Add SPI hit-finding

473ed929 · Egor Sobolev · 69b20b72 · 473ed929 · 473ed929
Commit 473ed929 authored 1 year ago by Egor Sobolev
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -91,6 +91,7 @@
    "\n",
    "# Additional processing\n",
    "count_lit_pixels = False # Count the number of pixels registering photons\n",
+    "spi_hitfinding = False  # Find hits using lit-pixel counter\n",
    "\n",
    "# Optional auxiliary devices\n",
    "use_ppu_device = ''  # Device ID for a pulse picker device to only process picked trains, empty string to disable\n",
@@ -108,6 +109,16 @@
    "recast_image_data = ''  # Cast data to a different dtype before saving\n",
    "compress_fields = ['gain', 'mask']  # Datasets in image group to compress.\n",
    "\n",
+    "# SPI hit-finder parameters\n",
+    "spi_hf_modules = [3, 4, 8, 15]  # Use specified modules for SPI hitfinding\n",
+    "spi_hf_mode = \"adaptive\"  # The method to compute threshold for hitscores in SPI hitfinding: `fixed` or `adaptive`\n",
+    "spi_hf_snr = 4.0  # Siginal-to-noise ration for adaptive threshold in SPI hitfinding\n",
+    "spi_hf_min_scores = 100  # The minimal size of events to compute adaptive threshold in SPI hitfinding\n",
+    "spi_hf_fixed_threshold = 0  # The fixed threshold value\n",
+    "spi_hf_hitrate_window_size = 200  # The window size for runnig average of hitrate in trains\n",
+    "spi_hf_miss_fraction = 1  # The fraction of misses to select along with hits\n",
+    "spi_hf_miss_fraction_base = \"hit\"  # The base to compute the number of misses to select: the number of hits (`hit`) or misses (`miss`)\n",
+    "\n",
    "# Plotting parameters\n",
    "skip_plots = False # exit after writing corrected files and metadata\n",
    "cell_id_preview = 1 # cell Id used for preview in single-shot plots\n",

 %% 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/MID/202201/p002834/raw" # the folder to read data from, required
 out_folder = "/gpfs/exfel/data/scratch/esobolev/pycal_litfrm/p002834/r0225"  # the folder to output to, required
 metadata_folder = ""  # Directory containing calibration_metadata.yml when run by xfel-calibrate
 sequences = [-1] # sequences to correct, set to -1 for all, range allowed
 overwrite = False  # IGNORED, NEEDED FOR COMPATIBILITY.
 modules = [-1] # modules to correct, set to -1 for all, range allowed
 train_ids = [-1] # train IDs to correct, set to -1 for all, range allowed
 run = 225 # runs to process, required

 karabo_id = "MID_DET_AGIPD1M-1" # karabo karabo_id
 karabo_da = ['-1']  # a list of data aggregators names, Default [-1] for selecting all data aggregators
 receiver_template = "{}CH0" # inset for receiver devices
 path_template = 'RAW-R{:04d}-{}-S{:05d}.h5' # the template to use to access data
 instrument_source_template = '{}/DET/{}:xtdf'  # path in the HDF5 file to images
 index_source_template = 'INDEX/{}/DET/{}:xtdf/'  # path in the HDF5 file to images
 ctrl_source_template = '{}/MDL/FPGA_COMP'  # path to control information
 karabo_id_control = "MID_EXP_AGIPD1M1" # karabo-id for control device

 slopes_ff_from_files = "" # Path to locally stored SlopesFF and BadPixelsFF constants, loaded in precorrection notebook

 creation_time = ""  # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
 cal_db_interface = "tcp://max-exfl-cal001:8015#8045" # the database interface to use
 cal_db_timeout = 30000 # in milliseconds
 creation_date_offset = "00:00:00" # add an offset to creation date, e.g. to get different constants
 cal_db_root = ""  # The calibration database root path to access constant files. e.g. accessing constants from the test database /gpfs/exfel/d/cal_tst/caldb_store.

 mem_cells = -1  # Number of memory cells used, set to 0 to automatically infer
 bias_voltage = -1  # bias voltage, set to 0 to use stored value in slow data.
 acq_rate = -1. # the detector acquisition rate, use 0 to try to auto-determine
 gain_setting = -1  # the gain setting, use -1 to use value stored in slow data.
 gain_mode = -1  # gain mode (0: adaptive, 1-3 fixed high/med/low, -1: read from CONTROL data)
 max_pulses = [0, 352, 1] # range list [st, end, step] of memory cell indices to be processed within a train. 3 allowed maximum list input elements.
 mem_cells_db = -1  # set to a value different than 0 to use this value for DB queries
 integration_time = -1 # integration time, negative values for auto-detection.

 # 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
 photon_energy = -1.0 # photon energy in keV, non-positive value for XGM autodetection
 rounding_threshold = 0.5 # the fraction to round to down, 0.5 for standard rounding rule
 cs_mg_adjust = 7e3  # Value to adjust medium gain when correcting with current source. This is used when `adjust_mg_baseline` is True.

 # Correction Booleans
 only_offset = False # Apply only Offset correction. if False, Offset is applied by Default. if True, Offset is only applied.
 # TODO: Remove this boolean parameter an replace rel_gain_mode with it.
 rel_gain = False
 rel_gain_mode = "off"  # Select relative gain correction. Choices [`PC`, `CS`, `off`]. (`PC`: Pulse Capacitor, `CS`: Current Source, `off`: Disable relative gain correction). Default: off.
 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 corrected
 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
 round_photons = False  # Round to absolute number of photons, only use with gain corrections

 # Additional processing
 count_lit_pixels = False # Count the number of pixels registering photons
+spi_hitfinding = False  # Find hits using lit-pixel counter

 # Optional auxiliary devices
 use_ppu_device = ''  # Device ID for a pulse picker device to only process picked trains, empty string to disable
 ppu_train_offset = 0  # When using the pulse picker, offset between the PPU's sequence start and actually picked train
 require_ppu_trigger = False  # Optional protection against running without PPU or without triggering trains.

 use_litframe_finder = 'off' # Process only illuminated frames: 'off' - disable, 'device' - use online device data, 'offline' - use offline algorithm, 'auto' - choose online/offline source automatically (default)
 litframe_device_id = '' # Device ID for a lit frame finder device, empty string to auto detection
 energy_threshold = -1000 # The low limit for the energy (uJ) exposed by frames subject to processing. If -1000, selection by pulse energy is disabled
 use_super_selection = 'cm' # Make a common selection for entire run: 'off' - disable, 'final' - enable for final selection, 'cm' - enable only for common mode correction

 use_xgm_device = ''  # DoocsXGM device ID to obtain actual photon energy, operating condition else.

 # Output parameters
 recast_image_data = ''  # Cast data to a different dtype before saving
 compress_fields = ['gain', 'mask']  # Datasets in image group to compress.

+# SPI hit-finder parameters
+spi_hf_modules = [3, 4, 8, 15]  # Use specified modules for SPI hitfinding
+spi_hf_mode = "adaptive"  # The method to compute threshold for hitscores in SPI hitfinding: `fixed` or `adaptive`
+spi_hf_snr = 4.0  # Siginal-to-noise ration for adaptive threshold in SPI hitfinding
+spi_hf_min_scores = 100  # The minimal size of events to compute adaptive threshold in SPI hitfinding
+spi_hf_fixed_threshold = 0  # The fixed threshold value
+spi_hf_hitrate_window_size = 200  # The window size for runnig average of hitrate in trains
+spi_hf_miss_fraction = 1  # The fraction of misses to select along with hits
+spi_hf_miss_fraction_base = "hit"  # The base to compute the number of misses to select: the number of hits (`hit`) or misses (`miss`)
+
 # Plotting parameters
 skip_plots = False # exit after writing corrected files and metadata
 cell_id_preview = 1 # cell Id used for preview in single-shot plots
 cmap = "viridis"  # matplolib.colormap for almost all heatmap. Other options ['plasma', 'inferno', 'magma', 'cividis', 'jet', ...]

 # Parallelization parameters
 chunk_size = 1000  # Size of chunk for image-wise correction
 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
 max_nodes = 8 # Maximum number of SLURM jobs to split correction work into
 max_tasks_per_worker = 1  # the number of tasks a correction pool worker process can complete before it will exit and be replaced with a fresh worker process. Leave as -1 to keep worker alive as long as pool.


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

 %% Cell type:code id: tags:

 ``` python
 import itertools
 import math
 import multiprocessing
 import os
 import sys
 import warnings
 from datetime import timedelta
 from logging import warning
 from pathlib import Path

 import tabulate
 from dateutil import parser
 from IPython.display import Latex, Markdown, display

 warnings.filterwarnings('ignore')
 import h5py
 import matplotlib
 import matplotlib.pyplot as plt
 import yaml
 from extra_data import by_id, RunDirectory, stack_detector_data
 from extra_geom import AGIPD_1MGeometry, AGIPD_500K2GGeometry
 from matplotlib.colors import LogNorm

 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")

 import cal_tools.restful_config as rest_cfg
 from cal_tools import agipdalgs as calgs
 from cal_tools.agipdlib import (
    AgipdCorrections,
    AgipdCtrl,
    CellRange,
    LitFrameSelection,
 )
 from cal_tools.ana_tools import get_range
 from cal_tools.calcat_interface import (
    AGIPD_CalibrationData,
    CalCatError,
 )
 from cal_tools.enums import AgipdGainMode, BadPixels
 from cal_tools.plotting import agipd_single_module_geometry
 from cal_tools.step_timing import StepTimer
 from cal_tools.tools import (
    calcat_creation_time,
    latex_warning,
    map_modules_from_folder,
    module_index_to_qm,
    write_constants_fragment,
 )
 ```

 %% Cell type:code id: tags:

 ``` python
 in_folder = Path(in_folder)
 out_folder = Path(out_folder)
 run_folder = in_folder / f'r{run:04d}'

 step_timer = StepTimer()
 ```

 %% 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 hierarchy and dependability for correction booleans are defined
 corr_bools = {}

 cs_corr = False
 pc_corr = False
 if rel_gain_mode.lower() == "off":
    # TODO: Remove this part after replacing rel_gain with rel_gain_mode
    if rel_gain:
        pc_corr = True

 elif rel_gain_mode.lower() == "cs":
    cs_corr = True

 elif rel_gain_mode.lower() == "pc":
    pc_corr = True

 else:
    raise ValueError(
        "Selected `rel_gain_mode` is unexpected. "
        "Please select between CS or PC.")

 # 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["cs_corr"] = cs_corr
    corr_bools["pc_corr"] = pc_corr
    corr_bools["adjust_mg_baseline"] = adjust_mg_baseline
    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
    corr_bools["round_photons"] = round_photons
    corr_bools["count_lit_pixels"] = count_lit_pixels

 # Many corrections don't apply to fixed gain mode; will explicitly disable later if detected
 disable_for_fixed_gain = [
    "adjust_mg_baseline",
    "blc_set_min",
    "force_hg_if_below",
    "force_mg_if_below",
    "low_medium_gap",
    "melt_snow",
    "pc_corr",
    "cs_corr",
 ]
 ```

 %% Cell type:code id: tags:

 ``` python
 if sequences == [-1]:
    sequences = None

 dc = RunDirectory(run_folder)

 ctrl_src = ctrl_source_template.format(karabo_id_control)
 instrument_src = instrument_source_template.format(karabo_id, receiver_template)
 index_src = index_source_template.format(karabo_id, receiver_template)
 ```

 %% Cell type:code id: tags:

 ``` python
 # Create output folder
 out_folder.mkdir(parents=True, exist_ok=True)

 # Evaluate detector instance for mapping
 instrument = karabo_id.split("_")[0]
 if "AGIPD1M" in karabo_id:
    nmods = 16
 elif "AGIPD500K" in karabo_id:
    nmods = 8
 else:
    nmods = 1

 # Evaluate requested modules
 if karabo_da[0] == '-1':
    if modules[0] == -1:
        modules = list(range(nmods))
    mod_indices = modules if nmods > 1 else [0]
    karabo_da = ["AGIPD{:02d}".format(i) for i in modules]
 else:  # TODO: fix this with the new CALCAT metadata for module indices.
    modules = [int(x[-2:]) for x in karabo_da]
    mod_indices = modules if nmods > 1 else [0]

 print("Process modules:", ', '.join(module_index_to_qm(x) for x in mod_indices))
 print(f"Detector in use is {karabo_id}")
 print(f"Instrument {instrument}")
 ```

 %% Cell type:code id: tags:

 ``` python
 train_available = False

 for m in modules:
    try:
        # Attempt to select the module. If no trains are available, ValueError might be raised
        if len(dc[instrument_src.format(m), 'image.data'].drop_empty_trains().train_ids) > 0:
            train_available = True
            break  # Found a module with available trains.
    except ValueError as e:
        warning(f"Missing module {m} from data: {e}")

 if not train_available:
    # Execute this block if no modules with trains were found.
    latex_warning("No trains available to correct for selected modules.")
    sys.exit(0)
 ```

 %% Cell type:code id: tags:

 ``` python
 if use_ppu_device and use_ppu_device in dc.control_sources:
    # Obtain trains to process if using a pulse picker device and it's present.

    seq_start = dc[use_ppu_device, 'trainTrigger.sequenceStart.value'].ndarray()

    # The trains picked are the unique values of trainTrigger.sequenceStart
    # minus the first (previous trigger before this run).
    start_train_ids = np.unique(seq_start)[1:] + ppu_train_offset

    train_ids = []
    for train_id in start_train_ids:
        n_trains = dc[
            use_ppu_device, 'trainTrigger.numberOfTrains'
        ].select_trains(by_id[[train_id]]).ndarray()[0]
        train_ids.extend(list(range(train_id, train_id + n_trains)))

    if train_ids:
        print(f'PPU device {use_ppu_device} triggered for {len(train_ids)} train(s)')
    elif require_ppu_trigger:
        raise RuntimeError(f'PPU device {use_ppu_device} not triggered but required, aborting!')
    else:
        print(f'PPU device {use_ppu_device} not triggered, processing all valid trains')
        train_ids = None

 elif use_ppu_device:
    # PPU configured but not present.

    if require_ppu_trigger:
        raise RuntimeError(f'PPU device {use_ppu_device} required but not found, aborting!')
    else:
        print(f'PPU device {use_ppu_device} configured but not found, processing all valid trains')
        train_ids = None

 elif train_ids != [-1]:
    # Specific trains passed by parameter, convert to ndarray.
    train_ids = np.array(train_ids)

    print(f'Processing up to {len(train_ids)} manually selected train(s)')

 else:
    # No PPU configured.
    print(f'Processing all valid trains')
    train_ids = None
 ```

 %% Cell type:code id: tags:

 ``` python
 # set everything up filewise
 mapped_files, _, total_sequences, _, _ =  map_modules_from_folder(
    str(in_folder), run, path_template, karabo_da, sequences
 )
 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
 first_mod_channel = sorted(modules)[0]
 instrument_src_first_mod = [
    s for s in list(dc.all_sources) if f"{first_mod_channel}CH" in s][0]

 agipd_cond = AgipdCtrl(
    run_dc=dc,
    image_src=instrument_src_first_mod,
    ctrl_src=ctrl_src,
    raise_error=False,  # to be able to process very old data without gain_setting value
 )
 ```

 %% Cell type:code id: tags:

 ``` python
 # Run's creation time:
 creation_time = calcat_creation_time(in_folder, run, creation_time)
 offset = parser.parse(creation_date_offset)
 delta = timedelta(hours=offset.hour, minutes=offset.minute, seconds=offset.second)
 creation_time += delta
 print(f"Creation time: {creation_time}")

 if acq_rate == -1.:
    acq_rate = agipd_cond.get_acq_rate()
 if mem_cells == -1:
    mem_cells = agipd_cond.get_num_cells()
 # TODO: look for alternative for passing creation_time
 if gain_setting == -1:
    gain_setting = agipd_cond.get_gain_setting(creation_time)
 if bias_voltage == -1:
    for m in modules:
        bias_voltage = agipd_cond.get_bias_voltage(karabo_id_control, module=m)
        # Accept non-zero value for a bias voltage from any module.
        if bias_voltage != 0.:
            break
 if integration_time == -1:
    integration_time = agipd_cond.get_integration_time()
 if gain_mode == -1:
    gain_mode = agipd_cond.get_gain_mode()
 else:
    gain_mode = AgipdGainMode(gain_mode)
 ```

 %% Cell type:code id: tags:

 ``` python
 mem_cells_db = mem_cells if mem_cells_db == -1 else mem_cells_db

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

 %% Cell type:code id: tags:

 ``` python
 print(f"Using {creation_time} as creation time")
 print("Operating conditions are:")
 print(f"• Bias voltage: {bias_voltage}")
 print(f"• Memory cells: {mem_cells_db}")
 print(f"• Acquisition rate: {acq_rate}")
 print(f"• Gain setting: {gain_setting}")
 print(f"• Gain mode: {gain_mode.name}")
 print(f"• Integration time: {integration_time}")
 print(f"• Photon Energy: 9.2")
 ```

 %% Cell type:code id: tags:

 ``` python
 if gain_mode:
    for to_disable in disable_for_fixed_gain:
        if corr_bools.get(to_disable, False):
            warning(f"{to_disable} correction was requested, but does not apply to fixed gain mode")
            corr_bools[to_disable] = False
 ```

 %% Cell type:code id: tags:

 ``` python
 if use_litframe_finder != 'off':
    from extra_redu import make_litframe_finder, LitFrameFinderError

    if use_litframe_finder not in ['auto', 'offline', 'online']:
        raise ValueError("Unexpected value in 'use_litframe_finder'.")

    inst = karabo_id_control[:3]
    litfrm = make_litframe_finder(inst, dc, litframe_device_id)
    try:
        get_data = {'auto': litfrm.read_or_process, 'offline': litfrm.process, 'online': litfrm.read}
        r = get_data[use_litframe_finder]()
        cell_sel = LitFrameSelection(r, train_ids, max_pulses, energy_threshold, use_super_selection)
        print(f"{cell_sel.msg()}\n")
        cell_sel.print_report()
        if np.count_nonzero(r.nLitFrame.value) == 0:  # No lit frames.
            latex_warning(
                "No lit frames identified using AGIPD LitFrameFinder."
                " Offline correction will not be performed.")
            sys.exit(0)
    except LitFrameFinderError as err:
        print(cell_sel.msg())
        warning(f"Cannot use AgipdLitFrameFinder due to:\n{err}")
        cell_sel = CellRange(max_pulses, max_cells=mem_cells)
 else:
    # Use range selection
    cell_sel = CellRange(max_pulses, max_cells=mem_cells)
    print(cell_sel.msg())
 ```

 %% Cell type:code id: tags:

 ``` python
 photon_energy_warn = None
 if photon_energy <= 0.0:
    if use_xgm_device:
        # Try to obtain photon energy from XGM device.
        wavelength_data = dc[use_xgm_device, 'pulseEnergy.wavelengthUsed']

        try:
            from scipy.constants import h, c, e

            # Read wavelength as a single value and convert to hv.
            photon_energy = (h * c / e) / (wavelength_data.as_single_value(rtol=1e-2) * 1e-6)
            print(f'Obtained photon energy {photon_energy:.3f} keV from {use_xgm_device}')
        except ValueError:
            photon_energy_warn = 'XGM source available but photon energy varies greater than 1%'
    else:
        photon_energy_warn = 'Neither explicit photon energy nor XGM device configured'

 rounding_threshold_warn = None
 if rounding_threshold <= .0 or 1. <= rounding_threshold:
    rounding_threshold_warn = 'Round threshold is out of (0, 1) range. Use standard 0.5 value.'
    rounding_threshold = 0.5

 if round_photons:
    if photon_energy_warn:
        warning(photon_energy_warn + ', photon rounding disabled!')
        round_photons = False
    else:
        print(f'Photon energy for rounding: {photon_energy:.3f} keV')
        if rounding_threshold_warn:
            warning(rounding_threshold_warn)
 ```

 %% Cell type:code id: tags:

 ``` python
 if count_lit_pixels:
    if round_photons:
        data_units = 'photon'
        litpx_threshold = 1.
    else:
        data_units = 'keV'
        if photon_energy_warn:
            warning(photon_energy_warn + '. Use 12 keV for lit-pixel counter threshold')
            litpx_threshold = 12.
        else:
            litpx_threshold = photon_energy

        if rounding_threshold_warn:
            warning(rounding_threshold_warn)

    if not xray_gain:
        # convert photon energy to ADU (for AGIPD approx. 1 keV = 7 ADU)
        # it looks that rounding to photons can be applied to data in ADU as well
        litpx_threshold *= 7.
        data_units = 'ADU'

    litpx_threshold *= rounding_threshold
    print(f"Count lit-pixels with signal above {litpx_threshold:.3g} {data_units}")
 else:
    # dummy value, that is not expected to be used
    litpx_threshold = 42
 ```

 %% Cell type:code id: tags:

 ``` python
 agipd_corr = AgipdCorrections(
    mem_cells,
    cell_sel,
    h5_data_path=instrument_src,
    h5_index_path=index_src,
    corr_bools=corr_bools,
    gain_mode=gain_mode,
    comp_threads=os.cpu_count() // n_cores_files,
    train_ids=train_ids
 )

 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
 agipd_corr.photon_energy = photon_energy
 agipd_corr.rounding_threshold = rounding_threshold
 agipd_corr.cs_mg_adjust = cs_mg_adjust
 agipd_corr.litpx_threshold = litpx_threshold

 agipd_corr.compress_fields = compress_fields
 if recast_image_data:
    agipd_corr.recast_image_fields['data'] = np.dtype(recast_image_data)
 ```

 %% Cell type:markdown id: tags:

 ## Retrieving constants

 %% Cell type:code id: tags:

 ``` python
 def get_constants_and_update_metadata(cal_data, main_metadata, constants):
    try:
        metadata = cal_data.metadata(constants)
        for key, value in metadata.items():
            main_metadata.setdefault(key, {}).update(value)
    except CalCatError as e:  # TODO: replace when API errors are improved.
        warning(f"CalCatError: {e}")
 ```

 %% Cell type:code id: tags:

 ``` python
 step_timer.start()
 # Instantiate agipd_cal with the read operating conditions.
 agipd_cal = AGIPD_CalibrationData(
    detector_name=karabo_id,
    modules=karabo_da,
    sensor_bias_voltage=bias_voltage,
    memory_cells=mem_cells,
    acquisition_rate=acq_rate,
    integration_time=integration_time,
    source_energy=9.2,
    gain_mode=gain_mode,
    gain_setting=gain_setting,
    event_at=creation_time,
    client=rest_cfg.calibration_client(),
    caldb_root=Path(cal_db_root) if cal_db_root else None,
 )

 # Prepare lists of expected calibrations
 dark_constants = ["Offset", "Noise", "BadPixelsDark"]
 if not gain_mode:  # Adaptive gain
    dark_constants.append("ThresholdsDark")

 agipd_metadata = agipd_cal.metadata(dark_constants)

 agipd_cal.gain_mode = None  # gain_mode is not used for gain constants
 pc_constants, ff_constants, cs_constants = [], [], []

 if agipd_corr.corr_bools.get('xray_corr'):
    ff_constants = list(agipd_cal.illuminated_calibrations)
    get_constants_and_update_metadata(
        agipd_cal, agipd_metadata, ff_constants)

 if any(agipd_corr.relgain_bools):

    if cs_corr:
        # Integration time is not used with CS
        agipd_cal.integration_time = None
        cs_constants = ["SlopesCS", "BadPixelsCS"]
        get_constants_and_update_metadata(
            agipd_cal, agipd_metadata, cs_constants)


    else:  # rel_gain_mode == "pc" or "off"
        pc_constants = ["SlopesPC", "BadPixelsPC"]
        get_constants_and_update_metadata(
            agipd_cal, agipd_metadata, pc_constants)

 step_timer.done_step("Constants were retrieved in")

 relgain_alias = "CS" if cs_corr else "PC"
 print("Preparing constants ("
      f"FF: {agipd_corr.corr_bools.get('xray_corr', False)}, "
      f"{relgain_alias}: {any(agipd_corr.relgain_bools)}, "
      f"BLC: {any(agipd_corr.blc_bools)})")
 # Display retrieved calibration constants timestamps
 agipd_cal.display_markdown_retrieved_constants(metadata=agipd_metadata)
 ```

 %% Cell type:code id: tags:

 ``` python
 # Validate constants availability and exclude modules with no offsets.
 for da, calibrations in agipd_metadata.items():
    mod = modules[karabo_da.index(da)]
    # Constants to error out for when missing.
    error_missing_constants = {"Offset"}
    if not gain_mode:
        error_missing_constants |= {"ThresholdsDark"}

    error_missing_constants -= set(calibrations)
    if error_missing_constants:
        warning(f"Offset constant is not available to correct {da}.")
        # Remove module from files to process.
        del mapped_files[module_index_to_qm(mod)]
        karabo_da.remove(da)
        modules.remove(mod)

    warn_missing_constants = set(dark_constants + pc_constants + ff_constants + cs_constants)
    warn_missing_constants -= error_missing_constants
    warn_missing_constants -= set(calibrations)
    if warn_missing_constants:
        warning(f"Constants {warn_missing_constants} were not retrieved for {da}.")

 if not mapped_files:  # Offsets are missing for all modules.
    raise Exception("Could not find offset constants for any modules, will not correct data.")
 ```

 %% Cell type:code id: tags:

 ``` python
 # Record constant details in YAML metadata
 write_constants_fragment(
    out_folder=(metadata_folder or out_folder),
    det_metadata=agipd_metadata,
    caldb_root=agipd_cal.caldb_root)
 ```

 %% Cell type:code id: tags:

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

 def load_constants(da, module):
    """
    Initialize constants data from previously retrieved metadata.

    Args:
        da (str): Data Aggregator (Karabo DA)
        module (int): Module index

    Returns:
        (int, dict, str): Module index, {constant name: creation time}, Karabo DA
    """
    const_data = dict()
    variant = dict()
    for cname, mdata in agipd_metadata[da].items():
        dataset = mdata["dataset"]
        with h5py.File(agipd_cal.caldb_root / mdata["path"], "r") as cf:  # noqa
            const_data[cname] = np.copy(cf[f"{dataset}/data"])
            variant[cname] = cf[dataset].attrs["variant"] if cf[dataset].attrs.keys() else 0  # noqa
    agipd_corr.init_constants(const_data, module, variant)


 step_timer.start()
 with multiprocessing.Pool(processes=len(modules)) as pool:
    pool.starmap(load_constants, zip(karabo_da, modules))
 step_timer.done_step(f'Constants were loaded in ')
 ```

 %% Cell type:code id: tags:

 ``` python
 # Store timestamps for Offset, SlopesPC/SlopesCS, and SlopesFF
 # in YAML file for time-summary table.
 timestamps = {}

 for mod, mod_mdata in agipd_metadata.items():
    modno = int(mod[-2:])

    module_timestamps = {}

    # Store few time stamps if exists
    # Add NA to keep array structure
    for key in ['Offset', f'Slopes{relgain_alias}', 'SlopesFF']:
        if key in mod_mdata:
            module_timestamps[key] = mod_mdata[key]["begin_validity_at"]
        else:
            module_timestamps[key] = "NA"

    timestamps[module_index_to_qm(modno)] = module_timestamps

 seq = sequences[0] if sequences else 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:markdown id: tags:

 ## Data processing ##

 %% Cell type:code id: tags:

 ``` python
 # allocate memory for images and hists
 n_images_max = mem_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.start()
 all_imgs_counts = []
 if max_tasks_per_worker == -1:
    max_tasks_per_worker = None
 with multiprocessing.Pool(maxtasksperchild=max_tasks_per_worker) as pool:
    step_timer.done_step('Started 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")
        step_timer.start()
        img_counts = pool.starmap(
            agipd_corr.read_file,
            zip(range(len(file_batch)), file_batch, [not common_mode]*len(file_batch))
        )
        step_timer.done_step(f'Loading data from files')

        all_imgs_counts += img_counts
        if not np.any(img_counts):
            # Skip any further processing and output if there are no images to
            # correct in this file.
            continue

        if mask_zero_std:
            # 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:
            # In common mode corrected is enabled.
            # Cell selection is only activated after common mode correction.
            # Perform cross-file correction parallel over asics
            image_files_idx = [i_proc for i_proc, n_img in enumerate(img_counts) if n_img > 0]
            pool.starmap(agipd_corr.cm_correction, itertools.product(
                image_files_idx, range(16)  # 16 ASICs per module
            ))
            step_timer.done_step("Common-mode correction")

            img_counts = pool.map(agipd_corr.apply_selected_pulses, image_files_idx)
            step_timer.done_step("Applying selected cells after common mode correction")

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

        # Peform additional processing
        if count_lit_pixels:
            pool.starmap(agipd_corr.litpixel_counter, imagewise_chunks(img_counts))
            step_timer.done_step("Lit-pixel counting")

        # Save corrected data
        pool.starmap(agipd_corr.write_file, [
            (i_proc, file_name, str(out_folder / Path(file_name).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 skip_plots:
    print("Skipping plots as configured.")
    sys.exit(0)
 elif not np.any(all_imgs_counts):
    latex_warning(f"All sequence files contain no data for correction.")
    sys.exit(0)
 ```

 %% Cell type:code id: tags:

 ``` python
 def do_2d_plot(data, edges, y_axis, x_axis, title=""):
    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(111)
    extent = np.array(
        [np.nanmin(edges[1]), np.nanmax(edges[1]),
         np.nanmin(edges[0]), np.nanmax(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)
    ax.set_title(title)
    cb = fig.colorbar(im)
    cb.set_label("Counts")
 ```

 %% Cell type:code id: tags:

 ``` python
 def get_trains_data(data_folder, source, include, detector_id, tid=None, modules=16, fillvalue=None, mod_starts_at=0):
    """Load single train for all module

    :param data_folder: Path to folder with data
    :param source: Data source to be loaded
    :param include: Inset of file name to be considered
    :param detector_id: The karabo id of the detector to get data for
    :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
    """
    try:
        run_data = RunDirectory(data_folder, include)
    except FileNotFoundError:
        warning(f'No corrected files for {include}. Skipping plots.')
        sys.exit(0)
    if tid is not None:
        tid, data = run_data.select(
            f'{detector_id}/DET/*', source).train_from_id(tid, keep_dims=True)
    else:
        # A first full trainId for all available modules is of interest.
        tid, data = next(run_data.select(
            f'{detector_id}/DET/*', source).trains(require_all=True, keep_dims=True))

    stacked_data = stack_detector_data(
        train=data, data=source, fillvalue=fillvalue, modules=modules,
        starts_at=mod_starts_at,
    )

    return tid, stacked_data
 ```

 %% Cell type:code id: tags:

 ``` python
 if "AGIPD500K" in karabo_id:
    geom = AGIPD_500K2GGeometry.from_origin()
 elif "AGIPD1M" in karabo_id:
    geom = AGIPD_1MGeometry.from_quad_positions(quad_pos=[
        (-525, 625),
        (-550, -10),
        (520, -160),
        (542.5, 475),
    ])
 else:  # single module AGIPD detector
    geom = agipd_single_module_geometry()
 ```

 %% Cell type:code id: tags:

 ``` python
 include = '*S00000*' if sequences is None else f'*S{sequences[0]:05d}*'
 mod_starts_at = 0 if nmods > 1 else modules[0]  # TODO: use CALCAT metadata for the detector.
 tid, corrected = get_trains_data(out_folder, 'image.data', include, karabo_id, modules=nmods, mod_starts_at=mod_starts_at)

 _, gains = get_trains_data(out_folder, 'image.gain', include, karabo_id, tid, modules=nmods, mod_starts_at=mod_starts_at)
 _, mask = get_trains_data(out_folder, 'image.mask', include, karabo_id, tid, modules=nmods, mod_starts_at=mod_starts_at)
 _, blshift = get_trains_data(out_folder, 'image.blShift', include, karabo_id, tid, modules=nmods, mod_starts_at=mod_starts_at)
 _, cellId = get_trains_data(out_folder, 'image.cellId', include, karabo_id, tid, modules=nmods, mod_starts_at=mod_starts_at)
 _, pulseId = get_trains_data(out_folder, 'image.pulseId', include, karabo_id, tid, modules=nmods, fillvalue=0, mod_starts_at=mod_starts_at)
 _, raw = get_trains_data(run_folder, 'image.data', include, karabo_id, tid, modules=nmods, mod_starts_at=mod_starts_at)
 ```

 %% Cell type:code id: tags:

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

 %% Cell type:code id: tags:

 ``` python
 # As part of data reduction efforts, the DAQ now has an option to discard AGIPD gain data
 # when it is known that all data is in the same gain stage. In such cases, the gain data
 # will be set to zeros. Consequently, the signal vs. analog gain 2D histogram can be skipped.
 gain = raw[:, 1, ...]
 if gain.max() > 0:
    signal = raw[:, 0, ...]
    display(Markdown("### Signal vs. Analogue Gain"))
    hist, bins_x, bins_y = calgs.histogram2d(
        signal.flatten().astype(np.float32),
        gain.flatten().astype(np.float32),
        bins=(100, 100),
        range=[
            np.percentile(signal, [0.02, 99.8]),
            np.percentile(gain, [0.02, 99.8]),
            ],
    )
    do_2d_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
 vmin, vmax = np.nanmin(corrected), np.nanmax(corrected)
 hist, bins_x, bins_y = calgs.histogram2d(
    corrected.flatten().astype(np.float32),
    gains.flatten().astype(np.float32), bins=(100, 3),
    range=[
        # The range boundaries and decided by DET expert.
        [max(vmin, -50), min(vmax, 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.nanmin(pulseId[pulseId>=0]), np.nanmax(pulseId[pulseId>=0])]


 def clamp(value, min_value, max_value):
    return max(min_value, min(value, max_value))


 # Modify pulse_range, if only one pulse is selected.
 if pulse_range[0] == pulse_range[1]:
    pulse_range = [0, pulse_range[1]+int(acq_rate)]

 mean_data = np.nanmean(corrected, axis=(2, 3))
 vmin, vmax = np.nanmin(mean_data), np.nanmax(mean_data)
 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=[[clamp(vmin, -50, -0.2), min(vmax, 1000)], pulse_range],
 )
 do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id", title="Signal-Pulse ID")

 if vmax > 1000:  # a zoom out plot.
    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=[[clamp(vmin, -50, -0.2), min(vmax, 20000)], pulse_range]
    )
    do_2d_plot(hist, (bins_x, bins_y), "Signal (ADU)", "Pulse id", title="Signal-Pulse ID (Extended View)")
 ```

 %% 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(np.nanpercentile(corrected_ave/10**6, [2, 98]))
 plt.grid()
 plt.xlabel('Illuminated corrected [MADU] ')
 _ = plt.ylabel('Estimated baseline shift [ADU]')
 ```

 %% Cell type:code id: tags:

 ``` python
 if cell_id_preview not in cellId[:, 0]:
    print(f"WARNING: The selected cell_id_preview value {cell_id_preview} is not available in the corrected data.")
    cell_id_preview = cellId[:, 0][0]
    cell_idx_preview = 0
    print(f"Previewing the first available cellId: {cell_id_preview}.")
 else:
    cell_idx_preview = np.where(cellId[:, 0] == cell_id_preview)[0][0]
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Raw preview ###\n'))
 if cellId.shape[0] != 1:
    display(Markdown(f'Mean over images of the RAW data\n'))
    fig = plt.figure(figsize=(20, 10))
    ax = fig.add_subplot(111)
    data = np.mean(raw[slice(*cell_sel.crange), 0, ...], axis=0)
    vmin, vmax = np.percentile(data, [5, 95])
    ax = geom.plot_data_fast(data, ax=ax, vmin=vmin, vmax=vmax, cmap=cmap)
    pass
 else:
    print("Skipping mean RAW preview for single memory cell, "
          f"see single shot image for selected cell ID {cell_id_preview}.")
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown(f'Single shot of the RAW data from cell {cell_id_preview} \n'))
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = np.percentile(raw[cell_idx_preview, 0, ...], [5, 95])
 ax = geom.plot_data_fast(
    raw[cell_idx_preview, 0, ...], ax=ax, vmin=vmin, vmax=vmax, cmap=cmap)
 pass
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown('### Corrected preview ###\n'))
 if cellId.shape[0] != 1:
    display(Markdown('### Mean CORRECTED Preview ###\n'))
    display(Markdown(f'A mean across train: {tid}\n'))
    fig = plt.figure(figsize=(20, 10))
    ax = fig.add_subplot(111)
    data = np.mean(corrected, axis=0)
    vmax = np.nanpercentile(data, 99.8)
    ax = geom.plot_data_fast(data, ax=ax, vmin=max(-50, np.nanmin(data)), vmax=vmax, cmap=cmap)
    pass
 else:
    print("Skipping mean CORRECTED preview for single memory cell, "
          f"see single shot image for selected cell ID {cell_id_preview}.")
 ```

 %% Cell type:code id: tags:

 ``` python
 display(Markdown(f'A single shot of the CORRECTED image from cell {cell_id_preview} \n'))
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmax = np.nanpercentile(corrected[cell_idx_preview], 99.8)
 ax = geom.plot_data_fast(
    corrected[cell_idx_preview],
    ax=ax,
    vmin=max(-50, np.nanmin(corrected[cell_idx_preview])),
    vmax=vmax,
    cmap=cmap,
 )
 pass
 ```

 %% Cell type:code id: tags:

 ``` python
 fig = plt.figure(figsize=(20, 10))
 ax = fig.add_subplot(111)
 vmin, vmax = get_range(corrected[cell_idx_preview], 5, -50)
 nbins = int((vmax + 50) / 2)
 h = ax.hist(corrected[cell_idx_preview].flatten(),
            bins=nbins, range=(-50, vmax),
            histtype='stepfilled', log=True)
 plt.xlabel('[ADU]')
 plt.ylabel('Counts')
 ax.grid()
 plt.title(f'Log-scaled histogram for corrected data for cell {cell_idx_preview}')
 pass
 ```

 %% 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 = 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')
 plt.title(f'Overlaid Histograms for corrected data for multiple gains')
 pass
 ```

 %% 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=cmap, vmin=-0.3, vmax=2.3)  # Extend cmap for wrong gain values.
 pass
 ```

 %% 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 {cell_id_preview} \n'))
 ```

 %% Cell type:code id: tags:

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

 %% Cell type:code id: tags:

 ``` python
 if round_photons:
    display(Markdown('### Photonization histograms ###'))

    x_preround = (agipd_corr.hist_bins_preround[1:] + agipd_corr.hist_bins_preround[:-1]) / 2
    x_postround = (agipd_corr.hist_bins_postround[1:] + agipd_corr.hist_bins_postround[:-1]) / 2
    x_photons = np.arange(0, (x_postround[-1] + 1) / photon_energy)

    fig, ax = plt.subplots(ncols=1, nrows=1, clear=True)
    ax.plot(x_preround, agipd_corr.shared_hist_preround, '.-', color='C0')
    ax.bar(x_postround, agipd_corr.shared_hist_postround, photon_energy, color='C1', alpha=0.5)
    ax.set_yscale('log')
    ax.set_ylim(0, max(agipd_corr.shared_hist_preround.max(), agipd_corr.shared_hist_postround.max())*3)
    ax.set_xlim(x_postround[0], x_postround[-1]+1)
    ax.set_xlabel('Photon energy / keV')
    ax.set_ylabel('Intensity')
    ax.vlines(x_photons * photon_energy, *ax.get_ylim(), color='k', linestyle='dashed')

    phx = ax.twiny()
    phx.set_xlim(x_postround[0] / photon_energy, (x_postround[-1]+1)/photon_energy)
    phx.set_xticks(x_photons)
    phx.set_xlabel('# Photons')
    pass
 ```

 %% 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)
 geom.plot_data_fast(
    np.mean(mask>0, axis=0), vmin=0, ax=ax, vmax=1, cmap=cmap)
 pass
 ```

 %% 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=cmap)
 pass
 ```

--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify_Summary_NBC.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify_Summary_NBC.ipynb
@@ -19,7 +19,19 @@
    "karabo_id = \"SPB_DET_AGIPD1M-1\" # karabo karabo_id\n",
    "modules = [-1]\n",
    "karabo_da = ['-1']  # a list of data aggregators names, Default [-1] for selecting all data aggregators\n",
-    "rel_gain_mode = \"off\"  # Select relative gain correction. Choices [`PC`, `CS`, `off`]. (`PC`: Pulse Capacitor, `CS`: Current Source, `off`: Disable relative gain correction). Default: off."
+    "rel_gain_mode = \"off\"  # Select relative gain correction. Choices [`PC`, `CS`, `off`]. (`PC`: Pulse Capacitor, `CS`: Current Source, `off`: Disable relative gain correction). Default: off.\n",
+    "# Additional processing\n",
+    "spi_hitfinding = False  # Find hits using lit-pixel counter\n",
+    "\n",
+    "# SPI hit-finder parameters\n",
+    "spi_hf_modules = [3, 4, 8, 15]  # Use specified modules for SPI hitfinding\n",
+    "spi_hf_mode = \"adaptive\"  # The method to compute threshold for hitscores in SPI hitfinding: `fixed` or `adaptive`\n",
+    "spi_hf_snr = 4.0  # Siginal-to-noise ration for adaptive threshold in SPI hitfinding\n",
+    "spi_hf_min_scores = 100  # The minimal size of events to compute adaptive threshold in SPI hitfinding\n",
+    "spi_hf_fixed_threshold = 0  # The fixed threshold value\n",
+    "spi_hf_hitrate_window_size = 200  # The window size for runnig average of hitrate in trains\n",
+    "spi_hf_miss_fraction = 1  # The fraction of misses to select along with hits\n",
+    "spi_hf_miss_fraction_base = \"hit\"  # The base to compute the number of misses to select: the number of hits (`hit`) or misses (`miss`)"
   ]
  },
  {
@@ -29,12 +41,22 @@
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
+    "from logging import warning\n",
    "\n",
    "import yaml\n",
    "\n",
    "import tabulate\n",
    "from cal_tools.tools import CalibrationMetadata\n",
-    "from IPython.display import Latex, display"
+    "from IPython.display import Latex, display, Markdown\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "matplotlib.use(\"agg\")\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from extra_data import RunDirectory\n",
+    "from extra_redu.fileutils import StackedPulseSource, exdf_save\n",
+    "from extra_redu.spi import SpiHitfinder"
   ]
  },
  {
@@ -110,11 +132,60 @@
    "for const in ['Offset', f'Slopes{rel_gain_alias}', 'SlopesFF']:\n",
    "    print_const_table(const)"
   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if spi_hitfinding:\n",
+    "    display(Markdown(\"# SPI hit finding\"))\n",
+    "    try:\n",
+    "        dc = RunDirectory(out_folder)\n",
+    "        litpx_src = StackedPulseSource.from_datacollection(\n",
+    "            dc, f\"{karabo_id}/CORR/(?P<key>\\d+)CH0:output\", \"litpx\")\n",
+    "    except ValueError:\n",
+    "        litpx_src = None\n",
+    "        warning(\"The data sources of the number of lit-pixels are not found. \"\n",
+    "                \"Use `count_lit_pixels = True`\")\n",
+    "\n",
+    "if litpx_src is not None:    \n",
+    "    hitfinder = SpiHitfinder(\n",
+    "        modules=spi_hf_modules,\n",
+    "        mode=spi_hf_mode,\n",
+    "        snr=spi_hf_snr,\n",
+    "        min_scores=spi_hf_min_scores,\n",
+    "        fixed_threshold=spi_hf_fixed_threshold,\n",
+    "        hitrate_window_size=spi_hf_hitrate_window_size,\n",
+    "        miss_fraction=spi_hf_miss_fraction,\n",
+    "        miss_fraction_base=spi_hf_miss_fraction_base,\n",
+    "    )\n",
+    "    hitfinder.find_hits(litpx_src)\n",
+    "\n",
+    "    # write hit-finder data in file\n",
+    "    sources = {\n",
+    "        f\"{karabo_id}/REDU/SPI_HITFINDER\": hitfinder,\n",
+    "    }\n",
+    "    exdf_save(out_folder, \"REDU00\", run, sources, sequence_size=3500)\n",
+    "\n",
+    "    # draw plots\n",
+    "    display(Markdown(\"## Hit-rate plot\"))\n",
+    "    hitfinder.plot_hitrate()\n",
+    "    plt.show()\n",
+    "\n",
+    "    display(Markdown(\"## Hitscore histogram\"))\n",
+    "    hitfinder.plot_hitscore_hist()\n",
+    "    plt.show()\n",
+    "\n",
+    "    display(Markdown(\"## Hitscore plots\"))\n",
+    "    hitfinder.plot_hitscore()"
+   ]
  }
 ],
 "metadata": {
  "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python3",
   "language": "python",
   "name": "python3"
  },
@@ -128,7 +199,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.11"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id: tags:

 # Summary of the AGIPD offline correction #

 %% Cell type:code id: tags:

 ``` python
 run = 11 # runs to process, required
 out_folder = "/gpfs/exfel/data/scratch/ahmedk/test/AGIPD_Corr" # path to output to, required
 metadata_folder = ""  # Directory containing calibration_metadata.yml when run by xfel-calibrate
 karabo_id = "SPB_DET_AGIPD1M-1" # karabo karabo_id
 modules = [-1]
 karabo_da = ['-1']  # a list of data aggregators names, Default [-1] for selecting all data aggregators
 rel_gain_mode = "off"  # Select relative gain correction. Choices [`PC`, `CS`, `off`]. (`PC`: Pulse Capacitor, `CS`: Current Source, `off`: Disable relative gain correction). Default: off.
+# Additional processing
+spi_hitfinding = False  # Find hits using lit-pixel counter
+
+# SPI hit-finder parameters
+spi_hf_modules = [3, 4, 8, 15]  # Use specified modules for SPI hitfinding
+spi_hf_mode = "adaptive"  # The method to compute threshold for hitscores in SPI hitfinding: `fixed` or `adaptive`
+spi_hf_snr = 4.0  # Siginal-to-noise ration for adaptive threshold in SPI hitfinding
+spi_hf_min_scores = 100  # The minimal size of events to compute adaptive threshold in SPI hitfinding
+spi_hf_fixed_threshold = 0  # The fixed threshold value
+spi_hf_hitrate_window_size = 200  # The window size for runnig average of hitrate in trains
+spi_hf_miss_fraction = 1  # The fraction of misses to select along with hits
+spi_hf_miss_fraction_base = "hit"  # The base to compute the number of misses to select: the number of hits (`hit`) or misses (`miss`)
 ```

 %% Cell type:code id: tags:

 ``` python
 from pathlib import Path
+from logging import warning

 import yaml

 import tabulate
 from cal_tools.tools import CalibrationMetadata
-from IPython.display import Latex, display
+from IPython.display import Latex, display, Markdown
+
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.use("agg")
+%matplotlib inline
+
+from extra_data import RunDirectory
+from extra_redu.fileutils import StackedPulseSource, exdf_save
+from extra_redu.spi import SpiHitfinder
 ```

 %% Cell type:code id: tags:

 ``` python
 out_folder = Path(out_folder)
 metadata = CalibrationMetadata(metadata_folder or out_folder)
 const_dict = metadata.setdefault("retrieved-constants", {})
 time_dict = const_dict.setdefault("time-summary", {})

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

 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]

 # This is needed only if AGIPD Correction notebook had no precorrection notebooks for retrieving constants
 # gather all generated sequence yml files for time summary of retrieved constant under retrieved-constants in metadata.yml
 for fn in sorted(out_folder.glob("retrieved_constants_*.yml")):
    with fn.open("r") as fd:
        fdict = yaml.safe_load(fd)
    # append different sequences' time summary to the main yaml
    time_dict.update(fdict["time-summary"])
    fn.unlink()

 metadata.save()
 ```

 %% Cell type:code id: tags:

 ``` python
 def print_const_table(const):
    print(f"{const} constants were injected on:")
    table_data = {}
    for seq, mod_data in time_dict.items():
        for mod, const_data in mod_data.items():
            timestamp = const_data[const]
            table_data.setdefault(timestamp, []).append(f"{seq}:{mod}")
    table = []
    if not len(table_data):
        table.append(["No constants retrieved"])
    elif len(table_data) == 1:
        table.append([[*table_data][0], "All modules"])
    else:
        for timestamp, seqmod in table_data.items():
            table.append([timestamp, seqmod[0]])
            for further_module in seqmod[1:]:
                table.append(["", further_module])
    display(Latex(tabulate.tabulate(table,
                 tablefmt="latex",
                 headers=["Timestamps", "Modules and sequences"])))
 rel_gain_alias = "CS" if rel_gain_mode.lower() == "cs" else "PC"  # 'off' or 'pc'
 for const in ['Offset', f'Slopes{rel_gain_alias}', 'SlopesFF']:
    print_const_table(const)
 ```
+
+%% Cell type:code id: tags:
+
+``` python
+if spi_hitfinding:
+    display(Markdown("# SPI hit finding"))
+    try:
+        dc = RunDirectory(out_folder)
+        litpx_src = StackedPulseSource.from_datacollection(
+            dc, f"{karabo_id}/CORR/(?P<key>\d+)CH0:output", "litpx")
+    except ValueError:
+        litpx_src = None
+        warning("The data sources of the number of lit-pixels are not found. "
+                "Use `count_lit_pixels = True`")
+
+if litpx_src is not None:
+    hitfinder = SpiHitfinder(
+        modules=spi_hf_modules,
+        mode=spi_hf_mode,
+        snr=spi_hf_snr,
+        min_scores=spi_hf_min_scores,
+        fixed_threshold=spi_hf_fixed_threshold,
+        hitrate_window_size=spi_hf_hitrate_window_size,
+        miss_fraction=spi_hf_miss_fraction,
+        miss_fraction_base=spi_hf_miss_fraction_base,
+    )
+    hitfinder.find_hits(litpx_src)
+
+    # write hit-finder data in file
+    sources = {
+        f"{karabo_id}/REDU/SPI_HITFINDER": hitfinder,
+    }
+    exdf_save(out_folder, "REDU00", run, sources, sequence_size=3500)
+
+    # draw plots
+    display(Markdown("## Hit-rate plot"))
+    hitfinder.plot_hitrate()
+    plt.show()
+
+    display(Markdown("## Hitscore histogram"))
+    hitfinder.plot_hitscore_hist()
+    plt.show()
+
+    display(Markdown("## Hitscore plots"))
+    hitfinder.plot_hitscore()
+```