Enhance ruff checks and update dependencies to v0.6.2

examples/Example6.py

@@ -58,8 +58,6 @@ def generate(ref="Example6_PyNN", add_inputs=True):
         "L6": {"E": 14395, "I": 2948},
     }
 
-    scale = 0.1
-
     pops = []
     input_pops = []
     pop_dict = {}

neuromllite/BBPConnectomeReader.py

@@ -56,10 +56,10 @@ def parse_group(self, g):
                     self.pre_pop = node._v_name.replace("-", "_")
                     self.post_pop = None
                     # print("Conn %s -> ?"%(self.pre_pop))
-                elif self.pre_pop != None and self.post_pop == None:
+                elif self.pre_pop is not None and self.post_pop is None:
                     self.post_pop = node._v_name.replace("-", "_")
                     # print("Conn2 %s -> %s"%(self.pre_pop,self.post_pop))
-                elif self.pre_pop != None and self.post_pop != None:
+                elif self.pre_pop is not None and self.post_pop is not None:
                     # print("Conn3 %s -> %s"%(self.pre_pop,self.post_pop))
                     pass
 
@@ -192,7 +192,7 @@ def parse_dataset(self, d):
                         )
 
         # Projection
-        elif self.pre_pop != None and self.post_pop != None:
+        elif self.pre_pop is not None and self.post_pop is not None:
             proj_id = "Proj__%s__%s" % (self.pre_pop, self.post_pop)
             synapse = "gaba"
             if (

neuromllite/ConnectivityHandler.py

@@ -122,7 +122,7 @@ def _scale_individual_weight(self, weight, projName):
 
         if self.scale_by_post_pop_cond:
             gbase_nS = self._get_gbase_nS(projName)
-            if gbase_nS != None:
+            if gbase_nS is not None:
                 weight *= gbase_nS
 
         if not orig_weight == weight:
@@ -138,7 +138,7 @@ def _scale_population_weight(self, weight, projName):
 
         if self.scale_by_post_pop_cond:
             gbase_nS = self._get_gbase_nS(projName)
-            if gbase_nS != None:
+            if gbase_nS is not None:
                 weight *= gbase_nS
 
         if not orig_weight == weight:

neuromllite/MatrixHandler.py

@@ -220,7 +220,7 @@ def finalise_document(self):
                         self._get_conn_label(matrix_number_conns, pclass)
                     ][pre_pop_i][post_pop_i] += abs(self.proj_conns[projName])
 
-                    cond_scale = gbase_nS if gbase_nS != None else 1.0
+                    cond_scale = gbase_nS if gbase_nS is not None else 1.0
                     tot_scaled = (
                         abs(self.proj_tot_weight[projName]) * cond_scale / num_post
                     )
@@ -489,7 +489,7 @@ def handle_projection(
         if synapse_obj:
             self.proj_syn_objs[projName] = synapse_obj
             erev = self.get_reversal_potential_mV(synapse_obj)
-            if erev != None and erev < self.CUTOFF_INH_SYN_MV:
+            if erev is not None and erev < self.CUTOFF_INH_SYN_MV:
                 proj_type = "inhibitory"
 
         if self.nl_network:

neuromllite/NetworkGenerator.py

@@ -594,7 +594,7 @@ def _extract_pynn_components_to_neuroml(nl_model, nml_doc=None):
                 cell.id = c.id
 
     for s in nl_model.synapses:
-        if nml_doc.get_by_id(s.id) == None:
+        if nml_doc.get_by_id(s.id) is None:
             if s.pynn_synapse_type and s.pynn_receptor_type:
                 import neuroml
 
@@ -720,7 +720,7 @@ def generate_neuroml2_from_network(
             if incl not in nml_doc.includes:
                 nml_doc.includes.append(incl)
 
-        if nml_doc.get_by_id(i.id) == None:
+        if nml_doc.get_by_id(i.id) is None:
             if i.neuroml2_source_file:
                 incl = neuroml.IncludeType(
                     locate_file(i.neuroml2_source_file, base_dir)
@@ -768,7 +768,7 @@ def generate_neuroml2_from_network(
                     if p.component == c.id:
                         pass
 
-            if nml_doc.get_by_id(c.id) == None:  # cell (of any type) is not in nml_doc
+            if nml_doc.get_by_id(c.id) is None:  # cell (of any type) is not in nml_doc
                 if incl not in nml_doc.includes:
                     nml_doc.includes.append(incl)
 
@@ -842,7 +842,7 @@ def generate_neuroml2_from_network(
                 )
 
     for s in nl_model.synapses:
-        if nml_doc.get_by_id(s.id) == None:
+        if nml_doc.get_by_id(s.id) is None:
             if s.neuroml2_source_file:
                 incl = neuroml.IncludeType(
                     locate_file(s.neuroml2_source_file, base_dir)
@@ -894,7 +894,7 @@ def generate_neuroml2_from_network(
         print_v(nml_doc.summary())
 
     # Save to file
-    if target_dir == None:
+    if target_dir is None:
         target_dir = base_dir
     if format == "xml":
         if not nml_file_name:
@@ -949,7 +949,7 @@ def _generate_neuron_files_from_neuroml(network, verbose=False, dir_for_mod_file
     temp_nml_doc = NeuroMLDocument(id="temp")
 
     dirs_for_mod_files = []
-    if dir_for_mod_files != None:
+    if dir_for_mod_files is not None:
         dirs_for_mod_files.append(os.path.abspath(dir_for_mod_files))
 
     for c in network.cells:
@@ -1036,7 +1036,7 @@ def generate_and_run(
     Generates the network in the specified simulator and runs, if appropriate
     """
 
-    if network == None:
+    if network is None:
         network = load_network(simulation.network)
 
     print_v(
@@ -1247,7 +1247,7 @@ def generate_value_array(node, index, context='{0}', param='value'):
 
             temp_nml_doc = _extract_pynn_components_to_neuroml(network)
 
-            summary = temp_nml_doc.summary()
+            temp_nml_doc.summary()
             from pyneuroml.pynml import convert_to_units
 
             sim_file_info["inputs"] = {}
@@ -1762,7 +1762,7 @@ def get_source_id(spiketrain):
                 return traces, events
 
         elif simulator == "NetPyNE":
-            if target_dir == None:
+            if target_dir is None:
                 target_dir = "./"
 
             _generate_neuron_files_from_neuroml(network, dir_for_mod_files=target_dir)
@@ -1804,7 +1804,7 @@ def get_source_id(spiketrain):
             for pop_id in trace_pop_indices_seg_ids:
                 for index in trace_pop_indices_seg_ids[pop_id]:
                     seg_ids = trace_pop_indices_seg_ids[pop_id][index]
-                    if seg_ids == None:
+                    if seg_ids is None:
                         simConfig.recordTraces[
                             "%s.%s.%s.v" % (simulation.id, pop_id, index)
                         ] = {
@@ -1853,7 +1853,7 @@ def get_source_id(spiketrain):
                     % (projName, ptype, prePop, postPop, synapse)
                 )
 
-                preComp = netpyne_handler.pop_ids_vs_components[prePop]
+                netpyne_handler.pop_ids_vs_components[prePop]
 
                 for conn in netpyne_handler.connections[projName]:
                     (
@@ -1905,10 +1905,8 @@ def get_source_id(spiketrain):
                     ):  # check if postsyn is in this node's list of gids
                         sim.net._addCellConn(connParam, pre_id, post_id)
 
-            stims = (
-                sim.net.addStims()
-            )  # add external stimulation to cells (IClamps etc)
-            simData = (
+            (sim.net.addStims())  # add external stimulation to cells (IClamps etc)
+            (
                 sim.setupRecording()
             )  # setup variables to record for each cell (spikes, V traces, etc)
             sim.runSim()  # run parallel Neuron simulation
@@ -1932,7 +1930,7 @@ def get_source_id(spiketrain):
                 all_v = []
                 for index in indices_segids:
                     seg_ids = indices_segids[index]
-                    if seg_ids == None:
+                    if seg_ids is None:
                         ref = "%s.%s.%s.v" % (simulation.id, pop_id, index)
                         # print('Ref: %s, existing: %s'%(ref, sim.allSimData.keys()))
                         v = [
@@ -2062,7 +2060,7 @@ def get_source_id(spiketrain):
                             "Recording in pop %s, cell index %i, seg ids: %s"
                             % (p.id, cell_index, seg_ids)
                         )
-                        if seg_ids == None:
+                        if seg_ids is None:
                             quantity = "%s/%i/%s/v" % (p.id, cell_index, p.component)
                             if not p.has_positions():
                                 quantity = "%s[%i]/v" % (p.id, cell_index)

neuromllite/SonataReader.py

@@ -1332,7 +1332,7 @@ def main(args=None):
         # id = '300_pointneurons'
         # filename = '/home/padraig/git/sonatapg/examples/%s/config.json'%id
 
-        nml_doc = get_neuroml_from_sonata(filename, id, generate_lems=True)
+        get_neuroml_from_sonata(filename, id, generate_lems=True)
         """
         nml_file_name = '%s.net.nml'%id
         nml_file_name += '.h5'

neuromllite/gui/NMLliteUI.py

@@ -341,8 +341,8 @@ def __init__(self, nml_sim_file, parent=None):
             self.plot2DTab.setLayout(self.plot2DTabLayout)
 
             for plot2D in self.simulation.plots2D:
-                info = self.simulation.plots2D[plot2D]
-                pLayout = self.add_tab(
+                self.simulation.plots2D[plot2D]
+                self.add_tab(
                     plot2D, self.plot2DTab, figure=True, toolbar=True, options=True
                 )
 
@@ -354,8 +354,8 @@ def __init__(self, nml_sim_file, parent=None):
             self.plot3DTab.setLayout(self.plot3DTabLayout)
 
             for plot3D in self.simulation.plots3D:
-                info = self.simulation.plots3D[plot3D]
-                pLayout = self.add_tab(
+                self.simulation.plots3D[plot3D]
+                self.add_tab(
                     plot3D, self.plot3DTab, figure=True, toolbar=False, options=True
                 )
 
@@ -1049,7 +1049,7 @@ def replotSimResults(self):
             hm = ax_heatmap.pcolormesh(heat_array, cmap=cm)
             # cbar = ax_heatmap.colorbar(im)
 
-            if self.heatmapColorbar == None:
+            if self.heatmapColorbar is None:
                 self.heatmapColorbar = self.heatmapFigure.colorbar(hm)
                 self.heatmapColorbar.set_label("Firing rate")
 

neuromllite/test/layout/Layout.py

@@ -2,8 +2,6 @@
     RelativeLayout,
     Cell,
     Synapse,
-    InputSource,
-    Input,
     RectangularRegion,
 )
 from neuromllite.NetworkGenerator import generate_network

neuromllite/test/sonata/test_sonata_reader.py

@@ -25,7 +25,7 @@ def main():
 
         print("****        Testing %s (%s)         \n" % (id, filename))
 
-        nml_doc = get_neuroml_from_sonata(filename, id, generate_lems=True)
+        get_neuroml_from_sonata(filename, id, generate_lems=True)
 
         nml_file = "%s.net.nml" % id
 

neuromllite/test/test_base.py

@@ -213,7 +213,6 @@ class NewNetwork(Base):
         nety = NewNetwork.from_dict(datay)
         str_nety = str(nety)
 
-        verbose = False
         print("----- Before -----")
         print(str_orig)
         print("----- After via %s -----" % filenamej)

neuromllite/utils.py

@@ -280,7 +280,7 @@ def create_new_model(
     net.id = reference
 
     print(net.to_json())
-    if network_filename == None:
+    if network_filename is None:
         network_filename = "%s.json" % net.id
     new_file = net.to_json_file(network_filename)
 
@@ -296,7 +296,7 @@ def create_new_model(
         record_traces={"all": "*"},
     )
 
-    if simulation_filename == None:
+    if simulation_filename is None:
         simulation_filename = "%s.json" % sim.id
     sim.to_json_file(simulation_filename)
 

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = neuromllite
-version = 0.6.1
+version = 0.6.2
 author = Padraig Gleeson
 author_email = p.gleeson@gmail.com
 url = https://github.com/NeuroML/NeuroMLlit
@@ -21,12 +21,12 @@ classifiers =
 
 [options]
 install_requires =
-    libNeuroML>=0.5.1
+    libNeuroML>=0.6.7
     pyyaml
     numpy
     tables
     h5py
-    modelspec>=0.2.6
+    modelspec>=0.3.9
     ppft[dill]
 
 packages = find: