diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index 81134340ab4facbb443af90bd9ed7b1a73526778..9770751f9a3bc86acfe33f00c52f71cf52d7369b 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -459,17 +459,12 @@
" if use_litframe_finder not in ['auto', 'offline', 'online']:\n",
" raise ValueError(\"Unexpected value in 'use_litframe_finder'.\")\n",
"\n",
- " selection_type = {'off': 0, 'cm': 1, 'final': 2}\n",
- " if use_super_selection not in selection_type:\n",
- " raise ValueError(\"Unexpected value in 'common_selection'.\")\n",
- "\n",
" inst = karabo_id_control[:3]\n",
" litfrm = make_litframe_finder(inst, dc, litframe_device_id)\n",
" try:\n",
- " get_data = {'auto': litfrm.read_or_process, 'offline':litfrm.process, 'online': litfrm.read}\n",
+ " get_data = {'auto': litfrm.read_or_process, 'offline': litfrm.process, 'online': litfrm.read}\n",
" r = get_data[use_litframe_finder]()\n",
- " cell_sel = LitFrameSelection(r, train_ids, max_pulses, energy_threshold,\n",
- " selection_type[use_super_selection])\n",
+ " cell_sel = LitFrameSelection(r, train_ids, max_pulses, energy_threshold, use_super_selection)\n",
" cell_sel.print_report()\n",
" except LitFrameFinderError as err:\n",
" warn(f\"Cannot use AgipdLitFrameFinder due to:\\n{err}\")\n",
@@ -720,13 +715,13 @@
" # In common mode corrected is enabled.\n",
" # Cell selection is only activated after common mode correction.\n",
" # Perform cross-file correction parallel over asics\n",
- " file_ix = [i_proc for i_proc, n_img in enumerate(img_counts) if n_img > 0]\n",
+ " image_files_idx = [i_proc for i_proc, n_img in enumerate(img_counts) if n_img > 0]\n",
" pool.starmap(agipd_corr.cm_correction, itertools.product(\n",
- " file_ix, range(16) # 16 ASICs per module\n",
+ " image_files_idx, range(16) # 16 ASICs per module\n",
" ))\n",
" step_timer.done_step(\"Common-mode correction\")\n",
"\n",
- " img_counts = pool.map(agipd_corr.apply_selected_pulses, file_ix)\n",
+ " img_counts = pool.map(agipd_corr.apply_selected_pulses, image_files_idx)\n",
" step_timer.done_step(\"Applying selected cells after common mode correction\")\n",
" \n",
" # Perform image-wise correction\"\n",
diff --git a/src/cal_tools/agipdlib.py b/src/cal_tools/agipdlib.py
index cb12a91b6c8a019d8e49c3c46ee664e98464b8a0..39fb187b74fe8a65333b700eecabb716712c04f8 100644
--- a/src/cal_tools/agipdlib.py
+++ b/src/cal_tools/agipdlib.py
@@ -303,6 +303,8 @@ class CellSelection:
"""Returns mask of cells selected for processing
:param train_sel: list of a train ids selected for processing
+ :param nfrm: the number of frames expected for every train in
+ the list `train_sel`
:param cm: flag indicates the final selection or interim selection
for common-mode correction
@@ -1033,7 +1035,7 @@ class AgipdCorrections:
ntrains = data_dict["n_valid_trains"][0]
train_ids = data_dict["valid_trains"][:ntrains]
- nimg_in_trains = data_dict["nimg_in_trains"][:ntrains].astype(int)
+ nimg_in_trains = data_dict["nimg_in_trains"][:ntrains]
# Initializing can_calibrate array
can_calibrate = self.cell_sel.get_cells_on_trains(
@@ -1544,7 +1546,7 @@ class AgipdCorrections:
self.shared_dict[i]["cm_presel"] = sharedmem.empty(1, dtype="b")
self.shared_dict[i]["n_valid_trains"] = sharedmem.empty(1, dtype="i4") # noqa
self.shared_dict[i]["valid_trains"] = sharedmem.empty(1024, dtype="u8") # noqa
- self.shared_dict[i]["nimg_in_trains"] = sharedmem.empty(1024, dtype="i4") # noqa
+ self.shared_dict[i]["nimg_in_trains"] = sharedmem.empty(1024, dtype="i8") # noqa
if self.corr_bools.get("round_photons"):
self.shared_hist_preround = sharedmem.empty(len(self.hist_bins_preround) - 1, dtype="i8")
@@ -1660,30 +1662,36 @@ class LitFrameSelection(CellSelection):
train_ids: List[int],
crange: Optional[List[int]] = None,
energy_threshold: float = -1000,
- super_selection: int = 0):
+ use_super_selection: str = 'off'):
"""Initialize lit frame selection
:param litfrmdata: AgipdLitFrameFinder output data
:param train_ids: the list of selected trains
:param crange: range parameters of selected cells,
list up to 3 elements
+ :param energy_threshold: the minimum allowed value for
+ pulse energy
+ :param use_super_selection: the stage when super selection
+ should be applied: `off`, `cm` or `final`
"""
from extra_redu import FrameSelection, SelType
self.dev = litfrmdata.meta.litFrmDev
self.crange = validate_selected_pulses(crange, self.ncell_max)
self.energy_threshold = energy_threshold
- self.super_selection = super_selection
- if super_selection == 0:
+ self.use_super_selection = use_super_selection
+
+ if use_super_selection == 'off':
self.cm_sel_type = SelType.ROW
self.final_sel_type = SelType.CELL
- elif super_selection == 1:
+ elif use_super_selection == 'cm':
self.cm_sel_type = SelType.SUPER_ROW
self.final_sel_type = SelType.CELL
- elif super_selection == 2:
+ elif use_super_selection == 'final':
self.cm_sel_type = SelType.SUPER_ROW
self.final_sel_type = SelType.SUPER_CELL
else:
- raise ValueError("param 'super_selection' takes only 0,1,2")
+ raise ValueError("param 'use_super_selection' takes only "
+ "'off', 'cm' or 'final'")
self._sel = FrameSelection(
litfrmdata, guess_missed=True, crange=slice(*self.crange),