📝 update of tutorial

Author: Tesshub

tutorials/Identification_python model of an opaque wall.ipynb

@@ -72,8 +72,6 @@
     "import pandas as pd\n",
     "import datetime as dt\n",
     "\n",
-    "from docutils.nodes import reference\n",
-    "\n",
     "from corrai.base.model import PyModel\n",
     "\n",
     "class OpaqueWallSimple(PyModel):\n",
@@ -439,7 +437,7 @@
    "id": "9a24e457cc8ad243",
    "metadata": {},
    "source": [
-    "from corrai.optimize import SciOptimizer, MixedProblem\n",
+    "from corrai.optimize import SciOptimizer\n",
     "\n",
     "sci_opt = SciOptimizer(\n",
     "    model=OpaqueWallSimple(),\n",
@@ -848,6 +846,31 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "You can use the function `plot_parameter_forest` from **corrai.optimize**, or as a method directly implemented in `PymooModelEvaluator`.\n",
+    "\n",
+    "This function provides a compact visualization of the calibrated parameters, showing the optimized value together with the lower and upper bounds defined for each parameter. It can be displayed either in absolute values, relative values, or normalized coordinates, making it easy to identify parameters that converge near their admissible limits."
+   ],
+   "id": "825951bc1bc47737"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "pymoo_ev.plot_parameter_forest(\n",
+    "    optimal_values=res.X[i],\n",
+    "    mode=\"absolute\",\n",
+    "    width=300,\n",
+    "    height=300,\n",
+    ")"
+   ],
+   "id": "16afc5745eea4fd6",
+   "outputs": [],
+   "execution_count": null
+  },
   {
    "cell_type": "markdown",
    "id": "6af5abd476a2d1ce",