Harmonized signature of inject_current (now inject_square_current) ac…

…ross modules

README.md

@@ -98,11 +98,14 @@ reference_data = neuroelectro.NeuroElectroSummary(
     ephysprop = {'name':'Spike Half-Width'}) # Electrophysiological property name. 
 # Get and verify summary data for the combination above from neuroelectro.org. 
 reference_data.get_values()
-spikewidth_test = tests.InjectedCurrentSpikeWidthTest(
+spikewidth_test = tests.InjectedCurrentAPWidthTest(
                 observation = {'mean':reference_data.mean,
                                'std':reference_data.std},
                 name = 'Spike Width',
-                params={'injected_current':{'ampl':0.0053}}) # 0.0053 nA (5.3 pA) of injected current.  
+                params={'injected_square_current':{'amplitude':5.3*pq.pA,
+                                                   'delay':50.0*pq.ms,
+                                                   'duration':500.0*pq.ms}}) 
+                # 5.3 pA of injected current in a 500 ms square pulse.  
 
 # Create a test suite from these two tests.  
 suite = sciunit.TestSuite('Neuron Tests',(spikewidth_test,vm_test))

docs/chapter3.ipynb

@@ -144,11 +144,13 @@
     "    ephysprop = {'name':'Spike Half-Width'}) # Ephys property name in the NeuroElectro ontology. \n",
     "reference_data.get_values() # Get and verify summary data for the combination above from neuroelectro.org. \n",
     "\n",
-    "spikewidth_test = tests.InjectedCurrentSpikeWidthTest(\n",
+    "spikewidth_test = tests.InjectedCurrentAPWidthTest(\n",
     "                observation = {'mean':reference_data.mean*pq.ms, # In milliseconds.  \n",
     "                               'std':reference_data.std*pq.ms},\n",
     "                name = 'Spike Width',\n",
-    "                params={'injected_current':{'ampl':0.006}}) # 0.0053 nanoamps of injected current.  "
+    "                params={'injected_square_current':{'amplitude':6.0*pq.pA,\n",
+    "                                            'delay':50.0*pq.ms,\n",
+    "                                            'duration':500.0*pq.ms}}) # 6 pA of injected current in a 500 ms square pulse.  "
    ]
   },
   {

neuronunit/capabilities/__init__.py

@@ -104,6 +104,21 @@ def inject_current(self,current):
 
 		raise NotImplementedError()
 
+	def inject_square_current(self,current):
+		"""Injects somatic current into the model.  
+
+	    Parameters
+	    ----------
+	    current : a dictionary like:
+	    		      	{'amplitude':-10.0*pq.pA, 
+	    		      	 'delay':100*pq.ms, 
+	    		      	 'duration':500*pq.ms}}
+	    		  where 'pq' is the quantities package
+	    This describes the current to be injected.  
+	    """
+
+		raise NotImplementedError()
+
 
 class Runnable(sciunit.Capability):
     """Capability for models that can be run."""

neuronunit/neuroconstruct/capabilities.py

@@ -130,11 +130,12 @@ class Current(object):
 		duration = 0
 		offset = 0
 
-	def inject_current(self,injected_current):
+	def inject_square_current(self,injected_current):
 		self.prepare()
 		cmd = 'import %s as j;' % JUTILS_PATH
 		cmd += 'import sys;'
-		cmd += 'err = j.sim.set_current_ampl(%f);' % injected_current['ampl']
+		cmd += 'err = j.sim.set_current_ampl(%f);' % \
+					injected_current['amplitude']
 		cmd += 'channel.send(err);'
 		channel = self.gateway.remote_exec(cmd)
 		#print(cmd)

neuronunit/tests/__init__.py

@@ -124,22 +124,22 @@ class InputResistanceTest(VmTest):
 
 	units = pq.ohm*1e6
 
-	params = {'injected_current': 
+	params = {'injected_square_current': 
 				{'amplitude':-10.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
 
 	ephysprop_name = 'Input Resistance'
 
 	def generate_prediction(self, model, verbose=False):
 		"""Implementation of sciunit.Test.generate_prediction."""
-		model.inject_current(self.params['injected_current']) 
+		model.inject_square_current(self.params['injected_square_current']) 
 		vm = model.get_membrane_potential() 
 
 		def get_segment(self,vm,start,finish):
 			start = int((start/vm.sampling_period).simplified)
 			finish = int((finish/vm.sampling_period).simplified)
 			return vm[start:finish]
 
-		i = self.params['injected_current']
+		i = self.params['injected_square_current']
 		start, stop = -11*pq.ms, -1*pq.ms
 		before = get_segment(self,vm,start+i['delay'],
 									 stop+i['delay'])
@@ -198,7 +198,7 @@ class InjectedCurrentAPWidthTest(APWidthTest):
 
 	required_capabilities = (cap.ReceivesCurrent,)
 
-	params = {'injected_current': 
+	params = {'injected_square_current': 
 				{'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
 
 	name = "Injected current AP width test"
@@ -208,7 +208,7 @@ class InjectedCurrentAPWidthTest(APWidthTest):
 				   "is injected into cell.")
 
 	def generate_prediction(self, model, verbose=False):
-		model.inject_current(self.params['injected_current']) 
+		model.inject_square_current(self.params['injected_square_current']) 
 		return super(InjectedCurrentAPWidthTest,self).\
 			generate_prediction(model, verbose=verbose)
 
@@ -273,7 +273,7 @@ class InjectedCurrentAPAmplitudeTest(APAmplitudeTest):
 
 	required_capabilities = (cap.ReceivesCurrent,)
 
-	params = {'injected_current': 
+	params = {'injected_square_current': 
 				{'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
 
 	name = "Injected current AP amplitude test"
@@ -283,7 +283,7 @@ class InjectedCurrentAPAmplitudeTest(APAmplitudeTest):
 				   "is injected into cell.")
 
 	def generate_prediction(self, model, verbose=False):
-		model.inject_current(self.params['injected_current']) 
+		model.inject_square_current(self.params['injected_square_current']) 
 		return super(InjectedCurrentAPAmplitudeTest,self).\
 				generate_prediction(model, verbose=verbose)
 
@@ -334,7 +334,7 @@ class InjectedCurrentAPThresholdTest(APThresholdTest):
 
 	required_capabilities = (cap.ReceivesCurrent,)
 
-	params = {'injected_current': 
+	params = {'injected_square_current': 
 				{'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
 
 	name = "Injected current AP threshold test"
@@ -343,7 +343,7 @@ class InjectedCurrentAPThresholdTest(APThresholdTest):
 				   "action potentials are produced under current injection.")
 
 	def generate_prediction(self, model, verbose=False):
-		model.inject_current(self.params['injected_current']) 
+		model.inject_square_current(self.params['injected_square_current']) 
 		return super(InjectedCurrentAPThresholdTest,self).\
 				generate_prediction(model, verbose=verbose)
 
@@ -357,7 +357,7 @@ class RheobaseTest(VmTest):
 	required_capabilities = (cap.ReceivesCurrent,
 							 cap.ProducesSpikes)
 
-	params = {'injected_current': 
+	params = {'injected_square_current': 
 				{'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
 
 	name = "Rheobase test"
@@ -406,7 +406,9 @@ def threshold_FI(self, model, units, guess=None, verbose=False):
 
 		def f(ampl):
 			if float(ampl) not in lookup:
-				model.inject_current({'amplitude':ampl}) 
+				current = params.copy()
+				current['amplitude'] = ampl
+				model.inject_square_current(current) 
 				n_spikes = model.get_spike_count()
 				if verbose:
 					print("Injected %s current and got %d spikes" % \
@@ -459,6 +461,9 @@ class RestingPotentialTest(VmTest):
 
 	required_capabilities = (cap.ReceivesCurrent,)
 
+	params = {'injected_square_current': 
+				{'amplitude':0.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
+
 	name = "Resting potential test"
 
 	description = ("A test of the resting potential of a cell "
@@ -481,7 +486,7 @@ def validate_observation(self, observation):
 	def generate_prediction(self, model, verbose=False):
 		"""Implementation of sciunit.Test.generate_prediction."""
 		model.rerun = True
-		model.inject_current({'amplitude':0.0*pq.pA}) 
+		model.inject_square_current(self.params['injected_square_current']) 
 		median = model.get_median_vm() # Use median for robustness.  
 		std = model.get_std_vm()
 		prediction = {'mean':median, 'std':std}

neuronunit/tests/dynamics.py

@@ -99,7 +99,7 @@ def validate_observation(self, observation):
                                                      nonunited_keys=['cv'])
 
     def generate_prediction(self, model, verbose=False):
-        model.inject_current(observation['current']) 
+        model.inject_square_current(observation['current']) 
         spike_train = model.get_spike_train()
         if len(spike_train) >= 3:
             isis = isi(spike_train)