diff --git a/notebooks/iMinuit.ipynb b/notebooks/iMinuit.ipynb
index a144c7197fdbe80ef9ccefa343ad4abbbb1f2c29..aef86e197b0917c4affe468cdb91f343d25d07b8 100644
--- a/notebooks/iMinuit.ipynb
+++ b/notebooks/iMinuit.ipynb
@@ -12,6 +12,23 @@
"iMinuit: \n",
"https://iminuit.readthedocs.io/en/latest/index.html#\n",
"\n",
+ "iminuit is a Python frontend to the Minuit library in C++, an integrated software that combines a \n",
+ "local minimizer (called Migrad) and two error calculators (called Hesse and the Minos). \n",
+ "You provide it an analytical function, which accepts one or several parameters, and an initial guess \n",
+ "of the parameter values. It will then find a local minimum of this function starting from the initial guess. \n",
+ "In that regard, iminuit minimizer is like other local minimizers, like those in scipy.optimize.\n",
+ "In addition, iminuit has the ability to compute uncertainty estimates for model parameters. \n",
+ "iminuit was designed to solve statistics problems, where uncertainty estimates are an essential part of \n",
+ "the result. The two ways of computing uncertainty estimates, Hesse and the Minos, have different \n",
+ "advantages and disadvantages.\n",
+ "\n",
+ "To correctly compute the uncertainties, it needs to know if you are doing a max likelihood \n",
+ "or a least squares fit:\n",
+ "\n",
+ "errordef = 0.5 for negative log-likelihood functions\n",
+ "\n",
+ "errordef = 1 for least-squares functions\n",
+ "\n",
"Here below a quick summary of: \n",
"https://iminuit.readthedocs.io/en/latest/tutorials.html \n",
"\n"
@@ -19,7 +36,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -31,12 +48,12 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -52,18 +69,21 @@
"def line(x, a, b):\n",
" return a + x * b\n",
"\n",
+ "# generate random toy data with random offsets in y\n",
+ "np.random.seed(12345)\n",
"data_x = np.linspace(0, 1, 10)\n",
- "# precomputed random numbers from a normal distribution\n",
- "offsets = np.array([-0.49783783, -0.33041722, -1.71800806, 1.60229399, 1.36682387,\n",
- " -1.15424221, -0.91425267, -0.03395604, -1.27611719, -0.7004073 ])\n",
- "data_y = line(data_x, 1, 2) + 0.1 * offsets # generate some data points with random offsets\n",
- "plt.plot(data_x, data_y, \"o\")\n",
+ "\n",
+ "data_yerr = 0.1 # could also be an array\n",
+ "data_y = line(data_x, 1, 2) + data_yerr * np.random.randn(len(data_x))\n",
+ "\n",
+ "# draw toy data\n",
+ "plt.errorbar(data_x, data_y, data_yerr, fmt=\"o\")\n",
"plt.xlim(-0.1, 1.1);"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -83,148 +103,25 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 10,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<table>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td/>\n",
- "<th title=\"Variable name\">\n",
- "Name\n",
- "</th>\n",
- "<th title=\"Value of parameter\">\n",
- "Value\n",
- "</th>\n",
- "<th title=\"Hesse error\">\n",
- "Hesse Error\n",
- "</th>\n",
- "<th title=\"Minos lower error\">\n",
- "Minos Error-\n",
- "</th>\n",
- "<th title=\"Minos upper error\">\n",
- "Minos Error+\n",
- "</th>\n",
- "<th title=\"Lower limit of the parameter\">\n",
- "Limit-\n",
- "</th>\n",
- "<th title=\"Upper limit of the parameter\">\n",
- "Limit+\n",
- "</th>\n",
- "<th title=\"Is the parameter fixed in the fit\">\n",
- "Fixed\n",
- "</th>\n",
- "</tr>\n",
- "<tr style=\"background-color:#FFFFFF;\">\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "a\n",
- "</td>\n",
- "<td>\n",
- "5.00\n",
- "</td>\n",
- "<td>\n",
- "0.10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "yes\n",
- "</td>\n",
- "</tr>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td>\n",
- "1\n",
- "</td>\n",
- "<td>\n",
- "b\n",
- "</td>\n",
- "<td>\n",
- "5.00\n",
- "</td>\n",
- "<td>\n",
- "0.10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
- ],
- "text/plain": [
- "------------------------------------------------------------------------------------------\n",
- "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
- "------------------------------------------------------------------------------------------\n",
- "| 0 | a | 5.00 | 0.10 | | | 0 | | yes |\n",
- "| 1 | b | 5.00 | 0.10 | | | 0 | 10 | |\n",
- "------------------------------------------------------------------------------------------"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Minuit uses introspection to detect the parameter names of your function - it means \n",
- "#\n",
- "# A complete instance of Minuit contains the starting point of each parameter (<name>) and \n",
- "# the step size (<err_name>) for the dimension corresponding to that parameter.\n",
- "#\n",
- "# To correctly compute the uncertainties, it needs to know if you are doing a max likelihood \n",
- "# or a least squares fit:\n",
- "# errordef = 0.5 for negative log-likelihood functions\n",
- "# errordef = 1 for least-squares functions\n",
- "#\n",
- "# You can specify the limits on the parameters to be fit (\"limit_varname\")\n",
- "# lower limit: use limit_<name> = (<value>, None) or (<value>, float(\"infinity\"))\n",
- "# upper limit: use limit_<name> = (None, <value>) or (-float(\"infinity\"), <value>)\n",
- "# two-sided limit: use limit_<name> = (<min_value>, <max_value>)\n",
- "#\n",
- "# You can fix some of the parameters (ignore that dimension in the fit) setting <fix_name> = True/False\n",
- "# fix_a=True,\n",
- "\n",
+ "# configure minuit\n",
"m = Minuit(least_squares, \n",
- " a=5, b=5,\n",
- " fix_a = True,\n",
- " error_a=0.1, error_b=0.1,\n",
- " limit_a=(0, None), limit_b=(0, 10),\n",
- " errordef=1)\n",
+ " a=5, b=5\n",
+ " )\n",
"\n",
- "m.get_param_states()\n",
"\n",
- "\n"
+ "m.errors = (0.1, 0.1)\n",
+ "\n",
+ "m.limits['a']=(0,None)\n",
+ "m.limits['a']=(0,10)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -238,7 +135,10 @@
"m.migrad()\n",
"\n",
"# To change the value of a fixed parameter (or reset it to a different value) you can access it as:\n",
- "m.values[\"a\"] = 0.5"
+ "m.values[\"a\"] = 0.5\n",
+ "\n",
+ "# errordef = 1 for least-squares functions\n",
+ "m.errordef=Minuit.LEAST_SQUARES"
]
},
{
@@ -250,202 +150,115 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
- "<tr>\n",
- "<td colspan=\"2\" title=\"Minimum value of function\">\n",
- "FCN = 10.39\n",
- "</td>\n",
- "<td align=\"center\" colspan=\"3\" title=\"No. of calls in last algorithm and total number of calls\">\n",
- "Ncalls = 42 (106 total)\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td colspan=\"2\" title=\"Estimated distance to minimum and target threshold\">\n",
- "EDM = 4.32E-06 (Goal: 1E-05)\n",
- "</td>\n",
- "<td align=\"center\" colspan=\"3\" title=\"Increase in FCN which corresponds to 1 standard deviation\">\n",
- "up = 1.0\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td align=\"center\" title=\"Validity of the migrad call\">\n",
- "Valid Min.\n",
- "</td>\n",
- "<td align=\"center\" title=\"Validity of parameters\">\n",
- "Valid Param.\n",
- "</td>\n",
- "<td align=\"center\" title=\"Is EDM above goal EDM?\">\n",
- "Above EDM\n",
- "</td>\n",
- "<td align=\"center\" colspan=\"2\" title=\"Did last migrad call reach max call limit?\">\n",
- "Reached call limit\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td align=\"center\" title=\"Did Hesse fail?\">\n",
- "Hesse failed\n",
- "</td>\n",
- "<td align=\"center\" title=\"Has covariance matrix\">\n",
- "Has cov.\n",
- "</td>\n",
- "<td align=\"center\" title=\"Is covariance matrix accurate?\">\n",
- "Accurate\n",
- "</td>\n",
- "<td align=\"center\" title=\"Is covariance matrix positive definite?\">\n",
- "Pos. def.\n",
- "</td>\n",
- "<td align=\"center\" title=\"Was positive definiteness enforced by Minuit?\">\n",
- "Forced\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td align=\"center\" style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n",
- "<table>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td/>\n",
- "<th title=\"Variable name\">\n",
- "Name\n",
- "</th>\n",
- "<th title=\"Value of parameter\">\n",
- "Value\n",
- "</th>\n",
- "<th title=\"Hesse error\">\n",
- "Hesse Error\n",
- "</th>\n",
- "<th title=\"Minos lower error\">\n",
- "Minos Error-\n",
- "</th>\n",
- "<th title=\"Minos upper error\">\n",
- "Minos Error+\n",
- "</th>\n",
- "<th title=\"Lower limit of the parameter\">\n",
- "Limit-\n",
- "</th>\n",
- "<th title=\"Upper limit of the parameter\">\n",
- "Limit+\n",
- "</th>\n",
- "<th title=\"Is the parameter fixed in the fit\">\n",
- "Fixed\n",
- "</th>\n",
- "</tr>\n",
- "<tr style=\"background-color:#FFFFFF;\">\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "a\n",
- "</td>\n",
- "<td>\n",
- "0.99\n",
- "</td>\n",
- "<td>\n",
- "0.06\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td>\n",
- "1\n",
- "</td>\n",
- "<td>\n",
- "b\n",
- "</td>\n",
- "<td>\n",
- "1.95\n",
- "</td>\n",
- "<td>\n",
- "0.10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 5.17 </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 153 </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.69e-07 (Goal: 0.0002) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of gradient evaluations in last call and total number\"> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Parameters </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th title=\"Variable name\"> Name </th>\n",
+ " <th title=\"Value of parameter\"> Value </th>\n",
+ " <th title=\"Hesse error\"> Hesse Error </th>\n",
+ " <th title=\"Minos lower error\"> Minos Error- </th>\n",
+ " <th title=\"Minos upper error\"> Minos Error+ </th>\n",
+ " <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
+ " <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
+ " <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 0 </th>\n",
+ " <td> a </td>\n",
+ " <td> 0.99 </td>\n",
+ " <td> 0.06 </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> 0 </td>\n",
+ " <td> 10 </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 1 </th>\n",
+ " <td> b </td>\n",
+ " <td> 2.11 </td>\n",
+ " <td> 0.10 </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th> a </th>\n",
+ " <th> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> a </th>\n",
+ " <td> 0.00345 </td>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> b </th>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " <td> 0.00982 </td>\n",
+ " </tr>\n",
+ "</table>"
],
"text/plain": [
- "------------------------------------------------------------------\n",
- "| FCN = 10.39 | Ncalls=42 (106 total) |\n",
- "| EDM = 4.32E-06 (Goal: 1E-05) | up = 1.0 |\n",
- "------------------------------------------------------------------\n",
- "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
- "------------------------------------------------------------------\n",
- "| True | True | False | False |\n",
- "------------------------------------------------------------------\n",
- "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
- "------------------------------------------------------------------\n",
- "| False | True | True | True | False |\n",
- "------------------------------------------------------------------\n",
- "------------------------------------------------------------------------------------------\n",
- "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
- "------------------------------------------------------------------------------------------\n",
- "| 0 | a | 0.99 | 0.06 | | | 0 | | |\n",
- "| 1 | b | 1.95 | 0.10 | | | 0 | 10 | |\n",
- "------------------------------------------------------------------------------------------"
+ "┌──────────────────────────────────┬──────────────────────────────────────â”\n",
+ "│ FCN = 5.17 │ Nfcn = 153 │\n",
+ "│ EDM = 1.69e-07 (Goal: 0.0002) │ │\n",
+ "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
+ "│ Valid Minimum │ Valid Parameters │ 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",
+ "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────â”\n",
+ "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
+ "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
+ "│ 0 │ a │ 0.99 │ 0.06 │ │ │ 0 │ 10 │ │\n",
+ "│ 1 │ b │ 2.11 │ 0.10 │ │ │ │ │ │\n",
+ "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
+ "┌───┬───────────────────â”\n",
+ "│ │ a b │\n",
+ "├───┼───────────────────┤\n",
+ "│ a │ 0.00345 -0.00491 │\n",
+ "│ b │ -0.00491 0.00982 │\n",
+ "└───┴───────────────────┘"
]
},
- "execution_count": 6,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -458,14 +271,57 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x7f8068d2cc70>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# draw data and fitted line\n",
+ "plt.errorbar(data_x, data_y, data_yerr, fmt=\"o\", label=\"data\")\n",
+ "plt.plot(data_x, line(data_x, *m.values), label=\"fit\")\n",
+ "\n",
+ "# display legend with some fit info\n",
+ "fit_info = [\n",
+ " f\"$\\\\chi^2$ / $n_\\\\mathrm{{dof}}$ = {m.fval:.1f} / {len(data_x) - m.nfit}\", \n",
+ "]\n",
+ "for p, v, e in zip(m.parameters, m.values, m.errors):\n",
+ " fit_info.append(f\"{p} = ${v:.3f} \\\\pm {e:.3f}$\")\n",
+ "\n",
+ "plt.legend(title=\"\\n\".join(fit_info))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "FMin(fval=10.387015571126001, edm=4.319932643302712e-06, tolerance=0.1, nfcn=42, ncalls=106, 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"
+ "<FMin edm=1.6860438405671432e-07 edm_goal=0.0002 errordef=1.0 fval=5.170397387684738 has_accurate_covar=True has_covariance=True has_made_posdef_covar=False has_parameters_at_limit=False has_posdef_covar=True has_reached_call_limit=False has_valid_parameters=True hesse_failed=False is_above_max_edm=False is_valid=True nfcn=153 ngrad=0>\n"
]
}
],
@@ -473,8 +329,8 @@
"from pprint import pprint\n",
"\n",
"# To understand the results of the fit there are two dict-like objects:\n",
- "# The first one is\n",
- "pprint (m.get_fmin())\n",
+ "# The first one is m.fmin\n",
+ "pprint (m.fmin)\n",
"\n",
"# The most important one here is is_valid. If this is false, the fit did not converge and the result is useless !\n",
"#\n",
@@ -505,49 +361,29 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[Param(number=0, name='a', value=0.9908538664000521, error=0.05876818861364508, is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=False, lower_limit=0.0, upper_limit=None),\n",
- " Param(number=1, name='b', value=1.945147699139382, error=0.09907833701567537, is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=True, lower_limit=0.0, upper_limit=10.0)]\n"
+ "(Param(number=0, name='a', value=0.9936315825735781, error=0.05877309679439413, merror=None, is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=True, lower_limit=0.0, upper_limit=10.0),\n",
+ " Param(number=1, name='b', value=2.1116927916108037, error=0.0990891371833569, merror=None, is_const=False, is_fixed=False, has_limits=False, has_lower_limit=False, has_upper_limit=False, lower_limit=None, upper_limit=None))\n"
]
}
],
"source": [
- "# The second one is a list of dict-like objects which contain information about the fitted parameters:\n",
- "pprint(m.get_param_states())\n",
+ "# The second one is a list o|f dict-like objects which contain information about the fitted parameters:\n",
+ "pprint(m.params)\n",
"\n",
"# Important fields are:\n",
- "# index: parameter index.\n",
+ "# number: parameter index.\n",
"# name: parameter name.\n",
"# value: value of the parameter at the minimum.\n",
"# error: uncertainty estimate for the parameter value."
]
},
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "True\n",
- "True\n"
- ]
- }
- ],
- "source": [
- "# if you need just the basic\n",
- "print (m.migrad_ok())\n",
- "print (m.matrix_accurate())"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -557,110 +393,115 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td/>\n",
- "<th title=\"Variable name\">\n",
- "Name\n",
- "</th>\n",
- "<th title=\"Value of parameter\">\n",
- "Value\n",
- "</th>\n",
- "<th title=\"Hesse error\">\n",
- "Hesse Error\n",
- "</th>\n",
- "<th title=\"Minos lower error\">\n",
- "Minos Error-\n",
- "</th>\n",
- "<th title=\"Minos upper error\">\n",
- "Minos Error+\n",
- "</th>\n",
- "<th title=\"Lower limit of the parameter\">\n",
- "Limit-\n",
- "</th>\n",
- "<th title=\"Upper limit of the parameter\">\n",
- "Limit+\n",
- "</th>\n",
- "<th title=\"Is the parameter fixed in the fit\">\n",
- "Fixed\n",
- "</th>\n",
- "</tr>\n",
- "<tr style=\"background-color:#FFFFFF;\">\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "a\n",
- "</td>\n",
- "<td>\n",
- "0.99\n",
- "</td>\n",
- "<td>\n",
- "0.06\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td>\n",
- "1\n",
- "</td>\n",
- "<td>\n",
- "b\n",
- "</td>\n",
- "<td>\n",
- "1.95\n",
- "</td>\n",
- "<td>\n",
- "0.10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 5.17 </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 163 </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.69e-07 (Goal: 0.1) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of gradient evaluations in last call and total number\"> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Parameters </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th title=\"Variable name\"> Name </th>\n",
+ " <th title=\"Value of parameter\"> Value </th>\n",
+ " <th title=\"Hesse error\"> Hesse Error </th>\n",
+ " <th title=\"Minos lower error\"> Minos Error- </th>\n",
+ " <th title=\"Minos upper error\"> Minos Error+ </th>\n",
+ " <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
+ " <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
+ " <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 0 </th>\n",
+ " <td> a </td>\n",
+ " <td> 0.99 </td>\n",
+ " <td> 0.06 </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> 0 </td>\n",
+ " <td> 10 </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 1 </th>\n",
+ " <td> b </td>\n",
+ " <td> 2.11 </td>\n",
+ " <td> 0.10 </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th> a </th>\n",
+ " <th> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> a </th>\n",
+ " <td> 0.00345 </td>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> b </th>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " <td> 0.00982 </td>\n",
+ " </tr>\n",
+ "</table>"
],
"text/plain": [
- "------------------------------------------------------------------------------------------\n",
- "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
- "------------------------------------------------------------------------------------------\n",
- "| 0 | a | 0.99 | 0.06 | | | 0 | | |\n",
- "| 1 | b | 1.95 | 0.10 | | | 0 | 10 | |\n",
- "------------------------------------------------------------------------------------------"
+ "┌──────────────────────────────────┬──────────────────────────────────────â”\n",
+ "│ FCN = 5.17 │ Nfcn = 163 │\n",
+ "│ EDM = 1.69e-07 (Goal: 0.1) │ │\n",
+ "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
+ "│ Valid Minimum │ Valid Parameters │ 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",
+ "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────â”\n",
+ "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
+ "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
+ "│ 0 │ a │ 0.99 │ 0.06 │ │ │ 0 │ 10 │ │\n",
+ "│ 1 │ b │ 2.11 │ 0.10 │ │ │ │ │ │\n",
+ "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
+ "┌───┬───────────────────â”\n",
+ "│ │ a b │\n",
+ "├───┼───────────────────┤\n",
+ "│ a │ 0.00345 -0.00491 │\n",
+ "│ b │ -0.00491 0.00982 │\n",
+ "└───┴───────────────────┘"
]
},
- "execution_count": 10,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -679,186 +520,131 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
- "<tr>\n",
- "<td/>\n",
- "\n",
- "<th>\n",
- "a\n",
- "</th>\n",
- "<th>\n",
- "b\n",
- "</th>\n",
- "</tr>\n",
- "<tr>\n",
- "<th>\n",
- "a\n",
- "</th>\n",
- "<td>\n",
- " 0.003\n",
- "</td>\n",
- "<td style=\"background-color:rgb(140,140,250)\">\n",
- "-0.005\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<th>\n",
- "b\n",
- "</th>\n",
- "<td style=\"background-color:rgb(140,140,250)\">\n",
- "-0.005\n",
- "</td>\n",
- "<td>\n",
- " 0.010\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
+ " <tr>\n",
+ " <td></td>\n",
+ " <th> a </th>\n",
+ " <th> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> a </th>\n",
+ " <td> 0.00345 </td>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> b </th>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " <td> 0.00982 </td>\n",
+ " </tr>\n",
+ "</table>"
],
"text/plain": [
- "---------------------\n",
- "| | a b |\n",
- "---------------------\n",
- "| a | 0.003 -0.005 |\n",
- "| b | -0.005 0.010 |\n",
- "---------------------"
+ "┌───┬───────────────────â”\n",
+ "│ │ a b │\n",
+ "├───┼───────────────────┤\n",
+ "│ a │ 0.00345 -0.00491 │\n",
+ "│ b │ -0.00491 0.00982 │\n",
+ "└───┴───────────────────┘"
]
},
- "execution_count": 11,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Covariance - colored table\n",
- "m.matrix()"
+ "m.covariance"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "---------------------\n",
- "| | a b |\n",
- "---------------------\n",
- "| a | 0.003 -0.005 |\n",
- "| b | -0.005 0.010 |\n",
- "---------------------\n",
- "[[ 0.00345475 -0.00490941]\n",
- " [-0.00490941 0.00981865]]\n"
+ "0.0034546038589760453 -0.004909182102917668 -0.004909182102917668 0.009818323004527202\n"
]
}
],
"source": [
"# to access the matrix\n",
- "cov = m.matrix()\n",
- "print (cov)\n",
- "\n",
- "# or as a numpy array\n",
- "npcov = m.np_matrix()\n",
- "print (npcov)"
+ "cov = m.covariance\n",
+ "print (cov[0,0], cov[0,1], cov[1,0], cov[1,1]) "
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
- "<tr>\n",
- "<td/>\n",
- "\n",
- "<th>\n",
- "a\n",
- "</th>\n",
- "<th>\n",
- "b\n",
- "</th>\n",
- "</tr>\n",
- "<tr>\n",
- "<th>\n",
- "a\n",
- "</th>\n",
- "<td>\n",
- " 1.00\n",
- "</td>\n",
- "<td style=\"background-color:rgb(140,140,250)\">\n",
- "-0.84\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<th>\n",
- "b\n",
- "</th>\n",
- "<td style=\"background-color:rgb(140,140,250)\">\n",
- "-0.84\n",
- "</td>\n",
- "<td>\n",
- " 1.00\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
+ " <tr>\n",
+ " <td></td>\n",
+ " <th> a </th>\n",
+ " <th> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> a </th>\n",
+ " <td> 1 </td>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.843 </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> b </th>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.843 </td>\n",
+ " <td> 1 </td>\n",
+ " </tr>\n",
+ "</table>"
],
"text/plain": [
- "-----------------\n",
- "| | a b |\n",
- "-----------------\n",
- "| a | 1.0 -0.8 |\n",
- "| b | -0.8 1.0 |\n",
- "-----------------"
+ "┌───┬───────────────â”\n",
+ "│ │ a b │\n",
+ "├───┼───────────────┤\n",
+ "│ a │ 1 -0.843 │\n",
+ "│ b │ -0.843 1 │\n",
+ "└───┴───────────────┘"
]
},
- "execution_count": 13,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Correlation - colored table\n",
- "m.matrix(correlation=True)"
+ "m.covariance.correlation()"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "-----------------\n",
- "| | a b |\n",
- "-----------------\n",
- "| a | 1.0 -0.8 |\n",
- "| b | -0.8 1.0 |\n",
- "-----------------\n",
- "[[ 1. -0.84293683]\n",
- " [-0.84293683 1. ]]\n"
+ "1.0 -0.8429297028614006 -0.8429297028614006 1.0\n"
]
}
],
"source": [
- "# to access the matrix\n",
- "corr = m.matrix(correlation=True)\n",
- "print (corr)\n",
"\n",
- "# or as a numpy array\n",
- "npcov = m.np_matrix(correlation=True)\n",
- "print (npcov)"
+ "# to access the matrix\n",
+ "corr = m.covariance.correlation()\n",
+ "print (corr[0,0], corr[0,1], corr[1,0], corr[1,1]) "
]
},
{
@@ -870,7 +656,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
@@ -891,166 +677,165 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
- "<tr>\n",
- "<th title=\"Parameter name\">\n",
- "a\n",
- "</th>\n",
- "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
- "Valid\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Lower and upper minos error of the parameter\">\n",
- "Error\n",
- "</td>\n",
- "<td>\n",
- "-0.06\n",
- "</td>\n",
- "<td>\n",
- " 0.06\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Validity of lower/upper minos error\">\n",
- "Valid\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Did scan hit limit of any parameter?\">\n",
- "At Limit\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Did scan hit function call limit?\">\n",
- "Max FCN\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"New minimum found when doing scan?\">\n",
- "New Min\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n",
- "\n",
- "<table>\n",
- "<tr>\n",
- "<th title=\"Parameter name\">\n",
- "b\n",
- "</th>\n",
- "<td align=\"center\" colspan=\"2\" style=\"background-color:#92CCA6;\">\n",
- "Valid\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Lower and upper minos error of the parameter\">\n",
- "Error\n",
- "</td>\n",
- "<td>\n",
- "-0.10\n",
- "</td>\n",
- "<td>\n",
- " 0.10\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Validity of lower/upper minos error\">\n",
- "Valid\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "True\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Did scan hit limit of any parameter?\">\n",
- "At Limit\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"Did scan hit function call limit?\">\n",
- "Max FCN\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "<tr>\n",
- "<td title=\"New minimum found when doing scan?\">\n",
- "New Min\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "<td style=\"background-color:#92CCA6;\">\n",
- "False\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 5.17 </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of function evaluations in last call and total number\"> Nfcn = 195 </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.69e-07 (Goal: 0.1) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center\" title=\"No. of gradient evaluations in last call and total number\"> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Parameters </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
+ " <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
+ " <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th title=\"Variable name\"> Name </th>\n",
+ " <th title=\"Value of parameter\"> Value </th>\n",
+ " <th title=\"Hesse error\"> Hesse Error </th>\n",
+ " <th title=\"Minos lower error\"> Minos Error- </th>\n",
+ " <th title=\"Minos upper error\"> Minos Error+ </th>\n",
+ " <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
+ " <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
+ " <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 0 </th>\n",
+ " <td> a </td>\n",
+ " <td> 0.99 </td>\n",
+ " <td> 0.06 </td>\n",
+ " <td> -0.06 </td>\n",
+ " <td> 0.06 </td>\n",
+ " <td> 0 </td>\n",
+ " <td> 10 </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> 1 </th>\n",
+ " <td> b </td>\n",
+ " <td> 2.11 </td>\n",
+ " <td> 0.10 </td>\n",
+ " <td> -0.10 </td>\n",
+ " <td> 0.10 </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " <td> </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> a </th>\n",
+ " <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th title=\"Lower and upper minos error of the parameter\"> Error </th>\n",
+ " <td> -0.06 </td>\n",
+ " <td> 0.06 </td>\n",
+ " <td> -0.1 </td>\n",
+ " <td> 0.1 </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th title=\"Validity of lower/upper minos error\"> Valid </th>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th title=\"Did scan hit limit of any parameter?\"> At Limit </th>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th title=\"Did scan hit function call limit?\"> Max FCN </th>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th title=\"New minimum found when doing scan?\"> New Min </th>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
+ " </tr>\n",
+ "</table><table>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " <th> a </th>\n",
+ " <th> b </th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> a </th>\n",
+ " <td> 0.00345 </td>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> b </th>\n",
+ " <td style=\"background-color:rgb(140,140,250);color:black\"> -0.00491 <strong>(-0.843)</strong> </td>\n",
+ " <td> 0.00982 </td>\n",
+ " </tr>\n",
+ "</table>"
],
"text/plain": [
- "-------------------------------------------------\n",
- "| a | Valid |\n",
- "-------------------------------------------------\n",
- "| Error | -0.06 | 0.06 |\n",
- "| Valid | True | True |\n",
- "| At Limit | False | False |\n",
- "| Max FCN | False | False |\n",
- "| New Min | False | False |\n",
- "-------------------------------------------------\n",
- "-------------------------------------------------\n",
- "| b | Valid |\n",
- "-------------------------------------------------\n",
- "| Error | -0.10 | 0.10 |\n",
- "| Valid | True | True |\n",
- "| At Limit | False | False |\n",
- "| Max FCN | False | False |\n",
- "| New Min | False | False |\n",
- "-------------------------------------------------"
+ "┌──────────────────────────────────┬──────────────────────────────────────â”\n",
+ "│ FCN = 5.17 │ Nfcn = 195 │\n",
+ "│ EDM = 1.69e-07 (Goal: 0.1) │ │\n",
+ "├───────────────┬──────────────────┼──────────────────────────────────────┤\n",
+ "│ Valid Minimum │ Valid Parameters │ 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",
+ "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────â”\n",
+ "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n",
+ "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
+ "│ 0 │ a │ 0.99 │ 0.06 │ -0.06 │ 0.06 │ 0 │ 10 │ │\n",
+ "│ 1 │ b │ 2.11 │ 0.10 │ -0.10 │ 0.10 │ │ │ │\n",
+ "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
+ "┌──────────┬───────────────────────┬───────────────────────â”\n",
+ "│ │ a │ b │\n",
+ "├──────────┼───────────┬───────────┼───────────┬───────────┤\n",
+ "│ Error │ -0.06 │ 0.06 │ -0.1 │ 0.1 │\n",
+ "│ Valid │ True │ True │ True │ True │\n",
+ "│ At Limit │ False │ False │ False │ False │\n",
+ "│ Max FCN │ False │ False │ False │ False │\n",
+ "│ New Min │ False │ False │ False │ False │\n",
+ "└──────────┴───────────┴───────────┴───────────┴───────────┘\n",
+ "┌───┬───────────────────â”\n",
+ "│ │ a b │\n",
+ "├───┼───────────────────┤\n",
+ "│ a │ 0.00345 -0.00491 │\n",
+ "│ b │ -0.00491 0.00982 │\n",
+ "└───┴───────────────────┘"
]
},
- "execution_count": 16,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -1061,143 +846,47 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
- "data": {
- "text/html": [
- "<table>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td/>\n",
- "<th title=\"Variable name\">\n",
- "Name\n",
- "</th>\n",
- "<th title=\"Value of parameter\">\n",
- "Value\n",
- "</th>\n",
- "<th title=\"Hesse error\">\n",
- "Hesse Error\n",
- "</th>\n",
- "<th title=\"Minos lower error\">\n",
- "Minos Error-\n",
- "</th>\n",
- "<th title=\"Minos upper error\">\n",
- "Minos Error+\n",
- "</th>\n",
- "<th title=\"Lower limit of the parameter\">\n",
- "Limit-\n",
- "</th>\n",
- "<th title=\"Upper limit of the parameter\">\n",
- "Limit+\n",
- "</th>\n",
- "<th title=\"Is the parameter fixed in the fit\">\n",
- "Fixed\n",
- "</th>\n",
- "</tr>\n",
- "<tr style=\"background-color:#FFFFFF;\">\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "a\n",
- "</td>\n",
- "<td>\n",
- " 0.99\n",
- "</td>\n",
- "<td>\n",
- " 0.06\n",
- "</td>\n",
- "<td>\n",
- "-0.06\n",
- "</td>\n",
- "<td>\n",
- " 0.06\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "<tr style=\"background-color:#F4F4F4;\">\n",
- "<td>\n",
- "1\n",
- "</td>\n",
- "<td>\n",
- "b\n",
- "</td>\n",
- "<td>\n",
- " 1.95\n",
- "</td>\n",
- "<td>\n",
- " 0.10\n",
- "</td>\n",
- "<td>\n",
- "-0.10\n",
- "</td>\n",
- "<td>\n",
- " 0.10\n",
- "</td>\n",
- "<td>\n",
- "0\n",
- "</td>\n",
- "<td>\n",
- "10\n",
- "</td>\n",
- "<td>\n",
- "\n",
- "</td>\n",
- "</tr>\n",
- "</table>\n"
- ],
- "text/plain": [
- "------------------------------------------------------------------------------------------\n",
- "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
- "------------------------------------------------------------------------------------------\n",
- "| 0 | a | 0.99 | 0.06 | -0.06 | 0.06 | 0 | | |\n",
- "| 1 | b | 1.95 | 0.10 | -0.10 | 0.10 | 0 | 10 | |\n",
- "------------------------------------------------------------------------------------------"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(Param(number=0, name='a', value=0.9936315825735781, error=0.058772096719407974, merror=(-0.05875142510027605, 0.05879936709187449), is_const=False, is_fixed=False, has_limits=True, has_lower_limit=True, has_upper_limit=True, lower_limit=0.0, upper_limit=10.0),\n",
+ " Param(number=1, name='b', value=2.1116927916108037, error=0.0990874512969589, merror=(-0.09912333982025215, 0.09905012403395125), is_const=False, is_fixed=False, has_limits=False, has_lower_limit=False, has_upper_limit=False, lower_limit=None, upper_limit=None))\n"
+ ]
}
],
"source": [
- "m.get_param_states()"
+ "pprint(m.params)"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "array([0.99085387, 1.9451477 ])\n",
- "array([0.05876843, 0.09907874])\n",
- "array([[0.05866313, 0.09929038],\n",
- " [0.05888831, 0.09888435]])\n",
- "array([[ 0.00345475, -0.00490941],\n",
- " [-0.00490941, 0.00981865]])\n"
+ "<ValueView a=0.9936315825735781 b=2.1116927916108037>\n",
+ "<ErrorView a=0.058772096719407974 b=0.0990874512969589>\n",
+ "{'a': <MError number=0 name='a' lower=-0.05875142510027605 upper=0.05879936709187449 is_valid=True lower_valid=True upper_valid=True at_lower_limit=False at_upper_limit=False at_lower_max_fcn=False at_upper_max_fcn=False lower_new_min=False upper_new_min=False nfcn=16 min=0.9936315825735781>,\n",
+ " 'b': <MError number=1 name='b' lower=-0.09912333982025215 upper=0.09905012403395125 is_valid=True lower_valid=True upper_valid=True at_lower_limit=False at_upper_limit=False at_lower_max_fcn=False at_upper_max_fcn=False lower_new_min=False upper_new_min=False nfcn=16 min=2.1116927916108037>}\n",
+ "[[ 0.0034546 -0.00490918]\n",
+ " [-0.00490918 0.00981832]]\n"
]
}
],
"source": [
"# Other ways to access the fits results\n",
- "pprint(m.np_values())\n",
- "pprint(m.np_errors())\n",
- "pprint(m.np_merrors()) # The layout returned by Minuit.np_merrors() follows the convention \n",
+ "pprint(m.values)\n",
+ "pprint(m.errors)\n",
+ "pprint(m.merrors) # The layout returned by Minuit.np_merrors() follows the convention \n",
" # [abs(delta_down), delta_up] that is used by matplotlib.pyplot.errorbar.\n",
- "pprint(m.np_covariance())"
+ "pprint(m.covariance)"
]
},
{
@@ -1209,22 +898,22 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.contour.ContourSet at 0x120abe7d0>"
+ "<matplotlib.contour.ContourSet at 0x7f806cd93f40>"
]
},
- "execution_count": 19,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
@@ -1237,17 +926,17 @@
],
"source": [
"plt.figure(figsize=[5,5])\n",
- "m.draw_mncontour('a','b', nsigma=4, numpoints=100) # nsigma=4 says: draw four contours from sigma=1 to 4v"
+ "m.draw_mncontour('a','b', cl=(0.68, 0.9, 0.99), size=100) "
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1264,12 +953,12 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1286,12 +975,12 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -1331,7 +1020,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.7"
+ "version": "3.8.5"
}
},
"nbformat": 4,