linting

Author: apdavison

.github/workflows/full-test.yml

@@ -70,7 +70,7 @@ jobs:
         # stop the build if there are Python syntax errors or undefined names
         flake8 pyNN --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 pyNN --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        flake8 pyNN --count --exit-zero --max-complexity=20 --max-line-length=127 --statistics
     - name: Run unit and system tests
       run: |
         pytest -v --cov=pyNN --cov-report=term test

pyNN/arbor/cells.py

@@ -101,7 +101,7 @@ def _build_decor(self, i):
             decor.set_ion(ion_name,
                           int_con=ionic_species.internal_concentration,
                           ext_con=ionic_species.external_concentration,
-                          rev_pot=ionic_species.reversal_potential) #method="nernst/na")
+                          rev_pot=ionic_species.reversal_potential)  # method="nernst/na")
         for native_name, region_params in mechanism_parameters.items():
             for region, params in region_params.items():
                 if native_name == "hh":
@@ -124,14 +124,15 @@ def _build_decor(self, i):
         for current_source in self.current_sources[i]:
             location_generator = current_source["location_generator"]
             mechanism = getattr(arbor, current_source["model_name"])
-            for locset, label in  location_generator.generate_locations(morph, label=f"{current_source['model_name']}_label"):
-                #decor.place(locset, mechanism(start, stop - start, current=amplitude), "iclamp_label")
+            for locset, label in location_generator.generate_locations(morph, label=f"{current_source['model_name']}_label"):
                 decor.place(locset, mechanism(**current_source["parameters"].evaluate()), label)
 
         # add spike source
-        decor.place('"root"', arbor.threshold_detector(-10), "detector")  # todo: allow user to choose location and threshold value
+        decor.place('"root"', arbor.threshold_detector(-10), "detector")
+        # todo: allow user to choose location and threshold value
 
-        policy = arbor.cv_policy_max_extent(10.0)  # to do: allow user to specify this value and/or the policy more generally
+        policy = arbor.cv_policy_max_extent(10.0)
+        # to do: allow user to specify this value and/or the policy more generally
         decor.discretization(policy)
 
         return decor

pyNN/arbor/morphology.py

@@ -6,7 +6,6 @@
 from .. import morphology as base_morphology
 
 
-
 # --- MorphologyFilters ---
 
 class with_label(base_morphology.with_label):
@@ -48,7 +47,6 @@ def get_region(self):
         raise NotImplementedError
 
 
-
 # --- IonChannelDistributions ---
 
 
@@ -68,11 +66,13 @@ def resolve(self):
             value = self.value_provider.get_value()
         return region, value
 
+
 class by_distance(base_morphology.by_distance, HasSelector):
 
     def resolve(self):
         raise NotImplementedError
 
+
 class by_diameter(base_morphology.by_diameter, HasSelector):
 
     def resolve(self):

pyNN/arbor/projections.py

@@ -91,7 +91,7 @@ def arbor_connections(self, gid):
                                 cg.weight,
                                 cg.delay
                             )
-                    )
+                        )
                 else:
                     raise NotImplementedError()
             return connections

pyNN/arbor/recording.py

@@ -48,9 +48,11 @@ def _localize_variables(self, variables, locations):
                     location_generator = item
                 else:
                     raise ValueError("'locations' should be a str, list, LocationGenerator or None")
-                morphology = self.population.celltype.parameter_space["morphology"].base_value  # todo: handle inhomogeneous morphologies in a Population
+                morphology = self.population.celltype.parameter_space["morphology"].base_value
+                # todo: handle inhomogeneous morphologies in a Population
                 for locset, label in location_generator.generate_locations(morphology, label="recording-point"):
-                    short_label = label[len("recording-point-"):]  # not sure we need the 'recording-point' in the first place
+                    short_label = label[len("recording-point-"):]
+                    # not sure we need the 'recording-point' in the first place
                     for var_name in variables:
                         resolved_variables.append(recording.Variable(location=locset, name=var_name, label=short_label))
         return resolved_variables

pyNN/arbor/simulator.py

@@ -35,36 +35,12 @@ def build_mechanisms():
         return mech_path
 
 
-class Cell(int):  # (, common.IDMixin):
+class Cell(int):
 
     def __init__(self, n):
         """Create an ID object with numerical value `n`."""
-        #int.__init__(n)
-        #common.IDMixin.__init__(self)
         self.gid = n
-        #self.morph = None  #morph
-        #self.decor = None  # decor
-        #self.labels = None  # labels
         self.local = True
-        #self.decor.place('"root"', arbor.threshold_detector(-10), f"detector-{self.gid}")
-
-    # def __lt__(self, other):
-    #     return self.gid < other.gid
-
-    # def __lte__(self, other):
-    #     return self.gid <= other.gid
-
-    # def __gt__(self, other):
-    #     return self.gid > other.gid
-
-    # def __gte__(self, other):
-    #     return self.gid >= other.gid
-
-    # def __eq__(self, other):
-    #     return self.gid == other.gid
-
-    # def __ne__(self, other):
-    #     return self.gid != other.gid
 
 
 class NetworkRecipe(arbor.recipe):
@@ -206,7 +182,8 @@ def run(self, simtime):
             hints = {}
             decomp = arbor.partition_load_balance(recipe, self.arbor_context, hints)
             self.arbor_sim = arbor.simulation(recipe, self.arbor_context, decomp, self.rng_seed)
-            self.arbor_sim.record(arbor.spike_recording.all)  # todo: for now record all, but should be controlled by population.record()
+            self.arbor_sim.record(arbor.spike_recording.all)
+            # todo: for now record all, but should be controlled by population.record()
             for recorder in self.recorders:
                 recorder._set_arbor_sim(self.arbor_sim)
         self.t += simtime

pyNN/arbor/standardmodels.py

@@ -244,8 +244,6 @@ def get_schema(self):
                 "cl": IonicSpecies
             }
         }
-        #for name, ion_channel in self.ion_channels.items():
-        #    schema[name] = ion_channel.get_schema()
         return schema
 
     def translate(self, parameters, copy=True):
@@ -254,7 +252,6 @@ def translate(self, parameters, copy=True):
             _parameters = deepcopy(parameters)
         else:
             _parameters = parameters
-        cls = self.__class__
         if parameters.schema != self.get_schema():
             # should replace this with a PyNN-specific exception type
             raise Exception(f"Schemas do not match: {parameters.schema} != {self.get_schema()}")

pyNN/brian2/recording.py

@@ -39,10 +39,11 @@ def _create_device(self, group, variable):
             varname = translations[variable.name]['translated_name']
             neurons_to_record = np.sort(np.fromiter(
                 self.recorded[variable], dtype=int)) - self.population.first_id
-            self._devices[variable.name] = brian2.StateMonitor(group, varname,
-                                                          record=neurons_to_record,
-                                                          when='end',
-                                                          dt=self.sampling_interval * ms)
+            self._devices[variable.name] = brian2.StateMonitor(
+                group, varname,
+                record=neurons_to_record,
+                when='end',
+                dt=self.sampling_interval * ms)
         simulator.state.network.add(self._devices[variable.name])
 
     def _record(self, variable, new_ids, sampling_interval=None):

pyNN/connectors.py

@@ -195,7 +195,7 @@ def _standard_connect(self, projection, connection_map_generator, distance_map=N
             _proceed = False
             if source_mask is True or source_mask.any():
                 _proceed = True
-            elif type(source_mask) == np.ndarray:
+            elif isinstance(source_mask, np.ndarray):
                 if source_mask.dtype == bool:
                     if source_mask.any():
                         _proceed = True

pyNN/morphology.py

@@ -19,8 +19,9 @@
 
 
 def _download_file(url):
-    import requests  # consider rewriting using just standard library
-                     # to avoid adding another dependency
+    import requests
+    # consider rewriting using just standard library
+    # to avoid adding another dependency
     local_filename = url.split('/')[-1]
     r = requests.get(url, stream=True)
     with open(local_filename, 'wb') as f:
@@ -61,7 +62,6 @@ def soma_index(self):
         return self._soma_index
 
 
-
 class NeuroMLMorphology(Morphology):
     """
 
@@ -100,7 +100,8 @@ def labels(self):
 
     @property
     def soma_index(self):
-        return self.labels().get("soma", 0)  # todo: more robust way to handle morphologies without a declared soma, e.g. single dendrites
+        return self.labels().get("soma", 0)
+        # todo: more robust way to handle morphologies without a declared soma, e.g. single dendrites
 
     @property
     def path_lengths(self):
@@ -185,7 +186,6 @@ class SynapseDistribution(NeuriteDistribution):
     pass
 
 
-
 class uniform(IonChannelDistribution, SynapseDistribution):
     # we inherit from two parents, because we want to use the name "uniform" for both
     # the implementation behaves differently depending on context
@@ -219,12 +219,10 @@ def value_in(self, morphology, index):
         return self.absence
 
 
-
 class MorphologyFilter(object):
     pass
 
 
-
 class dendrites(MorphologyFilter):
 
     def __init__(self, fraction_along=None):
@@ -274,7 +272,6 @@ def __init__(self, *labels):
         self.labels = labels
 
 
-
 class LocationGenerator:
 
     def lazily_evaluate(self, mask=None, shape=None):