Change to DemoTestUnit

DemoTestUnit/capabilities/__init__.py

@@ -0,0 +1,42 @@
+import sciunit
+
+# ===============================================================================
+
+class SomaProducesMembranePotential(sciunit.Capability):
+    """Enables recording membrane potential from soma """
+
+    def get_soma_membrane_potential(self, tstop):
+        """
+        Run simulation for time 'tstop', specified in ms, while 
+        recording the somatic membrane potential.
+        Must return a dict of the form:
+        	{'T': list1, 'V': list2 } where,
+        		list1 = time series (in ms)
+        		list2 = membrane potential series (in mV)
+        """
+        raise NotImplementedError()
+
+    # this will be used by our second test (test for input resistance)    
+    def get_soma_membrane_potential_eFEL_format(self, tstop, start, stop):
+        traces = self.get_soma_membrane_potential(tstop)
+        efel_trace = {'T' : traces['T'],
+                      'V' : traces['V'],
+                      'stim_start' : [start],
+                      'stim_end'   : [stop]}
+        return efel_trace
+
+# ===============================================================================
+
+class SomaReceivesStepCurrent(sciunit.Capability):
+    """ Enables step current stimulus into soma """
+
+    def inject_soma_square_current(self, current):
+        """
+        Input current is specified in the form of a dict with keys:
+            'delay'     : (value in ms),
+            'duration'  : (value in ms),
+            'amplitude' : (value in nA)
+        """
+        raise NotImplementedError()
+
+# ===============================================================================

DemoTestUnit/tests/__init__.py

@@ -0,0 +1,157 @@
+import sciunit
+import efel
+import numpy
+
+import os
+import json
+import matplotlib
+# To avoid figures being plotted on screen (we wish to save to file directly)
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+import DemoTestUnit.capabilities as cap
+
+# ===============================================================================
+
+class RestingPotential(sciunit.Test):
+    """Test the cell's resting membrane potential"""
+    score_type = sciunit.scores.ZScore
+    description = ("Test the cell's resting membrane potential")
+
+    def __init__(self,
+                 observation={'mean':None, 'std':None},
+                 name="Resting Membrane Potential Test"):
+        self.required_capabilities += (cap.SomaProducesMembranePotential,)
+        sciunit.Test.__init__(self, observation, name)
+
+    def validate_observation(self, observation):
+        try:
+            assert len(observation.keys()) == 2
+            for key, val in observation.items():
+                assert key in ["mean", "std"]
+                assert (isinstance(val, int) or isinstance(val, float))
+        except Exception:
+            raise sciunit.errors.ObservationError(
+                ("Observation must return a dictionary of the form:"
+                 "{'mean': NUM1, 'std': NUM2}"))
+
+    def generate_prediction(self, model):
+        self.trace = model.get_soma_membrane_potential(tstop=50.0)
+        prediction = numpy.mean(self.trace['V'])
+        return prediction
+
+    def compute_score(self, observation, prediction):
+        score = sciunit.scores.ZScore.compute(observation, prediction)
+        return score
+
+    def bind_score(self, score, model, observation, prediction):
+        self.figures = []
+        self.target_dir = os.path.join("./validation_results", self.name, model.name)
+        if not os.path.exists(self.target_dir):
+            os.makedirs(self.target_dir)
+
+        # create relevant output files
+        # 1. JSON data: observation, prediction, score, run_times
+        validation_data = {
+            "observation": observation,
+            "prediction": prediction,
+            "score": score.score,
+        }
+        with open(os.path.join(self.target_dir, 'basic_data.json'), 'w') as f:
+            json.dump(validation_data, f, indent=4)
+        self.figures.append(os.path.join(self.target_dir, 'basic_data.json'))
+
+        # 2. JSON data: save Vm vs t traces
+        with open(os.path.join(self.target_dir, 'trace_data.json'), 'w') as f:
+            json.dump(self.trace, f, indent=4)
+        self.figures.append(os.path.join(self.target_dir, 'trace_data.json'))
+
+        # 3. Vm traces as PDF
+        fig = plt.figure()
+        plt.plot(self.trace["T"], self.trace["V"])
+        plt.title("Somatic Vm vs t")
+        plt.xlabel("Time (ms)")
+        plt.ylabel("Membrane potential (mV)")
+        plt.show()
+        fig.savefig(os.path.join(self.target_dir, "trace_plot.pdf"))
+        self.figures.append(os.path.join(self.target_dir, "trace_plot.pdf"))
+
+        score.related_data["figures"] = self.figures
+        return score
+
+# ===============================================================================
+
+class InputResistance(sciunit.Test):
+    """Test the cell's input resistance"""
+    score_type = sciunit.scores.ZScore
+    description = ("Test the cell's input resistance")
+
+    def __init__(self,
+                 observation={'mean':None, 'std':None},
+                 name="Input Resistance Test"):
+        self.required_capabilities += (cap.SomaReceivesStepCurrent, cap.SomaProducesMembranePotential,)
+        sciunit.Test.__init__(self, observation, name)
+
+    def validate_observation(self, observation):
+        try:
+            assert len(observation.keys()) == 2
+            for key, val in observation.items():
+                assert key in ["mean", "std"]
+                assert (isinstance(val, int) or isinstance(val, float))
+        except Exception:
+            raise sciunit.errors.ObservationError(
+                ("Observation must return a dictionary of the form:"
+                 "{'mean': NUM1, 'std': NUM2}"))
+
+    def generate_prediction(self, model):
+        efel.reset()
+        model.inject_soma_square_current(current={'delay':20.0,
+                                                  'duration':50.0,
+                                                  'amplitude':-5.0})
+        self.trace = model.get_soma_membrane_potential_eFEL_format(tstop=100.0,
+                                                              start=20.0,
+                                                              stop =70.0)
+        efel.setDoubleSetting('stimulus_current', -5.0)
+        prediction = efel.getFeatureValues([self.trace], ['ohmic_input_resistance_vb_ssse'])[0]["ohmic_input_resistance_vb_ssse"][0]
+        return prediction
+
+    def compute_score(self, observation, prediction):
+        score = sciunit.scores.ZScore.compute(observation, prediction)
+        return score
+
+    def bind_score(self, score, model, observation, prediction):
+        self.figures = []
+        self.target_dir = os.path.join("./validation_results", self.name, model.name)
+        if not os.path.exists(self.target_dir):
+            os.makedirs(self.target_dir)
+
+        # create relevant output files
+        # 1. JSON data: observation, prediction, score, run_times
+        validation_data = {
+            "observation": observation,
+            "prediction": prediction,
+            "score": score.score,
+        }
+        with open(os.path.join(self.target_dir, 'basic_data.json'), 'w') as f:
+            json.dump(validation_data, f, indent=4)
+        self.figures.append(os.path.join(self.target_dir, 'basic_data.json'))
+
+        # 2. JSON data: save Vm vs t traces
+        with open(os.path.join(self.target_dir, 'trace_data.json'), 'w') as f:
+            json.dump(self.trace, f, indent=4)
+        self.figures.append(os.path.join(self.target_dir, 'trace_data.json'))
+
+        # 3. Vm traces as PDF
+        fig = plt.figure()
+        plt.plot(self.trace["T"], self.trace["V"])
+        plt.title("Somatic Vm vs t")
+        plt.xlabel("Time (ms)")
+        plt.ylabel("Membrane potential (mV)")
+        plt.show()
+        fig.savefig(os.path.join(self.target_dir, "trace_plot.pdf"))
+        self.figures.append(os.path.join(self.target_dir, "trace_plot.pdf"))
+
+        score.related_data["figures"] = self.figures
+        return score
+
+# ===============================================================================

README.md

@@ -1,3 +1,3 @@
-# PackageName
+# DemoTestUnit
 
 A simple SciUnit library for demonstrating data-driven testing of electrophysiological features extracted from computational models.

setup.py

@@ -4,16 +4,16 @@
     from distutils.core import setup
 
 setup(
-    name='PackageName',
-    version='0.1',
+    name='DemoTestUnit',
+    version='1.2.0',
     author='Shailesh Appukuttan, Andrew Davison',
     author_email='[email protected]',
-    packages=['PackageName',
-              'PackageName.tests',
-              'PackageName.capabilities',
-              'PackageName.scores',
-              'PackageName.plots',],
-    url='https://github.com/appukuttan-shailesh/PackageName',
+    packages=['DemoTestUnit',
+              'DemoTestUnit.tests',
+              'DemoTestUnit.capabilities',
+              'DemoTestUnit.scores',
+              'DemoTestUnit.plots',],
+    url='https://github.com/appukuttan-shailesh/DemoTestUnit',
     keywords=['electrophysiology', 'electrical',
               'testing', 'validation framework'],
     license='MIT',