Fixing np.logical_and/or problems in Connection ProcessModels (#412)

* fixing np.logical_and/or problems

* fixing linting issues

Co-authored-by: gkarray <[email protected]>
Co-authored-by: PhilippPlank <[email protected]>

src/lava/magma/core/model/py/connection.py

@@ -186,18 +186,16 @@ def _build_active_traces(self) -> None:
         """Build and store boolean numpy arrays specifying which x and y
         traces are active."""
         # Shape : (2, )
-        self._active_x_traces = np.logical_or(
-            self._active_x_traces_per_dependency[0],
-            self._active_x_traces_per_dependency[1],
-            self._active_x_traces_per_dependency[2],
-        )
+        self._active_x_traces = \
+            self._active_x_traces_per_dependency[0] \
+            | self._active_x_traces_per_dependency[1] \
+            | self._active_x_traces_per_dependency[2]
 
         # Shape : (3, )
-        self._active_y_traces = np.logical_or(
-            self._active_y_traces_per_dependency[0],
-            self._active_y_traces_per_dependency[1],
-            self._active_y_traces_per_dependency[2],
-        )
+        self._active_y_traces = \
+            self._active_y_traces_per_dependency[0] \
+            | self._active_y_traces_per_dependency[1] \
+            | self._active_y_traces_per_dependency[2]
 
     def _build_learning_rule_appliers(self) -> None:
         """Build and store LearningRuleApplier for each active learning
@@ -493,7 +491,7 @@ def _record_pre_spike_times(self, s_in: np.ndarray) -> None:
             Pre-synaptic spikes.
         """
         self.x0[s_in] = True
-        multi_spike_x = np.logical_and(self.tx > 0, s_in)
+        multi_spike_x = (self.tx > 0) & s_in
 
         x_traces = self._x_traces
         x_traces[:, multi_spike_x] = self._add_impulse(
@@ -519,7 +517,7 @@ def _record_post_spike_times(self, s_in_bap: np.ndarray) -> None:
             Post-synaptic spikes.
         """
         self.y0[s_in_bap] = True
-        multi_spike_y = np.logical_and(self.ty > 0, s_in_bap)
+        multi_spike_y = (self.ty > 0) & s_in_bap
 
         y_traces = self._y_traces
         y_traces[:, multi_spike_y] = self._add_impulse(
@@ -956,12 +954,8 @@ def _evaluate_trace(
 
         t_diff = t_eval - t_spikes
 
-        decay_only = np.logical_and(
-            np.logical_or(t_spikes == 0, t_diff < 0), broad_taus > 0
-        )
-        decay_spike_decay = np.logical_and(
-            t_spikes != 0, t_diff >= 0, broad_taus > 0
-        )
+        decay_only = ((t_spikes == 0) | (t_diff < 0)) & (broad_taus > 0)
+        decay_spike_decay = (t_spikes != 0) & (t_diff >= 0) & (broad_taus > 0)
 
         result = trace_values.copy()
 
@@ -1160,7 +1154,7 @@ def _record_pre_spike_times(self, s_in: np.ndarray) -> None:
         """
 
         self.x0[s_in] = True
-        multi_spike_x = np.logical_and(self.tx > 0, s_in)
+        multi_spike_x = (self.tx > 0) & s_in
 
         x_traces = self._x_traces
         x_traces[:, multi_spike_x] += self._x_impulses[:, np.newaxis]
@@ -1182,7 +1176,7 @@ def _record_post_spike_times(self, s_in_bap: np.ndarray) -> None:
         """
 
         self.y0[s_in_bap] = True
-        multi_spike_y = np.logical_and(self.ty > 0, s_in_bap)
+        multi_spike_y = (self.ty > 0) & s_in_bap
 
         y_traces = self._y_traces
         y_traces[:, multi_spike_y] += self._y_impulses[:, np.newaxis]
@@ -1377,12 +1371,8 @@ def _evaluate_trace(
 
         t_diff = t_eval - t_spikes
 
-        decay_only = np.logical_and(
-            np.logical_or(t_spikes == 0, t_diff < 0), broad_taus > 0
-        )
-        decay_spike_decay = np.logical_and(
-            t_spikes != 0, t_diff >= 0, broad_taus > 0
-        )
+        decay_only = ((t_spikes == 0) | (t_diff < 0)) & (broad_taus > 0)
+        decay_spike_decay = (t_spikes != 0) & (t_diff >= 0) & (broad_taus > 0)
 
         result = trace_values.copy()