjetq1fv0afPapydbVi0qCelSqk3vp/MXVayHFvKPlkiq5a7\nsbGs7t2bcnXr0n3+fMyt1Pu3lPJGjQJxVghR92XfMyRZIIyL0GpJTUxkvqcnDq6upCYk4Obnh+ej\noab8vgFr0LCbnZznPG8xCAfU2yMqM1PD8ePXadnS+bnX3LiRwu3b6TRoYK9a3IJ6+DCbDz7YzuHD\n19iwYQC1apVVre0sstjKZq5znbcYRFkKtqHgP9pOT2fru++SGB7OgA0bKFOtmmptS69OjUnqEEVR\nmj7VYBPgpBrJSUVL+u3baDUaFBMTSjk6MjYyErNixeg+fz4eI0agKMo/ioMmK2/bX5hiShe60o72\nLGYh4YSplruFhekLi4MQgjVrIunWbQUffLAdrVYYxZbixYqZMX9+DyZMaELLlgtVPXr18TyQF01Y\nwDxVj441t7KiV0AAHqNG4d+smTzW1Ai98D54RVEiyJlzMAcOK4py5dHjyqDDQ4alQuv8hg3EBQXR\ne1nOaGRcUBAWpUrh6OEB5PQsFBMTbp49S9KZM1zYtInqnTrhOnRonuI0xJXy2LOSv7jONTrRJc/b\nVeeVoiiMH9+UlJRMAgNjjG6uYtSoRjRoYE+/fmuIi0tm3Dh1dslVUPCiCQ44spoVXOcaPirNAymK\ngtf77+Pg5sbaAQNo9O67tP7Pf1BM5PoZY/DCISZFUSq/6IeFEJdVzyif5BCT8dj67rvcPHuWWt27\nczU4mMqtW+M5diwm5uaYmJpy++JFdo4fT/mGDSlfvz7BP/1E47Fj8XrvvTzHesAD1rKaTDLpz8An\nR23qyqVLtxk4cB2zZ79B06aV0Gi0Rnez3eXLybRpE8DEiU354AN1t1JPIYXVrMQCC/rSv8Bbc/yj\n7Rs3WNOvH1Z2dvRaskRuHa4n8kY5Se8ili/nbkwMTs2bU6FxYyytrZ88t9jbm5pdu9J4zBgsS5Xi\nzMqVPLh7F88xY/IVS4uWIPYRQgj9GYAzL/xcUyCjR2/BwsKUWbPe+MdKrWdXbRna4yLx8cfNGTvW\nU9W2NWjYxU4ucp5BDKY86s3HaDIz2TlxIjGBgQzYsIHy9eqp1raUuxcVCIQQReJ3zkuRjM3tqCgR\numjRk8d7vvxSrBs0SGiys598b2WvXiJoypQnj1OTkvIV67w4J34S34tgcUhohTbfOT/P5s3nhZvb\nHHHv3kMhhBAajVZotTlxsrM14sqVZDF9+iGxcmWE6rHzIzb2rqhY8RexZk2kTtoPFSHiRzFVnBNn\n1W978WIx3c5OnFm9WvW2pX969N6Z6/uq3ItX0inb6tW5ExVF2s2cQ3BSExLwemrt+6l587gbG8uA\nDRu4Ex3NoR9/JDU+PmeYISAgT7Fq48Jo3mUVK7jCZXryZoGHQLRawYYN53jzzTrMnXuKMWMaY21t\n+a+hpQULQjh48Aq2tsVZs+Yse/fGMnduN4P2KqpUKc3WrYPo2HEpdnZW+PhUUbV9N9wpix2rWE4i\nibTGW7X7Jdx8fbFv0IBVvXtz/fhx2v/4o9w63ACMa/BUKpJqdOpEiXLlsLS2JuPePawrVUIxMSHp\nzBkO//wzPfz9ubBlC0d//ZVipUvTb80aMlNTOTl3bp5jlcGWEYzGGmtmM5OLFGxFT1paJkuWnKZs\n2enExNxl9OhGQE63/PGBPrt2RbFnTyy+vq7MnNmF4GA/srK0pKRkFii2GtzcHFi5si/9+q1h/371\ntmh/zAknRjOGs5xhFztVPfTJ0cOD0SdPkhgezrJOnZ58yJD0RxYISS+EEGgyMsh++JD9X39N5Jo1\nrB04kJZffIFFiRKc37iRSs2a0fKLLzC3sqJSs2b5Pr7SHHPeoBu96cdWtrCR9Twkfzf9lyplyaZN\nA5kzpyu3bz8gICCMBw+yMDFRMDU1QaPRsmBBKJ07V6dp00oA7NwZxalTN56cg336dCIHDxpuPUfb\ntlVZs6Yf/fuv1cnWHNZYM4wRxBHLdrapWiSs7Ox4e8cOKnh5Mb9xY26clKvr9UlOUkt6JYRg67vv\nYlu9OtaVKtFg0CAOPho+qD9wIDZOTty7coUto0bR4rPPqNq2LfevXeNhcjLl6+d9m+sMMtjJdqKJ\noie9qf7oHIT80GoFx45d48yZJBo2tKdJk0rs2RPD+vXn+OijZlSvnnPyW48eKxg4sD5vvunCjz8e\nYv/+ONLTs7C3L8Hq1f0oWdIwu8cePHiZPn1WM2vWG/Tvr/7k70MesoTFOOBAN3qouh0KwNl169j2\n7rt0mzuXOr17q9r260zu5ioZDUVR6D53Li0+/ZQGgwYBcP3oUUpXqYKNkxMAJ+fOxallS2ycnYna\nuZMl7dqx76uvmF23LvevXctTPEss6cmbdKcnG1jHVjaTQUa+cjcxUWjWzIkePWqTnZ0zvGRhYcrd\nuw+pUCFnee3vvx/D3r4E9eqV448/TnD7djqLF/fk1KnRlCxpQUREYr5iq6FVq8oEBg7ho492M2PG\nUdXbL0YxfBlOEklsZiNaCn6m9tPq9unD4F272DFuHME//2wUNykWdbJASAaVEh9P1oMHTw4dCp4+\nncyUFCo0bkzi6dOEL1lC66+/ZuDGjdTp3Zszq1blK05NavEeH5BJJn8yizji8p2zvX1JWrTIueM6\nPT2LO3ceULy4OWFhCcyefYIRIzy4cOE2CQmpdO9em+rVbblz5wGXLt3BxqYYkHMIUFycukervgpX\nVweCg/2YN+8U06YdUr19SywZgi93uMMG1qleJBw9PBhx5AgRy5axc/x4WSR0TBYIyaBKOTpStnZt\n5ri6snPCBE4vW0b9t97CokQJLh84QGVvbxoOHgxAyvXrBVrJUpzi9KYvnXmDNaxkB9vJIm9bfTyr\nQ4dq1K5dlipVZvC//x1h1CgPmjatRHT0HcqXL0H79jn7C+3eHU3btlWoUKEUx4/nHPwzcOBaOnZc\nSkJCaoFyyCtnZxsCA4fw22/H2L1b3aNdIadIDGYoKaSwjjVo0Kjavo2TE8MOHODGiRNsGzMGoVW3\nCEn/T85BSEYhcvVqLEqWpHz9+tg4O3Ng6lTSkpLoMnMmADdOnSJy1Spqdu1KFW/vAsdLJ51tbCGe\nG7xJX5xwKlB7V67co1w5qyenvDVuPI9p09rTrl01Lly4xZIl4ZQrV4Ju3Wrxzjtb6datJhMnNmPc\nuB04OJTkyy9bFfg15dXff8cxYMBajh8fhbOz+nctZ5HFSpY/ueta7a1QMlJSWP7GG9jWqkX3efMw\nMdXtVitFlZyDkIxevf79qfnGG9g4O5OWlETowoVPdn5Njovj8oEDZD14gJ2LiyrxrLCiHwNoS3uW\ns5RAdpFNdr7bc3a2eVIckpLScHAoibd3FQD8/UMxMVHw8qrI5s0XcHa2YeLEZgCULVucYsVyekVa\nrX4/4Hh7V+GDD7wYOXKzToZqzDHnLd4miyxWs6JAf7+5sSxVird37CA5JoZNw4ej1ajbU5FkgZCM\nkFmxYtg3bEhxW1sy7t8ncs0a7kRFUX/gQEraq7vNdn0a8B7juMlN5jCbGxT8OMxy5axwd3fAyelX\n+vdfw+nTibzzTmPs7KzYtOkCn37aHIBbt9KxsDDF1DTnw5shNv/77LOW3LnzAH//UJ20b4YZAxkE\nKKxkeYGH9J5lUbIkg7ZtIzU+ng2DB6PNVrcIve4MNsSkKEoZYBU5O8PGAf2FEPdyuW4H0BQ4KITo\n8YL25BBTEXL4l184NWcOJR0dqdSsGbW6daNyK90NwwgE4YSxix140YTW+BR4SCQ0NJ60tCzc3Bwo\nWdKCUaM2Y25uyh9/dCUrK+fsiR9/PMTUqW1xc3Mw2H5OJ05cp2/fNcTEjNPZxoMaNKxlNRlk8BZv\nY465qu1nPXjA6t69sShZkt7Ll2Nqrm77RZlRbtanKMo04LYQYrqiKJ+Rc0Ld57lc1wawAt6RBeL1\nkhwXR8b9+9g3bJjr84+3DlfTfe6xiY2kkkJv+mKPgyrtCiH4+uv9tGlThXbtqnH8+HWWL4/Azs6K\nr75qrUqMgmjceB7ffdeGLl1q6iyGBg0bWEcqqQxisKon1AFkZ2Swpm9fFFNT+q5ahZmleqfrFWXG\nOgfRE3i82U4A0Cu3i4QQ+wH9LvOQjELpKlWeWxwA9k+axOaRI8m4f1+1mNbYMJiheNGURfhzgL9V\nWYWjKArVq5dh2LBNTJ68n+++O0DZssWZOLHpy39YD7p3r8W+fepvxfE0U0zpTV+ssWYpAfm+H+V5\nzCwt6b9uHYqJCav79CH7Yf7unpf+nyELRHkhRCKAECIBKGfAXKRCqMWnn6KYmPBnw4bE7N2rWrsK\nCo1ozLuMJYZoFjCPmyQVuN3hw91Zvrw3Go3giy9a8sknLShRwiLXCeKsLP1OuMbF3aNmTfWOKn0e\nE0zoRW/ssGMpAfneAuV5TC0s6LtqFeZWVqzs1YusBw9Ubf91o9MhJkVRAuEfm8Ur5JxI9xWwWAhh\n+9S1t4UQuf4PVRTFG/hIDjFJuYnauZMto0ZRu2dP2k+bhkWJEqq1LRCc5Dh72YMXTWhJa9WHRp51\n82Ya9ev/yciR7owb1wR7+5I6jQfQoMGfzJ/f/cl+UrqmRftomXE8Q/BV9eAhAG12Nht9fUlNSGDg\n5s2q/p8oaox1DuIc4COESFQUxQHYL4So85xrX6lATJ48+cljHx8ffHx8VM5aMlYPk5PZOX48V4KD\n6bV4Mc4tW6ra/j2S2cVOrnKVTnSmHvVV29o6N9HRd/jllyOsWHGGPn3qMGKEO02bVtLJJHZYWAI9\ne67U6SR1bgSCHWzjCpcZynCssFK1fa1Gw+YRI0iOjWXQtm1YlNR9oS0MgoKCCAoKevJ4ypQpRlkg\npgF3hBDTXjRJ/ehaH3IKRPcXtCd7EBIXNm9m67vvUn/gQNp+/z3mxdX9ZBpLLNvZSnGK8wbdcFBp\nEvt5EhNTWbQojIULQzEzM2HECHeGDHGlfHn1PhH7+W3C2dmGb77xUa3NVyUQ7GInMUQxjBGqFwmh\n1bLlnXe4de4cb2/f/o+TDaUcxtqDsAVWA07AFaCfECJZUZRG5KxYGv3ougNAbaAkcBsYIYQIzKU9\nWSAkANJv3WL7+++TEBZGr4AAKjVR91xmDRpOcZL97KUe9WlLe9Xf2J4lhODQoSv4+4eyadMF2rSp\ngp+fO50718DMLP+f+mfOPMYff5wgONiPsmV1+xqeRyDYzU7iiMUXP4pRTN32tVq2v/8+8SEhDN65\nk2KlS6vafmFnlAVCbbJASM+KXL2aHePG4e7nh/fkyaove0wnnX3sIZIztKEtjfFSfYvr3Ny/n8Hq\n1ZEsXBhKVNQd+vaty6BBDWjRwilPQ1BXrtzD3X0uJ0+OomrVMjrM+OUEgm1sIYEEhjJM9XkeIQQ7\nJ0zganAwQ3bvprit7ct/6DUhC4T02kpNTGTrO+9wNyaGXgEBOLq7qx4jgQS2s5UHPOANulGVqqrH\neJ7Y2LusXHmGZcsiuHEjBQeHktjZWWFvXwJ3dweaNq2Ep2fFJ4cXPbZnTwxDh27gs89aMH68cSy1\n1aJlHWsww4w36aN6+0IIAj/5hNi9exkSGIiVnZ3qMQojWSCk15oQgtPLlrH7o4/wev99Wn7xhep3\n2goEkY+O3XTCiY50pjT6G8oQQnDzZjo3b6Zx82Y68fEphITEc+zYdUJC4nFyssHNzQF3dwdu3kxj\n+fIzLFnSi3btquktx1eRQQbz+JMWtMKDRqq3L4Rg75dfcmnbNobu2UOJ8uVVj1HYyAIhScD969fZ\nMnIkaUlJ9FqyhPL11D9VLZNMDnGAYxylGc1pQSvVt5XIq6wsDefP3yI0NIHQ0HhSUzOZOrWtXpbP\n5kcSSSxkPsMYoZNFAEIIgr75hrNr1jB0715KOTqqHqMwkQVCkh4RQhDq78/eL76g2ccf0/zjj3Wy\nTfRd7rKLHdzgBp3pQh3q6nRZbFETThhB7OddxmKJbrbMODB1KqeXLmXovn1YV6yokxiFgSwQkvSM\n5Lg4Nvn5kf3gAT38/SlXt65O4kQTzQ62UpJSvEFXyqPubrRF2SY2kEkmfemvs+IaPH06p+bNw3ff\nPmycnXUSw9jJAiFJuRBaLSfnziVo0iS8xo2j5WefYWqh/l3SGjSc4Dh/s58GuNKGtqrfOVwUZZHF\nfObgiReeqLtU+WlHfv2V4zNn4rt/P6WrVNFZHGMlC4QkvcC9q1fZPnYsyXFxdJs7F6fmzXUSJ400\n9hLIec7RlvZ40Egvy2ILs9vcYgHzGIIvFdDdMNDxWbM4/PPPDN27F9saNXQWxxjJAiFJLyGEIHL1\nagI//pjK3t60nzZNZ+PSN7jOdrbxkIe0pZ2cn3iJCE6zjz28y3s6m48AODVvHge++44he/ZgV7u2\nzuIYG1kgJOkVZaamcvDHHzk1dy7NPvqIZhMnYlZM3Tt7IWdZ7CUuspdAQKEt7ahFbVkonmMj69Gi\npTd9dRondNEi9n/1FUP27KFcnVy3hityZIGQpDy6Ex3N7g8/JCkykk6//kqtbt10slGeQHCOs+xj\nL2aY4UMbauMiC8UzMslkNjPpSW+qodt7N8KXLmXPZ58xeNcu7Bs00GksYyALhCTlU9SuXeyaMAGb\nypXpPGMGdi4uOomjRcs5zhLEfkxQ8KEtLtSRheIpZ4jgIH/zDmN1PncTsWIFuyZOZPDOnTi4uek0\nlqHJAiFJBaDJyuL4779z8IcfcPX1xXvSJIrZ2OgklhYtFzhPEPsQCLxpQx3qyslscnpb8/iT1vhQ\nB90sS37a2XXr2P7eeww/cICytWrpPJ6hyAIhSSpITUxk75dfErV9O21/+AE3X1/Vz8R+TCC4yAX2\ns49ssvGhDXWp99oXihBOcp7zDGKwXuKdmj+f4GnTGHH4cJHdlkMWCElS0fUTJ9jxwQcIrZYuv/+u\n+nbiT3s8mR3EfjLIwBsf6tPgtS0UGWTwC9MZx0RKop+tQvZ99RWx+/YxLChIJ/fJGJosEJKkMqHV\ncnrZMvZ+8QXVOnSg/U8/UdJBd4cHCQRRRBHEPh7wAG98aEDD17JQrGMNFahIM3Rzv8qzhFbLyl69\nKFO9Op1//VUvMfXpRQXi9fvfJUkqUExMcB06lPfOn6dE+fL8Ub8+wT//jCYzUzfxUKhJTUYymq50\n4wTH+Z3fCCMUDRqdxDRWbrgTRoje4ikmJvQKCODCxo2c27BBb3GNgexBSJIKbl+8yK4PP+TOpUt0\nmjGDml266DSeQBBLDPvZRwr3aY0PrrhhivobDxobLVr+x38ZwlDsdXzk69OuHz/O8m7dGHn0KGWq\nGdc26QUhh5gkSU8ubtvGrokTKVurFp1+/ZWyNWvqPGYssQSxjzvcoQlNaYyn6sd2GptAdqNBQ2d0\nW4ifdWzmTMIDAvALDtbJDZSGIAuEJOmRJjOTo7/9RvC0aXiMHEmr//wHy1KldB73Otc4wmGiuEQj\nPPHES6+HFunTLW6ykAV8xKd67TUJIVjTrx8lypen6x9/6C2uLsk5CEnSI1MLC1p88gljIiJIjY9n\ntosL4UuXIrRancatSCX60p/RjCGTDP5kFgEsIoLTZJOt09j6Zkc5SlOGaKL0GldRFHr4+xMTGMiZ\nlSv1GtsQZA9CknTs2tGj7PjgA0zMzeny++9UaKT+UZq5ySKLc5wlhFMkEE8DXGlEIxwoGieoHeMI\n17mu8/2ZchMfGsqyjh0ZfuhQod/YTw4xSZKBCa2WsIAA9n35JTW7daPd99/r9caru9wllFOEEooV\nVnjQiIa4FupzKW6SxDKWMJGPDRL/1Lx5HJ81i5FHj2JuZWWQHNQgC4QkGYmH9+7x97ffcnrJElp+\n+SVe77+Pqbn+zqzWoiWGaEI4RRSXqEktPGhMVaoWunsqMsnkR6byKV8YpNAJIdgwZAimlpb09PfX\ne3y1yAIhSUbm1vnz7JwwgXtXrtB5xgyqd+yo9xzSSec0YYRwiodk4I4H7ngUiontW9xiA+soQxn6\n0M9gmxpmpqYy39OTFp9/jpuvr0FyKChZICTJCAkhuLh1K7smTqRc3br4TJmCo7u7/vNAEM8NQjhF\nBKepQEU8aIQLdTBHf72bV6FFy1GOcIAgfGiDF00N3vNJOnOGgDZt8A0Kony9egbNJT9kgZAkI5b9\n8CEn58whePp0Knp64j15Mo4eHgbJ5dmJ7fo0wB0PKlDR4FuP3+IWG1mPgkIvelOWsgbN52mhixZx\n5JdfGHXiBObFC9e8jiwQklQIZD14QMij3UMdPTxoPWkSFT09DZbPXe4STihhhGKKKXWoS1Wq4Uxl\nvfYsjLHX8CwhBOvffhtLa2u6zZlj6HTyRBYISSpEsh8+JGTBAg799BP2DRviPXmyTneMfRmB4ApX\niOIiMcSQRCKVcKICFXDAEUcqYIutTt60n+41vElvbI2o1/CsjPv3+aNePfqtWUOlpk0Nnc4rkwVC\nkgqh7IwMQhcu5NCPP1Kubl28J0/GqVkzQ6fFAx5wmTgSiH/yK5007B+VC8dHRaM89phhlq8YhaHX\nkJuQBQsIX7KEYX//rZMjanVBFghJKsSyMzIIDwjg4A8/ULZmTbwnT8a5ZUtDp/UPD3jwpGA8/nqH\n29hii+OTnoYjDji+dElqAglsZbNRzjW8jFajYZ6HB+1++knnGzaqRRYISSoCNJmZhC9dysHvv6d0\nlSp4T55MFW9vQ6f1XFlkcZMk4rnxpKeRSAJWlMARBxxwpDz22GNPSUpxlkhOcZJk7tIKb7xoUih6\nDc86OXcusXv20G/NGkOn8kpkgZCkIkSTlcXpZcs4+P33WFeqhPekSVRp06ZQDGlo0XKHOyQ86mkk\nkUgiSdwjmRrUpBGNqUXtQr1t+cN795hRuTLjoqOxKmv8vR9ZICSpCNJmZ3P6r784+P33lLS3x3vy\nZKq2a1coCsWztGgLZW/hedYNGoRT8+Z4vf++oVN5KVkgJKkI02Znc2bVKg5OnUpxW1taT5pE9Y4d\nC2WhKCqidu1i/1dfMerECUOn8lKyQEjSa0Cr0RC5ejUHp07FolQpvCdNokaXLrJQGIBWo2GGszND\nAgMpV7euodN5IVkgJOk1IrRazq5dy4HvvsOsWDFaT5pErW7dZKHQs8DPPgOgw7RpBs7kxWSBkKTX\nkNBqObdhAwe+/RYTMzNaT5pE7R49ZKHQk5tnz7KkfXsmXrmCiVn+7gfRB1kgJOk1JrRaLmzezN/f\nfovQavGeNAmXXr1QTIrOpLCxmu/lRZvvvqNGp06GTuW5ZIGQJOnJ7rF/T5mCJiOD1l9/Td2+fWWh\n0KHjs2Zx9fBh+ixfbuhUnksWCEmSnhBCELVjB39PmUJmairNP/mE+m+9hZmlpaFTK3LSb99mZvXq\nTLh8mWI2NoZOJ1eyQEiS9C9CCGICAznyyy8knj5Nk/Hj8Rw7Fktra0OnVqSs7tOH6p0702jUKEOn\nkqsXFQjZt5Sk15SiKFTv2JHBu3YxePdukiIimFG5Mpv8/LgSHIz8wKUOV19fwgMCDJ1GvhisB6Eo\nShlgFVAZiAP6CyHuPXONK/AnUArQAD8IIVY/pz3Zg5CkAkpNTCR8yRJC/f1RFAW34cNxHTqUkg4O\nhk6t0NJkZfFrpUr4BQdjW6OGodP5F6McYlIUZRpwWwgxXVGUz4AyQojPn7mmBiCEENGKojgCpwAX\nIcT9XNqTBUKSVCKE4GpwMKELF3J+wwYqt26N+4gR1OjSBVNz4zqGtDDYOWECljY2tJkyxdCp/Iux\nFojzgLcQIlFRFAcgSAjh8pKfCQP6CCGic3lOFghJ0oGMlBTOrllDqL8/d2NiaDh0KO5+ftjVrm3o\n1AqN+JAQVvfpw7joaKNbNWascxDlhRCJAEKIBKDciy5WFMULMM+tOEiSpDuWpUrh7ueHX3Awvvv3\ngxAs9vZmYcuWhC5cSGZqqqFTNHoO7u4opqYkRUYaOpU80WkPQlGUQMD+6W8BAvgKWCyEsH3q2ttC\niFz3xn00vLQfGCKEyHX3K9mDkCT90WRlcWn7dsIWLuTygQO49O6Nx4gRVGrWTN6p/Rzr336bah06\n4DZsmKFT+YcX9SB0ev+3EKLD855TFCVRURT7p4aYkp5zXSlgK/Dl84rDY998882TP/v4+ODj45Of\ntCVJeglTc3NcevbEpWdPUuLjOb10KZv8/HImtv38cB0yRE5sP+VubCyJp09Trl49Q6dCUFAQQUFB\nr3StoSep7wghpr1gktoc2AlsEkLMfEl7sgchSQb0r4ltb2/c/fyo+cYbRr0XkS4JrZaTc+ey/+uv\nafHppzT78EOj+7sw1klqW2A14ARcAfoJIZIVRWkEvCOEGK0oytvAQiCS/x+eGiaEOJ1Le7JASJKR\nkBPbOb2GLSNHkpmWRs9FiyhXp46hU8qVURYItckCIUnG6db/tXevMVLddRjHv48CIkWuK1DLTcu1\nAt3VpEWstbWNpZoIkYZeYmuEGhOtUXqJ6QtrGo2+8wWpJtYWCDFqGhtJrdeYSg2htEh3qFCoRaFl\nC0tLAEuhtIX9+eIc6Lj97+6BsnNmhueTEM5hzwzPDMM88ztn5sz27bSvWMHm1asZNWUKbUuW8NHF\nixk0dGjZ0frFyalh7T33MO+uu/jEHXfwnvfW71eouiDMrHSpA9ttS5YwYd68pjmw3ShTQzUXhJnV\nldc6O099Ypv8E9uzrr+eEZMnlx3tjDTa1FDNBWFmdSki2L1+PZVVq3huzRqGT5rERdddx0euvpox\nsz7SskQAAAcKSURBVGYxYPDgsiP26eDOnTyydClvHT3aMFNDNReEmdW9ruPH2fX442x7+GFeXLeO\nAzt2MGrKFMa1tjKurS37vbWV948cWXZUoDHeoVSEC8LMGs7xY8d4eetWOtvb6axU6KxU2Ld5M0Na\nWt4ujbw4ho0fX9PjGKemhiNHWLBqVcNNDdVcEGbWFKKriwM7dtBZqbC3vT0rj/Z2uk6c+L9J4/y2\nNkZPm3bWX9E3y9RQzQVhZk3ttc7OrDAqlVOlcXjPHsbMmsXYvDDGtbUxdvZsBg4ZckZ/RzNNDdVc\nEGZ2znnj8GH2bd789qRRqbB/+3ZGTJ58ato4v62NsRdfzHkf7Plcoa8fOMBT993Hk8uXN83UUM0F\nYWYGnHjzTV559tlTu6he2bKFvU8/zaChQxnX2sqYOXMYPnEiw8aPZ0hLC1sfeojKypXMWLiQy+6+\nm9FTp5Z9E846F4SZWQ+iq4tDu3axt72dl7ds4dWODg53dHB4zx4+fNVVzF22jOETJpQds9+4IMzM\nLKlevzDIzMzqmAvCzMySXBBmZpbkgjAzsyQXhJmZJbkgzMwsyQVhZmZJLggzM0tyQZiZWZILwszM\nklwQZmaW5IIwM7MkF4SZmSW5IMzMLMkFYWZmSS4IMzNLckGYmVmSC8LMzJJcEGZmluSCMDOzpHOm\nINauXVt2hNPWiJmhMXM3YmZozNyNmBkaM/e7zeyCqGONmBkaM3cjZobGzN2ImaExc7sgzMysX7gg\nzMwsSRFRdoazQlJz3BAzsxqLCKX+vGkKwszMzi7vYjIzsyQXhJmZJTVVQUiaL2m7pH9J+k4P2yyW\ntFXSPyX9otYZU/rKLenHktolPS3pOUkHysjZLVNfmSdIeizPXJF0bRk5uyuQe6Kkv0ranOf/UBk5\nu2V6UNI+Sc/0ss1ySc/n93VrLfP1kKfXzJKmS1ov6Zik22udrycFct+UPzYqktZJml3rjIlMfWX+\nQp65XdJTkj5Z+Mojoil+kZXdDmASMBCoADO6bTMF2AQMy9dbGiF3t+1vAx6o98zAz4Cv5cszgZ2N\ncF8DDwFfypevAFbXQe7LgFbgmR5+fi3w+3z5UmBDA2RuAT4OfB+4vey8p5F7LjA8X57fIPf1kKrl\n2cC2otfdTBPEJcDzEfFCRLwF/BpY0G2brwI/iYhXASJif40zphTJXe1G4Fc1SdazIpm7gGH58gjg\npRrm60mR3BcBjwFExNrEz2suItYBB3vZZAGwOt/2SWC4pLG1yNaTvjJHxP6I2AQcr12qvhXIvSEi\n/puvbgAuqEmwXhTIfLRqdSjZ/81CmqkgLgB2V6138M5/vGnA9Hw0XC/pmpql61mR3EC2+wOYTP4E\nVqIime8Fbpa0G3gU+GaNsvWmSO4KsAhA0heBoZJG1ibeGet+u16iDp64zgG3An8sO0QRkhZK2gb8\nDlhS9HLNVBCp9/F2fw/vALLdTJcDNwEPSBr2jkvVVpHcJ90A/CbyWbFERTLfCKyMiAnA54F6ON5T\nJPddwBWSNgGfInuyratXuQmn8xiys0DSlcBXgOSxznoTEWsiYiawEPhB0csN6L9INdcBTKxaHw/s\nSWzzRER0AbskPQdMJTsuUZYiuU+6Afh6vyfqW5HMS4FrIBvLJQ2W1FLybr0+c0fEXt6eIM4DFkXE\n4ZolPDMdwISq9d4eQ/YuSZoD3A/Mj4jedv3VnYhYJ+lCSaMios83uzTTBLERmCJpkqRBZE+mj3Tb\nZg3wGQBJLWTl8J+apnynIrmRNB0YEREbah0woUjmF4CrASTNBN5XB8d8+swtabSkk6/I7wZW1Dhj\nT0R6UoDsNtwCIGkucCgi9tUqWC96y9x9u3rSY+58N+/DwM0R8e+apupdb5kvrFr+GDCwSDlAE00Q\nEXFC0m3AX8iK78GI2CbpXmBjRDwaEX+W9FlJW8l2G9xZ9iuAIrnzTW8gO6hauoKZ7wR+LmkZ2UGx\nL5eXOFMw9xXAjyR1AX8HvlFa4JykX5LlGi3pReB7wCAgIuL+iPiDpM9J2gEcIdv1Uaq+MucH0f8B\nfADokvQt4KKIeK2szNB3buC7wCjgp/kLibci4pKy8kKhzIsk3QK8CbwOLC583eXvzjYzs3rUTLuY\nzMzsLHJBmJlZkgvCzMySXBBmZpbkgjAzsyQXhJmZJbkgzMwsyQVhZmZJLgizfiTpt5I25l9QdWvZ\necxOhz9JbdaPJI2IiEOSBpOdC+rysk/vYlaUJwiz/vVtSRWyL5cZT3aCSLOG0DQn6zOrN5I+TXb2\n4Esj4g1JfwMGlxzLrDBPEGb9ZzhwMC+HGWTfZ2zWMFwQZv3nT8DA/PTyPwSeKDmP2WnxQWozM0vy\nBGFmZkkuCDMzS3JBmJlZkgvCzMySXBBmZpbkgjAzsyQXhJmZJbkgzMws6X+fe4YW87TEXwAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.draw_mncontour('a','b',nsigma=3)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IrqCel+c2rSLEtFCcP3QTK+ZERR8YdRe4+wmHfS/Z2PER72sPcwTIyXnemMng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\n",
"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": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hOyL6ADPLBn4LDDpaAEh3P3n5J2S2zGTjjzbS7pR2vp1XFb1EpCG+5AmY2WC8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\nBQERkTSmICAiksb+PyujxHOkUUNvAAAAAElFTkSuQmCC\n",
"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": 25,
"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": 26,
"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": 27,
"metadata": {
"collapsed": false
},
"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": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib64/python3.4/site-packages/ipykernel/__main__.py:1: InitialParamWarning: errordef is not given. Default to 1.\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"m=Minuit(chi2,x0=0,error_x0=1,Gamma=1,error_Gamma=1)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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]
},
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"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" FCN = 18.097376960150946 | \n",
" TOTAL NCALL = 33 | \n",
" NCALLS = 33 | \n",
"
\n",
" \n",
" EDM = 1.7129910452155893e-07 | \n",
" GOAL EDM = 1e-05 | \n",
" \n",
" UP = 1.0 | \n",
"
\n",
"
\n",
" \n",
" \n",
" \n",
" Valid | \n",
" Valid Param | \n",
" Accurate Covar | \n",
" PosDef | \n",
" Made PosDef | \n",
"
\n",
" \n",
" True | \n",
" True | \n",
" True | \n",
" True | \n",
" False | \n",
"
\n",
" \n",
" Hesse Fail | \n",
" HasCov | \n",
" Above EDM | \n",
" | \n",
" Reach calllim | \n",
"
\n",
" \n",
" False | \n",
" True | \n",
" False | \n",
" | \n",
" False | \n",
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" \n",
" \n",
" + | \n",
" Name | \n",
" Value | \n",
" Parab Error | \n",
" Minos Error- | \n",
" Minos Error+ | \n",
" Limit- | \n",
" Limit+ | \n",
" FIXED | \n",
"
\n",
" \n",
" \n",
" 1 | \n",
" x0 | \n",
" -0.0351926 | \n",
" 0.0673422 | \n",
" 0 | \n",
" 0 | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" 2 | \n",
" Gamma | \n",
" 1.03425 | \n",
" 0.0300784 | \n",
" 0 | \n",
" 0 | \n",
" | \n",
" | \n",
" | \n",
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"text/plain": [
"({'fval': 18.097376960150946, 'up': 1.0, 'is_above_max_edm': False, 'has_valid_parameters': True, 'edm': 1.7129910452155893e-07, 'has_made_posdef_covar': False, 'has_covariance': True, 'has_posdef_covar': True, 'nfcn': 33, 'has_accurate_covar': True, 'hesse_failed': False, 'is_valid': True, 'has_reached_call_limit': False},\n",
" [{'is_const': False, 'number': 0, 'upper_limit': 0.0, 'lower_limit': 0.0, 'name': 'x0', 'is_fixed': False, 'error': 0.06734219710972826, 'value': -0.0351926437845268, 'has_upper_limit': False, 'has_limits': False, 'has_lower_limit': False},\n",
" {'is_const': False, 'number': 1, 'upper_limit': 0.0, 'lower_limit': 0.0, 'name': 'Gamma', 'is_fixed': False, 'error': 0.030078423287488033, 'value': 1.034253164644145, 'has_upper_limit': False, 'has_limits': False, 'has_lower_limit': False}])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.migrad()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Gamma': 1.034253164644145, 'x0': -0.0351926437845268}"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.values"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"18.097376960150946"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.fval"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Gamma': 0.030078423287488033, 'x0': 0.06734219710972826}"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.errors"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((0.004534971511565494, -2.272188453114644e-05),\n",
" (-2.272188453114644e-05, 0.0009047115474613024))"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.matrix()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([-0.30116278, -0.29582729, -0.29049181, -0.28515632, -0.27982084,\n",
" -0.27448535, -0.26914986, -0.26381438, -0.25847889, -0.25314341,\n",
" -0.24780792, -0.24247243, -0.23713695, -0.23180146, -0.22646597,\n",
" -0.22113049, -0.215795 , -0.21045952, -0.20512403, -0.19978854,\n",
" -0.19445306, -0.18911757, -0.18378208, -0.1784466 , -0.17311111,\n",
" -0.16777563, -0.16244014, -0.15710465, -0.15176917, -0.14643368,\n",
" -0.14109819, -0.13576271, -0.13042722, -0.12509174, -0.11975625,\n",
" -0.11442076, -0.10908528, -0.10374979, -0.0984143 , -0.09307882,\n",
" -0.08774333, -0.08240785, -0.07707236, -0.07173687, -0.06640139,\n",
" -0.0610659 , -0.05573041, -0.05039493, -0.04505944, -0.03972396,\n",
" -0.03438847, -0.02905298, -0.0237175 , -0.01838201, -0.01304653,\n",
" -0.00771104, -0.00237555, 0.00295993, 0.00829542, 0.01363091,\n",
" 0.01896639, 0.02430188, 0.02963736, 0.03497285, 0.04030834,\n",
" 0.04564382, 0.05097931, 0.0563148 , 0.06165028, 0.06698577,\n",
" 0.07232125, 0.07765674, 0.08299223, 0.08832771, 0.0936632 ,\n",
" 0.09899869, 0.10433417, 0.10966966, 0.11500514, 0.12034063,\n",
" 0.12567612, 0.1310116 , 0.13634709, 0.14168258, 0.14701806,\n",
" 0.15235355, 0.15768903, 0.16302452, 0.16836001, 0.17369549,\n",
" 0.17903098, 0.18436647, 0.18970195, 0.19503744, 0.20037292,\n",
" 0.20570841, 0.2110439 , 0.21637938, 0.22171487, 0.22705036]),\n",
" array([ 0.92950681, 0.93189365, 0.9342805 , 0.93666734, 0.93905418,\n",
" 0.94144103, 0.94382787, 0.94621471, 0.94860156, 0.9509884 ,\n",
" 0.95337524, 0.95576209, 0.95814893, 0.96053577, 0.96292262,\n",
" 0.96530946, 0.96769631, 0.97008315, 0.97246999, 0.97485684,\n",
" 0.97724368, 0.97963052, 0.98201737, 0.98440421, 0.98679105,\n",
" 0.9891779 , 0.99156474, 0.99395159, 0.99633843, 0.99872527,\n",
" 1.00111212, 1.00349896, 1.0058858 , 1.00827265, 1.01065949,\n",
" 1.01304633, 1.01543318, 1.01782002, 1.02020686, 1.02259371,\n",
" 1.02498055, 1.0273674 , 1.02975424, 1.03214108, 1.03452793,\n",
" 1.03691477, 1.03930161, 1.04168846, 1.0440753 , 1.04646214,\n",
" 1.04884899, 1.05123583, 1.05362268, 1.05600952, 1.05839636,\n",
" 1.06078321, 1.06317005, 1.06555689, 1.06794374, 1.07033058,\n",
" 1.07271742, 1.07510427, 1.07749111, 1.07987795, 1.0822648 ,\n",
" 1.08465164, 1.08703849, 1.08942533, 1.09181217, 1.09419902,\n",
" 1.09658586, 1.0989727 , 1.10135955, 1.10374639, 1.10613323,\n",
" 1.10852008, 1.11090692, 1.11329376, 1.11568061, 1.11806745,\n",
" 1.1204543 , 1.12284114, 1.12522798, 1.12761483, 1.13000167,\n",
" 1.13238851, 1.13477536, 1.1371622 , 1.13954904, 1.14193589,\n",
" 1.14432273, 1.14670958, 1.14909642, 1.15148326, 1.15387011,\n",
" 1.15625695, 1.15864379, 1.16103064, 1.16341748, 1.16580432]),\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": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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rNwgO9uWFF/rQv7+HLlui91JMMUv4nBH40YveWse5L/veew9DaqrJb6pXq5vo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\nZmY5LgxmZpbjwmBmZjl/Az/KpeeBDkoGAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.draw_mncontour('x0','Gamma',nsigma=3)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mnF7PJhDE9XpjOd06Xiust5RNbIhZBoMos47jFQzT5kT0kOMO6sfM+cg8hZQp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\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": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6emBp4tq/HzpWT+fqUefxfw8doFqlagGI1PhcER/yR444pZ369eG6Bz/hlg8H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\nCGEJ3xhjIoQlfGOMiRD/Dy1bbaSTZLRLAAAAAElFTkSuQmCC\n",
"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": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}