From 3ce8f69ed773ada28572305256beff2541ccebc2 Mon Sep 17 00:00:00 2001
From: Philipp Schmidt <philipp.schmidt@xfel.eu>
Date: Fri, 13 May 2022 21:24:22 +0200
Subject: [PATCH] Make constant axis ordering configurable and adjust based on
calibration type
---
notebooks/LPD/LPD_Correct_Fast.ipynb | 36 +++++++++++++++++++++-------
1 file changed, 27 insertions(+), 9 deletions(-)
diff --git a/notebooks/LPD/LPD_Correct_Fast.ipynb b/notebooks/LPD/LPD_Correct_Fast.ipynb
index 91fe5d360..f624f5fae 100644
--- a/notebooks/LPD/LPD_Correct_Fast.ipynb
+++ b/notebooks/LPD/LPD_Correct_Fast.ipynb
@@ -306,39 +306,57 @@
"metadata": {},
"outputs": [],
"source": [
+ "# These are intended in order cell, X, Y, gain\n",
"ccv_offsets = {}\n",
"ccv_gains = {}\n",
"ccv_masks = {}\n",
"\n",
+ "ccv_shape = (mem_cells, 256, 256, 3)\n",
+ "\n",
+ "constant_order = {\n",
+ " 'Offset': (1, 0, 2, 3),\n",
+ " 'BadPixelsDark': (1, 0, 2, 3),\n",
+ " 'RelativeGain': (1, 0, 2, 3),\n",
+ " 'FFMap': (0, 1, 2, 3),\n",
+ " 'BadPixelsFF': (0, 1, 2, 3),\n",
+ " 'GainAmpMap': (0, 1, 2, 3),\n",
+ "}\n",
+ "\n",
"def prepare_constants(wid, index, aggregator):\n",
" consts = {calibration_name: entry['data']\n",
" for (aggregator_, calibration_name), entry\n",
" in const_data.items()\n",
" if aggregator == aggregator_}\n",
" \n",
+ " def _prepare_data(calibration_name, dtype):\n",
+ " return consts[calibration_name] \\\n",
+ " .transpose(constant_order[calibration_name]) \\\n",
+ " .astype(dtype, copy=True) # Make sure array is contiguous.\n",
+ " \n",
" if offset_corr and 'Offset' in consts:\n",
- " ccv_offsets[aggregator] = consts['Offset'].astype(np.float32)\n",
+ " ccv_offsets[aggregator] = _prepare_data('Offset', np.float32)\n",
" else:\n",
- " ccv_offsets[aggregator] = np.zeros((256, 256, mem_cells, 3), dtype=np.float32)\n",
+ " ccv_offsets[aggregator] = np.zeros(ccv_shape, dtype=np.float32)\n",
" \n",
- " ccv_gains[aggregator] = np.ones((256, 256, mem_cells, 3), dtype=np.float32)\n",
+ " ccv_gains[aggregator] = np.ones(ccv_shape, dtype=np.float32)\n",
" \n",
" if 'BadPixelsDark' in consts:\n",
- " ccv_masks[aggregator] = consts['BadPixelsDark'].astype(np.uint32)\n",
+ " ccv_masks[aggregator] = _prepare_data('BadPixelsDark', np.uint32)\n",
" else:\n",
- " ccv_masks[aggregator] = np.zeros((256, 256, mem_cells, 3), dtype=np.uint32)\n",
+ " ccv_masks[aggregator] = np.zeros(ccv_shape, dtype=np.uint32)\n",
" \n",
" if rel_gain and 'RelativeGain' in consts:\n",
- " ccv_gains[aggregator] *= consts['RelativeGain']\n",
+ " ccv_gains[aggregator] *= _prepare_data('RelativeGain', np.float32)\n",
" \n",
" if ff_map and 'FFMap' in consts:\n",
- " ccv_gains[aggregator] *= consts['FFMap']\n",
+ " ccv_gains[aggregator] *= _prepare_data('FFMap', np.float32)\n",
" \n",
" if 'BadPixelsFF' in consts:\n",
- " np.bitwise_or(ccv_masks[aggregator], consts['BadPixelsFF'], out=ccv_masks[aggregator])\n",
+ " np.bitwise_or(ccv_masks[aggregator], _prepare_data('BadPixelsFF', np.uint32),\n",
+ " out=ccv_masks[aggregator])\n",
" \n",
" if gain_amp_map and 'GainAmpMap' in consts:\n",
- " ccv_gains[aggregator] *= consts['GainAmpMap']\n",
+ " ccv_gains[aggregator] *= _prepare_data('GainAmpMap', np.float32)\n",
" \n",
" print('.', end='', flush=True)\n",
" \n",
--
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