Regenerated

NetPyNE/ACnet/LEMS_MediumNet_netpyne.py

@@ -58,7 +58,7 @@
 
 class NetPyNESimulation():
 
-    def __init__(self, tstop=500.0, dt=0.01, seed=123456789, save_json=False):
+    def __init__(self, tstop=500.0, dt=0.01, seed=123456789, save_json=False, abs_tol=None):
 
         self.setup_start = time.time()
 
@@ -89,6 +89,13 @@ def __init__(self, tstop=500.0, dt=0.01, seed=123456789, save_json=False):
         self.simConfig.duration = self.simConfig.tstop = tstop # Duration of the simulation, in ms
         self.simConfig.dt = dt # Internal integration timestep to use
 
+        # cvode
+        if abs_tol is not None:
+            self.simConfig.cvode_active = True
+            self.simConfig.cvode_atol = abs_tol
+        else:
+            self.simConfig.cvode_active = False
+
         # Seeds for randomizers (connectivity, input stimulation and cell locations)
         # Note: locations and connections should be fully specified by the structure of the NeuroML,
         # so seeds for conn & loc shouldn't affect networks structure/behaviour
@@ -1402,7 +1409,7 @@ def save_results(self):
     save_json = '-json' in sys.argv
     no_run = '-norun' in sys.argv
 
-    ns = NetPyNESimulation(tstop=500.0, dt=0.01, seed=123456789, save_json=save_json)
+    ns = NetPyNESimulation(tstop=500.0, dt=0.01, seed=123456789, save_json=save_json, abs_tol=None)
 
     if not no_run:
       ns.run()

NetPyNE/GapJunctions/LEMS_GJ_netpyne.py

@@ -32,7 +32,7 @@
 
 class NetPyNESimulation():
 
-    def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False):
+    def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False, abs_tol=None):
 
         self.setup_start = time.time()
 
@@ -63,6 +63,13 @@ def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False):
         self.simConfig.duration = self.simConfig.tstop = tstop # Duration of the simulation, in ms
         self.simConfig.dt = dt # Internal integration timestep to use
 
+        # cvode
+        if abs_tol is not None:
+            self.simConfig.cvode_active = True
+            self.simConfig.cvode_atol = abs_tol
+        else:
+            self.simConfig.cvode_active = False
+
         # Seeds for randomizers (connectivity, input stimulation and cell locations)
         # Note: locations and connections should be fully specified by the structure of the NeuroML,
         # so seeds for conn & loc shouldn't affect networks structure/behaviour
@@ -200,7 +207,7 @@ def save_results(self):
     save_json = '-json' in sys.argv
     no_run = '-norun' in sys.argv
 
-    ns = NetPyNESimulation(tstop=700.0, dt=0.01, seed=123456789, save_json=save_json)
+    ns = NetPyNESimulation(tstop=700.0, dt=0.01, seed=123456789, save_json=save_json, abs_tol=None)
 
     if not no_run:
       ns.run()

NetPyNE/LEMS_2007One_netpyne.py

@@ -30,7 +30,7 @@
 
 class NetPyNESimulation():
 
-    def __init__(self, tstop=520.0, dt=0.001, seed=123456789, save_json=False):
+    def __init__(self, tstop=520.0, dt=0.001, seed=123456789, save_json=False, abs_tol=None):
 
         self.setup_start = time.time()
 
@@ -51,6 +51,13 @@ def __init__(self, tstop=520.0, dt=0.001, seed=123456789, save_json=False):
         self.simConfig.duration = self.simConfig.tstop = tstop # Duration of the simulation, in ms
         self.simConfig.dt = dt # Internal integration timestep to use
 
+        # cvode
+        if abs_tol is not None:
+            self.simConfig.cvode_active = True
+            self.simConfig.cvode_atol = abs_tol
+        else:
+            self.simConfig.cvode_active = False
+
         # Seeds for randomizers (connectivity, input stimulation and cell locations)
         # Note: locations and connections should be fully specified by the structure of the NeuroML,
         # so seeds for conn & loc shouldn't affect networks structure/behaviour
@@ -175,7 +182,7 @@ def save_results(self):
     save_json = '-json' in sys.argv
     no_run = '-norun' in sys.argv
 
-    ns = NetPyNESimulation(tstop=520.0, dt=0.001, seed=123456789, save_json=save_json)
+    ns = NetPyNESimulation(tstop=520.0, dt=0.001, seed=123456789, save_json=save_json, abs_tol=None)
 
     if not no_run:
       ns.run()

NetPyNE/TwoCells/LEMS_TwoCell_netpyne.py

@@ -51,7 +51,7 @@
 
 class NetPyNESimulation():
 
-    def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False):
+    def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False, abs_tol=None):
 
         self.setup_start = time.time()
 
@@ -82,6 +82,13 @@ def __init__(self, tstop=700.0, dt=0.01, seed=123456789, save_json=False):
         self.simConfig.duration = self.simConfig.tstop = tstop # Duration of the simulation, in ms
         self.simConfig.dt = dt # Internal integration timestep to use
 
+        # cvode
+        if abs_tol is not None:
+            self.simConfig.cvode_active = True
+            self.simConfig.cvode_atol = abs_tol
+        else:
+            self.simConfig.cvode_active = False
+
         # Seeds for randomizers (connectivity, input stimulation and cell locations)
         # Note: locations and connections should be fully specified by the structure of the NeuroML,
         # so seeds for conn & loc shouldn't affect networks structure/behaviour
@@ -435,7 +442,7 @@ def save_results(self):
     save_json = '-json' in sys.argv
     no_run = '-norun' in sys.argv
 
-    ns = NetPyNESimulation(tstop=700.0, dt=0.01, seed=123456789, save_json=save_json)
+    ns = NetPyNESimulation(tstop=700.0, dt=0.01, seed=123456789, save_json=save_json, abs_tol=None)
 
     if not no_run:
       ns.run()

NetPyNE/UI/LEMS_HHSimple_netpyne.py

@@ -30,7 +30,7 @@
 
 class NetPyNESimulation():
 
-    def __init__(self, tstop=450.0, dt=0.05, seed=123456789, save_json=False):
+    def __init__(self, tstop=450.0, dt=0.05, seed=123456789, save_json=False, abs_tol=None):
 
         self.setup_start = time.time()
 
@@ -51,6 +51,13 @@ def __init__(self, tstop=450.0, dt=0.05, seed=123456789, save_json=False):
         self.simConfig.duration = self.simConfig.tstop = tstop # Duration of the simulation, in ms
         self.simConfig.dt = dt # Internal integration timestep to use
 
+        # cvode
+        if abs_tol is not None:
+            self.simConfig.cvode_active = True
+            self.simConfig.cvode_atol = abs_tol
+        else:
+            self.simConfig.cvode_active = False
+
         # Seeds for randomizers (connectivity, input stimulation and cell locations)
         # Note: locations and connections should be fully specified by the structure of the NeuroML,
         # so seeds for conn & loc shouldn't affect networks structure/behaviour
@@ -181,7 +188,7 @@ def save_results(self):
     save_json = '-json' in sys.argv
     no_run = '-norun' in sys.argv
 
-    ns = NetPyNESimulation(tstop=450.0, dt=0.05, seed=123456789, save_json=save_json)
+    ns = NetPyNESimulation(tstop=450.0, dt=0.05, seed=123456789, save_json=save_json, abs_tol=None)
 
     if not no_run:
       ns.run()

NeuroML2/LEMS_HHSmall.xml

@@ -2,7 +2,7 @@
 
     <!-- 
 
-        This LEMS file has been automatically generated using PyNeuroML v1.1.14 (libNeuroML v0.5.8)
+        This LEMS file has been automatically generated using PyNeuroML v1.2.7 (libNeuroML v0.5.9)
 
      -->
 

NeuroML2/LEMS_HybridSmall.xml

@@ -2,7 +2,7 @@
 
     <!-- 
 
-        This LEMS file has been automatically generated using PyNeuroML v1.1.14 (libNeuroML v0.5.8)
+        This LEMS file has been automatically generated using PyNeuroML v1.2.7 (libNeuroML v0.5.9)
 
      -->
 

NeuroML2/LEMS_HybridTut.xml

@@ -2,7 +2,7 @@
 
     <!-- 
 
-        This LEMS file has been automatically generated using PyNeuroML v1.1.14 (libNeuroML v0.5.8)
+        This LEMS file has been automatically generated using PyNeuroML v1.2.7 (libNeuroML v0.5.9)
 
      -->
 

NeuroML2/LEMS_M1.xml

@@ -2,7 +2,7 @@
 
     <!-- 
 
-        This LEMS file has been automatically generated using PyNeuroML v1.1.14 (libNeuroML v0.5.8)
+        This LEMS file has been automatically generated using PyNeuroML v1.2.7 (libNeuroML v0.5.9)
 
      -->