Merge pull request #12 from tudo-astroparticlephysics/update_fitting

* Fix typos
* harmonize matplotlib usage

Author: LukasBeiske

smd_handson_fitting.ipynb

@@ -7,24 +7,6 @@
     "<span style=\"font-size: 3rem; font-weight: bold;\">SMD Hands-On Estimators / Fitting</span>"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%javascript\n",
-    "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
-    "<div id=\"toc\"></div>"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -133,28 +115,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# create some randomized example data points\n",
     "rng = np.random.default_rng(1337)\n",
     "\n",
     "N = 100\n",
     "true_parameters = np.array([2, 1, 0.5])\n",
-    "y_unc = rng.uniform(0.1, 0.4, N)\n",
-    "\n",
     "x = np.linspace(0, 4 * np.pi, N)\n",
     "y = linear_combination(x, funcs, true_parameters)\n",
     "\n",
     "# add some noise for to simulate measurement uncertainty\n",
+    "y_unc = rng.uniform(0.1, 0.4, N)\n",
     "y += rng.normal(0, y_unc)\n",
     "\n",
     "plt.figure()\n",
     "plt.errorbar(x, y, yerr=y_unc, ls='', marker='.', color='k')\n",
     "plt.xlabel('x')\n",
-    "plt.ylabel('y')"
+    "plt.ylabel('y');"
    ]
   },
   {
@@ -213,7 +192,7 @@
     "\n",
     "fig, ax = plt.subplots()\n",
     "plot = ax.matshow(W)\n",
-    "fig.colorbar(plot)"
+    "fig.colorbar(plot);"
    ]
   },
   {
@@ -239,7 +218,7 @@
    "outputs": [],
    "source": [
     "def solve_linear_least_squares(A, W, y):\n",
-    "    \"\"\"Solve the linear least sqaures problem\n",
+    "    \"\"\"Solve the linear least squares problem\n",
     "    \n",
     "    Parameters\n",
     "    ----------\n",
@@ -378,7 +357,7 @@
     ")\n",
     "\n",
     "\n",
-    "ax.legend()"
+    "ax.legend();"
    ]
   },
   {
@@ -389,10 +368,9 @@
    "source": [
     "corr = cov_to_corr(cov)\n",
     "\n",
-    "fig = plt.figure(constrained_layout=True)\n",
-    "ax = fig.add_subplot(1, 1, 1)\n",
+    "fig, ax = plt.subplots()\n",
     "m = ax.matshow(corr, cmap='RdBu_r', vmin=-1, vmax=1)\n",
-    "fig.colorbar(m)"
+    "fig.colorbar(m);"
    ]
   },
   {
@@ -469,7 +447,7 @@
     ")\n",
     "\n",
     "\n",
-    "ax.legend()"
+    "ax.legend();"
    ]
   },
   {
@@ -512,9 +490,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = pd.read_csv('resources/spalt.csv')\n",
@@ -547,9 +523,7 @@
     "    xlabel=r'$\\varphi \\,\\, / \\,\\, \\mathrm{rad}$',\n",
     "    ylabel=r'$I \\,\\, / \\,\\, \\mathrm{A}$',\n",
     ")\n",
-    "ax.legend(loc='best')\n",
-    "\n",
-    "None # to not have mpl objects in the output"
+    "ax.legend(loc='best');"
    ]
   },
   {
@@ -583,10 +557,10 @@
     "E_MAX = 175\n",
     "\n",
     "# normally distributed signal\n",
-    "higgs_signal = np.random.normal(126, 5, 500)\n",
+    "higgs_signal = rng.normal(126, 5, 500)\n",
     "\n",
     "# exponentially distributed background\n",
-    "background = np.random.exponential(50, size=20000)\n",
+    "background = rng.exponential(50, size=20000)\n",
     "\n",
     "# combine signal and background\n",
     "measured = np.append(higgs_signal, background)\n",
@@ -598,7 +572,7 @@
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.hist(measured, bins=100)\n",
-    "ax.set_xlabel('$m \\,/\\, \\mathrm{GeV}$')\n",
+    "ax.set_xlabel(r'$m \\,/\\, \\mathrm{GeV}$')\n",
     "ax.margins(x=0)\n",
     "None"
    ]
@@ -714,7 +688,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "from scipy.optimize import minimize\n",
@@ -768,7 +744,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "e = np.linspace(E_MIN, E_MAX, 1000)\n",
     "\n",
     "\n",
@@ -778,7 +753,7 @@
     "ax.plot(e, pdf(e, *result.x))\n",
     "ax.axvline(result.x[0], color='C2', lw=2)\n",
     "\n",
-    "ax.set_xlabel('$m \\,/\\, \\mathrm{GeV}$')\n",
+    "ax.set_xlabel(r'$m \\,/\\, \\mathrm{GeV}$')\n",
     "ax.margins(x=0)"
    ]
   },
@@ -904,9 +879,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "N = np.genfromtxt(\"resources/muon_data.txt\")\n",
@@ -1000,17 +973,18 @@
    "source": [
     "plt.figure()\n",
     "\n",
-    "plt.axvline(m.values['tau'], color='C1')\n",
+    "plt.axvline(m.values['tau'], color='C1', label=\"Fit-Result\")\n",
     "plt.plot(m.values['tau'], m.fval, color='C1', marker='o', zorder=3)\n",
     "\n",
-    "plt.axvline(m.values['tau'] + m.merrors['tau'].lower, color='C2')\n",
-    "plt.axvline(m.values['tau'] + m.merrors['tau'].upper, color='C2')\n",
-    "plt.axhline(m.fval + 1, color='C3')\n",
+    "plt.axvline(m.values['tau'] + m.merrors['tau'].lower, color='C2', label=\"Lower Error\")\n",
+    "plt.axvline(m.values['tau'] + m.merrors['tau'].upper, color='C2', label=\"Upper Error\")\n",
+    "plt.axhline(m.fval + 1, color='C3', label=\"NLL + 1\")\n",
     "plt.axvline(pdg_reference, color='k', label='PDG')\n",
     "plt.plot(tau, ts)\n",
     "\n",
     "plt.xlabel('τ / µs')\n",
     "plt.ylabel('NLL')\n",
+    "plt.legend()\n",
     "None"
    ]
   },
@@ -1021,7 +995,7 @@
    "outputs": [],
    "source": [
     "plt.figure()\n",
-    "m.draw_mncontour('tau', 'p', size=250)"
+    "m.draw_mncontour('tau', 'p', size=250);"
    ]
   },
   {
@@ -1048,7 +1022,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,