From 377060993addf17d38aa79f6e2306beb8d1b4d7f Mon Sep 17 00:00:00 2001
From: Abelardo Moralejo Olaizola <moralejo@ifae.es>
Date: Fri, 15 Nov 2024 15:39:08 +0100
Subject: [PATCH] Addressed more comments by reviewer

---
 notebooks/LST1_observation_simulator.ipynb | 25 ++++++++++++++--------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/notebooks/LST1_observation_simulator.ipynb b/notebooks/LST1_observation_simulator.ipynb
index 919ab3748..6afcba744 100644
--- a/notebooks/LST1_observation_simulator.ipynb
+++ b/notebooks/LST1_observation_simulator.ipynb
@@ -20,7 +20,7 @@
     "from scipy.stats import moyal, norm, skewnorm\n",
     "from pathlib import Path\n",
     "from lstchain.io.io import get_resource_path\n",
-    "from lstchain import version"
+    "from lstchain.version import version as lstchain_version"
    ]
   },
   {
@@ -271,8 +271,14 @@
     "# We will apply the Dominguez EBL model to simulate the absorption\n",
     "\n",
     "dominguez_ebl_file = get_resource_path(f'data/ebl_dominguez11.fits.gz')\n",
-    "dominguez = EBLAbsorptionNormSpectralModel.read(dominguez_ebl_file, redshift=redshift)\n",
+    "ebl_model = EBLAbsorptionNormSpectralModel.read(dominguez_ebl_file, redshift=redshift)\n",
     "\n",
+    "\n",
+    "###############################################################################################\n",
+    "#\n",
+    "# In case you want to use other EBL models available in gammapy (e.g. Franceschini) you can try \n",
+    "# the lines below: \n",
+    "#\n",
     "# Make sure we have the necessary EBL absorption data:\n",
     "# try:\n",
     "#     os.environ['GAMMAPY_DATA']\n",
@@ -286,9 +292,10 @@
     "#     gammapy_data = ebl_file[:ebl_file.find('/ebl/')]\n",
     "#     os.environ['GAMMAPY_DATA'] = gammapy_data\n",
     "#     print('Set GAMMAPY_DATA to', gammapy_data)\n",
-    "\n",
-    "\n",
-    "# dominguez = EBLAbsorptionNormSpectralModel.read_builtin(\"dominguez\", redshift=redshift)"
+    "#\n",
+    "#\n",
+    "# ebl_model = EBLAbsorptionNormSpectralModel.read_builtin(\"franceschini\", redshift=redshift)\n",
+    "#"
    ]
   },
   {
@@ -353,7 +360,7 @@
    "source": [
     "# After EBL absorption:\n",
     "def dFdE(E):\n",
-    "    return intrinsic_dFdE(E) * dominguez.evaluate(E, redshift, 1)"
+    "    return intrinsic_dFdE(E) * ebl_model.evaluate(E, redshift, 1)"
    ]
   },
   {
@@ -933,7 +940,7 @@
     "        str += '\\nPoint-like source'\n",
     "    else:\n",
     "        str += f'\\nSource radius: {source_radius:.2f}$^\\\\circ$ ({cut_efficiency:.0%} containment)'\n",
-    "    str += f'\\n\\ncta-lstchain {version.version}'\n",
+    "    str += f'\\n\\ncta-lstchain {lstchain_version}'\n",
     "    \n",
     "    ax.text(0.05, 0.38, str, \n",
     "            transform=ax.transAxes, fontsize=12, verticalalignment='top',\n",
@@ -974,7 +981,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c927ed19",
+   "id": "d56d43a4",
    "metadata": {},
    "source": [
     "### NOTE: in reality, other systematics from data-MC discrepancies (in Aeff, energy-dependent) may make the point-to point fluctuations larger than shown above. Systematic errors of around a few percent (independent in each bin) are to be expected.\n"
@@ -983,7 +990,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f3089dde",
+   "id": "29c63cec",
    "metadata": {},
    "outputs": [],
    "source": []