docs: add live plot of fit with custom callback

cspell.json

@@ -181,6 +181,8 @@
         "unflattened",
         "unnormalized",
         "vstack",
-        "xlabel"
+        "xlabel",
+        "xlim",
+        "ylim"
     ]
 }

docs/usage.ipynb

@@ -242,9 +242,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": [
      "hide-cell"
     ]
@@ -271,7 +268,7 @@
     "def indicate_masses():\n",
     "    plt.xlabel(\"$m$ [GeV]\")\n",
     "    for i, p in enumerate(intermediate_states):\n",
-    "        plt.axvline(\n",
+    "        plt.gca().axvline(\n",
     "            x=p.mass, linestyle=\"dotted\", label=p.name, color=colors[i]\n",
     "        )"
    ]
@@ -319,9 +316,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    },
     "tags": [
      "hide-cell"
     ]
@@ -365,10 +359,8 @@
     "initial_parameters = {\n",
     "    \"C[J/\\\\psi(1S) \\\\to f_{0}(1500)_{0} \\\\gamma_{+1};f_{0}(1500) \\\\to \\\\pi^{0}_{0} \\\\pi^{0}_{0}]\": 1.0\n",
     "    + 0.0j,\n",
-    "    \"m_f(0)(500)\": 0.6,\n",
     "    \"Gamma_f(0)(500)\": 0.3,\n",
-    "    \"Gamma_f(0)(980)\": 0.2,\n",
-    "    \"m_f(0)(1370)\": 1.3,\n",
+    "    \"Gamma_f(0)(980)\": 0.1,\n",
     "    \"m_f(0)(1710)\": 1.75,\n",
     "    \"Gamma_f(0)(1710)\": 0.2,\n",
     "}\n",
@@ -377,15 +369,71 @@
     "print(\"Number of free parameters:\", len(initial_parameters))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{tip}\n",
+    "Insert behavior into the {class}`.Optimizer` by defining a custom {class}`.Callback` class. Here's one that live updates a plot of the latest fit model!\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
    "outputs": [],
    "source": [
-    "minuit2 = Minuit2(callback=CSVSummary(\"traceback.csv\", step_size=2))\n",
-    "result = minuit2.optimize(estimator, initial_parameters)\n",
-    "result"
+    "import os\n",
+    "\n",
+    "from IPython.display import clear_output\n",
+    "from tensorwaves.optimizer.callbacks import Callback, CallbackList\n",
+    "\n",
+    "\n",
+    "class PyplotCallback(Callback):\n",
+    "    def __init__(self, step_size=10):\n",
+    "        self.__step_size = step_size\n",
+    "        self.__fig, self.__ax = plt.subplots(1, figsize=(8, 5))\n",
+    "        self.__latest_parameters = {}\n",
+    "\n",
+    "    def on_iteration_end(self, function_call, logs=None):\n",
+    "        self.__latest_parameters = logs[\"parameters\"]\n",
+    "        if function_call % self.__step_size != 0:\n",
+    "            return\n",
+    "        if \"READTHEDOCS\" in os.environ:\n",
+    "            return\n",
+    "        self.update_plot()\n",
+    "        clear_output(wait=True)\n",
+    "        display(plt.gcf())\n",
+    "\n",
+    "    def on_function_call_end(self):\n",
+    "        self.update_plot()\n",
+    "\n",
+    "    def update_plot(self):\n",
+    "        bins = 100\n",
+    "        data = data_set[\"m_3+4\"]\n",
+    "        phsp = phsp_set[\"m_3+4\"]\n",
+    "        intensity.update_parameters(self.__latest_parameters)\n",
+    "        intensities = intensity(phsp_set)\n",
+    "        self.__ax.cla()\n",
+    "        self.__ax.hist(data, bins=bins, alpha=0.5, label=\"data\", density=True)\n",
+    "        self.__ax.hist(\n",
+    "            phsp,\n",
+    "            weights=intensities,\n",
+    "            bins=bins,\n",
+    "            histtype=\"step\",\n",
+    "            color=\"red\",\n",
+    "            label=\"fit model\",\n",
+    "            density=True,\n",
+    "        )\n",
+    "        self.__ax.set_xlim((0.25, 2.5))\n",
+    "        self.__ax.set_ylim((0, 1.9))\n",
+    "        indicate_masses()\n",
+    "        plt.gcf().legend()"
    ]
   },
   {
@@ -394,8 +442,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "intensity.update_parameters(result[\"parameter_values\"])\n",
-    "compare_model(\"m_3+4\", data_set, phsp_set, intensity)"
+    "minuit2 = Minuit2(\n",
+    "    callback=CallbackList(\n",
+    "        [CSVSummary(\"traceback.csv\", step_size=2), PyplotCallback()]\n",
+    "    )\n",
+    ")\n",
+    "fit_result = minuit2.optimize(estimator, initial_parameters)\n",
+    "fit_result"
    ]
   },
   {