chore: ran black

Author: jnsbck

ephyspy/allen_sdk/ephys_features.py

@@ -531,9 +531,11 @@ def find_widths(v, t, spike_indexes, peak_indexes, trough_indexes, clipped=None)
     width_starts = np.zeros_like(trough_indexes) * np.nan
     width_starts[use_indexes] = np.array(
         [
-            pk - np.flatnonzero(v[pk:spk:-1] <= wl)[0]
-            if np.flatnonzero(v[pk:spk:-1] <= wl).size > 0
-            else np.nan
+            (
+                pk - np.flatnonzero(v[pk:spk:-1] <= wl)[0]
+                if np.flatnonzero(v[pk:spk:-1] <= wl).size > 0
+                else np.nan
+            )
             for pk, spk, wl in zip(
                 peak_indexes[use_indexes],
                 spike_indexes[use_indexes],
@@ -545,9 +547,11 @@ def find_widths(v, t, spike_indexes, peak_indexes, trough_indexes, clipped=None)
 
     width_ends[use_indexes] = np.array(
         [
-            pk + np.flatnonzero(v[pk:tr] <= wl)[0]
-            if np.flatnonzero(v[pk:tr] <= wl).size > 0
-            else np.nan
+            (
+                pk + np.flatnonzero(v[pk:tr] <= wl)[0]
+                if np.flatnonzero(v[pk:tr] <= wl).size > 0
+                else np.nan
+            )
             for pk, tr, wl in zip(
                 peak_indexes[use_indexes],
                 trough_indexes[use_indexes].astype(int),

ephyspy/features/spike_features.py

@@ -590,9 +590,11 @@ def _plot(self, ax: Optional[Axes] = None, selected_idxs=None, **kwargs) -> Axes
 
             width_idx = np.array(
                 [
-                    pk - np.flatnonzero(v[pk:spk:-1] <= wl)[0]
-                    if np.flatnonzero(v[pk:spk:-1] <= wl).size > 0
-                    else np.nan
+                    (
+                        pk - np.flatnonzero(v[pk:spk:-1] <= wl)[0]
+                        if np.flatnonzero(v[pk:spk:-1] <= wl).size > 0
+                        else np.nan
+                    )
                     for pk, spk, wl in zip(
                         peak_idxs,
                         spike_idxs,

ephyspy/features/sweepset_features.py

@@ -285,7 +285,11 @@ def _aggregate(self, fts):
                 "selected_idx": hyperpol_idx[median_idx(fts)],
             }
         )
-        med = float("nan") if len(fts) == 0 or np.all(np.isnan(fts)) else np.nanmedian(fts).item()
+        med = (
+            float("nan")
+            if len(fts) == 0 or np.all(np.isnan(fts))
+            else np.nanmedian(fts).item()
+        )
         return med
 
 

ephyspy/features/utils.py

@@ -266,7 +266,7 @@ def sag_period(sweep: EphysSweep, where_sag: ndarray) -> float:
 def where_stimulus(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]:
     """Checks where the stimulus is unequal to current at t=0.
 
-    Checks where stimulus is unequal to current at t=0 for a single sweep or each 
+    Checks where stimulus is unequal to current at t=0 for a single sweep or each
     sweep in a sweepset.
 
     Args:
@@ -326,7 +326,7 @@ def has_stimulus(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]
 
     Returns:
         bool: True if sweep has stimulus."""
-    return np.any(data.i.T*where_stimulus(data) != 0, axis=0)
+    return np.any(data.i.T * where_stimulus(data) != 0, axis=0)
 
 
 def is_hyperpol(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]:
@@ -338,7 +338,7 @@ def is_hyperpol(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]:
 
     Returns:
         bool: True if sweep is hyperpolarizing."""
-    return np.any(data.i.T*where_stimulus(data) < 0, axis=0)
+    return np.any(data.i.T * where_stimulus(data) < 0, axis=0)
 
 
 def is_depol(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]:
@@ -350,7 +350,7 @@ def is_depol(data: Union[EphysSweep, EphysSweepSet]) -> Union[bool, ndarray]:
 
     Returns:
         bool: True if sweep is depolarizing."""
-    return np.any(data.i.T*where_stimulus(data) > 0, axis=0)
+    return np.any(data.i.T * where_stimulus(data) > 0, axis=0)
 
 
 def has_rebound(feature: Any, T_rebound: float = 0.3) -> bool:

ephyspy/utils.py

@@ -169,8 +169,12 @@ def stimulus_type(sweep_or_sweepset: Union[EphysSweep, EphysSweepSet]) -> str:
     if np.all(slope == 0):
         return "long_square"
 
-    rel_slope_change = np.abs(slope - slope[0]) / slope[0] if np.all(slope > 0) else np.nan
-    if np.all(rel_slope_change < 0.001) and not np.isnan(rel_slope_change):  # same slope
+    rel_slope_change = (
+        np.abs(slope - slope[0]) / slope[0] if np.all(slope > 0) else np.nan
+    )
+    if np.all(rel_slope_change < 0.001) and not np.isnan(
+        rel_slope_change
+    ):  # same slope
         return "ramp"
     else:
         return "unknown"