get badpx due to bad gain separation

This MR has two features.

1. The ability to define an offset or noise threshold for each gain. The old parameter is left for back compatibility with old configurations and for the ability to give on threshold for all 3 gains.
2. Catching the bad pixel for bad gain separation depending on a gain_sigma which can be configured for the notebook.

Report sample for two modules: CharacterizeDarkImages.pdf

@jsztuk @karnem

notebooks/AGIPD/Characterize_AGIPD_Gain_Darks_NBC.ipynb

 %% Cell type:markdown id: tags:
 
 # Characterize Dark Images #
 
 Author: S. Hauf, Version: 0.1
 
 The following code analyzes a set of dark images taken with the AGIPD detector to deduce detector offsets , noise, bad-pixel maps and thresholding. All four types of constants are evaluated per-pixel and per-memory cell. Data for the detector's three gain stages needs to be present, separated into separate runs.
 
 The evaluated calibration constants are stored locally and injected in the calibration data base.
 
 %% Cell type:code id: tags:
 
 ``` python
 cluster_profile = "noDB" # The ipcluster profile to use
-in_folder = "/gpfs/exfel/d/raw/SPB/202030/p900138/" # path to input data, required
+in_folder = "/gpfs/exfel/d/raw/SPB/202030/p900138" # path to input data, required
 out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/AGIPDb1" # path to output to, required
 sequences = [0] # sequence files to evaluate.
 modules = [-1]  # list of modules to evaluate, RANGE ALLOWED
-run_high = 264 # run number in which high gain data was recorded, required
-run_med = 265 # run number in which medium gain data was recorded, required
-run_low = 266 # run number in which low gain data was recorded, required
+run_high = 199 # run number in which high gain data was recorded, required
+run_med = 201 # run number in which medium gain data was recorded, required
+run_low = 202 # run number in which low gain data was recorded, required
 
 karabo_id = "SPB_DET_AGIPD1M-1" # karabo karabo_id
 karabo_da = [-1]  # data aggregators
 receiver_id = "{}CH0" # inset for receiver devices
 path_template = 'RAW-R{:04d}-{}-S{:05d}.h5' # the template to use to access data
 h5path = '/INSTRUMENT/{}/DET/{}:xtdf/image' # path in the HDF5 file to images
 h5path_idx = '/INDEX/{}/DET/{}:xtdf/image' # path in the HDF5 file to images
 h5path_ctrl = '/CONTROL/{}/MDL/FPGA_COMP_TEST' # path to control information
-karabo_id_control = "SPB_IRU_AGIPD1M1" # karabo-id for control device
-karabo_da_control = 'DA02' # karabo DA for control infromation
+karabo_id_control = "SPB_IRU_AGIPD1M1" # karabo-id for control device '
+karabo_da_control = "AGIPD1MCTRL00" # karabo DA for control infromation
 
 use_dir_creation_date = True  # use dir creation date as data production reference date
 cal_db_interface = "tcp://max-exfl016:8020" # the database interface to use
 cal_db_timeout = 3000000 # timeout on caldb requests"
 local_output = True # output constants locally
 db_output = False # output constants to database
 
 mem_cells = 0 # number of memory cells used, set to 0 to automatically infer
 bias_voltage = 300 # detector bias voltage
 gain_setting = 0.1 # the gain setting, use 0.1 to try to auto-determine
 acq_rate = 0. # the detector acquisition rate, use 0 to try to auto-determine
 interlaced = False # assume interlaced data format, for data prior to Dec. 2017
 rawversion = 2 # RAW file format version
 
 thresholds_offset_sigma = 3. # thresholds in terms of n sigma noise for offset deduced bad pixels
 thresholds_offset_hard = [0, 0] # This offset threshold is left for back compatability or for defining threshold for the 3 gains
 thresholds_offset_hard_hg = [4000, 8500] # High-gain thresholds in absolute ADU terms for offset deduced bad pixels
 thresholds_offset_hard_mg = [4000, 8500] # Medium-gain thresholds in absolute ADU terms for offset deduced bad pixels
 thresholds_offset_hard_lg = [4000, 8500] # Low-gain thresholds in absolute ADU terms for offset deduced bad pixels
 
 thresholds_noise_sigma = 5. # thresholds in terms of n sigma noise for offset deduced bad pixels
 thresholds_noise_hard = [0, 0] # This noise threshold is left for back compatability or for defining threshold for the 3 gains
 thresholds_noise_hard_hg = [4, 20] # High-gain thresholds in absolute ADU terms for offset deduced bad pixels
 thresholds_noise_hard_mg = [4, 20] # Medium-gain thresholds in absolute ADU terms for offset deduced bad pixels
 thresholds_noise_hard_lg = [4, 20] # Low-gain thresholds in absolute ADU terms for offset deduced bad pixels
 
 gain_sep_sigma = 5. # gain separation sigma
 
 high_res_badpix_3d = False # set this to True if you need high-resolution 3d bad pixel plots. Runtime: ~ 1h
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # imports and things that do not usually need to be changed
 from datetime import datetime
 import dateutil.parser
 import warnings
 warnings.filterwarnings('ignore')
 from collections import OrderedDict
 import os
 import h5py
 import numpy as np
 import matplotlib
 import tabulate
 
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
 from IPython.display import display, Markdown, Latex
 %matplotlib inline
 
 from cal_tools.tools import (map_gain_stages, parse_runs,
                              run_prop_seq_from_path, get_notebook_name,
                              get_dir_creation_date, save_const_to_h5,
                              get_random_db_interface, get_from_db)
 from cal_tools.influx import InfluxLogger
 from cal_tools.enums import BadPixels
 from cal_tools.plotting import show_overview, plot_badpix_3d, create_constant_overview
 from cal_tools.agipdlib import get_gain_setting
 
 # make sure a cluster is running with ipcluster start --n=32, give it a while to start
 from ipyparallel import Client
 
 view = Client(profile=cluster_profile)[:]
 view.use_dill()
 
 from iCalibrationDB import ConstantMetaData, Constants, Conditions, Detectors, Versions
 
 gains = np.arange(3)
 
 IL_MODE = interlaced
 max_cells = mem_cells
 
 offset_runs = OrderedDict()
 offset_runs["high"] = run_high
 offset_runs["med"] = run_med
 offset_runs["low"] = run_low
 
 creation_time=None
 if use_dir_creation_date:
     creation_time = get_dir_creation_date(in_folder, run_high)
 print(f"Using {creation_time} as creation time of constant.")
 
 run, prop, seq = run_prop_seq_from_path(in_folder)
 
 cal_db_interface = get_random_db_interface(cal_db_interface)
 print(f'Calibration database interface: {cal_db_interface}')
 
 loc = None
 instrument = karabo_id.split("_")[0]
 if instrument == "SPB":
     dinstance = "AGIPD1M1"
 else:
     dinstance = "AGIPD1M2"
 print(f"Detector in use is {karabo_id}")
 print(f"Instrument {instrument}")
 print(f"Detector instance {dinstance}")
 
 logger = InfluxLogger(detector="AGIPD", instrument=instrument, mem_cells=mem_cells,
                       notebook=get_notebook_name(), proposal=prop)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 control_fname = '{}/r{:04d}/RAW-R{:04d}-{}-S00000.h5'.format(in_folder, run_high,
                                                              run_high, karabo_da_control)
 
 if gain_setting == 0.1:
     if creation_time.replace(tzinfo=None) < dateutil.parser.parse('2020-01-31'):
         print("Set gain-setting to None for runs taken before 2020-01-31")
         gain_setting = None
     else:
         try:
             gain_setting = get_gain_setting(control_fname, h5path_ctrl)
         except Exception as e:
             print(f'Error while reading gain setting from: \n{control_fname}')
             print(e)
             print("Gain setting is not found in the control information")
             print("Data will not be processed")
             sequences = []
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 if karabo_da[0] == -1:
     if modules[0] == -1:
         modules = list(range(16))
     karabo_da = ["AGIPD{:02d}".format(i) for i in modules]
 else:
     modules = [int(x[-2:]) for x in karabo_da]
-
 h5path = h5path.format(karabo_id, receiver_id)
 h5path_idx = h5path_idx.format(karabo_id, receiver_id)
-h5path_ctrl = h5path_ctrl.format(karabo_id_control)
+if '{' in h5path_ctrl:
+    h5path_ctrl = h5path_ctrl.format(karabo_id_control)
 
 print("Parameters are:")
 print(f"Proposal: {prop}")
 print(f"Memory cells: {mem_cells}/{max_cells}")
 print("Runs: {}".format([ v for v in offset_runs.values()]))
 print(f"Sequences: {sequences}")
 print(f"Interlaced mode: {IL_MODE}")
 print(f"Using DB: {db_output}")
 print(f"Input: {in_folder}")
 print(f"Output: {out_folder}")
 print(f"Bias voltage: {bias_voltage}V")
 print(f"Gain setting: {gain_setting}")
 ```
 
 %% Cell type:markdown id: tags:
 
 The following lines will create a queue of files which will the be executed module-parallel. Distiguishing between different gains.
 
 %% Cell type:code id: tags:
 
 ``` python
 # set everything up filewise
 os.makedirs(out_folder, exist_ok=True)
 gmf = map_gain_stages(in_folder, offset_runs, path_template, karabo_da, sequences)
 gain_mapped_files, total_sequences, total_file_size = gmf
 print(f"Will process a total of {total_sequences} sequences.")
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Calculate Offsets, Noise and Thresholds ##
 
 The calculation is performed per-pixel and per-memory-cell. Offsets are simply the median value for a set of dark data taken at a given gain, noise the standard deviation, and gain-bit values the medians of the gain array.
 
 %% Cell type:code id: tags:
 
 ``` python
 import copy
 from functools import partial
 def characterize_module(il_mode, cells, bp_thresh, rawversion, loc, acq_rate,
                         prev_offset, prev_noise, h5path, h5path_idx, inp):
     import numpy as np
     import copy
     import h5py
     from cal_tools.enums import BadPixels
     from cal_tools.agipdlib import get_num_cells, get_acq_rate
 
     filename, filename_out, channel = inp
 
     if cells == 0:
         cells = get_num_cells(filename, loc, channel)
 
     print(f"Using {cells} memory cells")
 
     if acq_rate == 0.:
         acq_rate = get_acq_rate(filename, loc, channel)
 
     thresholds_offset_hard, thresholds_offset_sigma, thresholds_noise_hard, thresholds_noise_sigma = bp_thresh
 
     infile = h5py.File(filename, "r", driver="core")
 
     h5path = h5path.format(channel)
     h5path_idx = h5path_idx.format(channel)
 
     if rawversion == 2:
         count = np.squeeze(infile[f"{h5path_idx}/count"])
         first = np.squeeze(infile[f"{h5path_idx}/first"])
         last_index = int(first[count != 0][-1]+count[count != 0][-1])
         first_index = int(first[count != 0][0])
     else:
         status = np.squeeze(infile[f"{h5path_idx}/status"])
         if np.count_nonzero(status != 0) == 0:
             return
         last = np.squeeze(infile[f"{h5path_idx}/last"])
         first = np.squeeze(infile[f"{h5path_idx}/first"])
         last_index = int(last[status != 0][-1]) + 1
         first_index = int(first[status != 0][0])
     im = np.array(infile[f"{h5path}/data"][first_index:last_index,...])
     cellIds = np.squeeze(infile[f"{h5path}/cellId"][first_index:last_index,...])
 
     infile.close()
 
     if il_mode:
         ga = im[1::2, 0, ...]
         im = im[0::2, 0, ...].astype(np.float32)
         cellIds = cellIds[::2]
     else:
         ga = im[:, 1, ...]
         im = im[:, 0, ...].astype(np.float32)
 
     im = np.rollaxis(im, 2)
     im = np.rollaxis(im, 2, 1)
 
     ga = np.rollaxis(ga, 2)
     ga = np.rollaxis(ga, 2, 1)
 
     mcells = cells #max(cells, np.max(cellIds)+1)
     offset = np.zeros((im.shape[0], im.shape[1], mcells))
     gains = np.zeros((im.shape[0], im.shape[1], mcells))
     noise = np.zeros((im.shape[0], im.shape[1], mcells))
 
     for cc in np.unique(cellIds[cellIds < mcells]):
         cellidx = cellIds == cc
         offset[...,cc] = np.median(im[..., cellidx], axis=2)
         noise[...,cc] = np.std(im[..., cellidx], axis=2)
         gains[...,cc] = np.median(ga[..., cellidx], axis=2)
 
     # bad pixels
     bp = np.zeros(offset.shape, np.uint32)
     # offset related bad pixels
     offset_mn = np.nanmedian(offset, axis=(0,1))
     offset_std = np.nanstd(offset, axis=(0,1))
 
     # Bad pixels during bad gain separation.
     if prev_offset is not None:
         bad_sep = (offset-prev_offset) / np.sqrt(noise**2 + prev_noise**2)
         bp[(bad_sep)<gain_sep_sigma]|= BadPixels.GAIN_THRESHOLDING_ERROR.value
 
     bp[(offset < offset_mn-thresholds_offset_sigma*offset_std) |
        (offset > offset_mn+thresholds_offset_sigma*offset_std)] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
     bp[(offset < thresholds_offset_hard[0]) | (
         offset > thresholds_offset_hard[1])] |= BadPixels.OFFSET_OUT_OF_THRESHOLD.value
     bp[~np.isfinite(offset)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
 
     # noise related bad pixels
     noise_mn = np.nanmedian(noise, axis=(0,1))
     noise_std = np.nanstd(noise, axis=(0,1))
     bp[(noise < noise_mn-thresholds_noise_sigma*noise_std) |
        (noise > noise_mn+thresholds_noise_sigma*noise_std)] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
     bp[(noise < thresholds_noise_hard[0]) | (noise > thresholds_noise_hard[1])] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
     bp[~np.isfinite(noise)] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
 
 
     return offset, noise, gains, bp, cells, acq_rate
 
 offset_g = OrderedDict()
 noise_g = OrderedDict()
 gain_g = OrderedDict()
 badpix_g = OrderedDict()
 gg = 0
 
 start = datetime.now()
 all_cells = []
 all_acq_rate = []
 offset = None
 noise = None
 if thresholds_offset_hard == [0, 0]:
     thresholds_offset_hard = [thresholds_offset_hard_hg, thresholds_offset_hard_mg, thresholds_offset_hard_lg]
-if thresholds_noise_hard == [0, 0]
+if thresholds_noise_hard == [0, 0]:
     thresholds_noise_hard = [thresholds_noise_hard_hg, thresholds_noise_hard_mg, thresholds_noise_hard_lg]
 for gain, mapped_files in gain_mapped_files.items():
 
     inp = []
     dones = []
     for i in modules:
         qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
         if qm in mapped_files and not mapped_files[qm].empty():
             fname_in = mapped_files[qm].get()
             dones.append(mapped_files[qm].empty())
         else:
             continue
         fout = os.path.abspath("{}/{}".format(out_folder, (os.path.split(fname_in)[-1]).replace("RAW", "CORR")))
         inp.append((fname_in, fout, i))
     first = False
     p = partial(characterize_module, IL_MODE, max_cells,
                (thresholds_offset_hard[gg], thresholds_offset_sigma,
                 thresholds_noise_hard[gg], thresholds_noise_sigma),
                 rawversion, karabo_id, acq_rate, offset, noise, h5path, h5path_idx)
     #results = list(map(p, inp))
     results = view.map_sync(p, inp)
     for ii, r in enumerate(results):
         i = modules[ii]
         offset, noise, gain, bp, thiscell, thisacq = r
         all_cells.append(thiscell)
         all_acq_rate.append(thisacq)
         qm = "Q{}M{}".format(i//4 +1, i % 4 + 1)
         if qm not in offset_g:
             offset_g[qm] = np.zeros((offset.shape[0], offset.shape[1], offset.shape[2], 3))
             noise_g[qm] = np.zeros_like(offset_g[qm])
             gain_g[qm] = np.zeros_like(offset_g[qm])
             badpix_g[qm] = np.zeros_like(offset_g[qm], np.uint32)
 
         offset_g[qm][...,gg] = offset
         noise_g[qm][...,gg] = noise
         gain_g[qm][...,gg] = gain
         badpix_g[qm][...,gg] = bp
     gg +=1
 
 duration = (datetime.now()-start).total_seconds()
 logger.runtime_summary_entry(success=True, runtime=duration,
                                    total_sequences=total_sequences,
                                    filesize=total_file_size)
 logger.send()
 max_cells = np.max(all_cells)
 print(f"Using {max_cells} memory cells")
 acq_rate = np.max(all_acq_rate)
 print(f"Using {acq_rate} MHz acquisition rate")
 ```
 
 %% Cell type:markdown id: tags:
 
 The thresholds for gain switching are then defined as the mean value between in individual gain bit levels. Note that these thresholds need to be refined with charge induced thresholds, as the two are not the same.
 
 %% Cell type:code id: tags:
 
 ``` python
 thresholds_g = {}
 for qm in gain_g.keys():
     thresholds_g[qm] = np.zeros((gain_g[qm].shape[0], gain_g[qm].shape[1], gain_g[qm].shape[2], 5))
     thresholds_g[qm][...,0] = (gain_g[qm][...,1]+gain_g[qm][...,0])/2
     thresholds_g[qm][...,1] = (gain_g[qm][...,2]+gain_g[qm][...,1])/2
     for i in range(3):
         thresholds_g[qm][...,2+i] = gain_g[qm][...,i]
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 res = OrderedDict()
 for i in modules:
     qm = "Q{}M{}".format(i//4+1, i%4+1)
     res[qm] = {'Offset': offset_g[qm],
                'Noise': noise_g[qm],
                'ThresholdsDark': thresholds_g[qm],
                'BadPixelsDark': badpix_g[qm]
                }
 
 if local_output:
     for qm in offset_g.keys():
         ofile = "{}/agipd_offset_store_{}_{}.h5".format(out_folder,
                                                         "{}-{}-{}".format(*offset_runs.values()),
                                                         qm)
         store_file = h5py.File(ofile, "w")
         store_file["{}/Offset/0/data".format(qm)] = offset_g[qm]
         store_file["{}/Noise/0/data".format(qm)] = noise_g[qm]
         store_file["{}/Threshold/0/data".format(qm)] = thresholds_g[qm]
         store_file["{}/BadPixels/0/data".format(qm)] = badpix_g[qm]
         store_file.close()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # Retrieve existing constants for comparison
 clist = ["Offset", "Noise", "ThresholdsDark", "BadPixelsDark"]
 old_const = {}
 old_mdata = {}
 detinst = getattr(Detectors, dinstance)
 
 print('Retrieve pre-existing constants for comparison.')
 
 for qm in res:
     for const in res[qm]:
         metadata = ConstantMetaData()
         dconst = getattr(Constants.AGIPD, const)()
         dconst.data = res[qm][const]
         metadata.calibration_constant = dconst
 
         # Setting conditions
         condition = Conditions.Dark.AGIPD(memory_cells=max_cells,
                                           bias_voltage=bias_voltage,
                                           acquisition_rate=acq_rate,
                                           gain_setting=gain_setting)
         device = getattr(detinst, qm)
         data, mdata = get_from_db(device,
                                   getattr(Constants.AGIPD, const)(),
                                   condition,
                                   None,
                                   cal_db_interface, creation_time=creation_time,
                                   verbosity=2, timeout=cal_db_timeout)
 
         old_const[const] = data
         if mdata is not None and data is not None:
             time = mdata.calibration_constant_version.begin_at
             old_mdata[const] = time.isoformat()
             os.makedirs('{}/old/'.format(out_folder), exist_ok=True)
             save_const_to_h5(mdata, '{}/old/'.format(out_folder))
         else:
             old_mdata[const] = "Not found"
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 proposal = list(filter(None, in_folder.strip('/').split('/')))[-2]
 file_loc = 'proposal:{} runs:{} {} {}'.format(proposal, run_low, run_med, run_high)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 for qm in res:
 
     print(f"Injecting constants {const} with conditions:\n")
     print("1. memory_cells: {}\n2. bias_voltage: {}\n"
               "3. acquisition_rate: {}\n4. gain_setting: {}\n"
               "5. creation_time: {}\n".format(max_cells, bias_voltage,
                                            acq_rate, gain_setting,
                                            creation_time))
     for const in res[qm]:
         metadata = ConstantMetaData()
         dconst = getattr(Constants.AGIPD, const)()
         dconst.data = res[qm][const]
         metadata.calibration_constant = dconst
 
         # set the operating condition
         condition = Conditions.Dark.AGIPD(memory_cells=max_cells,
                                           bias_voltage=bias_voltage,
                                           acquisition_rate=acq_rate,
                                           gain_setting=gain_setting)
         detinst = getattr(Detectors, dinstance)
         device = getattr(detinst, qm)
 
         metadata.detector_condition = condition
 
         # specify the a version for this constant
         if creation_time is None:
             metadata.calibration_constant_version = Versions.Now(device=device)
         else:
             metadata.calibration_constant_version = Versions.Timespan(device=device,
                                                                       start=creation_time)
 
         metadata.calibration_constant_version.raw_data_location = file_loc
 
         if db_output:
             try:
                 metadata.send(cal_db_interface, timeout=cal_db_timeout)
                 msg = 'Const {} for module {} was injected to the calibration DB. Begin at: {}'
                 print(msg.format(const, qm,
                                  metadata.calibration_constant_version.begin_at))
             except Exception as e:
                 print("Error:", e)
 
         if local_output:
             save_const_to_h5(metadata, out_folder)
             print("Calibration constant {} is stored locally.\n".format(const))
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 qm = "Q{}M{}".format(modules[0]//4+1, modules[0] % 4+1)
 display(Markdown('## Position of the module {} and it\'s ASICs##'.format(qm)))
 
 fig, ax = plt.subplots(1, figsize=(10, 10))
 ax.set_axis_off()
 
 ax.set_xlim(0, 90)
 ax.set_ylim(0, 75)
 
 asic_pos = 5
 
 q_st = 8
 
 l_y = 6
 l_x = 5
 
 for iq, q_x in enumerate([[43,66],[45,34],[4,32],[2,64]]):
     for im in range(4):
         x = q_x[0]
         for i_as in range(8):
             ax.add_patch(matplotlib.patches.Rectangle((x,q_x[1]-q_st*im), l_x, l_y, linewidth=2, edgecolor='c',
                                            facecolor='lightblue', fill=True))
 
             if iq*4+im == modules[0]:
                 ax.add_patch(matplotlib.patches.Rectangle((x,q_x[1]-q_st*im), l_x, l_y/2, linewidth=2,edgecolor='c',
                                            facecolor='sandybrown', fill=True))
                 ax.add_patch(matplotlib.patches.Rectangle((x,(q_x[1]-q_st*im+3)), l_x, l_y/2, linewidth=2,edgecolor='c',
                                            facecolor='sandybrown', fill=True))
             x += asic_pos
 
         if iq*4+im == modules[0]:
             # Change Text for current processed module.
             ax.text(q_x[0]+13, q_x[1]-q_st*im+1.5, 'Q{}M{}'.format(
                 iq+1, im+1),  fontsize=28, color='mediumblue',weight='bold')
         else:
             ax.text(q_x[0]+14, q_x[1]-q_st*im+1.5, 'Q{}M{}'.format(
                                         iq+1, im+1),  fontsize=25, color='k')
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Single-Cell Overviews ##
 
 Single cell overviews allow to identify potential effects on all memory cells, e.g. on sensor level. Additionally, they should serve as a first sanity check on expected behaviour, e.g. if structuring on the ASIC level is visible in the offsets, but otherwise no immediate artifacts are visible.
 
 %% Cell type:markdown id: tags:
 
 ### High Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 cell = 3
 gain = 0
 out_folder = None
 show_overview(res, cell, gain, out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()))
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Medium Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 cell = 3
 gain = 1
 show_overview(res, cell, gain, out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()))
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Low Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 cell = 3
 gain = 2
 show_overview(res, cell, gain, out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()))
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Global Bad Pixel Behaviour ##
 
 The following plots show the results of bad pixel evaluation for all evaluated memory cells. Cells are stacked in the Z-dimension, while pixels values in x/y are rebinned with a factor of 2. This excludes single bad pixels present only in disconnected pixels. Hence, any bad pixels spanning at least 4 pixels in the x/y-plane, or across at least two memory cells are indicated. Colors encode the bad pixel type, or mixed type.
 
 %% Cell type:markdown id: tags:
 
 ### High Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 cols = {BadPixels.NOISE_OUT_OF_THRESHOLD.value: (BadPixels.NOISE_OUT_OF_THRESHOLD.name, '#FF000080'),
         BadPixels.OFFSET_NOISE_EVAL_ERROR.value: (BadPixels.OFFSET_NOISE_EVAL_ERROR.name, '#0000FF80'),
         BadPixels.OFFSET_OUT_OF_THRESHOLD.value: (BadPixels.OFFSET_OUT_OF_THRESHOLD.name, '#FFDF0080'),
         BadPixels.GAIN_THRESHOLDING_ERROR.value: (BadPixels.GAIN_THRESHOLDING_ERROR.name, '#BF2080'),
         BadPixels.OFFSET_OUT_OF_THRESHOLD.value | BadPixels.NOISE_OUT_OF_THRESHOLD.value: ('MIXED', '#DD00DD80')}
 
 rebin = 8 if not high_res_badpix_3d else 2
 
 gain = 0
 for mod, data in badpix_g.items():
     plot_badpix_3d(data[...,gain], cols, title=mod, rebin_fac=rebin)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Medium Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 gain = 1
 for mod, data in badpix_g.items():
     plot_badpix_3d(data[...,gain], cols, title=mod, rebin_fac=rebin)
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Low Gain ###
 
 %% Cell type:code id: tags:
 
 ``` python
 gain = 2
 for mod, data in badpix_g.items():
     plot_badpix_3d(data[...,gain], cols, title=mod, rebin_fac=rebin)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Aggregate values, and per Cell behaviour ##
 
 The following tables and plots give an overview of statistical aggregates for each constant, as well as per cell behavior.
 
 %% Cell type:code id: tags:
 
 ``` python
 create_constant_overview(offset_g, "Offset (ADU)", max_cells, 4000, 8000,
                          out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()),
                          badpixels=[badpix_g, np.nan]))
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 create_constant_overview(noise_g, "Noise (ADU)", max_cells, 0, 100,
                          out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()),
                          badpixels=[badpix_g, np.nan])
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # Plot only three gain threshold maps.
 bp_thresh = OrderedDict()
 for mod, con in badpix_g.items():
     bp_thresh[mod] = np.zeros((con.shape[0], con.shape[1], con.shape[2], 5), dtype=con.dtype)
     bp_thresh[mod][...,:2] = con[...,:2]
     bp_thresh[mod][...,2:] = con
 
 
 create_constant_overview(thresholds_g, "Threshold (ADU)", max_cells, 4000, 10000, 5,
                          out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()),
                          badpixels=[bp_thresh, np.nan],
                          gmap=['HG-MG Threshold', 'MG-LG Threshold', 'High gain', 'Medium gain', 'low gain'],
                          marker=['d','d','','','']
                          )
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 bad_pixel_aggregate_g = OrderedDict()
 for m, d in badpix_g.items():
     bad_pixel_aggregate_g[m] = d.astype(np.bool).astype(np.float)
 create_constant_overview(bad_pixel_aggregate_g, "Bad pixel fraction", max_cells, 0, 0.10, 3,
                          out_folder=out_folder, infix="{}-{}-{}".format(*offset_runs.values()))
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Summary tables ##
 
 The following tables show summary information for the evaluated module. Values for currently evaluated constants are compared with values for pre-existing constants retrieved from the calibration database.
 
 %% Cell type:code id: tags:
 
 ``` python
 table = []
 gain_names = ['High', 'Medium', 'Low']
 for qm in badpix_g.keys():
     for gain in range(3):
 
         l_data = []
         l_data_old = []
 
         data = np.copy(badpix_g[qm][:,:,:,gain])
         datau32 = data.astype(np.uint32)
         l_data.append(data)
         l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.NOISE_OUT_OF_THRESHOLD.value))
         l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.OFFSET_OUT_OF_THRESHOLD.value))
         l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.OFFSET_NOISE_EVAL_ERROR.value))
         l_data.append(datau32 - np.bitwise_or(datau32,BadPixels.GAIN_THRESHOLDING_ERROR.value))
 
         if old_const['BadPixelsDark'] is not None:
             dataold = np.copy(old_const['BadPixelsDark'][:, :, :, gain])
             datau32old = dataold.astype(np.uint32)
             l_data_old.append(dataold)
             l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.NOISE_OUT_OF_THRESHOLD.value))
             l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.OFFSET_OUT_OF_THRESHOLD.value))
             l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.OFFSET_NOISE_EVAL_ERROR.value))
             l_data_old.append(datau32old - np.bitwise_or(datau32old,BadPixels.GAIN_THRESHOLDING_ERROR.value))
 
         l_data_name = ['All bad pixels', 'NOISE_OUT_OF_THRESHOLD',
                        'OFFSET_OUT_OF_THRESHOLD', 'OFFSET_NOISE_EVAL_ERROR', 'GAIN_THRESHOLDING_ERROR']
 
         l_threshold = ['', '{}'.format(thresholds_noise_sigma), '{}'.format(thresholds_offset_sigma),
                       '{}/{}'.format(thresholds_offset_hard[gain], thresholds_noise_hard[gain]), '']
 
         for i in range(len(l_data)):
             line = ['{}, {} gain'.format(l_data_name[i], gain_names[gain]),
                       l_threshold[i],
                       len(l_data[i][l_data[i]>0].flatten())
                    ]
             if old_const['BadPixelsDark'] is not None:
                 line += [len(l_data_old[i][l_data_old[i]>0].flatten())]
             else:
                 line += ['-']
 
             table.append(line)
         table.append(['', '', '', ''])
 
 display(Markdown('### Number of bad pixels ###'.format(qm)))
 if len(table)>0:
     md = display(Latex(tabulate.tabulate(table, tablefmt='latex',
                                      headers=["Pixel type", "Threshold", "New constant", "Old constant "])))
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 
 header = ['Parameter',
           "New constant", "Old constant ",
           "New constant", "Old constant ",
           "New constant", "Old constant ",
           "New constant", "Old constant "]
 
 for const in ['Offset', 'Noise', 'ThresholdsDark']:
     if const != 'ThresholdsDark':
         table = [['','High gain', 'High gain', 'Medium gain', 'Medium gain', 'Low gain', 'Low gain']]
     else:
         table = [['','HG-MG threshold', 'HG-MG threshold', 'MG-LG threshold', 'MG-LG threshold']]
     for qm in res.keys():
 
         data = np.copy(res[qm][const])
         if const == 'ThresholdsDark':
             data[...,0][res[qm]['BadPixelsDark'][...,0]>0] = np.nan
             data[...,1][res[qm]['BadPixelsDark'][...,1]>0] = np.nan
         else:
             data[res[qm]['BadPixelsDark']>0] = np.nan
 
         if old_const[const] is not None and old_const['BadPixelsDark'] is not None:
             dataold = np.copy(old_const[const])
             if const == 'ThresholdsDark':
                 dataold[...,0][old_const['BadPixelsDark'][...,0]>0] = np.nan
                 dataold[...,1][old_const['BadPixelsDark'][...,1]>0] = np.nan
             else:
                 dataold[old_const['BadPixelsDark']>0] = np.nan
 
         f_list = [np.nanmedian, np.nanmean, np.nanstd, np.nanmin, np.nanmax]
         n_list = ['Median', 'Mean', 'Std', 'Min', 'Max']
 
         for i, f in enumerate(f_list):
             line = [n_list[i]]
             for gain in range(3):
                 # Compare only 3 threshold gain-maps
                 if gain == 2 and const == 'ThresholdsDark':
                     continue
                 line.append('{:6.1f}'.format(f(data[...,gain])))
                 if old_const[const] is not None and old_const['BadPixelsDark'] is not None:
                     line.append('{:6.1f}'.format(f(dataold[...,gain])))
                 else:
                     line.append('-')
 
             table.append(line)
 
     display(Markdown('### {} [ADU], good pixels only ###'.format(const)))
     md = display(Latex(tabulate.tabulate(table, tablefmt='latex', headers=header)))
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 ```