diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index 668f0c3cd8f7a134ed6d6f3f679de3f261b78eb3..26ce0bcb14e57864ec1604d9cdaa19a7369cef3b 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -17,22 +17,22 @@
"metadata": {},
"outputs": [],
"source": [
- "in_folder = \"/gpfs/exfel/exp/HED/202031/p900174/raw\" # the folder to read data from, required\n",
- "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/hibef_agipd2\" # the folder to output to, required\n",
+ "in_folder = \"/gpfs/exfel/exp/SPB/202131/p900230/raw\" # the folder to read data from, required\n",
+ "out_folder = \"/gpfs/exfel/data/scratch/esobolev/pycal_litfrm/p900230\" # the folder to output to, required\n",
"sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
"modules = [-1] # modules to correct, set to -1 for all, range allowed\n",
"train_ids = [-1] # train IDs to correct, set to -1 for all, range allowed\n",
- "run = 155 # runs to process, required\n",
+ "run = 275 # runs to process, required\n",
"\n",
- "karabo_id = \"HED_DET_AGIPD500K2G\" # karabo karabo_id\n",
+ "karabo_id = \"SPB_DET_AGIPD1M-1\" # karabo karabo_id\n",
"karabo_da = ['-1'] # a list of data aggregators names, Default [-1] for selecting all data aggregators\n",
"receiver_id = \"{}CH0\" # inset for receiver devices\n",
"path_template = 'RAW-R{:04d}-{}-S{:05d}.h5' # the template to use to access data\n",
"h5path = 'INSTRUMENT/{}/DET/{}:xtdf/' # path in the HDF5 file to images\n",
"h5path_idx = 'INDEX/{}/DET/{}:xtdf/' # path in the HDF5 file to images\n",
"h5path_ctrl = '/CONTROL/{}/MDL/FPGA_COMP' # path to control information\n",
- "karabo_id_control = \"HED_EXP_AGIPD500K2G\" # karabo-id for control device\n",
- "karabo_da_control = 'AGIPD500K2G00' # karabo DA for control infromation\n",
+ "karabo_id_control = \"SPB_IRU_AGIPD1M1\" # karabo-id for control device\n",
+ "karabo_da_control = 'AGIPD1MCTRL00' # karabo DA for control infromation\n",
"\n",
"slopes_ff_from_files = \"\" # Path to locally stored SlopesFF and BadPixelsFF constants\n",
"\n",
@@ -44,6 +44,9 @@
"use_ppu_device = '' # Device ID for a pulse picker device to only process picked trains, empty string to disable\n",
"ppu_train_offset = 0 # When using the pulse picker, offset between the PPU's sequence start and actually picked train\n",
"\n",
+ "use_litframe_device = '' # Device ID for a lit frame finder device to only process illuminated frames, empty string to disable\n",
+ "energy_threshold = None # Energy threshold is the low limit for the energy exposed by frames subject to processing\n",
+ "\n",
"max_cells = 0 # number of memory cells used, set to 0 to automatically infer\n",
"bias_voltage = 300 # Bias voltage\n",
"acq_rate = 0. # the detector acquisition rate, use 0 to try to auto-determine\n",
@@ -146,6 +149,8 @@
"from cal_tools import agipdalgs as calgs\n",
"from cal_tools.agipdlib import (\n",
" AgipdCorrections,\n",
+ " CellRange,\n",
+ " LitFrameSelection,\n",
" get_acq_rate,\n",
" get_gain_mode,\n",
" get_integration_time,\n",
@@ -383,6 +388,32 @@
"print(f\"Maximum memory cells to calibrate: {max_cells}\")"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if use_litframe_device:\n",
+ " # check run for the AgipdLitFrameFinder device\n",
+ " try: dc\n",
+ " except NameError: dc = RunDirectory(in_folder / f'r{run:04d}')\n",
+ "\n",
+ " if use_litframe_device + ':output' in dc.instrument_sources:\n",
+ " # Use selection provided by the AgipdLitFrameFinder (if the device is recorded)\n",
+ " cell_sel = LitFrameSelection(use_litframe_device, dc, train_ids, max_pulses, energy_threshold)\n",
+ " train_ids = cell_sel.train_ids\n",
+ " else:\n",
+ " # Use range selection (if the device is not recorded)\n",
+ " print(\"WARNING: LitFrameFinder device is not found.\")\n",
+ " cell_sel = CellRange(max_pulses, max_cells=max_cells)\n",
+ "else:\n",
+ " # Use range selection\n",
+ " cell_sel = CellRange(max_pulses, max_cells=max_cells)\n",
+ "\n",
+ "print(cell_sel.mesg())"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -464,7 +495,7 @@
"source": [
"agipd_corr = AgipdCorrections(\n",
" max_cells,\n",
- " max_pulses,\n",
+ " cell_sel,\n",
" h5_data_path=h5path,\n",
" h5_index_path=h5path_idx,\n",
" corr_bools=corr_bools,\n",
@@ -1216,9 +1247,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.7"
+ "version": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/src/cal_tools/agipdlib.py b/src/cal_tools/agipdlib.py
index feb27962279b1f2047fa433417d8e5be1559278d..286ae4dc40bece6e24d8b0ebce7d1cf68f517e02 100644
--- a/src/cal_tools/agipdlib.py
+++ b/src/cal_tools/agipdlib.py
@@ -191,7 +191,7 @@ class AgipdCorrections:
def __init__(
self,
max_cells,
- max_pulses,
+ cell_sel,
h5_data_path="INSTRUMENT/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/",
h5_index_path="INDEX/SPB_DET_AGIPD1M-1/DET/{}CH0:xtdf/",
corr_bools: Optional[dict] = None,
@@ -204,8 +204,7 @@ class AgipdCorrections:
:param max_cells: maximum number of memory cells to handle, e.g. if
calibration constants only exist for a subset of cells
- :param max_pulses: a range list of pulse indices used for
- calibration and histogram. [start, end, step]
+ :param cell_sel: the CellSelection indicates cells selected for calibration
:param h5_data_path: path in HDF5 file which is prefixed to the
image/data section
:param h5_index_path: path in HDF5 file which is prefixed to the
@@ -218,7 +217,8 @@ class AgipdCorrections:
The following example shows a typical use case:
.. code-block:: python
- agipd_corr = AgipdCorrections(max_cells, max_pulses,
+ cell_sel = CellRange(max_pulses, max_cells)
+ agipd_corr = AgipdCorrections(max_cells, cell_sel,
h5_data_path=h5path,
h5_index_path=h5path_idx,
corr_bools=corr_bools)
@@ -256,8 +256,7 @@ class AgipdCorrections:
self.comp_threads = comp_threads
self.train_ids = np.array(train_ids) if train_ids is not None else None
- self.start, self.last, self.step = self._validate_selected_pulses(
- max_pulses, max_cells)
+ self.cell_sel = cell_sel
# Correction parameters
self.baseline_corr_noise_threshold = -1000
@@ -331,14 +330,29 @@ class AgipdCorrections:
try:
f = h5py.File(file_name, "r")
- (_, first_index, last_index,
- _, valid_indices) = self.get_valid_image_idx(idx_base, f)
+ (valid, first_index, last_index,
+ train_ids, valid_indices) = self.get_valid_image_idx(idx_base, f)
if len(valid_indices) == 0:
# If there's not a single valid index, exit early.
data_dict['nImg'][0] = 0
return 0
+ # store valid trains in shared memory
+ valid_train_ids = train_ids[valid]
+ 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
+
+ # get cell selection for the images in this file
+ if self.cell_sel is None:
+ data_dict["cm_presel"][0] = False
+ self.cm_presel_train_ids = None
+ else:
+ cm = self.cell_sel.CM_NONE if apply_sel_pulses else self.cell_sel.CM_PRESEL
+ img_selected = self.cell_sel.select_trains(valid_train_ids, cm=cm)
+ data_dict["cm_presel"][0] = (cm == self.cell_sel.CM_PRESEL)
+
group = f[agipd_base]['image']
allcells = np.squeeze(group['cellId'])
allpulses = np.squeeze(group['pulseId'])
@@ -346,7 +360,7 @@ class AgipdCorrections:
firange = self.gen_valid_range(first_index, last_index,
self.max_cells, allcells,
allpulses, valid_indices,
- apply_sel_pulses)
+ img_selected)
if firange is None:
# gen_valid_range() returns None if there are no cells
@@ -847,20 +861,22 @@ class AgipdCorrections:
"""Select sharedmem data indices to correct based on selected
pulses indices.
-
:param i_proc: the index of sharedmem for a given file/module
:return n_img: number of images to correct
"""
-
data_dict = self.shared_dict[i_proc]
n_img = data_dict['nImg'][0]
- allpulses = data_dict['pulseId'][:n_img]
+ if not data_dict["cm_presel"][0]:
+ return n_img
+
+ ntrains = data_dict["n_valid_trains"][0]
+ train_ids = data_dict["valid_trains"][:ntrains]
- # Initializing can_calibrate array
- can_calibrate = self.choose_selected_pulses(
- allpulses,
- can_calibrate=np.ones(shape=(len(allpulses),), dtype=np.bool))
+ ## Initializing can_calibrate array
+ can_calibrate = self.cell_sel.select_trains(train_ids, cm=self.cell_sel.CM_FINSEL)
+ if np.all(can_calibrate):
+ return n_img
# Only select data corresponding to selected pulses
# and overwrite data in shared-memory leaving
@@ -884,99 +900,11 @@ class AgipdCorrections:
return n_img
- def _validate_selected_pulses(
- self, max_pulses: List[int],
- max_cells: int,
- ) -> List[int]:
- """Validate the selected pulses given from the notebook
-
- Validate the selected range of pulses to correct raw data
- of at least one image.
-
- 1) A pulse indices within one train can't be greater
- than the operating memory cells.
- 2) Validate the order of the given raneg of pulses.
- 3) Raise value error if generate list of pulses is empty.
-
- :param max_pulses: a list of at most 3 elements defining the
- range of pulses to calibrate.
- :param max_cells: operating memory cells.
-
- :return adjusted_range: An adjusted range of pulse indices to correct.
- """
-
- # Validate selected pulses range:
- # 1) A pulseId can't be greater than the operating memory cells.
- pulses_range = [max_cells if p > max_cells else p for p in max_pulses]
-
- if pulses_range != max_pulses:
- print(
- "WARNING: \"max_pulses\" list has been modified from "
- f"{max_pulses} to {pulses_range}. As the number of "
- "operating memory cells are less than the selected "
- "maximum pulse."
- )
-
- if len(pulses_range) == 1:
- adjusted_range = (0, pulses_range[0], 1)
- elif len(pulses_range) == 2:
- adjusted_range = (pulses_range[0], pulses_range[1], 1)
- elif len(pulses_range) == 3:
- adjusted_range = tuple(pulses_range)
- else:
- raise ValueError(
- "ERROR: Wrong length for the list of pulses indices range. "
- "Please check the given range for \"max_pulses\":"
- f"{max_pulses}. \"max_pulses\" needs to be a list of "
- "3 elements, [start, last, step]")
-
- if adjusted_range[0] > adjusted_range[1]:
- raise ValueError(
- "ERROR: Pulse range start is greater than range end. "
- "Please check the given range for \"max_pulses\":"
- f"{max_pulses}. \"max_pulses\" needs to be a list of "
- "3 elements, [start, last, step]")
-
- if not np.all([isinstance(p, int) for p in max_pulses]):
- raise TypeError(
- "ERROR: \"max_pulses\" elements needs to be integers:"
- f" {max_pulses}.")
-
- print(
- "A range of pulse indices is selected to correct:"
- f" {pulses_range}")
-
- return adjusted_range
-
- def choose_selected_pulses(self, allpulses: np.array,
- can_calibrate: np.array) -> np.array:
- """
- Choose given selected pulse from pulseId array of
- raw data. The selected pulses range is validated then
- used to add a booleans in can_calibrate and guide the
- later appliance.
-
-
- :param allpulses: array of pulseIds from raw data
- :param can_calibrate: array of booleans for indices to calibrate
- :return can_calibrate: array of booleans to calibrate depending on
- selected pulses
- """
-
- # Check interesection between array of booleans and
- # array of pulses to calibrate.
- can_calibrate &= (
- (allpulses >= allpulses[self.start]) &
- (allpulses <= allpulses[self.last-1]) &
- (((allpulses - allpulses[self.start]) % allpulses[self.step]) == 0) # noqa
- )
- return can_calibrate
-
def gen_valid_range(self, first_index: int, last_index: int,
max_cells: int, allcells: np.array,
allpulses: np.array,
valid_indices: Optional[np.array] = None,
- apply_sel_pulses: Optional[bool] = True
+ img_selected: Optional[np.array] = None,
) -> np.array:
""" Validate the arrays of image.cellId and image.pulseId
to check presence of data and to avoid empty trains.
@@ -990,12 +918,8 @@ class AgipdCorrections:
:param allcells: array of image.cellsIds of raw data
:param allpulses: array of image.pulseIds of raw data
:param valid_indices: validated indices of image.data
- :param apply_sel_pulses: A flag for applying selected pulses
- after validation for correction
:return firange: An array of validated image.data
indices to correct
- # TODO: Ignore rows (32 pulse) of empty pulses even if
- common-mode is selected
"""
if valid_indices is not None:
@@ -1010,9 +934,9 @@ class AgipdCorrections:
if not np.any(can_calibrate):
return
- if apply_sel_pulses:
- can_calibrate = self.choose_selected_pulses(
- allpulses, can_calibrate=can_calibrate)
+ if img_selected is not None:
+ can_calibrate &= img_selected
+
if valid_indices is None:
firange = np.arange(first_index, last_index)
else:
@@ -1466,7 +1390,6 @@ class AgipdCorrections:
:param shape: Shape of expected data (nImg, x, y)
:param n_cores_files: Number of files, handled in parallel
"""
-
self.shared_dict = []
for i in range(n_cores_files):
self.shared_dict.append({})
@@ -1485,3 +1408,147 @@ class AgipdCorrections:
self.shared_dict[i]["raw_data"] = sharedmem.empty(shape, dtype="f4") # noqa
self.shared_dict[i]["rel_corr"] = sharedmem.empty(shape, dtype="f4") # noqa
self.shared_dict[i]["t0_rgain"] = sharedmem.empty(shape, dtype="u2") # noqa
+ # Valid trains
+ self.shared_dict[i]["cm_presel"] = sharedmem.empty(1, dtype="b")
+ self.shared_dict[i]["n_valid_trains"] = sharedmem.empty(1, dtype="i4")
+ self.shared_dict[i]["valid_trains"] = sharedmem.empty(1024, dtype="u8")
+
+
+class CellSelection:
+ """Selection of detector memory cells (abstract class)"""
+ row_size = 32
+ ncell_max = 352
+ CM_NONE = 0
+ CM_PRESEL = 1
+ CM_FINSEL = 2
+
+ def select_trains(self, trains_sel, cm=0):
+ """Returns mask of cells selected for processing
+
+ :param train_sel: list of a train ids selected for processing
+ :param cm: flag indicates the final selection or interim selection for cm correction
+ """
+ raise NotImplemented
+
+ def mesg(self):
+ """Return log message on initialization"""
+ raise NotImplemented
+
+ @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 CellRange(CellSelection):
+ """Selection of detector memory cells given as a range"""
+
+ def __init__(self, crange, max_cells):
+ """Initialize range selection
+
+ :param crange: range parameters of selected cells, list up to 3 elements
+ :param max_cells: number of exposed cells
+ """
+ self.max_cells = max_cells
+ self.crange = crange
+ self.flag = np.zeros(self.max_cells, bool)
+ self.flag_cm = np.zeros(self.ncell_max, bool)
+ self.flag[slice(*crange)] = True
+ 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]
+
+ def mesg(self):
+ return (
+ f"Use range selection with crange={self.crange}, max_cells={self.max_cells}\n"
+ f"Frames per train: {self.flag.sum()}"
+ )
+ def select_trains(self, train_sel, cm=0):
+ return np.tile(self._sel_for_cm(self.flag, self.flag_cm, cm), len(train_sel))
+
+
+class LitFrameSelection(CellSelection):
+ """Selection of detector memery cells indicated as lit frames by the AgipdLitFrameFinder"""
+
+ def __init__(self, dev, dc, train_ids, crange=None, energy_threshold=None):
+ """Initialize lit frame selection
+
+ :param dev: AgipdLitFrameFinder device name
+ :param dc: EXtra-data DataCollection of a run
+ :param train_ids: the list of selected trains
+ :param crange: range parameters of selected cells, list up to 3 elements
+ """
+ # read AgipdLitFrameFinder data
+ self.dev = dev
+ self.crange = crange
+ self.ethr = energy_threshold
+ intr_src = dev + ':output'
+ nfrm = dc[intr_src, 'data.nFrame'].ndarray()
+ litfrm_train_ids = dc[intr_src, 'data.trainId'].ndarray()
+ litfrm = dc[intr_src, 'data.nPulsePerFrame'].ndarray() > 0
+ if energy_threshold is not None and 'data.energyPerFrame' in dc.keys_for_source(intr_src):
+ litfrm &= dc[intr_src, 'data.energyPerFrame'].ndarray() > energy_threshold
+
+ # apply range selection
+ if crange is None:
+ cellsel = np.ones(self.ncell_max, bool)
+ else:
+ cellsel = np.zeros(self.ncell_max, bool)
+ cellsel[slice(*crange)] = True
+
+ # update train selection, removing trains without litframe data
+ if train_ids is None:
+ train_ids = np.unique(litfrm_train_ids)
+ ntrain = len(train_ids)
+ else:
+ ntrain_orig = len(train_ids)
+ train_ids, _, litfrm_train_idx = np.intersect1d(train_ids, litfrm_train_ids, return_indices=True)
+ litfrm = litfrm[litfrm_train_idx]
+ nfrm = nfrm[litfrm_train_idx]
+ ntrain = len(train_ids)
+ if ntrain != ntrain_orig:
+ print(f"Lit frame data missed for {ntrain_orig - ntrain}. Skip them.")
+
+ # initiate instance attributes
+ nimg = np.sum(nfrm)
+ self.train_ids = train_ids
+ self.count = nfrm
+ self.first = np.zeros(ntrain, int)
+ self.flag = np.zeros(nimg, bool)
+ self.flag_cm = np.zeros(nimg, bool)
+ self.mn_sel = nfrm.max()
+ self.mx_sel = 0
+ # compress frame pattern
+ i0 = 0
+ for i in range(ntrain):
+ n = nfrm[i]
+ iN = i0 + n
+ trnflg = litfrm[i] & cellsel
+ trnflg[n:] = False
+ self.first[i] = i0
+ self.flag[i0:iN] = trnflg[:n]
+ self.flag_cm[i0:iN] = trnflg.reshape(-1, self.row_size).any(1).repeat(self.row_size)[:n]
+ nlit = trnflg[:n].sum()
+ self.mn_sel = min(self.mn_sel, nlit)
+ self.mx_sel = max(self.mx_sel, nlit)
+
+ def mesg(self):
+ mn, mx = self.count.min(), self.count.max()
+ srng = f"{self.mn_sel}" if self.mn_sel == self.mx_sel else f"{self.mn_sel}~{self.mx_sel}"
+ return (
+ f"Use lit frame selection from {self.dev}, crange={self.crange}\n"
+ f"Frames per train: {srng}"
+ )
+
+ def select_trains(self, train_sel, cm=0):
+ train_idx = np.flatnonzero(np.in1d(self.train_ids, train_sel))
+ i0 = self.first[train_idx]
+ iN = i0 + self.count[train_idx]
+ idx = np.concatenate([np.arange(i0[i], iN[i], dtype=int) for i in range(train_idx.size)])
+
+ return self._sel_for_cm(self.flag[idx], self.flag_cm[idx], cm)