diff --git a/cal_tools/cal_tools/agipdlib.py b/cal_tools/cal_tools/agipdlib.py
index 48156b02223fd0a95d3b7022d7ee0d1b04d6fff3..83f8dcb0d319e250bc943a8808b9106092f2eda3 100644
--- a/cal_tools/cal_tools/agipdlib.py
+++ b/cal_tools/cal_tools/agipdlib.py
@@ -15,8 +15,7 @@ from cal_tools.cython import agipdalgs as calgs
def get_num_cells(fname, loc, module):
with h5py.File(fname, "r") as f:
- cells = \
- f[f"INSTRUMENT/{loc}/DET/{module}CH0:xtdf/image/cellId"][()]
+ cells = f[f"INSTRUMENT/{loc}/DET/{module}CH0:xtdf/image/cellId"][()]
if cells.shape[0] == 0:
return None
maxcell = np.max(cells)
@@ -88,7 +87,7 @@ def get_gain_setting(fname: str, h5path_ctrl: str) -> int:
gain-setting 1: setupr@dark=8, setupr@slopespc=40
gain-setting 0: setupr@dark=0, setupr@slopespc=32
- patternTypeIndex 1: High-gian
+ patternTypeIndex 1: High-gain
patternTypeIndex 2: Medium-gain
patternTypeIndex 3: Low-gain
patternTypeIndex 4: SlopesPC
@@ -187,9 +186,7 @@ class AgipdCorrections:
const_yaml = metadata["retrieved-constants"]
for mod in modules:
- qm = f"Q{mod // 4 + 1}M{mod % 4 + 1}"
- agipd_corr.initialize_from_yaml(karabo_da,
- const_yaml, mod)
+ agipd_corr.initialize_from_yaml(karabo_da, const_yaml, mod)
data_shape = (n_images_max, 512, 128)
agipd_corr.allocate_images(data_shape, n_cores_files)
@@ -315,8 +312,7 @@ class AgipdCorrections:
data_dict['rawgain'][:n_img] = raw_data[:, 1]
data_dict['cellId'][:n_img] = allcells[firange]
data_dict['pulseId'][:n_img] = allpulses[firange]
- data_dict['trainId'][:n_img] = np.squeeze(
- group['trainId'][:][firange])
+ data_dict['trainId'][:n_img] = np.squeeze(group['trainId'][:][firange])
except Exception as e:
print(f'Error during reading data from file {file_name}: {e}')
print(f'Error traceback: {traceback.format_exc()}')
@@ -354,10 +350,10 @@ class AgipdCorrections:
# Copy any other data from the input file.
# This includes indexes, so it's important that the corrected data
# we write is aligned with the raw data.
- with h5py.File(file_name, 'r') as infile:
- self.copy_and_sanitize_non_cal_data(infile, outfile,
- agipd_base,
- idx_base, trains)
+ with h5py.File(file_name, "r") as infile:
+ self.copy_and_sanitize_non_cal_data(
+ infile, outfile, agipd_base, idx_base, trains
+ )
# All corrected data goes in a /INSTRUMENT/.../image group
image_grp = outfile[data_path]
@@ -545,9 +541,9 @@ class AgipdCorrections:
cellid = self.shared_dict[i_proc]['cellId'][first:last]
# output is saved in sharedmem to pass for correct_agipd()
# as this function takes about 3 seconds.
- self.shared_dict[i_proc]['msk'][first:last] = \
- calgs.gain_choose_int(gain,
- self.mask[module_idx][:, cellid]) # noqa
+ self.shared_dict[i_proc]["msk"][first:last] = calgs.gain_choose_int(
+ gain, self.mask[module_idx][:, cellid]
+ )
if hasattr(self, "rel_gain"):
# Get the correct rel_gain depending on cell-id
@@ -620,14 +616,12 @@ class AgipdCorrections:
# if baseline correction was not requested
# msk and rel_corr will still be empty shared_mem arrays
if not any(self.blc_bools):
- msk = calgs.gain_choose_int(gain,
- self.mask[module_idx][:, cellid])
+ msk = calgs.gain_choose_int(gain, self.mask[module_idx][:, cellid])
# same for relative gain and then bad pixel mask
if hasattr(self, "rel_gain"):
# Get the correct rel_gain depending on cell-id
- rel_corr = calgs.gain_choose(gain,
- self.rel_gain[module_idx][:, cellid]) # noqa
+ rel_corr = calgs.gain_choose(gain, self.rel_gain[module_idx][:, cellid])
# Correct for relative gain
if self.corr_bools.get("rel_gain") and hasattr(self, "rel_gain"):
@@ -690,11 +684,9 @@ class AgipdCorrections:
# Copy the data across into the existing shared-memory array
mask[...] = msk[...]
- def get_valid_image_idx(self, idx_base: str, infile: str,
- index_v: Optional[int] = 2):
- """ Return the indices of valid data
- """
- if index_v == 2:
+ def get_valid_image_idx(self, idx_base: str, infile: str, raw_format_version: int = 2):
+ """Return the indices of valid data"""
+ if raw_format_version == 2:
count = np.squeeze(infile[idx_base + "image/count"])
first = np.squeeze(infile[idx_base + "image/first"])
if np.count_nonzero(count != 0) == 0:
@@ -719,13 +711,16 @@ class AgipdCorrections:
# Creating an array of validated indices.
# If all indices were validated this array will be the same,
# as what is stored at /DET/image/trainId
- valid_indices = np.concatenate([np.arange(validf[i],
- validf[i]+validc[i])
- for i in range(validf.size)],
- axis=0)
+ valid_indices = np.concatenate(
+ [
+ np.arange(validf[i], validf[i] + validc[i])
+ for i in range(validf.size)
+ ],
+ axis=0,
+ )
valid_indices = np.squeeze(valid_indices).astype(np.int32)
- elif index_v == 1:
+ elif raw_format_version == 1:
status = np.squeeze(infile[idx_base + "image/status"])
if np.count_nonzero(status != 0) == 0:
raise IOError(f"File {infile} has no valid counts")
@@ -743,10 +738,9 @@ class AgipdCorrections:
valid_indices = None
else:
raise AttributeError(
- f"Not a known raw format version: {index_v}")
+ f"Not a known raw format version: {raw_format_version}")
- return (valid, first_index, last_index, idxtrains,
- valid_indices)
+ return (valid, first_index, last_index, idxtrains, valid_indices)
def apply_selected_pulses(self, i_proc: int) -> int:
"""Select sharedmem data indices to correct based on selected