Merge pull request #1063 from SpiNNakerManchester/flake8_tidy_up

Add flake8 back for spynnaker_integration_tests, spynnaker8 directories

.github/workflows/python_actions.yml

@@ -70,12 +70,12 @@ jobs:
         coveralls-token: ${{ secrets.GITHUB_TOKEN }}
 
     - name: Lint with flake8
-      run: flake8 spynnaker unittests
+      run: flake8 spynnaker spynnaker8 unittests spynnaker_integration_tests
 
     - name: Lint with pylint
       uses: ./support/actions/pylint
       with:
-        package: spynnaker
+        package: spynnaker spynnaker8
 
     - name: Run rat copyright enforcement
       if: matrix.python-version == 3.8

spynnaker8/models/connectors/multapse_connector.py

@@ -56,6 +56,6 @@ def __init__(self, n, allow_self_connections=True,
                     "spynnaker.pyNN.models.neural_projections.connectors."
                     "MultapseConnector")
         super(MultapseConnector, self).__init__(
-            num_synapses=n, allow_self_connections=allow_self_connections,
+            n=n, allow_self_connections=allow_self_connections,
             with_replacement=with_replacement, safe=safe, verbose=verbose,
             rng=rng)

spynnaker8/models/recorder.py

@@ -25,11 +25,11 @@ class Recorder(_BaseClass):
     # DO NOT DEFINE SLOTS! Multiple inheritance problems otherwise.
     # __slots__ = []
 
-    def __init__(self, population):
+    def __init__(self, population, vertex):
         """
         :param population: the population to record for
         :type population: ~spynnaker.pyNN.models.populations.Population
         """
         moved_in_v6("spynnaker8.models.recorder",
                     "spynnaker.pyNN.models.recorder")
-        super(Recorder, self).__init__(population)
+        super(Recorder, self).__init__(population, vertex)

spynnaker8/models/synapse_dynamics/timing_dependence/timing_dependence_recurrent.py

@@ -15,9 +15,9 @@
 from spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (
     TimingDependenceRecurrent as
     _BaseClass)
+from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
 
 _defaults = _BaseClass.default_parameters
-from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
 
 
 class TimingDependenceRecurrent(_BaseClass):

spynnaker8/models/synapse_dynamics/timing_dependence/timing_dependence_spike_nearest_pair.py

@@ -16,9 +16,9 @@
 from spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (
     TimingDependenceSpikeNearestPair as
     _BaseClass)
+from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
 
 _defaults = _BaseClass.default_parameters
-from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
 
 
 class TimingDependenceSpikeNearestPair(_BaseClass):

spynnaker8/models/synapse_dynamics/timing_dependence/timing_dependence_vogels_2011.py

@@ -15,9 +15,10 @@
 from spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (
     TimingDependenceVogels2011 as
     _BaseClass)
+from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
 
 _defaults = _BaseClass.default_parameters
-from spynnaker.pyNN.utilities.utility_calls import moved_in_v6
+
 
 class TimingDependenceVogels2011(_BaseClass):
     """
@@ -41,4 +42,4 @@ def __init__(
                     "spynnaker.pyNN.models.neuron.plasticity.stdp."
                     "timing_dependence.TimingDependenceVogels2011")
         super(TimingDependenceVogels2011, self).__init__(
-            tau=tau, alpha=alpha, A_plus=A_plus, A_minus=A_minus)
+            tau=tau, alpha=alpha, A_plus=A_plus, A_minus=A_minus)

spynnaker_integration_tests/test_big_connection/test_big_connection.py

@@ -72,4 +72,4 @@ def report_file(self):
             style += "advanced"
         else:
             style += "simple"
-        return "{}_test_big_connection".format()
+        return "{}_test_big_connection".format(style)

spynnaker_integration_tests/test_many_boards/many_boards.py

@@ -78,7 +78,6 @@ def report_file(self):
         return "{}_n_boards={}_n_neurons={}_simtime={}".format(
             style, self.n_boards, self.n_neurons, self.simtime)
 
-
     def do_run(self):
         report_file = self.report_file()
         t_before = time.time()

spynnaker_integration_tests/test_record_sdram/__init__.py

@@ -11,4 +11,4 @@
 # GNU General Public License for more details.
 #
 # You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.

spynnaker_integration_tests/test_record_sdram_no_extra_mons/__init__.py

@@ -11,4 +11,4 @@
 # GNU General Public License for more details.
 #
 # You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.

spynnaker_integration_tests/test_selective_recording/test_recording_on_off.py

@@ -13,7 +13,6 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-import os
 from neo.io import PickleIO
 from spinn_front_end_common.utilities.exceptions import ConfigurationException
 import spynnaker8 as sim

spynnaker_integration_tests/test_selective_recording/test_sampling.py

@@ -26,14 +26,10 @@ def medium(self):
         simtime = 1000
         spike_rate = 5
         n_neurons = 320
-        spike_rec_indexes = list(range(0, 100, 2)) \
-                            + list(range(100, 200, 3)) \
-                            + list(range(200, 300, 1)) \
-                            + list(range(300, 320, 4))
-        v_rec_indexes = list(range(0, 100, 1)) \
-                        + list(range(100, 200, 3)) \
-                        + list(range(200, 300, 4)) \
-                        + list(range(300, 320, 2))
+        spike_rec_indexes = list(range(0, 100, 2)) + list(range(100, 200, 3)) \
+            + list(range(200, 300, 1)) + list(range(300, 320, 4))
+        v_rec_indexes = list(range(0, 100, 1)) + list(range(100, 200, 3)) \
+            + list(range(200, 300, 4)) + list(range(300, 320, 2))
         v_rate = 3
         pop = ps.create_population(sim, n_neurons=n_neurons, label="test",
                                    spike_rate=spike_rate,

spynnaker_integration_tests/test_selective_recording/test_sampling_by_compare.py

@@ -23,16 +23,17 @@
 """
 This is the original way of testing selective recording.
 
-It worked by running the same seeded script twice. 
+It worked by running the same seeded script twice.
 Once recording all and once with selective recording on.
 
-The main selective recording is now done by test_sampling 
+The main selective recording is now done by test_sampling
 based on the PatternSpiker.
 
 This is kept mainly for all the useful compare methods.
 Which is why most tests are commented out.
 """
 
+
 def run_script(
         simtime, n_neurons, run_split=1,
         record_spikes=False, spike_rate=None, spike_rec_indexes=None,

spynnaker_integration_tests/test_stdp/test_STDP_pair_multiplicative.py

@@ -20,7 +20,6 @@
 from spinnaker_testbase import BaseTestCase
 
 
-
 def post_spike_same_time():
     """ Check that the offsets between send times of different spike source
         arrays don't change the outcome of STDP

spynnaker_integration_tests/test_struct_pl/test_structural_formation_to_full.py

@@ -58,6 +58,7 @@ def structural_formation_to_full():
 
     return conns, num_rewires
 
+
 class TestStructuralFormationToFull(BaseTestCase):
     def do_run(self):
         conns, num_rewires = structural_formation_to_full()

spynnaker_integration_tests/test_various/test_record_packets_per_timestep.py

@@ -32,9 +32,9 @@ def do_run(self):
 
         weight = 5
         delay = 5
-        proj = sim.Projection(pop_src, pop_lif, sim.OneToOneConnector(),
-                              sim.StaticSynapse(weight=weight, delay=delay),
-                              receptor_type="excitatory")
+        sim.Projection(pop_src, pop_lif, sim.OneToOneConnector(),
+                       sim.StaticSynapse(weight=weight, delay=delay),
+                       receptor_type="excitatory")
 
         pop_lif.record("packets-per-timestep")
         sim.run(runtime)