fix: F841 (unused variables)

After deleting variables, some bare expressions were deleted:

Bare expressions deleted (no side effects — entire line removed):

File: brain_observatory/circle_plots.py
Deleted expression: r * np.cos(angle), r * np.sin(angle)
Why: Pure arithmetic
────────────────────────────────────────
File: brain_observatory/ecephys/.../view_blocks.py
Deleted expression: recorded_blocks[0] → pass
Why: Bare index in else clause
────────────────────────────────────────
File: brain_observatory/eye_tracking/.../DLC_Ellipse_Fitting.py
Deleted expression: 1-frac_completed
Why: Pure arithmetic
────────────────────────────────────────
File: internal/brain_observatory/demix_report.py
Deleted expression: np.zeros(mask.shape)
Why: Array allocated and discarded
────────────────────────────────────────
File: internal/brain_observatory/frame_stream.py
Deleted expression: np.prod(self.frame_shape)
Why: Pure computation
────────────────────────────────────────
File: internal/brain_observatory/roi_filter.py
Deleted expression: create_feature_array(...) → pass
Why: Returns value, no mutation
────────────────────────────────────────
File: internal/ephys/plot_qc_figures.py
Deleted expression: cell_features["long_squares"]["sweeps"]
Why: Dict lookup
────────────────────────────────────────
File: internal/ephys/plot_qc_figures3.py
Deleted expression: cell_features["long_squares"]["sweeps"]
Why: Dict lookup
────────────────────────────────────────
File: internal/model/GLM.py
Deleted expression: kbasprs['b']
Why: Dict lookup
────────────────────────────────────────
File: internal/model/biophysical/fit_stage_1.py
Deleted expression: neuronal_model_data['specimen_id']
Why: Dict lookup
────────────────────────────────────────
File: internal/model/biophysical/make_deap_fit_json.py
Deleted expression: os.path.realpath(os.curdir)
Why: Pure path op
────────────────────────────────────────
File: internal/model/biophysical/passive_fitting/preprocess.py
Deleted expression: down_idxs[1] - down_idxs[0]
Why: Pure arithmetic
────────────────────────────────────────
File: internal/model/glif/error_functions.py
Deleted expression: input_data['subthreshold_long_square_voltage_variance'], np.arange(...)*experiment.neuron.dt, [e.data['interpolated_ISI']] (dead code after raise)
Why: Dict lookup, pure computation, unreachable
────────────────────────────────────────
File: internal/model/glif/preprocess_neuron.py
Deleted expression: sweep_index[...][RESTING_POTENTIAL]*1e-3, long_square_config['all'], long_square_config['subthreshold'], np.mean(El_test_list)
Why: Dict lookups, pure computation
────────────────────────────────────────
File: internal/model/glif/spike_cutting.py
Deleted expression: np.var(xdata)
Why: Pure computation
────────────────────────────────────────
File: internal/pipeline_modules/IVSCC/ephys_nwb/qc.py
Deleted expression: sweep_data['response'], sweep_data['sampling_rate']
Why: Dict lookups
────────────────────────────────────────
File: internal/pipeline_modules/run_neuropil_correction.py
Deleted expression: np.array([...]).mean()
Why: Pure computation
────────────────────────────────────────
File: internal/pipeline_modules/run_observatory_container_thumbnails.py
Deleted expression: input_file['output_json']
Why: Dict lookup
────────────────────────────────────────
File: model/glif/glif_neuron_methods.py
Deleted expression: tcs['voltage'][-1]
Why: Dict/index lookup
────────────────────────────────────────
File: brain_observatory/behavior/swdb/summary_figures.py
Deleted expression: int(session.metadata['ophys_frame_rate'])
Why: Pure type conversion

Bare expressions kept (have side effects):

┌────────────────────────────────────────────────────────┬──────────────────────────────────┐
│                       Expression                       │             Why kept             │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ hero_sweep.sweep_feature("adapt"/"latency"/"mean_isi") │ Forces lazy feature caching      │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ swp.sweep_feature("v_baseline")                        │ Same caching pattern             │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ i_vec.as_numpy()                                       │ NEURON vector conversion         │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ subprocess.check_output([...]) (3 calls)               │ Runs external fitting processes  │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ p.map(func, types)                                     │ Multiprocessing execution        │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ description.manifest.get_path(...)                     │ Manifest path resolution         │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ plt.subplot2grid(...)                                  │ Creates subplot in figure layout │
├────────────────────────────────────────────────────────┼──────────────────────────────────┤
│ All allensdk/test/ expressions                         │ Smoke tests exercising APIs      │
└────────────────────────────────────────────────────────┴──────────────────────────────────┘

Author: galenlynch

allensdk/api/queries/cell_types_api.py

@@ -330,7 +330,7 @@ def save_ephys_data(self, specimen_id, file_name):
         try:
             file_url = results[0]['ephys_result'][
                 'well_known_files'][0]['download_link']
-        except Exception as _:
+        except Exception as _:  # noqa: F841
             raise Exception("Specimen %d has no ephys data" % specimen_id)
 
         self.retrieve_file_over_http(self.api_url + file_url, file_name)

allensdk/brain_observatory/behavior/swdb/summary_figures.py

@@ -383,7 +383,7 @@ def plot_max_proj_and_roi_masks(session, save_dir=None):
     mask = np.empty(session.segmentation_mask_image.data.shape, dtype='float')
     mask[:] = np.nan
     mask[tmp > 0] = 1
-    cax = ax[2].imshow(mask, cmap='hsv', alpha=0.4, vmin=0, vmax=1)
+    ax[2].imshow(mask, cmap='hsv', alpha=0.4, vmin=0, vmax=1)
 
     if save_dir:
         ut.save_figure(fig, figsize, save_dir, 'roi_masks', str(session.metadata['ophys_experiment_id']))
@@ -445,9 +445,6 @@ def plot_experiment_summary_figure(session, save_dir=None):
     title = meta['driver_line'][0] + ', ' + meta['targeted_structure'] + ', ' + str(meta['imaging_depth']) + ', ' + \
             session.task_parameters['stage']
 
-    interval_seconds = 600
-    ophys_frame_rate = int(session.metadata['ophys_frame_rate'])
-
     figsize = [2 * 11, 2 * 8.5]
     fig = plt.figure(figsize=figsize, facecolor='white')
 

allensdk/brain_observatory/behavior/sync/process_sync.py

@@ -33,7 +33,6 @@ def filter_digital(rising, falling, threshold=0.0001):
 
 def calculate_delay(sync_data, stim_vsync_fall, sample_frequency):
     # from http://stash.corp.alleninstitute.org/projects/INF/repos/lims2_modules/browse/CAM/ophys_time_sync/ophys_time_sync.py
-    ASSUMED_DELAY = 0.0351
     DELAY_THRESHOLD = 0.001
     FIRST_ELEMENT_INDEX = 0
     ROUND_PRECISION = 4

allensdk/brain_observatory/brain_observatory_plotting.py

@@ -670,7 +670,7 @@ def _plot_3sb(sg, nm1, ns, save_dir):
         ax1 = plt.subplot2grid((6, 6), (0, 0), colspan=4)  # full trace
         ax2 = plt.subplot2grid((6, 6), (0, 4))  # histogram of F
         ax14 = plt.subplot2grid((6, 6), (1, 4))  # speed tuning
-        ax16 = plt.subplot2grid((6, 6), (1, 5), sharex=ax14, sharey=ax14)
+        plt.subplot2grid((6, 6), (1, 5), sharex=ax14, sharey=ax14)
         ax15 = plt.subplot2grid((6, 6), (1, 0), colspan=4)
         ax3 = plt.subplot2grid((6, 6), (2, 0), colspan=2)  # Ori tuning
         ax4 = plt.subplot2grid((6, 6), (2, 2), colspan=2)  # sf tuning

allensdk/brain_observatory/circle_plots.py

@@ -611,9 +611,6 @@ def show_group_labels(self, groups=None, color=None, fontdict=None):
         r = self.inner_radius*.5
         angle = 90.0
 
-        x = r * np.cos(angle)
-        y = r * np.sin(angle)
-
         for group in groups:
             off = self.group_offset_map[group]
 

allensdk/brain_observatory/ecephys/stimulus_table/visualization/view_blocks.py

@@ -42,7 +42,7 @@ def get_blocks(table):
                 )
             )
         else:
-            recorded_block = recorded_blocks[0]
+            pass
 
         start = block["Start"].values[0]
         end = block["End"].values[-1]

allensdk/brain_observatory/eye_tracking/stage_2/DLC_Ellipse_Fitting.py

@@ -61,7 +61,6 @@ def fit_ellipse(h5name):
         #fit ellipses to the pupil & eye points in 4/25
 
         frac_completed = max(1,float(j))/len(df)
-        frac_rem = 1-frac_completed
         tot_time_est = (time.time() - loop_t0)/frac_completed
         progress_str = "{:10.2f} {:10.2f} {:5s} {:10.2f}".format(time.time()-last_loop_time, time.time()-loop_t0, "{0:.0%}".format(frac_completed), tot_time_est)
         logger.info('Ellipse fit: {}'.format(progress_str))

allensdk/brain_observatory/observatory_plots.py

@@ -71,7 +71,6 @@ def plot_cell_correlation(sig_corrs, labels, colors, scale=15):
         alpha = 1.0
 
     ax = plt.gca()
-    ps = []
     for sig_corr, color, label in zip(sig_corrs, colors, labels):
         ax.hist(sig_corr, bins=30, range=[-1,1],
                 histtype='stepfilled',

allensdk/ephys/extract_cell_features.py

@@ -137,9 +137,9 @@ def extract_cell_features(data_set,
             hero_sweep = sweep
 
     if hero_sweep:
-        adapt = hero_sweep.sweep_feature("adapt")
-        latency = hero_sweep.sweep_feature("latency")
-        mean_isi = hero_sweep.sweep_feature("mean_isi")
+        hero_sweep.sweep_feature("adapt")
+        hero_sweep.sweep_feature("latency")
+        hero_sweep.sweep_feature("mean_isi")
     else:
         raise ft.FeatureError("Could not find hero sweep.")
 

allensdk/ephys/feature_extractor.py

@@ -397,7 +397,6 @@ def process_instance(self, name, v, curr, t, onset, dur, stim_name):
                     close_idx += 1
                 spk["downstroke"] = dvdt[close_idx]
 
-        features = {}
         feature.mean["base_v"] = v[np.where((t > onset - 0.1) & (t < onset - 0.001))].mean() # baseline voltage, 100ms before stim
         feature.mean["spikes"] = spikes
         isi_cv = self.isicv(spikes)