From d5c6b94287287992c6e9b6450c170bf0c453e58a Mon Sep 17 00:00:00 2001
From: David Hammer <dhammer@mailbox.org>
Date: Thu, 25 Mar 2021 10:36:49 +0100
Subject: [PATCH] Satisfying flake8 for agipdlib.py
---
cal_tools/cal_tools/agipdlib.py | 58 +++++++++++++++++----------------
1 file changed, 30 insertions(+), 28 deletions(-)
diff --git a/cal_tools/cal_tools/agipdlib.py b/cal_tools/cal_tools/agipdlib.py
index 103bbc162..a098b07b8 100644
--- a/cal_tools/cal_tools/agipdlib.py
+++ b/cal_tools/cal_tools/agipdlib.py
@@ -329,7 +329,7 @@ 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]) # noqa
except Exception as e:
print(f'Error during reading data from file {file_name}: {e}')
print(f'Error traceback: {traceback.format_exc()}')
@@ -502,10 +502,10 @@ class AgipdCorrections:
t0 = self.thresholds[module_idx][0]
t1 = self.thresholds[module_idx][1]
- # load raw_data and rgain to be used during gain_correction if requested
if self.corr_bools.get("melt_snow"):
- self.shared_dict[i_proc]["t0_rgain"][first:last] = rawgain / t0[cellid, ...]
- self.shared_dict[i_proc]["raw_data"][first:last] = np.copy(data)
+ # load raw_data and rgain to be used during gain_correction
+ self.shared_dict[i_proc]["t0_rgain"][first:last] = rawgain / t0[cellid, ...] # noqa
+ self.shared_dict[i_proc]["raw_data"][first:last] = np.copy(data) # noqa
# Often most pixels are in high-gain, so it's more efficient to
# set the whole output block to zero than select the right pixels.
@@ -522,10 +522,10 @@ class AgipdCorrections:
# force into high or medium gain if requested
if self.corr_bools.get("force_mg_if_below"):
- gain[(gain == 2) & ((data - offsetb[1]) < self.mg_hard_threshold)] = 1
+ gain[(gain == 2) & ((data - offsetb[1]) < self.mg_hard_threshold)] = 1 # noqa
if self.corr_bools.get("force_hg_if_below"):
- gain[(gain > 0) & ((data - offsetb[0]) < self.hg_hard_threshold)] = 0
+ gain[(gain > 0) & ((data - offsetb[0]) < self.hg_hard_threshold)] = 0 # noqa
# choose constants according to gain setting
off = calgs.gain_choose(gain, offsetb)
@@ -637,7 +637,7 @@ class AgipdCorrections:
# 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])
+ rel_corr = calgs.gain_choose(gain, self.rel_gain[module_idx][:, cellid]) # noqa
# Correct for relative gain
if self.corr_bools.get("rel_gain") and hasattr(self, "rel_gain"):
@@ -700,7 +700,9 @@ 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, raw_format_version: int = 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"])
@@ -962,11 +964,11 @@ class AgipdCorrections:
# Extract parameters through identifying
# unique trains, index and numbers.
- uq, fidxv, cntsv = np.unique(trains, return_index=True, return_counts=True)
+ uq, fidxv, cntsv = np.unique(trains, return_index=True, return_counts=True) # noqa
# Validate calculated CORR INDEX contents by checking
# difference between trainId stored in RAW data and trains from
- train_diff = np.isin(np.array(infile["/INDEX/trainId"]), uq, invert=True)
+ train_diff = np.isin(np.array(infile["/INDEX/trainId"]), uq, invert=True) # noqa
# Insert zeros for missing trains.
# fidxv and cntsv should have same length as
@@ -1244,12 +1246,12 @@ class AgipdCorrections:
"""Initialize calibration constants from a yaml file
:param karabo_da: a karabo data aggregator
- :param const_yaml: from the "retrieved-constants" part of a yaml
- file from pre-notebook, which consists of metadata of either the constant
+ :param const_yaml: from the "retrieved-constants" part of a yaml file
+ from pre-notebook, which consists of metadata of either the constant
file path or the empty constant shape, and the creation-time of the
retrieved constants
- :param module_idx: Index of module.
- :return when: Dictionary of retrieved constants with their creation-time.
+ :param module_idx: Index of module
+ :return when: dict of retrieved constants with their creation-time
"""
# string of the device name.
@@ -1357,17 +1359,17 @@ class AgipdCorrections:
:param constant_shape: Shape of expected constants (gain, cells, x, y)
"""
for module_idx in modules:
- self.offset[module_idx] = sharedmem.empty(constant_shape, dtype="f4")
+ self.offset[module_idx] = sharedmem.empty(constant_shape, dtype="f4") # noqa
if self.gain_mode is AgipdGainMode.ADAPTIVE_GAIN:
- self.thresholds[module_idx] = sharedmem.empty(constant_shape, dtype="f4")
- self.noise[module_idx] = sharedmem.empty(constant_shape, dtype="f4")
+ self.thresholds[module_idx] = sharedmem.empty(constant_shape, dtype="f4") # noqa
+ self.noise[module_idx] = sharedmem.empty(constant_shape, dtype="f4") # noqa
- self.md_additional_offset[module_idx] = sharedmem.empty(constant_shape[1:], dtype="f4")
- self.rel_gain[module_idx] = sharedmem.empty(constant_shape, dtype="f4")
- self.frac_high_med[module_idx] = sharedmem.empty(constant_shape[1], dtype="f4")
+ self.md_additional_offset[module_idx] = sharedmem.empty(constant_shape[1:], dtype="f4") # noqa
+ self.rel_gain[module_idx] = sharedmem.empty(constant_shape, dtype="f4") # noqa
+ self.frac_high_med[module_idx] = sharedmem.empty(constant_shape[1], dtype="f4") # noqa
self.mask[module_idx] = sharedmem.empty(constant_shape, dtype="i4")
- self.xray_cor[module_idx] = sharedmem.empty(constant_shape[1:], dtype="f4")
+ self.xray_cor[module_idx] = sharedmem.empty(constant_shape[1:], dtype="f4") # noqa
def allocate_images(self, shape, n_cores_files):
"""
@@ -1381,18 +1383,18 @@ class AgipdCorrections:
self.shared_dict = []
for i in range(n_cores_files):
self.shared_dict.append({})
- self.shared_dict[i]["cellId"] = sharedmem.empty(shape[0], dtype="u2")
- self.shared_dict[i]["pulseId"] = sharedmem.empty(shape[0], dtype="u8")
- self.shared_dict[i]["trainId"] = sharedmem.empty(shape[0], dtype="u8")
+ self.shared_dict[i]["cellId"] = sharedmem.empty(shape[0], dtype="u2") # noqa
+ self.shared_dict[i]["pulseId"] = sharedmem.empty(shape[0], dtype="u8") # noqa
+ self.shared_dict[i]["trainId"] = sharedmem.empty(shape[0], dtype="u8") # noqa
self.shared_dict[i]["moduleIdx"] = sharedmem.empty(1, dtype="i4")
self.shared_dict[i]["nImg"] = sharedmem.empty(1, dtype="i4")
self.shared_dict[i]["mask"] = sharedmem.empty(shape, dtype="u4")
self.shared_dict[i]["data"] = sharedmem.empty(shape, dtype="f4")
self.shared_dict[i]["rawgain"] = sharedmem.empty(shape, dtype="u2")
self.shared_dict[i]["gain"] = sharedmem.empty(shape, dtype="u1")
- self.shared_dict[i]["blShift"] = sharedmem.empty(shape[0], dtype="f4")
+ self.shared_dict[i]["blShift"] = sharedmem.empty(shape[0], dtype="f4") # noqa
# Parameters shared between image-wise correction functions
self.shared_dict[i]["msk"] = sharedmem.empty(shape, dtype="i4")
- self.shared_dict[i]["raw_data"] = sharedmem.empty(shape, dtype="f4")
- self.shared_dict[i]["rel_corr"] = sharedmem.empty(shape, dtype="f4")
- self.shared_dict[i]["t0_rgain"] = sharedmem.empty(shape, dtype="u2")
+ 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
--
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