rm unused imports

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

@@ -6,7 +6,7 @@
 
 import typing as ty
 import logging
-from abc import ABC, abstractmethod
+from abc import ABC
 
 if ty.TYPE_CHECKING:
     from lava.magma.core.process.process import AbstractProcess

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

@@ -116,21 +116,21 @@ def _get_var(self):
         data_port = self.process_to_service
         # Header corresponds to number of values
         # Data is either send once (for int) or one by one (array)
-        if isinstance(var, int) or isinstance(var, np.integer):
+        if isinstance(var, int) or isinstance(var, np.int32):
             data_port.send(enum_to_np(1))
             data_port.send(enum_to_np(var))
         elif isinstance(var, np.ndarray):
             # FIXME: send a whole vector (also runtime_service.py)
             var_iter = np.nditer(var, order="C")
-            num_items: np.integer = np.prod(var.shape)
+            num_items: np.int32 = np.prod(var.shape)
             data_port.send(enum_to_np(num_items))
             for value in var_iter:
                 data_port.send(enum_to_np(value, np.float64))
         elif isinstance(var, str):
             encoded_str = list(var.encode("ascii"))
             data_port.send(enum_to_np(len(encoded_str)))
             for ch in encoded_str:
-                data_port.send(enum_to_np(ch, d_type=np.integer))
+                data_port.send(enum_to_np(ch, d_type=np.int32))
 
     def _set_var(self):
         """Handles the set Var command from runtime service."""
@@ -141,7 +141,7 @@ def _set_var(self):
 
         # 2. Receive Var data
         data_port = self.service_to_process
-        if isinstance(var, int) or isinstance(var, np.integer):
+        if isinstance(var, int) or isinstance(var, np.int32):
             # First item is number of items (1) - not needed
             data_port.recv()
             # Data to set

tests/lava/magma/core/learning/test_learning_rule.py

@@ -60,15 +60,15 @@ def create_network(
     dense_inp = Dense(weights=np.eye(size, size) * 2.0)
 
     lif_0 = LIF(
-        shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0.0, name="lif_pre"
+        shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0, name="lif_pre"
     )
 
     dense = LearningDense(
         weights=weights_init, learning_rule=learning_rule, name="plastic_dense"
     )
 
     lif_1 = LIF(
-        shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0.0, name="lif_post"
+        shape=(size,), du=du, dv=dv, vth=vth, bias_mant=0, name="lif_post"
     )
 
     spike_gen.s_out.connect(dense_inp.s_in)