diff --git a/notebooks/ePix100/Correction_ePix100_NBC.ipynb b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
index 878edb18e2346902e08fe6d2d56be7176afff197..34fdbf8aa78773bfd9fb5dd33bf1747411c532cd 100644
--- a/notebooks/ePix100/Correction_ePix100_NBC.ipynb
+++ b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
@@ -6,7 +6,7 @@
"source": [
"# ePIX Data Correction ##\n",
"\n",
- "Authors: Q. Tian S. Hauf, Version 1.0\n",
+ "Authors: Q. Tian S. Hauf M. Cascella, Version 1.0\n",
"\n",
"The following notebook provides Offset correction of images acquired with the ePix100 detector."
]
@@ -49,6 +49,9 @@
"photon_energy = 8.0 # Photon energy to calibrate in number of photons, 0 for calibration in keV\n",
"\n",
"relative_gain = False # Apply relative gain correction.\n",
+ "common_mode = True # Apply common mode correction.\n",
+ "cm_min_frac = 0.25 # No CM correction is performed if after masking the ratio of good pixels falls below this \n",
+ "cm_noise_sigma = 5. # CM correction noise standard deviation\n",
"\n",
"split_evt_primary_threshold = 7. # primary threshold for split event correction\n",
"split_evt_secondary_threshold = 5. # secondary threshold for split event correction\n",
@@ -105,8 +108,8 @@
"metadata": {},
"outputs": [],
"source": [
- "# TODO: expose to first cell after fixing common mode correction.\n",
- "common_mode = False # Apply common mode correction.\n",
+ "# TODO: expose to first cell after fixing clustering.\n",
+ "pattern_classification = False # do clustering.\n",
"\n",
"h5path = h5path.format(karabo_id, receiver_id)\n",
"h5path_t = h5path_t.format(karabo_id, receiver_id)\n",
@@ -360,18 +363,72 @@
"# ************************Calculators************************ #\n",
"if common_mode:\n",
" commonModeBlockSize = [x//2, y//2]\n",
- " commonModeAxisR = 'row'\n",
- " cmCorrection = xcal.CommonModeCorrection(\n",
+ "\n",
+ " cmCorrectionB = xcal.CommonModeCorrection(sensorSize, \n",
+ " commonModeBlockSize, \n",
+ " 'block',\n",
+ " nCells = memoryCells, \n",
+ " noiseMap = const_data['Noise'],\n",
+ " runParallel=run_parallel,\n",
+ " stats=True,\n",
+ " minFrac = cm_min_frac,\n",
+ " noiseSigma = cm_noise_sigma,\n",
+ " )\n",
+ " cmCorrectionR = xcal.CommonModeCorrection(sensorSize, \n",
+ " commonModeBlockSize, \n",
+ " 'row',\n",
+ " nCells = memoryCells, \n",
+ " noiseMap = const_data['Noise'],\n",
+ " runParallel=run_parallel,\n",
+ " stats=True\n",
+ " minFrac = cm_min_frac,\n",
+ " noiseSigma = cm_noise_sigma,\n",
+ " )\n",
+ " cmCorrectionC = xcal.CommonModeCorrection(sensorSize, \n",
+ " commonModeBlockSize, \n",
+ " 'col',\n",
+ " nCells = memoryCells, \n",
+ " noiseMap = const_data['Noise'],\n",
+ " runParallel=run_parallel,\n",
+ " stats=True\n",
+ " minFrac = cm_min_frac,\n",
+ " noiseSigma = cm_noise_sigma,\n",
+ " )\n",
+ "\n",
+ " histCalCMCor = xcal.HistogramCalculator(\n",
+ " sensorSize,\n",
+ " bins=1050,\n",
+ " range=[-50, 1000],\n",
+ " parallel=run_parallel,\n",
+ " nCells=memoryCells,\n",
+ " cores=cpuCores,\n",
+ " blockSize=blockSize,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "if pattern_classification:\n",
+ " patternClassifier = xcal.PatternClassifier(\n",
" [x, y],\n",
- " commonModeBlockSize,\n",
- " commonModeAxisR,\n",
+ " const_data[\"Noise\"],\n",
+ " split_evt_primary_threshold,\n",
+ " split_evt_secondary_threshold,\n",
+ " split_evt_mip_threshold,\n",
+ " tagFirstSingles=0,\n",
" nCells=memoryCells,\n",
- " noiseMap=const_data[\"Noise\"],\n",
+ " cores=cpuCores,\n",
+ " allowElongated=False,\n",
+ " blockSize=[x, y],\n",
" runParallel=run_parallel,\n",
- " stats=True,\n",
" )\n",
"\n",
- " histCalCMCor = xcal.HistogramCalculator(\n",
+ " histCalSECor = xcal.HistogramCalculator(\n",
" sensorSize,\n",
" bins=1050,\n",
" range=[-50, 1000],\n",
@@ -379,31 +436,7 @@
" nCells=memoryCells,\n",
" cores=cpuCores,\n",
" blockSize=blockSize,\n",
- " )\n",
- "\n",
- "patternClassifier = xcal.PatternClassifier(\n",
- " [x, y],\n",
- " const_data[\"Noise\"],\n",
- " split_evt_primary_threshold,\n",
- " split_evt_secondary_threshold,\n",
- " split_evt_mip_threshold,\n",
- " tagFirstSingles=0,\n",
- " nCells=memoryCells,\n",
- " cores=cpuCores,\n",
- " allowElongated=False,\n",
- " blockSize=[x, y],\n",
- " runParallel=run_parallel,\n",
- ")\n",
- "\n",
- "histCalSECor = xcal.HistogramCalculator(\n",
- " sensorSize,\n",
- " bins=1050,\n",
- " range=[-50, 1000],\n",
- " parallel=run_parallel,\n",
- " nCells=memoryCells,\n",
- " cores=cpuCores,\n",
- " blockSize=blockSize,\n",
- ")"
+ " )"
]
},
{
@@ -413,10 +446,14 @@
"outputs": [],
"source": [
"if common_mode:\n",
- " cmCorrection.debug()\n",
+ " cmCorrectionB.debug()\n",
+ " cmCorrectionR.debug()\n",
+ " cmCorrectionC.debug()\n",
" histCalCMCor.debug()\n",
- "patternClassifier.debug()\n",
- "histCalSECor.debug()"
+ "\n",
+ "if pattern_classification:\n",
+ " patternClassifier.debug()\n",
+ " histCalSECor.debug()"
]
},
{
@@ -477,6 +514,21 @@
" # Offset correction.\n",
" data = offsetCorrection.correct(data.astype(np.float32))\n",
"\n",
+ " # Common Mode correction.\n",
+ " if common_mode:\n",
+ "# ddsetcm = ofile.create_dataset(\n",
+ "# h5path+\"/pixels_cm\",\n",
+ "# oshape,\n",
+ "# chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ "# dtype=np.float32)\n",
+ "\n",
+ " data = cmCorrectionB.correct(data.astype(np.float32)) #correct for the Block common mode\n",
+ " data = cmCorrectionR.correct(data.astype(np.float32)) #correct for the Row common mode\n",
+ " data = cmCorrectionC.correct(data.astype(np.float32)) #correct for the Col common mode\n",
+ "\n",
+ " histCalCMCor.fill(data)\n",
+ "# ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
+ "\n",
" # relative gain correction.\n",
" if relative_gain:\n",
" data = gainCorrection.correct(data.astype(np.float32))\n",
@@ -492,20 +544,14 @@
" \"\"\"The gain correction is currently applying an absolute correction\n",
" (not a relative correction as the implied by the name);\n",
" it changes the scale (the unit of measurement) of the data from ADU\n",
- " to either keV or n_of_photons. But the common mode correction\n",
+ " to either keV or n_of_photons. But the pattern classification\n",
" relies on comparing data with the noise map, which is still in ADU.\n",
"\n",
" The best solution is probably to do a relative gain correction first\n",
- " (correct) and apply the global absolute gain to the data at the end,\n",
- " after common mode and clustering.\n",
+ " and apply the global absolute gain to the data at the end,\n",
+ " after clustering.\n",
" \"\"\"\n",
- " if common_mode:\n",
- " ddsetcm = ofile.create_dataset(\n",
- " h5path+\"/pixels_cm\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.float32)\n",
- "\n",
+ " if pattern_classification:\n",
" ddsetc = ofile.create_dataset(\n",
" h5path+\"/pixels_classified\",\n",
" oshape,\n",
@@ -518,10 +564,6 @@
" chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
" dtype=np.int32, compression=\"gzip\")\n",
"\n",
- " # row common mode correction.\n",
- " data = cmCorrection.correct(data)\n",
- " histCalCMCor.fill(data)\n",
- " ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
"\n",
" data, patterns = patternClassifier.classify(data)\n",
"\n",
@@ -565,6 +607,7 @@
" 'label': 'CM corr.'\n",
" })\n",
"\n",
+ "if pattern_classification:\n",
" ho, eo, co, so = histCalSECor.get()\n",
" d.append({\n",
" 'x': co,\n",