typing

spynnaker/plot_utils.py

@@ -186,6 +186,7 @@ def plot_spikes(spikes, title="spikes"):
     spike_data = np.loadtxt("spikes.csv", delimiter=',')
     plot_spikes(spike_data)
     doubled_spike_data = np.loadtxt("spikes.csv", delimiter=',')
+    _i: int
     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

@@ -324,7 +324,7 @@ def __in_selector(self, n: int, selector: Selector) -> bool:
 
     def _write_current_source_parameters(
             self, spec: DataSpecificationBase):
-        n_atoms = self._vertex_slice.n_atoms
+        n_atoms: int = self._vertex_slice.n_atoms
 
         spec.comment(
             f"\nWriting Current Source Parameters for {n_atoms} Neurons:\n")
@@ -350,7 +350,7 @@ def _write_current_source_parameters(
         if current_sources:
             # Array to keep track of the number of each type of current source
             # (there are four, but they are numbered 1 to 4, so five elements)
-            cs_index_array = [0, 0, 0, 0, 0]
+            cs_index_array: List[int] = [0, 0, 0, 0, 0]
 
             # Data sent to the machine will be current sources per neuron
             # This array will have the first entry indicating the number of

spynnaker/pyNN/utilities/neo_buffer_database.py

@@ -20,8 +20,8 @@
 import re
 import struct
 from typing import (
-    Any, Collection, Dict, Iterable, List, Optional, Sequence, Tuple, Union,
-    TYPE_CHECKING)
+    Any, Collection, Dict, Iterable, List, Optional, Sequence, Set, Tuple,
+    TYPE_CHECKING, Union)
 
 import numpy
 from numpy import floating, integer, uint8, uint32
@@ -785,7 +785,7 @@ def __combine_indexes(
         data_set = set(data_indexes)
         indexes = [i for i in view_indexes if i in data_set]
         # check for missing and report
-        view_set = set(view_indexes)
+        view_set: Set[int] = set(view_indexes)
         missing = view_set.difference(data_indexes)
         if missing:
             logger.warning("No {} available for neurons {}",