From 0bdb6e2cb536d24ff88738ce1a1a9de62800b112 Mon Sep 17 00:00:00 2001
From: Karim Ahmed <karim.ahmed@xfel.eu>
Date: Mon, 11 Nov 2019 17:38:29 +0100
Subject: [PATCH] add bools to control correction
---
...orrectionNotebook_NewDAQ_FastCCD_NBC.ipynb | 353 +++++++++++-------
1 file changed, 210 insertions(+), 143 deletions(-)
diff --git a/notebooks/FastCCD/CorrectionNotebook_NewDAQ_FastCCD_NBC.ipynb b/notebooks/FastCCD/CorrectionNotebook_NewDAQ_FastCCD_NBC.ipynb
index c80d17a47..7f3c35ece 100644
--- a/notebooks/FastCCD/CorrectionNotebook_NewDAQ_FastCCD_NBC.ipynb
+++ b/notebooks/FastCCD/CorrectionNotebook_NewDAQ_FastCCD_NBC.ipynb
@@ -18,13 +18,12 @@
"ExecuteTime": {
"end_time": "2018-12-06T15:54:23.218849Z",
"start_time": "2018-12-06T15:54:23.166497Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
"in_folder = \"/gpfs/exfel/exp/SCS/201802/p002170/raw/\" # input folder, required\n",
- "out_folder = '/gpfs/exfel/data/scratch/xcal/test/' # output folder, required\n",
+ "out_folder = '/gpfs/exfel/data/scratch/ahmedk/test/fastccd' # output folder, required\n",
"path_template = 'RAW-R{:04d}-{}-S{{:05d}}.h5' # path template in hdf5 file\n",
"path_inset = 'DA05'\n",
"run = 277 # run number\n",
@@ -42,16 +41,23 @@
"sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
"chunk_size_idim = 1 # H5 chunking size of output data\n",
"overwrite = True # overwrite existing files\n",
- "do_pattern_classification = True # classify split events\n",
"sequences_per_node = 1 # sequences to correct per node\n",
"limit_images = 0 # limit images per file \n",
- "correct_offset_drift = False # correct for offset drifts\n",
"use_dir_creation_date = True # use dir creation data for calDB queries\n",
"time_offset_days = 0 # offset in days for calibration parameters\n",
"photon_energy_gain_map = 2. # energy in keV\n",
"fix_temperature = 0. # fix temperature to this value, set to 0 to use slow control value\n",
"flipped_between = [\"2019-02-01\", \"2019-04-02\"] # detector was flipped during this timespan\n",
"temp_limits = 5 # limits within which temperature is considered the same\n",
+ "commonModeAxis = 1 # Axis along which common mode will be calculated (0: along rows, 1: along columns)\n",
+ "\n",
+ "# Correction Booleans\n",
+ "only_offset = False # Only apply offset correction\n",
+ "cti_corr = True # Apply CTI correction\n",
+ "relgain_corr = True # Apply relative gain correction\n",
+ "common_mode_corr = True # Apply commonMode correction\n",
+ "correct_offset_drift = False # correct for offset drifts\n",
+ "do_pattern_classification = True # classify split events\n",
"\n",
"def balance_sequences(in_folder, run, sequences, sequences_per_node):\n",
" import glob\n",
@@ -74,6 +80,38 @@
" return sequences"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Fill dictionaries comprising bools and arguments for correction and data analysis\n",
+ "\n",
+ "# Here the herarichy and dependability for correction booleans are defined \n",
+ "corr_bools = {}\n",
+ "\n",
+ "# offset is at the bottom of AGIPD correction pyramid.\n",
+ "corr_bools[\"only_offset\"] = only_offset\n",
+ "\n",
+ "# Dont apply any corrections if only_offset is requested \n",
+ "if not only_offset:\n",
+ " \n",
+ " # Apply relative gain correction, only if requested\n",
+ " if relgain_corr:\n",
+ " corr_bools[\"relgain\"] = relgain_corr\n",
+ " \n",
+ " # Apply CTI correction, only if requested\n",
+ " if cti_corr:\n",
+ " corr_bools[\"cti\"] = cti_corr\n",
+ " \n",
+ " corr_bools[\"common_mode\"] = common_mode_corr\n",
+ " corr_bools[\"offset_drift\"] = correct_offset_drift\n",
+ " corr_bools[\"pattern_class\"] = do_pattern_classification\n",
+ "# Here the herarichy and dependability for data analysis booleans and arguments are defined \n",
+ "data_analysis_parms = {}"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -187,7 +225,7 @@
"\n",
"sensorSize = [x, y]\n",
"chunkSize = 100 #Number of images to read per chunk\n",
- "blockSize = [sensorSize[0]//2, sensorSize[1]//4] #Sensor area will be analysed according to blocksize\n",
+ "blockSize = [sensorSize[0]//2, sensorSize[1]] #Sensor area will be analysed according to blocksize\n",
"xcal.defaultBlockSize = blockSize\n",
"memoryCells = 1 #FastCCD has 1 memory cell\n",
"#Specifies total number of images to proceed\n",
@@ -227,8 +265,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T15:54:24.088948Z",
"start_time": "2018-12-06T15:54:24.059925Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -448,104 +485,100 @@
"ExecuteTime": {
"end_time": "2018-12-06T15:54:28.343869Z",
"start_time": "2018-12-06T15:54:28.271344Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
"## relative gain\n",
+ "if corr_bools.get('relgain'):\n",
+ " metadata = ConstantMetaData()\n",
+ " relgain = Constants.CCD(DetectorTypes.fastCCD).RelativeGain()\n",
+ " metadata.calibration_constant = relgain\n",
"\n",
- "metadata = ConstantMetaData()\n",
- "relgain = Constants.CCD(DetectorTypes.fastCCD).RelativeGain()\n",
- "metadata.calibration_constant = relgain\n",
- "\n",
- "# set the operating condition\n",
- "condition = Conditions.Illuminated.CCD(bias_voltage=bias_voltage,\n",
- " integration_time=integration_time,\n",
- " gain_setting=0,\n",
- " temperature=temperature_k,\n",
- " pixels_x=1934,\n",
- " pixels_y=960, photon_energy=photon_energy_gain_map)\n",
- "device = Detectors.fastCCD1\n",
+ " # set the operating condition\n",
+ " condition = Conditions.Illuminated.CCD(bias_voltage=bias_voltage,\n",
+ " integration_time=integration_time,\n",
+ " gain_setting=0,\n",
+ " temperature=temperature_k,\n",
+ " pixels_x=1934,\n",
+ " pixels_y=960, photon_energy=photon_energy_gain_map)\n",
+ " device = Detectors.fastCCD1\n",
"\n",
"\n",
- "metadata.detector_condition = condition\n",
+ " metadata.detector_condition = condition\n",
"\n",
- "# specify the a version for this constant\n",
- "metadata.calibration_constant_version = Versions.Now(device=device)\n",
- "metadata.retrieve(cal_db_interface)\n",
+ " # specify the a version for this constant\n",
+ " metadata.calibration_constant_version = Versions.Now(device=device)\n",
+ " metadata.retrieve(cal_db_interface)\n",
"\n",
- "relGain = relgain.data[::-1,...]\n"
+ " relGain = relgain.data[::-1,...]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
- "relGainCA = copy.copy(relGain)\n",
- "relGainC = relGainCA[:relGainCA.shape[0]//2,...]\n",
- "ctiA = np.ones(relGainCA.shape[:2])\n",
- "cti = np.ones(relGainC.shape[:2])\n",
- "i = 0\n",
- "idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
- "mn1 = np.nanmean(relGainC[i, ~idx, 0])\n",
- "\n",
- "for i in range(1, relGainC.shape[0]):\n",
+ "if corr_bools.get('cti'):\n",
+ " relGainCA = copy.copy(relGain)\n",
+ " relGainC = relGainCA[:relGainCA.shape[0]//2,...]\n",
+ " ctiA = np.ones(relGainCA.shape[:2])\n",
+ " cti = np.ones(relGainC.shape[:2])\n",
+ " i = 0\n",
" idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
- " mn2 = np.nanmean(relGainC[i, ~idx, 0])\n",
- " cti[i,:] = mn2/mn1\n",
- "ctiA[:relGainCA.shape[0]//2,...] = cti\n",
+ " mn1 = np.nanmean(relGainC[i, ~idx, 0])\n",
"\n",
- "relGainC = relGainCA[relGainCA.shape[0]//2:,...]\n",
+ " for i in range(1, relGainC.shape[0]):\n",
+ " idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
+ " mn2 = np.nanmean(relGainC[i, ~idx, 0])\n",
+ " cti[i,:] = mn2/mn1\n",
+ " ctiA[:relGainCA.shape[0]//2,...] = cti\n",
"\n",
+ " relGainC = relGainCA[relGainCA.shape[0]//2:,...]\n",
"\n",
- "cti = np.ones(relGainC.shape[:2])\n",
- "i = -1\n",
- "idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
- "mn1 = np.nanmean(relGainC[i, ~idx, 0])\n",
"\n",
- "for i in range(relGainC.shape[0]-1, 1, -1):\n",
+ " cti = np.ones(relGainC.shape[:2])\n",
+ " i = -1\n",
" idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
- " mn2 = np.nanmean(relGainC[i, ~idx, 0])\n",
- " cti[i,:] = mn2/mn1\n",
- "\n",
- "ctiA[relGainCA.shape[0]//2:,...] = cti\n",
- "\n",
- "relGainCA = copy.copy(relGain)\n",
- "relGainC = relGainCA[:relGainCA.shape[0]//2,...]\n",
- "for i in range(relGainC.shape[1]):\n",
- " idx = (relGainC[:,i, 0] < 0.95) | (relGainC[:,i,0] > 1.05)\n",
- " relGainC[idx,i,0] = np.nanmean(relGainC[~idx,i,0])\n",
- " relGainC[idx,i,1] = np.nanmean(relGainC[~idx,i,1])\n",
- " relGainC[idx,i,2] = np.nanmean(relGainC[~idx,i,2])\n",
- "relGainCA[:relGainCA.shape[0]//2,...] = relGainC\n",
- "relGainC = relGainCA[relGainCA.shape[0]//2:,...]\n",
- "for i in range(relGainC.shape[1]):\n",
- " idx = (relGainC[:,i, 0] < 0.95) | (relGainC[:,i,0] > 1.05)\n",
- " relGainC[idx,i,0] = np.nanmean(relGainC[~idx,i,0])\n",
- " relGainC[idx,i,1] = np.nanmean(relGainC[~idx,i,1])\n",
- " relGainC[idx,i,2] = np.nanmean(relGainC[~idx,i,2])\n",
- "relGainCA[relGainCA.shape[0]//2:,...] = relGainC\n",
- "relGainC = relGainCA*ctiA[...,None]\n",
- "\n",
- "relGain = relGainC"
+ " mn1 = np.nanmean(relGainC[i, ~idx, 0])\n",
+ "\n",
+ " for i in range(relGainC.shape[0]-1, 1, -1):\n",
+ " idx = (relGainC[i, :, 0] < 0.9) | (relGainC[i,:,0] > 1.1)\n",
+ " mn2 = np.nanmean(relGainC[i, ~idx, 0])\n",
+ " cti[i,:] = mn2/mn1\n",
+ "\n",
+ " ctiA[relGainCA.shape[0]//2:,...] = cti\n",
+ "\n",
+ " relGainCA = copy.copy(relGain)\n",
+ " relGainC = relGainCA[:relGainCA.shape[0]//2,...]\n",
+ " for i in range(relGainC.shape[1]):\n",
+ " idx = (relGainC[:,i, 0] < 0.95) | (relGainC[:,i,0] > 1.05)\n",
+ " relGainC[idx,i,0] = np.nanmean(relGainC[~idx,i,0])\n",
+ " relGainC[idx,i,1] = np.nanmean(relGainC[~idx,i,1])\n",
+ " relGainC[idx,i,2] = np.nanmean(relGainC[~idx,i,2])\n",
+ " relGainCA[:relGainCA.shape[0]//2,...] = relGainC\n",
+ " relGainC = relGainCA[relGainCA.shape[0]//2:,...]\n",
+ " for i in range(relGainC.shape[1]):\n",
+ " idx = (relGainC[:,i, 0] < 0.95) | (relGainC[:,i,0] > 1.05)\n",
+ " relGainC[idx,i,0] = np.nanmean(relGainC[~idx,i,0])\n",
+ " relGainC[idx,i,1] = np.nanmean(relGainC[~idx,i,1])\n",
+ " relGainC[idx,i,2] = np.nanmean(relGainC[~idx,i,2])\n",
+ " relGainCA[relGainCA.shape[0]//2:,...] = relGainC\n",
+ " relGainC = relGainCA*ctiA[...,None]\n",
+ "\n",
+ " relGain = relGainC"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import dateutil.parser\n",
"flipped_between = [dateutil.parser.parse(d) for d in flipped_between]\n",
- "flip_rgain = creation_time >= flipped_between[0] and creation_time <= flipped_between[1]\n",
+ "flip_rgain = creation_time.replace(tzinfo=None) >= flipped_between[0] and creation_time.replace(tzinfo=None) <= flipped_between[1]\n",
"flip_rgain &= (metadata.calibration_constant_version.begin_at.replace(tzinfo=None) >= flipped_between[0] \n",
" and metadata.calibration_constant_version.begin_at.replace(tzinfo=None) <= flipped_between[1])\n",
"print(\"Accounting for flipped detector: {}\".format(flip_rgain))\n"
@@ -558,21 +591,20 @@
"ExecuteTime": {
"end_time": "2018-12-06T15:54:28.771629Z",
"start_time": "2018-12-06T15:54:28.346051Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
"#************************Calculators************************#\n",
"\n",
- "\n",
- "cmCorrection = xcal.CommonModeCorrection([x, y], \n",
- " commonModeBlockSize, \n",
- " commonModeAxisR,\n",
- " nCells = memoryCells, \n",
- " noiseMap = noiseMap,\n",
- " runParallel=True,\n",
- " stats=True)\n",
+ "if corr_bools.get('common_mode'):\n",
+ " cmCorrection = xcal.CommonModeCorrection([x, y],\n",
+ " commonModeBlockSize, \n",
+ " commonModeAxis,\n",
+ " nCells = memoryCells,\n",
+ " stride=10,\n",
+ " runParallel=True,\n",
+ " stats=True, minFrac=0)\n",
"\n",
"patternClassifierLH = xcal.PatternClassifier([x//2, y], \n",
" noiseMap[:x//2, :], \n",
@@ -612,8 +644,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:08:51.886343Z",
"start_time": "2018-12-06T16:08:51.842837Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -638,7 +669,15 @@
" range=[-50, 1000],\n",
" nCells=memoryCells, \n",
" cores=cpuCores,\n",
- " blockSize=blockSize)"
+ " blockSize=blockSize)\n",
+ "\n",
+ "\n",
+ "histCalCommonModeCor = xcal.HistogramCalculator([x, y], \n",
+ " bins=500, \n",
+ " range=[-50, 1000],\n",
+ " nCells=memoryCells, \n",
+ " cores=cpuCores,\n",
+ " blockSize=blockSize)"
]
},
{
@@ -655,8 +694,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:08:52.441784Z",
"start_time": "2018-12-06T16:08:52.437284Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -673,13 +711,13 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:08:53.042555Z",
"start_time": "2018-12-06T16:08:53.034522Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
"histCalOffsetCor.debug()\n",
- "histCalPcorr.debug()\n"
+ "histCalCommonModeCor.debug()\n",
+ "histCalPcorr.debug()"
]
},
{
@@ -689,8 +727,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:08:53.551111Z",
"start_time": "2018-12-06T16:08:53.531064Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -721,8 +758,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:55.917179Z",
"start_time": "2018-12-06T16:09:01.603633Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -735,7 +771,8 @@
"offsetMap = np.squeeze(offsetMap)\n",
"noiseMap = np.squeeze(noiseMap)\n",
"badPixelMap = np.squeeze(badPixelMap)\n",
- "relGain = np.squeeze(relGain)\n",
+ "if corr_bools.get('relgain'):\n",
+ " relGain = np.squeeze(relGain)\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",
@@ -778,20 +815,31 @@
" if not flip_rgain: # rgain was taken during flipped orientation\n",
" fstride = -1\n",
" \n",
- " data = np.bitwise_and(data, 0b0011111111111111).astype(np.float32) \n",
- " omap = np.repeat(offsetMap[...,None,:], data.shape[2], axis=2)\n",
- " rmap = np.repeat(relGain[:,::fstride,None,:], data.shape[2], axis=2)\n",
+ " data = np.bitwise_and(data, 0b0011111111111111).astype(np.float32)\n",
+ "\n",
+ " # creating maps for correction usage\n",
+ " omap = np.repeat(offsetMap[...,None,:], data.shape[2], axis=2) \n",
" nmap = np.repeat(noiseMap[...,None,:], data.shape[2], axis=2)\n",
" bmap = np.repeat(badPixelMap[...,None,:], data.shape[2], axis=2)\n",
+ "\n",
+ " # selecting element value related to the gain of the pixel.\n",
" offset = np.choose(gain, (omap[...,0], omap[...,1], omap[...,2]))\n",
- " rg = np.choose(gain, (rmap[...,0], rmap[...,1], rmap[...,2]))\n",
" noise = np.choose(gain, (nmap[...,0], nmap[...,1], nmap[...,2]))\n",
" bpix = np.choose(gain, (bmap[...,0], bmap[...,1], bmap[...,2]))\n",
+ "\n",
+ " # same for relative gain if correction is available\n",
+ " if corr_bools.get('relgain'):\n",
+ " rmap = np.repeat(relGain[:,::fstride,None,:], data.shape[2], axis=2)\n",
+ " rg = np.choose(gain, (rmap[...,0], rmap[...,1], rmap[...,2]))\n",
" \n",
+ " # Apply offset correction\n",
" data -= offset\n",
- " data *= rg\n",
+ "\n",
+ " if corr_bools.get('relgain'):\n",
+ " # Apply relative gain correction\n",
+ " data *= rg\n",
" \n",
- " if correct_offset_drift:\n",
+ " if corr_bools.get(\"offset_drift\"):\n",
" lhd = np.mean(data[x//2-10:x//2,y//2-5:y//2+5,:], axis=(0,1))\n",
" data[:x//2, :, :] -= lhd\n",
" drift_lh.append(lhd)\n",
@@ -811,7 +859,7 @@
" mean_im = np.nanmean(data, axis=2)\n",
" single_im = data[...,0]\n",
" \n",
- " if do_pattern_classification:\n",
+ " if corr_bools.get(\"pattern_class\"):\n",
" \n",
" ddsetcm = ofile.create_dataset(h5path+\"/pixels_cm\",\n",
" oshape,\n",
@@ -831,9 +879,16 @@
"\n",
" patternClassifierLH._noisemap = noise[:x//2, :, :]\n",
" patternClassifierUH._noisemap = noise[x//2:, :, :]\n",
+ " \n",
+ " # common mode correction\n",
+ " if corr_bools.get(\"common_mode\"):\n",
+ " cellTable = np.zeros(data.shape[2], np.int32) # Common mode correction\n",
+ " data = cmCorrection.correct(data.astype(np.float32), cellTable=cellTable, noiseMap=noise) \n",
"\n",
- " data = cmCorrection.correct(data) # correct for the row common mode\n",
- " ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
+ " # correct for the row common mode\n",
+ " ddsetcm[...] = np.moveaxis(data, 2, 0)\n",
+ "\n",
+ " histCalCommonModeCor.fill(data)\n",
"\n",
" dataLH = data[:x//2, :, :]\n",
" dataUH = data[x//2:, :, :]\n",
@@ -871,12 +926,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.094985Z",
"start_time": "2018-12-06T16:10:55.918900Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if correct_offset_drift:\n",
+ "if corr_bools.get(\"offset_drift\"):\n",
" lhds = np.concatenate(drift_lh)\n",
" uhds = np.concatenate(drift_uh)\n",
" fig = plt.figure(figsize=(10,5))\n",
@@ -894,12 +948,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.126409Z",
"start_time": "2018-12-06T16:10:56.096242Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" print(\"******************LOWER HEMISPHERE******************\\n\")\n",
"\n",
" patternStatsLH = patternClassifierLH.getPatternStats()\n",
@@ -951,12 +1004,13 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.176160Z",
"start_time": "2018-12-06T16:10:56.127853Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
+ " print(\"******************UPPER HEMISPHERE******************\\n\")\n",
+ "\n",
" patternStatsUH = patternClassifierUH.getPatternStats()\n",
" fig = plt.figure(figsize=(15,15))\n",
" ax = fig.add_subplot(4,4,1)\n",
@@ -996,9 +1050,7 @@
" pmap = ax.imshow(patternStatsUH[m][j], interpolation=\"nearest\", vmax=np.median(patternStatsUH[m][j]))\n",
" ax.set_title(m+\"(\"+str(j)+\")\")\n",
" cb = fig.colorbar(pmap)\n",
- " k+=1\n",
- "\n",
- " print(\"******************UPPER HEMISPHERE******************\\n\") "
+ " k+=1\n"
]
},
{
@@ -1008,12 +1060,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.190150Z",
"start_time": "2018-12-06T16:10:56.177570Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" t0 = PrettyTable()\n",
" t0.title = \"Total number of Counts after all corrections\"\n",
" t0.field_names = [\"Hemisphere\",\"Singles\", \"First-singles\", \"Clusters\"]\n",
@@ -1041,12 +1092,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.203219Z",
"start_time": "2018-12-06T16:10:56.191509Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" doublesLH = patternStatsLH['doubles'][0] + patternStatsLH['doubles'][1] + patternStatsLH['doubles'][2] + patternStatsLH['doubles'][3]\n",
" triplesLH = patternStatsLH['triples'][0] + patternStatsLH['triples'][1] + patternStatsLH['triples'][2] + patternStatsLH['triples'][3]\n",
" quadsLH = patternStatsLH['quads'][0] + patternStatsLH['quads'][1] + patternStatsLH['quads'][2] + patternStatsLH['quads'][3]\n",
@@ -1070,12 +1120,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:10:56.212586Z",
"start_time": "2018-12-06T16:10:56.204731Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" fig = plt.figure(figsize=(10,5))\n",
" ax = fig.add_subplot(1,2,1)\n",
" labels = ['singles', 'doubles', 'triples', 'quads']\n",
@@ -1097,8 +1146,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:13:12.889583Z",
"start_time": "2018-12-06T16:13:11.122653Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -1115,7 +1163,17 @@
" },\n",
" \n",
" ]\n",
- " \n",
+ "\n",
+ "if corr_bools.get(\"pattern_class\") and corr_bools.get(\"common_mode\"):\n",
+ " hcm,ecm,ccm,scm = histCalCommonModeCor.get()\n",
+ " d.append({'x': ccm,\n",
+ " 'y': hcm,\n",
+ " 'y_err': np.sqrt(hcm[:]),\n",
+ " 'drawstyle': 'steps-mid',\n",
+ " 'errorstyle': 'bars',\n",
+ " 'errorcoarsing': 2,\n",
+ " 'label': 'CommonMode corr.'\n",
+ " })\n",
"\n",
"fig = xana.simplePlot(d, aspect=1, x_label='Energy(ADU)', \n",
" y_label='Number of occurrences', figsize='2col',\n",
@@ -1130,12 +1188,11 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:12:57.289742Z",
"start_time": "2018-12-06T16:12:45.529734Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" h1,e1L,c1L,s1L = histCalPcorr.get()\n",
" h1s,e1Ls,c1Ls,s1Ls = histCalPcorrS.get()\n",
"\n",
@@ -1174,8 +1231,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:11:08.317130Z",
"start_time": "2018-12-06T16:11:05.788655Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -1185,7 +1241,7 @@
" x_range=(0,y),\n",
" y_range=(0,x), vmin=-50, vmax=500)\n",
"\n",
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" fig = xana.heatmapPlot(mean_im_cc,\n",
" x_label='Columns', y_label='Rows',\n",
" lut_label='Signal (ADU)',\n",
@@ -1209,8 +1265,7 @@
"ExecuteTime": {
"end_time": "2018-12-06T16:11:10.908912Z",
"start_time": "2018-12-06T16:11:08.318486Z"
- },
- "collapsed": true
+ }
},
"outputs": [],
"source": [
@@ -1220,7 +1275,7 @@
" x_range=(0,y),\n",
" y_range=(0,x), vmin=-50, vmax=500)\n",
"\n",
- "if do_pattern_classification:\n",
+ "if corr_bools.get(\"pattern_class\"):\n",
" fig = xana.heatmapPlot(single_im_cc,\n",
" x_label='Columns', y_label='Rows',\n",
" lut_label='Signal (ADU)',\n",
@@ -1231,9 +1286,21 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": []
}
--
GitLab