diff --git a/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb b/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
index a1e303b74d6c616adf235f2dd6d4eba5cfd0f391..cb1e13f64cfc762a9080a2c63c551e93b582d8ea 100644
--- a/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
+++ b/notebooks/pnCCD/Characterize_pnCCD_Dark_NBC.ipynb
@@ -49,7 +49,7 @@
"fix_temperature_top = 0. # fix temperature of top pnCCD sensor in K. Set to 0, to use the value from slow data\n",
"fix_temperature_bot = 0. # fix temperature of bottom pnCCD sensor in K. Set to 0, to use the value from slow data\n",
"temp_limits = 5 # temperature limits in which calibration parameters are considered equal\n",
- "gain = -1 # the detector's gain setting. Set to -1 to use the value from the slow data\n",
+ "gain = -1 # the detector's gain setting. Set to -1 to use the value from the slow data.\n",
"bias_voltage = 0. # the detector's bias voltage. set to 0. to use the value from slow data.\n",
"integration_time = 70 # detector's integration time.\n",
"\n",
@@ -426,24 +426,23 @@
"def correct_image(wid, idx, d):\n",
" d -= offsetMap\n",
" offset_corr_data[idx, ...] = d\n",
- " histCalCorrected.fill(d)\n",
"\n",
" d = np.squeeze(cmCorrection.correct(\n",
" d[..., np.newaxis],\n",
" cellTable=np.zeros((1,), np.int32)\n",
" ))\n",
- " histCalCMCorrected.fill(d)\n",
" corr_data[idx, ...] = d\n",
"\n",
"context = psh.context.ThreadContext(num_workers=10)\n",
"\n",
- "corr_data = context.alloc(\n",
- " shape=(n_trains, sensorSize[0], sensorSize[1]), dtype=np.float32)\n",
- "offset_corr_data = context.alloc(\n",
- " shape=(n_trains, sensorSize[0], sensorSize[1]), dtype=np.float32)\n",
+ "data_shape = (n_trains, sensorSize[0], sensorSize[1])\n",
"\n",
- "context.map(correct_image, data.copy())\n",
+ "corr_data = context.alloc(shape=data_shape, dtype=np.float32)\n",
+ "offset_corr_data = context.alloc(shape=data_shape, dtype=np.float32)\n",
"\n",
+ "context.map(correct_image, data.copy())\n",
+ "histCalCorrected.fill(offset_corr_data)\n",
+ "histCalCMCorrected.fill(corr_data)\n",
"step_timer.done_step(f'Offset and common mode corrections are applied.')"
]
},
@@ -675,22 +674,19 @@
" d_off[d_off_cm > event_threshold] = np.nan\n",
" d_off_cm[d_off_cm > event_threshold] = np.nan \n",
"\n",
- " histCalCorrected.fill(d_off[..., np.newaxis])\n",
- "\n",
" offset_corr_data[idx, ...] = d_off # Will be used for plotting\n",
- " \n",
" corr_data[idx, ...] = np.squeeze(d_off_cm)\n",
- " histCalCorrected.fill(d_off)\n",
- " histCalCMCorrected.fill(d_off_cm)\n",
+ "\n",
"context = psh.context.ThreadContext(num_workers=10)\n",
"\n",
- "corr_data = context.alloc(\n",
- " shape=(n_trains, sensorSize[0], sensorSize[1]), dtype=np.float32)\n",
- "offset_corr_data = context.alloc(\n",
- " shape=(n_trains, sensorSize[0], sensorSize[1]), dtype=np.float32)\n",
+ "corr_data = context.alloc(shape=data_shape, dtype=np.float32)\n",
+ "offset_corr_data = context.alloc(shape=data_shape, dtype=np.float32)\n",
"\n",
"context.map(correct_image, data.copy())\n",
"\n",
+ "histCalCorrected.fill(offset_corr_data)\n",
+ "histCalCMCorrected.fill(corr_data)\n",
+ "\n",
"print(f\"A total number of {n_trains} images are processed.\")\n",
"step_timer.done_step(\"Final iteration is Performed.\")"
]
diff --git a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
index 181eb982561d35509a0eae294ea901c96e0b8be4..54f5d78c1e7968212de1414d5340b37a4d7cd176 100644
--- a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
+++ b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
@@ -18,34 +18,29 @@
"outputs": [],
"source": [
"in_folder = \"/gpfs/exfel/exp/SQS/202031/p900166/raw\" # input folder\n",
- "out_folder = '' # output folder\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/remove\" # output folder\n",
"run = 347 # which run to read data from\n",
- "sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
+ "sequences = [0] # sequences to correct, set to -1 for all, range allowed\n",
"sequences_per_node = 1 # number of sequences running on the same slurm node.\n",
"\n",
"karabo_da = 'PNCCD01' # data aggregators\n",
"karabo_id = \"SQS_NQS_PNCCD1MP\" # karabo prefix of PNCCD devices\n",
"receiver_id = \"PNCCD_FMT-0\" # inset for receiver devices\n",
"path_template = 'RAW-R{:04d}-{}-S{:05d}.h5' # the template to use to access data\n",
- "instrument_source_template = '{}/CAL/{}:output' # path to data in the HDF5 file \n",
- "\n",
- "overwrite = True # keep this as True to not overwrite the output \n",
+ "instrument_source_template = '{}/CAL/{}:output' # template for data source name, will be filled with karabo_id and receiver_id.\n",
"\n",
"# Parameters affecting data correction.\n",
+ "commonModeAxis = 0 # axis along which common mode will be calculated, 0 = row, and 1 = column\n",
"commonModeBlockSize = [512, 512] # size of the detector in pixels for common mode calculations\n",
- "commonModeAxis = 0 # axis along which common mode will be calculated, 0 = row, and 1 = column \n",
"split_evt_primary_threshold = 4. # primary threshold for split event classification in terms of n sigma noise\n",
"split_evt_secondary_threshold = 3. # secondary threshold for split event classification in terms of n sigma noise\n",
"saturated_threshold = 32000. # full well capacity in ADU\n",
- "chunk_size_idim = 1 # H5 chunking size of output data\n",
- "# ONLY FOR TESTING\n",
- "limit_images = 0 # this parameter is used for limiting number of images to correct from a sequence file. ONLY FOR TESTING.\n",
"\n",
"# Conditions for retrieving calibration constants\n",
"set_parameters_manually = False # Boolean for setting parameter values manually instead of reading control values.\n",
"fix_temperature_top = 0. # fix temperature for top sensor in K, set to 0. to use value from slow data.\n",
"fix_temperature_bot = 0. # fix temperature for bottom senspr in K, set to 0. to use value from slow data.\n",
- "gain = -1 # the detector's gain setting, It is later read from file and this value is overwritten\n",
+ "gain = -1 # the detector's gain setting. Set to -1 to use the value from the slow data.\n",
"bias_voltage = 0. # the detector's bias voltage. set to 0. to use value from slow data.\n",
"integration_time = 70 # detector's integration time\n",
"photon_energy = 1.6 # Al fluorescence in keV\n",
@@ -61,6 +56,12 @@
"relgain = True # Apply relative gain correction\n",
"pattern_classification = True # classify split events\n",
"\n",
+ "# parameters affecting stored output data.\n",
+ "chunk_size_idim = 1 # H5 chunking size of output data\n",
+ "overwrite = True # keep this as True to not overwrite the output \n",
+ "# ONLY FOR TESTING\n",
+ "limit_images = 0 # this parameter is used for limiting number of images to correct from a sequence file. ONLY FOR TESTING.\n",
+ "\n",
"# TODO: REMOVE\n",
"db_module = \"\"\n",
"\n",
@@ -134,8 +135,14 @@
"source": [
"# Calibration Database Settings, and Some Initial Run Parameters & Paths:\n",
"display(Markdown('### Initial Settings and Paths'))\n",
- "pixels_x = 1024 # rows of pnCCD in pixels \n",
- "pixels_y = 1024 # columns of pnCCD in pixels\n",
+ "\n",
+ "# Sensor size and block size definitions (important for common mode and other corrections):\n",
+ "pixels_x = 1024 # rows of pnCCD in pixels \n",
+ "pixels_y = 1024 # columns of pnCCD in pixels\n",
+ "sensorSize = [pixels_x, pixels_y]\n",
+ "# For xcal.HistogramCalculators.\n",
+ "blockSize = [pixels_x//2, pixels_y//2] # sensor area will be analysed according to blockSize.\n",
+ "\n",
"print(f\"pnCCD size is: {pixels_x}x{pixels_y} pixels.\")\n",
"print(f'Calibration database interface selected: {cal_db_interface}')\n",
"\n",
@@ -213,7 +220,7 @@
"outputs": [],
"source": [
"seq_files = []\n",
- "for f in run_dc.select(f\"*{karabo_id}*\").files:\n",
+ "for f in run_dc.select(instrument_src).files:\n",
" fpath = Path(f.filename)\n",
" if fpath.match(f\"*{karabo_da}*.h5\"):\n",
" seq_files.append(fpath)\n",
@@ -305,18 +312,6 @@
"b_range = Event_Bin_Dict[\"b_range\"]"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Sensor size and block size definitions (important for common mode and other corrections):\n",
- "sensorSize = [pixels_x, pixels_y]\n",
- "blockSize = [sensorSize[0]//2, sensorSize[1]//2] # sensor area will be analysed according to blockSize\n",
- "xcal.defaultBlockSize = blockSize # for xcal.HistogramCalculators "
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -478,7 +473,7 @@
"if corr_bools.get('common_mode'):\n",
" # Common Mode Correction Calculator:\n",
" cmCorrection = xcal.CommonModeCorrection([pixels_x, pixels_y],\n",
- " blockSize,\n",
+ " commonModeBlockSize,\n",
" commonModeAxis,\n",
" parallel=False, dType=np.float32, stride=1,\n",
" noiseMap=constants[\"Noise\"].astype(np.float32), minFrac=0.25)\n",
@@ -515,8 +510,8 @@
" patternClassifierLH._noisemap = constants[\"Noise\"][:, :pixels_x//2]\n",
" patternClassifierRH._noisemap = constants[\"Noise\"][:, pixels_x//2:]\n",
" # Setting bad pixels:\n",
- " patternClassifierLH.setBadPixelMask(constants[\"BadPixelsDark\"][:, :pixels_y//2] != 0)\n",
- " patternClassifierRH.setBadPixelMask(constants[\"BadPixelsDark\"][:, pixels_y//2:] != 0)"
+ " patternClassifierLH.setBadPixelMask(constants[\"BadPixelsDark\"][:, :pixels_x//2] != 0)\n",
+ " patternClassifierRH.setBadPixelMask(constants[\"BadPixelsDark\"][:, pixels_x//2:] != 0)"
]
},
{
@@ -593,17 +588,15 @@
" Equating bad pixels' values to np.nan,\n",
" so that the pattern classifier ignores them:\n",
" \"\"\"\n",
- " histCalRaw.fill(d) # filling histogram with raw uncorrected data\n",
+ " d = d.copy()\n",
"\n",
" # TODO: To clear this up. Is it on purpose to save corrected data with nans?\n",
" d[bpix != 0] = np.nan\n",
- " d -= offset # offset correction\n",
+ " d -= offset # offset correction\n",
"\n",
" # TODO: to clear this up. why save the badpixels map in the corrected data?\n",
" bpix_data[index, ...] = bpix\n",
" data[index, ...] = d\n",
- " histCalOffsetCor.fill(d) # filling histogram with offset corrected data\n",
- "\n",
"\n",
"def common_mode(wid, index, d):\n",
" \"\"\"common-mode correction.\n",
@@ -613,17 +606,15 @@
" # we equate these values to np.nan so that the pattern classifier ignores them:\n",
" d[d >= saturated_threshold] = np.nan\n",
" data[index, ...] = d\n",
- " histCalCommonModeCor.fill(d) # filling histogram with common mode corrected data\n",
"\n",
"\n",
"def gain_correction(wid, index, d):\n",
" \"\"\"relative gain correction.\"\"\"\n",
- " d /= rg \n",
+ " d /= relativegain \n",
" data[index, ...] = d\n",
- " histCalGainCor.fill(d) # filling histogram with gain corrected data\n",
"\n",
"\n",
- "def pattern_classification(wid, index, d):\n",
+ "def pattern_classification_correction(wid, index, d):\n",
" \"\"\"pattern classification correction.\n",
" data set to save split event corrected images\n",
" \n",
@@ -643,10 +634,9 @@
" patterns[:, pixels_x//2:] = np.squeeze(patternsRH)\n",
" d[d < split_evt_primary_threshold*noise] = 0\n",
" data[index, ...] = d\n",
- " histCalPcorr.fill(d) # filling histogram with split events corrected data\n",
" ptrn_data[index, ...] = patterns\n",
- " d[patterns != 100] = np.nan # Discard doubles, triples, quadruple, clusters, first singles\n",
- " histCalPcorrS.fill(d) # filling histogram with singles events data"
+ " d[patterns != 100] = np.nan # Discard doubles, triples, quadruple, clusters, first singles\n",
+ " filtered_data[index, ...] = d"
]
},
{
@@ -655,7 +645,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# 10 is a number chosen after testing 1 ... 71 parallel threads\n",
+ "# 10 is a number chosen after testing 1 ... 71 parallel threads for a node with 72 cpus.\n",
"parallel_num_threads = 10\n",
"context = psh.context.ThreadContext(num_workers=parallel_num_threads)\n",
"\n",
@@ -664,11 +654,7 @@
"offset = np.squeeze(constants[\"Offset\"])\n",
"noise = np.squeeze(constants[\"Noise\"])\n",
"bpix = np.squeeze(constants[\"BadPixelsDark\"])\n",
- "rg = constants.get(\"RelativeGain\")\n",
- "\n",
- "corr_comp_fields = {}\n",
- "kw = {\"compression\": \"gzip\"}\n",
- "comp_fields = [\"gain\", \"patterns\", \"pixels_classified\"]"
+ "relativegain = constants.get(\"RelativeGain\")"
]
},
{
@@ -677,14 +663,25 @@
"metadata": {},
"outputs": [],
"source": [
- "def write_dataset(ofile, data, field, kw={}):\n",
- " ofile.create_dataset(\n",
- " f\"{data_path}/{field}\",\n",
- " data=data,\n",
- " chunks=(chunk_size_idim, pixels_x, pixels_y),\n",
- " dtype=data.dtype,\n",
- " **kw,\n",
- " )"
+ "def write_datasets(corr_arrays, ofile):\n",
+ " \"\"\"\n",
+ " Creating datasets first then adding data.\n",
+ " To have metadata together available at the start of the file,\n",
+ " so it's quick to see what the file contains\n",
+ " \"\"\"\n",
+ " comp_fields = [\"gain\", \"patterns\", \"pixels_classified\"]\n",
+ " img_grp = ofile[data_path]\n",
+ "\n",
+ " for field, data in corr_arrays.items():\n",
+ " kw = dict(chunks=(chunk_size_idim, pixels_x, pixels_y))\n",
+ " if field in comp_fields:\n",
+ " kw[\"compression\"] = \"gzip\"\n",
+ "\n",
+ " img_grp.create_dataset(\n",
+ " field, shape=data.shape, dtype=data.dtype, **kw)\n",
+ "\n",
+ " for field, data in corr_arrays.items():\n",
+ " img_grp[field][:] = data"
]
},
{
@@ -712,60 +709,79 @@
"\n",
" print(f\"Correcting file: {seq_f} of shape {data_shape}.\")\n",
"\n",
- " data_dc = f_dc.select(\n",
- " instrument_src, \"data.image\",\n",
- " require_all=True).select_trains(np.s_[:n_trains])\n",
+ " data_dc = f_dc.select(instrument_src, \"data.image\", require_all=True).select_trains(np.s_[:n_trains]) # noqa\n",
+ "\n",
" raw_data = data_dc[instrument_src, \"data.image\"].ndarray().astype(np.float32)\n",
+ "\n",
" if seq_n == 0:\n",
" raw_plt = raw_data.copy() # plot first sequence only\n",
- " # Allocating shared arrays for data arrays for each correction stage.\n",
- " data = context.alloc(shape=data_shape, dtype=np.float32)\n",
"\n",
" step_timer.start()\n",
- " bpix_data = context.alloc(shape=data_shape)\n",
"\n",
+ " # Allocating shared arrays for data arrays for each correction stage.\n",
+ " data = context.alloc(shape=data_shape, dtype=np.float32)\n",
+ " bpix_data = context.alloc(shape=data_shape, dtype=np.uint32)\n",
+ " histCalRaw.fill(raw_data) # filling histogram with raw uncorrected data\n",
+ " \n",
+ " # Applying offset correction\n",
" context.map(offset_correction, raw_data)\n",
+ " histCalOffsetCor.fill(data) # filling histogram with offset corrected data\n",
+ "\n",
" if seq_n == 0:\n",
" off_data = data.copy() # plot first sequence only\n",
"\n",
+ "\n",
" corr_arrays = {\n",
- " \"pixels\": off_data.astype(np.float32),\n",
- " \"mask\": bpix_data.astype(np.uint32), \n",
+ " \"pixels\": data.copy(),\n",
+ " \"mask\": bpix_data, \n",
" }\n",
" step_timer.done_step(f'offset correction.')\n",
"\n",
" if corr_bools.get('common_mode'):\n",
" step_timer.start()\n",
+ "\n",
+ " # Applying common mode correction\n",
" context.map(common_mode, data)\n",
" if seq_n == 0:\n",
" cm_data = data.copy() # plot first sequence only\n",
- " corr_arrays[\"pixels_cm\"] = cm_data.astype(np.float32)\n",
+ " corr_arrays[\"pixels_cm\"] = data.copy()\n",
+ " histCalCommonModeCor.fill(data) # filling histogram with common mode corrected data\n",
+ "\n",
" step_timer.done_step(f'common-mode correction.')\n",
"\n",
" if corr_bools.get('relgain'):\n",
+ "\n",
" step_timer.start()\n",
+ "\n",
+ " # Applying gain correction\n",
" context.map(gain_correction, data)\n",
" if seq_n == 0:\n",
" rg_data = data.copy() # plot first sequence only\n",
- " corr_arrays[\"gain\"] = np.repeat(\n",
- " rg[np.newaxis, ...],\n",
- " n_trains, axis=0).astype(np.float32) # TODO: confirm that we need to keep saving a constant\n",
+ " # TODO: Why storing a repeated constant for each image in corrected files.\n",
+ " corr_arrays[\"gain\"] = np.repeat(relativegain[np.newaxis, ...], n_trains, axis=0).astype(np.float32) # noqa\n",
+ " histCalGainCor.fill(data) # filling histogram with gain corrected data\n",
" step_timer.done_step(f'gain correction.')\n",
"\n",
" if corr_bools.get('pattern_class'):\n",
" step_timer.start()\n",
"\n",
" ptrn_data = context.alloc(shape=data_shape, dtype=np.int32)\n",
+ " filtered_data = context.alloc(shape=data_shape, dtype=np.int32)\n",
+ " # Applying pattern classification correction\n",
" # Even thougth data is indeed of dtype np.float32,\n",
" # not specifiying this again screw with the data quality.\n",
- " context.map(pattern_classification, data.astype(np.float32))\n",
+ " context.map(pattern_classification_correction, data.astype(np.float32))\n",
"\n",
" if seq_n == 0:\n",
" cls_data = data.copy() # plot first sequence only\n",
" # split event corrected images plotted for first sequence only\n",
" # (also these events are only singles events):\n",
- " corr_arrays[\"pixels_classified\"] = cls_data.astype(np.float32)\n",
+ " corr_arrays[\"pixels_classified\"] = data.copy()\n",
" corr_arrays[\"patterns\"] = ptrn_data\n",
+ "\n",
+ " histCalPcorr.fill(data) # filling histogram with split events corrected data\n",
+ " # filling histogram with corr data after discarding doubles, triples, quadruple, clusters, and first singles\n",
+ " histCalPcorrS.fill(filtered_data)\n",
" step_timer.done_step(f'pattern classification correction.')\n",
"\n",
" step_timer.start()\n",
@@ -778,11 +794,9 @@
" sfile, ofile,\n",
" [\"INSTRUMENT/\"+instrument_src+\"/data/image\"],\n",
" )\n",
- " for field, arr in corr_arrays.items():\n",
- " write_dataset(\n",
- " ofile, arr, field,\n",
- " kw=kw if field in comp_fields else {})\n",
" # TODO: to clear this up: why save corrected data in data/pixels rather than data/image.\n",
+ " write_datasets(corr_arrays, ofile)\n",
+ "\n",
" step_timer.done_step(f'Storing data.')"
]
},
@@ -801,6 +815,21 @@
"step_timer.print_summary()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"In addition to offset correction, the following corrections were performed:\")\n",
+ "for k, v in corr_bools.items():\n",
+ " if v:\n",
+ " print(\" -\", k.upper())\n",
+ "\n",
+ "print(f\"Total processing time {step_timer.timespan():.01f} s\")\n",
+ "step_timer.print_summary()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -1221,9 +1250,7 @@
" doubles = []\n",
" triples = []\n",
" quads = []\n",
- " with H5File(\n",
- " f\"{out_folder}/{path_template.format(run, karabo_da, sequences[0]).replace('RAW', 'CORR')}\"\n",
- " ) as dc:\n",
+ " with H5File(f\"{out_folder}/{seq_files[0].name.replace('RAW', 'CORR')}\") as dc: # noqa\n",
" data = dc[instrument_src, \"data.pixels_classified\"].ndarray()\n",
" patterns = dc[instrument_src, \"data.patterns\"].ndarray()\n",
" # events' patterns indices are as follows: 100 (singles), 101 (first singles), 200 - 203 (doubles),\n",