resolve pylinting issues

nest · Aug 8, 2024 · 62f81e9 · 62f81e9
1 parent 051aba9
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diff --git a/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_data-ignore_and_fire.py
@@ -58,26 +58,24 @@ def generate_reference_data(neuron_model="ignore_and_fire"):
     model_instance.create()
     model_instance.connect()
 
-    ## connectivity at start of simulation
-    subset_size = 2000  ## number of pre- and post-synaptic neurons weights are extracted from
+    # connectivity at start of simulation
+    subset_size = 2000  # number of pre- and post-synaptic neurons weights are extracted from
     pop_pre = model_instance.nodes["pop_E"][:subset_size]
     pop_post = model_instance.nodes["pop_E"][:subset_size]
     C = model_instance.get_connectivity(
         pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_presim.dat"
     )
 
-    ## simulate
+    # simulate
     model_instance.simulate(model_instance.pars["T"])
 
-    ## save parameters to file
+    # save parameters to file
     model_instance.save_parameters("model_instance_parameters", model_instance.pars["data_path"])
 
-    ## connectivity at end of simulation
+    # connectivity at end of simulation
     C = model_instance.get_connectivity(
         pop_pre, pop_post, model_instance.pars["data_path"] + "/" + "connectivity_postsim.dat"
     )
 
-    return
-
 
 generate_reference_data(neuron_model=sys.argv[1])
diff --git a/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py b/pynest/examples/ignore_and_fire/generate_reference_figures-ignore_and_fire.py
@@ -147,53 +147,53 @@ def plot_weight_distributions(whist_presim, whist_postsim, weights, pars, path="
 def generate_reference_figures(neuron_model="ignore_and_fire"):
     """Generate and store set of reference data"""
 
-    ## raster plot
+    # raster plot
     parameters = model.get_default_parameters()
 
     parameters["neuron_model"] = neuron_model
 
     parameters["record_spikes"] = True
     parameters["record_weights"] = True
 
-    ## fetch node ids
+    # fetch node ids
     model_instance = model.Model(parameters)
     model_instance.create()
 
-    ## create subfolder for figures (if necessary)
+    # create subfolder for figures (if necessary)
     os.system("mkdir -p " + model_instance.pars["data_path"])
 
-    ## load spikes from reference data
+    # load spikes from reference data
     spikes = model.load_spike_data(
         model_instance.pars["data_path"],
         "spikes-%d" % (np.array(model_instance.nodes["spike_recorder"])[0]),
     )
     # plot_spikes(spikes, model_instance.nodes, model_instance.pars, model_instance.pars["data_path"])
     plot_spikes(spikes, model_instance.nodes, model_instance.pars, "figures")
 
-    ## load connectivity from reference data
+    # load connectivity from reference data
     connectivity_presim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_presim")
     connectivity_postsim = model.load_connectivity_data(model_instance.pars["data_path"], "connectivity_postsim")
 
-    ## create connectivity matrices before and after simulation for a subset of neurons
+    # create connectivity matrices before and after simulation for a subset of neurons
     subset_size = 100
     pop_pre = np.array(model_instance.nodes["pop_E"])[:subset_size]
     pop_post = np.array(model_instance.nodes["pop_E"])[:subset_size]
     W_presim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_presim, pop_pre, pop_post)
     W_postsim, pop_pre, pop_post = model.get_connectivity_matrix(connectivity_postsim, pop_pre, pop_post)
 
-    ## plot connectivity matrices
+    # plot connectivity matrices
     # plot_connectivity_matrix(W_presim, pop_pre, pop_post, "_presim", model_instance.pars["data_path"])
     # plot_connectivity_matrix(W_postsim, pop_pre, pop_post, "_postsim", model_instance.pars["data_path"])
     plot_connectivity_matrix(W_presim, pop_pre, pop_post, model_instance.pars, "_presim", "figures")
     plot_connectivity_matrix(W_postsim, pop_pre, pop_post, model_instance.pars, "_postsim", "figures")
 
-    ## compute weight distributions
+    # compute weight distributions
     # weights = np.arange(29.5,34.1,0.05)
     weights = np.arange(0.0, 150.1, 0.5)
     whist_presim = model.get_weight_distribution(connectivity_presim, weights)
     whist_postsim = model.get_weight_distribution(connectivity_postsim, weights)
 
-    ## plot weight distributions
+    # plot weight distributions
     # plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars["data_path"])
     plot_weight_distributions(whist_presim, whist_postsim, weights, model_instance.pars, "figures")
 

diff --git a/pynest/examples/ignore_and_fire/model-ignore_and_fire.py b/pynest/examples/ignore_and_fire/model-ignore_and_fire.py
@@ -95,6 +95,8 @@ def __init__(self, parameters):
         Returns
         -------
 
+        none
+
         """
 
         print("\nInitialising model and simulation...")
@@ -143,6 +145,9 @@ def __init__(self, parameters):
         elif self.pars["neuron_model"] == "ignore_and_fire":
             self.__neuron_params = {}
 
+        else:
+            return None
+
     def __derived_parameters(self, parameters):
         """
         Set additional parameters derived from base parameters.
@@ -185,8 +190,6 @@ def __derived_parameters(self, parameters):
         if self.pars["N_rec_spikes"] == "all":
             self.pars["N_rec_spikes"] = self.pars["N"]
 
-        return
-
     def create(self):
         """
         Create and configure all network nodes (neurons + recording and stimulus devices),
@@ -207,10 +210,12 @@ def create(self):
             nest.GetLocalNodeCollection(pop_all).V_m = random_vm
         elif self.pars["neuron_model"] == "ignore_and_fire":
             # pop_all = nest.Create("ignore_and_fire", self.pars["N"]) # overall population
-            # pop_all.rate = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["rate_dist"][0],high=self.pars["ignore_and_fire_pars"]["rate_dist"][1],size=self.pars["N"])
-            # pop_all.phase = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["phase_dist"][0],high=self.pars["ignore_and_fire_pars"]["phase_dist"][1],size=self.pars["N"])
+            # pop_all.rate = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["rate_dist"][0],high=self.pars
+            # ["ignore_and_fire_pars"]["rate_dist"][1],size=self.pars["N"])
+            # pop_all.phase = np.random.uniform(low=self.pars["ignore_and_fire_pars"]["phase_dist"][0],
+            # high=self.pars["ignore_and_fire_pars"]["phase_dist"][1],size=self.pars["N"])
 
-            ## better, but not working yet:
+            # better, but not working yet:
             pop_all.rate = nest.random.uniform(
                 min=self.pars["ignore_and_fire_pars"]["rate_dist"][0],
                 max=self.pars["ignore_and_fire_pars"]["rate_dist"][1],
@@ -265,24 +270,22 @@ def create(self):
         self.nodes["spike_recorder"] = spike_recorder
         # self.nodes["weight_recorder"] = weight_recorder
 
-        return
-
     def connect(self):
         """
         Connect network and devices.
 
         """
 
         print("\nConnecting network and devices...")
-        ## fetch neuron populations and device ids
+        # fetch neuron populations and device ids
         pop_all = self.nodes["pop_all"]
         pop_E = self.nodes["pop_E"]
         pop_I = self.nodes["pop_I"]
         poisson = self.nodes["poisson"]
         spike_recorder = self.nodes["spike_recorder"]
 
         # connect network
-        ## EE connections (plastic)
+        # EE connections (plastic)
         nest.Connect(
             pop_E,
             pop_E,
@@ -295,7 +298,7 @@ def connect(self):
             syn_spec="excitatory_plastic",
         )
 
-        ## EI connections (static)
+        # EI connections (static)
         nest.Connect(
             pop_E,
             pop_I,
@@ -308,7 +311,7 @@ def connect(self):
             syn_spec="excitatory_static",
         )
 
-        ## IE and II connections (static)
+        # IE and II connections (static)
         nest.Connect(
             pop_I,
             pop_all,
@@ -339,8 +342,6 @@ def connect(self):
         nest.Prepare()
         nest.Cleanup()
 
-        return
-
     def simulate(self, t_sim):
         """
         Run simulation.
@@ -356,8 +357,6 @@ def simulate(self, t_sim):
 
         nest.Simulate(t_sim)
 
-        return
-
     def save_parameters(self, filename_root, path):
         """
         Save model-instance parameters to file.
@@ -376,8 +375,6 @@ def save_parameters(self, filename_root, path):
 
         json.dump(self.pars, open("%s/%s.json" % (path, filename_root), "w"), indent=4)
 
-        return
-
     def get_connectivity(self, pop_pre, pop_post, filename=None):
         """
         Extract connectivity for subpopulations pop_pre and pop_post
@@ -464,7 +461,7 @@ def get_data_file_list(path, label):
 
     """
 
-    ## get list of files names
+    # get list of files names
     files = []
     for file_name in os.listdir(path):
         if file_name.endswith(".dat") and file_name.startswith(label):
@@ -513,14 +510,14 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3):
 
     files = get_data_file_list(path, label)
 
-    ## open spike files and read data
+    # open spike files and read data
     spikes = []
     for file_name in files:
         try:
             spikes += [
                 np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)
-            ]  ## load spike file while skipping the header
-        except:
+            ]  # load spike file while skipping the header
+        except ValueError:
             print("Error: %s" % sys.exc_info()[1])
             print(
                 "Remove non-numeric entries from file %s (e.g. in file header) \
@@ -530,8 +527,8 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3):
 
     spikes = np.concatenate(spikes)
 
-    ## extract spikes in specified time interval
-    if time_interval != None:
+    # extract spikes in specified time interval
+    if time_interval is not None:
         if type(time_interval) == tuple:
             ind = (spikes[:, 1] >= time_interval[0]) * (spikes[:, 1] <= time_interval[1])
             spikes = spikes[ind, :]
@@ -540,7 +537,7 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3):
 
     if type(pop) == nest.NodeCollection:
         spikes_subset = []
-        for cn, nid in enumerate(pop):  ## loop over all neurons
+        for cn, nid in enumerate(pop):  # loop over all neurons
             print(
                 "Spike extraction from %d/%d (%d%%) neurons completed"
                 % (cn + 1, len(pop), 1.0 * (cn + 1) / len(pop) * 100),
@@ -549,7 +546,7 @@ def load_spike_data(path, label, time_interval=None, pop=None, skip_rows=3):
             ind = np.where(spikes[:, 0] == nid)[0]
             spikes_subset += list(spikes[ind, :])
         spikes = np.array(spikes_subset)
-    elif pop == None:
+    elif pop is None:
         pass
     else:
         print("Warning: pop must be a NEST NodeCollection or None. All spikes are loaded.")
@@ -571,7 +568,8 @@ def load_connectivity_data(path, label, skip_rows=1):
                     Connectivity file label (file name root).
 
     skip_rows:      int, optional
-                    Number of rows to be skipped while reading connectivity files (to remove file headers). The default is 1.
+                    Number of rows to be skipped while reading connectivity files (to remove file headers).
+                    The default is 1.
 
     Returns
     -------
@@ -589,15 +587,16 @@ def load_connectivity_data(path, label, skip_rows=1):
 
     files = get_data_file_list(path, label)
 
-    ## open weight files and read data
+    # open weight files and read data
     C = []
     for file_name in files:
         try:
-            C += [np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)]  ## load file while skipping the header
-        except:
+            C += [np.loadtxt("%s/%s" % (path, file_name), skiprows=skip_rows)]  # load file while skipping the header
+        except ValueError:
             print("Error: %s" % sys.exc_info()[1])
             print(
-                "Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter 'skip_rows'.\n"
+                "Remove non-numeric entries from file %s (e.g. in file header) by specifying (optional) parameter \
+                'skip_rows'.\n"
                 % (file_name)
             )
 
@@ -709,10 +708,10 @@ def get_connectivity_matrix(connectivity, pop_pre=[], pop_post=[]):
     if len(pop_post) == 0:
         pop_post = np.unique(connectivity[:, 1])
 
-    ## initialise weight matrix
-    W = np.zeros([len(pop_post), len(pop_pre)])  ## convention: pre = columns, post = rows
+    # initialise weight matrix
+    W = np.zeros([len(pop_post), len(pop_pre)])  # convention: pre = columns, post = rows
 
-    ## fill weight matrix
+    # fill weight matrix
     for c in range(connectivity.shape[0]):
         W[get_index(pop_post, connectivity[c, 1]), get_index(pop_pre, connectivity[c, 0])] = connectivity[c, 2]
 

diff --git a/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py b/pynest/examples/ignore_and_fire/parameter_dict-ignore_and_fire.py
@@ -51,7 +51,7 @@
 pars["theta"] = 20.0  # spike threshold(mV)
 pars["V_reset"] = 0.0  # reset potential(mV)
 
-## needed for ignore_and_fire version of the model
+# needed for ignore_and_fire version of the model
 pars["ignore_and_fire_pars"] = {}
 pars["ignore_and_fire_pars"]["rate_dist"] = [0.5, 1.5]
 pars["ignore_and_fire_pars"]["phase_dist"] = [0.01, 1.0]