diff --git a/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb b/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
index ca345b5b104b1a46c06b9d4a01ccd3f0ccede587..0e05035d434af2710418b0db025d1fdcbbb6e032 100644
--- a/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
+++ b/notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb
@@ -26,7 +26,7 @@
"source": [
"cluster_profile = \"noDB\" # The ipcluster profile to use\n",
"in_folder = \"/gpfs/exfel/d/raw/SPB/202030/p900138/\" # path to input data, required\n",
- "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/AGIPD3\" # path to output to, required\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/AGIPDb1\" # path to output to, required\n",
"sequences = [0] # sequence files to evaluate.\n",
"modules = [-1] # list of modules to evaluate, RANGE ALLOWED\n",
"run_high = 264 # run number in which high gain data was recorded, required\n",
@@ -57,10 +57,18 @@
"rawversion = 2 # RAW file format version\n",
"\n",
"thresholds_offset_sigma = 3. # thresholds in terms of n sigma noise for offset deduced bad pixels\n",
- "thresholds_offset_hard = [4000, 8500] # thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_offset_hard = [0, 0] # This offset threshold is left for back compatability or for defining threshold for the 3 gains\n",
+ "thresholds_offset_hard_hg = [4000, 8500] # High-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_offset_hard_mg = [4000, 8500] # Medium-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_offset_hard_lg = [4000, 8500] # Low-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
"\n",
"thresholds_noise_sigma = 5. # thresholds in terms of n sigma noise for offset deduced bad pixels\n",
- "thresholds_noise_hard = [4, 20] # thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_noise_hard = [0, 0] # This noise threshold is left for back compatability or for defining threshold for the 3 gains\n",
+ "thresholds_noise_hard_hg = [4, 20] # High-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_noise_hard_mg = [4, 20] # Medium-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "thresholds_noise_hard_lg = [4, 20] # Low-gain thresholds in absolute ADU terms for offset deduced bad pixels\n",
+ "\n",
+ "gain_sep_sigma = 5. # gain separation sigma\n",
"\n",
"high_res_badpix_3d = False # set this to True if you need high-resolution 3d bad pixel plots. Runtime: ~ 1h"
]
@@ -251,13 +259,13 @@
"import copy\n",
"from functools import partial\n",
"def characterize_module(il_mode, cells, bp_thresh, rawversion, loc, acq_rate,\n",
- " h5path, h5path_idx, inp):\n",
+ " prev_offset, prev_noise, h5path, h5path_idx, inp):\n",
" import numpy as np\n",
" import copy\n",
" import h5py\n",
" from cal_tools.enums import BadPixels\n",
" from cal_tools.agipdlib import get_num_cells, get_acq_rate\n",
- " \n",
+ "\n",
" filename, filename_out, channel = inp\n",
" \n",
" if cells == 0:\n",
@@ -267,7 +275,7 @@
" \n",
" if acq_rate == 0.:\n",
" acq_rate = get_acq_rate(filename, loc, channel)\n",
- " \n",
+ "\n",
" thresholds_offset_hard, thresholds_offset_sigma, thresholds_noise_hard, thresholds_noise_sigma = bp_thresh \n",
"\n",
" infile = h5py.File(filename, \"r\", driver=\"core\")\n",
@@ -300,7 +308,7 @@
" else:\n",
" ga = im[:, 1, ...]\n",
" im = im[:, 0, ...].astype(np.float32)\n",
- " \n",
+ "\n",
" im = np.rollaxis(im, 2)\n",
" im = np.rollaxis(im, 2, 1)\n",
"\n",
@@ -311,29 +319,33 @@
" offset = np.zeros((im.shape[0], im.shape[1], mcells))\n",
" gains = np.zeros((im.shape[0], im.shape[1], mcells))\n",
" noise = np.zeros((im.shape[0], im.shape[1], mcells))\n",
- " \n",
+ "\n",
" for cc in np.unique(cellIds[cellIds < mcells]):\n",
" cellidx = cellIds == cc\n",
" offset[...,cc] = np.median(im[..., cellidx], axis=2)\n",
" noise[...,cc] = np.std(im[..., cellidx], axis=2)\n",
" gains[...,cc] = np.median(ga[..., cellidx], axis=2)\n",
- " \n",
+ "\n",
" # bad pixels\n",
" bp = np.zeros(offset.shape, np.uint32)\n",
" # offset related bad pixels\n",
" offset_mn = np.nanmedian(offset, axis=(0,1))\n",
- " offset_std = np.nanstd(offset, axis=(0,1)) \n",
- " \n",
+ " offset_std = np.nanstd(offset, axis=(0,1))\n",
+ "\n",
+ " # Bad pixels during bad gain separation.\n",
+ " if prev_offset is not None:\n",
+ " bad_sep = (offset-prev_offset) / np.sqrt(noise**2 + prev_noise**2)\n",
+ " bp[(bad_sep)<gain_sep_sigma]|= BadPixels.GAIN_THRESHOLDING_ERROR.value\n",
+ "\n",
" bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |\n",
" (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value\n",
" bp[(offset < thresholds_offset_hard[0]) | (\n",
" offset > thresholds_offset_hard[1])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value\n",
" bp[~np.isfinite(offset)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value\n",
- " \n",
+ "\n",
" # noise related bad pixels\n",
" noise_mn = np.nanmedian(noise, axis=(0,1))\n",
" noise_std = np.nanstd(noise, axis=(0,1)) \n",
- " \n",
" bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |\n",
" (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value\n",
" bp[(noise < thresholds_noise_hard[0]) | (noise > thresholds_noise_hard[1])] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value\n",
@@ -341,8 +353,7 @@
"\n",
"\n",
" return offset, noise, gains, bp, cells, acq_rate\n",
- " \n",
- " \n",
+ "\n",
"offset_g = OrderedDict()\n",
"noise_g = OrderedDict()\n",
"gain_g = OrderedDict()\n",
@@ -352,8 +363,14 @@
"start = datetime.now()\n",
"all_cells = []\n",
"all_acq_rate = []\n",
+ "offset = None\n",
+ "noise = None\n",
+ "if thresholds_offset_hard == [0, 0]:\n",
+ " thresholds_offset_hard = [thresholds_offset_hard_hg, thresholds_offset_hard_mg, thresholds_offset_hard_lg]\n",
+ "if thresholds_noise_hard == [0, 0]\n",
+ " thresholds_noise_hard = [thresholds_noise_hard_hg, thresholds_noise_hard_mg, thresholds_noise_hard_lg]\n",
"for gain, mapped_files in gain_mapped_files.items():\n",
- " \n",
+ "\n",
" inp = []\n",
" dones = []\n",
" for i in modules:\n",
@@ -367,9 +384,9 @@
" inp.append((fname_in, fout, i))\n",
" first = False\n",
" p = partial(characterize_module, IL_MODE, max_cells,\n",
- " (thresholds_offset_hard, thresholds_offset_sigma,\n",
- " thresholds_noise_hard, thresholds_noise_sigma), \n",
- " rawversion, karabo_id, acq_rate, h5path, h5path_idx)\n",
+ " (thresholds_offset_hard[gg], thresholds_offset_sigma,\n",
+ " thresholds_noise_hard[gg], thresholds_noise_sigma), \n",
+ " rawversion, karabo_id, acq_rate, offset, noise, h5path, h5path_idx)\n",
" #results = list(map(p, inp))\n",
" results = view.map_sync(p, inp)\n",
" for ii, r in enumerate(results):\n",
@@ -383,7 +400,7 @@
" noise_g[qm] = np.zeros_like(offset_g[qm])\n",
" gain_g[qm] = np.zeros_like(offset_g[qm])\n",
" badpix_g[qm] = np.zeros_like(offset_g[qm], np.uint32)\n",
- " \n",
+ "\n",
" offset_g[qm][...,gg] = offset\n",
" noise_g[qm][...,gg] = noise\n",
" gain_g[qm][...,gg] = gain\n",
@@ -447,7 +464,7 @@
" 'ThresholdsDark': thresholds_g[qm],\n",
" 'BadPixelsDark': badpix_g[qm] \n",
" }\n",
- " \n",
+ "\n",
"if local_output:\n",
" for qm in offset_g.keys():\n",
" ofile = \"{}/agipd_offset_store_{}_{}.h5\".format(out_folder,\n",
@@ -566,7 +583,7 @@
" print(msg.format(const, qm,\n",
" metadata.calibration_constant_version.begin_at))\n",
" except Exception as e:\n",
- " print(e)\n",
+ " print(\"Error:\", e)\n",
"\n",
" if local_output:\n",
" save_const_to_h5(metadata, out_folder)\n",
@@ -610,7 +627,7 @@
" ax.add_patch(matplotlib.patches.Rectangle((x,(q_x[1]-q_st*im+3)), l_x, l_y/2, linewidth=2,edgecolor='c',\n",
" facecolor='sandybrown', fill=True))\n",
" x += asic_pos\n",
- " \n",
+ "\n",
" if iq*4+im == modules[0]:\n",
" # Change Text for current processed module.\n",
" ax.text(q_x[0]+13, q_x[1]-q_st*im+1.5, 'Q{}M{}'.format(\n",
@@ -720,7 +737,8 @@
"source": [
"cols = {BadPixels.NOISE_OUT_OF_THRESHOLD.value: (BadPixels.NOISE_OUT_OF_THRESHOLD.name, '#FF000080'),\n",
" BadPixels.OFFSET_NOISE_EVAL_ERROR.value: (BadPixels.OFFSET_NOISE_EVAL_ERROR.name, '#0000FF80'),\n",
- " BadPixels.OFFSET_OUT_OF_THRESHOLD.value: (BadPixels.OFFSET_OUT_OF_THRESHOLD.name, '#00FF0080'),\n",
+ " BadPixels.OFFSET_OUT_OF_THRESHOLD.value: (BadPixels.OFFSET_OUT_OF_THRESHOLD.name, '#FFDF0080'),\n",
+ " BadPixels.GAIN_THRESHOLDING_ERROR.value: (BadPixels.GAIN_THRESHOLDING_ERROR.name, '#BF2080'),\n",
" BadPixels.OFFSET_OUT_OF_THRESHOLD.value | BadPixels.NOISE_OUT_OF_THRESHOLD.value: ('MIXED', '#DD00DD80')}\n",
"\n",
"rebin = 8 if not high_res_badpix_3d else 2\n",
@@ -815,8 +833,8 @@
" bp_thresh[mod] = np.zeros((con.shape[0], con.shape[1], con.shape[2], 5), dtype=con.dtype)\n",
" bp_thresh[mod][...,:2] = con[...,:2]\n",
" bp_thresh[mod][...,2:] = con\n",
- " \n",
- " \n",
+ "\n",
+ "\n",
"create_constant_overview(thresholds_g, \"Threshold (ADU)\", max_cells, 4000, 10000, 5,\n",
" out_folder=out_folder, infix=\"{}-{}-{}\".format(*offset_runs.values()),\n",
" badpixels=[bp_thresh, np.nan],\n",
@@ -867,6 +885,7 @@
" l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.NOISE_OUT_OF_THRESHOLD.value))\n",
" l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.OFFSET_OUT_OF_THRESHOLD.value))\n",
" l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.OFFSET_NOISE_EVAL_ERROR.value))\n",
+ " l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.GAIN_THRESHOLDING_ERROR.value))\n",
"\n",
" if old_const['BadPixelsDark'] is not None:\n",
" dataold = np.copy(old_const['BadPixelsDark'][:, :, :, gain])\n",
@@ -875,12 +894,13 @@
" l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.NOISE_OUT_OF_THRESHOLD.value))\n",
" l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.OFFSET_OUT_OF_THRESHOLD.value))\n",
" l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.OFFSET_NOISE_EVAL_ERROR.value))\n",
+ " l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.GAIN_THRESHOLDING_ERROR.value))\n",
"\n",
" l_data_name = ['All bad pixels', 'NOISE_OUT_OF_THRESHOLD', \n",
- " 'OFFSET_OUT_OF_THRESHOLD', 'OFFSET_NOISE_EVAL_ERROR']\n",
+ " 'OFFSET_OUT_OF_THRESHOLD', 'OFFSET_NOISE_EVAL_ERROR', 'GAIN_THRESHOLDING_ERROR']\n",
"\n",
" l_threshold = ['', '{}'.format(thresholds_noise_sigma), '{}'.format(thresholds_offset_sigma),\n",
- " '{}/{}'.format(thresholds_offset_hard, thresholds_noise_hard)]\n",
+ " '{}/{}'.format(thresholds_offset_hard[gain], thresholds_noise_hard[gain]), '']\n",
"\n",
" for i in range(len(l_data)):\n",
" line = ['{}, {} gain'.format(l_data_name[i], gain_names[gain]),\n",