diff --git a/setup.py b/setup.py
index b2b0f3f66bb9affb38b67d59df92fc50adf06143..320b5926c3bf775fba9ce6b6c6a6567fa9fcdf5e 100644
--- a/setup.py
+++ b/setup.py
@@ -101,7 +101,7 @@ install_requires = [
"tabulate==0.8.6",
"traitlets==4.3.3",
"xarray==2022.3.0",
- "EXtra-redu==0.0.7",
+ "EXtra-redu==0.0.8",
"rich==12.6.0",
"httpx==0.23.0",
]
diff --git a/src/cal_tools/agipdlib.py b/src/cal_tools/agipdlib.py
index 2f4046a6c88193319545e9102b7d704a57cae8fc..011905554fbdc24453ccfb552ec845c517ff8ad4 100644
--- a/src/cal_tools/agipdlib.py
+++ b/src/cal_tools/agipdlib.py
@@ -316,17 +316,21 @@ 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
: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 cellid: array of cell IDs in the same sequence as images to
+ filter
:param cm: flag indicates the final selection or interim selection
for common-mode correction
-
- :return: boolean array with flags indicating images for processing
+ :returns:
+ - boolean array with flags indicating images for processing
+ - integer array with number of selected frames in trains
"""
raise NotImplementedError
@@ -337,17 +341,6 @@ class CellSelection:
"""
raise NotImplementedError
- @staticmethod
- def _sel_for_cm(flag, flag_cm, cm):
- if cm == CellSelection.CM_NONE:
- return flag
- elif cm == CellSelection.CM_PRESEL:
- return flag_cm
- elif cm == CellSelection.CM_FINSEL:
- return flag[flag_cm]
- else:
- raise ValueError("param 'cm' takes only 0,1,2")
-
class AgipdCorrections:
@@ -535,7 +528,6 @@ 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
if "AGIPD500K" in agipd_base:
agipd_comp = components.AGIPD500K(im_dc)
@@ -550,8 +542,11 @@ 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)
@@ -1022,11 +1017,13 @@ 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, nimg_in_trains = self.cell_sel.get_cells_on_trains(
+ train_ids, nimg_in_trains, cellid, cm=self.cell_sel.CM_FINSEL
)
+ data_dict["nimg_in_trains"][:ntrains] = nimg_in_trains
if np.all(can_calibrate):
return n_img
@@ -1624,6 +1621,7 @@ 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 (
@@ -1633,10 +1631,24 @@ 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
@@ -1670,14 +1682,11 @@ class LitFrameSelection(CellSelection):
self.use_super_selection = use_super_selection
if use_super_selection == 'off':
- self.cm_sel_type = SelType.ROW
- self.final_sel_type = SelType.CELL
+ self.sel_type = [SelType.CELL, SelType.ROW, SelType.CELL]
elif use_super_selection == 'cm':
- self.cm_sel_type = SelType.SUPER_ROW
- self.final_sel_type = SelType.CELL
+ self.sel_type = [SelType.CELL, SelType.SUPER_ROW, SelType.CELL]
elif use_super_selection == 'final':
- self.cm_sel_type = SelType.SUPER_ROW
- self.final_sel_type = SelType.SUPER_CELL
+ self.sel_type = [SelType.SUPER_CELL, SelType.SUPER_ROW, SelType.SUPER_CELL]
else:
raise ValueError("param 'use_super_selection' takes only "
"'off', 'cm' or 'final'")
@@ -1713,12 +1722,16 @@ 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:
+ if cm < 0 or cm > 2:
+ raise ValueError("param 'cm' takes only 0,1,2")
+
+ (sel, counts), = self._sel.litframes_on_trains(
+ train_sel, nfrm, cellid, [self.sel_type[cm]])
- 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)
+ return sel, counts
def filter_trains(self, train_sel: np.ndarray):
return self._sel.filter_trains(train_sel, drop_empty=True)