Merge dev into master

.circleci/config.yml

@@ -0,0 +1,65 @@
+defaults: &defaults
+  working_directory: ~/markovmodel/PyEMMA
+  docker:
+    - image: continuumio/miniconda3
+
+inst_conda_bld: &inst_conda_bld
+  - run: conda config --add channels conda-forge
+  - run: conda config --set always_yes true
+  - run: conda config --set quiet true
+  - run: conda install conda-build
+
+version: 2
+
+jobs:
+  build:
+    <<: *defaults
+    parallelism: 1
+    steps:
+      - checkout
+      - run: git fetch --unshallow || true
+      - run: apt-get install -y cpp gcc
+      - run: apt-get install -y libx11-6 python-dev git build-essential
+      - run: apt-get install -y autoconf automake gcc g++ make gfortran
+      - run: apt-get install -y python-tables
+      - run: apt-get install -y libhdf5-serial-dev
+
+      - run: conda config --add channels conda-forge
+      - run: conda config --set always_yes true
+      - run: conda config --set quiet true
+      - run: conda install conda-build
+      - run: pip install pip --upgrade;
+      - run: conda install numpy;
+      - run: conda install numba;
+      - run: conda install dask;
+      - run: pip install tables
+      - run: pip install scipy==1.5.4
+      - run: pip install coverage
+      - run: pip install cython
+      - run: pip install asciiplotlib;
+      - run: pip install ipfx
+      - run: pip install streamlit
+      - run: pip install sklearn
+      - run: pip install seaborn
+      - run: pip install frozendict
+      - run: pip install igor
+      #- run: pip install plotly
+      - run: pip install allensdk==0.16.3
+      - run: pip install --upgrade colorama
+      - run: pip install -e .
+      - run: rm -rf /opt/conda/lib/python3.8/site-packages/sciunit
+      - run: git clone -b neuronunit https://github.com/russelljjarvis/jit_hub.git
+      - run: cd jit_hub; pip install -e .; cd ..;
+      - run: git clone -b neuronunit_reduced_cells https://github.com/russelljjarvis/BluePyOpt.git
+      - run: cd BluePyOpt; pip install -e .
+      - run: git clone -b dev https://github.com/russelljjarvis/sciunit.git
+
+      - run: cd sciunit; pip install -e .; cd ..;
+      - run: pip install git+https://github.com/russelljjarvis/eFEL
+      - run: pip install coveralls
+      - run: sh build.sh
+      - run: sh test.sh;
+      #- run: cd neuronunit/unit_test; coveralls -m unittest rheobase_model_test.py; cd -;
+      #- run: cd neuronunit/unit_test; coverage report
+      #- store_artifacts:
+      #    path: htmlcov

.gitattributes

@@ -0,0 +1,14 @@
+# Set the default behavior, in case people don't have core.autocrlf set.
+* text=auto
+
+# Explicitly declare text files you want to always be normalized and converted
+# to native line endings on checkout.
+*.c text
+*.h text
+
+# Declare files that will always have CRLF line endings on checkout.
+*.sln text eol=crlf
+
+# Denote all files that are truly binary and should not be modified.
+*.png binary
+*.jpg binary

.gitignore

@@ -1,4 +1,10 @@
+*.vscode
 *.py[co]
+*.pkl
+*.p
+*do-not-add*
+neuronunit/examples/model_zoo/*.nml
+*.hoc
 
 # Packages
 *.egg
@@ -66,3 +72,6 @@ neuronunit/models/NeuroML2/*.py
 neuronunit/unit_test/bbp.py
 neuronunit/unit_test/get_tau.py
 scratch
+GeneratedFiles
+docs/tmp/
+tmp/

.travis.yml

@@ -3,32 +3,43 @@
 language: python
 python:
   # We don't actually use the Travis Python, but this keeps it organized.
-  - "2.7"
   - "3.5"
   - "3.6"
+  - "3.7"
+  - "3.8"
 install:
   - sudo apt-get update
   # We do this conditionally because it saves us some downloading if the
   # version is the same.
-  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
-      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
-    else
-      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
-    fi
+  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
   - bash miniconda.sh -b -p $HOME/miniconda
   - export PATH="$HOME/miniconda/bin:$PATH"
   - hash -r
   - conda config --set always_yes yes --set changeps1 no
-  - conda update -q conda
+  - conda update -q --all
   # Useful for debugging any issues with conda
   - conda info -a
-  - pip install --no-cache-dir -r requirements.txt . 
+  - pip install -U pip
+  - pip install .
+  - pip install sklearn
+  - pip install seaborn
   - pip install coveralls
+  - pip install pylmeasure # required by morphology tests
+  - sh build.sh
+
 ######################################################
 
-script: 
+script:
   - export NC_HOME='.' # NeuroConstruct isn't used but tests need this
-                       # variable set to pass.  
-  - sh test.sh  
+                       # variable set to pass.
+<<<<<<< HEAD
+  - cd neuronunit/unit_test; python -m unittest scores_unit_test.py; cd -;
+  - cd neuronunit/unit_test; python -m unittest rheobase_dtc_test.py; cd -;
+  #- sh test.sh
+=======
+  #- cd neuronunit/unit_test; python -m unittest scores_unit_test.py; cd -;
+  #- cd neuronunit/unit_test; python -m unittest rheobase_model_test.py; cd -;
+  - sh test.sh
+>>>>>>> 9fb0c2e613a1bf059f38eeeae80582d0cfb11f2f
 after_success:
   - coveralls

Dockerfile

@@ -1,18 +1,54 @@
-FROM russelljarvis/neuronunit
+FROM scidash/neuronunit-optimization
 USER jovyan
-RUN sudo /opt/conda/bin/pip install psutil
+RUN pip install psutil
 ENV QT_QPA_PLATFORM offscreen
-RUN sudo rm -rf /opt/conda/lib/python3.5/site-packages/neuronunit-0.1.8.8-py3.5.egg/neuronunit
-RUN sudo rm -rf $HOME/neuronunit
-COPY . $HOME/neuronunit
-
 RUN pip install dask
 RUN pip install distributed
-RUN sudo pip uninstall -y sciunit
-RUN sudo pip install git+https://github.com/scidash/sciunit@dev
-#COPY BluePyOpt ~/HOME/BluePyOpt
-#RUN pip install -e $HOME/BluePyOpt
-WORKDIR $HOME
+RUN sudo apt-get update
+RUN pip install ioloop
+RUN sudo chown -R jovyan /home/jovyan
+RUN pip install git+https://github.com/OpenSourceBrain/OpenCortex
+RUN git clone https://github.com/vrhaynes/AllenInstituteNeuroML.git
+RUN pip install PyLEMS
+
+# RUN sudo /opt/conda/bin/pip install git+https://github.com/python-quantities/python-quantities
+# RUN sudo /opt/conda/bin/pip install git+https://github.com/scidash/sciunit@dev
+RUN sudo chown -R jovyan /home/jovyan
+WORKDIR /home/jovyan/neuronunit/neuronunit/unit_test
+RUN sudo chown -R jovyan /home/jovyan
+RUN git clone https://github.com/vrhaynes/AllenInstituteNeuroML.git
+RUN pip install git+https://github.com/OpenSourceBrain/OpenCortex
+# RUN sudo apt-get -y install ipython ipython-notebook
+# RUN sudo -H /opt/conda/bin/pip install jupyter
+# ADD neuronunit/unit_test/post_install.sh .
 
+RUN git clone https://github.com/OpenSourceBrain/osb-model-validation.git
+WORKDIR osb-model-validation
+RUN python setup.py install 
+RUN pip --no-cache-dir install \
+        ipykernel \
+        jupyter \
+        matplotlib \
+        numpy \
+        scipy \
+        sklearn \
+        pandas \
+        Pillow 
 
+RUN sudo /opt/conda/bin/python3 -m ipykernel.kernelspec
+
+# Then install the Jupyter Notebook using:
+RUN pip install jupyter
+
+RUN sudo /opt/conda/bin/pip uninstall -y tornado
+RUN pip install tornado==4.5.3
+RUN /opt/conda/bin/python3 -m pip install ipykernel
+RUN /opt/conda/bin/python3 -m ipykernel install --user
+RUN pip install deap
+WORKDIR $HOME
+# ADD . neuronunit
+# WORKDIR neuronunit
+# RUN sudo /opt/conda/bin/pip install -e .
+#RUN bash post_install.sh
+ENTRYPOINT /bin/bash
 

README.md

@@ -1,9 +1,14 @@
+### Circle CI russelljjarvis/optimization build:
+[![Build Status](https://circleci.com/gh/russelljjarvis/neuronunit/tree/optimization.svg?style=svg)](https://app.circleci.com/pipelines/github/russelljjarvis/neuronunit/)
+### Travis CI scidash/optimization build:
+[![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=optimization)](https://travis-ci.org/scidash/neuronunit?branch=optimization)  
 | Master  | Dev |
 | ------------- | ------------- |
 | [![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=master)](https://travis-ci.org/scidash/neuronunit) | [![Travis](https://travis-ci.org/scidash/neuronunit.svg?branch=dev)](https://travis-ci.org/scidash/neuronunit)  |
 | [![RTFD](https://readthedocs.org/projects/neuronunit/badge/?version=master)](http://neuronunit.readthedocs.io/en/latest/?badge=master) | [![RTFD](https://readthedocs.org/projects/neuronunit/badge/?version=dev)](http://neuronunit.readthedocs.io/en/latest/?badge=dev) |
 | [![Coveralls](https://coveralls.io/repos/github/scidash/neuronunit/badge.svg?branch=master)](https://coveralls.io/github/scidash/neuronunit?branch=master) | [![Coveralls](https://coveralls.io/repos/github/scidash/neuronunit/badge.svg?branch=dev)](https://coveralls.io/github/scidash/neuronunit?branch=dev) |
-| [![Requirements](https://requires.io/github/scidash/neuronunit/requirements.svg?branch=master)](https://requires.io/github/scidash/neuronunit/requirements/?branch=master) |  [![Requirements](https://requires.io/github/scidash/neuronunit/requirements.svg?branch=dev)](https://requires.io/github/scidash/neuronunit/requirements/?branch=dev) | 
+| [![Requirements](https://requires.io/github/scidash/neuronunit/requirements.svg?branch=master)](https://requires.io/github/scidash/neuronunit/requirements/?branch=master) |  [![Requirements](https://requires.io/github/scidash/neuronunit/requirements.svg?branch=dev)](https://requires.io/github/scidash/neuronunit/requirements/?branch=dev) |
+| [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/scidash/neuronunit/master) |
 
 
 
@@ -43,13 +48,13 @@ model = ChannelModel(channel_file_path, channel_index=0, name=channel_model_name
 # Get the experiment data from ChannelWorm and instantiate the test
 doi = '10.1083/jcb.200203055'
 fig = '2B'
-sample_data = GraphData.objects.get(graph__experiment__reference__doi=doi, 
+sample_data = GraphData.objects.get(graph__experiment__reference__doi=doi,
                                     graph__figure_ref_address=fig)
 voltage, current_per_farad = sample_data.asunitedarray()
-patch_capacitance = pq.Quantity(1e-13,'F') # Assume recorded patch had this capacitance; 
+patch_capacitance = pq.Quantity(1e-13,'F') # Assume recorded patch had this capacitance;
                                            # an arbitrary scaling factor.  
 current = current_per_farad * patch_capacitance
-observation = {'v':voltage, 
+observation = {'v':voltage,
                'i':current}
 test = IVCurvePeakTest(observation)
 
@@ -62,7 +67,7 @@ score.plot(rd['v'],rd['i_pred'],same_fig=True,color='r',label='Predicted (model)
 ![png](https://raw.githubusercontent.com/scidash/assets/master/figures/SCU_IVCurve_Model_6_0.png)
 
 ```
-score.summarize() 
+score.summarize()
 """ OUTPUT:
 Model EGL-19.channel (ChannelModel) achieved score Fail on test 'IV Curve Test (IVCurvePeakTest)'. ===
 """
@@ -89,27 +94,27 @@ neurolex_id = 'nifext_128' # Cerebellar Granule Cell
 # Specify reference data for a test of resting potential for a granule cell.  
 reference_data = neuroelectro.NeuroElectroSummary(
     neuron = {'nlex_id':neurolex_id}, # Neuron type.  
-    ephysprop = {'name':'Resting Membrane Potential'}) # Electrophysiological property name. 
-# Get and verify summary data for the combination above from neuroelectro.org. 
+    ephysprop = {'name':'Resting Membrane Potential'}) # Electrophysiological property name.
+# Get and verify summary data for the combination above from neuroelectro.org.
 reference_data.get_values()
 vm_test = tests.RestingPotentialTest(
                 observation = {'mean':reference_data.mean,
-                               'std':reference_data.std},
+                               'sd':reference_data.std},
                 name = 'Resting Potential')
 
 # Specify reference data for a test of action potential width.  
 reference_data = neuroelectro.NeuroElectroSummary(
     neuron = {'nlex_id':neurolex_id}, # Neuron type.  
-    ephysprop = {'name':'Spike Half-Width'}) # Electrophysiological property name. 
-# Get and verify summary data for the combination above from neuroelectro.org. 
+    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.InjectedCurrentAPWidthTest(
                 observation = {'mean':reference_data.mean,
-                               'std':reference_data.std},
+                               'sd':reference_data.std},
                 name = 'Spike Width',
                 params={'injected_square_current':{'amplitude':5.3*pq.pA,
                                                    'delay':50.0*pq.ms,
-                                                   'duration':500.0*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.  
@@ -121,7 +126,7 @@ for model_name in model_names # Iterate through a list of models downloaded from
     model = nc_models.OSBModel(*model_info)
     models.append(model) # Add to the list of models to be tested.  
 
-score_matrix = suite.judge(models,stop_on_error=True) 
+score_matrix = suite.judge(models,stop_on_error=True)
 score_matrix.view()
 ```
 ### Score Matrix for Test Suite 'Neuron Tests'
@@ -152,7 +157,7 @@ NeuronUnit is based on [SciUnit](http://github.com/scidash/sciunit), a disciplin
 2. Check that the model has the capabilities required to take the test.     
 1. Make the model take the test.  
 2. Generate a score from that test run.  
-1. Bind the score to the specific model/test combination and any related data from test execution. 
+1. Bind the score to the specific model/test combination and any related data from test execution.
 1. Visualize the score (i.e. print or display the result of the test).  
 
 Here, we will break down how this is accomplished in NeuronUnit.  Although NeuronUnit contains several model and test classes that make it easy to work with standards in neuron modeling and electrophysiology data reporting, here we will use toy model and test classes constructed on-the-fly so the process of model and test construction is fully transparent.  
@@ -179,7 +184,7 @@ Let's see what the `neuronunit.capabilities.ProducesMembranePotential` capabilit
 ```python
 class ProducesMembranePotential(Capability):
 	"""Indicates that the model produces a somatic membrane potential."""
-	
+
 	def get_membrane_potential(self):
 		"""Must return a neo.core.AnalogSignal."""
 		raise NotImplementedError()
@@ -205,12 +210,12 @@ Now we can then construct a simple test to use on this model or any other test t
 ```python
 class ToyAveragePotentialTest(sciunit.Test):
 	"""Tests the average membrane potential of a neuron."""
-	
+
 	def __init__(self,
-			     observation={'mean':None,'std':None},
+			     observation={'mean':None,'sd':None},
 			     name="Average potential test"):
 		"""Takes the mean and standard deviation of reference membrane potentials."""
-		
+
 		sciunit.Test.__init__(self,observation,name) # Call the base constructor.  
 		self.required_capabilities += (neuronunit.capabilities.ProducesMembranePotential,)
 							  		   # This test will require a model to express the above capabilities
@@ -219,18 +224,18 @@ class ToyAveragePotentialTest(sciunit.Test):
 	score_type = sciunit.scores.ZScore # The test will return this kind of score.  
 
 	def validate_observation(self, observation):
-	    """An optional method that makes sure an observation to be used as 
+	    """An optional method that makes sure an observation to be used as
 	    reference data has the right form"""
 		try:
 			assert type(observation['mean']) is quantities.Quantity # From the 'quantities' package
-			assert type(observation['std']) is quantities.Quantity
+			assert type(observation['sd']) is quantities.Quantity
 		except Exception as e:
 			raise sciunit.ObservationError(("Observation must be of the form "
-									"{'mean':float*mV,'std':float*mV}")) 
+									"{'mean':float*mV,'sd':float*mV}"))
 
 	def generate_prediction(self, model):
 		"""Implementation of sciunit.Test.generate_prediction."""
-		vm = model.get_median_vm() # If the model has the capability 'ProducesMembranePotential', 
+		vm = model.get_median_vm() # If the model has the capability 'ProducesMembranePotential',
 		                           # then it implements this method
 		prediction = {'mean':vm}
 		return prediction
@@ -247,8 +252,8 @@ The test constructor takes an observation to parameterize the test, e.g.:
 
 ```python
 from quantities import mV
-my_observation = {'mean':-60.0*mV, 
-                  'std':3.5*mV}
+my_observation = {'mean':-60.0*mV,
+                  'sd':3.5*mV}
 my_average_potential_test = ToyAveragePotentialTest(my_observation, name='my_average_potential_test')
 ```