typing fixes

spynnaker/plot_utils.py

@@ -17,6 +17,8 @@
 from types import ModuleType
 from typing import Optional
 import numpy as np
+from numpy.typing import NDArray
+
 # pylint: disable=invalid-name
 plt: Optional[ModuleType]
 try:
@@ -185,8 +187,7 @@ def plot_spikes(spikes, title="spikes"):
 if __name__ == "__main__":
     spike_data = np.loadtxt("spikes.csv", delimiter=',')
     plot_spikes(spike_data)
-    doubled_spike_data = np.loadtxt("spikes.csv", delimiter=',')
-    _i: int
+    doubled_spike_data: NDArray = np.loadtxt("spikes.csv", delimiter=',')
     for _i, doubled_spike_data_i in enumerate(doubled_spike_data):
         doubled_spike_data_i[0] = doubled_spike_data[_i][0] + 5
     plot_spikes([spike_data, doubled_spike_data])

spynnaker/pyNN/models/neuron/population_machine_neurons.py

@@ -317,7 +317,8 @@ def _write_neuron_core_parameters(
         # Write the keys
         spec.write_array(keys)
 
-    def __in_selector(self, n: int, selector: Selector) -> bool:
+    def __in_selector(
+            self, n: Union[int, numpy.integer], selector: Selector) -> bool:
         if isinstance(selector, Container):
             return n in selector
         return n == selector
@@ -363,9 +364,9 @@ def _write_current_source_parameters(
                 cs_id = current_source.current_source_id
 
                 # Only use IDs that are on this core
-                for i, n in enumerate(self._vertex_slice.get_raster_ids()):
+                for i, raster_id in enumerate(self._vertex_slice.get_raster_ids()):
                     if self.__in_selector(
-                            n, current_source_id_list[current_source]):
+                            raster_id, current_source_id_list[current_source]):
                         # I think this is now right, but test it more...
                         neuron_current_sources[i][0] += 1
                         neuron_current_sources[i].append(cs_id)
@@ -378,17 +379,16 @@ def _write_current_source_parameters(
 
             # Now loop over the neurons on this core and write the current
             # source ID and index for sources attached to each neuron
-            for n in range(n_atoms):
-                n_current_sources = neuron_current_sources[n][0]
+            for atom in range(n_atoms):
+                n_current_sources = neuron_current_sources[atom][0]
                 spec.write_value(n_current_sources)
                 if n_current_sources != 0:
                     for csid in range(n_current_sources * 2):
-                        spec.write_value(neuron_current_sources[n][csid+1])
+                        spec.write_value(neuron_current_sources[atom][csid+1])
 
             # Write the number of each type of current source
-            for n in range(1, len(cs_index_array)):
-                w = cs_index_array[n]
-                spec.write_value(w)
+            for cs_index in range(1, len(cs_index_array)):
+                spec.write_value(cs_index_array[cs_index])
 
             # Now loop over the current sources and write the data required
             # for each type of current source