diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index c07c86dfb..809779425 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -16,7 +16,7 @@ jobs:
         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 }}
 
diff --git a/CHANGES.md b/CHANGES.md
index 1122ddf2d..005f0c6c3 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -1,3 +1,43 @@
+# Version 1.0.7
+
+**New features**
+
+- Introducing `batchtools` subpackage for parameters exploration and optimization
+
+- Added progress-bar indicating network creation progress. Toggle the progress bar with cfg.progressBar
+
+- cfg.connRandomSecFromList and cfg.distributeSynsUniformly can now be overriden in individual conn rule
+
+- Added ability to use `sec`, `loc`, `preSec` and `preLoc` from list in connList-type connParams
+
+- Updated tests.examples.utils to allow for dynamic pathing
+
+- Dropped python2 support
+
+**Bug fixes**
+
+- Better handling of exceptions in `importCellParams()` (incl. `h.load_file()` - issue 782)
+
+- Pandas deprecated parameter fix
+
+- Fixed pointer id overflow on MPI (e.g. for gap junctions)
+
+- preSec and preLoc are no longer lost for inverse pointer connection
+
+- Fixed crash due to use of matplotlib.TextArea deprecated param (credit: Christian O'Reilly)
+
+- syncLines in rasterPlot restored
+
+- Fixed a bug in `gatherDataFromFiles()` where cellGids for node 0 were lost
+
+- Fixed generating rhythmic spiking pattern with 'uniform' option
+
+- Fixed misleading console output when cfg.recordStims is On
+
+- The colors in CSD plots are now properly aligned vertically with the CSD time-series overlays (credit: Sam Neymotin)
+
+- Update mkdir to makedirs (credit: Jacob Sprouse)
+
 # Version 1.0.6
 
 **New features**
diff --git a/doc/build.py b/doc/build.py
index 3a3e6b5ff..75f572aff 100644
--- a/doc/build.py
+++ b/doc/build.py
@@ -51,7 +51,7 @@
 13) Announce the new release
     13a) New release announcement text:
          NetPyNE v#.#.# is now available. For a complete list of changes and bug fixes see: https://github.com/suny-downstate-medical-center/netpyne/releases/tag/v#.#.#
-         See here for instructions to install or update to the latest version: http://www.netpyne.org/install.html
+         See here for instructions to install or update to the latest version: https://www.netpyne.org/documentation/installation
     13b) Announce on NEURON forum:
          https://www.neuron.yale.edu/phpBB/viewtopic.php?f=45&t=3685&sid=9c380fe3a835babd47148c81ae71343e
     13c) Announce to Google group:
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 52deba232..4979653f7 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.0.6'
+version = '1.0.7'
 # The full version, including alpha/beta/rc tags.
-release = '1.0.6'
+release = '1.0.7'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
diff --git a/doc/source/user_documentation.rst b/doc/source/user_documentation.rst
index 8843f4c64..1fcd64eed 100644
--- a/doc/source/user_documentation.rst
+++ b/doc/source/user_documentation.rst
@@ -1183,6 +1183,65 @@ From a specific section and location, you can record section variables such as v
 	# is equivalent to recording soma.myPP.V in NEURON. 
 	simConfig.recordTraces['VmyPP'] = {'sec': 'soma', 'pointp': 'myPP', 'var': 'V'} 
 
+	## Recording from Synaptic Currents Mechanisms 
+
+	# Example of recording from an excitatory synaptic mechanism
+	# record the 'i' variable (current) from an excitatory synaptic mechanism located
+	# in the middle of the 'dend' section. This is equivalent to recording
+	# dend(0.5).exc._ref_i in NEURON.
+	simConfig.recordTraces['iExcSyn'] = {'sec': 'dend',  'loc': 0.5, 'synMech': 'exc',  'var': 'i'}
+
+	# Example of recording from an inhibitory synaptic mechanism
+	# record the 'i' variable (current) from an inhibitory synaptic mechanism located
+	# at 0.3 of the 'soma' section. This is equivalent to recording
+	# soma(0.3).inh._ref_i in NEURON.
+	simConfig.recordTraces['iInhSyn'] = {'sec': 'soma', 'loc': 0.3, 'synMech': 'inh',  'var': 'i'}
+
+	# Example of recording multiple synaptic currents
+	# Recording synaptic currents
+	simConfig.recordSynapticCurrents = True
+	synaptic_curr = [
+    		('AMPA', 'i'),   # Excitatory synaptic current
+    		('NMDA', 'i'),   # Excitatory synaptic current
+    		('GABA_A', 'i')  # Inhibitory synaptic current
+	]
+	if simConfig.recordSynapticCurrents:
+    	for syn_curr in synaptic_curr:
+        	trace_label = f'i__soma_0__{syn_curr[0]}__{syn_curr[1]}'
+        	simConfig.recordTraces.update({trace_label: {'sec': 'soma_0', 'loc': 0.5, 'mech': syn_curr[0], 'var': syn_curr[1]}})
+
+The names ``'iExcSyn'`` , ``'iInhSyn'`` , ``'AMPA'`` , ``'NMDA'`` , ``'GABA_A'`` are those defined by the user in ``netParams.synMechParams``, and can be found using ``netParams.synMechParams.keys()``. The variables that can be paired with the synaptic mechanism in ``synaptic_curr`` tuples can be found by inspecting the MOD file that is used to define that synaptic mechanism, and the ones that can be recorded are defined as ``RANGE`` type in the MOD file.
+
+Example:
+
+* A synaptic mechanism defined in ``netParams.synMechParamsas`` as ``'AMPA'`` that uses the ``MyExp2SynBB.mod`` template.
+
+	* The user can go to the source ``/mod`` folder, and open the ``MyExp2SynBB.mod`` file, to inspect which variables are defined as ``RANGE``, and those can be recorded.
+	* The user can also modify the variable type, and define as ``RANGE``, and then recompile the mechanism to make it recordable in netpyne.
+
+* Example of variables that can be recorded for the given file
+
+	* The user can record ``'tau1'``, ``'tau2'``, ``'e'``, ``'i'``, ``'g'``, ``'Vwt'``, ``'gmax'``.
+
+ 
+.. code-block:: python
+
+   : $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ 
+   NEURON {
+   :  THREADSAFE
+     POINT_PROCESS MyExp2SynBB
+     RANGE tau1, tau2, e, i, g, Vwt, gmax
+     NONSPECIFIC_CURRENT i
+   }
+
+Those should be specified in  ``synaptic_curr`` as:
+
+.. code-block:: python
+
+	synaptic_curr = [
+    	('AMPA', 'i'),    # Excitatory synaptic current
+    	('AMPA', 'g'),    # Channel conductance
+	]	
 
 .. _package_functions:
 
@@ -2547,3 +2606,498 @@ The code for neural network optimization through evolutionary algorithm used in
 .. Adding cell classes
 .. --------------------
 
+Running a Batch Job
+===================
+
+The NetPyNE batchtools subpackage provides a method of automating job submission and reporting
+
+A diagram of the object interfaces...
+
+::
+
+ batch<-->\               /---> configuration_0 >---\
+           \             /                         specs---\
+           \<--->dispatcher_0                               sim_0
+           \             \                         comm ---/
+           \              \---<    results_0    <---/
+            \
+            \               /---> configuration_1 >---\
+            \              /                         specs---\
+            \<--->dispatcher_1                                sim_1
+             \             \                         comm ---/
+             \              \---<    results_1    <---/
+             \
+             \
+             ...
+
+While objects and interfaces can be handled directly, batchtools offers simple wrapper commands applicable to most use-cases, where
+automatic parameter searches can be done by specifying a search space and algorithm through `netpyne.batchtools.search`, and
+parameter to model translation and result communication is handled through `netpyne.batchtools.specs` and `netpyne.batchtools.comm` respectively.
+
+A diagram of the wrapper interactions...
+
+::
+
+ netpyne.batchtools.search.search(   ) ----------------------------\                 host
+        |                                                          |
+        | search(   )                                              |
+ ==============================================================================================
+        |                                                      comm.initialize(   )
+        |                                                      comm.send(   )
+        |  cfg = netpyne.batchtools.specs.SimConfig(   )       comm.close(   )
+        |            |                                           ^     ^
+        v            v                                           |     |
+        cfg.update_cfg() ----------------------------------------/     |
+                                                                       |
+        send(   )                                               netpyne.batchtools.comm(   )
+                                                                                    simulation
+
+1. Setting up batchtools
+------------------------
+Beyond the necessary dependency installations for NetPyNE and NEURON, the following `pip` installations are preferred.
+
+The NetPyNE installation should be handled as a development installation to allow for up to date fixes::
+
+    git clone https://github.com/Neurosim-lab/netpyne.git
+    cd netpyne
+    pip install -e .
+
+A development install of the batchtools subpackage::
+
+    git clone https://github.com/jchen6727/batchtk.git
+    cd batchtk
+    pip install -e .
+
+Ray is a dependency for batchtools, and should be installed with the following command::
+
+    pip install -U ray[default]
+
+2. Examples
+-----------
+Examples of NetPyNE batchtools usage can be found in the ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples>`_.
+
+Examples of the underlying batchtk package can be in the ``examples`` directory `on the batchtk github <https://github.com/jchen6727/batchtk/tree/release/examples>`_.
+
+3. Retrieving batch configuration values through the ``specs`` object
+---------------------------------------------------------------------
+Each simulation is able to retrieve relevant configurations through the ``specs`` object, and communicate with
+the dispatcher through the ``comm`` object.
+
+importing the relevant objects
+
+.. code-block:: python
+
+     from netpyne.batchtools import specs, comm
+     cfg = specs.SimConfig()  # create a SimConfig object, can be provided with a dictionary on initial call to set initial values
+     netParams = specs.NetParams()  # create a netParams object
+
+``netpyne.batchtools.specs`` behaves similarly to ``netpyne.sim.specs`` except in the following cases:
+
+* ``netpyne.batchtools.specs`` automatically captures relevant configuration mappings created by the ``dispatcher`` upon initialization
+
+   * these mappings can be retrieved via ``specs.get_mappings()``
+
+* the SimConfig object created by ``netpyne.batch.specs.SimConfig()`` will update itself with relevant configuration mappings through the ``update()`` method
+
+.. code-block:: python
+
+    from netpyne.batchtools import specs # import the custom batch specs
+    cfg = specs.SimConfig()              # create a SimConfig object
+    cfg.update()                         # update the cfg object with any relevant mappings for this particular batch job
+
+The ``update`` method will update the ``SimConfig`` object ``first`` with values supplied in the argument call, and ``then`` with the configuration mappings captured in ``specs`` (see: ``specs.get_mappings()``)
+
+This replaces the previous idiom for updating the SimConfig object with mappings from the batched job submission
+
+.. code-block:: python
+
+    try:
+        from __main__ import cfg  # import SimConfig object with params from parent module
+    except:
+        from cfg import cfg  # if no simConfig in parent module, import directly from tut8_cfg module
+
+4. Additional functionality within the simConfig object
+-------------------------------------------------------
+
+Rather than handling custom ``SimConfig`` object attribute declaration through the ``batch`` ``initCfg`` argument, the new NetPyNE batchtools subpackage moves the custom declaration of ``SimConfig`` attributes to the actual ``SimConfig`` object, allowing them both during instantiation of the object as well as when calling ``cfg.update()``
+
+.. code-block:: python
+
+        from netpyne.batchtools import specs                     # import the custom batch specs
+        cfg = specs.SimConfig({'foo': 0, 'bar': 1, 'baz': 2})    # create a SimConfig object, initializes it with a dictionary {'foo': 0} such that
+        assert cfg.foo == 0                                      # cfg.foo == 0
+        assert cfg.bar == 1                                      # cfg.bar == 1
+        assert cfg.baz == 2                                      # cfg.baz == 2
+        cfg.update({'foo': 3})                                   # update the cfg object with any relevant mappings for this particular batch job
+        assert cfg.foo == 3                                      # cfg.foo == 3
+        assert cfg.bar == 1                                      # cfg.bar remains unchanged
+        assert cfg.baz == 2                                      # cfg.baz remains unchanged
+
+``cfg.update()`` supports also supports the optional argument ``force_match``, which forces values in the update dictionary to match existing attributes within the ``SimConfig`` object. This setting is recommended to be set to ``True`` in order to prevent the unanticipated creation of new attributes within the ``SimConfig`` object at runtime ...
+
+.. code-block:: python
+
+        from netpyne.batchtools import specs                     # import the custom batch specs
+        cfg = specs.SimConfig({'type': 0})                       # create a SimConfig object, initializes it with a dictionary {'type': 0} such that
+        assert cfg.foo == 0                                      # cfg.type == 0
+        try:
+            cfg.update({'typo': 1}, force_match=True)            # cfg.typo is not defined, so this line will raise an AttributeError
+        except Exception as e:
+            print(e)
+        cfg.update({'typo': 1})                                  # without force_match, the typo attribute cfg.fooo is created and set to 1
+        assert cfg.type == 0                                     # cfg.type remains unchanged due to a typo in the attribute name 'type' -> 'typo'
+        assert cfg.typo == 1                                     # instead, cfg.typo is created and set to the value 1
+
+Both the initialization of the ``cfg`` object with ``specs.SimConfig()`` and the subsequent call to ``cfg.update()`` handle nested containers...
+
+.. code-block:: python
+
+        from netpyne.batchtools import specs
+        cfg = specs.SimConfig({'foo': {'val0': 0, 'arr0': [0, 1, 2]}})
+        assert cfg.foo['val0'] == 0
+        assert cfg.foo['arr0'][0] == 0
+        cfg.update({'foo': {'val0': 10,           # update cfg.foo['val0'] to 10
+                            'arr0': {0: 20        # update cfg.arr0[0] to 20
+                                     1: 30}}})    # update cfg.arr0[1] to 30
+        assert cfg.foo['val0'] == 10
+        assert cfg.foo['arr0'][0] == 20
+        assert cfg.foo['arr0'][1] == 30
+        assert cfg.foo['arr0'][2] == 2            # cfg.arr0[2] remains unchanged
+
+After updating the ``cfg`` object with the supplied dictionary, further updates will be made as appropriate by the calling ``batch`` processes search parameters...
+
+5. Communicating results to the ``dispatcher`` with the ``comm`` object
+-----------------------------------------------------------------------
+
+Prior batched simulations relied on ``.pkl`` files to communicate data. The ``netpyne.batch`` subpackage uses a specific ``comm`` object to send custom data back
+The ``comm`` object determines the method of communication based on the batch job submission type.
+
+In terms of the simulation, the following functions are available to the user:
+
+* **comm.initialize()**: establishes a connection with the batch ``dispatcher`` for sending data
+
+* **comm.send(<data>)**: sends ``<data>`` to the batch ``dispatcher``
+
+    * for ``search`` jobs, it is important to match the data sent with the metric specified in the search function
+
+* **comm.close()**: closes and cleans up the connection with the batch ``dispatcher``
+
+6. Specifying a batch job
+-------------------------
+Batch job handling is implemented with methods from ``netpyne.batchtools.search``
+
+**search**
+
+.. code-block:: python
+
+    def search(job_type: str, # the submission engine to run a single simulation (e.g. 'sge', 'sh')
+               comm_type: str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem')
+               run_config: Dict,  # batch configuration, (keyword: string pairs to customize the submit template)
+               params: Dict,  # search space (dictionary of parameter keys: tune search spaces)
+               algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+               label: Optional[str] = 'search',  # label for the search
+               output_path: Optional[str] = '../batch',  # directory for storing generated files
+               checkpoint_path: Optional[str] = '../ray',  # directory for storing checkpoint files
+               max_concurrent: Optional[int] = 1,  # number of concurrent trials to run at one time
+               batch: Optional[bool] = True,  # whether concurrent trials should run synchronously or asynchronously
+               num_samples: Optional[int] = 1,  # number of trials to run
+               metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+               mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+               algorithm_config: Optional[dict] = None,  # additional configuration for the search algorithm
+               ) -> tune.ResultGrid: # results of the search
+
+The basic search implemented with the ``search`` function uses ``ray.tune`` as the search algorithm backend, returning a ``tune.ResultGrid`` which can be used to evaluate the search space and results. It takes the following parameters;
+
+* **job_type**: either "``sge``" or "``sh``", specifying how the job should be submitted, "``sge``" will submit batch jobs through the Sun Grid Engine. "``sh``" will submit bach jobs through the shell on a local machine
+* **comm_type**: either "``socket``" or "``filesystem``", specifying how the job should communicate with the dispatcher
+* **run_config**: a dictionary of keyword: string pairs to customize the submit template, the expected keyword: string pairs are dependent on the job_type::
+
+    =======
+    sge
+    =======
+    queue: the queue to submit the job to (#$ -q {queue})
+    cores: the number of cores to request for the job (#$ -pe smp {cores})
+    vmem: the amount of memory to request for the job (#$ -l h_vmem={vmem})
+    realtime: the amount of time to request for the job (#$ -l h_rt={realtime})
+    command: the command to run for the job
+
+    example:
+    run_config = {
+        'queue': 'cpu.q',       # request job to be run on the 'cpu.q' queue
+        'cores': 8,             # request 8 cores for the job
+        'vmem': '8G',           # request 8GB of memory for the job
+        'realtime': '24:00:00', # set timeout of the job to 24 hours
+        'command': 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'
+    } # set the command to be run to 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'
+
+    =======
+    sh
+    =======
+    command: the command to run for the job
+
+    example:
+    run_config = {
+        'command': 'mpiexec -n 8 nrniv -python -mpi init.py'
+    } # set the command to be run
+
+* **params**: a dictionary of config values to perform the search over. The keys of the dictionary should match the keys of the config object to be updated. Lists or numpy generators >2 values will force a grid search over the values; otherwise, a list of two values will create a uniform distribution sample space.
+
+    **usage 1**: updating a constant value specified in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # take a config object with the following parameter ``foo``
+        cfg = specs.SimConfig()
+        cfg.foo = 0
+        cfg.update()
+
+        # specify a search space for ``foo`` such that a simulation will run with:
+        # cfg.foo = 0
+        # cfg.foo = 1
+        # cfg.foo = 2
+        # ...
+        # cfg.foo = 9
+
+        # using:
+        params = {
+            'foo': range(10)
+        }
+
+    **usage 2**: updating a nested object in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # to update a nested object, the package uses the `.` operator to specify reflection into the object.
+        # take a config object with the following parameter object ``foo``
+        cfg = specs.SimConfig()
+        cfg.foo = {'bar': 0, 'baz': 0}
+        cfg.update()
+
+        # specify a search space for ``foo['bar']`` with `foo.bar` such that a simulation will run:
+        # cfg.foo['bar'] = 0
+        # cfg.foo['bar'] = 1
+        # cfg.foo['bar'] = 2
+        # ...
+        # cfg.foo['bar'] = 9
+
+        # using:
+        params = {
+            'foo.bar': range(10)
+        }
+
+        # this reflection works with nested objects as well...
+        # i.e.
+        # cfg.foo = {'bar': {'baz': 0}}
+        # params = {'foo.bar.baz': range(10)}
+
+    **usage 3**: updating a list object in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # to update a nested object, the package uses the `.` operator to specify reflection into the object.
+        # take a config object with the following
+        cfg = specs.SimConfig()
+        cfg.foo = [0, 1, 4, 9, 16]
+        cfg.update()
+
+        # specify a search space for ``foo[0]`` with `foo.0` such that a simulation will run:
+        # cfg.foo[0] = 0
+        # cfg.foo[0] = 1
+        # cfg.foo[0] = 2
+        # ...
+        # cfg.foo[0] = 9
+
+        # using:
+        params = {
+            'foo.0': range(10)
+        }
+
+        # this reflection works with nested objects as well...
+
+* **algorithm** : the search algorithm (supported within ``ray.tune``)
+
+    **Supported algorithms**
+
+.. code-block:: python
+
+        * "variant_generator": grid and random based search of the parameter space (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "random": grid and random based search of the parameter space (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "axe": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "bayesopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "hyperopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "bohb": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "nevergrad": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "optuna": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "hebo": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "sigopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "zoopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+
+* **label**: a label for the search, used for output file naming
+
+* **output_path**: the directory for storing generated files, can be a relative or absolute path
+
+* **checkpoint_path**: the directory for storing checkpoint files (maintained by ``ray.tune``)in case the search needs to be restored, can be a relative or absolute path
+
+* **max_concurrent**: the number of concurrent trials to run at one time, it is recommended to keep in mind the resource usage of each trial to avoid overscheduling
+
+* **batch**: whether concurrent trials should run synchronously or asynchronously
+
+* **num_samples**: the number of trials to run, for any grid search, each value in the grid will be sampled ``num_samples`` times.
+
+* **metric**: the metric to optimize (this should match some key: value pair in the returned data)
+
+* **mode**: either 'min' or 'max' (whether to minimize or maximize the metric)
+
+* **algorithm_config**: additional configuration for the search algorithm (see the `optuna docs <https://docs.ray.io/en/latest/tune/api/suggestion.html>`_)
+
+7. Batch searches on the Rosenbrock function (some simple examples)
+-------------------------------------------------------------------
+The ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock>`_ contains multiple methods of performing automatic parameter search of a
+2 dimensional Rosenbrock function.  These examples are used to quickly demonstrate some of the functionality of batch communications rather than the full process of running parameter searches on a detailed
+NEURON simulation (see 7. Performing parameter optimization searches (CA3 example)) and therefore only contain the a `batch.py` file containing the script detailing the parameter space and search method, and a
+`rosenbrock.py` file containing the function to explore, and the appropriate declarations and calls for batch automation and communication (rather than the traditional `cfg.py`, `netParams.py`, and `init.py` files).
+
+1. `basic_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock>`_
+
+This demonstrates a basic grid search of the Rosenbrock function using the new ``batchtools``, where the search space is defined as the cartesian product of ``params['x0']`` and ``params['x1']``
+
+.. code-block:: python
+
+        # from batch.py
+        params = {'x0': [0, 3],
+                  'x1': [0, 3],
+                 }
+
+that is, with the values ``cfg.x0``, ``cfg.x1`` iterating over: ``[(0, 0), (0, 3), (3, 0), (3, 3)]`` list
+
+2. `coupled_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock>`_
+
+This demonstrates a basic paired grid search, where ``x0`` is ``[0, 1, 2]`` and x1[n] is ``x0[n]**2``
+
+.. code-block:: python
+
+        # from batch.py
+        x0 = numpy.arange(0, 3)
+        x1 = x0**2
+
+        x0_x1 = [*zip(x0, x1)]
+        params = {'x0_x1': x0_x1
+                  }
+
+the ``x0`` and ``x1`` values are paired together to create a search space ``x0_x1`` iterating over: ``[(0, 0), (1, 1), (2, 4)]`` list
+
+then, in the ``rosenbrock.py`` file, a list of two values ``cfg.x0_x1`` is created to capture the ``x0_x1`` values, which is then unpacked into individual ``x0`` and ``x1`` values
+
+.. code-block:: python
+
+        # from rosenbrock.py
+        cfg.x0_x1 = [1, 1]
+
+        cfg.update_cfg()
+
+        # -------------- unpacking x0_x1 list  -------------- #
+        x0, x1 = cfg.x0_x1
+
+then the Rosenbrock function is evaluated with the unpacked ``x0`` and ``x1``
+
+3. `random_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/random_rosenbrock>`_
+
+This demonstrates a grid search over a nested object, where ``xn`` is a list of 2 values which are independently modified to search the cartesian product of ``[0, 1, 2, 3, 4]`` and ``[0, 1, 2, 3, 4]``
+
+.. code-block:: python
+
+        # from batch.py
+        params = {'xn.0': numpy.arange(0, 5),
+                  'xn.1': numpy.arange(0, 5)
+                 }
+
+By using ``xn.0`` and ``xn.1`` we can reference the 0th and 1st elements of the list, which is created and modified in rosenbrock.py
+
+.. code-block:: python
+
+        # from rosenbrock.py
+        cfg.xn = [1, 1]
+
+        cfg.update_cfg()
+
+        # ---------------- unpacking x list  ---------------- #
+        x0, x1 = cfg.xn
+
+
+8. Performing parameter optimization searches (CA3 example)
+-----------------------------------------------------------
+The ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples>`_ shows both a ``grid`` based search as well as an ``optuna`` based optimization.
+
+In the ``CA3`` example, we tune the ``PYR->BC`` ``NMDA`` and ``AMPA`` synaptic weights, as well as the ``BC->PYR`` ``GABA`` synaptic weight. Note the search space is defined
+
+.. code-block:: python
+
+        # from optuna_search.py
+        params = {'nmda.PYR->BC' : [1e-3, 1.8e-3],
+                  'ampa.PYR->BC' : [0.2e-3, 0.5e-3],
+                  'gaba.BC->PYR' : [0.4e-3, 1.0e-3],
+                 }
+
+in both ``optuna_search.py``, defining the upper and lower bounds of the search space, while in ``grid_search.py`` the search space is defined
+
+.. code-block:: python
+
+        # from grid_search.py
+        params = {'nmda.PYR->BC' : numpy.linspace(1e-3, 1.8e-3, 3),
+                  'ampa.PYR->BC' : numpy.linspace(0.2e-3, 0.5e-3, 3),
+                  'gaba.BC->PYR' : numpy.linspace(0.4e-3, 1.0e-3, 3),
+                 }
+
+which defines ``3x3x3`` specific values to search over
+
+Note that the ``metric`` specifies a specific ``string`` (``loss``) to report and optimize around. This value is generated and ``sent`` by the ``init.py`` simulation
+
+.. code-block:: python
+
+        # from init.py
+        results['PYR_loss'] = (results['PYR'] - 3.33875)**2
+        results['BC_loss']  = (results['BC']  - 19.725 )**2
+        results['OLM_loss'] = (results['OLM'] - 3.470  )**2
+        results['loss'] = (results['PYR_loss'] + results['BC_loss'] + results['OLM_loss']) / 3
+        out_json = json.dumps({**inputs, **results})
+
+        print(out_json)
+        #TODO put all of this in a single function.
+        comm.send(out_json)
+        comm.close()
+
+The ``out_json`` output contains a dictionary which includes the ``loss`` metric (calculated as the MSE between observed and expected values)
+
+In a multi-objective optimization, the relevant ``PYR_loss``, ``BC_loss``, and ``OLM_loss`` components are additionally included (see ``mo_optuna_search.py``)
+
+9. Multiprocessing and parallelization
+--------------------------------------
+When using ``mpiexec`` to run simulations, it is important to only have one thread handle communications with the ``dispatcher`` host. To do this, encapsulate calls to ``comm.send()`` and ``comm.close()`` within
+a conditional block which checks that the proper thread (set as the 0th thread) executes communication calls as follows...
+
+.. code-block:: python
+
+    out_json = json.dumps({**inputs, **results}) # prepare message to send to host...
+    if comm.is_host():                           # only single thread enters this execution block...
+        comm.send(out_json)
+        comm.close()
+
+9. Parameter Importance Evaluation Using fANOVA
+-----------------------------------------------
+A new feature in the batchtools beta release is the ability to evaluate parameter importance using a functional ANOVA inspired algorithm via the ``Optuna`` and ``scikit-learn`` libraries.
+(See `the original Hutter paper <http://proceedings.mlr.press/v32/hutter14.pdf>`_  and its `citation <https://automl.github.io/fanova/cite.html>`_)
+
+Currently, only unpaired single parameter importance to a single metric score is supported through the ``NetPyNE.batchtools.analysis`` ``Analyzer`` object, with an example of its usage
+`here <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock>`_:
+
+To run the example, generate an output ``grid.csv`` using ``batch.py``, then loading that ``grid.csv`` into the ``Analyzer`` object. Then, using ``run_analysis`` will generate, per parameter, a single score indicative of the estimated ``importance`` of the parameter: that is, the estimated effect on the total variance of the model within the given bounds.
+
+.. code-block:: python
+
+        # from analysis.py
+        from netpyne.batchtools.analysis import Analyzer
+
+        analyzer = Analyzer(params = ['x.0', 'x.1', 'x.2', 'x.3'], metrics = ['fx']) # specify the parameter space and metrics of the batch function
+        analyzer.load_file('grid.csv') # load the grid file generated by the batch run
+        results = analyzer.run_analysis() # run fANOVA analysis and store the importance values in a results dictionary
+
diff --git a/examples/HHTut/src/HHTut.py b/examples/HHTut/src/HHTut.py
index 9ff40e99a..2d06c28e0 100644
--- a/examples/HHTut/src/HHTut.py
+++ b/examples/HHTut/src/HHTut.py
@@ -70,7 +70,6 @@
 
 # Saving
 simConfig.filename = 'HHTut'  # Set file output name
-simConfig.saveFileStep = 1000 # step size in ms to save data to disk
 simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file
 simConfig.saveJson = True
 
diff --git a/examples/HybridTut/src/cfg.py b/examples/HybridTut/src/cfg.py
index 3000a52a0..ab178d5c2 100644
--- a/examples/HybridTut/src/cfg.py
+++ b/examples/HybridTut/src/cfg.py
@@ -23,7 +23,6 @@
 
 # Saving
 cfg.filename = 'mpiHybridTut'  # Set file output name
-cfg.saveFileStep = 1000 # step size in ms to save data to disk
 cfg.savePickle = False # Whether or not to write spikes etc. to a .mat file
 cfg.saveJson = False # Whether or not to write spikes etc. to a .mat file
 cfg.saveMat = False # Whether or not to write spikes etc. to a .mat file
diff --git a/examples/M1/src/cfg.py b/examples/M1/src/cfg.py
index 89238f443..5c61b8684 100644
--- a/examples/M1/src/cfg.py
+++ b/examples/M1/src/cfg.py
@@ -34,7 +34,6 @@
 
 # Saving
 cfg.filename = 'data/M1_ynorm_izhi'  # Set file output name
-cfg.saveFileStep = 1000 # step size in ms to save data to disk
 cfg.savePickle = False # save to pickle file
 cfg.saveJson = False # save to json file
 cfg.saveMat = False # save to mat file
diff --git a/examples/RL_arm/params.py b/examples/RL_arm/params.py
index a863d05ae..cf56ffe75 100644
--- a/examples/RL_arm/params.py
+++ b/examples/RL_arm/params.py
@@ -308,7 +308,6 @@
 
 # Saving
 simConfig.filename = 'simdata'  # Set file output name
-simConfig.saveFileStep = 1000 # step size in ms to save data to disk
 simConfig.savePickle = True # Whether or not to write spikes etc. to a .mat file
 simConfig.saveJson = False # Whether or not to write spikes etc. to a .mat file
 simConfig.saveMat = False # Whether or not to write spikes etc. to a .mat file
diff --git a/examples/netClamp/src/cfg.py b/examples/netClamp/src/cfg.py
index 88d0c68db..dfac67e86 100644
--- a/examples/netClamp/src/cfg.py
+++ b/examples/netClamp/src/cfg.py
@@ -1,4 +1,3 @@
-import imp
 from netpyne import specs
 
 # Simulation options
@@ -14,4 +13,4 @@
 cfg.recordStim = True  # required for netClamp to work
 
 cfg.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True}      # Plot a raster
-cfg.analysis['plotTraces'] = {'include': [5]}      # Plot recorded traces for this list of cells
\ No newline at end of file
+cfg.analysis['plotTraces'] = {'include': [5]}      # Plot recorded traces for this list of cells
diff --git a/examples/rxd_buffering/src/netParams.py b/examples/rxd_buffering/src/netParams.py
index 06a2014b2..22190e495 100644
--- a/examples/rxd_buffering/src/netParams.py
+++ b/examples/rxd_buffering/src/netParams.py
@@ -1,4 +1,3 @@
-import imp
 from netpyne import specs
 
 netParams = specs.NetParams()  # object of class NetParams to store the network parameters
@@ -27,4 +26,4 @@
 netParams.rxdParams['reactions'] = {'buffering': {'reactant': '2 * ca + buf', 'product': 'cabuf', 'rate_f': kf, 'rate_b': kb}}
 
 ### rates
-netParams.rxdParams['rates'] = {'degradation': {'species': 'buf', 'rate': '-1e-3 * buf'}}
\ No newline at end of file
+netParams.rxdParams['rates'] = {'degradation': {'species': 'buf', 'rate': '-1e-3 * buf'}}
diff --git a/netpyne/__init__.py b/netpyne/__init__.py
index 0dae0c25b..182303abb 100644
--- a/netpyne/__init__.py
+++ b/netpyne/__init__.py
@@ -4,7 +4,7 @@
 NetPyNE consists of a number of sub-packages and modules.
 """
 
-__version__ = '1.0.6'
+__version__ = '1.0.7'
 import os, sys
 
 display = os.getenv('DISPLAY')
diff --git a/netpyne/analysis/csd.py b/netpyne/analysis/csd.py
index ba61f7100..25cbb0ffb 100644
--- a/netpyne/analysis/csd.py
+++ b/netpyne/analysis/csd.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
-
 try:
     basestring
 except NameError:
diff --git a/netpyne/analysis/dipole.py b/netpyne/analysis/dipole.py
index 165467d4b..46c9da25c 100644
--- a/netpyne/analysis/dipole.py
+++ b/netpyne/analysis/dipole.py
@@ -3,24 +3,13 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 import os
-from builtins import range
-from builtins import round
-from builtins import str
 
 try:
     basestring
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/filter.py b/netpyne/analysis/filter.py
index 6e907cd85..2f812fc00 100644
--- a/netpyne/analysis/filter.py
+++ b/netpyne/analysis/filter.py
@@ -21,11 +21,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 import warnings
 
 import numpy as np
diff --git a/netpyne/analysis/info.py b/netpyne/analysis/info.py
index 76ef99d29..2871dc920 100644
--- a/netpyne/analysis/info.py
+++ b/netpyne/analysis/info.py
@@ -3,20 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 try:
     basestring
 except NameError:
     basestring = str
 
-from builtins import zip
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/interactive.py b/netpyne/analysis/interactive.py
index cf2b478b8..3079d3cf1 100644
--- a/netpyne/analysis/interactive.py
+++ b/netpyne/analysis/interactive.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 try:
diff --git a/netpyne/analysis/lfp.py b/netpyne/analysis/lfp.py
index 56dd2b7e6..8f2c43fe4 100644
--- a/netpyne/analysis/lfp.py
+++ b/netpyne/analysis/lfp.py
@@ -3,23 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import range
-from builtins import round
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/lfp_orig.py b/netpyne/analysis/lfp_orig.py
index e68ab66cd..d124ebc25 100644
--- a/netpyne/analysis/lfp_orig.py
+++ b/netpyne/analysis/lfp_orig.py
@@ -3,23 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import range
-from builtins import round
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/network.py b/netpyne/analysis/network.py
index 068451b7f..8426feafe 100644
--- a/netpyne/analysis/network.py
+++ b/netpyne/analysis/network.py
@@ -3,26 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import open
-from builtins import next
-from builtins import range
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from builtins import zip
-
-from builtins import round
-from future import standard_library
 
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/rxd.py b/netpyne/analysis/rxd.py
index 3678d6171..75e0fcfbf 100644
--- a/netpyne/analysis/rxd.py
+++ b/netpyne/analysis/rxd.py
@@ -3,19 +3,11 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
 try:
     basestring
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/spikes.py b/netpyne/analysis/spikes.py
index 98fc65297..2d42e241a 100644
--- a/netpyne/analysis/spikes.py
+++ b/netpyne/analysis/spikes.py
@@ -3,19 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
-
-from builtins import round
-from builtins import open
-from builtins import range
-
 try:
     to_unicode = unicode
 except NameError:
@@ -83,7 +70,7 @@ def prepareSpikeData(
             orderBy = 'gid'
         elif orderBy == 'pop':
             df['popInd'] = df['pop'].astype('category')
-            df['popInd'].cat.set_categories(sim.net.pops.keys(), inplace=True)
+            df['popInd'] = df['popInd'].cat.set_categories(sim.net.pops.keys())
             orderBy = 'popInd'
         elif isinstance(orderBy, basestring) and not isinstance(cells[0]['tags'][orderBy], Number):
             orderBy = 'gid'
@@ -91,7 +78,7 @@ def prepareSpikeData(
         if isinstance(orderBy, list):
             if 'pop' in orderBy:
                 df['popInd'] = df['pop'].astype('category')
-                df['popInd'].cat.set_categories(sim.net.pops.keys(), inplace=True)
+                df['popInd'] = df['popInd'].cat.set_categories(sim.net.pops.keys())
                 orderBy[orderBy.index('pop')] = 'popInd'
             keep = keep + list(set(orderBy) - set(keep))
         elif orderBy not in keep:
diff --git a/netpyne/analysis/spikes_legacy.py b/netpyne/analysis/spikes_legacy.py
index 66e92bc2f..e35c2c0e8 100755
--- a/netpyne/analysis/spikes_legacy.py
+++ b/netpyne/analysis/spikes_legacy.py
@@ -3,25 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import dict
-from builtins import round
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from builtins import range
-from builtins import zip
-
-from future import standard_library
 
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/tools.py b/netpyne/analysis/tools.py
index 9d68db49f..0a11e507e 100644
--- a/netpyne/analysis/tools.py
+++ b/netpyne/analysis/tools.py
@@ -3,19 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
-
-from builtins import round
-from builtins import open
-from builtins import range
-
 try:
     to_unicode = unicode
 except NameError:
diff --git a/netpyne/analysis/traces.py b/netpyne/analysis/traces.py
index 517428287..0c4b9f38f 100644
--- a/netpyne/analysis/traces.py
+++ b/netpyne/analysis/traces.py
@@ -3,21 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import range
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/utils.py b/netpyne/analysis/utils.py
index 2b49515f0..bfc666d27 100644
--- a/netpyne/analysis/utils.py
+++ b/netpyne/analysis/utils.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import round
-from builtins import open
-from builtins import range
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
@@ -22,9 +13,6 @@
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/analysis/wrapper.py b/netpyne/analysis/wrapper.py
index 701b0ffd2..93626d4cf 100644
--- a/netpyne/analysis/wrapper.py
+++ b/netpyne/analysis/wrapper.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 try:
diff --git a/netpyne/batch/__init__.py b/netpyne/batch/__init__.py
index 74580c1ae..62da2bb9b 100644
--- a/netpyne/batch/__init__.py
+++ b/netpyne/batch/__init__.py
@@ -3,11 +3,4 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from .batch import Batch
diff --git a/netpyne/batch/asd_parallel.py b/netpyne/batch/asd_parallel.py
index 8e7ccdbdd..45ef1e9a1 100644
--- a/netpyne/batch/asd_parallel.py
+++ b/netpyne/batch/asd_parallel.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
diff --git a/netpyne/batch/batch.py b/netpyne/batch/batch.py
index a3eac6b41..6c24a9c9f 100644
--- a/netpyne/batch/batch.py
+++ b/netpyne/batch/batch.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
@@ -76,37 +62,40 @@ def tupleToStr(obj):
 class Batch(object):
     """
     Class that handles batch simulations on NetPyNE.
-    Relevant Attributes:
-        batchLabel : str
-            The label of the batch used for directory/file naming of batch generated files.
-        cfgFile : str
-            The path of the file containing the `netpyne.simConfig.SimConfig` object
-        cfg : `netpyne.simConfig.SimConfig`
-            The `netpyne.simConfig.SimConfig` object
-        N.B. either cfg or cfgFile should be specified #TODO: replace with typechecked single argument
-        netParamsFile : str
-            The path of the file containing the `netpyne.netParams.NetParams` object
-        netParams : `netpyne.netParams.NetParams`
-            The `netpyne.netParams.NetParams` object
-        N.B. either netParams or netParamsFile should be specified #TODO: replace with typechecked single argument
-        initCfg : dict
-            params dictionary that is used to modify the batch cfg prior to any algorithm based parameter modifications
-        saveFolder : str
-            The path of the folder where the batch will be saved (defaults to batchLabel)
-        method : str
-            The algorithm method used for batch
-        runCfg : dict
-            Keyword: Arg dictionary used to generate submission templates (see utils.py)
-        evolCfg : dict #TODO: replace with algoCfg? to merge with optimCfg
-            Keyword: Arg dictionary used to define evolutionary algorithm parameters (see evol.py)
-        optimCfg : dict #TODO: replace with algoCfg? to merge with evolCfg
-            Keyword: Arg dictionary used to define optimization algorithm parameters
-            (see asd_parallel.py, optuna_parallel.py, sbi_parallel.py)
-        params : list
-            Dictionary of parameters to be explored per algorithm (grid, evol, asd, optuna, sbi)
-            (see relevant algorithm script for details)
-        seed : int
-            Seed for random number generator for some algorithms
+
+    Attributes
+    ----------
+
+    batchLabel : str
+        The label of the batch used for directory/file naming of batch generated files.
+    cfgFile : str
+        The path of the file containing the `netpyne.simConfig.SimConfig` object
+    cfg : `netpyne.simConfig.SimConfig`
+        The `netpyne.simConfig.SimConfig` object
+    N.B. either cfg or cfgFile should be specified #TODO: replace with typechecked single argument
+    netParamsFile : str
+        The path of the file containing the `netpyne.netParams.NetParams` object
+    netParams : `netpyne.netParams.NetParams`
+        The `netpyne.netParams.NetParams` object
+    N.B. either netParams or netParamsFile should be specified #TODO: replace with typechecked single argument
+    initCfg : dict
+        params dictionary that is used to modify the batch cfg prior to any algorithm based parameter modifications
+    saveFolder : str
+        The path of the folder where the batch will be saved (defaults to batchLabel)
+    method : str
+        The algorithm method used for batch
+    runCfg : dict
+        Keyword: Arg dictionary used to generate submission templates (see utils.py)
+    evolCfg : dict #TODO: replace with algoCfg? to merge with optimCfg
+        Keyword: Arg dictionary used to define evolutionary algorithm parameters (see evol.py)
+    optimCfg : dict #TODO: replace with algoCfg? to merge with evolCfg
+        Keyword: Arg dictionary used to define optimization algorithm parameters
+        (see asd_parallel.py, optuna_parallel.py, sbi_parallel.py)
+    params : list
+        Dictionary of parameters to be explored per algorithm (grid, evol, asd, optuna, sbi)
+        (see relevant algorithm script for details)
+    seed : int
+        Seed for random number generator for some algorithms
     """
 
     def __init__(
diff --git a/netpyne/batch/evol.py b/netpyne/batch/evol.py
index e74521fce..f9622d246 100644
--- a/netpyne/batch/evol.py
+++ b/netpyne/batch/evol.py
@@ -3,20 +3,7 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
 from ctypes import util
-from future import standard_library
-
-standard_library.install_aliases()
 
 # required to make json saving work in Python 2/3
 try:
diff --git a/netpyne/batch/grid.py b/netpyne/batch/grid.py
index adec7a77f..dee221e96 100644
--- a/netpyne/batch/grid.py
+++ b/netpyne/batch/grid.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
@@ -24,7 +10,6 @@
     to_unicode = str
 
 import pandas as pd
-import imp
 import os, sys
 import glob
 from time import sleep
diff --git a/netpyne/batch/optuna_parallel.py b/netpyne/batch/optuna_parallel.py
index a0abee973..7af3793d2 100644
--- a/netpyne/batch/optuna_parallel.py
+++ b/netpyne/batch/optuna_parallel.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
diff --git a/netpyne/batch/sbi_parallel.py b/netpyne/batch/sbi_parallel.py
index 5700548da..9ef44cb10 100644
--- a/netpyne/batch/sbi_parallel.py
+++ b/netpyne/batch/sbi_parallel.py
@@ -2,20 +2,8 @@
 Module for SBI optimization
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import zip
-
-from builtins import range
-from builtins import open
-from builtins import str
 from lib2to3.pytree import NegatedPattern
-from future import standard_library
 
-standard_library.install_aliases()
 
 # required to make json saving work in Python 2/3
 try:
@@ -32,16 +20,11 @@
 from neuron import h
 from sbi import utils as utils
 from sbi import analysis as analysis
-from sbi.inference.base import infer
-from sbi.analysis.plot import pairplot
 from sbi import utils
 from sbi import analysis
-from sbi import inference
-from sbi.inference import SNPE, SNLE, SNRE, simulate_for_sbi, prepare_for_sbi
+from sbi.inference import SNPE, SNLE, SNRE, simulate_for_sbi
+from sbi.inference.trainers.base import process_simulator, process_prior
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from scipy.stats import kurtosis
 
 pc = h.ParallelContext()  # use bulletin board master/slave
 
@@ -200,7 +183,11 @@ def objectiveFunc(param):
             updateBestFit(candidate, fitness)
             return torch.as_tensor(fitness + sum_statistics)
 
-        simulator, prior = prepare_for_sbi(lambda param: objectiveFunc(param), prior)
+        # Check prior, return PyTorch prior.
+        prior, _, prior_returns_numpy = process_prior(prior)
+        # Check simulator, returns PyTorch simulator able to simulate batches.
+        simulator = process_simulator(simulator, prior, prior_returns_numpy)
+
         inference = sbi_md[sbi_method](prior=prior)
 
         if inference_type == 'single':
diff --git a/netpyne/batch/utils.py b/netpyne/batch/utils.py
index 3ddfdc514..99bdbe759 100644
--- a/netpyne/batch/utils.py
+++ b/netpyne/batch/utils.py
@@ -3,16 +3,8 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
 import builtins
 
-from future import standard_library
-
-standard_library.install_aliases()
-
 import numpy as np
 import json
 import pickle
@@ -36,11 +28,13 @@ def createFolder(folder):
 
     import os
 
-    if not os.path.exists(folder):
-        try:
-            os.mkdir(folder)
-        except OSError:
-            print(' Could not create %s' % (folder))
+    # If file path does not exist, it will create the file path (parent and sub-directories)
+    
+    try:
+        os.makedirs(folder, exist_ok=True)
+    except Exception as e:
+        print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+        raise SystemExit('Could not create %s' % (folder))
 
 
 # -------------------------------------------------------------------------------
diff --git a/netpyne/batchtools/__init__.py b/netpyne/batchtools/__init__.py
new file mode 100644
index 000000000..5a373d286
--- /dev/null
+++ b/netpyne/batchtools/__init__.py
@@ -0,0 +1,27 @@
+from netpyne.batchtools.runners import NetpyneRunner
+from batchtk.runtk import dispatchers
+from netpyne.batchtools import submits
+from batchtk import runtk
+from netpyne.batchtools.analysis import Analyzer
+
+specs = NetpyneRunner()
+
+from netpyne.batchtools.comm import Comm
+
+comm = Comm()
+
+dispatchers = dispatchers
+submits = submits
+runtk = runtk
+
+
+"""
+def analyze_from_file(filename):
+    analyzer = Fanova()
+    analyzer.load_file(filename)
+    analyzer.run_analysis(
+"""
+
+#from ray import tune as space.comm
+#list and lb ub
+
diff --git a/netpyne/batchtools/analysis.py b/netpyne/batchtools/analysis.py
new file mode 100644
index 000000000..561e339ab
--- /dev/null
+++ b/netpyne/batchtools/analysis.py
@@ -0,0 +1,55 @@
+import pandas
+from collections import namedtuple
+import numpy
+
+from optuna.importance._fanova._fanova import _Fanova
+
+
+class Fanova(object):
+    def __init__(self, n_trees: int = 64, max_depth: int = 64, seed: int | None = None) -> None:
+        self._evaluator = _Fanova(
+            n_trees=n_trees,
+            max_depth=max_depth,
+            min_samples_split=2,
+            min_samples_leaf=1,
+            seed=seed,
+        )
+
+    def evaluate(self, X: pandas.DataFrame, y: pandas.DataFrame) -> dict:
+        assert X.shape[0] == y.shape[0] # all rows must be present
+        assert y.shape[1] == 1 # only evaluation for single metric supported
+
+        evaluator = self._evaluator
+        #mins, maxs = X.min().values, X.max().values #in case bound matching is necessary.
+        search_spaces = numpy.array([X.min().values, X.max().values]).T # bounds
+        column_to_encoded_columns = [numpy.atleast_1d(i) for i in range(X.shape[1])] # encoding (no 1 hot/categorical)
+        evaluator.fit(X.values, y.values.ravel(), search_spaces, column_to_encoded_columns)
+        importances = numpy.array(
+            [evaluator.get_importance(i)[0] for i in range(X.shape[1])]
+        )
+        return {col: imp for col, imp in zip(X.columns, importances)}
+
+
+class Analyzer(object):
+    def __init__(self,
+                 params: list, # list of parameters
+                 metrics: list, # list of metrics
+                 evaluator = Fanova()) -> None:
+        self.params = params
+        self.metrics = metrics
+        self.data = None
+        self.evaluator = evaluator
+
+    def load_file(self,
+                  filename: str # filename (.csv) containing the completed batchtools trials
+                  ) -> None:
+        data = pandas.read_csv(filename)
+        param_space = data[["config/{}".format(param) for param in self.params]]
+        param_space = param_space.rename(columns={'config/{}'.format(param): param for param in self.params})
+        results = data[self.metrics]
+        self.data = namedtuple('data', ['param_space', 'results'])(param_space, results)
+
+    def run_analysis(self) -> dict:
+        return self.evaluator.evaluate(self.data.param_space, self.data.results)
+
+
diff --git a/netpyne/batchtools/comm.py b/netpyne/batchtools/comm.py
new file mode 100644
index 000000000..1a8a12763
--- /dev/null
+++ b/netpyne/batchtools/comm.py
@@ -0,0 +1,48 @@
+from netpyne.batchtools import specs
+from batchtk.runtk.runners import get_class
+from batchtk import runtk
+from neuron import h
+import warnings
+HOST = 0 # for the purposes of send and receive with mpi.
+
+class Comm(object):
+    def __init__(self, runner = specs):
+        self.runner = runner
+        h.nrnmpi_init()
+        self.pc = h.ParallelContext()
+        self.rank = self.pc.id()
+        self.connected = False
+
+    def initialize(self):
+        if self.is_host():
+            try:
+                self.runner.connect()
+                self.connected = True
+            except Exception as e:
+                print("Failed to connect to the Dispatch Server, failover to Local mode. See: {}".format(e))
+                self.runner._set_inheritance('file') #TODO or could change the inheritance of the runner ...
+                self.runner.env[runtk.MSGOUT] = "{}/{}.out".format(self.runner.cfg.saveFolder, self.runner.cfg.simLabel)
+
+    def set_runner(self, runner_type):
+        self.runner = get_class(runner_type)()
+    def is_host(self):
+        return self.rank == HOST
+    def send(self, data):
+        if self.is_host():
+            if self.connected:
+                self.runner.send(data)
+            else:
+                self.runner.write(data)
+
+    def recv(self): #TODO to be tested, broadcast to all workers?
+        if self.is_host() and self.connected:
+            data = self.runner.recv()
+        else:
+            data = None
+        #data = self.is_host() and self.runner.recv()
+        #probably don't put a blocking statement in a boolean evaluation...
+        self.pc.barrier()
+        return self.pc.py_broadcast(data, HOST)
+
+    def close(self):
+        self.runner.close()
diff --git a/netpyne/batchtools/docs/batchtools.ipynb b/netpyne/batchtools/docs/batchtools.ipynb
new file mode 100644
index 000000000..22fe3489f
--- /dev/null
+++ b/netpyne/batchtools/docs/batchtools.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Jupyter Tutorial: The NetPyNE batchtools subpackage\n",
+    "How to use the `specs` and `comm` to communicate with the `batchtools` `dispatcher`\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "89ec6ca2392a9a0d"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For each individual `sim`, communication with the `batchtools` `dispatcher` occurs through the `specs` and `comm` objects"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "be50f40d8e61a944"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "from netpyne.batchtools import specs, comm"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6f321aedb7faf945",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "the `specs` object is an instantiation of a custom class extending the `batchtk` `Runner` ..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5f2f08f0b5e582c3"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "help(type(specs))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "29fa261236494bc3",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "From this `specs` object, we can similarly call `specs.NetParams` and `specs.SimConfig` to create the NetPyNE objects..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "64ead24451bbad4a"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "help(specs.NetParams)\n",
+    "help(specs.SimConfig)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "43d263d080800019",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The `batchtools` job submission tool uses `environmental variables` to pass values to our `config` object created by `specs.SimConfig`, these `environmental variables` are captured during the `specs` `object creation` which occurs during the batchtools `import` (from the `batchtools` `__init__.py`:\n",
+    "```\n",
+    "from netpyne.batchtools.runners import NetpyneRunner\n",
+    "specs = NetpyneRunner()\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "710cc6084bd7af02"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's `export` some `environmental variables` to pass values to our `config` object. When this is handled by the `batchtools` `subpackage`, this occurs automatically..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "52704684f5e80f3c"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "%env STRRUNTK0   =foo.bar=baz\n",
+    "%env FLOATRUNTK1 =float_val=7.7\n",
+    "from netpyne.batchtools import NetpyneRunner\n",
+    "specs = NetpyneRunner()"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "50de117ff7f43aa6",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "One way of retrieving these values is by calling `specs.get_mappings()`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "fac14e517044b980"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(specs.get_mappings())"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "257fad390f4abce",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now, let's create our `config` object using the `specs.SimConfig()` constructor\n",
+    "This `config` object will hold a `dictionary` such that the initial values `foo['bar']` = `not_baz` and a `float_val` = `3.3`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "92d41061bb828744"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "cfg = specs.SimConfig()\n",
+    "cfg.foo = {'bar': 'not_baz', 'qux': 'quux'}\n",
+    "cfg.float_val = 3.3\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "ca121d6ab30c3e7b",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Finally, calling the `cfg.update_cfg()` method will overwrite the original values with our environment values, (`baz` and `7.7`)...\n",
+    "\n",
+    "in NetPyNE, this was originally handled with the:\n",
+    "```\n",
+    "try:\n",
+    "    from __main__ import cfg\n",
+    "except:\n",
+    "    from cfg import cfg\n",
+    "```\n",
+    "API idiom in the `netParams.py` file...\n",
+    " \n",
+    "as well as the \n",
+    "```\n",
+    "cfg, netParams = sim.readCmdLineArgs(simConfigDefault='src/cfg.py', netParamsDefault='src/netParams.py')\n",
+    "```\n",
+    "API idiom in the `init.py` file...\n",
+    "\n",
+    "using the `batchtools` subpackage, we can treat the `cfg` as an object and pass it between scripts via `import` statements...\n",
+    "in `netParams.py`...\n",
+    "```\n",
+    "from cfg import cfg\n",
+    "cfg.update()\n",
+    "```\n",
+    "in `init.py`...\n",
+    "```\n",
+    "from netParams import cfg, netParams\n",
+    "sim.createSimulateAnalyze(simConfig=cfg, netParams=netParams)\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6ea43f729d0685d4"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(\"prior to  cfg.update()\")\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))\n",
+    "print()\n",
+    "cfg.update() # call update_cfg to update values in the cfg object with values assigned by batch\n",
+    "print(\"after the cfg.update()\")\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "a9426b6e6594961",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Finally, the `comm object` is used to report to the monitoring `dispatcher object`\n",
+    "the means of communication is dependent on which `dispatcher object` is instantiated, and communicated through environmental variables\n",
+    "in this case, since there is no `dispatcher object` the `comm` methods will simply perform `pass operations`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "65bbb0ef2c76295a"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.initialize() # initializes comm object, establishing channel to communicate with the host dispatcher object"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "e9141d91d6e02aa3",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(comm.is_host()) # returns a boolean IF the calling process is the 0th ranked  parallelcontext, similar to sim.pc.rank == 0"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5ed6a524bd8a3e0b",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.send('message') # sends 'message' to the `dispatcher object`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "1966edbf32649352",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.close() #finalizes communication, closes any resources used to communicate with the `dispatcher object`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "34f021af4127363c"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "648746fff96b8a72"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/netpyne/batchtools/docs/batchtools.rst b/netpyne/batchtools/docs/batchtools.rst
new file mode 100644
index 000000000..c21bf4da9
--- /dev/null
+++ b/netpyne/batchtools/docs/batchtools.rst
@@ -0,0 +1,435 @@
+Running a Batch Job
+===================
+
+The NetPyNE batchtools subpackage provides a method of automating job submission and reporting
+
+A diagram of the object interfaces...
+
+::
+
+ batch<-->\               /---> configuration_0 >---\
+           \             /                         specs---\
+           \<--->dispatcher_0                               sim_0
+           \             \                         comm ---/
+           \              \---<    results_0    <---/
+            \
+            \               /---> configuration_1 >---\
+            \              /                         specs---\
+            \<--->dispatcher_1                                sim_1
+             \             \                         comm ---/
+             \              \---<    results_1    <---/
+             \
+             \
+             ...
+
+While objects and interfaces can be handled directly, batchtools offers simple wrapper commands applicable to most use-cases, where
+automatic parameter searches can be done by specifying a search space and algorithm through `netpyne.batchtools.search`, and
+parameter to model translation and result communication is handled through `netpyne.batchtools.specs` and `netpyne.batchtools.comm` respectively.
+
+A diagram of the wrapper interactions...
+
+::
+
+ netpyne.batchtools.search.search(   ) ----------------------------\                 host
+        |                                                          |
+        | search(   )                                              |
+ ==============================================================================================
+        |                                                      comm.initialize(   )
+        |                                                      comm.send(   )
+        |  cfg = netpyne.batchtools.specs.SimConfig(   )       comm.close(   )
+        |            |                                           ^     ^
+        v            v                                           |     |
+        cfg.update_cfg() ----------------------------------------/     |
+                                                                       |
+        send(   )                                               netpyne.batchtools.comm(   )
+                                                                                    simulation
+
+1. Setting up batchtools
+------------------------
+Beyond the necessary dependency installations for NetPyNE and NEURON, several additional `pip` installations are required.
+
+The NetPyNE installation should be handled as a development installation of the repository branch `batch`::
+
+    git clone https://github.com/Neurosim-lab/netpyne.git
+    cd netpyne
+    git checkout batch
+    pip install -e .
+
+The batchtools installation either::
+
+    pip install -u batchtk
+
+or a development install (recommended)::
+
+    git clone https://github.com/jchen6727/batchtk.git
+    cd batchtk
+    pip install -e .
+
+Ray is a dependency for batchtools, and should be installed with the following command::
+
+    pip install -u ray[default]
+
+2. Examples
+-----------
+Examples of NetPyNE batchtools usage can be found in the ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples>`_.
+
+Examples of the underlying batchtk package can be in the ``examples`` directory `on the batchtk github <https://github.com/jchen6727/batchtk/tree/release/examples>`_.
+
+3. Retrieving batch configuration values through the ``specs`` object
+---------------------------------------------------------------------
+Each simulation is able to retrieve relevant configurations through the ``specs`` object, and communicate with
+the dispatcher through the ``comm`` object.
+
+importing the relevant objects::
+
+     from netpyne.batchtools import specs, comm
+     cfg = specs.SimConfig()  # create a SimConfig object, can be provided with a dictionary on initial call to set initial values
+     netParams = specs.NetParams()  # create a netParams object
+
+``netpyne.batchtools.specs`` behaves similarly to ``netpyne.sim.specs`` except in the following cases:
+
+* ``netpyne.batchtools.specs`` automatically captures relevant configuration mappings created by the ``dispatcher`` upon initialization
+
+   * these mappings can be retrieved via ``specs.get_mappings()``
+
+* the SimConfig object created by ``netpyne.batch.specs.SimConfig()`` will update itself with relevant configuration mappings through the ``update()`` method::
+
+    from netpyne.batchtools import specs # import the custom batch specs
+    cfg = specs.SimConfig()              # create a SimConfig object
+    cfg.update()                         # update the cfg object with any relevant mappings for this particular batch job
+
+The ``update`` method will update the ``SimConfig`` object with the configuration mappings captured in ``specs`` (see: ``specs.get_mappings()``)
+
+This replaces the previous idiom for updating the SimConfig object with mappings from the batched job submission::
+
+    try:
+        from __main__ import cfg  # import SimConfig object with params from parent module
+    except:
+        from cfg import cfg  # if no simConfig in parent module, import directly from tut8_cfg module
+
+
+
+
+4. Communicating results to the ``dispatcher`` with the ``comm`` object
+-----------------------------------------------------------------------
+
+Prior batched simulations relied on ``.pkl`` files to communicate data. The ``netpyne.batch`` subpackage uses a specific ``comm`` object to send custom data back
+The ``comm`` object determines the method of communication based on the batch job submission type.
+
+In terms of the simulation, the following functions are available to the user:
+
+* **comm.initialize()**: establishes a connection with the batch ``dispatcher`` for sending data
+
+* **comm.send(<data>)**: sends ``<data>`` to the batch ``dispatcher``
+
+    * for ``search`` jobs, it is important to match the data sent with the metric specified in the search function
+
+* **comm.close()**: closes and cleans up the connection with the batch ``dispatcher``
+
+5. Specifying a batch job
+-------------------------
+Batch job handling is implemented with methods from ``netpyne.batchtools.search``
+
+**search**
+
+.. code-block:: python
+
+    def search(job_type: str, # the submission engine to run a single simulation (e.g. 'sge', 'sh')
+               comm_type: str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem')
+               run_config: Dict,  # batch configuration, (keyword: string pairs to customize the submit template)
+               params: Dict,  # search space (dictionary of parameter keys: tune search spaces)
+               algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+               label: Optional[str] = 'search',  # label for the search
+               output_path: Optional[str] = '../batch',  # directory for storing generated files
+               checkpoint_path: Optional[str] = '../ray',  # directory for storing checkpoint files
+               max_concurrent: Optional[int] = 1,  # number of concurrent trials to run at one time
+               batch: Optional[bool] = True,  # whether concurrent trials should run synchronously or asynchronously
+               num_samples: Optional[int] = 1,  # number of trials to run
+               metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+               mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+               algorithm_config: Optional[dict] = None,  # additional configuration for the search algorithm
+               ) -> tune.ResultGrid: # results of the search
+
+The basic search implemented with the ``search`` function uses ``ray.tune`` as the search algorithm backend, returning a ``tune.ResultGrid`` which can be used to evaluate the search space and results. It takes the following parameters;
+
+* **job_type**: either "``sge``" or "``sh``", specifying how the job should be submitted, "``sge``" will submit batch jobs through the Sun Grid Engine. "``sh``" will submit bach jobs through the shell on a local machine
+* **comm_type**: either "``socket``" or "``filesystem``", specifying how the job should communicate with the dispatcher
+* **run_config**: a dictionary of keyword: string pairs to customize the submit template, the expected keyword: string pairs are dependent on the job_type::
+
+    =======
+    sge
+    =======
+    queue: the queue to submit the job to (#$ -q {queue})
+    cores: the number of cores to request for the job (#$ -pe smp {cores})
+    vmem: the amount of memory to request for the job (#$ -l h_vmem={vmem})
+    realtime: the amount of time to request for the job (#$ -l h_rt={realtime})
+    command: the command to run for the job
+
+    example:
+    run_config = {
+        'queue': 'cpu.q',       # request job to be run on the 'cpu.q' queue
+        'cores': 8,             # request 8 cores for the job
+        'vmem': '8G',           # request 8GB of memory for the job
+        'realtime': '24:00:00', # set timeout of the job to 24 hours
+        'command': 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'
+    } # set the command to be run to 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'
+
+    =======
+    sh
+    =======
+    command: the command to run for the job
+
+    example:
+    run_config = {
+        'command': 'mpiexec -n 8 nrniv -python -mpi init.py'
+    } # set the command to be run
+
+* **params**: a dictionary of config values to perform the search over. The keys of the dictionary should match the keys of the config object to be updated. Lists or numpy generators >2 values will force a grid search over the values; otherwise, a list of two values will create a uniform distribution sample space.
+
+    **usage 1**: updating a constant value specified in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # take a config object with the following parameter ``foo``
+        cfg = specs.SimConfig()
+        cfg.foo = 0
+        cfg.update()
+
+        # specify a search space for ``foo`` such that a simulation will run with:
+        # cfg.foo = 0
+        # cfg.foo = 1
+        # cfg.foo = 2
+        # ...
+        # cfg.foo = 9
+
+        # using:
+        params = {
+            'foo': range(10)
+        }
+
+    **usage 2**: updating a nested object in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # to update a nested object, the package uses the `.` operator to specify reflection into the object.
+        # take a config object with the following parameter object ``foo``
+        cfg = specs.SimConfig()
+        cfg.foo = {'bar': 0, 'baz': 0}
+        cfg.update()
+
+        # specify a search space for ``foo['bar']`` with `foo.bar` such that a simulation will run:
+        # cfg.foo['bar'] = 0
+        # cfg.foo['bar'] = 1
+        # cfg.foo['bar'] = 2
+        # ...
+        # cfg.foo['bar'] = 9
+
+        # using:
+        params = {
+            'foo.bar': range(10)
+        }
+
+        # this reflection works with nested objects as well...
+        # i.e.
+        # cfg.foo = {'bar': {'baz': 0}}
+        # params = {'foo.bar.baz': range(10)}
+
+    **usage 3**: updating a list object in the ``SimConfig`` object
+
+.. code-block:: python
+
+        # to update a nested object, the package uses the `.` operator to specify reflection into the object.
+        # take a config object with the following
+        cfg = specs.SimConfig()
+        cfg.foo = [0, 1, 4, 9, 16]
+        cfg.update()
+
+        # specify a search space for ``foo[0]`` with `foo.0` such that a simulation will run:
+        # cfg.foo[0] = 0
+        # cfg.foo[0] = 1
+        # cfg.foo[0] = 2
+        # ...
+        # cfg.foo[0] = 9
+
+        # using:
+        params = {
+            'foo.0': range(10)
+        }
+
+        # this reflection works with nested objects as well...
+
+* **algorithm** : the search algorithm (supported within ``ray.tune``)
+
+    **Supported algorithms**
+
+.. code-block:: python
+
+        * "variant_generator": grid and random based search of the parameter space (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "random": grid and random based search of the parameter space (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "axe": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "bayesopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "hyperopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "bohb": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "nevergrad": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "optuna": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "hebo": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "sigopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+        * "zoopt": optimization algorithm (see: https://docs.ray.io/en/latest/tune/api/suggestion.html)
+
+* **label**: a label for the search, used for output file naming
+
+* **output_path**: the directory for storing generated files, can be a relative or absolute path
+
+* **checkpoint_path**: the directory for storing checkpoint files in case the search needs to be restored, can be a relative or absolute path
+
+* **max_concurrent**: the number of concurrent trials to run at one time, it is recommended to keep in mind the resource usage of each trial to avoid overscheduling
+
+* **batch**: whether concurrent trials should run synchronously or asynchronously
+
+* **num_samples**: the number of trials to run, for any grid search, each value in the grid will be sampled ``num_samples`` times.
+
+* **metric**: the metric to optimize (this should match some key: value pair in the returned data)
+
+* **mode**: either 'min' or 'max' (whether to minimize or maximize the metric)
+
+* **algorithm_config**: additional configuration for the search algorithm (see the `optuna docs <https://docs.ray.io/en/latest/tune/api/suggestion.html>`_)
+
+6. Batch searches on the Rosenbrock function (some simple examples)
+-------------------------------------------------------------------
+The ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock>`_ contains multiple methods of performing automatic parameter search of a
+2 dimensional Rosenbrock function.  These examples are used to quickly demonstrate some of the functionality of batch communications rather than the full process of running parameter searches on a detailed
+NEURON simulation (see 7. Performing parameter optimization searches (CA3 example)) and therefore only contain the a `batch.py` file containing the script detailing the parameter space and search method, and a
+`rosenbrock.py` file containing the function to explore, and the appropriate declarations and calls for batch automation and communication (rather than the traditional `cfg.py`, `netParams.py`, and `init.py` files).
+
+1. `basic_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock>`_
+
+This demonstrates a basic grid search of the Rosenbrock function using the new ``batchtools``, where the search space is defined as the cartesian product of ``params['x0']`` and ``params['x1']``
+
+.. code-block:: python
+
+        # from batch.py
+        params = {'x0': [0, 3],
+                  'x1': [0, 3],
+                 }
+
+that is, with the values ``cfg.x0``, ``cfg.x1`` iterating over: ``[(0, 0), (0, 3), (3, 0), (3, 3)]`` list
+
+2. `coupled_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock>`_
+
+This demonstrates a basic paired grid search, where ``x0`` is ``[0, 1, 2]`` and x1[n] is ``x0[n]**2``
+
+.. code-block:: python
+
+        # from batch.py
+        x0 = numpy.arange(0, 3)
+        x1 = x0**2
+
+        x0_x1 = [*zip(x0, x1)]
+        params = {'x0_x1': x0_x1
+                  }
+
+the ``x0`` and ``x1`` values are paired together to create a search space ``x0_x1`` iterating over: ``[(0, 0), (1, 1), (2, 4)]`` list
+
+then, in the ``rosenbrock.py`` file, a list of two values ``cfg.x0_x1`` is created to capture the ``x0_x1`` values, which is then unpacked into individual ``x0`` and ``x1`` values
+
+.. code-block:: python
+
+        # from rosenbrock.py
+        cfg.x0_x1 = [1, 1]
+
+        cfg.update_cfg()
+
+        # -------------- unpacking x0_x1 list  -------------- #
+        x0, x1 = cfg.x0_x1
+
+then the Rosenbrock function is evaluated with the unpacked ``x0`` and ``x1``
+
+3. `random_rosenbrock <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/random_rosenbrock>`_
+
+This demonstrates a grid search over a nested object, where ``xn`` is a list of 2 values which are independently modified to search the cartesian product of ``[0, 1, 2, 3, 4]`` and ``[0, 1, 2, 3, 4]``
+
+.. code-block:: python
+
+        # from batch.py
+        params = {'xn.0': numpy.arange(0, 5),
+                  'xn.1': numpy.arange(0, 5)
+                 }
+
+By using ``xn.0`` and ``xn.1`` we can reference the 0th and 1st elements of the list, which is created and modified in rosenbrock.py
+
+.. code-block:: python
+
+        # from rosenbrock.py
+        cfg.xn = [1, 1]
+
+        cfg.update_cfg()
+
+        # ---------------- unpacking x list  ---------------- #
+        x0, x1 = cfg.xn
+
+
+7. Performing parameter optimization searches (CA3 example)
+-----------------------------------------------------------
+The ``examples`` directory `on the NetPyNE github <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples>`_ shows both a ``grid`` based search as well as an ``optuna`` based optimization.
+
+In the ``CA3`` example, we tune the ``PYR->BC`` ``NMDA`` and ``AMPA`` synaptic weights, as well as the ``BC->PYR`` ``GABA`` synaptic weight. Note the search space is defined
+
+.. code-block:: python
+
+        # from optuna_search.py
+        params = {'nmda.PYR->BC' : [1e-3, 1.8e-3],
+                  'ampa.PYR->BC' : [0.2e-3, 0.5e-3],
+                  'gaba.BC->PYR' : [0.4e-3, 1.0e-3],
+                 }
+
+in both ``optuna_search.py``, defining the upper and lower bounds of the search space, while in ``grid_search.py`` the search space is defined
+
+.. code-block:: python
+
+        # from grid_search.py
+        params = {'nmda.PYR->BC' : numpy.linspace(1e-3, 1.8e-3, 3),
+                  'ampa.PYR->BC' : numpy.linspace(0.2e-3, 0.5e-3, 3),
+                  'gaba.BC->PYR' : numpy.linspace(0.4e-3, 1.0e-3, 3),
+                 }
+
+which defines ``3x3x3`` specific values to search over
+
+Note that the ``metric`` specifies a specific ``string`` (``loss``) to report and optimize around. This value is generated and ``sent`` by the ``init.py`` simulation
+
+.. code-block:: python
+
+        # from init.py
+        results['PYR_loss'] = (results['PYR'] - 3.33875)**2
+        results['BC_loss']  = (results['BC']  - 19.725 )**2
+        results['OLM_loss'] = (results['OLM'] - 3.470  )**2
+        results['loss'] = (results['PYR_loss'] + results['BC_loss'] + results['OLM_loss']) / 3
+        out_json = json.dumps({**inputs, **results})
+
+        print(out_json)
+        #TODO put all of this in a single function.
+        comm.send(out_json)
+        comm.close()
+
+The ``out_json`` output contains a dictionary which includes the ``loss`` metric (calculated as the MSE between observed and expected values)
+
+In a multi-objective optimization, the relevant ``PYR_loss``, ``BC_loss``, and ``OLM_loss`` components are additionally included (see ``mo_optuna_search.py``)
+
+8. Parameter Importance Evaluation Using fANOVA
+-----------------------------------------------
+A new feature in the batchtools beta release is the ability to evaluate parameter importance using a functional ANOVA inspired algorithm via the `Optuna` and `scikit-learn` libraries.
+(See `the original Hutter paper <http://proceedings.mlr.press/v32/hutter14.pdf>`_  and its `citation <https://automl.github.io/fanova/cite.html>`_)
+
+Currently, only unpaired single parameter importance to a single metric score is supported through the `NetPyNE.batchtools.analysis` `Analyzer` object, with an example of its usage
+`here <https://github.com/suny-downstate-medical-center/netpyne/tree/batch/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock>`_:
+
+In its current iteration, demonstrating the example requires generating an output `grid.csv` using `batch.py`, then loading that `grid.csv` into the `Analyzer` object. Then, using `run_analysis` will generate, per parameter, a single score indicative of the estimated `importance` of the parameter: that is, the estimated effect on the total variance of the model within the given bounds.
+
+.. code-block:: python
+
+        # from analysis.py
+        from netpyne.batchtools.analysis import Analyzer
+
+        analyzer = Analyzer(params = ['x.0', 'x.1', 'x.2', 'x.3'], metrics = ['fx']) # specify the parameter space and metrics of the batch function
+        analyzer.load_file('grid.csv') # load the grid file generated by the batch run
+        results = analyzer.run_analysis() # run fANOVA analysis and store the importance values in a results dictionary
+
diff --git a/netpyne/batchtools/evol.py b/netpyne/batchtools/evol.py
new file mode 100644
index 000000000..a30d29982
--- /dev/null
+++ b/netpyne/batchtools/evol.py
@@ -0,0 +1,6 @@
+import numpy
+from cmaes import CMA
+
+from netpyne.batchtools import specs, comm
+
+# ---- Rosenbrock Function & Constant Definition ---- #
diff --git a/netpyne/batchtools/examples/CA3/README.md b/netpyne/batchtools/examples/CA3/README.md
new file mode 100644
index 000000000..e69de29bb
diff --git a/netpyne/batchtools/examples/CA3/cfg.py b/netpyne/batchtools/examples/CA3/cfg.py
new file mode 100644
index 000000000..8780792fc
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/cfg.py
@@ -0,0 +1,48 @@
+from netpyne.batchtools import specs
+
+### config ###
+
+cfg = specs.SimConfig()
+
+cfg.duration = 1000
+cfg.dt = 0.1
+cfg.hparams = {'v_init': -65.0}
+cfg.verbose = False
+cfg.recordTraces = {}  # don't save this
+cfg.recordStim = False
+cfg.recordStep = 0.1            # Step size in ms to save data (eg. V traces, LFP, etc)
+cfg.filename = '00'         # Set file output name
+cfg.savePickle = False        # Save params, network and sim output to pickle file
+cfg.saveDat = False
+cfg.saveJson = True
+cfg.printRunTime = 0.1 
+cfg.recordLFP = None # don't save this
+cfg.simLabel = 'ca3'
+cfg.saveFolder = '.'
+
+
+cfg.analysis['plotRaster'] = {'saveFig': True} # raster ok
+cfg.analysis['plotTraces'] = { } # don't save this
+cfg.analysis['plotLFPTimeSeries'] = { }  # don't save this 
+
+cfg.cache_efficient = True # better with MPI?
+""" remove all of the unecessary data """
+cfg.saveCellSecs = False
+cfg.saveCellConns = False
+
+cfg.nmda={#NMDA search space
+    "PYR->BC" : 1.38e-3,
+    "PYR->OLM": 0.7e-3,
+    "PYR->PYR": 0.004e-3,
+}
+cfg.ampa={#AMPA search space
+    "PYR->BC" : 0.36e-3,
+    "PYR->OLM": 0.36e-3,
+    "PYR->PYR": 0.02e-3,
+}
+
+cfg.gaba = {#GABA search space
+    "BC->BC"  : 4.5e-3,
+    "BC->PYR" : 0.72e-3,
+    "OLM->PYR": 72e-3,
+}
diff --git a/netpyne/batchtools/examples/CA3/grid_search.py b/netpyne/batchtools/examples/CA3/grid_search.py
new file mode 100644
index 000000000..3ad5dedd2
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/grid_search.py
@@ -0,0 +1,41 @@
+from netpyne.batchtools.search import search
+import numpy
+
+params = {'nmda.PYR->BC' : numpy.linspace(1e-3, 1.8e-3, 3),
+          #'nmda.PYR->OLM': numpy.linspace(0.4e-3, 1.0e-3, 3),
+          #'nmda.PYR->PYR': numpy.linspace(0.001e-3, 0.007e-3, 3),
+          'ampa.PYR->BC' : numpy.linspace(0.2e-3, 0.5e-3, 3),
+          #'ampa.PYR->OLM': numpy.linspace(0.2e-3, 0.5e-3, 3),
+          #'ampa.PYR->PYR': numpy.linspace(0.01e-3, 0.03e-3, 3),
+          #'gaba.BC->BC'  : numpy.linspace(1e-3, 7e-3, 3),
+          'gaba.BC->PYR' : numpy.linspace(0.4e-3, 1.0e-3, 3),
+          #'gaba.OLM->PYR': numpy.linspace(40e-3, 100e-3, 3),
+          }
+
+# use batch_shell_config if running directly on the machine
+shell_config = {'command': 'mpiexec -np 4 nrniv -python -mpi init.py',}
+
+# use batch_sge_config if running on a
+sge_config = {
+    'queue': 'cpu.q',
+    'cores': 5,
+    'vmem': '4G',
+    'realtime': '00:30:00',
+    'command': 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'}
+
+
+run_config = sge_config
+
+search(job_type = 'sge', # or 'sh'
+       comm_type = 'socket',
+       label = 'grid',
+
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = run_config,
+       num_samples = 1,
+       metric = 'loss',
+       mode = 'min',
+       algorithm = "variant_generator",
+       max_concurrent = 9)
diff --git a/netpyne/batchtools/examples/CA3/init.py b/netpyne/batchtools/examples/CA3/init.py
new file mode 100644
index 000000000..b84a47b9a
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/init.py
@@ -0,0 +1,27 @@
+from netpyne.batchtools import specs, comm
+from netpyne import sim
+from netParams import netParams, cfg
+import json
+
+comm.initialize()
+
+sim.createSimulate(netParams=netParams, simConfig=cfg)
+print('completed simulation...')
+#comm.pc.barrier()
+#sim.gatherData()
+if comm.is_host():
+    netParams.save("{}/{}_params.json".format(cfg.saveFolder, cfg.simLabel))
+    print('transmitting data...')
+    inputs = specs.get_mappings()
+    #print(json.dumps({**inputs}))
+    results = sim.analysis.popAvgRates(show=False)
+
+    results['PYR_loss'] = (results['PYR'] - 3.33875)**2
+    results['BC_loss']  = (results['BC']  - 19.725 )**2
+    results['OLM_loss'] = (results['OLM'] - 3.470  )**2
+    results['loss'] = (results['PYR_loss'] + results['BC_loss'] + results['OLM_loss']) / 3
+    out_json = json.dumps({**inputs, **results})
+
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
diff --git a/netpyne/batchtools/examples/CA3/mo_optuna_search.py b/netpyne/batchtools/examples/CA3/mo_optuna_search.py
new file mode 100644
index 000000000..25f478051
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mo_optuna_search.py
@@ -0,0 +1,50 @@
+from netpyne.batchtools.search import ray_optuna_search
+from netpyne.batchtools import dispatchers, submits
+import batchtk
+
+from ray import tune
+
+params = {'nmda.PYR->BC' : tune.uniform(1e-3, 1.8e-3),
+          #'nmda.PYR->OLM': tune.uniform(0.4e-3, 1.0e-3),
+          #'nmda.PYR->PYR': tune.uniform(0.001e-3, 0.007e-3),
+          'ampa.PYR->BC' : tune.uniform(0.2e-3, 0.5e-3),
+          #'ampa.PYR->OLM': tune.uniform(0.2e-3, 0.5e-3),
+          #'ampa.PYR->PYR': tune.uniform(0.01e-3, 0.03e-3),
+          #'gaba.BC->BC'  : tune.uniform(1e-3, 7e-3),
+          'gaba.BC->PYR' : tune.uniform(0.4e-3, 1.0e-3),
+          #'gaba.OLM->PYR': tune.uniform(40e-3, 100e-3),
+          }
+
+# use batch_shell_config if running directly on the machine
+shell_config = {'command': 'mpiexec -np 4 nrniv -python -mpi init.py',}
+
+# use batch_sge_config if running on a
+sge_config = {
+    'queue': 'cpu.q',
+    'cores': 5,
+    'vmem': '4G',
+    'realtime': '00:30:00',
+    'command': 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'}
+
+run_config = sge_config
+
+Dispatcher = dispatchers.INETDispatcher
+Submit = submits.SGESubmitSOCK
+metrics = ['PYR_loss', 'BC_loss', 'OLM_loss', 'loss']
+
+ray_study = ray_optuna_search(
+                dispatcher_constructor = Dispatcher,
+                submit_constructor=Submit,
+                params = params,
+                run_config = run_config,
+                max_concurrent = 3,
+                output_path = '../mo_batch',
+                checkpoint_path = '../ray',
+                label = 'mo_search',
+                num_samples = 15,
+                metric = metrics,
+                mode = ['min', 'min', 'min', 'loss'],)
+
+results = {
+    metric: ray_study.results.get_best_result(metric, 'min') for metric in metrics
+}
diff --git a/netpyne/batchtools/examples/CA3/mod/CA1ih.mod b/netpyne/batchtools/examples/CA3/mod/CA1ih.mod
new file mode 100644
index 000000000..93d435e30
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/CA1ih.mod
@@ -0,0 +1,64 @@
+: $Id: CA1ih.mod,v 1.4 2010/12/13 21:35:47 samn Exp $ 
+TITLE Ih CA3
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX hcurrent
+  NONSPECIFIC_CURRENT ih
+  RANGE g, e, v50, htau, hinf
+  RANGE gfactor
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  g= 0.0001	(mho/cm2)
+  e= -30	(mV)
+  v50=-82	(mV)
+  gfactor = 1
+}
+ 
+STATE {
+  h
+}
+ 
+ASSIGNED {
+  ih	  (mA/cm2) 
+  hinf
+  htau    (ms)
+  v	  (mV)
+}
+
+PROCEDURE iassign () { ih=g*h*(v-e)*gfactor }
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  h'= (hinf- h)/ htau
+}
+
+INITIAL { 
+  rates(v)
+  h = hinf
+  iassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  UNITSOFF
+  : HCN1
+  :hinf = 1/(1+exp(0.151*(v-v50)))
+  :htau = exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75))))
+
+  : HCN2
+  hinf = 1/(1+exp((v-v50)/10.5))
+  htau = (1/(exp(-14.59-0.086*v)+exp(-1.87+0.0701*v))) 
+  UNITSON
+}
+
diff --git a/netpyne/batchtools/examples/CA3/mod/CA1ika.mod b/netpyne/batchtools/examples/CA3/mod/CA1ika.mod
new file mode 100644
index 000000000..9e4fe6922
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/CA1ika.mod
@@ -0,0 +1,85 @@
+: $Id: CA1ika.mod,v 1.2 2010/12/01 05:06:07 samn Exp $ 
+TITLE Ika CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX kacurrent
+  NONSPECIFIC_CURRENT ika, ikad
+  RANGE g, gd, e, ninf, ntau, ndinf, ndtau, linf, ltau
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  g= 0.048	(mho/cm2)
+  gd= 0		(mho/cm2)
+  e= -90	(mV)
+}
+ 
+STATE {
+  n
+  nd : distal
+  l
+}
+ 
+ASSIGNED {
+  v	(mV)
+  ika	(mA/cm2) 
+  ikad	(mA/cm2)
+  ninf
+  ntau  (ms)
+  ndinf
+  ndtau (ms)
+  linf
+  ltau	(ms)
+}
+
+PROCEDURE iassign () {
+  ika=g*n*l*(v-e)
+  ikad=gd*nd*l*(v-e)
+}
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  n'= (ninf- n)/ ntau
+  l'= (linf- l)/ ltau
+  nd'= (ndinf-nd)/ndtau
+}
+
+INITIAL { 
+  rates(v)
+  n = ninf
+  l = linf
+  iassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = exp(-0.038*(1.5+1/(1+exp(v+40)/5))*(v-11))
+  b =	exp(-0.038*(0.825+1/(1+exp(v+40)/5))*(v-11))
+  ntau=4*b/(1+a)
+  if (ntau<0.1) {ntau=0.1}
+  ninf=1/(1+a)
+	
+  a=exp(-0.038*(1.8+1/(1+exp(v+40)/5))*(v+1))
+  b=exp(-0.038*(0.7+1/(1+exp(v+40)/5))*(v+1))
+  ndtau=2*b/(1+a)
+  if (ndtau<0.1) {ndtau=0.1}
+  ndinf=1/(1+a)
+
+  a = exp(0.11*(v+56))
+  ltau=0.26*(v+50)
+  if (ltau<2) {ltau=2}
+  linf=1/(1+a)
+  UNITSON
+}
+
diff --git a/netpyne/batchtools/examples/CA3/mod/CA1ikdr.mod b/netpyne/batchtools/examples/CA3/mod/CA1ikdr.mod
new file mode 100644
index 000000000..4c5236362
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/CA1ikdr.mod
@@ -0,0 +1,60 @@
+: $Id: CA1ikdr.mod,v 1.2 2010/12/01 05:10:52 samn Exp $ 
+TITLE IKDR CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX kdrcurrent
+  NONSPECIFIC_CURRENT ik
+  RANGE g, e, ninf, ntau
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  g = 0.010	(mho/cm2)
+  e = -90	(mV)
+}
+ 
+STATE {
+  n 
+}
+ 
+ASSIGNED {
+  v	  (mV)
+  ik	  (mA/cm2) 
+  ninf
+  ntau    (ms)
+}
+
+PROCEDURE iassign () { ik=g*n*(v-e) }
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  n'= (ninf- n)/ ntau
+}
+
+INITIAL { 
+  rates(v)
+  n = ninf
+  iassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = exp(-0.11*(v-13))
+  b = exp(-0.08*(v-13)) 	
+  ntau=50*b/(1+a)
+  if (ntau<2) {ntau=2}
+  ninf=1/(1+a)
+  UNITSON
+}
+
diff --git a/netpyne/batchtools/examples/CA3/mod/CA1ina.mod b/netpyne/batchtools/examples/CA3/mod/CA1ina.mod
new file mode 100644
index 000000000..d33ab9739
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/CA1ina.mod
@@ -0,0 +1,89 @@
+: $Id: CA1ina.mod,v 1.4 2010/11/30 19:50:00 samn Exp $ 
+TITLE INa CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+
+NEURON {
+  SUFFIX nacurrent
+  NONSPECIFIC_CURRENT ina
+  RANGE g, e, vi, ki
+  RANGE minf,hinf,iinf,mtau,htau,itau : testing
+}
+  
+PARAMETER {
+  : v	    (mV)
+  celsius	    (degC)
+  g = 0.032   (mho/cm2)
+  e = 55	    (mV)
+  vi = -60    (mV)
+  ki = 0.8
+}
+ 
+STATE {
+  m
+  h
+  I : i 
+}
+ 
+ASSIGNED {
+  i (mA/cm2)
+  ina	(mA/cm2) 
+  minf
+  mtau    (ms)
+  hinf
+  htau	(ms)
+  iinf
+  itau	(ms)
+  v	(mV) : testing
+}
+
+: PROCEDURE iassign () { ina=g*m*m*m*h*i*(v-e) }
+PROCEDURE iassign () { i=g*m*m*m*h*I*(v-e) ina=i}
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  m' = (minf - m) / mtau
+  h' = (hinf - h) / htau
+  : i' = (iinf - i) / itau	    
+  I' = (iinf - I) / itau	    
+}
+
+INITIAL { 
+  rates(v)
+  h = hinf
+  m = minf
+  : i = iinf
+  I = iinf
+  iassign() : testing
+}
+
+
+PROCEDURE rates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = 0.4*(v+30)/(1-exp(-(v+30)/7.2))
+  b = 0.124*(v+30)/(exp((v+30)/7.2)-1) 	
+  mtau=0.5/(a+b)
+  if (mtau<0.02) {mtau=0.02}
+  minf=a/(a+b)
+  a = 0.03*(v+45)/(1-exp(-(v+45)/1.5))
+  b = 0.01*(v+45)/(exp((v+45)/1.5)-1)
+  htau=0.5/(a+b)
+  if (htau<0.5) {htau=0.5}
+  hinf=1/(1+exp((v+50)/4))
+  a =	exp(0.45*(v+66))
+  b = exp(0.09*(v+66))
+  itau=3000*b/(1+a)
+  if (itau<10) {itau=10}
+  iinf=(1+ki*exp((v-vi)/2))/(1+exp((v-vi)/2))
+  UNITSON
+}
+
diff --git a/netpyne/batchtools/examples/CA3/mod/MyExp2SynBB.mod b/netpyne/batchtools/examples/CA3/mod/MyExp2SynBB.mod
new file mode 100644
index 000000000..9a68baef1
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/MyExp2SynBB.mod
@@ -0,0 +1,67 @@
+: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ 
+NEURON {
+:  THREADSAFE
+  POINT_PROCESS MyExp2SynBB
+  RANGE tau1, tau2, e, i, g, Vwt, gmax
+  NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+  (nA) = (nanoamp)
+  (mV) = (millivolt)
+  (uS) = (microsiemens)
+}
+
+PARAMETER {
+  tau1=.1 (ms) <1e-9,1e9>
+  tau2 = 10 (ms) <1e-9,1e9>
+  e=0	(mV)
+  gmax = 1e9 (uS)
+  Vwt   = 0 : weight for inputs coming in from vector
+}
+
+ASSIGNED {
+  v (mV)
+  i (nA)
+  g (uS)
+  factor
+  etime (ms)
+}
+
+STATE {
+  A (uS)
+  B (uS)
+}
+
+INITIAL {
+  LOCAL tp
+
+  Vwt = 0    : testing
+
+  if (tau1/tau2 > .9999) {
+    tau1 = .9999*tau2
+  }
+  A = 0
+  B = 0
+  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
+  factor = -exp(-tp/tau1) + exp(-tp/tau2)
+  factor = 1/factor
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  g = B - A
+  if (g>gmax) {g=gmax}: saturation
+  i = g*(v - e)
+}
+
+DERIVATIVE state {
+  A' = -A/tau1
+  B' = -B/tau2
+}
+
+NET_RECEIVE(w (uS)) {LOCAL ww
+  ww=w
+  A = A + ww*factor
+  B = B + ww*factor
+}
diff --git a/netpyne/batchtools/examples/CA3/mod/MyExp2SynNMDABB.mod b/netpyne/batchtools/examples/CA3/mod/MyExp2SynNMDABB.mod
new file mode 100644
index 000000000..01291643a
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/MyExp2SynNMDABB.mod
@@ -0,0 +1,108 @@
+: $Id: MyExp2SynNMDABB.mod,v 1.4 2010/12/13 21:28:02 samn Exp $ 
+NEURON {
+:  THREADSAFE
+  POINT_PROCESS MyExp2SynNMDABB
+  RANGE tau1, tau2, e, i, iNMDA, s, sNMDA, r, tau1NMDA, tau2NMDA, Vwt, smax, sNMDAmax
+  NONSPECIFIC_CURRENT i, iNMDA
+}
+
+UNITS {
+  (nA) = (nanoamp)
+  (mV) = (millivolt)
+  (uS) = (microsiemens)
+}
+
+PARAMETER {
+  tau1     =   0.1 (ms) <1e-9,1e9>
+  tau2     =  10 (ms) <1e-9,1e9>	
+  tau1NMDA = 15  (ms)
+  tau2NMDA = 150 (ms)
+  e        = 0	(mV)
+  mg       = 1
+  r        = 1
+  smax     = 1e9 (1)
+  sNMDAmax = 1e9 (1)
+  
+  Vwt   = 0 : weight for inputs coming in from vector
+}
+
+ASSIGNED {
+  v       (mV)
+  i       (nA)
+  iNMDA   (nA)
+  s       (1)
+  sNMDA   (1)
+  mgblock (1)
+  factor  (1)
+  factor2 (1)
+	
+  etime (ms)
+}
+
+STATE {
+  A  (1)
+  B  (1)
+  A2 (1)
+  B2 (1)
+}
+
+INITIAL {
+
+  LOCAL tp
+  
+  Vwt = 0 : testing
+
+  if (tau1/tau2 > .9999) {
+    tau1 = .9999*tau2
+  }
+  A = 0
+  B = 0	
+  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
+  factor = -exp(-tp/tau1) + exp(-tp/tau2)
+  factor = 1/factor
+  
+  if (tau1NMDA/tau2NMDA > .9999) {
+    tau1NMDA = .9999*tau2NMDA
+  }
+  A2 = 0
+  B2 = 0	
+  tp = (tau1NMDA*tau2NMDA)/(tau2NMDA - tau1NMDA) * log(tau2NMDA/tau1NMDA)
+  factor2 = -exp(-tp/tau1NMDA) + exp(-tp/tau2NMDA)
+  factor2 = 1/factor2  
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  : Jahr Stevens 1990 J. Neurosci
+  mgblock = 1.0 / (1.0 + 0.28 * exp(-0.062(/mV) * v) )
+  s     = B  - A
+  sNMDA = B2 - A2
+  if (s    >smax)     {s    =smax    }: saturation
+  if (sNMDA>sNMDAmax) {sNMDA=sNMDAmax}: saturation
+  i     = s     * (v - e) 
+  iNMDA = sNMDA * (v - e) * mgblock
+}
+
+DERIVATIVE state {
+  A'  = -A/tau1
+  B'  = -B/tau2	
+  A2' = -A2/tau1NMDA
+  B2' = -B2/tau2NMDA
+}
+
+NET_RECEIVE(w (uS)) {LOCAL ww
+  ww=w
+  :printf("NMDA Spike: %g\n", t)
+  if(r>=0){ : if r>=0, g = AMPA + NMDA*r
+    A  = A  + factor *ww
+    B  = B  + factor *ww
+    A2 = A2 + factor2*ww*r
+    B2 = B2 + factor2*ww*r
+  }else{
+    if(r>-1000){ : if r>-1, g = NMDA*r  
+      A2 = A2 - factor2*ww*r
+      B2 = B2 - factor2*ww*r
+    }
+    : if r<0 and r<>-1, g = 0
+  }
+}
diff --git a/netpyne/batchtools/examples/CA3/mod/aux_fun.inc b/netpyne/batchtools/examples/CA3/mod/aux_fun.inc
new file mode 100644
index 000000000..ccb579afb
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/aux_fun.inc
@@ -0,0 +1,43 @@
+: $Id: aux_fun.inc,v 1.1 2009/11/04 01:24:52 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+
+
+:-------------------------------------------------------------------
+FUNCTION fun1(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+	 fun1 = A*exp((v-V0)/B)
+}
+
+FUNCTION fun2(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+	 fun2 = A/(exp((v-V0)/B)+1)
+}
+
+FUNCTION fun3(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+    if(fabs((v-V0)/B)<1e-6) {
+    :if(v==V0) {
+        fun3 = A*B/1(mV) * (1- 0.5 * (v-V0)/B)
+    } else {
+        fun3 = A/1(mV)*(v-V0)/(exp((v-V0)/B)-1)
+    }
+}
+
+FUNCTION min(x,y) { if (x<=y){ min = x }else{ min = y } }
+FUNCTION max(x,y) { if (x>=y){ max = x }else{ max = y } }
diff --git a/netpyne/batchtools/examples/CA3/mod/caolmw.mod b/netpyne/batchtools/examples/CA3/mod/caolmw.mod
new file mode 100644
index 000000000..3ea21a7ef
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/caolmw.mod
@@ -0,0 +1,47 @@
+: $Id: caolmw.mod,v 1.2 2010/11/30 16:40:09 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {	
+  (mollar) = (1/liter)
+  (M)      = (mollar)
+  (mM)     = (millimollar)
+  (mA)     = (milliamp)
+  (mV)     = (millivolt)
+  (mS)     = (millisiemens)
+}
+
+NEURON {
+  SUFFIX Caolmw
+  USEION ca READ ica, cai WRITE cai
+  RANGE alpha, tau
+}
+	
+PARAMETER {
+  alpha = 0.002 (cm2-M/mA-ms)
+  tau   = 80    (ms)
+}
+    
+ASSIGNED { ica (mA/cm2) }
+
+INITIAL { cai = 0 }
+
+STATE { cai (mM) }
+
+BREAKPOINT { SOLVE states METHOD cnexp }
+
+DERIVATIVE states { cai' = -(1000) * alpha * ica - cai/tau }
diff --git a/netpyne/batchtools/examples/CA3/mod/icaolmw.mod b/netpyne/batchtools/examples/CA3/mod/icaolmw.mod
new file mode 100644
index 000000000..51112d099
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/icaolmw.mod
@@ -0,0 +1,51 @@
+: $Id: icaolmw.mod,v 1.2 2010/11/30 16:44:13 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (mS) = (millisiemens)
+}
+
+NEURON {
+  SUFFIX ICaolmw
+  USEION ca WRITE ica
+  RANGE gca,eca
+}
+
+PARAMETER {
+  gca = 1    (mS/cm2)
+  eca = 120  (mV)
+}
+    
+ASSIGNED { 
+  ica (mA/cm2)    
+  v   (mV)
+}
+
+PROCEDURE iassign () { ica = (1e-3) * gca * mcainf(v)^2 * (v-eca) }
+
+INITIAL {
+  iassign()
+}
+
+BREAKPOINT { iassign() }
+
+FUNCTION mcainf(v(mV)) { mcainf = fun2(v, -20, 1,  -9)*1(ms) }
+
+INCLUDE "aux_fun.inc"
diff --git a/netpyne/batchtools/examples/CA3/mod/iholmw.mod b/netpyne/batchtools/examples/CA3/mod/iholmw.mod
new file mode 100644
index 000000000..ccd919202
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/iholmw.mod
@@ -0,0 +1,60 @@
+: $Id: iholmw.mod,v 1.2 2010/11/30 16:34:22 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (mS) = (millisiemens)
+}
+
+NEURON {
+  SUFFIX Iholmw
+  NONSPECIFIC_CURRENT i
+  RANGE gh,eh
+}
+	
+PARAMETER {
+  gh = 0.15 (mS/cm2)
+  eh = -40  (mV)
+}
+    
+ASSIGNED { 
+  v (mV)
+  i (mA/cm2)
+}
+
+STATE { q }
+
+PROCEDURE iassign () { i = (1e-3) * gh * q * (v-eh) }
+
+INITIAL { 
+  q  = qinf(v) 
+  iassign()
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+
+DERIVATIVE states { q' = (qinf(v)-q)/qtau(v) }
+
+FUNCTION qinf(v(mV))     { qinf = fun2(v, -80, 1, 10)*1(ms) }
+FUNCTION qtau(v(mV))(ms) { qtau = 200(ms)/(exp((v+70(mV))/20(mV))+exp(-(v+70(mV))/20(mV))) + 5(ms) }
+
+INCLUDE "aux_fun.inc"
diff --git a/netpyne/batchtools/examples/CA3/mod/kcaolmw.mod b/netpyne/batchtools/examples/CA3/mod/kcaolmw.mod
new file mode 100644
index 000000000..b2368787e
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/kcaolmw.mod
@@ -0,0 +1,52 @@
+: $Id: kcaolmw.mod,v 1.2 2010/11/30 16:47:18 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA)     = (milliamp)
+  (mV)     = (millivolt)
+  (mS)     = (millisiemens)
+  (mollar) = (1/liter)
+  (mM)     = (millimollar)
+}
+
+NEURON {
+  SUFFIX KCaolmw
+  USEION k WRITE ik
+  USEION ca READ cai
+  RANGE gkca,ek,kd
+}
+	
+PARAMETER {
+  gkca =  10 (mS/cm2)
+  ek   = -90 (mV)
+  kd   =  30 (mM)
+}
+    
+ASSIGNED {    
+  cai (mM) 
+  v   (mV)
+  ik  (mA/cm2) 
+}
+
+PROCEDURE iassign () { ik  = (1e-3) * gkca * cai/(cai+kd) * (v-ek) }
+
+INITIAL {
+  iassign()
+}
+
+BREAKPOINT { iassign() }
diff --git a/netpyne/batchtools/examples/CA3/mod/kdrbwb.mod b/netpyne/batchtools/examples/CA3/mod/kdrbwb.mod
new file mode 100644
index 000000000..fc52ae534
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/kdrbwb.mod
@@ -0,0 +1,76 @@
+: $Id: kdrbwb.mod,v 1.4 2010/12/13 21:35:26 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (mS) = (millisiemens)
+}
+
+NEURON {
+  SUFFIX Kdrbwb
+  USEION k WRITE ik
+  RANGE phin,gkdr,ek
+  RANGE taon,ninf
+}
+	
+PARAMETER {
+  gkdr =   9 (mS/cm2)
+  ek   = -90 (mV)
+  phin = 5
+}
+    
+ASSIGNED {
+  v       (mV)
+  ik      (mA/cm2)
+  celsius (degC)
+  ninf    (1)
+  taon    (ms)
+}
+
+STATE { n }
+
+PROCEDURE iassign () { ik = (1e-3) * gkdr * n^4 * (v-ek) }
+
+INITIAL { 
+  rates(v)
+  n  = ninf
+  iassign()
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp	
+  iassign()
+}
+
+DERIVATIVE states { 
+  rates(v)
+  n' = (ninf-n)/taon
+}
+
+PROCEDURE rates(v(mV)) { LOCAL an, bn, q10
+  q10  = phin:^((celsius-27.0(degC))/10.0(degC))
+    
+  an = fun3(v,  -34,  -0.01,   -10)
+  bn = fun1(v,  -44,   0.125,  -80)
+    
+  ninf = an/(an+bn)
+  taon = 1./((an+bn)*q10)
+}
+
+INCLUDE "aux_fun.inc"
diff --git a/netpyne/batchtools/examples/CA3/mod/nafbwb.mod b/netpyne/batchtools/examples/CA3/mod/nafbwb.mod
new file mode 100644
index 000000000..37281dc94
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/mod/nafbwb.mod
@@ -0,0 +1,81 @@
+: $Id: nafbwb.mod,v 1.4 2010/12/13 21:35:08 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (mS) = (millisiemens)
+}
+
+NEURON {
+  SUFFIX Nafbwb
+  USEION na WRITE ina
+  RANGE phih
+  RANGE gna, ena, taoh : testing
+}
+	
+PARAMETER {
+  gna  = 35 (mS/cm2)
+  ena  = 55 (mV)
+  phih = 5
+}
+    
+ASSIGNED {
+  v       (mV)
+  ina     (mA/cm2)
+  minf    (1)
+  hinf    (1)
+  taoh    (ms)
+  celsius (degC)
+}
+
+STATE { h }
+
+PROCEDURE iassign () { ina = (1e-3) * gna * minf^3 * h * (v-ena) }
+
+INITIAL {
+  rates(v)
+  h = hinf
+  iassign()
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp	
+  iassign()
+}
+
+DERIVATIVE states { 
+  rates(v)
+  h' = (hinf-h)/taoh
+}
+
+PROCEDURE rates(v(mV)) { LOCAL am, bm, ah, bh, q10
+    
+  q10  = phih:^((celsius-27.0(degC))/10.0(degC))	
+    
+  am   = fun3(v,  -35, -0.1,    -10)
+  bm   = fun1(v,  -60,  4,      -18) 
+  minf = am/(am+bm)
+ 
+  ah   = fun1(v,  -58,    0.07,  -20)
+  bh   = fun2(v,  -28,    1,     -10)
+  hinf = ah/(ah+bh)
+  taoh = 1./((ah+bh)*q10)
+}
+
+INCLUDE "aux_fun.inc"
diff --git a/netpyne/batchtools/examples/CA3/netParams.py b/netpyne/batchtools/examples/CA3/netParams.py
new file mode 100644
index 000000000..156e9ff20
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/netParams.py
@@ -0,0 +1,321 @@
+from netpyne.batchtools import specs
+from cfg import cfg
+
+cfg.update_cfg()
+### params ###
+# Network parameters
+netParams = specs.NetParams()  # object of class NetParams to store the network parameters
+netParams.defaultThreshold = 0.0
+netParams.defineCellShapes = True       # sets 3d geometry aligned along the y-axis 
+
+
+###############################################################################
+## Cell types
+###############################################################################
+# Basket cell
+BasketCell = {'secs':{}}
+BasketCell['secs']['soma'] = {'geom': {}, 'mechs': {}}
+BasketCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1}
+BasketCell['secs']['soma']['mechs'] = {'pas': {'g': 0.1e-3, 'e': -65}, 'Nafbwb': {}, 'Kdrbwb': {}}
+netParams.cellParams['BasketCell'] = BasketCell
+
+
+# OLM cell
+OlmCell = {'secs':{}}
+OlmCell['secs']['soma'] = {'geom': {}, 'mechs': {}}
+OlmCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1}
+OlmCell['secs']['soma']['mechs'] = {
+    'pas': {'g': 0.1e-3, 'e': -65},
+    'Nafbwb': {}, 
+    'Kdrbwb': {},
+    'Iholmw': {},
+    'Caolmw': {},
+    'ICaolmw': {},
+    'KCaolmw': {}}
+netParams.cellParams['OlmCell'] = OlmCell
+
+	
+# Pyramidal cell
+PyrCell = {'secs':{}}
+PyrCell['secs']['soma'] = {'geom': {}, 'mechs': {}}
+PyrCell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'cm': 1, 'Ra': 150}
+PyrCell['secs']['soma']['mechs'] = {
+    'pas': {'g': 0.0000357, 'e': -70}, 
+    'nacurrent': {},
+    'kacurrent': {},
+    'kdrcurrent': {},
+    'hcurrent': {}}
+PyrCell['secs']['Bdend'] = {'geom': {}, 'mechs': {}}
+PyrCell['secs']['Bdend']['geom'] = {'diam': 2, 'L': 200, 'cm': 1, 'Ra': 150}
+PyrCell['secs']['Bdend']['topol'] = {'parentSec': 'soma', 'parentX': 0, 'childX': 0}
+PyrCell['secs']['Bdend']['mechs'] = {
+    'pas': {'g': 0.0000357, 'e': -70}, 
+    'nacurrent': {'ki': 1},
+    'kacurrent': {},
+    'kdrcurrent': {},
+    'hcurrent': {}}
+PyrCell['secs']['Adend1'] = {'geom': {}, 'mechs': {}}
+PyrCell['secs']['Adend1']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150}
+PyrCell['secs']['Adend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # here there is a change: connected to end soma(1) instead of soma(0.5)
+PyrCell['secs']['Adend1']['mechs'] = {
+    'pas': {'g': 0.0000357, 'e': -70}, 
+    'nacurrent': {'ki': 0.5},
+    'kacurrent': {'g': 0.072},
+    'kdrcurrent': {},
+    'hcurrent': {'v50': -82, 'g': 0.0002}}     
+PyrCell['secs']['Adend2'] = {'geom': {}, 'mechs': {}}
+PyrCell['secs']['Adend2']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150}
+PyrCell['secs']['Adend2']['topol'] = {'parentSec': 'Adend1', 'parentX': 1, 'childX': 0}
+PyrCell['secs']['Adend2']['mechs'] = {
+    'pas': {'g': 0.0000357, 'e': -70}, 
+    'nacurrent': {'ki': 0.5},
+    'kacurrent': {'g': 0, 'gd': 0.120},
+    'kdrcurrent': {},
+    'hcurrent': {'v50': -90, 'g': 0.0004}}        
+PyrCell['secs']['Adend3'] = {'geom': {}, 'mechs': {}}
+PyrCell['secs']['Adend3']['geom'] = {'diam': 2, 'L': 150, 'cm': 2, 'Ra': 150}
+PyrCell['secs']['Adend3']['topol'] = {'parentSec': 'Adend2', 'parentX': 1, 'childX': 0}
+PyrCell['secs']['Adend3']['mechs'] = {
+    'pas': {'g': 0.0000714, 'e': -70}, 
+    'nacurrent': {'ki': 0.5},
+    'kacurrent': {'g': 0, 'gd': 0.200},
+    'kdrcurrent': {},
+    'hcurrent': {'v50': -90, 'g': 0.0007}}
+netParams.cellParams['PyrCell'] = PyrCell
+
+
+###############################################################################
+## Synaptic mechs
+###############################################################################
+
+netParams.synMechParams['AMPAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.05, 'tau2': 5.3, 'e': 0}
+netParams.synMechParams['NMDA'] = {'mod': 'MyExp2SynNMDABB', 'tau1': 0.05, 'tau2': 5.3, 'tau1NMDA': 15, 'tau2NMDA': 150, 'r': 1, 'e': 0}
+netParams.synMechParams['GABAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.07, 'tau2': 9.1, 'e': -80}
+netParams.synMechParams['GABAs'] = {'mod': 'MyExp2SynBB', 'tau1': 0.2, 'tau2': 20, 'e': -80}
+netParams.synMechParams['GABAss'] = {'mod': 'MyExp2SynBB', 'tau1': 20, 'tau2': 40, 'e': -80}
+
+
+###############################################################################
+## Populations
+###############################################################################
+netParams.popParams['PYR'] = {'cellType': 'PyrCell', 'numCells': 800}
+netParams.popParams['BC'] = {'cellType': 'BasketCell', 'numCells': 200}
+netParams.popParams['OLM'] = {'cellType': 'OlmCell', 'numCells': 200}
+
+
+###############################################################################
+# Current-clamp to cells
+###############################################################################
+netParams.stimSourceParams['IClamp_PYR'] =  {'type': 'IClamp', 'del': 2*0.1, 'dur': 1e9, 'amp': 50e-3}
+netParams.stimSourceParams['IClamp_OLM'] =  {'type': 'IClamp', 'del': 2*0.1, 'dur': 1e9, 'amp': -25e-3}
+
+netParams.stimTargetParams['IClamp_PYR->PYR'] = {
+        'source': 'IClamp_PYR',
+        'sec': 'soma',
+        'loc': 0.5,
+        'conds': {'pop': 'PYR'}}
+
+netParams.stimTargetParams['IClamp_OLM->OLM'] = {
+        'source': 'IClamp_OLM',
+        'sec': 'soma',
+        'loc': 0.5,
+        'conds': {'pop': 'OLM'}}
+
+
+###############################################################################
+# Setting connections
+###############################################################################
+
+# PYR -> X, NMDA
+netParams.connParams['PYR->BC_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'},
+    'convergence': 100,
+    'weight': cfg.nmda['PYR->BC'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'NMDA'}
+
+netParams.connParams['PYR->OLM_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'},
+    'convergence': 10,
+    'weight': cfg.nmda['PYR->OLM'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'NMDA'}
+
+netParams.connParams['PYR->PYR_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'},
+    'convergence': 25,
+    'weight': cfg.nmda['PYR->PYR'],
+    'delay': 2,
+    'sec': 'Bdend',
+    'loc': 1.0,
+    'synMech': 'NMDA'}
+
+# PYR -> X, AMPA
+netParams.connParams['PYR->BC_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'},
+    'convergence': 100,
+    'weight': cfg.ampa['PYR->BC'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'AMPAf'}
+
+netParams.connParams['PYR->OLM_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'},
+    'convergence': 10,
+    'weight': cfg.ampa['PYR->OLM'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'AMPAf'}
+
+netParams.connParams['PYR->PYR_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'},
+    'convergence': 25,
+    'weight': cfg.ampa['PYR->PYR'],
+    'delay': 2,
+    'sec': 'Bdend',
+    'loc': 1.0,
+    'synMech': 'AMPAf'}
+
+# BC -> X, GABA
+netParams.connParams['BC->BC_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'BC'},
+    'convergence': 60,
+    'weight': cfg.gaba['BC->BC'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'GABAf'}
+
+netParams.connParams['BC->PYR_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'PYR'},
+    'convergence': 50,
+    'weight': cfg.gaba['BC->PYR'],
+    'delay': 2,
+    'sec': 'soma',
+    'loc': 0.5,
+    'synMech': 'GABAf'}
+
+
+# OLM -> PYR, GABA
+netParams.connParams['OLM->PYR_GABA'] = {'preConds': {'pop': 'OLM'}, 'postConds': {'pop': 'PYR'},
+    'convergence': 20,
+    'weight': cfg.gaba['OLM->PYR'],
+    'delay': 2,
+    'sec': 'Adend2',
+    'loc': 0.5,
+    'synMech': 'GABAs'}
+
+
+###############################################################################
+# Setting NetStims
+###############################################################################
+# to PYR
+netParams.stimSourceParams['NetStim_PYR_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_PYR_SOMA_AMPA->PYR'] = {
+        'source': 'NetStim_PYR_SOMA_AMPA',
+        'conds': {'pop': 'PYR'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 4*0.05e-3,  # different from published value
+        'delay': 2*0.1,
+        'synMech': 'AMPAf'}
+
+netParams.stimSourceParams['NetStim_PYR_ADEND3_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_PYR_ADEND3_AMPA->PYR'] = {
+        'source': 'NetStim_PYR_ADEND3_AMPA',
+        'conds': {'pop': 'PYR'},
+        'sec': 'Adend3',
+        'loc': 0.5,
+        'weight': 4*0.05e-3,  # different from published value
+        'delay': 2*0.1,
+        'synMech': 'AMPAf'}
+
+netParams.stimSourceParams['NetStim_PYR_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_PYR_SOMA_GABA->PYR'] = {
+        'source': 'NetStim_PYR_SOMA_GABA',
+        'conds': {'pop': 'PYR'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 0.012e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAf'}
+
+netParams.stimSourceParams['NetStim_PYR_ADEND3_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_PYR_ADEND3_GABA->PYR'] = {
+        'source': 'NetStim_PYR_ADEND3_GABA',
+        'conds': {'pop': 'PYR'},
+        'sec': 'Adend3',
+        'loc': 0.5,
+        'weight': 0.012e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAf'}
+
+netParams.stimSourceParams['NetStim_PYR_ADEND3_NMDA'] = {'type': 'NetStim', 'interval': 100, 'number': int((1000/100.0)*cfg.duration), 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_PYR_ADEND3_NMDA->PYR'] = {
+        'source': 'NetStim_PYR_ADEND3_NMDA',
+        'conds': {'pop': 'PYR'},
+        'sec': 'Adend3',
+        'loc': 0.5,
+        'weight': 6.5e-3,
+        'delay': 2*0.1,
+        'synMech': 'NMDA'}
+
+# to BC
+netParams.stimSourceParams['NetStim_BC_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_BC_SOMA_AMPA->BC'] = {
+        'source': 'NetStim_BC_SOMA_AMPA',
+        'conds': {'pop': 'BC'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 0.02e-3,
+        'delay': 2*0.1,
+        'synMech': 'AMPAf'}
+
+netParams.stimSourceParams['NetStim_BC_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_BC_SOMA_GABA->BC'] = {
+        'source': 'NetStim_BC_SOMA_GABA',
+        'conds': {'pop': 'BC'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 0.2e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAf'}
+
+# to OLM
+netParams.stimSourceParams['NetStim_OLM_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_OLM_SOMA_AMPA->OLM'] = {
+        'source': 'NetStim_OLM_SOMA_AMPA',
+        'conds': {'pop': 'OLM'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 0.0625e-3,
+        'delay': 2*0.1,
+        'synMech': 'AMPAf'}
+
+netParams.stimSourceParams['NetStim_OLM_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1}
+netParams.stimTargetParams['NetStim_OLM_SOMA_GABA->OLM'] = {
+        'source': 'NetStim_OLM_SOMA_GABA',
+        'conds': {'pop': 'OLM'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 0.2e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAf'}
+
+# Medial Septal inputs to BC and OLM cells
+netParams.stimSourceParams['Septal'] = {'type': 'NetStim', 'interval': 150, 'number': int((1000/150)*cfg.duration), 'start': 0, 'noise': 0}
+netParams.stimTargetParams['Septal->BC'] = {
+        'source': 'Septal',
+        'conds': {'pop': 'BC'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 1.6e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAss'}
+
+netParams.stimTargetParams['Septal->OLM'] = {
+        'source': 'Septal',
+        'conds': {'pop': 'OLM'},
+        'sec': 'soma',
+        'loc': 0.5,
+        'weight': 1.6e-3,
+        'delay': 2*0.1,
+        'synMech': 'GABAss'}
diff --git a/netpyne/batchtools/examples/CA3/optuna_search.py b/netpyne/batchtools/examples/CA3/optuna_search.py
new file mode 100644
index 000000000..3ee09881d
--- /dev/null
+++ b/netpyne/batchtools/examples/CA3/optuna_search.py
@@ -0,0 +1,39 @@
+from netpyne.batchtools.search import search
+
+params = {'nmda.PYR->BC' : [1e-3, 1.8e-3],
+          #'nmda.PYR->OLM': [0.4e-3, 1.0e-3],
+          #'nmda.PYR->PYR': [0.001e-3, 0.007e-3],
+          'ampa.PYR->BC' : [0.2e-3, 0.5e-3],
+          #'ampa.PYR->OLM': [0.2e-3, 0.5e-3],
+          #'ampa.PYR->PYR': [0.01e-3, 0.03e-3],
+          #'gaba.BC->BC'  : [1e-3, 7e-3],
+          'gaba.BC->PYR' : [0.4e-3, 1.0e-3],
+          #'gaba.OLM->PYR': [40e-3, 100e-3],
+          }
+
+# use batch_shell_config if running directly on the machine
+shell_config = {'command': 'mpiexec -np 4 nrniv -python -mpi init.py',}
+
+# use batch_sge_config if running on a
+sge_config = {
+    'queue': 'cpu.q',
+    'cores': 5,
+    'vmem': '4G',
+    'realtime': '00:30:00',
+    'command': 'mpiexec -n $NSLOTS -hosts $(hostname) nrniv -python -mpi init.py'}
+
+
+run_config = sge_config
+
+search(job_type = 'sge', # or sh
+       comm_type = 'socket',
+       label = 'optuna',
+       params = params,
+       output_path = '../optuna_batch',
+       checkpoint_path = '../ray',
+       run_config = run_config,
+       num_samples = 27,
+       metric = 'loss',
+       mode = 'min',
+       algorithm = 'optuna',
+       max_concurrent = 3)
diff --git a/netpyne/batchtools/examples/categorical_strings/batch.py b/netpyne/batchtools/examples/categorical_strings/batch.py
new file mode 100644
index 000000000..d8cf9ef17
--- /dev/null
+++ b/netpyne/batchtools/examples/categorical_strings/batch.py
@@ -0,0 +1,18 @@
+from netpyne.batchtools.search import search
+
+params = {
+          'param_str': [ 'string0', 'string1', 'string2' ]
+          }
+
+search(job_type = 'sh',
+       comm_type = 'socket',
+       label = 'categorical',
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python categorical.py'},
+       num_samples = 1,
+       metric = 'return',
+       mode = 'min',
+       algorithm = 'variant_generator',
+       max_concurrent = 3)
diff --git a/netpyne/batchtools/examples/categorical_strings/categorical.py b/netpyne/batchtools/examples/categorical_strings/categorical.py
new file mode 100644
index 000000000..622e90ca1
--- /dev/null
+++ b/netpyne/batchtools/examples/categorical_strings/categorical.py
@@ -0,0 +1,26 @@
+from netpyne.batchtools import specs, comm
+import json
+
+
+# ----------- cfg creation & batch update ----------- #
+
+cfg = specs.SimConfig()
+
+cfg.simLabel = 'categorical'
+cfg.saveFolder = '.'
+
+cfg.param_str = ['default']
+
+cfg.update_cfg()
+
+# --------------------------------------------------- #
+
+# comm creation, calculation and result transmission  #
+comm.initialize()
+
+out_json = json.dumps({'return': 0, 'param_str': str(cfg.param_str), 'type': str(type(cfg.param_str))})
+if comm.is_host():
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
+
diff --git a/netpyne/batchtools/examples/cfg_loading/batch.py b/netpyne/batchtools/examples/cfg_loading/batch.py
new file mode 100644
index 000000000..d8cf9ef17
--- /dev/null
+++ b/netpyne/batchtools/examples/cfg_loading/batch.py
@@ -0,0 +1,18 @@
+from netpyne.batchtools.search import search
+
+params = {
+          'param_str': [ 'string0', 'string1', 'string2' ]
+          }
+
+search(job_type = 'sh',
+       comm_type = 'socket',
+       label = 'categorical',
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python categorical.py'},
+       num_samples = 1,
+       metric = 'return',
+       mode = 'min',
+       algorithm = 'variant_generator',
+       max_concurrent = 3)
diff --git a/netpyne/batchtools/examples/cfg_loading/categorical.csv b/netpyne/batchtools/examples/cfg_loading/categorical.csv
new file mode 100644
index 000000000..e2ad391a6
--- /dev/null
+++ b/netpyne/batchtools/examples/cfg_loading/categorical.csv
@@ -0,0 +1,13 @@
+,data,return,timestamp,checkpoint_dir_name,done,training_iteration,trial_id,date,time_this_iter_s,time_total_s,pid,hostname,node_ip,time_since_restore,iterations_since_restore,config/param_str,config/saveFolder,config/simLabel,logdir
+0,"return                   0
+param_str          string0
+type         <class 'str'>
+dtype: object",0,1724028563,,False,1,de3ff_00000,2024-08-18_19-49-23,1.7559211254119873,1.7559211254119873,87347,Jamess-MacBook-Pro.local,127.0.0.1,1.7559211254119873,1,string0,../grid_batch,categorical_00000,de3ff_00000
+1,"return                   0
+param_str          string1
+type         <class 'str'>
+dtype: object",0,1724028563,,False,1,de3ff_00001,2024-08-18_19-49-23,1.754591941833496,1.754591941833496,87348,Jamess-MacBook-Pro.local,127.0.0.1,1.754591941833496,1,string1,../grid_batch,categorical_00001,de3ff_00001
+2,"return                   0
+param_str          string2
+type         <class 'str'>
+dtype: object",0,1724028563,,False,1,de3ff_00002,2024-08-18_19-49-23,1.7577638626098633,1.7577638626098633,87349,Jamess-MacBook-Pro.local,127.0.0.1,1.7577638626098633,1,string2,../grid_batch,categorical_00002,de3ff_00002
diff --git a/netpyne/batchtools/examples/cfg_loading/categorical.py b/netpyne/batchtools/examples/cfg_loading/categorical.py
new file mode 100644
index 000000000..3a8005872
--- /dev/null
+++ b/netpyne/batchtools/examples/cfg_loading/categorical.py
@@ -0,0 +1,13 @@
+from netpyne.batchtools import specs                     # import the custom batch specs
+cfg = specs.SimConfig({'type': 0})                       # create a SimConfig object, initializes it with a dictionary {'type': 0} such that
+print("cfg.type={}".format(cfg.type))                    # cfg.type == 0
+try:
+    cfg.update({'typo': 1}, force_match=True)            # cfg.typo is not defined, so this line will raise an AttributeError
+except AttributeError as e:
+    print(e)
+cfg.update({'typo': 1})                                  # without force_match, the typo attribute cfg.fooo is created and set to 1
+print("cfg.type={}".format(cfg.type))                    # cfg.type remains unchanged due to a typo in the attribute name 'type' -> 'typo'
+print("cfg.typo={}".format(cfg.typo))                    # instead, cfg.typo is created and set to the value 1
+
+cfg.test_mappings({'type': 0})                           # this will return True, as the mappings are valid
+cfg.test_mappings({'missing': 1})                        # this will raise an AttributeError, as the 'missing' attribute is not defined
\ No newline at end of file
diff --git a/netpyne/batchtools/examples/jupyter/batchtools.ipynb b/netpyne/batchtools/examples/jupyter/batchtools.ipynb
new file mode 100644
index 000000000..22fe3489f
--- /dev/null
+++ b/netpyne/batchtools/examples/jupyter/batchtools.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Jupyter Tutorial: The NetPyNE batchtools subpackage\n",
+    "How to use the `specs` and `comm` to communicate with the `batchtools` `dispatcher`\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "89ec6ca2392a9a0d"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "For each individual `sim`, communication with the `batchtools` `dispatcher` occurs through the `specs` and `comm` objects"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "be50f40d8e61a944"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "from netpyne.batchtools import specs, comm"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6f321aedb7faf945",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "the `specs` object is an instantiation of a custom class extending the `batchtk` `Runner` ..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5f2f08f0b5e582c3"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "help(type(specs))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "29fa261236494bc3",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "From this `specs` object, we can similarly call `specs.NetParams` and `specs.SimConfig` to create the NetPyNE objects..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "64ead24451bbad4a"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "help(specs.NetParams)\n",
+    "help(specs.SimConfig)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "43d263d080800019",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "The `batchtools` job submission tool uses `environmental variables` to pass values to our `config` object created by `specs.SimConfig`, these `environmental variables` are captured during the `specs` `object creation` which occurs during the batchtools `import` (from the `batchtools` `__init__.py`:\n",
+    "```\n",
+    "from netpyne.batchtools.runners import NetpyneRunner\n",
+    "specs = NetpyneRunner()\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "710cc6084bd7af02"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Let's `export` some `environmental variables` to pass values to our `config` object. When this is handled by the `batchtools` `subpackage`, this occurs automatically..."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "52704684f5e80f3c"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "%env STRRUNTK0   =foo.bar=baz\n",
+    "%env FLOATRUNTK1 =float_val=7.7\n",
+    "from netpyne.batchtools import NetpyneRunner\n",
+    "specs = NetpyneRunner()"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "50de117ff7f43aa6",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "One way of retrieving these values is by calling `specs.get_mappings()`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "fac14e517044b980"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(specs.get_mappings())"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "257fad390f4abce",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Now, let's create our `config` object using the `specs.SimConfig()` constructor\n",
+    "This `config` object will hold a `dictionary` such that the initial values `foo['bar']` = `not_baz` and a `float_val` = `3.3`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "92d41061bb828744"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "cfg = specs.SimConfig()\n",
+    "cfg.foo = {'bar': 'not_baz', 'qux': 'quux'}\n",
+    "cfg.float_val = 3.3\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "ca121d6ab30c3e7b",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Finally, calling the `cfg.update_cfg()` method will overwrite the original values with our environment values, (`baz` and `7.7`)...\n",
+    "\n",
+    "in NetPyNE, this was originally handled with the:\n",
+    "```\n",
+    "try:\n",
+    "    from __main__ import cfg\n",
+    "except:\n",
+    "    from cfg import cfg\n",
+    "```\n",
+    "API idiom in the `netParams.py` file...\n",
+    " \n",
+    "as well as the \n",
+    "```\n",
+    "cfg, netParams = sim.readCmdLineArgs(simConfigDefault='src/cfg.py', netParamsDefault='src/netParams.py')\n",
+    "```\n",
+    "API idiom in the `init.py` file...\n",
+    "\n",
+    "using the `batchtools` subpackage, we can treat the `cfg` as an object and pass it between scripts via `import` statements...\n",
+    "in `netParams.py`...\n",
+    "```\n",
+    "from cfg import cfg\n",
+    "cfg.update()\n",
+    "```\n",
+    "in `init.py`...\n",
+    "```\n",
+    "from netParams import cfg, netParams\n",
+    "sim.createSimulateAnalyze(simConfig=cfg, netParams=netParams)\n",
+    "```"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "6ea43f729d0685d4"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(\"prior to  cfg.update()\")\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))\n",
+    "print()\n",
+    "cfg.update() # call update_cfg to update values in the cfg object with values assigned by batch\n",
+    "print(\"after the cfg.update()\")\n",
+    "print(\"cfg.foo['bar'] = {}\".format(cfg.foo['bar']))\n",
+    "print(\"cfg.float_val = {}\".format(cfg.float_val))"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "a9426b6e6594961",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Finally, the `comm object` is used to report to the monitoring `dispatcher object`\n",
+    "the means of communication is dependent on which `dispatcher object` is instantiated, and communicated through environmental variables\n",
+    "in this case, since there is no `dispatcher object` the `comm` methods will simply perform `pass operations`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "65bbb0ef2c76295a"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.initialize() # initializes comm object, establishing channel to communicate with the host dispatcher object"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "e9141d91d6e02aa3",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "print(comm.is_host()) # returns a boolean IF the calling process is the 0th ranked  parallelcontext, similar to sim.pc.rank == 0"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5ed6a524bd8a3e0b",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.send('message') # sends 'message' to the `dispatcher object`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "1966edbf32649352",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "comm.close() #finalizes communication, closes any resources used to communicate with the `dispatcher object`"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "34f021af4127363c"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "648746fff96b8a72"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/batch.py b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/batch.py
new file mode 100644
index 000000000..ed6a05474
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/batch.py
@@ -0,0 +1,21 @@
+from netpyne.batchtools.search import search
+
+params = {'x0': [0, 3],
+          'x1': [0, 3]
+          }
+
+# use shell_config if running directly on the machine
+shell_config = {'command': 'python rosenbrock.py',}
+
+search(job_type = 'sh', # or sh
+       comm_type = 'socket',
+       label = 'optuna',
+       params = params,
+       output_path = '../optuna_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python rosenbrock.py'},
+       num_samples = 9,
+       metric = 'fx',
+       mode = 'min',
+       algorithm = 'optuna',
+       max_concurrent = 3)
diff --git a/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/cma_batch.py b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/cma_batch.py
new file mode 100644
index 000000000..9bde349c6
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/cma_batch.py
@@ -0,0 +1,31 @@
+from batchtk.runtk.trial import trial, LABEL_POINTER, PATH_POINTER
+from netpyne.batchtools import dispatchers, submits
+from cmaes import CMA
+import numpy
+import os
+Dispatcher = dispatchers.INETDispatcher
+Submit = submits.SHSubmitSOCK
+
+cwd = os.getcwd()
+
+def eval_rosenbrock(x0, x1, tid):
+    cfg = {
+        'x0': x0,
+        'x1': x1,
+    }
+    submit = Submit()
+    submit.update_templates(**{'command': 'python rosenbrock.py',})
+    label = 'rosenbrock'
+    return float(trial(cfg, label, tid, Dispatcher, cwd, '../cma', submit)['fx'])
+
+#data = eval_rosenbrock(1, 1, "x11")
+
+optimizer = CMA(mean=numpy.zeros(2), sigma=1.0)
+for generation in range(3):
+    solutions = []
+    for cand in range(optimizer.population_size):
+        x = optimizer.ask()
+        value = eval_rosenbrock(x[0], x[1], "{}_{}".format(cand, generation))
+        solutions.append((x, value))
+        print(f"#{generation} {value} (x1={x[0]}, x2 = {x[1]})")
+    optimizer.tell(solutions)
diff --git a/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/rosenbrock.py b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/rosenbrock.py
new file mode 100644
index 000000000..52fa35e86
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/basic_rosenbrock/rosenbrock.py
@@ -0,0 +1,37 @@
+from netpyne.batchtools import specs, comm
+import json
+
+# ---- Rosenbrock Function & Constant Definition ---- #
+
+"""
+The rosenbrock minimum is at (A, A**2), where rosenbrock(A, A**2) = 0
+"""
+def rosenbrock(x0, x1):
+    return 100 * (x1 - x0**2)**2 + (A - x0)**2
+
+A = 1
+# --------------------------------------------------- #
+
+# ----------- cfg creation & batch update ----------- #
+
+cfg = specs.SimConfig()
+
+cfg.simLabel = 'rosenbrock'
+cfg.saveFolder = '.'
+
+cfg.x0 = 1
+cfg.x1 = 1
+
+cfg.update_cfg()
+
+# --------------------------------------------------- #
+
+# comm creation, calculation and result transmission  #
+comm.initialize()
+
+out_json = json.dumps({'x0': cfg.x0, 'x1': cfg.x1, 'fx': rosenbrock(cfg.x0, cfg.x1)})
+if comm.is_host():
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
+
diff --git a/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/batch.py b/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/batch.py
new file mode 100644
index 000000000..350c99084
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/batch.py
@@ -0,0 +1,24 @@
+from netpyne.batchtools.search import search
+import numpy
+x0 = numpy.arange(0, 3)
+x1 = x0**2
+
+x0_x1 = [*zip(x0.tolist(), x1.tolist())]
+params = {'x0_x1': x0_x1
+          }
+
+# use shell_config if running directly on the machine
+shell_config = {'command': 'python rosenbrock.py',}
+
+search(job_type = 'sh', # or sh
+       comm_type = 'socket',
+       label = 'grid',
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python rosenbrock.py'},
+       num_samples = 1,
+       metric = 'fx',
+       mode = 'min',
+       algorithm = 'variant_generator',
+       max_concurrent = 3)
\ No newline at end of file
diff --git a/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/rosenbrock.py b/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/rosenbrock.py
new file mode 100644
index 000000000..cc957b9d3
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/coupled_rosenbrock/rosenbrock.py
@@ -0,0 +1,41 @@
+from netpyne.batchtools import specs, comm
+import json
+
+# ---- Rosenbrock Function & Constant Definition ---- #
+
+"""
+The rosenbrock minimum is at (A, A**2), where rosenbrock(A, A**2) = 0
+"""
+def rosenbrock(x0, x1):
+    return 100 * (x1 - x0**2)**2 + (A - x0)**2
+
+A = 1
+# --------------------------------------------------- #
+
+# ----------- cfg creation & batch update ----------- #
+
+cfg = specs.SimConfig()
+
+cfg.simLabel = 'rosenbrock'
+cfg.saveFolder = '.'
+
+cfg.x0_x1 = [1, 1]
+
+cfg.update_cfg()
+
+# --------------------------------------------------- #
+
+# -------------- unpacking x0_x1 list  -------------- #
+x0, x1 = cfg.x0_x1
+# --------------------------------------------------- #
+
+
+# comm creation, calculation and result transmission  #
+comm.initialize()
+
+out_json = json.dumps({'x0': x0, 'x1': x1, 'fx': rosenbrock(x0, x1)})
+if comm.is_host():
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
+
diff --git a/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/analysis.py b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/analysis.py
new file mode 100644
index 000000000..32914a419
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/analysis.py
@@ -0,0 +1,6 @@
+from netpyne.batchtools.analysis import Analyzer
+
+analyzer = Analyzer(params = ['x.0', 'x.1', 'x.2', 'x.3'], metrics = ['fx'])
+analyzer.load_file('optuna.csv')
+results = analyzer.run_analysis()
+
diff --git a/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/batch.py b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/batch.py
new file mode 100644
index 000000000..d6229949d
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/batch.py
@@ -0,0 +1,24 @@
+from netpyne.batchtools.search import search
+import numpy
+
+params = {'x.0': numpy.linspace(-1, 3, 5),
+          'x.1': numpy.linspace(-1, 3, 5),
+          'x.2': numpy.linspace(-1, 3, 5),
+          'x.3': numpy.linspace(-1, 3, 5),
+          }
+
+# use shell_config if running directly on the machine
+shell_config = {'command': 'python rosenbrock.py',}
+
+search(job_type = 'sh', # or sh
+       comm_type = 'socket',
+       label = 'grid',
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python rosenbrock.py'},
+       num_samples = 1,
+       metric = 'fx',
+       mode = 'min',
+       algorithm = 'variant_generator',
+       max_concurrent = 3)
diff --git a/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/rosenbrock.py b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/rosenbrock.py
new file mode 100644
index 000000000..e6ce3b29d
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/fanova_rosenbrock/rosenbrock.py
@@ -0,0 +1,49 @@
+from netpyne.batchtools import specs, comm
+import json
+
+# --- Rosenbrock Functions & Constant Definitions --- #
+
+"""
+The rosenbrock_v0 (coupled rosenbrock)
+"""
+
+A = 1
+
+
+def rosenbrock_v0(*args):
+    if len(args) % 2:
+        raise ValueError('rosenbrock_v0 requires an even number of arguments')
+    return sum(100 * (args[i]**2 - args[i+1])**2 + (args[i] - A)**2 for i in range(0, len(args), 2))
+
+
+"""
+The rosenbrock_v1
+"""
+
+
+def rosenbrock_v1(*args):
+    return sum(100 * (args[i+1] - args[i]**2)**2 + (A - args[i])**2 for i in range(0, len(args)))
+
+
+# --------------------------------------------------- #
+
+# ----------- cfg creation & batch update ----------- #
+
+cfg = specs.SimConfig({'x': [None] * 4})
+
+cfg.simLabel = 'rosenbrock'
+cfg.saveFolder = '.'
+
+cfg.update_cfg()
+
+# --------------------------------------------------- #
+
+# comm creation, calculation and result transmission  #
+comm.initialize()
+
+out_json = json.dumps({'x': cfg.x, 'fx': rosenbrock_v0(*cfg.x)})
+if comm.is_host():
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
+
diff --git a/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/batch.py b/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/batch.py
new file mode 100644
index 000000000..cf35c487d
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/batch.py
@@ -0,0 +1,23 @@
+from netpyne.batchtools.search import search
+import numpy
+
+
+params = {'xn.0': numpy.arange(0, 5),
+          'xn.1': numpy.arange(0, 5)
+          }
+
+# use shell_config if running directly on the machine
+shell_config = {'command': 'python rosenbrock.py',}
+
+search(job_type = 'sh', # or sh
+       comm_type = 'socket',
+       label = 'grid',
+       params = params,
+       output_path = '../grid_batch',
+       checkpoint_path = '../ray',
+       run_config = {'command': 'python rosenbrock.py'},
+       num_samples = 1,
+       metric = 'fx',
+       mode = 'min',
+       algorithm = 'variant_generator',
+       max_concurrent = 3)
\ No newline at end of file
diff --git a/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/rosenbrock.py b/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/rosenbrock.py
new file mode 100644
index 000000000..b1956b2db
--- /dev/null
+++ b/netpyne/batchtools/examples/rosenbrock/nested_rosenbrock/rosenbrock.py
@@ -0,0 +1,41 @@
+from netpyne.batchtools import specs, comm
+import json
+
+# ---- Rosenbrock Function & Constant Definition ---- #
+
+"""
+The rosenbrock minimum is at (A, A**2), where rosenbrock(A, A**2) = 0
+"""
+def rosenbrock(x0, x1):
+    return 100 * (x1 - x0**2)**2 + (A - x0)**2
+
+A = 1
+# --------------------------------------------------- #
+
+# ----------- cfg creation & batch update ----------- #
+
+cfg = specs.SimConfig()
+
+cfg.simLabel = 'rosenbrock'
+cfg.saveFolder = '.'
+
+cfg.xn = [1, 1]
+
+cfg.update_cfg()
+
+# --------------------------------------------------- #
+
+# ---------------- unpacking x list  ---------------- #
+x0, x1 = cfg.xn
+# --------------------------------------------------- #
+
+
+# comm creation, calculation and result transmission  #
+comm.initialize()
+
+out_json = json.dumps({'x0': x0, 'x1': x1, 'fx': rosenbrock(x0, x1)})
+if comm.is_host():
+    print(out_json)
+    comm.send(out_json)
+    comm.close()
+
diff --git a/netpyne/batchtools/header.py b/netpyne/batchtools/header.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/netpyne/batchtools/runners.py b/netpyne/batchtools/runners.py
new file mode 100644
index 000000000..8289c8421
--- /dev/null
+++ b/netpyne/batchtools/runners.py
@@ -0,0 +1,266 @@
+#from batchtk.runtk.utils import convert, set_map, create_script
+from batchtk import runtk
+from batchtk.runtk.runners import Runner, get_class
+import collections
+import os
+import collections
+
+def validate(element, container):
+    try:
+        match container:
+            case list(): #container is a list, check validity of index
+                assert int(element) < len(container)
+            case dict(): #container is a dictionary, check validity of key
+                assert element in container
+            case _: #invalid container type
+                assert element in container
+                #raise AttributeError("container type is not supported, cfg attributes support dictionary and "
+                #                     "list objects, container {} is of type {}".format(container, type(container)))
+    except Exception as e:
+        raise AttributeError("error when validating {} within container {}: {}".format(element, container, e))
+    return True #element is valid, return True for boolean
+
+"""
+def set_map(self, assign_path, value, force_match=False):
+    assigns = assign_path.split('.')
+    if len(assigns) == 1 and not (force_match and not validate(assigns[0], self)):
+        self.__setitem__(assigns[0], value)
+        return
+    crawler = self.__getitem__(assigns[0])
+    for gi in assigns[1:-1]:
+        if not (force_match and not validate(gi, crawler)):
+            try:
+                crawler = crawler.__getitem__(gi)
+            except TypeError: # case for lists.
+                crawler = crawler.__getitem__(int(gi))
+    if not (force_match and not validate(assigns[-1], crawler)):
+        try:
+            crawler.__setitem__(assigns[-1], value)
+        except TypeError:
+            crawler.__setitem__(int(assigns[-1]), value)
+    return
+"""
+
+
+def traverse(obj, path, force_match=False):
+    if len(path) == 1:
+        if not (force_match and not validate(path[0], obj)):
+            return obj
+    if not (force_match and not validate(path[0], obj)):
+        try:
+            crawler = obj.__getitem__(path[0])
+        except TypeError:  # use for indexing into a list or in case the dictionary entry? is an int.
+            crawler = obj.__getitem__(int(path[0]))
+        return traverse(crawler, path[1:], force_match)
+
+def set_map(self, assign_path, value, force_match=False):
+    assigns = assign_path.split('.')
+    traverse(self, assigns, force_match)[assigns[-1]] = value
+
+def get_map(self, assign_path, force_match=False):
+    assigns = assign_path.split('.')
+    return traverse(self, assigns, force_match)[assigns[-1]]
+
+def update_items(d, u, force_match = False):
+    for k, v in u.items():
+        try:
+            force_match and validate(k, d)
+            if isinstance(v, collections.abc.Container):
+                d[k] = update_items(d.get(k), v, force_match)
+            else:
+                d[k] = v
+        except Exception as e:
+            raise AttributeError("Error when calling update_items with force_match, item {} does not exist".format(k))
+    return d
+
+class NetpyneRunner(Runner):
+    """
+    runner for netpyne
+    see class runner
+    mappings <-
+    """
+    def __new__(cls, inherit=None, **kwargs):
+        _super = get_class(inherit)
+
+        def __init__(self, netParams=None, cfg=None, **kwargs):
+            """
+            NetpyneRunner constructor
+
+            Parameters
+            ----------
+            self - NetpyneRunner instance
+            netParams - optional netParams instance (defaults to None, created with method: get_NetParams)
+            cfg - optional SimConfig instance (defaults to None, created with method: get_SimConfig)
+                  N.B. requires cfg with the update_cfg method. see in get_SimConfig:
+                                   self.cfg = type("Runner_SimConfig", (specs.SimConfig,),
+                                   {'__mappings__': self.mappings,
+                                   'update_cfg': update_cfg})()
+            kwargs - Unused
+            """
+            _super.__init__(self, **kwargs)
+            self.netParams = netParams
+            self.cfg = cfg
+
+        def _set_inheritance(self, inherit):
+            """
+            Method for changing inheritance of NetpyneRunner
+            see runtk.RUNNERS
+            Parameters
+            ----------
+            self
+            inherit
+            """
+            if inherit in runtk.RUNNERS:
+                cls = type(self)
+                cls.__bases__ = (runtk.RUNNERS[inherit],)
+            else:
+                raise KeyError("inheritance {} not found in runtk.RUNNERS (please check runtk.RUNNERS for valid strings...".format(inherit))
+
+
+        def get_NetParams(self, netParamsDict=None):
+            """
+            Creates / Returns a NetParams instance
+            Parameters
+            ----------
+            self
+            netParamsDict - optional dictionary to create NetParams instance (defaults to None)
+                          - to be called during initial function call only
+
+            Returns
+            -------
+            NetParams instance
+
+            """
+            if self.netParams:
+                return self.netParams
+            else:
+                from netpyne import specs
+                self.netParams = specs.NetParams(netParamsDict)
+                return self.netParams
+
+        def test_mappings(self, mappings):
+            """
+            Tests mappings for validity
+
+            Parameters
+            ----------
+            mappings - dictionary of mappings to test
+
+            Returns
+            -------
+            bool - True if mappings are valid, False otherwise
+            """
+            for assign_path, value in mappings.items():
+                try:
+                    set_map(self, assign_path, value, force_match=True)
+                    print("successfully assigned: cfg.{} with value: {}".format(assign_path, value))
+                except Exception as e:
+                    raise Exception("failed on mapping: cfg.{} with value: {}\n{}".format(assign_path, value, e))
+            return True
+
+
+        def update_cfg(self, simConfigDict=None, force_match=False): #intended to take `cfg` instance as self
+            """
+            Updates the SimConfig instance with mappings to the runner, called from a SimConfig instance
+
+            Parameters
+            ----------
+            self - specs (NetpyneRunner) SimConfig instance
+            simConfigDict - optional dictionary to update SimConfig instance (defaults to None)
+                          - to be called during initial function call only
+
+            Returns
+            -------
+            None (updates SimConfig instance in place)
+            """
+            if simConfigDict:
+                update_items(self, simConfigDict, force_match)
+            for assign_path, value in self.__mappings__.items():
+                try:
+                    set_map(self, assign_path, value)
+                except Exception as e:
+                    raise Exception("failed on mapping: cfg.{} with value: {}\n{}".format(assign_path, value, e))
+
+        def get_SimConfig(self, simConfigDict=None):
+            """
+            Creates / Returns a SimConfig instance
+            Parameters
+            ----------
+            self - NetpyneRunner instance
+            simConfigDict - optional dictionary to create NetParams instance (defaults to None)
+                          - to be called during initial function call only
+
+            Returns
+            -------
+            SimConfig instance
+            """
+            if self.cfg:
+                if simConfigDict:
+                    update_items(self.cfg,simConfigDict, force_match=False)
+                return self.cfg
+            else:
+                from netpyne import specs
+                self.cfg = type("Runner_SimConfig", (specs.SimConfig,),
+                    {'__mappings__': self.mappings,
+                     'update_cfg': update_cfg,
+                     'update': update_cfg,
+                     'test_mappings': test_mappings})(simConfigDict)
+                return self.cfg
+
+        def set_SimConfig(self):
+            """
+            updates the SimConfig instance with mappings to the runner, called from a Runner instance
+
+            Parameters
+            ----------
+            self
+            """
+            # assumes values are only in 'cfg'
+            for assign_path, value in self.mappings.items():
+                try:
+                    set_map(self, "cfg.{}".format(assign_path), value)
+                except Exception as e:
+                    raise Exception("failed on mapping: cfg.{} with value: {}\n{}".format(assign_path, value, e))
+
+        def set_mappings(self, filter=''):
+            # arbitrary filter, can work with 'cfg' or 'netParams'
+            for assign_path, value in self.mappings.items():
+                if filter in assign_path:
+                    set_map(self, assign_path, value)
+
+        return type("NetpyneRunner{}".format(str(_super.__name__)), (_super,),
+                    {'__init__': __init__,
+                     '_set_inheritance': _set_inheritance,
+                     'get_NetParams': get_NetParams,
+                     'NetParams': get_NetParams,
+                     'SimConfig': get_SimConfig,
+                     'get_SimConfig': get_SimConfig,
+                     'set_SimConfig': set_SimConfig,
+                     'set_mappings': set_mappings,
+                     'test_mappings': test_mappings})(**kwargs) # need to override __init__ or else will call parent
+
+# use this test_list to check set_map ....
+test_list = {
+    'lists_of_dicts': [
+        {'a': 0, 'b': 1, 'c': 2},
+        {'d': 3, 'e': 4, 'f': 5},
+        {'g': 6, 'h': 7, 'i': 8}
+    ],
+    'dict_of_lists': {
+        'a': [0, 1, 2],
+        'b': [3, 4, 5],
+        'c': [6, 7, 8]
+    },
+    'dict_of_dicts': {
+        0: {'a': 0, 'b': 1, 'c': 2},
+        1: {'d': 3, 'e': 4, 'f': 5},
+        2: {'g': 6, 'h': 7, 'i': 8}
+    }
+}
+
+"""
+Test statements
+In [3]: set_map(test_list, 'lists_of_dicts.0.a', 'a', force_match = True)
+In [4]: set_map(test_list, 'lists_of_dicts.0.a', 0, force_match = True)
+In [5]: set_map(test_list, 'lists_of_dicts.0.d', 0, force_match = True)
+"""
\ No newline at end of file
diff --git a/netpyne/batchtools/search.py b/netpyne/batchtools/search.py
new file mode 100644
index 000000000..1b791c1d4
--- /dev/null
+++ b/netpyne/batchtools/search.py
@@ -0,0 +1,435 @@
+import ray
+import pandas
+import os
+from ray import tune, train
+from ray.air import session, RunConfig
+from ray.tune.search.basic_variant import BasicVariantGenerator
+from ray.tune.search import create_searcher, ConcurrencyLimiter, SEARCH_ALG_IMPORT
+from netpyne.batchtools import runtk
+from collections import namedtuple
+from batchtk.raytk.search import ray_trial, LABEL_POINTER
+from batchtk.utils import get_path
+import numpy
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+
+
+choice = tune.choice
+grid = tune.grid_search
+uniform = tune.uniform
+
+def ray_optuna_search(dispatcher_constructor: Callable, # constructor for the dispatcher (e.g. INETDispatcher)
+                      submit_constructor: Callable, # constructor for the submit (e.g. SHubmitSOCK)
+                      run_config: Dict, # batch configuration, (keyword: string pairs to customize the submit template)
+                      params: Dict, # search space (dictionary of parameter keys: tune search spaces)
+                      label: Optional[str] = 'optuna_search', # label for the search
+                      output_path: Optional[str] = '../batch', # directory for storing generated files
+                      checkpoint_path: Optional[str] = '../ray', # directory for storing checkpoint files
+                      max_concurrent: Optional[int] = 1, # number of concurrent trials to run at one time
+                      batch: Optional[bool] = True, # whether concurrent trials should run synchronously or asynchronously
+                      num_samples: Optional[int] = 1, # number of trials to run
+                      metric: Optional[str|list|tuple] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+                      mode: Optional[str|list|tuple] = "min", # either 'min' or 'max' (whether to minimize or maximize the metric
+                      optuna_config: Optional[dict] = None, # additional configuration for the optuna search algorithm
+                      ) -> namedtuple('Study', ['algo', 'results']):
+    """
+    ray_optuna_search(...)
+
+    Parameters
+    ----------
+    dispatcher_constructor:Callable, # constructor for the dispatcher (e.g. INETDispatcher)
+    submit_constructor:Callable, # constructor for the submit (e.g. SHubmitSOCK)
+    run_config:Dict, # batch configuration, (keyword: string pairs to customize the submit template)
+    params:Dict, # search space (dictionary of parameter keys: tune search spaces)
+    label:Optional[str] = 'optuna_search', # label for the search
+    output_path:Optional[str] = '../batch', # directory for storing generated files
+    checkpoint_path:Optional[str] = '../ray', # directory for storing checkpoint files
+    max_concurrent:Optional[int] = 1, # number of concurrent trials to run at one time
+    batch:Optional[bool] = True, # whether concurrent trials should run synchronously or asynchronously
+    num_samples:Optional[int] = 1, # number of trials to run
+    metric:Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+    mode:Optional[str] = "min", # either 'min' or 'max' (whether to minimize or maximize the metric
+    optuna_config:Optional[dict] = None, # additional configuration for the optuna search algorithm (incl. sampler, seed, etc.)
+
+    Creates
+    -------
+    <label>.csv: file containing the results of the search
+
+    Returns
+    -------
+    Study: namedtuple('Study', ['algo', 'results'])(algo, results), # named tuple containing the created algorithm and the results of the search
+    """
+    from ray.tune.search.optuna import OptunaSearch
+
+    ray.init(runtime_env={"working_dir": "."})# TODO needed for python import statements ?
+    if optuna_config == None:
+        optuna_config = {}
+
+    storage_path = get_path(checkpoint_path)
+    algo = ConcurrencyLimiter(searcher=OptunaSearch(metric=metric, mode=mode, **optuna_config),
+                              max_concurrent=max_concurrent,
+                              batch=batch) #TODO does max_concurrent and batch work?
+
+    submit = submit_constructor()
+    submit.update_templates(
+        **run_config
+    )
+    project_path = os.getcwd()
+
+    def run(config):
+        config.update({'saveFolder': output_path, 'simLabel': LABEL_POINTER})
+        data = ray_trial(config, label, dispatcher_constructor, project_path, output_path, submit)
+        if isinstance(metric, str):#TODO only Optuna supports multiobjective?
+            metrics = {'config': config, 'data': data, metric: data[metric]}
+            session.report(metrics)
+        elif isinstance(metric, (list, tuple)):
+            metrics = {k: data[k] for k in metric}
+            metrics['config'] = config
+            metrics['data'] = data
+            session.report(metrics)
+        else:
+            raise ValueError("metric must be a string or a list/tuple of strings")
+    tuner = tune.Tuner(
+        run,
+        tune_config=tune.TuneConfig(
+            search_alg=algo,
+            num_samples=num_samples,
+        ),
+        run_config=RunConfig(
+            storage_path=storage_path,
+            name=label,
+        ),
+        param_space=params,
+    )
+
+    results = tuner.fit()
+    resultsdf = results.get_dataframe()
+    resultsdf.to_csv("{}.csv".format(label))
+    #return namedtuple('Study', ['algo', 'results'])(algo, results)
+    return namedtuple('Study', ['algo', 'results'])(algo.searcher._ot_study, results)
+
+"""
+Parameters
+:
+space –
+Hyperparameter search space definition for Optuna’s sampler. This can be either a dict with parameter names as keys and optuna.distributions as values, or a Callable - in which case, it should be a define-by-run function using optuna.trial to obtain the hyperparameter values. The function should return either a dict of constant values with names as keys, or None. For more information, see https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html.
+Warning
+No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to tune.Tuner().
+metric – The training result objective value attribute. If None but a mode was passed, the anonymous metric _metric will be used per default. Can be a list of metrics for multi-objective optimization.
+mode – One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. Can be a list of modes for multi-objective optimization (corresponding to metric).
+points_to_evaluate – Initial parameter suggestions to be run first. This is for when you already have some good parameters you want to run first to help the algorithm make better suggestions for future parameters. Needs to be a list of dicts containing the configurations.
+sampler –
+Optuna sampler used to draw hyperparameter configurations. Defaults to MOTPESampler for multi-objective optimization with Optuna<2.9.0, and TPESampler in every other case. See https://optuna.readthedocs.io/en/stable/reference/samplers/index.html for available Optuna samplers.
+Warning
+Please note that with Optuna 2.10.0 and earlier default MOTPESampler/TPESampler suffer from performance issues when dealing with a large number of completed trials (approx. >100). This will manifest as a delay when suggesting new configurations. This is an Optuna issue and may be fixed in a future Optuna release.
+seed – Seed to initialize sampler with. This parameter is only used when sampler=None. In all other cases, the sampler you pass should be initialized with the seed already.
+evaluated_rewards –
+If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate.
+"""
+
+
+def ray_search(dispatcher_constructor: Callable, # constructor for the dispatcher (e.g. INETDispatcher)
+               submit_constructor: Callable, # constructor for the submit (e.g. SHubmitSOCK)
+               run_config: Dict, # batch configuration, (keyword: string pairs to customize the submit template)
+               params: Dict, # search space (dictionary of parameter keys: tune search spaces)
+               algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+               label: Optional[str] = 'search', # label for the search
+               output_path: Optional[str] = '../batch', # directory for storing generated files
+               checkpoint_path: Optional[str] = '../ray', # directory for storing checkpoint files
+               max_concurrent: Optional[int] = 1, # number of concurrent trials to run at one time
+               batch: Optional[bool] = True, # whether concurrent trials should run synchronously or asynchronously
+               num_samples: Optional[int] = 1, # number of trials to run
+               metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+               mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+               algorithm_config: Optional[dict] = None, # additional configuration for the search algorithm
+               ) -> tune.ResultGrid:
+    ray.init(runtime_env={"working_dir": "."}) # TODO needed for python import statements ?
+
+    if algorithm_config == None:
+        algorithm_config = {}
+
+    if 'metric' not in algorithm_config:
+        algorithm_config['metric'] = metric
+
+    if 'mode' not in algorithm_config:
+        algorithm_config['mode'] = mode
+
+    if 'max_concurrent' not in algorithm_config:
+        algorithm_config['max_concurrent'] = max_concurrent
+
+    if 'batch' not in algorithm_config:
+        algorithm_config['batch'] = batch
+
+    #TODO class this object for self calls? cleaner? vs nested functions
+    #TODO clean up working_dir and excludes
+    storage_path = get_path(checkpoint_path)
+    algo = create_searcher(algorithm, **algorithm_config) #concurrency may not be accepted by all algo
+    #search_alg – The search algorithm to use.
+    #  metric – The training result objective value attribute. Stopping procedures will use this attribute.
+    #  mode – One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute.
+    #  **kwargs – Additional parameters. These keyword arguments will be passed to the initialization function of the chosen class.
+    try:
+        algo = ConcurrencyLimiter(searcher=algo, max_concurrent=algorithm_config['max_concurrent'], batch=algorithm_config['batch'])
+    except:
+        pass
+
+    submit = submit_constructor()
+    submit.update_templates(
+        **run_config
+    )
+    project_path = os.getcwd()
+    def run(config):
+        config.update({'saveFolder': output_path, 'simLabel': LABEL_POINTER})
+        data = ray_trial(config, label, dispatcher_constructor, project_path, output_path, submit)
+        if isinstance(metric, str):
+            metrics = {'config': config, 'data': data, metric: data[metric]}
+            session.report(metrics)
+        elif isinstance(metric, (list, tuple)):
+            metrics = {k: data[k] for k in metric}
+            metrics['data'] = data
+            metrics['config'] = config
+            session.report(metrics)
+        else:
+            session.report({'data': data, 'config': config})
+
+    tuner = tune.Tuner(
+        run,
+        tune_config=tune.TuneConfig(
+            search_alg=algo,
+            num_samples=num_samples, # grid search samples 1 for each param
+            metric=algorithm_config['metric'],
+            mode=algorithm_config['mode'],
+        ),
+        run_config=RunConfig(
+            storage_path=storage_path,
+            name=algorithm,
+        ),
+        param_space=params,
+    )
+
+    results = tuner.fit()
+    resultsdf = results.get_dataframe()
+    resultsdf.to_csv("{}.csv".format(label))
+    return results
+
+
+#should be constant?
+constructors = namedtuple('constructors', 'dispatcher, submit')
+constructor_tuples = {
+    ('sge', 'socket'): constructors(runtk.dispatchers.INETDispatcher, runtk.submits.SGESubmitSOCK),
+    #('sge', 'unix'): constructors(runtk.dispatchers.UNIXDispatcher, runtk.submits.SGESubmitSOCK), #can't use AF_UNIX sockets on networked machines
+    ('sge', 'sfs' ): constructors(runtk.dispatchers.SFSDispatcher , runtk.submits.SGESubmitSFS ),
+    #('zsh', 'inet'): constructors(runtk.dispatchers.INETDispatcher, runtk.submits.ZSHSubmitSOCK), #TODO preferable to use AF_UNIX sockets on local machines
+    ('sh', 'socket'): constructors(runtk.dispatchers.UNIXDispatcher, runtk.submits.SHSubmitSOCK),
+    ('sh', 'sfs' ): constructors(runtk.dispatchers.SFSDispatcher , runtk.submits.SHSubmitSFS ),
+}#TODO, just say "socket"?
+
+"""
+some shim functions before ray_search
+"""
+def generate_constructors(job_type, comm_type = 'socket', **kwargs):
+    """"
+    returns the dispatcher, submit constructor pair for ray_search based on the job_type and comm_type inputs
+    """
+    if (job_type, comm_type) not in constructor_tuples:
+        raise ValueError("Invalid job_type or comm_type pairing")
+    return constructor_tuples[(job_type, comm_type)]
+
+def generate_parameters(params, algorithm, **kwargs):
+    """
+
+    Parameters
+    ----------
+    params = {'synMechTau2': [3.0, 5.0, 7.0], # assumes list of values by default if grid search-like algo
+		  #'synMechTau2': [3.0, 7.0], # assumes lower/upper bounds by default if evol-like algo
+          'connWeight' : paramtypes.sample_from(lambda _: numpy.random.uniform(0.005, 0.15))} # can optionally pass any of the paramtypes (= ray.tune data types)
+
+    Returns
+    -------
+    a dictionary of parameters for ray_search based on the input dictionary
+
+    """
+     #TODO: bloated function, prone to error
+    ray_params = {}
+    for param, space in params.items():
+        if   isinstance(space, (list, tuple, range, numpy.ndarray)) and algorithm in {'variant_generator'}:
+            ray_params[param] = tune.grid_search(space)
+        elif isinstance(space, (list, tuple)) and algorithm in SEARCH_ALG_IMPORT.keys():
+            if len(space) == 2: #if 2 sample from uniform lb, ub
+                ray_params[param] = tune.uniform(*space)
+            else: #otherwise treat as a list for a categorical search
+                ray_params[param] = tune.choice(space)
+        else: #assume a tune search space was defined
+            ray_params[param] = space
+    return ray_params
+
+def shim(job_type: str, # the submission engine to run a single simulation (e.g. 'sge', 'sh')
+         comm_type: str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem')
+         run_config: Dict,  # batch configuration, (keyword: string pairs to customize the submit template)
+         params: Dict,  # search space (dictionary of parameter keys: tune search spaces)
+         algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+         label: Optional[str] = 'search',  # label for the search
+         output_path: Optional[str] = '../batch',  # directory for storing generated files
+         checkpoint_path: Optional[str] = '../ray',  # directory for storing checkpoint files
+         max_concurrent: Optional[int] = 1,  # number of concurrent trials to run at one time
+         batch: Optional[bool] = True,  # whether concurrent trials should run synchronously or asynchronously
+         num_samples: Optional[int] = 1,  # number of trials to run
+         metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+         mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+         algorithm_config: Optional[dict] = None,  # additional configuration for the search algorithm
+         ):
+    kwargs = locals()
+    kwargs['dispatcher_constructor'], kwargs['submit_constructor'] = generate_constructors(**kwargs)
+    [kwargs.pop(args) for args in ['job_type', 'comm_type']]
+    kwargs['params'] = generate_parameters(**kwargs)
+    return kwargs
+
+
+def search(job_type: str, # the submission engine to run a single simulation (e.g. 'sge', 'sh')
+           comm_type: str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem')
+           run_config: Dict,  # batch configuration, (keyword: string pairs to customize the submit template)
+           params: Dict,  # search space (dictionary of parameter keys: tune search spaces)
+           algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+           label: Optional[str] = 'search',  # label for the search
+           output_path: Optional[str] = '../batch',  # directory for storing generated files
+           checkpoint_path: Optional[str] = '../ray',  # directory for storing checkpoint files
+           max_concurrent: Optional[int] = 1,  # number of concurrent trials to run at one time
+           batch: Optional[bool] = True,  # whether concurrent trials should run synchronously or asynchronously
+           num_samples: Optional[int] = 1,  # number of trials to run
+           metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+           mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+           algorithm_config: Optional[dict] = None,  # additional configuration for the search algorithm
+           ) -> tune.ResultGrid: # results of the search
+    """
+    search(...)
+
+    Parameters
+    ----------
+    job_type: str, # the submission engine to run a single simulation (e.g. 'sge', 'sh')
+    comm_type: str, # the method of communication between host dispatcher and the simulation (e.g. 'socket', 'filesystem')
+    run_config: Dict,  # batch configuration, (keyword: string pairs to customize the submit template)
+    params: Dict,  # search space (dictionary of parameter keys: tune search spaces)
+    algorithm: Optional[str] = "variant_generator", # search algorithm to use, see SEARCH_ALG_IMPORT for available options
+    label: Optional[str] = 'search',  # label for the search
+    output_path: Optional[str] = '../batch',  # directory for storing generated files
+    checkpoint_path: Optional[str] = '../ray',  # directory for storing checkpoint files
+    max_concurrent: Optional[int] = 1,  # number of concurrent trials to run at one time
+    batch: Optional[bool] = True,  # whether concurrent trials should run synchronously or asynchronously
+    num_samples: Optional[int] = 1,  # number of trials to run
+    metric: Optional[str] = "loss", # metric to optimize (this should match some key: value pair in the returned data
+    mode: Optional[str] = "min",  # either 'min' or 'max' (whether to minimize or maximize the metric
+    algorithm_config: Optional[dict] = None,  # additional configuration for the search algorithm
+
+    Creates
+    -------
+    <label>.csv: file containing the results of the search
+
+    Returns
+    -------
+    ResultGrid: tune.ResultGrid # results of the search
+    """
+    kwargs = locals()
+    kwargs = shim(**kwargs)
+    return ray_search(**kwargs)
+
+
+"""
+def ray_search(dispatcher_constructor, submit_constructor, algorithm = "variant_generator", label = 'search',
+               params = None, concurrency = 1, output_path = '../batch', checkpoint_path = '../ray',
+               run_config = None, num_samples = 1):
+    ray.init(
+        runtime_env={"working_dir": "."}) # needed for python import statements
+
+    #TODO class this object for self calls? cleaner? vs nested functions
+    #TODO clean up working_dir and excludes
+    if checkpoint_path[0] == '/':
+        storage_path = os.path.normpath(checkpoint_path)
+    elif checkpoint_path[0] == '.':
+        storage_path = os.path.normpath(os.path.join(os.getcwd(), checkpoint_path))
+    else:
+        raise ValueError("checkpoint_dir must be an absolute path (starts with /) or relative to the current working directory (starts with .)")
+    algo = create_searcher(algorithm, max_concurrent=concurrency, batch=True)
+    #algo = ConcurrencyLimiter(searcher=algo, max_concurrent=concurrency, batch=True)
+    submit = submit_constructor()
+    submit.update_templates(
+        **run_config
+    )
+    project_path = os.getcwd()
+    def run(config):
+        config.update({'saveFolder': output_path, 'simLabel': LABEL_POINTER})
+        data = ray_trial(config, label, dispatcher_constructor, project_path, output_path, submit)
+        session.report({'data': data, 'config': config})
+
+    tuner = tune.Tuner(
+        run,
+        tune_config=tune.TuneConfig(
+            search_alg=algo,
+            num_samples=num_samples, # grid search samples 1 for each param
+            metric="data"
+        ),
+        run_config=RunConfig(
+            storage_path=storage_path,
+            name=algorithm,
+        ),
+        param_space=params,
+    )
+
+    results = tuner.fit()
+    resultsdf = results.get_dataframe()
+    resultsdf.to_csv("{}.csv".format(label))
+"""
+
+
+
+
+"""
+from netpyne.batchtools import search, paramtypes
+import numpy
+
+https://docs.ray.io/en/latest/tune/api/doc/ray.tune.search.Searcher.html#ray.tune.search.Searcher
+
+
+
+params = {'synMechTau2': [3.0, 5.0, 7.0], # assumes list of values by default if grid search-like algo
+		  #'synMechTau2': [3.0, 7.0], # assumes lower/upper bounds by default if evol-like algo
+          'connWeight' : paramtypes.sample_from(lambda _: numpy.random.uniform(0.005, 0.15))} # can optionally pass any of the paramtypes (= ray.tune data types)
+
+run_config = {'sge': 5, 'command': 'python init.py'}
+
+#TODO rename ray_search to search
+search(dispatcher = 'inet', # defaults to 'inet' if no arg is passed?
+           submit = 'socket', # defaults to 'socket' if no arg is passed?
+           params = params,
+           run_config = run_config, #
+           algorithm = "variant_generator",
+           concurrency = 9,
+           output_path = '../batch_func',
+           checkpoint_path = '../grid_func',
+           label = 'func_search',
+           num_samples = 3)
+SEE:
+'variant_generator'
+'random' -> points to variant_generator
+'ax'
+'dragonfly'
+'skopt'
+'hyperopt'
+'bayesopt'
+'bohb'
+'nevergrad'
+'optuna'
+'zoopt'
+'sigopt'
+'hebo'
+'blendsearch'
+'cfo'
+"""
+
+
+
+
+
+
+
+
+
diff --git a/netpyne/batchtools/submits.py b/netpyne/batchtools/submits.py
new file mode 100644
index 000000000..1d27a41cc
--- /dev/null
+++ b/netpyne/batchtools/submits.py
@@ -0,0 +1,167 @@
+from batchtk.runtk import Submit, Template
+from batchtk import runtk
+
+SFS_HANDLES = {runtk.SUBMIT: '{output_path}/{label}.sh',
+               runtk.STDOUT: '{output_path}/{label}.run',
+               runtk.MSGOUT: '{output_path}/{label}.out',
+               runtk.SGLOUT: '{output_path}/{label}.sgl'}
+class SHSubmit(Submit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command'}
+    script_template = \
+        """\
+#!/bin/sh
+cd {project_path}
+export JOBID=$$
+{env}
+nohup {command} > {output_path}/{label}.run 2>&1 &
+pid=$!
+echo $pid >&1
+"""
+    script_handles = {
+        runtk.STDOUT: '{output_path}/{label}.run',
+        runtk.SUBMIT: '{output_path}/{label}.sh'}
+    def __init__(self, **kwargs):
+        super().__init__(
+            submit_template = Template(template="sh {output_path}/{label}.sh",
+                                       key_args={'project_path', 'output_path', 'label'}),
+            script_template = Template(template=self.script_template,
+                                       key_args=self.script_args),
+            handles = self.script_handles,
+        )
+    def set_handles(self):
+        pass
+
+    def submit_job(self):
+        proc = super().submit_job()
+        try:
+            self.job_id = int(proc.stdout)
+        except Exception as e:
+            raise(Exception("{}\nJob submission failed:\n{}\n{}\n{}\n{}".format(e, self.submit, self.script, proc.stdout, proc.stderr)))
+        if self.job_id < 0:
+            raise(Exception("Job submission failed:\n{}\n{}\n{}\n{}".format(self.submit, self.script, proc.stdout, proc.stderr)))
+        return self.job_id
+
+class SHSubmitSFS(SHSubmit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command'}
+    script_template = \
+        """\
+#!/bin/sh
+cd {project_path}
+export OUTFILE="{output_path}/{label}.out"
+export SGLFILE="{output_path}/{label}.sgl"
+export JOBID=$$
+{env}
+nohup {command} > {output_path}/{label}.run 2>&1 &
+pid=$!
+echo $pid >&1
+"""
+    script_handles = {runtk.SUBMIT: '{output_path}/{label}.sh',
+                      runtk.STDOUT: '{output_path}/{label}.run',
+                      runtk.MSGOUT: '{output_path}/{label}.out',
+                      runtk.SGLOUT: '{output_path}/{label}.sgl'}
+
+class SHSubmitSOCK(SHSubmit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command', 'sockname'}
+    script_template = \
+        """\
+#!/bin/sh
+cd {project_path}
+export SOCNAME="{sockname}"
+export JOBID=$$
+{env}
+nohup {command} > {output_path}/{label}.run 2>&1 &
+pid=$!
+echo $pid >&1
+"""
+    script_handles = {runtk.SUBMIT: '{output_path}/{label}.sh',
+                      runtk.STDOUT: '{output_path}/{label}.run',
+                      runtk.SOCKET: '{sockname}'}
+
+class SGESubmit(Submit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command', 'cores', 'vmem', }
+    script_template = \
+        """\
+#!/bin/bash
+#$ -N job{label}
+#$ -q {queue}
+#$ -pe smp {cores}
+#$ -l h_vmem={vmem}
+#$ -l h_rt={realtime}
+#$ -o {output_path}/{label}.run
+cd {project_path}
+source ~/.bashrc
+export JOBID=$JOB_ID
+{env}
+{command}
+"""
+    script_handles = {runtk.SUBMIT: '{output_path}/{label}.sh',
+                      runtk.STDOUT: '{output_path}/{label}.run'}
+    def __init__(self, **kwargs):
+        super().__init__(
+            submit_template = Template(template="qsub {output_path}/{label}.sh",
+                                       key_args={'output_path',  'label'}),
+            script_template = Template(template=self.script_template,
+                                       key_args=self.script_args),
+            handles = self.script_handles,
+            )
+
+    def submit_job(self, **kwargs):
+        proc = super().submit_job()
+        try:
+            self.job_id = proc.stdout.split(' ')[2]
+        except Exception as e: #not quite sure how this would occur
+            raise(Exception("{}\nJob submission failed:\n{}\n{}\n{}\n{}".format(e, self.submit, self.script, proc.stdout, proc.stderr)))
+        return self.job_id
+
+    def set_handles(self):
+        pass
+
+
+class SGESubmitSFS(SGESubmit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command', 'cores', 'vmem', }
+    script_template = \
+        """\
+#!/bin/bash
+#$ -N job{label}
+#$ -q {queue}
+#$ -pe smp {cores}
+#$ -l h_vmem={vmem}
+#$ -l h_rt={realtime}
+#$ -o {output_path}/{label}.run
+cd {project_path}
+source ~/.bashrc
+export JOBID=$JOB_ID
+export OUTFILE="{output_path}/{label}.out"
+export SGLFILE="{output_path}/{label}.sgl"
+{env}
+{command}
+"""
+    script_handles = {runtk.SUBMIT: '{output_path}/{label}.sh',
+                      runtk.STDOUT: '{output_path}/{label}.run',
+                      runtk.MSGOUT: '{output_path}/{label}.out',
+                      runtk.SGLOUT: '{output_path}/{label}.sgl',
+                      }
+
+class SGESubmitSOCK(SGESubmit):
+    script_args = {'label', 'project_path', 'output_path', 'env', 'command', 'cores', 'vmem', 'sockname'}
+    script_template = \
+        """\
+#!/bin/bash
+#$ -N job{label}
+#$ -q {queue}
+#$ -pe smp {cores}
+#$ -l h_vmem={vmem}
+#$ -l h_rt={realtime}
+#$ -o {output_path}/{label}.run
+cd {project_path}
+source ~/.bashrc
+export JOBID=$JOB_ID
+export SOCNAME="{sockname}"
+{env}
+{command}
+"""
+    script_handles = {runtk.SUBMIT: '{output_path}/{label}.sh',
+                      runtk.STDOUT: '{output_path}/{label}.run',
+                      runtk.SOCKET: '{sockname}'
+                      }
+
diff --git a/netpyne/batchtools/tests/gain.json b/netpyne/batchtools/tests/gain.json
new file mode 100644
index 000000000..cfe629c55
--- /dev/null
+++ b/netpyne/batchtools/tests/gain.json
@@ -0,0 +1,52 @@
+{
+    "EICellTypeGain": {
+        "PV": 0.6152864472687144,
+        "SOM": 0.35116819915031644,
+        "VIP": 1.1218458257976713,
+        "NGF": 2.7521920026073357
+    },
+    "IECellTypeGain": {
+        "PV": 2.9493206673798475,
+        "SOM": 0.13986148108703275,
+        "VIP": 0.10888755736754395,
+        "NGF": 0.11567059244128475
+    },
+    "EILayerGain": {
+        "1": 0.827750821266419,
+        "2": 0.8309591864633213,
+        "3": 0.2606080713427127,
+        "4": 0.3587959278506161,
+        "5A": 0.9777414771947963,
+        "5B": 1.2194384057226428,
+        "6": 2.661195004653538
+    },
+    "IILayerGain": {
+        "1": 1.1305254604890966,
+        "2": 1.1428945741399408,
+        "3": 0.2896903197397815,
+        "4": 1.086197796863865,
+        "5A": 0.5982384402236298,
+        "5B": 0.879452953038821,
+        "6": 0.4784462642417754
+    },
+    "EELayerGain": {
+        "2": 1.1392556114489,
+        "3": 0.2627680775261009,
+        "4": 0.7953549349740079,
+        "5A": 1.107062263790829,
+        "5B": 0.1873673565713772,
+        "6": 1.2260292755940738
+    },
+    "IELayerGain": {
+        "2": 0.8766937284048854,
+        "3": 0.5609281169449685,
+        "4": 1.870729555191084,
+        "5A": 3.566320604507868,
+        "5B": 0.7695785463331726,
+        "6": 3.9278827507598293
+    },
+    "thalamoCorticalGain": 2.1111391118965863,
+    "intraThalamicGain": 0.9843624229766335,
+    "EbkgThalamicGain": 3.9181565363409163,
+    "IbkgThalamicGain": 3.9227144872233324
+}
\ No newline at end of file
diff --git a/netpyne/batchtools/tests/map_example1.py b/netpyne/batchtools/tests/map_example1.py
new file mode 100644
index 000000000..53d0fd95a
--- /dev/null
+++ b/netpyne/batchtools/tests/map_example1.py
@@ -0,0 +1,17 @@
+from netpyne.batchtools import specs
+
+cfg = specs.SimConfig({
+    'list': [{'a': False, 'b': False, 'c': False},
+             {'d': False, 'e': False, 'f': False},
+             {'g': False, 'h': False, 'i': False}],
+    'dict': {'abc': [False, False, False],
+             'def': [False, False, False],
+             'ghi': [False, False, False]},
+    'val0': False, 'val1': False, 'val2': False}
+)
+
+cfg.test_mappings({'list.0.d': 3, # creation of a new element which does not exist.
+                   'list.1.a': 1,})
+
+
+
diff --git a/netpyne/batchtools/tests/map_example2.py b/netpyne/batchtools/tests/map_example2.py
new file mode 100644
index 000000000..f8fc3ec12
--- /dev/null
+++ b/netpyne/batchtools/tests/map_example2.py
@@ -0,0 +1,34 @@
+import json
+from netpyne.batchtools import specs
+
+
+cfg = specs.SimConfig(json.load(open('gain.json')))
+cfg.dict_of_arrays = {'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}
+print(cfg.EICellTypeGain)
+
+print(cfg.dict_of_arrays)
+
+# could additionally load the mappings from a json dict...
+cfg.update_cfg({'EICellTypeGain': {'PV': 1.0,
+                                   'SOM': 1.0}}, force_match = True)
+
+print(cfg.EICellTypeGain)
+
+try:
+    cfg.update_cfg({'EICellTypeGain': {'5A': 1.0}}, force_match = True)
+except Exception as e:
+    print('Error setting EICellTypeGain[5A] to 1.0', e)
+
+
+cfg.update_cfg({'dict_of_arrays': {'a': {0: 5,
+                                         1: 5},
+                                   'b': {0: 10}}})
+print(cfg.dict_of_arrays)
+"""
+output
+Warning: Could not import rxdmath module
+numprocs=1
+{PV: 0.6152864472687144, SOM: 0.35116819915031644, VIP: 1.1218458257976713, NGF: 2.7521920026073357}
+{PV: 1.0, SOM: 1.0, VIP: 1.1218458257976713, NGF: 2.7521920026073357}
+Error setting EICellTypeGain[5A] to 1.0 Error when calling update_items with force_match, item EICellTypeGain does not exist
+"""
\ No newline at end of file
diff --git a/netpyne/batchtools/tests/test_map.py b/netpyne/batchtools/tests/test_map.py
new file mode 100644
index 000000000..352c3ac43
--- /dev/null
+++ b/netpyne/batchtools/tests/test_map.py
@@ -0,0 +1,67 @@
+"""
+test_job.py
+this file runs a simple test between a dispatcher<->runner pair
+it does not start a subprocess but validates that the file is appropriate and the runner has imported the correct values
+to start bidirectional communication
+the created environment. for subprocess testing, use test_sh.py.
+"""
+
+import pytest
+import os
+
+import logging
+import json
+from collections import namedtuple
+
+from netpyne.batchtools.runners import NetpyneRunner, get_map
+Test = namedtuple('Test', ['cfg', 'mapping'])
+
+TESTS = [
+
+]
+
+cfg = {
+    'list': [{'a': False, 'b': False, 'c': False},
+             {'d': False, 'e': False, 'f': False},
+             {'g': False, 'h': False, 'i': False}],
+    'dict': {'abc': [False, False, False],
+             'def': [False, False, False],
+             'ghi': [False, False, False]},
+    'val0': False, 'val1': False, 'val2': False}
+
+cfgstr = json.dumps(cfg) # to duplicate the cfg dictionary use json.loads
+
+mapping_str = {
+    'list.0.a'  : True, # should exist
+    'dict.abc.0': True, # should exist
+    'val0': True,       # should exist
+    'list.0.d'  : True, # should not exist
+    'dict.abc.4': True, # should not exist
+    'val4': True        # should not exist
+}
+
+mapping_trav = {
+    'list.0.a'  : True, # should exist
+    'dict.abc.0': True, # should exist
+    'val0': True,       # should exist
+    'list.0.d'  : True, # should not exist
+    'dict.abc.4': True, # should not exist
+    'val4': True        # should not exist2
+}
+
+logger = logging.getLogger('test')
+logger.setLevel(logging.INFO)
+handler = logging.FileHandler('test_job.log')
+
+formatter = logging.Formatter('>>> %(asctime)s --- %(funcName)s --- %(levelname)s >>>\n%(message)s <<<\n')
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+class TestMap:
+    @pytest.fixture(params=TESTS)
+    def setup(self, request):
+        cfg = json.loads(request.param.cfg)
+        mapping = request.param.mapping
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/netpyne/batchtools/tests/test_trial.py b/netpyne/batchtools/tests/test_trial.py
new file mode 100644
index 000000000..698449319
--- /dev/null
+++ b/netpyne/batchtools/tests/test_trial.py
@@ -0,0 +1,2 @@
+from batchtk.runtk.trial import trial, LABEL_POINTER, PATH_POINTER
+
diff --git a/netpyne/cell/NML2Cell.py b/netpyne/cell/NML2Cell.py
index a63eb2fa3..92150a696 100644
--- a/netpyne/cell/NML2Cell.py
+++ b/netpyne/cell/NML2Cell.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from copy import deepcopy
 from neuron import h  # Import NEURON
 import numpy as np
diff --git a/netpyne/cell/NML2SpikeSource.py b/netpyne/cell/NML2SpikeSource.py
index b257cb687..567870c34 100644
--- a/netpyne/cell/NML2SpikeSource.py
+++ b/netpyne/cell/NML2SpikeSource.py
@@ -3,13 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from neuron import h  # Import NEURON
 from .compartCell import CompartCell
 from ..specs import Dict
diff --git a/netpyne/cell/__init__.py b/netpyne/cell/__init__.py
index 9731ff6b1..74a6d1ed2 100644
--- a/netpyne/cell/__init__.py
+++ b/netpyne/cell/__init__.py
@@ -3,13 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from .compartCell import CompartCell
 from .pointCell import PointCell
 from .NML2Cell import NML2Cell
diff --git a/netpyne/cell/cell.py b/netpyne/cell/cell.py
index de12a5923..0f85b66c2 100644
--- a/netpyne/cell/cell.py
+++ b/netpyne/cell/cell.py
@@ -3,23 +3,11 @@
 
 """
 
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-
-from builtins import zip
-from builtins import next
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 from numbers import Number
 from copy import deepcopy
 from neuron import h  # Import NEURON
diff --git a/netpyne/cell/compartCell.py b/netpyne/cell/compartCell.py
index d0f1c4646..43127010b 100644
--- a/netpyne/cell/compartCell.py
+++ b/netpyne/cell/compartCell.py
@@ -3,19 +3,6 @@
 
 """
 
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import super
-from builtins import next
-from builtins import zip
-from builtins import range
-
-from builtins import round
-from builtins import str
-
 from netpyne.specs.netParams import CellParams, SynMechParams
 
 try:
@@ -23,9 +10,6 @@
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from numbers import Number
 from copy import deepcopy
 from neuron import h  # Import NEURON
@@ -1089,12 +1073,11 @@ def __parsePointerParams(self, params):
 
     def __generatePointerIds(self, pointerParams, params):
         from .. import sim
-
         # see comments in `__parsePointerParams()` for more details
-        if hasattr(sim, 'rank'):
-            preToPostId = 1e9 * sim.rank + sim.net.lastPointerId  # global index for presyn gap junc
-        else:
-            preToPostId = sim.net.lastPointerId
+
+        if sim.net.lastPointerId > sim.net.maxPointerIdForGivenNode:
+            print(f"WARNING: potential overflow of pointer connection id!")
+        preToPostId = sim.net.lastPointerId
         sim.net.lastPointerId += 1  # keep track of num of gap juncs in this node
 
         if pointerParams['bidirectional']:
@@ -1115,6 +1098,8 @@ def __generatePointerIds(self, pointerParams, params):
             'preGid': self.gid,
             'sec': params.get('preSec', 'soma'),
             'loc': params.get('preLoc', 0.5),
+            'preSec': params.get('sec', 'soma'),
+            'preLoc': params.get('loc', 0.5),
             'weight': params.get('weight', 0.0),
             'synMech': params['synMech'],
             '__preCellSidePointerParams__': preCellSideParams,
@@ -1478,6 +1463,9 @@ def _setConnPointP(self, params, secLabels, weightIndex):
     def _setConnSynMechs(self, params, secLabels):
         from .. import sim
 
+        distributeSynsUniformly = params.get('distributeSynsUniformly', sim.cfg.distributeSynsUniformly)
+        connRandomSecFromList = params.get('connRandomSecFromList', sim.cfg.connRandomSecFromList)
+
         synsPerConn = params['synsPerConn']
         if not params.get('synMech'):
             if sim.net.params.synMechParams:  # if no synMech specified, but some synMech params defined
@@ -1514,20 +1502,17 @@ def _setConnSynMechs(self, params, secLabels):
                     synMechLocs = [i * (1.0 / synsPerConn) + 1.0 / synsPerConn / 2 for i in range(synsPerConn)]
             else:
                 # if multiple sections, distribute syns uniformly
-                if sim.cfg.distributeSynsUniformly:
+                if distributeSynsUniformly:
                     synMechSecs, synMechLocs = self._distributeSynsUniformly(secList=secLabels, numSyns=synsPerConn)
                 else:
-                    if not sim.cfg.connRandomSecFromList and synsPerConn == len(
-                        secLabels
-                    ):  # have list of secs that matches num syns
+                    # have list of secs that matches num syns
+                    if not connRandomSecFromList and synsPerConn == len(secLabels):
                         synMechSecs = secLabels
                         if isinstance(params['loc'], list):
                             if len(params['loc']) == synsPerConn:  # list of locs matches num syns
                                 synMechLocs = params['loc']
                             else:  # list of locs does not match num syns
-                                print(
-                                    "Error: The length of the list of locations does not match synsPerConn (with cfg.distributeSynsUniformly = False"
-                                )
+                                print("Error: The length of the list of locations does not match synsPerConn (with distributeSynsUniformly = False)")
                                 return
                         else:  # single loc
                             synMechLocs = [params['loc']] * synsPerConn
@@ -1536,7 +1521,7 @@ def _setConnSynMechs(self, params, secLabels):
                         synMechLocs = params['loc'] if isinstance(params['loc'], list) else [params['loc']]
 
                         # randomize the section to connect to and move it to beginning of list
-                        if sim.cfg.connRandomSecFromList and len(synMechSecs) >= synsPerConn:
+                        if connRandomSecFromList and len(synMechSecs) >= synsPerConn:
                             if len(synMechLocs) == 1:
                                 synMechLocs = [params['loc']] * synsPerConn
                             rand = h.Random()
@@ -1553,9 +1538,7 @@ def _setConnSynMechs(self, params, secLabels):
                                 rand.uniform(0, 1)
                                 synMechLocs = [rand.repick() for i in range(synsPerConn)]
                         else:
-                            print(
-                                "\nError: The length of the list of sections needs to be greater or equal to the synsPerConn (with cfg.connRandomSecFromList = True"
-                            )
+                            print("\nError: The length of the list of sections needs to be greater or equal to the synsPerConn (with connRandomSecFromList = True)")
                             return
 
         else:  # if 1 synapse
@@ -1564,7 +1547,7 @@ def _setConnSynMechs(self, params, secLabels):
             synMechLocs = params['loc'] if isinstance(params['loc'], list) else [params['loc']]
 
             # randomize the section to connect to and move it to beginning of list
-            if sim.cfg.connRandomSecFromList and len(synMechSecs) > 1:
+            if connRandomSecFromList and len(synMechSecs) > 1:
                 rand = h.Random()
                 preGid = params['preGid'] if isinstance(params['preGid'], int) else 0
                 rand.Random123(sim.hashStr('connSynMechsSecs'), self.gid, preGid)  # initialize randomizer
@@ -1695,7 +1678,7 @@ def calcAbsSegCoords(self):
         self._segCoords['d1'] = morphSegCoords['d1']
 
     def setImembPtr(self):
-        """Set PtrVector to point to the i_membrane_"""
+        """Set PtrVector to point to the `i_membrane_`"""
         jseg = 0
         for sec in list(self.secs.values()):
             hSec = sec['hObj']
diff --git a/netpyne/cell/inputs.py b/netpyne/cell/inputs.py
index 8c525666b..53109e4dc 100644
--- a/netpyne/cell/inputs.py
+++ b/netpyne/cell/inputs.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from numbers import Number
 
 try:
@@ -97,7 +89,9 @@ def createRhythmicPattern(params, rand):
     # Uniform Distribution
     elif distribution == 'uniform':
         n_inputs = params['repeats'] * freq * (stop - start) / 1000.0
-        t_array = rand.uniform(start, stop, int(n_inputs))
+        rand.uniform(start, stop)
+        vec = h.Vector(int(n_inputs))
+        t_array = np.array(vec.setrand(rand))
         if eventsPerCycle == 2:
             # Two arrays store doublet times
             t_input_low = t_array - 5
diff --git a/netpyne/cell/pointCell.py b/netpyne/cell/pointCell.py
index 86a921b5a..5d6dcd84a 100644
--- a/netpyne/cell/pointCell.py
+++ b/netpyne/cell/pointCell.py
@@ -7,23 +7,12 @@
 
 Contributors: salvadordura@gmail.com, samnemo@gmail.com
 """
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import super
-from builtins import zip
-from builtins import range
 
 try:
     basestring
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from copy import deepcopy
 from neuron import h  # Import NEURON
 import numpy as np
diff --git a/netpyne/conversion/__init__.py b/netpyne/conversion/__init__.py
index ec3c4a5b9..1c3149ace 100644
--- a/netpyne/conversion/__init__.py
+++ b/netpyne/conversion/__init__.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
-
 from .neuronPyHoc import importCell, importCellsFromNet, mechVarList, getSecName
 from .pythonScript import createPythonScript, createPythonNetParams, createPythonSimConfig
 from .excel import importConnFromExcel
diff --git a/netpyne/conversion/excel.py b/netpyne/conversion/excel.py
index 6bdffa029..86e2da5f5 100644
--- a/netpyne/conversion/excel.py
+++ b/netpyne/conversion/excel.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-
-from builtins import open
-from builtins import range
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-
-
 def importConnFromExcel(fileName, sheetName):
     """
     Function for/to <short description of `netpyne.conversion.excel.importConnFromExcel`>
diff --git a/netpyne/conversion/neuromlFormat.py b/netpyne/conversion/neuromlFormat.py
index 1df0d12ab..8a4b2e053 100644
--- a/netpyne/conversion/neuromlFormat.py
+++ b/netpyne/conversion/neuromlFormat.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-
-from builtins import str
-
-from builtins import range
-
 try:
     import neuroml
     from pyneuroml import pynml
diff --git a/netpyne/conversion/neuronPyHoc.py b/netpyne/conversion/neuronPyHoc.py
index 105b85b77..6f10a70ca 100644
--- a/netpyne/conversion/neuronPyHoc.py
+++ b/netpyne/conversion/neuronPyHoc.py
@@ -3,18 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-from builtins import range
-from builtins import dict
-
-from builtins import zip
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
 import os, sys, signal
 from numbers import Number
 from neuron import h
@@ -286,7 +274,9 @@ def importCell(fileName, cellName, cellArgs=None, cellInstance=False):
         cellArgs = []  # Define as empty list if not otherwise defined
 
     if fileName.endswith('.hoc') or fileName.endswith('.tem'):
-        h.load_file(fileName)
+        resultCode = h.load_file(fileName)
+        if resultCode == 0: # error
+            raise Exception(f"Error occured in h.load_file() when loading {fileName}. See above for details.")
         if not cellInstance:
             if isinstance(cellArgs, dict):
                 cell = getattr(h, cellName)(**cellArgs)  # create cell using template, passing dict with args
@@ -318,9 +308,7 @@ def importCell(fileName, cellName, cellArgs=None, cellInstance=False):
 
         cell = load(fileName)
     else:
-        print("File name should end in '.hoc', '.py', or '.swc'")
-        return
-
+        raise Exception("File name should end in '.hoc', '.py', or '.swc'")
     secDic, secListDic, synMechs, globs = getCellParams(cell, varList, origGlob)
 
     if fileName.endswith('.py'):
diff --git a/netpyne/conversion/pythonScript.py b/netpyne/conversion/pythonScript.py
index 462e29d99..9e4ec6ab0 100644
--- a/netpyne/conversion/pythonScript.py
+++ b/netpyne/conversion/pythonScript.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import open
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __version__
 
 
diff --git a/netpyne/metadata/__init__.py b/netpyne/metadata/__init__.py
index 7034d7f5a..8508d053c 100644
--- a/netpyne/metadata/__init__.py
+++ b/netpyne/metadata/__init__.py
@@ -3,11 +3,4 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from .metadata import metadata
diff --git a/netpyne/metadata/api.py b/netpyne/metadata/api.py
index 86ce34571..caf63ef55 100644
--- a/netpyne/metadata/api.py
+++ b/netpyne/metadata/api.py
@@ -3,16 +3,6 @@
 
 """
 
-
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
-
-
 def merge(source, destination):
     for key, value in list(source.items()):
         if isinstance(value, dict):
diff --git a/netpyne/metadata/metadata.py b/netpyne/metadata/metadata.py
index e4bc4d9c5..92d63a691 100644
--- a/netpyne/metadata/metadata.py
+++ b/netpyne/metadata/metadata.py
@@ -3,13 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 metadata = {
     # ---------------------------------------------------------------------------------------------------------------------
     # netParams
diff --git a/netpyne/network/__init__.py b/netpyne/network/__init__.py
index dee2888a3..288114749 100644
--- a/netpyne/network/__init__.py
+++ b/netpyne/network/__init__.py
@@ -3,12 +3,5 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from .network import Network
 from .pop import Pop
diff --git a/netpyne/network/conn.py b/netpyne/network/conn.py
index 9710c5165..033f5298f 100644
--- a/netpyne/network/conn.py
+++ b/netpyne/network/conn.py
@@ -3,27 +3,15 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import dict
-from builtins import range
-
-from builtins import round
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 import numpy as np
 from array import array as arrayFast
 from numbers import Number
-
+from tqdm import tqdm
 
 # -----------------------------------------------------------------------------
 # Connect Cells
@@ -418,6 +406,10 @@ def fullConn(self, preCellsTags, postCellsTags, connParam):
 
     if sim.cfg.verbose:
         print('Generating set of all-to-all connections (rule: %s) ...' % (connParam['label']))
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar:
+        pbar = tqdm(total=len(postCellsTags.items()), ascii=True,
+                    desc='  ' + connParam['label'], position=0, leave=True,
+                    bar_format= '{l_bar}{bar}| Creating synaptic connections for {n_fmt}/{total_fmt} postsynaptic cells on node %i (all-to-all connectivity)' % sim.rank)
 
     # get list of params that have a lambda function
     paramsStrFunc = [param for param in [p + 'Func' for p in self.connStringFuncParams] if param in connParam]
@@ -436,9 +428,11 @@ def fullConn(self, preCellsTags, postCellsTags, connParam):
         }
 
     for postCellGid in postCellsTags:  # for each postsyn cell
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
         if postCellGid in self.gid2lid:  # check if postsyn is in this node's list of gids
             for preCellGid, preCellTags in preCellsTags.items():  # for each presyn cell
                 self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
 
 
 # -----------------------------------------------------------------------------
@@ -518,6 +512,11 @@ def probConn(self, preCellsTags, postCellsTags, connParam):
     if sim.cfg.verbose:
         print('Generating set of probabilistic connections (rule: %s) ...' % (connParam['label']))
 
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar:
+        pbar = tqdm(total=len(postCellsTags.items()), ascii=True,
+                    desc='  ' + str(connParam['label']), position=0, leave=(sim.cfg.progressBar == 2),
+                    bar_format= '{l_bar}{bar}| Creating synaptic connections for {n_fmt}/{total_fmt} postsynaptic cells on node %i (probabilistic connectivity)' % sim.rank)
+
     allRands = self.generateRandsPrePost(preCellsTags, postCellsTags)
 
     # get list of params that have a lambda function
@@ -550,13 +549,14 @@ def probConn(self, preCellsTags, postCellsTags, connParam):
             probMatrix, allRands, connParam['disynapticBias'], prePreGids, postPreGids
         )
         for preCellGid, postCellGid in connGids:
+            if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
             for paramStrFunc in paramsStrFunc:  # call lambda functions to get weight func args
                 connParam[paramStrFunc + 'Args'] = {
                     k: v if isinstance(v, Number) else v(preCellsTags[preCellGid], postCellsTags[postCellGid])
                     for k, v in connParam[paramStrFunc + 'Vars'].items()
                 }
             self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
-
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
     # standard probabilistic conenctions
     else:
         # print('rank %d'%(sim.rank))
@@ -564,6 +564,7 @@ def probConn(self, preCellsTags, postCellsTags, connParam):
         # calculate the conn preGids of the each pre and post cell
         # for postCellGid,postCellTags in sorted(postCellsTags.items()):  # for each postsyn cell
         for postCellGid, postCellTags in postCellsTags.items():  # for each postsyn cell  # for each postsyn cell
+            if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
             if postCellGid in self.gid2lid:  # check if postsyn is in this node
                 for preCellGid, preCellTags in preCellsTags.items():  # for each presyn cell
                     probability = (
@@ -580,7 +581,7 @@ def probConn(self, preCellsTags, postCellsTags, connParam):
                                 )
                             # connParam[paramStrFunc+'Args'] = {k:v if isinstance(v, Number) else v(preCellTags,postCellTags) for k,v in connParam[paramStrFunc+'Vars'].items()}
                         self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
-
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
 
 # -----------------------------------------------------------------------------
 # Generate random unique integers
@@ -653,6 +654,10 @@ def convConn(self, preCellsTags, postCellsTags, connParam):
 
     if sim.cfg.verbose:
         print('Generating set of convergent connections (rule: %s) ...' % (connParam['label']))
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar:
+        pbar = tqdm(total=len(postCellsTags.items()), ascii=True,
+                    desc='  ' + connParam['label'], position=0, leave=(sim.cfg.progressBar == 2),
+                    bar_format= '{l_bar}{bar}| Creating synaptic connections for {n_fmt}/{total_fmt} postsynaptic cells on node %i (convergent connectivity)' % sim.rank)
 
     # get list of params that have a lambda function
     paramsStrFunc = [param for param in [p + 'Func' for p in self.connStringFuncParams] if param in connParam]
@@ -672,6 +677,7 @@ def convConn(self, preCellsTags, postCellsTags, connParam):
     hashPreCells = sim.hashList(preCellsTagsKeys)
 
     for postCellGid, postCellTags in postCellsTags.items():  # for each postsyn cell
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
         if postCellGid in self.gid2lid:  # check if postsyn is in this node
             convergence = (
                 connParam['convergenceFunc'][postCellGid]
@@ -704,6 +710,7 @@ def convConn(self, preCellsTags, postCellsTags, connParam):
 
                 if preCellGid != postCellGid:  # if not self-connection
                     self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
 
 
 # -----------------------------------------------------------------------------
@@ -736,6 +743,10 @@ def divConn(self, preCellsTags, postCellsTags, connParam):
 
     if sim.cfg.verbose:
         print('Generating set of divergent connections (rule: %s) ...' % (connParam['label']))
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar:
+        pbar = tqdm(total=len(preCellsTags.items()), ascii=True,
+                    desc='  ' + connParam['label'], position=0, leave=(sim.cfg.progressBar == 2),
+                    bar_format= '{l_bar}{bar}| Creating synaptic connections for {n_fmt}/{total_fmt} presynaptic cells on node %i (divergent connectivity)' % sim.rank)
 
     # get list of params that have a lambda function
     paramsStrFunc = [param for param in [p + 'Func' for p in self.connStringFuncParams] if param in connParam]
@@ -755,6 +766,7 @@ def divConn(self, preCellsTags, postCellsTags, connParam):
     hashPostCells = sim.hashList(postCellsTagsKeys)
 
     for preCellGid, preCellTags in preCellsTags.items():  # for each presyn cell
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
         divergence = (
             connParam['divergenceFunc'][preCellGid] if 'divergenceFunc' in connParam else connParam['divergence']
         )  # num of presyn conns / postsyn cell
@@ -781,6 +793,7 @@ def divConn(self, preCellsTags, postCellsTags, connParam):
 
             if preCellGid != postCellGid:  # if not self-connection
                 self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
 
 
 # -----------------------------------------------------------------------------
@@ -813,7 +826,10 @@ def fromListConn(self, preCellsTags, postCellsTags, connParam):
 
     if sim.cfg.verbose:
         print('Generating set of connections from list (rule: %s) ...' % (connParam['label']))
-
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar:
+        pbar = tqdm(total=len(connParam['connList']), ascii=True,
+                    desc='  ' + connParam['label'], position=0, leave=(sim.cfg.progressBar == 2),
+                    bar_format= '{l_bar}{bar}| Creating synaptic connections for {n_fmt}/{total_fmt} pairs of neurons on node %i (from list)' % sim.rank)
     orderedPreGids = sorted(preCellsTags)
     orderedPostGids = sorted(postCellsTags)
 
@@ -840,7 +856,20 @@ def fromListConn(self, preCellsTags, postCellsTags, connParam):
     if 'loc' in connParam and isinstance(connParam['loc'], list):
         connParam['locFromList'] = list(connParam['loc'])  # if delay is a list, copy to locFromList
 
+    if connParam.get('synsPerConn', 1) == 1:
+        if isinstance(connParam.get('sec'), list):
+            connParam['secFromList'] = list(connParam['sec'])
+    else:
+        pass # TODO: needs consistent handling
+
+    # for pointer connections (e.g. gap junctions) only:
+    if isinstance(connParam.get('preLoc'), list):
+        connParam['preLocFromList'] = list(connParam['preLoc'])
+    if isinstance(connParam.get('preSec'), list):
+        connParam['preSecFromList'] = list(connParam['preSec'])
+
     for iconn, (relativePreId, relativePostId) in enumerate(connParam['connList']):  # for each postsyn cell
+        if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.update(1)
         preCellGid = orderedPreGids[relativePreId]
         postCellGid = orderedPostGids[relativePostId]
         if postCellGid in self.gid2lid:  # check if postsyn is in this node's list of gids
@@ -851,10 +880,18 @@ def fromListConn(self, preCellsTags, postCellsTags, connParam):
                 connParam['delay'] = connParam['delayFromList'][iconn]
             if 'locFromList' in connParam:
                 connParam['loc'] = connParam['locFromList'][iconn]
-
+            if 'secFromList' in connParam:
+                connParam['sec'] = connParam['secFromList'][iconn]
+            if 'preLocFromList' in connParam:
+                connParam['preLoc'] = connParam['preLocFromList'][iconn]
+            if 'preSecFromList' in connParam:
+                connParam['preSec'] = connParam['preSecFromList'][iconn]
+
+            # TODO: consider cfg.allowSelfConns?
             if preCellGid != postCellGid:  # if not self-connection
                 self._addCellConn(connParam, preCellGid, postCellGid, preCellsTags)  # add connection
-
+    if sim.rank == 0 and not sim.cfg.verbose and sim.cfg.progressBar: pbar.close()
+    
 
 # -----------------------------------------------------------------------------
 # Set parameters and create connection
@@ -923,6 +960,11 @@ def _addCellConn(self, connParam, preCellGid, postCellGid, preCellsTags={}):
         if 'weightIndex' in connParam:
             params['weightIndex'] = connParam.get('weightIndex')
 
+        if 'distributeSynsUniformly' in connParam:
+            params['distributeSynsUniformly'] = connParam['distributeSynsUniformly']
+        if 'connRandomSecFromList' in connParam:
+            params['connRandomSecFromList'] = connParam['connRandomSecFromList']
+
         isGapJunction = 'gapJunction' in connParam  # deprecated way of defining gap junction
         if self.params.synMechParams.isPointerConn(params['synMech']) or isGapJunction:
             params['preLoc'] = connParam.get('preLoc')
diff --git a/netpyne/network/modify.py b/netpyne/network/modify.py
index f9c793c03..684204315 100644
--- a/netpyne/network/modify.py
+++ b/netpyne/network/modify.py
@@ -3,19 +3,9 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-
 # -----------------------------------------------------------------------------
 # Modify cell params
 # -----------------------------------------------------------------------------
-from future import standard_library
-
-standard_library.install_aliases()
-
 
 def modifyCells(self, params, updateMasterAllCells=False):
     """
diff --git a/netpyne/network/netrxd.py b/netpyne/network/netrxd.py
index 57c5bddeb..38e04ea66 100644
--- a/netpyne/network/netrxd.py
+++ b/netpyne/network/netrxd.py
@@ -3,23 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import dict
-from builtins import range
-
-from builtins import round
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 import copy
 
 try:
diff --git a/netpyne/network/network.py b/netpyne/network/network.py
index ac1a34a04..dc2b0a434 100644
--- a/netpyne/network/network.py
+++ b/netpyne/network/network.py
@@ -3,17 +3,9 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from ..specs import ODict
 from neuron import h  # import NEURON
-
+from tqdm import tqdm
 
 class Network(object):
     """
@@ -26,6 +18,8 @@ class Network(object):
     # initialize variables
     # -----------------------------------------------------------------------------
     def __init__(self, params=None):
+        from .. import sim
+
         self.params = params
 
         # params that can be expressed using string-based functions in connections
@@ -62,7 +56,12 @@ def __init__(self, params=None):
             {}
         )  # Empty dict for storing GID -> local index (key = gid; value = local id) -- ~x6 faster than .index()
         self.lastGid = 0  # keep track of last cell gid
-        self.lastPointerId = 0  # keep track of last gap junction gid
+
+        # keep track of last gap junction gid
+        intMax = 2**(32-1) # pointer connection id in NEURON is signed 32-bit int
+        maxPointerIdPerNode = int(intMax / sim.nhosts)
+        self.lastPointerId = sim.rank * maxPointerIdPerNode # to avoid overlap of gids from different nodes
+        self.maxPointerIdForGivenNode = self.lastPointerId + maxPointerIdPerNode
 
     # -----------------------------------------------------------------------------
     # Set network params
@@ -90,18 +89,22 @@ def createCells(self):
 
         sim.pc.barrier()
         sim.timing('start', 'createTime')
-        if sim.rank == 0:
+        if sim.rank == 0 and sim.cfg.verbose:
             print(("\nCreating network of %i cell populations on %i hosts..." % (len(self.pops), sim.nhosts)))
 
         self._setDiversityRanges()  # update fractions for rules
-
+        if sim.rank == 0 and not sim.cfg.verbose: pbar = tqdm(total=len(self.pops.values()), ascii=True,
+                                                              desc="\nCreating network of %i cell populations on %i hosts..." % (len(self.pops), sim.nhosts),
+                                                              position=-1, leave=True,
+                                                              bar_format='{l_bar}{bar}|') #{n_fmt}/{total_fmt} populations created on node %i' % sim.rank)
         for ipop in list(self.pops.values()):  # For each pop instantiate the network cells (objects of class 'Cell')
+            if sim.rank == 0 and not sim.cfg.verbose: pbar.update(1)
             newCells = ipop.createCells()  # create cells for this pop using Pop method
             self.cells.extend(newCells)  # add to list of cells
             sim.pc.barrier()
             if sim.rank == 0 and sim.cfg.verbose:
                 print(('Instantiated %d cells of population %s' % (len(newCells), ipop.tags['pop'])))
-
+        if sim.rank == 0 and not sim.cfg.verbose: pbar.close()
         if self.params.defineCellShapes:
             self.defineCellShapes()
 
diff --git a/netpyne/network/pop.py b/netpyne/network/pop.py
index be868610e..f20e4416d 100644
--- a/netpyne/network/pop.py
+++ b/netpyne/network/pop.py
@@ -3,21 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import map
-from builtins import range
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 from numpy import pi, sqrt, sin, cos, arccos
 import numpy as np
 from neuron import h  # Import NEURON
diff --git a/netpyne/network/shape.py b/netpyne/network/shape.py
index f09f2a1cc..8f8f1920c 100644
--- a/netpyne/network/shape.py
+++ b/netpyne/network/shape.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from neuron import h
 
 # -----------------------------------------------------------------------------
diff --git a/netpyne/network/stim.py b/netpyne/network/stim.py
index 20d002304..178fd15d2 100644
--- a/netpyne/network/stim.py
+++ b/netpyne/network/stim.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from numbers import Number
 
 try:
@@ -26,19 +18,19 @@ def addStims(self):
     """
     Internal function to add stims specified in specs.NetParams 
     Usage:
-        Creates and attaches stims to targets via CompartCell.addstim() based on entries in the specs.NetParams sub-
-        dictionaries -- specs.NetParams.stimSourceParams and specs.NetParams.stimTargetParams (see below)
-        NetParams.stimSourceParams entries contain key-value pairs to describe NEURON point processes specified by the
-        'type' entry (i.e. 'IClamp', 'VClamp', 'SEClamp', 'AlphaSynapse', 'VecStim')
-        NetParams.stimTargetParams entries contain key-value pairs to describe the post-synaptic connections for a
-        stimSourceParam entry specified by the 'source' entry, including a 'sec' and 'loc' entry (describing section
-        and location) for where the post-synaptic connection will exist and a 'conds' entry with a dictionary
-        specifying the cell criteria for the post-synaptic connections: (i.e. 'x', 'y', 'z' or 'xnorm', 'ynorm', 'znorm'
-        specifying cell criteria by location, 'cellList' specifying cell criteria by specific gid, or arbitrary
-        'key': 'value' tags.
-        For 'VecStim' point processes, it may be more convenient to create an artificial cell (i.e.netParams.popParams
-        see: netpyne/cell/pointCell.py) which allows pattern generation ('rhythmic', 'evoked', 'poisson', 'gauss')
-        by key-value entries in a 'spikePattern' dictionary.
+    Creates and attaches stims to targets via CompartCell.addstim() based on entries in the specs.NetParams sub-
+    dictionaries -- specs.NetParams.stimSourceParams and specs.NetParams.stimTargetParams (see below)
+    NetParams.stimSourceParams entries contain key-value pairs to describe NEURON point processes specified by the
+    'type' entry (i.e. 'IClamp', 'VClamp', 'SEClamp', 'AlphaSynapse', 'VecStim')
+    NetParams.stimTargetParams entries contain key-value pairs to describe the post-synaptic connections for a
+    stimSourceParam entry specified by the 'source' entry, including a 'sec' and 'loc' entry (describing section
+    and location) for where the post-synaptic connection will exist and a 'conds' entry with a dictionary
+    specifying the cell criteria for the post-synaptic connections: (i.e. 'x', 'y', 'z' or 'xnorm', 'ynorm', 'znorm'
+    specifying cell criteria by location, 'cellList' specifying cell criteria by specific gid, or arbitrary
+    'key': 'value' tags.
+    For 'VecStim' point processes, it may be more convenient to create an artificial cell (i.e.netParams.popParams
+    see: netpyne/cell/pointCell.py) which allows pattern generation ('rhythmic', 'evoked', 'poisson', 'gauss')
+    by key-value entries in a 'spikePattern' dictionary.
 
     Parameters
     ----------
diff --git a/netpyne/network/subconn.py b/netpyne/network/subconn.py
index f91b0cc07..e9b21ce8f 100644
--- a/netpyne/network/subconn.py
+++ b/netpyne/network/subconn.py
@@ -3,20 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import zip
-from builtins import range
-
-from builtins import round
-from builtins import next
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
 import numpy as np
 from neuron import h
 
diff --git a/netpyne/plotting/__init__.py b/netpyne/plotting/__init__.py
index 3576f367d..4f90ab992 100644
--- a/netpyne/plotting/__init__.py
+++ b/netpyne/plotting/__init__.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 import warnings
 
 warnings.filterwarnings("ignore")
diff --git a/netpyne/plotting/plotCSD.py b/netpyne/plotting/plotCSD.py
index fd9fbb5cf..3c8e20991 100644
--- a/netpyne/plotting/plotCSD.py
+++ b/netpyne/plotting/plotCSD.py
@@ -9,6 +9,14 @@
 import numpy as np
 import scipy
 
+def getPaddedCSD (CSDData, pad):
+  # pad the first/last row of CSDData by replication (to avoid edge artifacts when drawing colors plots)
+  npcsd = []
+  for i in range(pad): npcsd.append(CSDData[0,:])
+  for i in range(CSDData.shape[0]): npcsd.append(CSDData[i,:])
+  for i in range(pad): npcsd.append(CSDData[-1,:])
+  npcsd=np.array(npcsd)
+  return npcsd
 
 @exception
 def plotCSD(
@@ -31,6 +39,7 @@ def plotCSD(
     showFig=False,
     smooth=True,
     colorbar=True,
+    pad=1,
     **kwargs
 ):
     """
@@ -113,9 +122,13 @@ def plotCSD(
         **Default:** ``True``
 
     colorbar : bool
-        Whetehr or not to plot the colorbar
+        Whether or not to plot the colorbar
         **Default:** ``True``
 
+    pad : int
+        Amount to pad CSDData on top/bottom for more accurate interpolation at edges
+        **Default:** ``1``
+
     """
 
     # If there is no input data, get the data from the NetPyNE sim object
@@ -137,6 +150,9 @@ def plotCSD(
     else:
         pass # TODO: ensure time slicing works properly in case CSDData is passed as an argument
 
+    npcsd = CSDData
+    if pad > 0: npcsd = getPaddedCSD(CSDData, pad) # apply padding (replicate first,last rows)
+
     if timeRange is None:
         timeRange = [0, sim.cfg.duration]
 
@@ -145,12 +161,13 @@ def plotCSD(
 
     # PLOTTING
     X = np.arange(timeRange[0], timeRange[1], dt)  # X == tt
-    Y = np.arange(CSDData.shape[0])
+    Y = np.arange(npcsd.shape[0])
 
     # interpolation
-    CSD_spline = scipy.interpolate.RectBivariateSpline(Y, X, CSDData)
-    Y_plot = np.linspace(0, CSDData.shape[0], num=1000)
-    Z = CSD_spline(Y_plot, X)
+    fctr = int(1000 / CSDData.shape[0])
+    CSD_spline = scipy.interpolate.RectBivariateSpline(Y, X, npcsd)
+    Y_plot = np.linspace(-pad, npcsd.shape[0] + pad, num=int(1000*npcsd.shape[0]/CSDData.shape[0]))    
+    Z = CSD_spline(Y_plot, X)[pad*fctr:pad*fctr+1000,:]
 
     # plotting options
     plt.rcParams.update({'font.size': fontSize})
@@ -158,7 +175,7 @@ def plotCSD(
     xmax = int(X[-1]) + 1
     ymin = 0
     if ymax is None:
-        ymax = sim.cfg.recordLFP[-1][1] + spacing_um
+        ymax = sim.cfg.recordLFP[-1][1] + spacing_um + pad
     extent_xy = [xmin, xmax, ymax, ymin]
 
     # set up figure
@@ -229,7 +246,7 @@ def plotCSD(
                 subaxs[chan].margins(0.0, 0.01)
                 subaxs[chan].get_xaxis().set_visible(False)
                 subaxs[chan].get_yaxis().set_visible(False)
-                subaxs[chan].plot(X, CSDData[chan, :], color='green', linewidth=0.3, label='CSD time series')
+                subaxs[chan].plot(X, npcsd[pad+chan, :], color='green', linewidth=0.3, label='CSD time series')
                 if legendLabel:
                     subaxs[chan].legend(loc='upper right', fontsize=fontSize)
                     legendLabel = False
diff --git a/netpyne/plotting/plotRaster.py b/netpyne/plotting/plotRaster.py
index e4edc256b..8269cfbbd 100644
--- a/netpyne/plotting/plotRaster.py
+++ b/netpyne/plotting/plotRaster.py
@@ -392,6 +392,7 @@ def color(gid, _):
 
     # add spike lines
     if syncLines:
+        cellInds = list(set(spkInds))
         rasterPlotter.axis.vlines(spkTimes, 0, len(cellInds), 'red', linewidth=0.1)
 
     # add legend
diff --git a/netpyne/plotting/plotShape.py b/netpyne/plotting/plotShape.py
index 3ed443c2b..60aac3a1f 100644
--- a/netpyne/plotting/plotShape.py
+++ b/netpyne/plotting/plotShape.py
@@ -3,26 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import open
-from builtins import next
-from builtins import range
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from builtins import zip
-
-from builtins import round
-from future import standard_library
 
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
diff --git a/netpyne/sim/__init__.py b/netpyne/sim/__init__.py
index d77b648f7..d43f0f007 100644
--- a/netpyne/sim/__init__.py
+++ b/netpyne/sim/__init__.py
@@ -5,17 +5,8 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
 # check for -nogui option
-from future import standard_library
-
-standard_library.install_aliases()
 import sys
-
 if '-nogui' in sys.argv:
     import netpyne
 
diff --git a/netpyne/sim/gather.py b/netpyne/sim/gather.py
index 14411678c..566ba379a 100644
--- a/netpyne/sim/gather.py
+++ b/netpyne/sim/gather.py
@@ -3,18 +3,10 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 import os, pickle
-from builtins import zip
-from future import standard_library
 
 from netpyne.support.recxelectrode import RecXElectrode
 
-standard_library.install_aliases()
 import numpy as np
 from ..specs import Dict, ODict
 from . import setup
@@ -507,9 +499,9 @@ def sort(popKeyAndValue):
                         for key in singleNodeVecs:
                             allSimData[key] = list(fileData['simData'][key])
                         allPopsCellGids = {popLabel: [] for popLabel in nodePopsCellGids}
-                    else:
-                        for popLabel, popCellGids in nodePopsCellGids.items():
-                            allPopsCellGids[popLabel].extend(popCellGids)
+
+                    for popLabel, popCellGids in nodePopsCellGids.items():
+                        allPopsCellGids[popLabel].extend(popCellGids)
 
                     mergedFiles.append(file)
 
diff --git a/netpyne/sim/load.py b/netpyne/sim/load.py
index 6c7a76ad9..6753ff181 100644
--- a/netpyne/sim/load.py
+++ b/netpyne/sim/load.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import open
-from builtins import range
-
-# required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
 except NameError:
@@ -21,9 +12,6 @@
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 import sys
 from collections import OrderedDict
 from ..specs import Dict, ODict
diff --git a/netpyne/sim/run.py b/netpyne/sim/run.py
index cd3d9ced0..88544d066 100644
--- a/netpyne/sim/run.py
+++ b/netpyne/sim/run.py
@@ -3,18 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import round
-from builtins import range
-
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
 import numpy as np
 from neuron import h, init  # Import NEURON
 from . import utils
diff --git a/netpyne/sim/save.py b/netpyne/sim/save.py
index 05e31ee9f..5e21f6924 100644
--- a/netpyne/sim/save.py
+++ b/netpyne/sim/save.py
@@ -3,17 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import range
-from builtins import open
-from future import standard_library
-
-standard_library.install_aliases()
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
@@ -114,26 +103,30 @@ def saveData(include=None, filename=None, saveLFP=True):
             print(('Copying cfg file %s ... ' % simName))
             source = sim.cfg.backupCfgFile[0]
             targetFolder = sim.cfg.backupCfgFile[1]
-            # make dir
+
+            # make directories required to make the target folder
             try:
-                os.mkdir(targetFolder)
-            except OSError:
-                if not os.path.exists(targetFolder):
-                    print(' Could not create target folder: %s' % (targetFolder))
+                os.makedirs(targetFolder, exist_ok=True)
+            except Exception as e:
+                print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+                raise SystemExit('Could not create %s' % (targetFolder))
+                
             # copy file
             targetFile = targetFolder + '/' + simName + '_cfg.py'
             if os.path.exists(targetFile):
                 print(' Removing prior cfg file', targetFile)
                 os.system('rm ' + targetFile)
             os.system('cp ' + source + ' ' + targetFile)
-
-        # create folder if missing
+        # looks like the logic for sim.cfg.filename and targetFolder is not entirely there,
+        # could be calling os.path.dirname(None) ? #TODO
+        # create the missing folder & directory for folder if one or both are missing
         targetFolder = os.path.dirname(sim.cfg.filename)
-        if targetFolder and not os.path.exists(targetFolder):
+        if targetFolder:
             try:
-                os.mkdir(targetFolder)
-            except OSError:
-                print(' Could not create target folder: %s' % (targetFolder))
+                os.makedirs(targetFolder, exist_ok=True)
+            except Exception as e:
+                print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+                raise SystemExit('Could not create %s' % (targetFolder))
 
         # saving data
         if not include:
@@ -186,14 +179,14 @@ def saveData(include=None, filename=None, saveLFP=True):
                 if hasattr(sim.cfg, 'simLabel') and sim.cfg.simLabel:
                     filePath = os.path.join(sim.cfg.saveFolder, sim.cfg.simLabel + '_data' + timestampStr)
 
-            # create folder if missing
+            # also strange conditional ^^^, filePath must exist or an error occurs. make directories for the target folder if they do not already exist
             targetFolder = os.path.dirname(filePath)
-            if targetFolder and not os.path.exists(targetFolder):
+            if targetFolder:
                 try:
-                    os.mkdir(targetFolder)
-                except OSError:
-                    print(' Could not create target folder: %s' % (targetFolder))
-
+                    os.makedirs(targetFolder, exist_ok=True)
+                except Exception as e:
+                    print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+                    raise SystemExit('Could not create %s' % (targetFolder))
             # Save to pickle file
             if sim.cfg.savePickle:
                 import pickle
@@ -401,12 +394,12 @@ def intervalSave(simTime, gatherLFP=True):
             targetFolder = os.path.join(sim.cfg.saveFolder, 'interval_data')
         else:
             targetFolder = 'interval_data'
-
-        if targetFolder and not os.path.exists(targetFolder):
-            try:
-                os.makedirs(targetFolder)
-            except OSError:
-                print(' Could not create target folder: %s' % (targetFolder))
+        # how can targetFolder ^^^ NOT have a value within this conditional 
+        try:
+            os.makedirs(targetFolder, exist_ok=True)
+        except Exception as e:
+            print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+            raise SystemExit('Could not create %s' % (targetFolder))
 
         include = sim.cfg.saveDataInclude
 
@@ -635,8 +628,11 @@ def saveDataInNodes(filename=None, saveLFP=True, removeTraces=False, saveFolder=
     else:
         saveFolder = os.path.join(saveFolder, sim.cfg.simLabel + '_node_data')  # YES saveFolder
 
-    if not os.path.exists(saveFolder):
+    try:
         os.makedirs(saveFolder, exist_ok=True)
+    except Exception as e:
+        print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+        raise SystemExit('Could not create %s' % (saveFolder))
 
     sim.pc.barrier()
     if sim.rank == 0:
@@ -787,7 +783,7 @@ def saveModel(netParams, simConfig, srcPath, dstPath=None, exportNetParamsAsPyth
             shutil.rmtree(dstDir)
 
         # (re)create dstDir and create /src dir in it where files will be stored by default
-        os.makedirs(os.path.join(dstDir, 'src'))
+        os.makedirs(os.path.join(dstDir, 'src'), exist_ok=True)
         # create default index
         indexData = {
             'netParams': f"src/netParams{'.py' if exportNetParamsAsPython else '.json'}",
diff --git a/netpyne/sim/setup.py b/netpyne/sim/setup.py
index ea7879f2f..cc8646726 100644
--- a/netpyne/sim/setup.py
+++ b/netpyne/sim/setup.py
@@ -3,16 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-#
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
 import sys
 import os
 import numpy as np
@@ -473,6 +463,8 @@ def setupRecording():
                     break
 
     if sim.cfg.recordStim:
+        if sim.cfg.verbose:
+            print("   Recording stims")
         sim.simData['stims'] = Dict()
         for cell in sim.net.cells:
             cell.recordStimSpikes()
@@ -505,6 +497,8 @@ def setupRecording():
 
         # record h.t
         if sim.cfg.recordTime and len(sim.simData) > 0:
+            if sim.cfg.verbose:
+                print("   Recording h.t")
             try:
                 sim.simData['t'] = h.Vector()  # sim.cfg.duration/sim.cfg.recordStep+1).resize(0)
                 if hasattr(sim.cfg, 'use_local_dt') and sim.cfg.use_local_dt:
@@ -520,7 +514,8 @@ def setupRecording():
         # print recorded traces
         cat = 0
         total = 0
-        for key in sim.simData:
+        keys = [k for k in sim.simData.keys() if k not in ['t', 'stims', 'spkt', 'spkid']]
+        for key in keys:
             if sim.cfg.verbose:
                 print(("   Recording: %s:" % key))
             if len(sim.simData[key]) > 0:
diff --git a/netpyne/sim/utils.py b/netpyne/sim/utils.py
index c8af8108f..e233a4adc 100644
--- a/netpyne/sim/utils.py
+++ b/netpyne/sim/utils.py
@@ -3,26 +3,13 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-
-from builtins import next
-from builtins import dict
-from builtins import map
-from builtins import str
-
 from netpyne.support.recxelectrode import RecXElectrode
 
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 from time import time
 import hashlib
 import array
diff --git a/netpyne/sim/wrappers.py b/netpyne/sim/wrappers.py
index c1b4b7712..ed90c680c 100644
--- a/netpyne/sim/wrappers.py
+++ b/netpyne/sim/wrappers.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
-
 # ------------------------------------------------------------------------------
 # Wrapper to create network
 # ------------------------------------------------------------------------------
diff --git a/netpyne/specs/__init__.py b/netpyne/specs/__init__.py
index 464e7b367..6064f40e0 100644
--- a/netpyne/specs/__init__.py
+++ b/netpyne/specs/__init__.py
@@ -3,14 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
 from .dicts import Dict, ODict
 from .netParams import NetParams, CellParams
 from .simConfig import SimConfig
diff --git a/netpyne/specs/dicts.py b/netpyne/specs/dicts.py
index ac6ae1763..f610f9f4a 100644
--- a/netpyne/specs/dicts.py
+++ b/netpyne/specs/dicts.py
@@ -5,17 +5,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import range
-from builtins import dict
-from builtins import super
-from future import standard_library
-
-standard_library.install_aliases()
 from collections import OrderedDict
 
 # ----------------------------------------------------------------------------
diff --git a/netpyne/specs/netParams.py b/netpyne/specs/netParams.py
index 9a21854db..a483125a4 100644
--- a/netpyne/specs/netParams.py
+++ b/netpyne/specs/netParams.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
-from builtins import next
-from builtins import open
-from builtins import range
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
@@ -23,9 +14,6 @@
 except NameError:
     basestring = str
 
-from future import standard_library
-
-standard_library.install_aliases()
 from collections import OrderedDict
 from .dicts import Dict, ODict
 from .. import conversion
@@ -559,12 +547,12 @@ def save(self, filename):
         folder = filename.split(basename)[0]
         ext = basename.split('.')[1]
 
-        # make dir
+        # make directories if they do not already exist:
         try:
-            os.mkdir(folder)
-        except OSError:
-            if not os.path.exists(folder):
-                print(' Could not create', folder)
+            os.makedirs(folder, exist_ok=True)
+        except Exception as e:
+            print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+            raise SystemExit('Could not create %s' % (folder))
 
         dataSave = {'net': {'params': self.todict()}}
 
diff --git a/netpyne/specs/simConfig.py b/netpyne/specs/simConfig.py
index 5024c43f5..a4f318b3f 100644
--- a/netpyne/specs/simConfig.py
+++ b/netpyne/specs/simConfig.py
@@ -3,22 +3,12 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
 # required to make json saving work in Python 2/3
 try:
     to_unicode = unicode
 except NameError:
     to_unicode = str
 
-from builtins import open
-from future import standard_library
-
-standard_library.install_aliases()
-
 from collections import OrderedDict
 from .dicts import Dict, ODict
 
@@ -82,7 +72,7 @@ def __init__(self, simConfigDict=None):
         self.printPopAvgRates = False  # print population avg firing rates after run
         self.printSynsAfterRule = False  # print total of connections after each conn rule is applied
         self.verbose = False  # show detailed messages
-
+        self.progressBar = 2 # (0: no progress bar; 1: progress bar w/ leave = False; 2: progress bar w/ leave = True)
         # Recording
         self.recordCells = []  # what cells to record traces from (eg. 'all', 5, or 'PYR')
         self.recordTraces = {}  # Dict of traces to record
@@ -136,6 +126,25 @@ def __init__(self, simConfigDict=None):
                 else:
                     setattr(self, k, v)
 
+    def __repr__(self): #functions to make the cfg function more like a dictionary
+        return str(self.__dict__)
+
+
+    def __contains__(self, item):
+        return item in self.__dict__
+
+
+    def __iter__(self):
+        return iter(self.__dict__)
+
+
+    def get(self, k, d=None):
+        try:
+            return self.__getitem__(k)
+        except:
+            return d
+
+
     def __getitem__(self, k):
         try:
             return object.__getattribute__(self, k)
@@ -155,12 +164,12 @@ def save(self, filename):
         folder = filename.split(basename)[0]
         ext = basename.split('.')[1]
 
-        # make dir
+        # make directories if they do not already exist: 
         try:
-            os.mkdir(folder)
-        except OSError:
-            if not os.path.exists(folder):
-                print(' Could not create', folder)
+            os.makedirs(folder, exist_ok=True)
+        except Exception as e:
+            print('%s: Exception: %s,' % (os.path.abspath(__file__), e))
+            raise SystemExit('Could not create %s' % (folder))
 
         dataSave = {'simConfig': self.__dict__}
 
diff --git a/netpyne/specs/utils.py b/netpyne/specs/utils.py
index 3107da6d8..1488dfed1 100644
--- a/netpyne/specs/utils.py
+++ b/netpyne/specs/utils.py
@@ -3,13 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from numbers import Number
 from neuron import h
 from numpy import array, sin, cos, tan, exp, remainder, sqrt, arctan2, pi, mean, inf, dstack, unravel_index, argsort, zeros, ceil, copy, log, log10
diff --git a/netpyne/support/__init__.py b/netpyne/support/__init__.py
index 4985f96a3..b28ac292c 100644
--- a/netpyne/support/__init__.py
+++ b/netpyne/support/__init__.py
@@ -4,11 +4,3 @@
 This subpackage contains external modules or packages required by NetPyNE which are either not available via pip or have been slightly modified.
 
 """
-
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
diff --git a/netpyne/support/bsmart.py b/netpyne/support/bsmart.py
index 10a9dedcb..caa99f64c 100644
--- a/netpyne/support/bsmart.py
+++ b/netpyne/support/bsmart.py
@@ -42,15 +42,8 @@
 
 Version: 2019jun17 by Cliff Kerr (cliff@thekerrlab.com)
 """
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import absolute_import
-import numpy as np
-from builtins import range
-from future import standard_library
 
-standard_library.install_aliases()
+import numpy as np
 
 # ARMORF -- AR parameter estimation via LWR method modified by Morf.
 #
diff --git a/netpyne/support/filter.py b/netpyne/support/filter.py
index 237801179..93cc97f18 100644
--- a/netpyne/support/filter.py
+++ b/netpyne/support/filter.py
@@ -21,11 +21,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 import warnings
 
 import numpy as np
diff --git a/netpyne/support/morlet.py b/netpyne/support/morlet.py
index a58b1b793..abbbf911d 100644
--- a/netpyne/support/morlet.py
+++ b/netpyne/support/morlet.py
@@ -10,11 +10,6 @@
  subtract mean from time series within the wavelet class)
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 from netpyne import __gui__
 if __gui__:
     import matplotlib.pyplot as plt
diff --git a/netpyne/support/morphology.py b/netpyne/support/morphology.py
index 4b2025625..67ec1bccc 100644
--- a/netpyne/support/morphology.py
+++ b/netpyne/support/morphology.py
@@ -3,21 +3,8 @@
 
 """
 
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import absolute_import
-
 # Code adapted from https://github.com/ahwillia/PyNeuron-Toolbox under MIT license
 
-
-from builtins import zip
-from builtins import range
-
-from future import standard_library
-
-standard_library.install_aliases()
-from builtins import object
 import numpy as np
 import pylab as plt
 from netpyne import __gui__
diff --git a/netpyne/support/recxelectrode.py b/netpyne/support/recxelectrode.py
index 7c2a87746..42b1c2605 100644
--- a/netpyne/support/recxelectrode.py
+++ b/netpyne/support/recxelectrode.py
@@ -3,11 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import division
-from __future__ import unicode_literals
-from __future__ import absolute_import
-
 # Allen Institute Software License - This software license is the 2-clause BSD license plus clause a third
 # clause that prohibits redistribution for commercial purposes without further permission.
 #
@@ -38,10 +33,6 @@
 # Adapted to NetPyNE by salvadordura@gmail.com
 #
 
-from builtins import range
-from future import standard_library
-
-standard_library.install_aliases()
 import numpy as np
 import math
 
diff --git a/netpyne/support/scalebar.py b/netpyne/support/scalebar.py
index 786835f04..6f6af8890 100644
--- a/netpyne/support/scalebar.py
+++ b/netpyne/support/scalebar.py
@@ -9,13 +9,6 @@
 # Adapted from mpl_toolkits.axes_grid1
 # LICENSE: Python Software Foundation (http://docs.python.org/license.html)
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
 from netpyne import __gui__
 
 if __gui__:
@@ -60,7 +53,7 @@ def __init__(
                 bars.add_artist(Rectangle((0, 0), 0, sizey, ec=barcolor, lw=barwidth, fc="none"))
 
             if sizex and labelx:
-                self.xlabel = TextArea(labelx, minimumdescent=False)
+                self.xlabel = TextArea(labelx)
                 bars = VPacker(children=[bars, self.xlabel], align="center", pad=0, sep=sep)
             if sizey and labely:
                 self.ylabel = TextArea(labely)
diff --git a/netpyne/support/stackedBarGraph.py b/netpyne/support/stackedBarGraph.py
index 176c85bf2..b6a865f97 100644
--- a/netpyne/support/stackedBarGraph.py
+++ b/netpyne/support/stackedBarGraph.py
@@ -25,17 +25,6 @@
 #                                                                             #
 ###############################################################################
 
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import absolute_import
-from builtins import range
-from builtins import str
-from future import standard_library
-
-standard_library.install_aliases()
-from builtins import object
-
 __author__ = "Michael Imelfort; modified by salvadordura@gmail.com"
 __copyright__ = "Copyright 2014"
 __credits__ = ["Michael Imelfort"]
diff --git a/netpyne/tests/checks.py b/netpyne/tests/checks.py
index d02d40a0b..edd788b91 100644
--- a/netpyne/tests/checks.py
+++ b/netpyne/tests/checks.py
@@ -3,18 +3,6 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-# checks.py
-
-from future import standard_library
-
-standard_library.install_aliases()
-
-
 def checkOutput(modelName, verbose=False):
     """
     Function to compare the output of tutorials and examples with their expected output
diff --git a/netpyne/tests/tests.py b/netpyne/tests/tests.py
index 4ee255196..c78679014 100644
--- a/netpyne/tests/tests.py
+++ b/netpyne/tests/tests.py
@@ -3,20 +3,11 @@
 
 """
 
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import division
-from __future__ import absolute_import
-
-from builtins import str
-
 try:
     basestring
 except NameError:
     basestring = str
-from future import standard_library
 
-standard_library.install_aliases()
 import unittest
 import numbers
 import sys
diff --git a/netpyne/tests/validate_tests.py b/netpyne/tests/validate_tests.py
index c2678abe0..d4213c5c9 100644
--- a/netpyne/tests/validate_tests.py
+++ b/netpyne/tests/validate_tests.py
@@ -3,15 +3,6 @@
 
 """
 
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
-from future import standard_library
-
-standard_library.install_aliases()
-from builtins import object
 from .tests import *
 import netpyne.specs as specs
 
@@ -78,7 +69,6 @@ def loadSimConfigTests(self):
         #
         # # Saving
         # simConfigParams.simConfig.saveJson=1
-        # simConfigParams.simConfig.saveFileStep = simConfigParams.simConfig.dt # step size in ms to save data to disk
         #
         # self.paramsMap["simConfig"]["durationTest"].append(simConfigParams)
 
diff --git a/sdnotes.org b/sdnotes.org
index 0e2c18667..0dae07748 100644
--- a/sdnotes.org
+++ b/sdnotes.org
@@ -7135,7 +7135,6 @@ simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, et
 # Saving
 simConfig.filename = 'HHTut'  # Set file output name
 #simConfig.Label = 'sim1'
-simConfig.saveFileStep = 1000 # step size in ms to save data to disk
 simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file
 simConfig.recordStim = True
 simConfig.saveJson = True # save to json file
diff --git a/setup.py b/setup.py
index 754ffcf13..bb877427c 100644
--- a/setup.py
+++ b/setup.py
@@ -75,7 +75,7 @@
         # your project is installed. For an analysis of "install_requires" vs pip's
         # requirements files see:
         # https://packaging.python.org/en/latest/requirements.html
-        install_requires=["numpy", "scipy", "matplotlib", "matplotlib-scalebar", "future", "pandas", "bokeh", "schema", "lfpykit"],
+        install_requires=["numpy", "scipy", "matplotlib", "matplotlib-scalebar", "future", "pandas", "bokeh", "schema", "lfpykit", "tqdm"],
         # List additional groups of dependencies here (e.g. development
         # dependencies). You can install these using the following syntax,
         # for example:
diff --git a/tests/examples/test_HHTut.py b/tests/examples/test_HHTut.py
index c2abbe325..253ba73dc 100644
--- a/tests/examples/test_HHTut.py
+++ b/tests/examples/test_HHTut.py
@@ -4,9 +4,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-@pytest.mark.package_data(['examples/HHTut/', None])
+package_dir = NETPYNE_DIR + '/examples/HHTut/'
+@pytest.mark.package_data([package_dir, None])
 class TestHHTut():
     def test_run(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_HybridTut.py b/tests/examples/test_HybridTut.py
index 9fc96136e..1490cea87 100644
--- a/tests/examples/test_HybridTut.py
+++ b/tests/examples/test_HybridTut.py
@@ -5,9 +5,11 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-@pytest.mark.package_data(['examples/HybridTut/', 'mod'])
+package_dir = NETPYNE_DIR + '/examples/HybridTut/'
+
+@pytest.mark.package_data([package_dir, 'mod'])
 class TestHybridTut:
     def test_run(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_LFPrecording.py b/tests/examples/test_LFPrecording.py
index 3ec68b134..42e235d0f 100644
--- a/tests/examples/test_LFPrecording.py
+++ b/tests/examples/test_LFPrecording.py
@@ -5,10 +5,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/LFPrecording/', 'mod'])
+package_dir = NETPYNE_DIR + '/examples/LFPrecording/'
+@pytest.mark.package_data([package_dir, 'mod'])
 class TestLFPrecording:
     def test_cell_lfp(self, pkg_setup):
         import src.cell.init
diff --git a/tests/examples/test_M1.py b/tests/examples/test_M1.py
index 6af398481..981df1f3a 100644
--- a/tests/examples/test_M1.py
+++ b/tests/examples/test_M1.py
@@ -5,10 +5,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/M1/', 'mod'])
+package_dir = NETPYNE_DIR + '/examples/M1/'
+@pytest.mark.package_data([package_dir, 'mod'])
 class TestM1:
     def test_run(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_NeuroMLImport.py b/tests/examples/test_NeuroMLImport.py
index fe540b4c6..361fb864f 100644
--- a/tests/examples/test_NeuroMLImport.py
+++ b/tests/examples/test_NeuroMLImport.py
@@ -5,10 +5,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/NeuroMLImport/', '.'])
+package_dir = NETPYNE_DIR + '/examples/NeuroMLImport/'
+@pytest.mark.package_data([package_dir, '.'])
 class TestPTcell:
     def test_init(self, pkg_setup):
         import SimpleNet_import
diff --git a/tests/examples/test_PTcell.py b/tests/examples/test_PTcell.py
index a7599f9e0..0e3b5a793 100644
--- a/tests/examples/test_PTcell.py
+++ b/tests/examples/test_PTcell.py
@@ -5,10 +5,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/PTcell/', 'mod'])
+package_dir = NETPYNE_DIR + '/examples/PTcell/'
+@pytest.mark.package_data([package_dir, 'mod'])
 class TestPTcell:
     def test_init(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_batchCell.py b/tests/examples/test_batchCell.py
index 9ce282143..ac2898e21 100644
--- a/tests/examples/test_batchCell.py
+++ b/tests/examples/test_batchCell.py
@@ -8,10 +8,10 @@
 if "-nogui" not in sys.argv:
     sys.argv.append("-nogui")
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(["examples/batchCell/", "mod"])
+package_dir = NETPYNE_DIR + "/examples/batchCell/"
+@pytest.mark.package_data([package_dir, "mod"])
 class TestBatchCell:
     def batch_run(self, pkg_setup):
         """run a reduced version of the batchCell example"""
diff --git a/tests/examples/test_evolCell.py b/tests/examples/test_evolCell.py
index cf82f05b6..8c171bf35 100644
--- a/tests/examples/test_evolCell.py
+++ b/tests/examples/test_evolCell.py
@@ -9,10 +9,10 @@
 if "-nogui" not in sys.argv:
     sys.argv.append("-nogui")
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(["examples/evolCell/", "mod"])
+package_dir = NETPYNE_DIR + "/examples/evolCell/"
+@pytest.mark.package_data([package_dir, "mod"])
 class TestEvolCell:
     def evol_run(self, pkg_setup):
         """run a reduced version of the evolCell example"""
diff --git a/tests/examples/test_rxd_buffering.py b/tests/examples/test_rxd_buffering.py
index ce6bb14d7..dab5ab15d 100644
--- a/tests/examples/test_rxd_buffering.py
+++ b/tests/examples/test_rxd_buffering.py
@@ -3,9 +3,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-@pytest.mark.package_data(['examples/rxd_buffering/', None])
+package_dir = NETPYNE_DIR + '/examples/rxd_buffering/'
+@pytest.mark.package_data([package_dir, None])
 class Test_rxd_buffering():
     def test_buffering(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_rxd_net.py b/tests/examples/test_rxd_net.py
index 4da23a73a..0e7a9b23b 100644
--- a/tests/examples/test_rxd_net.py
+++ b/tests/examples/test_rxd_net.py
@@ -5,10 +5,10 @@
 if '-nogui' not in sys.argv:
     sys.argv.append('-nogui')
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/rxd_net/', 'mod'])
+package_dir = NETPYNE_DIR + '/examples/rxd_net/'
+@pytest.mark.package_data([package_dir, 'mod'])
 class TestRxdNet:
     def test_init(self, pkg_setup):
         import src.init
diff --git a/tests/examples/test_saving.py b/tests/examples/test_saving.py
index 451d29b71..0e3b838aa 100644
--- a/tests/examples/test_saving.py
+++ b/tests/examples/test_saving.py
@@ -5,10 +5,10 @@
     sys.argv.append('-nogui')
 
 
-from .utils import pkg_setup
+from .utils import pkg_setup, NETPYNE_DIR
 
-
-@pytest.mark.package_data(['examples/saving', None])
+package_dir = NETPYNE_DIR + '/examples/saving/'
+@pytest.mark.package_data([package_dir, None])
 class Test_saving():
     def test_init(self, pkg_setup):
         import src.init
diff --git a/tests/examples/utils.py b/tests/examples/utils.py
index 81d3ab0d3..4180a4d99 100644
--- a/tests/examples/utils.py
+++ b/tests/examples/utils.py
@@ -1,7 +1,10 @@
 import os
 import pytest
 import sys
+import inspect
+import netpyne
 
+NETPYNE_DIR = '/' + os.path.join(*inspect.getfile(netpyne).split('/')[:-2])
 def compile_neuron_mod_dir(pkg_dir):
     try:
         print('Compiling {}'.format(pkg_dir))
@@ -14,7 +17,6 @@ def compile_neuron_mod_dir(pkg_dir):
         print(err)
         return
 
-
 @pytest.fixture
 def pkg_setup(request):
     mark = request.node.get_closest_marker("package_data")
diff --git a/tut-backup/cells_netpyne2021 b/tut-backup/cells_netpyne2021
new file mode 160000
index 000000000..59af57aa4
--- /dev/null
+++ b/tut-backup/cells_netpyne2021
@@ -0,0 +1 @@
+Subproject commit 59af57aa4d83a2a660d9ad3c6bc3851bcc62ea3b