From f27bae9022ef99fbeb972d98fa6c8cb465e50892 Mon Sep 17 00:00:00 2001
From: Karim Ahmed <karim.ahmed@xfel.eu>
Date: Fri, 1 Oct 2021 19:17:34 +0200
Subject: [PATCH] [JUNGFRAU] Style changes for Dark and Correct notebooks
---
...Jungfrau_Gain_Correct_and_Verify_NBC.ipynb | 212 ++++++++++++------
...rk_analysis_all_gains_burst_mode_NBC.ipynb | 60 ++---
2 files changed, 176 insertions(+), 96 deletions(-)
diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index c386874f8..b62e4e1d5 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -166,8 +166,9 @@
" else:\n",
" table.append((fi, \"\", k, f))\n",
" fi += 1\n",
- " md = display(Latex(tabulate.tabulate(table, tablefmt='latex',\n",
- " headers=[\"#\", \"module\", \"# module\", \"file\"])))\n",
+ " md = display(Latex(tabulate.tabulate(\n",
+ " table, tablefmt='latex',\n",
+ " headers=[\"#\", \"module\", \"# module\", \"file\"])))\n",
"\n",
"# restore the queue\n",
"mapped_files, _, total_sequences, _, _ = map_modules_from_folder(\n",
@@ -341,7 +342,14 @@
"outputs": [],
"source": [
"# Correct a chunk of images for offset and gain\n",
- "def correct_chunk(offset_map, mask, gain_map, memory_cells, relative_gain, inp):\n",
+ "def correct_chunk(\n",
+ " offset_map,\n",
+ " mask,\n",
+ " gain_map,\n",
+ " memory_cells,\n",
+ " relative_gain,\n",
+ " inp,\n",
+ "):\n",
" fim_data = None\n",
" gim_data = None\n",
" rim_data = None\n",
@@ -352,46 +360,61 @@
" d, g, m, ind, copy_sample = inp\n",
" g[g==3] = 2\n",
"\n",
- " if copy_sample and ind==0:\n",
- " if memory_cells==1:\n",
+ " if copy_sample and ind == 0:\n",
+ " if memory_cells == 1:\n",
" rim_data = np.squeeze(copy.copy(d))\n",
" else:\n",
- " rim_data = np.squeeze(copy.copy(d[:,0,...]))\n",
+ " rim_data = np.squeeze(copy.copy(d[:, 0, ...]))\n",
"\n",
" # Select memory cells\n",
- " if memory_cells>1:\n",
+ " if memory_cells > 1:\n",
" m[m>16] = 0\n",
- " offset_map_cell = offset_map[m,...]\n",
- " mask_cell = mask[m,...]\n",
+ " offset_map_cell = offset_map[m, ...]\n",
+ " mask_cell = mask[m, ...]\n",
" else:\n",
" offset_map_cell = offset_map\n",
" mask_cell = mask\n",
"\n",
" # Offset correction\n",
- " offset = np.choose(g, (offset_map_cell[...,0], offset_map_cell[...,1], offset_map_cell[...,2]))\n",
+ " offset = np.choose(\n",
+ " g,\n",
+ " (offset_map_cell[..., 0],\n",
+ " offset_map_cell[..., 1],\n",
+ " offset_map_cell[..., 2]),\n",
+ " )\n",
" d -= offset\n",
"\n",
" # Gain correction\n",
" if relative_gain:\n",
- " if memory_cells>1:\n",
- " gain_map_cell = gain_map[m,...]\n",
+ " if memory_cells > 1:\n",
+ " gain_map_cell = gain_map[m, ...]\n",
" else:\n",
" gain_map_cell = gain_map\n",
- " cal = np.choose(g, (gain_map_cell[..., 0], gain_map_cell[..., 1], gain_map_cell[..., 2]))\n",
+ " cal = np.choose(\n",
+ " g,\n",
+ " (gain_map_cell[..., 0],\n",
+ " gain_map_cell[..., 1],\n",
+ " gain_map_cell[..., 2]),\n",
+ " )\n",
" d /= cal \n",
"\n",
- " msk = np.choose(g, (mask_cell[...,0], mask_cell[...,1], mask_cell[...,2]))\n",
+ " msk = np.choose(\n",
+ " g,\n",
+ " (mask_cell[..., 0],\n",
+ " mask_cell[..., 1],\n",
+ " mask_cell[..., 2]),\n",
+ " )\n",
"\n",
" # Store sample of data for plotting\n",
- " if copy_sample and ind==0:\n",
- " if memory_cells==1:\n",
+ " if copy_sample and ind == 0:\n",
+ " if memory_cells == 1:\n",
" fim_data = np.squeeze(copy.copy(d))\n",
" gim_data = np.squeeze(copy.copy(g))\n",
" msk_data = np.squeeze(copy.copy(msk))\n",
" else:\n",
- " fim_data = np.squeeze(copy.copy(d[:,1,...]))\n",
- " gim_data = np.squeeze(copy.copy(g[:,1,...]))\n",
- " msk_data = np.squeeze(copy.copy(msk[:,1,...]))\n",
+ " fim_data = np.squeeze(copy.copy(d[:, 1, ...]))\n",
+ " gim_data = np.squeeze(copy.copy(g[:, 1, ...]))\n",
+ " msk_data = np.squeeze(copy.copy(msk[:, 1, ...]))\n",
"\n",
" except Exception as e:\n",
" err = e\n",
@@ -435,16 +458,20 @@
" # Chunk always contains >= 1 complete image\n",
" chunk_shape = (chunk_size_idim, 1) + oshape[-2:]\n",
"\n",
- " ddset = outfile.create_dataset(h5path_f+\"/adc\",\n",
- " oshape,\n",
- " chunks=chunk_shape,\n",
- " dtype=np.float32)\n",
- "\n",
- " mskset = outfile.create_dataset(h5path_f+\"/mask\",\n",
- " oshape,\n",
- " chunks=chunk_shape,\n",
- " dtype=np.uint32,\n",
- " compression=\"gzip\", compression_opts=1, shuffle=True)\n",
+ " ddset = outfile.create_dataset(\n",
+ " h5path_f+\"/adc\",\n",
+ " oshape,\n",
+ " chunks=chunk_shape,\n",
+ " dtype=np.float32)\n",
+ "\n",
+ " mskset = outfile.create_dataset(\n",
+ " h5path_f+\"/mask\",\n",
+ " oshape,\n",
+ " chunks=chunk_shape,\n",
+ " dtype=np.uint32,\n",
+ " compression=\"gzip\",\n",
+ " compression_opts=1,\n",
+ " shuffle=True)\n",
" # Parallelize over chunks of images\n",
" inp = []\n",
" max_ind = oshape[0]\n",
@@ -452,15 +479,15 @@
"\n",
" # If chunk size is not given maximum 12+1 chunks is expected\n",
" if chunk_size == 0:\n",
- " chunk_size = max_ind//12\n",
+ " chunk_size = max_ind // 12\n",
" print(f'Chunk size: {chunk_size}')\n",
"\n",
" ts = time.time()\n",
" while ind<max_ind:\n",
- " d = infile[h5path_f+\"/adc\"][ind:ind+chunk_size,...].astype(np.float32)\n",
- " g = infile[h5path_f+\"/gain\"][ind:ind+chunk_size,...]\n",
+ " d = infile[h5path_f+\"/adc\"][ind:ind+chunk_size, ...].astype(np.float32)\n",
+ " g = infile[h5path_f+\"/gain\"][ind:ind+chunk_size, ...]\n",
" if h5path_f+\"/memoryCell\" in infile:\n",
- " m = infile[h5path_f+\"/memoryCell\"][ind:ind+chunk_size,...]\n",
+ " m = infile[h5path_f+\"/memoryCell\"][ind:ind+chunk_size, ...]\n",
" else:\n",
" m = None\n",
" print(f'To process: {d.shape}')\n",
@@ -471,7 +498,15 @@
" ts = time.time()\n",
"\n",
" print(f'Run {len(inp)} processes')\n",
- " p = partial(correct_chunk, offset_map, mask, gain_map, memory_cells, relative_gain)\n",
+ "\n",
+ " p = partial(\n",
+ " correct_chunk,\n",
+ " offset_map,\n",
+ " mask,\n",
+ " gain_map,\n",
+ " memory_cells,\n",
+ " relative_gain,\n",
+ " )\n",
"\n",
" r = pool.map(p, inp)\n",
" \n",
@@ -484,10 +519,10 @@
" ts = time.time()\n",
"\n",
" for rr in r:\n",
- " ind, cdata, cmask, _,_,_,_, err = rr\n",
+ " ind, cdata, cmask, _, _, _, _, err = rr\n",
" data_size = cdata.shape[0]\n",
- " ddset[ind:ind+data_size,...] = cdata\n",
- " mskset[ind:ind+data_size,...] = cmask\n",
+ " ddset[ind:ind+data_size, ...] = cdata\n",
+ " mskset[ind:ind+data_size, ...] = cmask\n",
" if err != '':\n",
" print(f'Error: {err}')\n",
"\n",
@@ -504,10 +539,21 @@
"outputs": [],
"source": [
"def do_2d_plot(data, edges, y_axis, x_axis, title):\n",
- " fig = plt.figure(figsize=(10,10))\n",
+ " fig = plt.figure(figsize=(10, 10))\n",
" ax = fig.add_subplot(111)\n",
- " extent = [np.min(edges[1]), np.max(edges[1]),np.min(edges[0]), np.max(edges[0])]\n",
- " im = ax.imshow(data[::-1,:], extent=extent, aspect=\"auto\", norm=LogNorm(vmin=1, vmax=np.max(data)))\n",
+ " extent = [\n",
+ " np.min(edges[1]),\n",
+ " np.max(edges[1]),\n",
+ " np.min(edges[0]),\n",
+ " np.max(edges[0]),\n",
+ " ]\n",
+ "\n",
+ " im = ax.imshow(\n",
+ " data[::-1, :],\n",
+ " extent=extent,\n",
+ " aspect=\"auto\",\n",
+ " norm=LogNorm(vmin=1, vmax=np.max(data))\n",
+ " )\n",
" ax.set_xlabel(x_axis)\n",
" ax.set_ylabel(y_axis)\n",
" ax.set_title(title)\n",
@@ -522,11 +568,17 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " h, ex, ey = np.histogram2d(rim_data[mod].flatten(),\n",
- " gim_data[mod].flatten(),\n",
- " bins=[100, 4],\n",
- " range=[[0, 10000], [0, 4]])\n",
- " do_2d_plot(h, (ex, ey), \"Signal (ADU)\", \"Gain Bit Value\", f'Module {mod}')"
+ " h, ex, ey = np.histogram2d(\n",
+ " rim_data[mod].flatten(),\n",
+ " gim_data[mod].flatten(),\n",
+ " bins=[100, 4],\n",
+ " range=[[0, 10000], [0, 4]],\n",
+ " )\n",
+ " do_2d_plot(\n",
+ " h, (ex, ey),\n",
+ " \"Signal (ADU)\",\n",
+ " \"Gain Bit Value\",\n",
+ " f\"Module {mod}\")"
]
},
{
@@ -545,11 +597,15 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " fig = plt.figure(figsize=(20,10))\n",
+ " fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " im = ax.imshow(np.mean(rim_data[mod],axis=0),\n",
- " vmin=min(0.75*np.median(rim_data[mod][rim_data[mod] > 0]), 2000),\n",
- " vmax=max(1.5*np.median(rim_data[mod][rim_data[mod] > 0]), 16000), cmap=\"jet\")\n",
+ "\n",
+ " im = ax.imshow(\n",
+ " np.mean(rim_data[mod],axis=0),\n",
+ " vmin=min(0.75*np.median(rim_data[mod][rim_data[mod] > 0]), 2000),\n",
+ " vmax=max(1.5*np.median(rim_data[mod][rim_data[mod] > 0]), 16000),\n",
+ " cmap=\"jet\")\n",
+ "\n",
" ax.set_title(f'Module {mod}')\n",
" cb = fig.colorbar(im, ax=ax)"
]
@@ -570,11 +626,15 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " fig = plt.figure(figsize=(20,10))\n",
+ " fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " im = ax.imshow(np.mean(fim_data[mod],axis=0),\n",
- " vmin=min(0.75*np.median(fim_data[mod][fim_data[mod] > 0]), -0.5),\n",
- " vmax=max(2.*np.median(fim_data[mod][fim_data[mod] > 0]), 100), cmap=\"jet\")\n",
+ "\n",
+ " im = ax.imshow(\n",
+ " np.mean(fim_data[mod],axis=0),\n",
+ " vmin=min(0.75*np.median(fim_data[mod][fim_data[mod] > 0]), -0.5),\n",
+ " vmax=max(2.*np.median(fim_data[mod][fim_data[mod] > 0]), 100),\n",
+ " cmap=\"jet\")\n",
+ "\n",
" ax.set_title(f'Module {mod}', size=18)\n",
" cb = fig.colorbar(im, ax=ax)"
]
@@ -595,11 +655,15 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " fig = plt.figure(figsize=(20,10))\n",
+ " fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " im = ax.imshow(fim_data[mod][0,...],\n",
- " vmin=min(0.75*np.median(fim_data[mod][0,...]), -0.5),\n",
- " vmax=max(2.*np.median(fim_data[mod][0,...]), 100), cmap=\"jet\")\n",
+ "\n",
+ " im = ax.imshow(\n",
+ " fim_data[mod][0, ...],\n",
+ " vmin=min(0.75*np.median(fim_data[mod][0, ...]), -0.5),\n",
+ " vmax=max(2.*np.median(fim_data[mod][0, ...]), 100),\n",
+ " cmap=\"jet\")\n",
+ "\n",
" ax.set_title(f'Module {mod}', size=18)\n",
" cb = fig.colorbar(im, ax=ax)"
]
@@ -620,13 +684,23 @@
"for mod in rim_data: \n",
" fig = plt.figure(figsize=(20,10))\n",
" ax = fig.add_subplot(211)\n",
- " h = ax.hist(fim_data[mod].flatten(), bins=1000, range=(-100, 1000), log=True)\n",
+ " h = ax.hist(\n",
+ " fim_data[mod].flatten(),\n",
+ " bins=1000,\n",
+ " range=(-100, 1000),\n",
+ " log=True,\n",
+ " )\n",
" l = ax.set_xlabel(\"Signal (keV)\")\n",
" l = ax.set_ylabel(\"Counts\")\n",
" _ = ax.set_title(f'Module {mod}')\n",
"\n",
" ax = fig.add_subplot(212)\n",
- " h = ax.hist(fim_data[mod].flatten(), bins=1000, range=(-1000, 10000), log=True)\n",
+ " h = ax.hist(\n",
+ " fim_data[mod].flatten(),\n",
+ " bins=1000,\n",
+ " range=(-1000, 10000),\n",
+ " log=True,\n",
+ " )\n",
" l = ax.set_xlabel(\"Signal (keV)\")\n",
" l = ax.set_ylabel(\"Counts\")\n",
" _ = ax.set_title(f'Module {mod}')"
@@ -648,10 +722,11 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " fig = plt.figure(figsize=(20,10))\n",
+ " fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " im = ax.imshow(np.max(gim_data[mod], axis=0), vmin=0,\n",
- " vmax=3, cmap=\"jet\")\n",
+ " im = ax.imshow(\n",
+ " np.max(gim_data[mod], axis=0),\n",
+ " vmin=0, vmax=3, cmap=\"jet\")\n",
" ax.set_title(f'Module {mod}', size=18)\n",
" cb = fig.colorbar(im, ax=ax)"
]
@@ -672,8 +747,11 @@
"source": [
"table = []\n",
"for item in BadPixels:\n",
- " table.append((item.name, f\"{item.value:016b}\"))\n",
- "md = display(Latex(tabulate.tabulate(table, tablefmt='latex', headers=[\"Bad pixel type\", \"Bit mask\"])))"
+ " table.append(\n",
+ " (item.name, f\"{item.value:016b}\"))\n",
+ "md = display(Latex(tabulate.tabulate(\n",
+ " table, tablefmt='latex',\n",
+ " headers=[\"Bad pixel type\", \"Bit mask\"])))"
]
},
{
@@ -692,9 +770,11 @@
"outputs": [],
"source": [
"for mod in rim_data: \n",
- " fig = plt.figure(figsize=(20,10))\n",
+ " fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " im = ax.imshow(np.log2(msk_data[mod][0,...]), vmin=0, vmax=32, cmap=\"jet\")\n",
+ " im = ax.imshow(\n",
+ " np.log2(msk_data[mod][0,...]),\n",
+ " vmin=0, vmax=32, cmap=\"jet\")\n",
" ax.set_title(f'Module {mod}', size=18)\n",
" cb = fig.colorbar(im, ax=ax)"
]
diff --git a/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
index 922d58f8c..cd604ac09 100644
--- a/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb
@@ -35,11 +35,11 @@
"h5path_cntrl = '/CONTROL/{}/DET/{}' # path to control data\n",
"karabo_da_control = \"JNGFRCTRL00\" # file inset for control data\n",
"\n",
- "use_dir_creation_date = True # use dir creation date\n",
+ "use_dir_creation_date = True # use dir creation date\n",
"cal_db_interface = 'tcp://max-exfl016:8016' # calibrate db interface to connect to\n",
"cal_db_timeout = 300000 # timeout on caldb requests\n",
- "local_output = True # output constants locally\n",
- "db_output = False # output constants to database\n",
+ "local_output = True # output constants locally\n",
+ "db_output = False # output constants to database\n",
"\n",
"integration_time = 1000 # integration time in us, will be overwritten by value in file\n",
"gain_setting = 0 # 0 for dynamic, forceswitchg1, forceswitchg2, 1 for dynamichg0, fixedgain1, fixgain2. Will be overwritten by value in file\n",
@@ -52,7 +52,7 @@
"memoryCells = 16 # number of memory cells\n",
"db_module = \"\" # ID of module in calibration database, this parameter is ignored in the notebook. TODO: remove from calibration_configurations.\n",
"manual_slow_data = False # if true, use manually entered bias_voltage and integration_time values\n",
- "time_limits = 0.025 # to find calibration constants later on, the integration time is allowed to vary by 0.5 us\n",
+ "time_limits = 0.025 # to find calibration constants later on, the integration time is allowed to vary by 0.5 us\n",
"operation_mode = '' # Detector operation mode, optional"
]
},
@@ -125,7 +125,9 @@
"xRange = [0, 0+sensorSize[0]]\n",
"yRange = [0, 0+sensorSize[1]]\n",
"gains = [0, 1, 2]\n",
+ "\n",
"h5path = h5path.format(karabo_id, receiver_id)\n",
+ "\n",
"creation_time = None\n",
"if use_dir_creation_date:\n",
" creation_time = get_dir_creation_date(in_folder, run_high)\n",
@@ -198,16 +200,12 @@
"\n",
" myRange = range(0, n_files)\n",
" control_path = h5path_cntrl.format(karabo_id_control, receiver_control_id)\n",
- " \n",
+ "\n",
" this_run_mcells, sc_start = check_memoryCells(fp_path.format(0).format(myRange[0]), control_path)\n",
- " \n",
+ "\n",
" if mod not in noise_map:\n",
" if not manual_slow_data:\n",
- " with h5py.File(fp_path.format(0), 'r') as f:\n",
" run_path = h5path_run.format(karabo_id_control, receiver_control_id)\n",
- " integration_time = float(f[f'{run_path}/exposureTime/value'][()]*1e6)\n",
- " bias_voltage = int(np.squeeze(f[f'{run_path}/vHighVoltage/value'])[0])\n",
- " \n",
" if r_n == run_high:\n",
" try:\n",
" gain_s = f[f'/RUN/{karabo_id_control}/DET/CONTROL/settings/value'][0].decode()\n",
@@ -241,7 +239,7 @@
" print(\"Reading data from {}\".format(fp_path))\n",
" print(\"Run is: {}\".format(r_n))\n",
" print(\"HDF5 path: {}\".format(h5path))\n",
- " \n",
+ "\n",
" imageRange = [0, filep_size*len(myRange)]\n",
" reader = JFChunkReader(filename = fp_path, readFun = jfreader.readData, size = filep_size, chunkSize = chunkSize,\n",
" path = h5path, image_range=imageRange, pixels_x = sensorSize[0], pixels_y = sensorSize[1],\n",
@@ -249,7 +247,7 @@
" memoryCells=this_run_mcells, blockSize=blockSize)\n",
"\n",
" for data in reader.readChunks():\n",
- " \n",
+ "\n",
" images = np.array(data[0], dtype=np.float)\n",
" gainmaps = np.array(data[1], dtype=np.uint16)\n",
" trainId = np.array(data[2])\n",
@@ -257,31 +255,32 @@
" acelltable = np.array(data[4])\n",
" n_tr += acelltable.shape[-1]\n",
" this_tr = acelltable.shape[-1]\n",
- " \n",
- " \n",
- " \n",
+ "\n",
" idxs = np.nonzero(trainId)[0]\n",
" images = images[..., idxs]\n",
" gainmaps = gainmaps[..., idxs]\n",
" fr_num = fr_num[..., idxs]\n",
" acelltable = acelltable[..., idxs]\n",
- " \n",
+ "\n",
" if memoryCells == 1:\n",
" acelltable -= sc_start\n",
"\n",
" n_empty_trains += this_tr - acelltable.shape[-1]\n",
" n_empty_sc += len(acelltable[acelltable > 15])\n",
"\n",
- " if gain > 0 and memoryCells == 16: ## throwing away all the SC entries except the first for lower gains\n",
+ " # throwing away all the SC entries except\n",
+ " # the first for lower gains.\n",
+ " if gain > 0 and memoryCells == 16: \n",
" acelltable[1:] = 255\n",
- " \n",
+ "\n",
" # makes 4-dim vecs into 3-dim\n",
" # makes 2-dim into 1-dim\n",
" # leaves 1-dim and 3-dim vecs\n",
"\n",
- " images, gainmaps, acelltable = rollout_data([images, gainmaps, acelltable]) \n",
- " \n",
- " images, gainmaps, acelltable = sanitize_data_cellid([images, gainmaps], acelltable) # removes entries with cellID 255\n",
+ " images, gainmaps, acelltable = rollout_data([images, gainmaps, acelltable])\n",
+ "\n",
+ " # removes entries with cellID 255\n",
+ " images, gainmaps, acelltable = sanitize_data_cellid([images, gainmaps], acelltable)\n",
" valid_data.append(images)\n",
" valid_cellids.append(acelltable)\n",
"\n",
@@ -294,10 +293,10 @@
" noise_map[mod][..., cell, gain] = np.std(thiscell, axis=2)\n",
" offset_map[mod][..., cell, gain] = np.mean(thiscell, axis=2)\n",
"\n",
- "\n",
" print(f'G{gain:01d} dark calibration')\n",
- " print('Missed {:d} out of {:d} trains'.format(n_empty_trains, n_tr))\n",
- " print('Lost {:d} images out of {:d}'.format(n_empty_sc, this_run_mcells * (n_tr - n_empty_trains)))"
+ " print(f'Missed {n_empty_trains:d} out of {n_tr:d} trains')\n",
+ " print(\n",
+ " f'Lost {n_empty_sc:d} images out of {this_run_mcells*(n_tr-n_empty_trains):d}') # noqa"
]
},
{
@@ -461,12 +460,13 @@
"for g_idx in gains:\n",
" for cell in range(memoryCells):\n",
" bad_pixels = bad_pixels_map[mod][:, :, cell, g_idx]\n",
- " fn_0 = heatmapPlot(np.swapaxes(bad_pixels, 0, 1), \n",
- " y_label=\"Row\",\n",
- " x_label=\"Column\",\n",
- " lut_label=f\"Badpixels {g_name[g_idx]} [ADCu]\",\n",
- " aspect=1.,\n",
- " vmin=0, title=f'G{g_idx} Bad pixel map - Cell {cell:02d} - Module {mod}')"
+ " fn_0 = heatmapPlot(\n",
+ " np.swapaxes(bad_pixels, 0, 1),\n",
+ " y_label=\"Row\",\n",
+ " x_label=\"Column\",\n",
+ " lut_label=f\"Badpixels {g_name[g_idx]} [ADCu]\",\n",
+ " aspect=1.,\n",
+ " vmin=0, title=f'G{g_idx} Bad pixel map - Cell {cell:02d} - Module {mod}')"
]
},
{
--
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