{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise 4: Least square fit to data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams[\"font.size\"] = 20\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# data\n", "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='d')\n", "dx = np.array([0.1,0.1,0.5,0.1,0.5,0.1,0.5,0.1,0.5,0.1], dtype='d')\n", "y = np.array([1.1 ,2.3 ,2.7 ,3.2 ,3.1 ,2.4 ,1.7 ,1.5 ,1.5 ,1.7 ], dtype='d')\n", "dy = np.array([0.15,0.22,0.29,0.39,0.31,0.21,0.13,0.15,0.19,0.13], dtype='d')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# define fit function \n", "def pol3(a0, a1, a2, a3):\n", " return a0 + x*a1 + a2*x**2 + a3*x**3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# least-squares function = sum of data residuals squared\n", "def LSQ(a0, a1, a2, a3):\n", " return np.sum((y - pol3(a0, a1, a2, a3)) ** 2 / dy ** 2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# import Minuit object\n", "from iminuit import Minuit" ] }, { "cell_type": "code", "execution_count": 6, "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", " \n", " \n", " \n", "
Migrad
FCN = 3.719 Nfcn = 103
EDM = 4.33e-16 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a0 -0.98 0.34
1 a1 2.52 0.30
2 a2 -0.48 0.06
3 a3 0.0259 0.0035
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a0 a1 a2 a3
a0 0.119 -0.096 (-0.931) 0.0183 (0.878) -0.000997 (-0.835)
a1 -0.096 (-0.931) 0.0893 -0.0178 (-0.987) 0.000996 (0.962)
a2 0.0183 (0.878) -0.0178 (-0.987) 0.00366 -0.000208 (-0.993)
a3 -0.000997 (-0.835) 0.000996 (0.962) -0.000208 (-0.993) 1.2e-05
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.719 │ Nfcn = 103 │\n", "│ EDM = 4.33e-16 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a0 │ -0.98 │ 0.34 │ │ │ │ │ │\n", "│ 1 │ a1 │ 2.52 │ 0.30 │ │ │ │ │ │\n", "│ 2 │ a2 │ -0.48 │ 0.06 │ │ │ │ │ │\n", "│ 3 │ a3 │ 0.0259 │ 0.0035 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌────┬─────────────────────────────────────────┐\n", "│ │ a0 a1 a2 a3 │\n", "├────┼─────────────────────────────────────────┤\n", "│ a0 │ 0.119 -0.096 0.0183 -0.000997 │\n", "│ a1 │ -0.096 0.0893 -0.0178 0.000996 │\n", "│ a2 │ 0.0183 -0.0178 0.00366 -0.000208 │\n", "│ a3 │ -0.000997 0.000996 -0.000208 1.2e-05 │\n", "└────┴─────────────────────────────────────────┘" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create instance of Minuit and use LSQ function to minimize\n", "LSQ.errordef = Minuit.LEAST_SQUARES\n", "m = Minuit(LSQ,a0=-1.3, a1=2.6 ,a2=-0.24 ,a3=0.005)\n", "# run migrad \n", "m.migrad()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chi2/ndof = 0.6198527004952333\n" ] } ], "source": [ "# get function value at the minimum, which is per definition a chi2\n", "# obtain chi2 / degree of freedom (dof)\n", "chi2 = m.fval / (len(y) - len(m.values))\n", "print (\"Chi2/ndof =\" , chi2)" ] }, { "cell_type": "code", "execution_count": 8, "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", " \n", " \n", " \n", "
Migrad
FCN = 3.719 Nfcn = 128
EDM = 1.06e-17 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a0 -0.98 0.34
1 a1 2.52 0.30
2 a2 -0.48 0.06
3 a3 0.0259 0.0035
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a0 a1 a2 a3
a0 0.119 -0.0961 (-0.931) 0.0183 (0.878) -0.000999 (-0.835)
a1 -0.0961 (-0.931) 0.0895 -0.0179 (-0.987) 0.000997 (0.962)
a2 0.0183 (0.878) -0.0179 (-0.987) 0.00366 -0.000208 (-0.993)
a3 -0.000999 (-0.835) 0.000997 (0.962) -0.000208 (-0.993) 1.2e-05
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.719 │ Nfcn = 128 │\n", "│ EDM = 1.06e-17 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a0 │ -0.98 │ 0.34 │ │ │ │ │ │\n", "│ 1 │ a1 │ 2.52 │ 0.30 │ │ │ │ │ │\n", "│ 2 │ a2 │ -0.48 │ 0.06 │ │ │ │ │ │\n", "│ 3 │ a3 │ 0.0259 │ 0.0035 │ │ │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌────┬─────────────────────────────────────────┐\n", "│ │ a0 a1 a2 a3 │\n", "├────┼─────────────────────────────────────────┤\n", "│ a0 │ 0.119 -0.0961 0.0183 -0.000999 │\n", "│ a1 │ -0.0961 0.0895 -0.0179 0.000997 │\n", "│ a2 │ 0.0183 -0.0179 0.00366 -0.000208 │\n", "│ a3 │ -0.000999 0.000997 -0.000208 1.2e-05 │\n", "└────┴─────────────────────────────────────────┘" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run covariance \n", "m.hesse()" ] }, { "cell_type": "code", "execution_count": 9, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
a0 a1 a2 a3
a0 0.119 -0.0961 (-0.931) 0.0183 (0.878) -0.000999 (-0.835)
a1 -0.0961 (-0.931) 0.0895 -0.0179 (-0.987) 0.000997 (0.962)
a2 0.0183 (0.878) -0.0179 (-0.987) 0.00366 -0.000208 (-0.993)
a3 -0.000999 (-0.835) 0.000997 (0.962) -0.000208 (-0.993) 1.2e-05
" ], "text/plain": [ "┌────┬─────────────────────────────────────────┐\n", "│ │ a0 a1 a2 a3 │\n", "├────┼─────────────────────────────────────────┤\n", "│ a0 │ 0.119 -0.0961 0.0183 -0.000999 │\n", "│ a1 │ -0.0961 0.0895 -0.0179 0.000997 │\n", "│ a2 │ 0.0183 -0.0179 0.00366 -0.000208 │\n", "│ a3 │ -0.000999 0.000997 -0.000208 1.2e-05 │\n", "└────┴─────────────────────────────────────────┘" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get covariance matrix\n", "m.covariance" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "┌────┬─────────────────────────────────────────┐\n", "│ │ a0 a1 a2 a3 │\n", "├────┼─────────────────────────────────────────┤\n", "│ a0 │ 0.119 -0.0961 0.0183 -0.000999 │\n", "│ a1 │ -0.0961 0.0895 -0.0179 0.000997 │\n", "│ a2 │ 0.0183 -0.0179 0.00366 -0.000208 │\n", "│ a3 │ -0.000999 0.000997 -0.000208 1.2e-05 │\n", "└────┴─────────────────────────────────────────┘\n" ] } ], "source": [ "#get correlation matrix in numpy array\n", "cov = m.covariance\n", "print (cov)" ] }, { "cell_type": "code", "execution_count": 11, "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", " \n", " \n", " \n", "
Migrad
FCN = 3.719 Nfcn = 300
EDM = 1.06e-17 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a0 -0.98 0.35 -0.34 0.34
1 a1 2.52 0.30 -0.30 0.30
2 a2 -0.48 0.06 -0.06 0.06
3 a3 0.0259 0.0035 -0.0035 0.0035
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a0 a1 a2 a3
Error -0.34 0.34 -0.3 0.3 -0.06 0.06 -0.0035 0.0035
Valid True True True True True True True True
At Limit False False False False False False False False
Max FCN False False False False False False False False
New Min False False False False False False False False
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a0 a1 a2 a3
a0 0.119 -0.0961 (-0.931) 0.0183 (0.878) -0.000999 (-0.835)
a1 -0.0961 (-0.931) 0.0895 -0.0179 (-0.987) 0.000997 (0.962)
a2 0.0183 (0.878) -0.0179 (-0.987) 0.00366 -0.000208 (-0.993)
a3 -0.000999 (-0.835) 0.000997 (0.962) -0.000208 (-0.993) 1.2e-05
" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 3.719 │ Nfcn = 300 │\n", "│ EDM = 1.06e-17 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ No Parameters at limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Below EDM threshold (goal x 10) │ Below call limit │\n", "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", "│ 0 │ a0 │ -0.98 │ 0.35 │ -0.34 │ 0.34 │ │ │ │\n", "│ 1 │ a1 │ 2.52 │ 0.30 │ -0.30 │ 0.30 │ │ │ │\n", "│ 2 │ a2 │ -0.48 │ 0.06 │ -0.06 │ 0.06 │ │ │ │\n", "│ 3 │ a3 │ 0.0259 │ 0.0035 │ -0.0035 │ 0.0035 │ │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n", "│ │ a0 │ a1 │ a2 │ a3 │\n", "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n", "│ Error │ -0.34 │ 0.34 │ -0.3 │ 0.3 │ -0.06 │ 0.06 │ -0.0035 │ 0.0035 │\n", "│ Valid │ True │ True │ True │ True │ True │ True │ True │ True │\n", "│ At Limit │ False │ False │ False │ False │ False │ False │ False │ False │\n", "│ Max FCN │ False │ False │ False │ False │ False │ False │ False │ False │\n", "│ New Min │ False │ False │ False │ False │ False │ False │ False │ False │\n", "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n", "┌────┬─────────────────────────────────────────┐\n", "│ │ a0 a1 a2 a3 │\n", "├────┼─────────────────────────────────────────┤\n", "│ a0 │ 0.119 -0.0961 0.0183 -0.000999 │\n", "│ a1 │ -0.0961 0.0895 -0.0179 0.000997 │\n", "│ a2 │ 0.0183 -0.0179 0.00366 -0.000208 │\n", "│ a3 │ -0.000999 0.000997 -0.000208 1.2e-05 │\n", "└────┴─────────────────────────────────────────┘" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# run minos error analysis\n", "# The Minos algorithm uses the profile likelihood method to compute\n", "# (generally asymmetric) confidence intervals.\n", "m.minos()" ] }, { "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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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get a 2D contour of the function around the minimum for 2 parameters\n", "# and draw a 2 D contours up to 4 sigma of a1 and a2 \n", "#m.draw_profile(\"a1\")\n", "m.draw_mncontour(\"a2\", \"a3\", cl=[1, 2, 3, 4])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n" ] } ], "source": [ "print(m.values,m.errors)\n", "a0_fit = m.values[\"a0\"]\n", "a1_fit = m.values[\"a1\"]\n", "a2_fit = m.values[\"a2\"]\n", "a3_fit = m.values[\"a3\"]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# display fitted function \n", "x_plot = np.linspace( 0.1, 10.1 , 200 )\n", "y_fit = a0_fit + a1_fit * x_plot + a2_fit * x_plot**2 + a3_fit * x_plot**3" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot data \n", "plt.figure()\n", "plt.errorbar(x, y, dy , dx, fmt=\"o\")\n", "plt.plot(x_plot,y_fit )\n", "plt.title(\"iminuit Fit Test\")\n", "plt.xlabel('x')\n", "plt.ylabel('f(x)')\n", "plt.xlim(-0.1, 10.1)\n", "\n", "# show the plot\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 4 }