From b62970eda01481fb764e1c61ba6ecbb42b4781fd Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Fri, 3 Sep 2021 20:23:54 +0200
Subject: [PATCH] started to remove h5py
---
notebooks/pnCCD/Correct_pnCCD_NBC.ipynb | 439 ++++++++++++------------
1 file changed, 219 insertions(+), 220 deletions(-)
diff --git a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
index 9bbaf925b..cffad2c30 100644
--- a/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
+++ b/notebooks/pnCCD/Correct_pnCCD_NBC.ipynb
@@ -23,7 +23,7 @@
"outputs": [],
"source": [
"in_folder = \"/gpfs/exfel/exp/SQS/202031/p900166/raw\" # input folder\n",
- "out_folder = '/gpfs/exfel/data/scratch/setoodeh/Test' # 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",
"\n",
@@ -34,8 +34,8 @@
"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",
"path_template_seqs = \"{}/r{:04d}/*PNCCD01-S*.h5\"\n",
- "h5path = '/INSTRUMENT/{}/CAL/{}:output/data/' # path to data in the HDF5 file \n",
- "h5path_ctrl = '/CONTROL/{}/CTRL/TCTRL'\n",
+ "h5path = '{}/CAL/{}:output' # path to data in the HDF5 file \n",
+ "h5path_ctrl = '{}/CTRL/TCTRL'\n",
"\n",
"overwrite = True # keep this as True to not overwrite the output \n",
"use_dir_creation_date = True # To obtain creation time of the run\n",
@@ -70,7 +70,12 @@
"cti = False # Apply charge transfer inefficiency correction (not implemented, yet)\n",
"do_pattern_classification = True # classify split events\n",
"\n",
- "saturated_threshold = 32000. # full well capacity in ADU"
+ "saturated_threshold = 32000. # full well capacity in ADU\n",
+ "\n",
+ "\n",
+ "def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da):\n",
+ " from xfel_calibrate.calibrate import balance_sequences as bs\n",
+ " return bs(in_folder, run, sequences, sequences_per_node, karabo_da)"
]
},
{
@@ -116,6 +121,7 @@
"warnings.filterwarnings('ignore')\n",
"\n",
"import h5py\n",
+ "from extra_data import H5File\n",
"import matplotlib.pyplot as plt\n",
"from iminuit import Minuit\n",
"from IPython.display import Markdown, display\n",
@@ -128,6 +134,7 @@
" get_constant_from_db_and_time,\n",
" get_dir_creation_date,\n",
" get_random_db_interface,\n",
+ " map_modules_from_folder,\n",
")\n",
"from iCalibrationDB import Conditions, ConstantMetaData, Constants, Detectors, Versions\n",
"from iCalibrationDB.detectors import DetectorTypes\n",
@@ -142,11 +149,7 @@
"from XFELDetAna import xfelpycaltools as xcal\n",
"from XFELDetAna.plotting.util import prettyPlotting\n",
"\n",
- "prettyPlotting=True\n",
- "\n",
- "\n",
- "if sequences[0] == -1:\n",
- " sequences = None"
+ "prettyPlotting=True"
]
},
{
@@ -157,6 +160,9 @@
"source": [
"# Calibration Database Settings, and Some Initial Run Parameters & Paths:\n",
"\n",
+ "if sequences[0] == -1:\n",
+ " sequences = None\n",
+ "\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",
@@ -165,7 +171,7 @@
"\n",
"proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]\n",
"file_loc =f'Proposal: {proposal}, Run: {run}'\n",
- "print(f'Proposal: {proposal}, Run: {run}')\n",
+ "print(file_loc)\n",
"\n",
"# Paths to the data:\n",
"ped_dir = \"{}/r{:04d}\".format(in_folder, run)\n",
@@ -189,10 +195,26 @@
"if not creation_time and use_dir_creation_date:\n",
" creation_time = get_dir_creation_date(in_folder, run)\n",
"\n",
- "\n",
"print(f\"Creation time: {creation_time}\")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set everything up filewise\n",
+ "mapped_files, _, total_sequences, _, _ = map_modules_from_folder(\n",
+ " in_folder=in_folder,\n",
+ " run=run,\n",
+ " path_template='RAW-R{:04d}-{}-S{:05d}.h5',\n",
+ " karabo_da=karabo_da,\n",
+ " sequences=sequences,\n",
+ " qm_naming=False,\n",
+ ")"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -344,8 +366,8 @@
"metadata": {},
"outputs": [],
"source": [
- "display(Markdown('### List of Files to be Processed'))\n",
- "print(\"Reading data from the following files:\")\n",
+ "display(Markdown('### List of files to be Processed'))\n",
+ "print(\"Reading data from the following files: \")\n",
"for i in range(len(file_list)):\n",
" print(file_list[i])"
]
@@ -645,36 +667,6 @@
"Applying offset and common mode corrections to the raw data"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2018-12-06T16:08:53.551111Z",
- "start_time": "2018-12-06T16:08:53.531064Z"
- }
- },
- "outputs": [],
- "source": [
- "def copy_and_sanitize_non_cal_data(infile: str, outfile: str, h5base: str):\n",
- " '''This function reads the .h5 data and writes the corrected .h5 data.'''\n",
- " if h5base.startswith(\"/\"):\n",
- " h5base = h5base[1:]\n",
- " dont_copy = ['image']\n",
- " dont_copy = [h5base+\"/{}\".format(do) for do in dont_copy]\n",
- "\n",
- " def visitor(k, item):\n",
- " if k not in dont_copy:\n",
- " if isinstance(item, h5py.Group):\n",
- " outfile.create_group(k)\n",
- " elif isinstance(item, h5py.Dataset):\n",
- " group = str(k).split(\"/\")\n",
- " group = \"/\".join(group[:-1])\n",
- " infile.copy(k, outfile[group])\n",
- " \n",
- " infile.visititems(visitor)"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -694,156 +686,164 @@
" relGain = constants[\"RelativeGain\"]\n",
"\n",
"for k, f in enumerate(file_list):\n",
- " with h5py.File(f, 'r', driver='core') as infile:\n",
- " out_fileb = \"{}/{}\".format(out_folder, f.split(\"/\")[-1])\n",
- " out_file = out_fileb.replace(\"RAW\", \"CORR\")\n",
+ " f_dc = H5File(f)\n",
+ " out_fileb = f\"{out_folder}/{f.split(\"/\")[-1]}\"\n",
+ " out_file = out_fileb.replace(\"RAW\", \"CORR\")\n",
"\n",
- " data = None\n",
- " noise = None\n",
- " \n",
- " try:\n",
- " with h5py.File(out_file, \"w\") as ofile:\n",
- " copy_and_sanitize_non_cal_data(infile, ofile, h5path)\n",
- " data = infile[h5path+\"/image\"][()]\n",
- " # Getting rid of empty frames:\n",
- " nzidx = np.count_nonzero(data, axis=(1, 2))\n",
- " data = data[nzidx != 0, ...]\n",
- " \n",
- " # If you want to analyze only a certain number of frames instead of all available good frames: \n",
- " if limit_images > 0:\n",
- " data = data[:limit_images,...]\n",
- " \n",
- " # used for array shapes in the corrected data sets that we create and save in this loop:\n",
- " oshape = data.shape\n",
- " \n",
- " data = np.moveaxis(data, 0, 2)\n",
- " \n",
- " # data set to save offset corrected images:\n",
- " ddset = ofile.create_dataset(h5path+\"/pixels\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.float32)\n",
- " # data set to create bad pixels:\n",
- " ddsetm = ofile.create_dataset(h5path+\"/mask\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.uint32, compression=\"gzip\")\n",
- " \n",
- " data = data.astype(np.float32) \n",
- " \n",
- " # Creating maps for correction usage:\n",
- " offset = np.repeat(offsetMap[...,None], data.shape[2], axis=2)\n",
- " noise = np.repeat(noiseMap[...,None], data.shape[2], axis=2)\n",
- " bpix = np.repeat(badPixelMap[...,None], data.shape[2], axis=2)\n",
- " if corr_bools.get('relgain'):\n",
- " rg = np.repeat(relGain[:,:,None], data.shape[2], axis=2) # rg = relative gain\n",
- " \n",
- " # non-corrected images for first sequence only:\n",
- " if k == 0:\n",
- " uncor = np.append(uncor, data, axis=2)\n",
- " \n",
- " histCalRaw.fill(data) # filling histogram with raw uncorrected data\n",
- " \n",
- " # equating bad pixels' values to np.nan so that the pattern classifier ignores them:\n",
- " data[bpix != 0] = np.nan\n",
- " \n",
- " data -= offset # offset correction \n",
- " \n",
- " # Offset corrected images for first sequence only:\n",
- " if k == 0:\n",
- " off_cor = np.append(off_cor, data, axis=2)\n",
- " histCalOffsetCor.fill(data) # filling histogram with offset corrected data\n",
- "\n",
- " ddset[...] = np.moveaxis(data, 2, 0)\n",
- " ddsetm[...] = np.moveaxis(bpix, 2, 0)\n",
- " ofile.flush()\n",
- "\n",
- " # cm: common mode\n",
- " if corr_bools.get('common_mode'):\n",
- " \n",
- " # data set to save common mode corrected images:\n",
- " ddsetcm = ofile.create_dataset(h5path+\"/pixels_cm\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.float32)\n",
- " \n",
- " # common mode correction:\n",
- " data = cmCorrection.correct(data.astype(np.float32), # common mode correction\n",
- " cellTable=np.zeros(data.shape[2], np.int32)) \n",
- " \n",
- " # discarding events caused by saturated pixels:\n",
- " # we equate these values to np.nan so that the pattern classifier ignores them:\n",
- " data[data >= saturated_threshold] = np.nan \n",
- " histCalCommonModeCor.fill(data) # filling histogram with common mode corrected data\n",
- " # common mode corrected images for first sequence only:\n",
- " if k == 0:\n",
- " cm_cor = np.append(cm_cor, data, axis=2)\n",
- " \n",
- " ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
- " \n",
- " if corr_bools.get('relgain'):\n",
- " # data set to save gain corrected images:\n",
- " ddsetg = ofile.create_dataset(h5path+\"/gain\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.float32, compression=\"gzip\")\n",
- " \n",
- " data /= rg # relative gain correction \n",
- " histCalGainCor.fill(data) # filling histogram with gain corrected data\n",
- " # gain corrected images for first sequence only:\n",
- " if k == 0:\n",
- " g_cor = np.append(g_cor, data, axis=2)\n",
- " ddsetg[...] = np.moveaxis(rg, 2, 0).astype(np.float32)\n",
- "\n",
- " if corr_bools.get('pattern_class'):\n",
- " # data set to save split event corrected images:\n",
- " # c: classifications, p: even patterns\n",
- " ddsetc = ofile.create_dataset(h5path+\"/pixels_classified\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.float32, compression=\"gzip\")\n",
- " # data set to save different valid patterns:\n",
- " ddsetp = ofile.create_dataset(h5path+\"/patterns\",\n",
- " oshape,\n",
- " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
- " dtype=np.int32, compression=\"gzip\")\n",
- " \n",
- " # The calculation of the cluster map:\n",
- " patternClassifierLH._noisemap = noise[:, :pixels_x//2, :]\n",
- " patternClassifierRH._noisemap = noise[:, pixels_x//2:, :]\n",
- "\n",
- " # Dividing the data into left and right hemispheres:\n",
- " dataLH = data[:, :pixels_x//2, :]\n",
- " dataRH = data[:, pixels_x//2:, :]\n",
- " \n",
- " # pattern classification on corrected data\n",
- " dataLH, patternsLH = patternClassifierLH.classify(dataLH) \n",
- " dataRH, patternsRH = patternClassifierRH.classify(dataRH)\n",
- "\n",
- " data[:, :pixels_x//2, :] = dataLH\n",
- " data[:, pixels_x//2:, :] = dataRH\n",
- "\n",
- " patterns = np.zeros(data.shape, patternsLH.dtype)\n",
- " patterns[:, :pixels_x//2, :] = patternsLH\n",
- " patterns[:, pixels_x//2:, :] = patternsRH\n",
- "\n",
- " data[data < split_evt_primary_threshold*noise] = 0\n",
- " ddsetc[...] = np.moveaxis(data, 2, 0)\n",
- " ddsetp[...] = np.moveaxis(patterns, 2, 0)\n",
- "\n",
- " histCalPcorr.fill(data) # filling histogram with split events corrected data\n",
- " data[patterns != 100] = np.nan # Discard doubles, triples, quadruple, clusters, first singles\n",
- " histCalPcorrS.fill(data) # filling histogram with singles events data\n",
- " \n",
- " # split event corrected images for first sequence only (also these events are only \n",
- " # singles events):\n",
- " if k == 0:\n",
- " final_cor = np.append(final_cor, data, axis=2)\n",
- " \n",
- " except Exception as e:\n",
- " print(f\"Couldn't calibrate data in {f}: {e}\\n\")\n",
+ " data = None\n",
+ " noise = None\n",
+ "\n",
+ " try:\n",
+ " with h5py.File(out_file, 'w') as ofile:\n",
+ " # Copy RAW non-calibrated sources.\n",
+ " with h5py.File(f, 'r') as sfile:\n",
+ " h5_copy_except.h5_copy_except_paths(\n",
+ " sfile, ofile,\n",
+ " [\"INSTRUMENT/\"+h5path+\"/data/image\"])\n",
+ "\n",
+ " oshape = f_dc[h5path][\"data.image\"].shape\n",
+ " n_imgs = oshape[0]\n",
+ " # If you want to analyze only a certain number of frames\n",
+ " # instead of all available good frames. \n",
+ " if limit_images > 0:\n",
+ " n_imgs = limit_images\n",
+ "\n",
+ " data_dc = f_dc.select(\n",
+ " h5path, \"data.image\", require_all=True).select_trains(np._s(n_imgs))\n",
+ "\n",
+ " data = np.moveaxis(data, 0, 2)\n",
+ "\n",
+ " # data set to save offset corrected images:\n",
+ " ddset = ofile.create_dataset(\n",
+ " h5path+\"/pixels\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.float32)\n",
+ "\n",
+ " # data set to create bad pixels:\n",
+ " ddsetm = ofile.create_dataset(\n",
+ " h5path+\"/mask\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.uint32, compression=\"gzip\")\n",
+ "\n",
+ " data = data.astype(np.float32) \n",
+ "\n",
+ " # Creating maps for correction usage:\n",
+ " offset = np.repeat(offsetMap[...,None], data.shape[2], axis=2)\n",
+ " noise = np.repeat(noiseMap[...,None], data.shape[2], axis=2)\n",
+ " bpix = np.repeat(badPixelMap[...,None], data.shape[2], axis=2)\n",
+ " if corr_bools.get('relgain'):\n",
+ " rg = np.repeat(relGain[:,:,None], data.shape[2], axis=2) # rg = relative gain\n",
+ "\n",
+ " # non-corrected images for first sequence only:\n",
+ " if k == 0:\n",
+ " uncor = np.append(uncor, data, axis=2)\n",
+ "\n",
+ " histCalRaw.fill(data) # filling histogram with raw uncorrected data\n",
+ "\n",
+ " # equating bad pixels' values to np.nan so that the pattern classifier ignores them:\n",
+ " data[bpix != 0] = np.nan\n",
+ "\n",
+ " data -= offset # offset correction \n",
+ "\n",
+ " # Offset corrected images for first sequence only:\n",
+ " if k == 0:\n",
+ " off_cor = np.append(off_cor, data, axis=2)\n",
+ " histCalOffsetCor.fill(data) # filling histogram with offset corrected data\n",
+ "\n",
+ " ddset[...] = np.moveaxis(data, 2, 0)\n",
+ " ddsetm[...] = np.moveaxis(bpix, 2, 0)\n",
+ " ofile.flush()\n",
+ "\n",
+ " # cm: common mode\n",
+ " if corr_bools.get('common_mode'):\n",
+ "\n",
+ " # data set to save common mode corrected images:\n",
+ " ddsetcm = ofile.create_dataset(h5path+\"/pixels_cm\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.float32)\n",
+ "\n",
+ " # common mode correction:\n",
+ " data = cmCorrection.correct(data.astype(np.float32), # common mode correction\n",
+ " cellTable=np.zeros(data.shape[2], np.int32)) \n",
+ "\n",
+ " # discarding events caused by saturated pixels:\n",
+ " # we equate these values to np.nan so that the pattern classifier ignores them:\n",
+ " data[data >= saturated_threshold] = np.nan \n",
+ " histCalCommonModeCor.fill(data) # filling histogram with common mode corrected data\n",
+ " # common mode corrected images for first sequence only:\n",
+ " if k == 0:\n",
+ " cm_cor = np.append(cm_cor, data, axis=2)\n",
+ "\n",
+ " ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
+ "\n",
+ " if corr_bools.get('relgain'):\n",
+ " # data set to save gain corrected images:\n",
+ " ddsetg = ofile.create_dataset(h5path+\"/gain\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.float32, compression=\"gzip\")\n",
+ "\n",
+ " data /= rg # relative gain correction \n",
+ " histCalGainCor.fill(data) # filling histogram with gain corrected data\n",
+ " # gain corrected images for first sequence only:\n",
+ " if k == 0:\n",
+ " g_cor = np.append(g_cor, data, axis=2)\n",
+ " ddsetg[...] = np.moveaxis(rg, 2, 0).astype(np.float32)\n",
+ "\n",
+ " if corr_bools.get('pattern_class'):\n",
+ " # data set to save split event corrected images:\n",
+ " # c: classifications, p: even patterns\n",
+ " ddsetc = ofile.create_dataset(h5path+\"/pixels_classified\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.float32, compression=\"gzip\")\n",
+ " # data set to save different valid patterns:\n",
+ " ddsetp = ofile.create_dataset(h5path+\"/patterns\",\n",
+ " oshape,\n",
+ " chunks=(chunk_size_idim, oshape[1], oshape[2]),\n",
+ " dtype=np.int32, compression=\"gzip\")\n",
+ "\n",
+ " # The calculation of the cluster map:\n",
+ " patternClassifierLH._noisemap = noise[:, :pixels_x//2, :]\n",
+ " patternClassifierRH._noisemap = noise[:, pixels_x//2:, :]\n",
+ "\n",
+ " # Dividing the data into left and right hemispheres:\n",
+ " dataLH = data[:, :pixels_x//2, :]\n",
+ " dataRH = data[:, pixels_x//2:, :]\n",
+ "\n",
+ " # pattern classification on corrected data\n",
+ " dataLH, patternsLH = patternClassifierLH.classify(dataLH) \n",
+ " dataRH, patternsRH = patternClassifierRH.classify(dataRH)\n",
+ "\n",
+ " data[:, :pixels_x//2, :] = dataLH\n",
+ " data[:, pixels_x//2:, :] = dataRH\n",
+ "\n",
+ " patterns = np.zeros(data.shape, patternsLH.dtype)\n",
+ " patterns[:, :pixels_x//2, :] = patternsLH\n",
+ " patterns[:, pixels_x//2:, :] = patternsRH\n",
+ "\n",
+ " data[data < split_evt_primary_threshold*noise] = 0\n",
+ " ddsetc[...] = np.moveaxis(data, 2, 0)\n",
+ " ddsetp[...] = np.moveaxis(patterns, 2, 0)\n",
+ "\n",
+ " histCalPcorr.fill(data) # filling histogram with split events corrected data\n",
+ " data[patterns != 100] = np.nan # Discard doubles, triples, quadruple, clusters, first singles\n",
+ " histCalPcorrS.fill(data) # filling histogram with singles events data\n",
+ "\n",
+ " # split event corrected images for first sequence only (also these events are only \n",
+ " # singles events):\n",
+ " if k == 0:\n",
+ " final_cor = np.append(final_cor, data, axis=2)\n",
+ "\n",
+ "except Exception as e:\n",
+ " print(f\"Couldn't calibrate data in {f}: {e}\\n\")\n",
"\n",
"print(\"In addition to offset correction, the following corrections were performed:\")\n",
+ "print[k for k, v in corr_bools.items() if v]\n",
+ "\n",
"for k, v in corr_bools.items():\n",
" if v:\n",
" print(k)"
@@ -1286,35 +1286,35 @@
" with h5py.File(\"{}/CORR-R{:04d}-PNCCD01-S{:05d}.h5\".format(out_folder, run, sequences[seq_num]), 'r', \n",
" driver='core') as infile:\n",
"\n",
- " data = infile[h5path+\"/pixels_classified\"][()].astype(np.float32) # classifications\n",
- " patterns = infile[h5path+\"/patterns\"][()].astype(np.float32) # event patterns\n",
- " \n",
- " # events' patterns indices are as follows: 100 (singles), 101 (first singles), 200 - 203 (doubles),\n",
- " # 300 - 303 (triples), and 400 - 403 (quadruples). Note that for the last three types of patterns, \n",
- " # there are left, right, up, and down indices.\n",
- "\n",
- " # Separating the events:\n",
- " # Singles and First Singles:\n",
- " for s in range(100, 102):\n",
- " single = copy.copy(data[...])\n",
- " single[patterns != s] = np.nan\n",
- " singles.append(single)\n",
- " \n",
- "\n",
- " for d in range(200, 204):\n",
- " double = copy.copy(data[...])\n",
- " double[patterns != d] = np.nan\n",
- " doubles.append(double)\n",
- "\n",
- " for t in range(300, 304):\n",
- " triple = copy.copy(data[...])\n",
- " triple[patterns != t] = np.nan\n",
- " triples.append(triple) \n",
- "\n",
- " for q in range(400, 404):\n",
- " quad = copy.copy(data[...])\n",
- " quad[patterns != q] = np.nan\n",
- " quads.append(quad)"
+ " data = infile[h5path+\"/pixels_classified\"][()].astype(np.float32) # classifications\n",
+ " patterns = infile[h5path+\"/patterns\"][()].astype(np.float32) # event patterns\n",
+ "\n",
+ " # events' patterns indices are as follows: 100 (singles), 101 (first singles), 200 - 203 (doubles),\n",
+ " # 300 - 303 (triples), and 400 - 403 (quadruples). Note that for the last three types of patterns, \n",
+ " # there are left, right, up, and down indices.\n",
+ "\n",
+ " # Separating the events:\n",
+ " # Singles and First Singles:\n",
+ " for s in range(100, 102):\n",
+ " single = copy.copy(data[...])\n",
+ " single[patterns != s] = np.nan\n",
+ " singles.append(single)\n",
+ "\n",
+ "\n",
+ " for d in range(200, 204):\n",
+ " double = copy.copy(data[...])\n",
+ " double[patterns != d] = np.nan\n",
+ " doubles.append(double)\n",
+ "\n",
+ " for t in range(300, 304):\n",
+ " triple = copy.copy(data[...])\n",
+ " triple[patterns != t] = np.nan\n",
+ " triples.append(triple) \n",
+ "\n",
+ " for q in range(400, 404):\n",
+ " quad = copy.copy(data[...])\n",
+ " quad[patterns != q] = np.nan\n",
+ " quads.append(quad)"
]
},
{
@@ -1330,7 +1330,6 @@
" hs, e = np.histogram(single.flatten(), bins=event_bins, range=b_range) # h: histogram counts, e: bin edges\n",
" h += hs\n",
" hA += hs # hA: counts all events (see below)\n",
- "\n",
" \n",
" # bin edges array has one extra element => need to plot from 0 to the one before the last element to have the \n",
" # same size as h-array => in what follows, we use e[:-1] (-1 means one before the last element)\n",
--
GitLab