From 45405f4a6bca1ebeeecbb0673ecbb8afb28e0a5e Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Mon, 16 Jan 2023 17:00:21 +0100
Subject: [PATCH] skip characterize_module and skip plots for module with no
data
---
notebooks/LPD/LPDChar_Darks_NBC.ipynb | 24 +++++++++++++++++++++++-
1 file changed, 23 insertions(+), 1 deletion(-)
diff --git a/notebooks/LPD/LPDChar_Darks_NBC.ipynb b/notebooks/LPD/LPDChar_Darks_NBC.ipynb
index 5f5749ee5..c9508aa5c 100644
--- a/notebooks/LPD/LPDChar_Darks_NBC.ipynb
+++ b/notebooks/LPD/LPDChar_Darks_NBC.ipynb
@@ -53,6 +53,9 @@
"thresholds_noise_hard = [1, 35] # bad pixel hard threshold\n",
"skip_first_ntrains = 10 # Number of first trains to skip\n",
"\n",
+ "# Parameters for plotting\n",
+ "skip_plots = False # exit after writing corrected files\n",
+ "\n",
"instrument = \"FXE\" # instrument name\n",
"ntrains = 100 # number of trains to use\n",
"high_res_badpix_3d = False # plot bad-pixel summary in high resolution\n",
@@ -74,6 +77,7 @@
"from collections import OrderedDict\n",
"from datetime import datetime\n",
"from functools import partial\n",
+ "from logging import warning\n",
"\n",
"warnings.filterwarnings('ignore')\n",
"\n",
@@ -228,7 +232,9 @@
" im = np.array(infile[\"{}/data\".format(instrument_src, channel)][first_image:last_image, ...])\n",
" cellid = np.squeeze(np.array(infile[\"{}/cellId\".format(instrument_src, channel)][first_image:last_image, ...]))\n",
" infile.close()\n",
- " \n",
+ " if im.shape[0] == 0: # No data\n",
+ " return None, None, channel, gg, cap, None, None, None, None\n",
+ "\n",
" cellid_pattern = cellid[:count[0]]\n",
"\n",
" im, g = splitOffGainLPD(im[:, 0, ...])\n",
@@ -323,6 +329,10 @@
"\n",
"for ir, r in enumerate(results):\n",
" offset, noise, i, gg, cap, bp, data, normal, cellid_pattern = r\n",
+ " if data is None:\n",
+ " warning(f\"No data for module {i} of gain {gg}\")\n",
+ " skip_plots = True\n",
+ " continue\n",
" qm = module_index_to_qm(i)\n",
" if qm not in offset_g[cap]:\n",
" offset_g[cap][qm] = np.zeros(\n",
@@ -348,6 +358,18 @@
" f\"Number of processed trains per cell: {data.shape[0]}.\")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if skip_plots:\n",
+ " import sys\n",
+ " print('Skipping plots')\n",
+ " sys.exit(0)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
--
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