[Jungfrau][Dark] Feat: Skip first ntrains

Description

This MR is addressing the request made by Marcin, https://git.xfel.eu/calibration/pycalibration/-/issues/122, to have a parameter in Jungfrau Darks to exclude the first number of trains from dark constants' processing. His analysis lead that the first trains of Jungfrau are a transient period that when we exclude we produce better dark constants

How Has This Been Tested?

Relevant Documents (optional)

Types of changes

• New feature (non-breaking change which adds functionality)

Checklist:

Reviewers

@mramilli @schmidtp @kluyvert

notebooks/Jungfrau/Jungfrau_dark_analysis_all_gains_burst_mode_NBC.ipynb

 offset_abs_threshold_high = [8000, 15000, 15000]  # absolute bad pixel threshold in terms of offset, upper values
 max_trains = 1000  # Maximum trains to process darks. Set to 0 to process all available train images. 1000 trains is enough resolution to create the dark constants
 min_trains = 100  # Minimum number of trains to process dark constants. Raise a warning if the run has fewer trains.
-skip_ntrains = 0  # Skip first number of trains and don't include them in dark processing.
+skip_first_ntrains = 0  # Skip first number of trains and don't include them in dark processing.
 manual_slow_data = False  # if true, use manually entered bias_voltage and integration_time values
 time_limits = 0.025  # to find calibration constants later on, the integration time is allowed to vary by 0.5 us
 creation_time = ""  # To overwrite the measured creation_time. Required Format: YYYY-MM-DD HR:MN:SC e.g. "2022-06-28 13:00:00"
         n_trains = run_dc[instrument_src, "data.adc"].shape[0]
         # load number of data available, including trains with empty data.
         all_trains = len(run_dc.train_ids)
-        instr_dc = run_dc.select(instrument_src, require_all=True)
+
         empty_trains = all_trains - n_trains
         if empty_trains != 0:
             print(f"{mod} has {empty_trains} empty trains out of {all_trains} trains")
+
+        if skip_first_ntrains:
+            print(f"Skip first {skip_first_ntrains} trains from processing as configured.")
+
+        instr_dc = run_dc.select(
+            instrument_src, require_all=True).select_trains(np.s_[skip_first_ntrains:])
+
         if max_trains > 0:
             n_trains = min(n_trains, max_trains)
         print(f"Processing {n_trains} images.")
         if n_trains < min_trains:
             warning(f"Less than {min_trains} trains are available in RAW data.")
 
-        if skip_ntrains:
-            print(f"Skip first {skip_ntrains} trains from processing as configured.")
-
         # Select only requested number of images to process darks.
-        instr_dc = instr_dc.select_trains(np.s_[skip_ntrains:n_trains])
+        instr_dc = instr_dc.select_trains(np.s_[:n_trains])
         images = np.transpose(
             instr_dc[instrument_src, "data.adc"].ndarray(), (3, 2, 1, 0))
         acelltable = np.transpose(instr_dc[instrument_src, "data.memoryCell"].ndarray())