Apply suggestions from code review

Co-authored-by: Dimitri Yatsenko <dimitri@datajoint.com>

Author: JaerongA

element_array_ephys/ephys_acute.py

@@ -158,8 +158,8 @@ def auto_generate_entries(cls, session_key):
             get_ephys_root_data_dir(), get_session_directory(session_key)
         )
         # search session dir and determine acquisition software
-        for ephys_pattern, ephys_acq_type in zip(
-            ["*.ap.meta", "*.oebin"], ["SpikeGLX", "Open Ephys"]
+        for ephys_pattern, ephys_acq_type in (
+            ("*.ap.meta", "SpikeGLX"), ("*.oebin", "Open Ephys")
         ):
             ephys_meta_filepaths = list(session_dir.rglob(ephys_pattern))
             if ephys_meta_filepaths:
@@ -271,8 +271,8 @@ def make(self, key):
         )
 
         # search session dir and determine acquisition software
-        for ephys_pattern, ephys_acq_type in zip(
-            ["*.ap.meta", "*.oebin"], ["SpikeGLX", "Open Ephys"]
+        for ephys_pattern, ephys_acq_type in (
+            ("*.ap.meta", "SpikeGLX"), ("*.oebin", "Open Ephys")
         ):
             ephys_meta_filepaths = list(session_dir.rglob(ephys_pattern))
             if ephys_meta_filepaths:

element_array_ephys/ephys_report.py

@@ -51,9 +51,7 @@ def make(self, key):
             ephys.CuratedClustering.Unit & key & "cluster_quality_label='good'"
         )  
 
-        shanks: np.ndarray = np.unique(
-            (probe.ProbeType.Electrode.proj("shank") & units).fetch("shank")
-        )
+        shanks = set((probe.ProbeType.Electrode & units).fetch("shank"))
 
         for shank_no in shanks:
 

element_array_ephys/plotting/unit_level.py

@@ -124,20 +124,20 @@ def plot_depth_waveforms(
 
     # Spacing between channels (in um)
     x_inc = np.abs(np.diff(coords[:, 0])).min()
-    y_inc = np.unique((np.abs(np.diff(coords[:, 1])))).max()
+    y_inc = (np.abs(np.diff(coords[:, 1]))).max()
 
-    time = np.arange(waveforms.shape[1]) * (1 / sampling_rate)
+    time = np.arange(waveforms.shape[1]) / sampling_rate
 
-    x_scale_factor = x_inc / (time + (1 / sampling_rate))[-1]
+    x_scale_factor = x_inc / (time[-1] + 1 / sampling_rate)
     time_scaled = time * x_scale_factor
 
     wf_amps = waveforms.max(axis=1) - waveforms.min(axis=1)
     max_amp = wf_amps.max()
     y_scale_factor = y_inc / max_amp
 
     unique_x_loc = np.sort(np.unique(coords[:, 0]))
-    xtick_label = list(map(str, map(int, unique_x_loc)))
-    xtick_loc = time_scaled[int(len(time_scaled) / 2) + 1] + unique_x_loc
+    xtick_label = [str(int(x)) for x in unique_x_loc]
+    xtick_loc = time_scaled[len(time_scaled) // 2 + 1] + unique_x_loc
 
     # Plot figure
     fig = go.Figure()