.. _build_scm_hbp:

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Build your own single hippocampal cell model using HBP data 
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This Use Case allows a user to configure the 
`BluePyOpt <http://bluepyopt.readthedocs.io/en/latest/>`_ to run an 
optimization choosing from HBP data for morphology, channel kinetics, features, 
and parameters. The current version allows to select among self-consistent 
configuration files from previous optimizations.

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By selecting this Use Case, three Jupyter Notebooks are cloned in a private 
existing/new Collab. The cloned Notebooks are:

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BuildOwn Single HippoCell - Config
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This allows the user to:

1 Choose and visualize an existing morphology from HBP data (the name and type 
of morphology are listed):

.. container:: bsp-container-center

  .. image:: images/byohd_browseall.png
     :width: 600px
     :align: right

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2 Choose a self-consistent set of configuration files (the four JSON files) for 
the chosen morphology; the list includes those available for the selected 
morphology:

.. container:: bsp-container-center

  .. image:: images/byohd_chose.png
     :width: 500px
     :align: right

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3 visualize the electrophysiological features that will be used as reference by 
the optimization process (“Features” tab); they cannot be changed; they are 
taken from the 'features.json' file.

.. container:: bsp-container-center

  .. image:: images/byohd_visualize.png
     :width: 600px
     :align: right

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4 Visualize and change the parameters of an existing optimization ("Parameters" 
tab). The user can change the values and save them by using the "Save 
new parameters" button:

.. container:: bsp-container-center

  .. image:: images/byohd_change.png
     :width: 600px
     :align: right

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5 Configure the BluePyOpt optimization algorithm, by defining the offspring 
size and the max number of generations; they are set by default to 10 and 2 
respectively.

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6 Generate the zip file needed to run the new optimization by "Create zip file".

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7 Log in to NSG (Neuroscience Gateway) by inserting his/her username and 
password and clicking on the "Login NSG" button:

.. container:: bsp-container-center

  .. image:: images/byohd_login.png
     :width: 300px
     :align: right

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8 Configure the parameters of the optimization job: number of nodes, number of 
cores and runtime. They are set by default to 2, 10 and 0.5 respectively. The 
maximum number of nodes available per job is 72. If you would require more than 
72 nodes please contact us at nsghelp@sdsc.edu. The maximum number of cores 
required per node is 24.

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9 run the optimization by clicking on the “Run NSG simulation”.

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10 the job has been submitted when you see:

.. container:: bsp-container-center

  .. image:: images/byohd_submitted.png
     :width: 300px
     :align: right

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You may check the job status by clicking on the “Check NSG simulation”. The 
status may be: QUEUE, COMMANDRENDERING, INPUTSTAGING, SUBMITTED, LOAD_RESULTS 
or COMPLETED
   
Once the job is COMPLETED, results are saved in the Collab storage, under the 
BluePyOptAll/resultsNSG/username/foldername (foldername is the name of the 
configuration set, where date and time are updated with the current date and 
time).

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11 If you are interested in looking at the code, click on “Click here to toggle on/off the source code” button:
 
.. container:: bsp-container-center

  .. image:: images/byohd_toggle.png
     :width: 300px
     :align: right

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BuildOwn Single HippoCell - NSG Job Manager
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.. include:: ../nsg_job_manager/nsg_job_manager.rst
   
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BuildOwn Single HippoCell - Analysis
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.. include:: ../analyze_run/analyze_run.rst