Updated exported cell, adding axon & fixing densities

Passing tests

.gitignore

@@ -31,3 +31,6 @@ generatedNeuroML2
 /NeuroML2/L23_NoHotSpot.cell.svg
 /NeuroML2/L23_IV.net.svg
 /NeuroML2/L23_morph.cell.svg
+/NEURON/temp.*
+/NeuroML2/*cell.png
+/NeuroML2/*old

NEURON/test/.test.l23.mep

@@ -1,9 +1,12 @@
 system: Testing a detailed cell
 
 experiments:
-  0.6:
+  0.3:
     expected:
-      spike times: [55.95, 68.85, 80.55, 90.325, 142.6, 156.45, 171.025, 185.575, 200.1, 214.65, 229.25, 243.85, 258.525, 273.2, 287.925]
-  0.7:
+      spike times: [71.275, 106.525, 153.125]
+  0.4:
     expected:
-      spike times: [54.825, 66.6, 76.75, 171.1, 182.05, 194.675, 207.7, 220.7, 233.675, 246.7, 259.725, 272.8, 285.9, 299.0]
+      spike times: [60.975, 79.25, 97.925, 117.75, 138.65, 160.225, 182.2, 204.475, 226.975, 249.65, 272.475, 295.425]
+  0.5:
+    expected:
+      spike times: [57.675, 72.375, 86.325, 99.95, 113.85, 128.825, 144.925, 161.55, 178.4, 195.35, 212.4, 229.525, 246.725, 263.975, 281.275, 298.6]

NEURON/test/.test.l23.pynrn.omt

@@ -4,22 +4,32 @@ target: test_iclamp.py
 engine: PyNEURON
 mep: .test.l23.mep
 experiments:
-  0.6:
+  0.3:
     observables:
       spike times:
         file: 
           path: voltage.dat
-          columns: [0,8]
+          columns: [0,5]
           scaling: [1000, 1000]
         spike detection: 
           method: threshold
         tolerance: 0
-  0.7:
+  0.4:
     observables:
       spike times:
         file: 
           path: voltage.dat
-          columns: [0,9]
+          columns: [0,6]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+        tolerance: 0
+  0.5:
+    observables:
+      spike times:
+        file: 
+          path: voltage.dat
+          columns: [0,7]
           scaling: [1000, 1000]
         spike detection: 
           method: threshold

NEURON/test/load_l23.hoc

@@ -1,10 +1,20 @@
 
+nrnpython("from neuron import h")
 nrnpython("import os")
-nrnpython("print os.getcwd()")
+nrnpython("print('Current dir: '+os.getcwd())")
 nrnpython("from test_iclamp import run_iclamp")
 
-nrnpython("run_iclamp([0], False, True)")
+celsius = 35
+
+nrnpython("mymodel = run_iclamp([0], True, True, None)")
+
+define_shape()
+
+count2=0
+forall {print "--------------- ", secname()} 
+forall { count2=count2+1 } 
+print "-- Number of sections: ", count2
 
 load_file("test/cellCheck.hoc")
 
-psection()
+cellInfo()

NEURON/test/singleCompAllChans.py

@@ -103,7 +103,7 @@ def run(tstop=10, dt=0.001):
 
 
 
-varlist = ['v']#, 'ica', 'cai']
+varlist = ['v', 'ica', 'cai', 'ina', 'ik']
 ds = create_dumps(comp, varlist)
 
 run(600, 0.001)

NEURON/test/test_iclamp.py

@@ -70,6 +70,10 @@ def run_iclamp(iRange, run, nogui, vFileName):
             lb.add_dendStim(model, data.iclampLoc[1], data.iclampLoc[2],
                      onset=data.iclampOnset, dur=data.iclampDur, amp=data.iclampAmp)
 
+    h('count=0')
+    h('forall {print "--------------- ", secname() \n count=count+1 } ')
+    h('print "Number of sections: ", count')
+
     if run:
         data.vdata, data.vDdata, data.gdata, data.idata, data.caDdata, data.vsec = [], [], [], [], [], []
         data.rates = []
@@ -83,8 +87,6 @@ def run_iclamp(iRange, run, nogui, vFileName):
         for k in sorted(data.__dict__.keys()):
             print("    %s:\t\t%s"%(k, data.__dict__[k]))
         SIM_currentSteps(data, model, data.iRange, data.BGROUND)
-
-        h('forall {print "--------------- ", secname() \n psection() } ')
 
         modelData = sc.emptyObject()
         lb.props(modelData)
@@ -125,6 +127,7 @@ def run_iclamp(iRange, run, nogui, vFileName):
 
         print("Done!")
 
+    return model
 
 
 if __name__ == "__main__":
@@ -136,4 +139,4 @@ def run_iclamp(iRange, run, nogui, vFileName):
         iRange = np.array([0.7])
         vFileName = './test/voltage.one.dat'
 
-    run_iclamp(iRange, True, nogui, vFileName)
+    model = run_iclamp(iRange, True, nogui, vFileName)

NeuroML2/.test.iv.jnmlnrn.omt

@@ -2,6 +2,35 @@
 
 target: LEMS_L23_IV.xml
 engine: jNeuroML_NEURON
-
-
-
+mep: ../NEURON/test/.test.l23.mep
+experiments:
+  0.3:
+    observables:
+      spike times:
+        file: 
+          path: Sim_L23_IV.L23_pop.v.dat
+          columns: [0,5]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+        tolerance: 0.009143
+  0.4:
+    observables:
+      spike times:
+        file: 
+          path: Sim_L23_IV.L23_pop.v.dat
+          columns: [0,6]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+        tolerance: 0.004739
+  0.5:
+    observables:
+      spike times:
+        file: 
+          path: Sim_L23_IV.L23_pop.v.dat
+          columns: [0,7]
+          scaling: [1000, 1000]
+        spike detection: 
+          method: threshold
+        tolerance: 0.002596

NeuroML2/GenerateIV.py

@@ -53,12 +53,12 @@ def generate(cell_id, duration, reference, iRange):
 
     cell_id = 'L23_NoHotSpot'
     reference = "L23_IV"
-    duration = 350
+    duration = 400
     iRange = np.arange(-0.1,0.8,0.1)
 
     if '-one' in sys.argv:
         iRange = np.array([0.7])
         reference = "L23_One"
-        duration = 350
+        duration = 400
 
     generate(cell_id, duration, reference, iRange)

NeuroML2/L23_IV.net.nml

@@ -1,9 +1,9 @@
 <neuroml xmlns="http://www.neuroml.org/schema/neuroml2"  xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta5.xsd" id="L23_IV">
     <notes>
 
-This NeuroML 2 file was generated by OpenCortex v0.0.6 using: 
-    libNeuroML v0.2.23
-    pyNeuroML v0.2.2
+This NeuroML 2 file was generated by OpenCortex v0.0.8 using: 
+    libNeuroML v0.2.29
+    pyNeuroML v0.2.10
 
     </notes>
     <property tag="Python random seed" value="1234"/>