David Hammer
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb

+ 9

− 12
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb

+ 9

− 12
 %% Cell type:markdown id: tags:

 # AGIPD Offline Correction #

 Author: European XFEL Detector Group, Version: 2.0

 Offline Calibration for the AGIPD Detector

 %% Cell type:code id: tags:

 ``` python
 in_folder = "/gpfs/exfel/exp/HED/202031/p900174/raw" # the folder to read data from, required
 out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/hibef_agipd2"  # the folder to output to, required
 sequences = [-1] # sequences to correct, set to -1 for all, range allowed
 modules = [-1] # modules to correct, set to -1 for all, range allowed
 run = 155 # runs to process, required

 karabo_id = "HED_DET_AGIPD500K2G" # karabo karabo_id
 karabo_da = ['-1']  # a list of data aggregators names, Default [-1] for selecting all 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/' # path in the HDF5 file to images
 h5path_idx = 'INDEX/{}/DET/{}:xtdf/' # path in the HDF5 file to images
 h5path_ctrl = '/CONTROL/{}/MDL/FPGA_COMP' # path to control information
 karabo_id_control = "HED_EXP_AGIPD500K2G" # karabo-id for control device
 karabo_da_control = 'AGIPD500K2G00' # karabo DA for control infromation

 slopes_ff_from_files = "" # Path to locally stored SlopesFF and BadPixelsFF constants

 use_dir_creation_date = True # use the creation data of the input dir for database queries
 cal_db_interface = "tcp://max-exfl016:8015#8045" # the database interface to use
 cal_db_timeout = 30000 # in milli seconds
 creation_date_offset = "00:00:00" # add an offset to creation date, e.g. to get different constants

 max_cells = 0 # number of memory cells used, set to 0 to automatically infer
 bias_voltage = 300 # Bias voltage
 acq_rate = 0. # the detector acquisition rate, use 0 to try to auto-determine
 gain_setting = 0.1 # the gain setting, use 0.1 to try to auto-determine
 photon_energy = 9.2 # photon energy in keV
 overwrite = True # set to True if existing data should be overwritten
 max_pulses = [0, 500, 1] # range list [st, end, step] of maximum pulse indices within a train. 3 allowed maximum list input elements.
 mem_cells_db = 0 # set to a value different than 0 to use this value for DB queries
 cell_id_preview = 1 # cell Id used for preview in single-shot plots

 # Correction parameters
 blc_noise_threshold = 5000 # above this mean signal intensity now baseline correction via noise is attempted
 cm_dark_fraction = 0.66 # threshold for fraction of  empty pixels to consider module enough dark to perform CM correction
 cm_dark_range = [-50.,30] # range for signal value ADU for pixel to be consider as a dark pixel
 cm_n_itr = 4 # number of iterations for common mode correction
 hg_hard_threshold = 1000 # threshold to force medium gain offset subtracted pixel to high gain
 mg_hard_threshold = 1000 # threshold to force medium gain offset subtracted pixel from low to medium gain
 noisy_adc_threshold = 0.25 # threshold to mask complete adc
 ff_gain = 7.2 # conversion gain for absolute FlatField constants, while applying xray_gain

 # Correction Booleans
 only_offset = False # Apply only Offset correction. if False, Offset is applied by Default. if True, Offset is only applied.
 rel_gain = False # do relative gain correction based on PC data
 xray_gain = False # do relative gain correction based on xray data
 blc_noise = False # if set, baseline correction via noise peak location is attempted
 blc_stripes = False # if set, baseline corrected via stripes
 blc_hmatch = False # if set, base line correction via histogram matching is attempted
 match_asics = False # if set, inner ASIC borders are matched to the same signal level
 adjust_mg_baseline = False # adjust medium gain baseline to match highest high gain value
 zero_nans = False # set NaN values in corrected data to 0
 zero_orange = False # set to 0 very negative and very large values in corrected data
 blc_set_min = False # Shift to 0 negative medium gain pixels after offset corr
 corr_asic_diag = False # if set, diagonal drop offs on ASICs are correted
 force_hg_if_below = False # set high gain if mg offset subtracted value is below hg_hard_threshold
 force_mg_if_below = False # set medium gain if mg offset subtracted value is below mg_hard_threshold
 mask_noisy_adc = False # Mask entire ADC if they are noise above a relative threshold
 common_mode = False # Common mode correction
 melt_snow = False # Identify (and optionally interpolate) 'snowy' pixels
 mask_zero_std = False # Mask pixels with zero standard deviation across train
 low_medium_gap = False # 5 sigma separation in thresholding between low and medium gain

 # Paralellization parameters
 chunk_size = 1000 # Size of chunk for image-weise correction
 chunk_size_idim = 1  # chunking size of imaging dimension, adjust if user software is sensitive to this.
 n_cores_correct = 16 # Number of chunks to be processed in parallel
 n_cores_files = 4 # Number of files to be processed in parallel
 sequences_per_node = 2 # number of sequence files per cluster node if run as slurm job, set to 0 to not run SLURM parallel

 def balance_sequences(in_folder, run, sequences, sequences_per_node, karabo_da):
    from xfel_calibrate.calibrate import balance_sequences as bs
    return bs(in_folder, run, sequences, sequences_per_node, karabo_da)
 ```

 %% Cell type:code id: tags:

 ``` python
 import copy
 from datetime import timedelta
 from dateutil import parser
 import gc
 import glob
 import itertools
 from IPython.display import HTML, display, Markdown, Latex
 import math
 from multiprocessing import Pool
 import os
 import re
 import sys
 import traceback
 from time import time, sleep, perf_counter
 import tabulate
 import warnings
 warnings.filterwarnings('ignore')
 import yaml

 from extra_geom import AGIPD_1MGeometry, AGIPD_500K2GGeometry
 from extra_data import RunDirectory, stack_detector_data
 from iCalibrationDB import Detectors
 from mpl_toolkits.mplot3d import Axes3D
 from matplotlib.ticker import LinearLocator, FormatStrFormatter
 from matplotlib.colors import LogNorm
 from matplotlib import cm as colormap
 import matplotlib.pyplot as plt
 import matplotlib
 matplotlib.use("agg")
 %matplotlib inline
 import numpy as np
 import seaborn as sns
 sns.set()
 sns.set_context("paper", font_scale=1.4)
 sns.set_style("ticks")

 from cal_tools.agipdlib import (AgipdCorrections, get_acq_rate,
                                get_gain_setting, get_num_cells)
 from cal_tools.cython import agipdalgs as calgs
 from cal_tools.ana_tools import get_range
 from cal_tools.enums import BadPixels
 from cal_tools.tools import get_dir_creation_date, map_modules_from_folder
 from cal_tools.step_timing import StepTimer

 import seaborn as sns
 sns.set()
 sns.set_context("paper", font_scale=1.4)
 sns.set_style("ticks")
 ```

 %% Cell type:markdown id: tags:

 ## Evaluated parameters ##

 %% Cell type:code id: tags:

 ``` python
 # Fill dictionaries comprising bools and arguments for correction and data analysis

 # Here the herarichy and dependability for correction booleans are defined
 corr_bools = {}

 # offset is at the bottom of AGIPD correction pyramid.
 corr_bools["only_offset"] = only_offset

 # Dont apply any corrections if only_offset is requested
 if not only_offset:
    corr_bools["adjust_mg_baseline"] = adjust_mg_baseline
    corr_bools["rel_gain"] = rel_gain
    corr_bools["xray_corr"] = xray_gain
    corr_bools["blc_noise"] = blc_noise
    corr_bools["blc_stripes"] = blc_stripes
    corr_bools["blc_hmatch"] = blc_hmatch
    corr_bools["blc_set_min"] = blc_set_min
    corr_bools["match_asics"] = match_asics
    corr_bools["corr_asic_diag"] = corr_asic_diag
    corr_bools["zero_nans"] = zero_nans
    corr_bools["zero_orange"] = zero_orange
    corr_bools["mask_noisy_adc"] = mask_noisy_adc
    corr_bools["force_hg_if_below"] = force_hg_if_below
    corr_bools["force_mg_if_below"] = force_mg_if_below
    corr_bools["common_mode"] = common_mode
    corr_bools["melt_snow"] = melt_snow
    corr_bools["mask_zero_std"] = mask_zero_std
    corr_bools["low_medium_gap"] = low_medium_gap

 ```

 %% Cell type:code id: tags:

 ``` python
 if in_folder[-1] == "/":
    in_folder = in_folder[:-1]
 if sequences[0] == -1:
    sequences = None

 control_fname = f'{in_folder}/r{run:04d}/RAW-R{run:04d}-{karabo_da_control}-S00000.h5'
 h5path_ctrl = h5path_ctrl.format(karabo_id_control)
 h5path = h5path.format(karabo_id, receiver_id)
 h5path_idx = h5path_idx.format(karabo_id, receiver_id)

 print(f'Path to control file {control_fname}')
 ```

 %% Cell type:code id: tags:

 ``` python
 # Create output folder
 os.makedirs(out_folder, exist_ok=overwrite)

 # Evaluate detector instance for mapping
 instrument = karabo_id.split("_")[0]
 if instrument == "SPB":
    dinstance = "AGIPD1M1"
    nmods = 16
 elif instrument == "MID":
    dinstance = "AGIPD1M2"
    nmods = 16
 elif instrument == "HED":
    dinstance = "AGIPD500K"
    nmods = 8

 # Evaluate requested modules
 if karabo_da[0] == '-1':
    if modules[0] == -1:
        modules = list(range(nmods))
    karabo_da = ["AGIPD{:02d}".format(i) for i in modules]
 else:
    modules = [int(x[-2:]) for x in karabo_da]

 def mod_name(modno):
    return f"Q{modno // 4 + 1}M{modno % 4 + 1}"

 print("Process modules: ", ', '.join(
    [mod_name(x) for x in modules]))
 print(f"Detector in use is {karabo_id}")
 print(f"Instrument {instrument}")
 print(f"Detector instance {dinstance}")
 ```

 %% Cell type:code id: tags:

 ``` python
 # Display Information about the selected pulses indices for correction.
 pulses_lst = list(range(*max_pulses)) if not (len(max_pulses)==1 and max_pulses[0]==0) else max_pulses

 try:
    if len(pulses_lst) > 1:
        print("A range of {} pulse indices is selected: from {} to {} with a step of {}"
               .format(len(pulses_lst), pulses_lst[0] , pulses_lst[-1] + (pulses_lst[1] - pulses_lst[0]),
                       pulses_lst[1] - pulses_lst[0]))
    else:
        print("one pulse is selected: a pulse of idx {}".format(pulses_lst[0]))
 except Exception as e:
    raise ValueError('max_pulses input Error: {}'.format(e))
 ```

 %% Cell type:code id: tags:

 ``` python
 # set everything up filewise
 mmf = map_modules_from_folder(in_folder, run, path_template,
                              karabo_da, sequences)
 mapped_files, mod_ids, total_sequences, sequences_qm, _ = mmf
 file_list = []

 # ToDo: Split table over pages
 print(f"Processing a total of {total_sequences} sequence files in chunks of {n_cores_files}")
 table = []
 ti = 0
 for k, files in mapped_files.items():
    i = 0
    for f in list(files.queue):
        file_list.append(f)
        if i == 0:
            table.append((ti, k, i, f))
        else:
            table.append((ti, "", i,  f))
        i += 1
        ti += 1
 md = display(Latex(tabulate.tabulate(table, tablefmt='latex',
                                     headers=["#", "module", "# module", "file"])))
 file_list = sorted(file_list, key=lambda name: name[-10:])
 ```

 %% Cell type:code id: tags:

 ``` python
 filename = file_list[0]
 channel = int(re.findall(r".*-AGIPD([0-9]+)-.*", filename)[0])

 # Evaluate number of memory cells
 mem_cells = get_num_cells(filename, karabo_id, channel)
 if mem_cells is None:
    raise ValueError(f"No raw images found in {filename}")

 mem_cells_db = mem_cells if mem_cells_db == 0 else mem_cells_db
 max_cells = mem_cells if max_cells == 0 else max_cells

 # Evaluate aquisition rate
 if acq_rate == 0:
    acq_rate = get_acq_rate((filename, karabo_id, channel))

 print(f"Maximum memory cells to calibrate: {max_cells}")
 ```

 %% Cell type:code id: tags:

 ``` python
 # Evaluate creation time
 creation_time = None
 if use_dir_creation_date:
    creation_time = get_dir_creation_date(in_folder, run)
    offset = parser.parse(creation_date_offset)
    delta = timedelta(hours=offset.hour,
                      minutes=offset.minute, seconds=offset.second)
    creation_time += delta

 # Evaluate gain setting
 if gain_setting == 0.1:
    if creation_time.replace(tzinfo=None) < 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("Set gain setting to 0")
            gain_setting = 0

 ```

 %% Cell type:code id: tags:

 ``` python
 print(f"Using {creation_time} as creation time")
 print(f"Operating conditions are:\n• Bias voltage: {bias_voltage}\n• Memory cells: {mem_cells_db}\n"
              f"• Acquisition rate: {acq_rate}\n• Gain setting: {gain_setting}\n• Photon Energy: {photon_energy}\n")
 ```

 %% Cell type:markdown id: tags:

 ## Data processing ##

 %% Cell type:code id: tags:

 ``` python
 agipd_corr = AgipdCorrections(max_cells, max_pulses,
                              h5_data_path=h5path,
                              h5_index_path=h5path_idx,
                              corr_bools=corr_bools)

 agipd_corr.baseline_corr_noise_threshold = -blc_noise_threshold
 agipd_corr.hg_hard_threshold = hg_hard_threshold
 agipd_corr.mg_hard_threshold = mg_hard_threshold

 agipd_corr.cm_dark_min = cm_dark_range[0]
 agipd_corr.cm_dark_max = cm_dark_range[1]
 agipd_corr.cm_dark_fraction = cm_dark_fraction
 agipd_corr.cm_n_itr = cm_n_itr
 agipd_corr.noisy_adc_threshold = noisy_adc_threshold
 agipd_corr.ff_gain = ff_gain
 ```

 %% Cell type:code id: tags:

 ``` python
 # Retrieve calibration constants to RAM
 agipd_corr.allocate_constants(modules, (3, mem_cells_db, 512, 128))

 const_yaml = None
 yaml_fn = f'{out_folder}/metadata.yml'
 if os.path.isfile(yaml_fn):
    with open(yaml_fn, "r") as fd:
        metadata = yaml.safe_load(fd)
        if "retrieved-constants" in metadata:
            const_yaml = metadata["retrieved-constants"]

 # retrive constants
 def retrieve_constants(mod):
    """
    Retrieve calibration constants and load them to shared memory

    Metadata for constants is taken from yml file or retrieved from the DB
    """
    device = getattr(getattr(Detectors, dinstance), mod_name(mod))
    err = ''
    try:
        # check if there is a yaml file in out_folder that has the device constants.
        if const_yaml and device.device_name in const_yaml:
            when = agipd_corr.initialize_from_yaml(const_yaml, mod, device)
        else:
            when = agipd_corr.initialize_from_db(cal_db_interface, creation_time, mem_cells_db, bias_voltage,
                                                 photon_energy, gain_setting, acq_rate, mod, device, False)
    except Exception as e:
        err = f"Error: {e}\nError traceback: {traceback.format_exc()}"
        when = None
    return err, mod, when, device.device_name


 ts = perf_counter()
 with Pool(processes=len(modules)) as pool:
    const_out = pool.map(retrieve_constants, modules)
 print(f"Constants were loaded in {perf_counter()-ts:.01f}s")
 ```

 %% Cell type:code id: tags:

 ``` python
 # allocate memory for images and hists
 n_images_max = max_cells*256
 data_shape = (n_images_max, 512, 128)
 agipd_corr.allocate_images(data_shape, n_cores_files)
 ```

 %% Cell type:code id: tags:

 ``` python
 def batches(l, batch_size):
    """Group a list into batches of (up to) batch_size elements"""
    start = 0
    while start < len(l):
        yield l[start:start + batch_size]
        start += batch_size
 ```

 %% Cell type:code id: tags:

 ``` python
 def imagewise_chunks(img_counts):
    """Break up the loaded data into chunks of up to chunk_size

    Yields (file data slot, start index, stop index)
    """
    for i_proc, n_img in enumerate(img_counts):
        n_chunks = math.ceil(n_img / chunk_size)
        for i in range(n_chunks):
            yield i_proc, i * n_img // n_chunks, (i+1) * n_img // n_chunks
 ```

 %% Cell type:code id: tags:

 ``` python
 step_timer = StepTimer()
 ```

 %% Cell type:code id: tags:

 ``` python
 with Pool() as pool:
    for file_batch in batches(file_list, n_cores_files):
        # TODO: Move some printed output to logging or similar
        print(f"Processing next {len(file_batch)} files:")
        for file_name in file_batch:
            print(" ", file_name)
        step_timer.start()

        img_counts = pool.starmap(agipd_corr.read_file, enumerate(file_batch))
        step_timer.done_step('Loading data from files')

        # Evaluate zero-data-std mask
        pool.starmap(agipd_corr.mask_zero_std, itertools.product(
            range(len(file_batch)), np.array_split(np.arange(agipd_corr.max_cells), n_cores_correct)
        ))
        step_timer.done_step('Mask 0 std')

        # Perform offset image-wise correction
        pool.starmap(agipd_corr.offset_correction, imagewise_chunks(img_counts))
        step_timer.done_step("Offset correction")


        if blc_noise or blc_stripes or blc_hmatch:
            # Perform image-wise correction
            pool.starmap(agipd_corr.baseline_correction, imagewise_chunks(img_counts))
            step_timer.done_step("Base-line shift correction")

        if common_mode:
            # Perform cross-file correction parallel over asics
            pool.starmap(agipd_corr.cm_correction, itertools.product(
                range(len(file_batch)), range(16)  # 16 ASICs per module
            ))
            step_timer.done_step("Common-mode correction")

        # Perform image-wise correction
        pool.starmap(agipd_corr.gain_correction, imagewise_chunks(img_counts))
        step_timer.done_step("Image-wise correction")

        # Save corrected data
        pool.starmap(agipd_corr.write_file, [
            (i_proc, file_name, os.path.join(out_folder, os.path.basename(file_name).replace("RAW", "CORR")))
            for i_proc, file_name in enumerate(file_batch)
        ])
        step_timer.done_step("Save")
 ```

 %% Cell type:code id: tags:

 ``` python
 print(f"Correction of {len(file_list)} files is finished")
 print(f"Total processing time {step_timer.timespan():.01f} s")
 print(f"Timing summary per batch of {n_cores_files} files:")
 step_timer.print_summary()
 ```

 %% Cell type:code id: tags:

 ``` python
 # if the yml file contains "retrieved-constants", that means a leading
 # notebook got processed and the reporting would be generated from it.
 fst_print = True
+timestamps = {}

-to_store = []
-line = []
 for i, (error, modno, when, mod_dev) in enumerate(const_out):
    qm = mod_name(modno)
    # expose errors while applying correction
    if error:
        print("Error: {}".format(error) )

    if not const_yaml or mod_dev not in const_yaml:
        if fst_print:
            print("Constants are retrieved with creation time: ")
            fst_print = False

-        line = [qm]
+        module_timestamps = {}

        # If correction is crashed
        if not error:
            print(f"{qm}:")
            for key, item in when.items():
                if hasattr(item, 'strftime'):
                    item = item.strftime('%y-%m-%d %H:%M')
                when[key] = item
                print('{:.<12s}'.format(key), item)

        # Store few time stamps if exists
        # Add NA to keep array structure
        for key in ['Offset', 'SlopesPC', 'SlopesFF']:
            if when and key in when and when[key]:
-                line.append(when[key])
+                module_timestamps[key] = when[key]
            else:
                if error is not None:
-                    line.append('Err')
+                    module_timestamps[key] = "Err"
                else:
-                    line.append('NA')
-
-        if len(line) > 0:
-            to_store.append(line)
+                    module_timestamps[key] = "NA"
+        timestamps[qm] = module_timestamps

 seq = sequences[0] if sequences else 0

-if len(to_store) > 0:
-    with open(f"{out_folder}/retrieved_constants_s{seq}.yml","w") as fyml:
-        yaml.safe_dump({"time-summary": {f"S{seq}":to_store}}, fyml)
+if len(timestamps) > 0:
+    with open(f"{out_folder}/retrieved_constants_s{seq}.yml","w") as fd:
+        yaml.safe_dump({"time-summary": {f"S{seq}": timestamps}}, fd)
 ```

 %% Cell type:code id: tags:

 ``` python
 def do_3d_plot(data, edges, x_axis, y_axis):
    fig = plt.figure(figsize=(10, 10))
    ax = fig.gca(projection='3d')

    # Make data.
    X = edges[0][:-1]
    Y = edges[1][:-1]
    X, Y = np.meshgrid(X, Y)
    Z = data.T

    # Plot the surface.
    surf = ax.plot_surface(X, Y, Z, cmap=colormap.coolwarm,
                           linewidth=0, antialiased=False)
    ax.set_xlabel(x_axis)
    ax.set_ylabel(y_axis)
    ax.set_zlabel("Counts")


 def do_2d_plot(data, edges, y_axis, x_axis):
    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111)
    extent = [np.min(edges[1]), np.max(edges[1]),
              np.min(edges[0]), np.max(edges[0])]
    im = ax.imshow(data[::-1, :], extent=extent, aspect="auto",
                   norm=LogNorm(vmin=1, vmax=max(10, np.max(data))))
    ax.set_xlabel(x_axis)
    ax.set_ylabel(y_axis)
    cb = fig.colorbar(im)
    cb.set_label("Counts")
 ```

 %% Cell type:code id: tags:

 ``` python
 def get_trains_data(run_folder, source, include, tid=None, path='*/DET/*', modules=16, fillvalue=np.nan):
    """
    Load single train for all module

    :param run_folder: Path to folder with data
    :param source: Data source to be loaded
    :param include: Inset of file name to be considered
    :param tid: Train Id to be loaded. First train is considered if None is given
    :param path: Path to find image data inside h5 file

    """
    run_data = RunDirectory(run_folder, include)
    if tid:
        tid, data = run_data.select('*/DET/*', source).train_from_id(tid)
        return tid, stack_detector_data(train=data, data=source, fillvalue=fillvalue, modules=modules)
    else:
        for tid, data in run_data.select('*/DET/*', source).trains(require_all=True):
            return tid, stack_detector_data(train=data, data=source, fillvalue=fillvalue, modules=modules)
    return None, None
 ```

 %% Cell type:code id: tags:

 ``` python
 if dinstance == "AGIPD500K":
    geom = AGIPD_500K2GGeometry.from_origin()
 else:
    geom = AGIPD_1MGeometry.from_quad_positions(quad_pos=[
        (-525, 625),
        (-550, -10),
        (520, -160),
        (542.5, 475),
    ])
 ```

 %% Cell type:code id: tags:

 ``` python
 include = '*S00000*' if sequences is None else f'*S{sequences[0]:05d}*'
 tid, corrected = get_trains_data(f'{out_folder}/', 'image.data', include, modules=nmods)
 _, gains = get_trains_data(f'{out_folder}/', 'image.gain', include, tid, modules=nmods)
 _, mask = get_trains_data(f'{out_folder}/', 'image.mask', include, tid, modules=nmods)
 _, blshift = get_trains_data(f'{out_folder}/', 'image.blShift', include, tid, modules=nmods)
 _, cellId = get_trains_data(f'{out_folder}/', 'image.cellId', include, tid, modules=nmods)
 _, pulseId = get_trains_data(f'{out_folder}/', 'image.pulseId', include, tid,
                             modules=nmods, fillvalue=0)
 _, raw = get_trains_data(f'{in_folder}/r{run:04d}/', 'image.data', include, tid, modules=nmods)
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown(f'## Preview and statistics for {gains.shape[0]} images of the train {tid} ##\n'))
 ```

 %% Cell type:markdown id: tags:

 ### Signal vs. Analogue Gain ###

 %% Cell type:code id: tags:

 ``` python
 hist, bins_x, bins_y = calgs.histogram2d(raw[:,0,...].flatten().astype(np.float32),
                                         raw[:,1,...].flatten().astype(np.float32),
                                         bins=(100, 100),
                                         range=[[4000, 8192], [4000, 8192]])
 do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Analogue gain (ADU)")
 do_3d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Analogue gain (ADU)")
 ```

 %% Cell type:markdown id: tags:

 ### Signal vs. Digitized Gain ###

 The following plot shows plots signal vs. digitized gain

 %% Cell type:code id: tags:

 ``` python
 hist, bins_x, bins_y = calgs.histogram2d(corrected.flatten().astype(np.float32),
                                         gains.flatten().astype(np.float32), bins=(100, 3),
                                         range=[[-50, 8192], [0, 3]])
 do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Gain bit value")
 ```

 %% Cell type:code id: tags:

 ``` python
 print(f"Gain statistics in %")
 table = [[f'{gains[gains==0].size/gains.size*100:.02f}',
          f'{gains[gains==1].size/gains.size*100:.03f}',
          f'{gains[gains==2].size/gains.size*100:.03f}']]
 md = display(Latex(tabulate.tabulate(table, tablefmt='latex',
                                     headers=["High", "Medium", "Low"])))
 ```

 %% Cell type:markdown id: tags:

 ### Intensity per Pulse ###

 %% Cell type:code id: tags:

 ``` python
 pulse_range = [np.min(pulseId[pulseId>=0]), np.max(pulseId[pulseId>=0])]

 mean_data = np.nanmean(corrected, axis=(2, 3))
 hist, bins_x, bins_y = calgs.histogram2d(mean_data.flatten().astype(np.float32),
                                      pulseId.flatten().astype(np.float32),
                                      bins=(100, int(pulse_range[1])),
                                      range=[[-50, 1000], pulse_range])

 do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id")
 do_3d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id")

 hist, bins_x, bins_y = calgs.histogram2d(mean_data.flatten().astype(np.float32),
                                      pulseId.flatten().astype(np.float32),
                                      bins=(100,  int(pulse_range[1])),
                                      range=[[-50, 200000], pulse_range])

 do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id")
 do_3d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id")
 ```

 %% Cell type:markdown id: tags:

 ### Baseline shift ###

 Estimated base-line shift with respect to the total ADU counts of corrected image.

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 h = ax.hist(blshift.flatten(), bins=100, log=True)
 _ = plt.xlabel('Baseline shift [ADU]')
 _ = plt.ylabel('Counts')
 _ = ax.grid()
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(10, 10))
 corrected_ave = np.nansum(corrected, axis=(2, 3))
 plt.scatter(corrected_ave.flatten()/10**6, blshift.flatten(), s=0.9)
 plt.xlim(-1, 1000)
 plt.grid()
 plt.xlabel('Illuminated corrected [MADU] ')
 _ = plt.ylabel('Estimated baseline shift [ADU]')
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Raw preview ###\n'))
 display(Markdown(f'Mean over images of the RAW data\n'))
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 data = np.mean(raw[:, 0, ...], axis=0)
 vmin, vmax = get_range(data, 5)
 ax = geom.plot_data_fast(data, ax=ax, cmap="jet", vmin=vmin, vmax=vmax)
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown(f'Single shot of the RAW data from cell {np.max(cellId[cell_id_preview])} \n'))
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = get_range(raw[cell_id_preview, 0, ...], 5)
 ax = geom.plot_data_fast(raw[cell_id_preview, 0, ...], ax=ax, cmap="jet", vmin=vmin, vmax=vmax)
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Corrected preview ###\n'))
 display(Markdown(f'A single shot image from cell {np.max(cellId[cell_id_preview])} \n'))
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = get_range(corrected[cell_id_preview], 7, -50)
 vmin = - 50
 ax = geom.plot_data_fast(corrected[cell_id_preview], ax=ax, cmap="jet", vmin=vmin, vmax=vmax)
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = get_range(corrected[cell_id_preview], 5, -50)
 nbins = np.int((vmax + 50) / 2)
 h = ax.hist(corrected[cell_id_preview].flatten(),
            bins=nbins, range=(-50, vmax),
            histtype='stepfilled', log=True)
 _ = plt.xlabel('[ADU]')
 _ = plt.ylabel('Counts')
 _ = ax.grid()
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Mean CORRECTED Preview ###\n'))
 display(Markdown(f'A mean across one train \n'))
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 data = np.mean(corrected, axis=0)
 vmin, vmax = get_range(data, 7)
 ax = geom.plot_data_fast(data, ax=ax, cmap="jet", vmin=-50, vmax=vmax)
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = get_range(corrected, 10, -100)
 vmax = np.nanmax(corrected)
 if vmax > 50000:
    vmax=50000
 nbins = np.int((vmax + 100) / 5)
 h = ax.hist(corrected.flatten(), bins=nbins,
            range=(-100, vmax), histtype='step', log=True, label = 'All')
 _ = ax.hist(corrected[gains == 0].flatten(), bins=nbins, range=(-100, vmax),
            alpha=0.5, log=True, label='High gain', color='green')
 _ = ax.hist(corrected[gains == 1].flatten(), bins=nbins, range=(-100, vmax),
            alpha=0.5, log=True, label='Medium gain', color='red')
 _ = ax.hist(corrected[gains == 2].flatten(), bins=nbins,
            range=(-100, vmax), alpha=0.5, log=True, label='Low gain', color='yellow')
 _ = ax.legend()
 _ = ax.grid()
 _ = plt.xlabel('[ADU]')
 _ = plt.ylabel('Counts')
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Maximum GAIN Preview ###\n'))
 display(Markdown(f'The per pixel maximum across one train for the digitized gain'))
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 ax = geom.plot_data_fast(np.max(gains, axis=0), ax=ax,
                         cmap="jet", vmin=-1, vmax=3)
 ```

 %% Cell type:markdown id: tags:

 ## Bad Pixels ##
 The mask contains dedicated entries for all pixels and memory cells as well as all three gains stages. Each mask entry is encoded in 32 bits as:

 %% Cell type:code id: tags:

 ``` python
 table = []
 for item in BadPixels:
    table.append((item.name, "{:016b}".format(item.value)))
 md = display(Latex(tabulate.tabulate(table, tablefmt='latex',
                                     headers=["Bad pixel type", "Bit mask"])))
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown(f'### Single Shot Bad Pixels ### \n'))
 display(Markdown(f'A single shot bad pixel map from cell {np.max(cellId[cell_id_preview])} \n'))
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 ax = geom.plot_data_fast(np.log2(mask[cell_id_preview]), ax=ax, vmin=0, vmax=32, cmap="jet")
 ```

 %% Cell type:markdown id: tags:

 ### Percentage of Bad Pixels across one train  ###

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 ax = geom.plot_data_fast(np.mean(mask>0, axis=0),
                         vmin=0, ax=ax, vmax=1, cmap="jet")
 ```

 %% Cell type:markdown id: tags:

 ### Percentage of Bad Pixels across one train. Only Dark Related ###

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 cm = np.copy(mask)
 cm[cm > BadPixels.NO_DARK_DATA.value] = 0
 ax = geom.plot_data_fast(np.mean(cm>0, axis=0),
                         vmin=0, ax=ax, vmax=1, cmap="jet")
 ```