Fix pylint errors

pynest/nest/raster_plot.py

@@ -55,17 +55,16 @@ def extract_events(data, time=None, sel=None):
     """
     val = []
 
+    t_min, t_max = 0, float("inf")
     if time:
         t_max = time[-1]
         if len(time) > 1:
             t_min = time[0]
-        else:
-            t_min = 0
 
     for v in data:
         t = v[1]
         node_id = v[0]
-        if time and (t < t_min or t >= t_max):
+        if not (t_min <= t < t_max):
             continue
         if not sel or node_id in sel:
             val.append(v)
@@ -131,6 +130,7 @@ def from_file_pandas(fname, **kwargs):
     """Use pandas."""
     data = None
     for f in fname:
+        # pylint: disable=possibly-used-before-assignment
         dataFrame = pandas.read_table(f, header=2, skipinitialspace=True)
         newdata = dataFrame.values
 

testsuite/pytests/connect_test_base.py

@@ -451,6 +451,9 @@ def get_degrees(fan, pop1, pop2):
         degrees = np.sum(M, axis=1)
     elif fan == "out":
         degrees = np.sum(M, axis=0)
+    else:
+        raise ValueError(f"fan must be 'in' or 'out', got '{fan}'.")
+
     return degrees
 
 

testsuite/pytests/test_clopath_synapse.py

@@ -127,6 +127,9 @@ def test_SynapseDepressionFacilitation(self):
                     "tau_u_bar_plus": 114.0,
                     "delay_u_bars": 5.0,
                 }
+            else:
+                raise ValueError(f"Unsupported neuron model '{nrn_model}'")
+
             syn_weights = []
             # Loop over pairs of spike trains
             for s_t_pre, s_t_post in zip(spike_times_pre, spike_times_post):
@@ -146,6 +149,8 @@ def test_SynapseDepressionFacilitation(self):
                     conn_weight = 80.0
                 elif nrn_model == "hh_psc_alpha_clopath":
                     conn_weight = 2000.0
+                else:
+                    raise ValueError(f"Unsupported neuron model '{nrn_model}'")
 
                 spike_gen_post = nest.Create("spike_generator", 1, {"spike_times": s_t_post})
 
@@ -176,6 +181,8 @@ def test_SynapseDepressionFacilitation(self):
                 correct_weights = [57.82638722, 72.16730112, 149.43359357, 103.30408341, 124.03640668, 157.02882555]
             elif nrn_model == "hh_psc_alpha_clopath":
                 correct_weights = [70.14343863, 99.49206222, 178.1028757, 119.63314118, 167.37750688, 178.83111685]
+            else:
+                raise ValueError(f"Unsupported neuron model '{nrn_model}'")
 
             self.assertTrue(np.allclose(syn_weights, correct_weights, rtol=1e-7))
 

testsuite/pytests/test_connect_tripartite_bernoulli.py

@@ -177,6 +177,9 @@ def get_degrees(fan, pop1, pop2):
         degrees = np.sum(M, axis=1)
     elif fan == "out":
         degrees = np.sum(M, axis=0)
+    else:
+        raise ValueError(f"fan must be 'in' or 'out', got '{fan}'.")
+
     return degrees
 
 

testsuite/pytests/test_stdp_nn_synapses.py

@@ -187,18 +187,16 @@ def reproduce_weight_drift(self, _pre_spikes, _post_spikes, _initial_weight):
                 t = _pre_spikes[pre_spikes_forced_to_grid.index(time_in_simulation_steps)]
 
                 # Evaluating the depression rule.
-                if self.synapse_parameters["synapse_model"] == "stdp_nn_restr_synapse":
-                    current_nearest_neighbour_pair_is_suitable = t_previous_post > t_previous_pre
-                    # If '<', t_previous_post has already been paired
-                    # with t_previous_pre, thus due to the restricted
-                    # pairing scheme we do not account it.
-                if (
-                    self.synapse_parameters["synapse_model"] == "stdp_nn_symm_synapse"
-                    or self.synapse_parameters["synapse_model"] == "stdp_nn_pre_centered_synapse"
-                ):
-                    # The current pre-spike is simply paired with the
-                    # nearest post-spike.
-                    current_nearest_neighbour_pair_is_suitable = True
+                # For the first two rules below, simply pair current pre-spike with nearest post-spike.
+                # For "nn_restr" and `post < pre`, the previous post has already been paired, thus due
+                #   to the restricted pairing scheme, we do not account it.
+                current_nearest_neighbour_pair_is_suitable = self.synapse_parameters["synapse_model"] in [
+                    "stdp_nn_symm_synapse",
+                    "stdp_nn_pre_centered_synapse",
+                ] or (
+                    self.synapse_parameters["synapse_model"] == "stdp_nn_restr_synapse"
+                    and t_previous_post > t_previous_pre
+                )
 
                 if current_nearest_neighbour_pair_is_suitable and t_previous_post != -1:
                     # Otherwise, if == -1, there have been