Formatting + minor typos

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