From 7328ee9522339d21d5b28c9ce915aeac7d094767 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Mon, 22 Jan 2024 10:36:45 +0100
Subject: [PATCH] correct-offset False by default

---
 notebooks/Jungfrau/Jungfrau_Create_Gain_maps_NBC.ipynb | 2 +-
 notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/notebooks/Jungfrau/Jungfrau_Create_Gain_maps_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Create_Gain_maps_NBC.ipynb
index b73e09827..c13351f6c 100755
--- a/notebooks/Jungfrau/Jungfrau_Create_Gain_maps_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Create_Gain_maps_NBC.ipynb
@@ -48,7 +48,7 @@
     "creation_time = \"\"  # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. \"2022-06-28 13:00:00\"\n",
     "g_map_old = ''  # '/gpfs/exfel/data/user/mramilli/jungfrau/module_PSI_gainmaps/M302/gainMaps_M302_2022-02-23.h5'  # old gain map file path to calculate gain ratios G0/G1 and G1/G2. Set to \"\" to get last gain constant from the database.\n",
     "old_gain_dataset_name = 'gain_map_g0'  # name of the data structure in the old gain map\n",
-    "correct_offset = True  # correct the photon peak value with a pedestal fit position\n",
+    "correct_offset = False  # correct the photon peak value with a pedestal fit position\n",
     "db_output = False\n",
     "local_output = True\n",
     "send_bpix = False  # TODO: check why separate BPx from Gain.\n",
diff --git a/notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb
index 9f1a6ae5e..5939c1e2a 100644
--- a/notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_gain_map_Summary_NBC.ipynb
@@ -137,14 +137,13 @@
     "         Path(out_folder) / f\"const_{cname}_{pdu}.h5\",\n",
     "         'r'\n",
     "      ) as f:\n",
-    "         \n",
     "         for j, g in enumerate(gains):\n",
     "            stacked_constants[g][i] = np.moveaxis(\n",
     "               np.mean(\n",
     "                  f[\"data\"][..., j],\n",
     "                  axis=-1\n",
     "               ), 0, 1).astype(np.float32 if cname == \"RelativeGain\" else np.uint32)\n",
-    "   \n",
+    "\n",
     "   display(Markdown(f\"### {cname} map per gain\"))\n",
     "   for g in gains:\n",
     "      fig, ax = plt.subplots(figsize=(18, 10))\n",
-- 
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