updates

notebooks/02_fit_ex_3_sol.ipynb

@@ -89,7 +89,9 @@
    "outputs": [],
    "source": [
     "# run migrad for minimization\n",
-    "m.migrad()"
+    "m.migrad()\n",
+    "chi2 = m.fval / (len(y) - len(m.values))\n",
+    "print (\"Chi2/ndof =\" , chi2)"
    ]
   },
   {
@@ -231,9 +233,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:ML]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-ML-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -245,7 +247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

notebooks/02_fit_exp_fit_iMinuit.ipynb

@@ -116,6 +116,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "m.fixed[\"c\"] = True\n",
     "m.migrad()"
    ]
   },
@@ -154,13 +155,6 @@
     "m.covariance"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copy covariance information to numpy arrays"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -192,7 +186,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "m.draw_mncontour('a', 'b')"
+    "m.draw_mncontour('a', 'b', cl=[1,2,3,4])"
    ]
   },
   {
@@ -209,6 +203,9 @@
    "outputs": [],
    "source": [
     "print(m.values,m.errors)\n",
+    "print (m.merrors['a'])\n",
+    "print (m.merrors['a'].lower)\n",
+    "print (m.merrors['a'].upper)\n",
     "a_fit = m.values[\"a\"]\n",
     "b_fit = m.values[\"b\"]\n",
     "c_fit = m.values[\"c\"]"
@@ -251,20 +248,13 @@
     "plt.xlim(-0.1, 4.1)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:ML]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-ML-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -276,7 +266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,

slides/02_fit_intro.md

@@ -306,7 +306,7 @@ Fit process with the minuit package
      # limit the range of b and fix parameter c
      m = Minuit(fcn,a=1,b=-0.7,c=1)
      m.migrad()                      # run minimizer
-     m.fixed["c"] = True             # fix or release  parameter c
+     m.fixed["c"] = False / True     # fix or release  parameter c
      m.migrad()                      # rerun minimizer
      # Might be useful to fix parameters or limit the range for some applications
 
@@ -341,10 +341,9 @@ Fit process with the minuit package
      ......
      m.hesse()                           # run covariance estimator
      m.matrix()                          # get covariance matrix
-     m.matrix(correlation=True)          # get full correlation matrix
-     cov = m.np_matrix()                 # save matrix to numpy
-     cor = m.np_matrix(correlation=True) 
-     print(cor[0, 1])      # print correlation between parameter 1 and 2
+     m.covariance                        # get full covariance matrix
+     cov = m.covariance                  # save matrix to access by numpy
+     print(cov[0, 1])             # print correlation between parameter 1 and 2
  ```
 \normalsize