Merge pull request #9 from OpenSourceBrain/experimental

Retested & eden fixes

.github/workflows/non-omv.yml

@@ -13,17 +13,22 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ 3.8, 3.9, "3.10" ]
+        python-version: [ 3.9, "3.10" ]
         runs-on: [ ubuntu-latest, macos-latest ]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python  ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version:  ${{ matrix.python-version }}
 
+    - name: Install HDF5 for pytables on macos-latest
+      if: ${{ matrix.runs-on == 'macos-latest' }}
+      run: |
+        brew install hdf5
+
     - name: Install OMV
       run: |
         pip install git+https://github.com/OpenSourceBrain/osb-model-validation

.github/workflows/omv-ci.yml

@@ -17,11 +17,12 @@ jobs:
         engine: [ NetPyNE, jNeuroML, jNeuroML_NEURON, jNeuroML_NetPyNE, jNeuroML_EDEN, jNeuroML_validate ]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python  ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version:  ${{ matrix.python-version }}
+
     - name: Install OMV
       run: |
         pip install git+https://github.com/OpenSourceBrain/osb-model-validation
@@ -32,6 +33,7 @@ jobs:
     - name: Run OMV tests on engine ${{ matrix.engine }}
       run: |
         omv all -V --engine=${{ matrix.engine }}
+        
     - name: OMV final version info
       run: |
         omv list -V # list installed engines

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/.test.exGJ.jnmleden.omt

@@ -14,7 +14,7 @@ experiments:
         spike detection:
           method: threshold
           threshold: -69.5
-        tolerance: 0.00034498419085947374
+        tolerance: 0.0003449267030756095
   cell1:
     observables:
       spike times:

NeuroML2/.test.exIzh.jnmleden.omt

@@ -13,4 +13,4 @@ experiments:
           scaling: [1000, 1000]
         spike detection:
           method: threshold
-        tolerance: 0.00014888369126089628
+        tolerance: 0.00014884116521364865

NeuroML2/.test.hh.jnmleden.omt

@@ -13,7 +13,7 @@ experiments:
           scaling: [1000, 1000]
         spike detection:
           method: threshold
-        tolerance: 0.00011745994437245375
+        tolerance: 0.0001175226231050635
   v1:
     observables:
       spike times:
@@ -23,7 +23,7 @@ experiments:
           scaling: [1000, 1000]
         spike detection:
           method: threshold
-        tolerance: 0.0001244750601708972
+        tolerance: 0.00012449165905872983
   v2:
     observables:
       spike times:

NeuroML2/.test.hhsim.jnmleden.omt

@@ -13,4 +13,4 @@ experiments:
           scaling: [1000, 1000]
         spike detection:
           method: threshold
-        tolerance: 0.0007232195903184108
+        tolerance: 0.0007232712525507523

NeuroML2/.test.hyizh.jnmleden.omt

@@ -13,4 +13,4 @@ experiments:
           scaling: [1000, 1000]
         spike detection:
           method: threshold
-        tolerance: 3.66282906798275e-05
+        tolerance: 3.6664955635370336e-05

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 v1.2.13 (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 v1.2.13 (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 v1.2.13 (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 v1.2.13 (libNeuroML v0.5.9)
 
      -->
 

NeuroML2/TwoCells/.test.two.jnmleden.omt

@@ -15,7 +15,7 @@ experiments:
         spike detection:
           method: threshold
           threshold: 0
-        tolerance: 0.017343922603342065
+        tolerance: 0.01734388742304312
   bask0:
     observables:
       spike times:
@@ -26,4 +26,4 @@ experiments:
         spike detection:
           method: threshold
           threshold: 0
-        tolerance: 0.0017869092394359062
+        tolerance: 0.001786960295444412

README.md

@@ -8,4 +8,3 @@ See also info about [NetPyNE on OSBv2](https://docs.opensourcebrain.org/OSBv2/Ne
 
 
 
-