diff --git a/src/cal_tools/agipdlib.py b/src/cal_tools/agipdlib.py
index 88c3413cc623efda893852293865abc2b90db701..3a9da4e2283e9d784def6537b8bda7e8e0163023 100644
--- a/src/cal_tools/agipdlib.py
+++ b/src/cal_tools/agipdlib.py
@@ -53,12 +53,19 @@ class AgipdCtrl:
self.ctrl_src = ctrl_src
self.raise_error = raise_error
- def get_num_cells(self) -> Optional[int]:
- """Read number of memory cells from fast data.
+ def _get_num_cells_ctrl(self) -> Optional[int]:
+ """Get number of cells from CONTROL source."""
+ # Attempt to look for number of cells in slow data
+ ncell_src = (
+ self.ctrl_src, "bunchStructure.nPulses.value")
+ if (
+ ncell_src[0] in self.run_dc.all_sources and
+ ncell_src[1] in self.run_dc.keys_for_source(ncell_src[0])
+ ):
+ return int(self.run_dc[ncell_src].as_single_value(reduce_by='max'))
- :return mem_cells: Number of memory cells
- return None, if no data available.
- """
+ def _get_num_cells_instr(self) -> Optional[int]:
+ """Get number of cells from INSTRUMENT source."""
cells = np.squeeze(
self.run_dc[
self.image_src, "image.cellId"].drop_empty_trains().ndarray()
@@ -70,6 +77,19 @@ class AgipdCtrl:
dists = [abs(o - maxcell) for o in options]
return options[np.argmin(dists)]
+ def get_num_cells(self) -> Optional[int]:
+ """Read number of memory cells from fast data.
+
+ :return mem_cells: Number of memory cells
+ return None, if no data available.
+ """
+ ncell = self._get_num_cells_ctrl()
+ if ncell is not None:
+ return ncell
+ # In case of data filtering at DAQ, this function produces
+ # wrong value.
+ return self._get_num_cells_instr()
+
def _get_acq_rate_ctrl(self) -> Optional[float]:
"""Get acquisition (repetition) rate from CONTROL source."""
# Attempt to look for acquisition rate in slow data
@@ -83,9 +103,6 @@ class AgipdCtrl:
# about bucketing the rate for managing meta-data.
return round(float(self.run_dc[rep_rate_src].as_single_value()), 1)
- def _get_acq_rate_instr(self) -> Optional[float]:
- """Get acquisition (repetition rate) from INSTRUMENT source."""
-
def _get_acq_rate_instr(self) -> Optional[float]:
"""Get acquisition (repetition rate) from INSTRUMENT source."""
@@ -298,7 +315,8 @@ class CellSelection:
raise NotImplementedError
def get_cells_on_trains(
- self, train_sel: np.ndarray, nfrm: np.ndarray, cm: int = 0
+ self, train_sel: np.ndarray, nfrm: np.ndarray,
+ cellid: np.ndarray, cm: int = 0
) -> np.array:
"""Returns mask of cells selected for processing
@@ -309,6 +327,8 @@ class CellSelection:
for common-mode correction
:return: boolean array with flags indicating images for processing
+ :return: integer array with number of frames to use in the second
+ step in two step selection procedure
"""
raise NotImplementedError
@@ -517,7 +537,7 @@ class AgipdCorrections:
n_valid_trains = len(valid_train_ids)
data_dict["n_valid_trains"][0] = n_valid_trains
data_dict["valid_trains"][:n_valid_trains] = valid_train_ids
- data_dict["nimg_in_trains"][:n_valid_trains] = nimg_in_trains
+ #data_dict["nimg_in_trains"][:n_valid_trains] = nimg_in_trains
if "AGIPD500K" in agipd_base:
agipd_comp = components.AGIPD500K(im_dc)
@@ -532,8 +552,11 @@ class AgipdCorrections:
cm = (self.cell_sel.CM_NONE if apply_sel_pulses
else self.cell_sel.CM_PRESEL)
- img_selected = self.cell_sel.get_cells_on_trains(
- np.array(valid_train_ids), nimg_in_trains, cm=cm)
+ cellid = np.squeeze(im_dc[agipd_base, "image.cellId"].ndarray())
+
+ img_selected, nimg_in_trains = self.cell_sel.get_cells_on_trains(
+ np.array(valid_train_ids), nimg_in_trains, cellid, cm=cm)
+ data_dict["nimg_in_trains"][:n_valid_trains] = nimg_in_trains
frm_ix = np.flatnonzero(img_selected)
data_dict["cm_presel"][0] = (cm == self.cell_sel.CM_PRESEL)
@@ -1004,10 +1027,11 @@ 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]
+ cellid = data_dict["cellId"][:n_img]
# Initializing can_calibrate array
- can_calibrate = self.cell_sel.get_cells_on_trains(
- train_ids, nimg_in_trains, cm=self.cell_sel.CM_FINSEL
+ can_calibrate, _ = self.cell_sel.get_cells_on_trains(
+ train_ids, nimg_in_trains, cellid, cm=self.cell_sel.CM_FINSEL
)
if np.all(can_calibrate):
return n_img
@@ -1606,6 +1630,7 @@ class CellRange(CellSelection):
self.flag_cm[:self.max_cells] = self.flag
self.flag_cm = (self.flag_cm.reshape(-1, self.row_size).any(1)
.repeat(self.row_size)[:self.max_cells])
+ self.sel_type = [self.flag, self.flag_cm, self.flag]
def msg(self):
return (
@@ -1615,10 +1640,24 @@ class CellRange(CellSelection):
)
def get_cells_on_trains(
- self, train_sel: np.ndarray, nfrm: np.ndarray, cm: int = 0
+ self, train_sel: np.ndarray, nfrm: np.ndarray,
+ cellid: np.ndarray, cm: int = 0
) -> np.array:
- return np.tile(self._sel_for_cm(self.flag, self.flag_cm, cm),
- len(train_sel))
+ if cm < 0 or cm > 2:
+ raise ValueError("param 'cm' takes only 0,1,2")
+
+ flag = self.sel_type[cm]
+ sel = np.zeros(np.sum(nfrm), bool)
+ counts = np.zeros(len(nfrm), int)
+ i0 = 0
+ for i, nfrm_i in enumerate(nfrm):
+ iN = i0 + nfrm_i
+ f = flag[cellid[i0:iN]]
+ sel[i0:iN] = f
+ counts[i] = np.sum(f)
+ i0 = iN
+
+ return sel, counts
def filter_trains(self, train_sel: np.ndarray):
return train_sel
@@ -1695,12 +1734,14 @@ class LitFrameSelection(CellSelection):
)
def get_cells_on_trains(
- self, train_sel: np.ndarray, nfrm: np.ndarray, cm: int = 0
+ self, train_sel: np.ndarray, nfrm: np.ndarray,
+ cellid: np.ndarray, cm: int = 0
) -> np.array:
cell_flags, cm_flags = self._sel.litframes_on_trains(
train_sel, nfrm, [self.final_sel_type, self.cm_sel_type])
- return self._sel_for_cm(cell_flags, cm_flags, cm)
+ sel = self._sel_for_cm(cell_flags, cm_flags, cm)
+ return sel, nfrm
def filter_trains(self, train_sel: np.ndarray):
return self._sel.filter_trains(train_sel, drop_empty=True)