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A cloud-based flow cytometry web application
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README.md

KeesaFlo

KeesaFlo cloud flow cytometry project

Latest Release: v1.0.0

Licence: Apache 2.0

Discussion:

For discussions see the Keesaco mailing list. (For access contact Jack McCrea)
Issues should be raised on the repository's issue list.

Documentation:

This project is prepared for Doxygen automatic documentation generation. Running Doxygen on Doxyfile.doxyfile (in the root directory of the repository) will generate documentation for the project into the Doxygen directory in HTML and LaTeX.
JavaScript documentation is performed automatically using js2doxy (acquired by Dependable - see dependencies), therefore perl is required to generate documentation for JavaScript.

For convenience, an HTML copy of the documentation is accessible from keesaco.com using the following link:
Doxygen output (Last updated 2 May 14)
Please note that this may not reflect all of the latest changes to the project and only shows documentation for code on the development branch.

Dependencies:

This project has a number of dependencies which are not committed to this repository. For convenience, the project has been prepared for automatic dependency acquisition using Dependable. To build the application download AppBuild.py and place it in the root directory of the project. Running python AppBuild.py will download and unzip all dependencies into their correct directories for the app to be run/deployed.

In addition to these dependencies, the application requires pyopenssl to run locally. As this is not installed into the application's files it is not downloaded by Dependable.

Image:

The provided Google Compute Engine image ‘keesaflo-worker’ contains the dependencies and directory structure of a worker instance. This is based on the ‘debian-7-wheezy-v20140415’ image, changes to which are listed below:

  1. Installed the R software environment.
  2. Installed the Bioconductor R packages ‘flowViz’ and ‘flowCore’.
  3. Made new directory ‘Analysis’ in the home directory.
  4. Copied the contents of the repository’s ‘compute-engine’ directory into ‘Analysis’.

To create a new image please follow Google’s documentation. To use this new image upload it to Google Cloud Storage and change the IMAGE_URL in the project configuration file.

Testing:

Automated unit testing is included in this project. Given that functionality largely relies on the Google Cloud Platform, these tests must be run using the dev_appserver. When the app is being served by the dev server, navigate to http://localhost:8080/\_ah/unittest/ (changing the port if necessary). New tests can be added by adding additional methods to existing Python files in the app/test directory, or by adding new test files to the app/test directory and then importing them from app/test/__init__.py.

Running with Local Dev Server:

  1. Download the Google App Engine Python SDK.
  2. Ensure Python 2.7 and pyOpenSSL are installed.
  3. Acquire dependencies using Dependable as described above.
  4. Add an (existing) application to the dev_appserver using the 'app' directory as the path.

Running with Vagrant:

  1. Install Vagrant
  2. Install VirtualBox
  3. From the repository folder, provision and start the VM with 'vagrant up' (This will take several minutes)
  4. SSH into the VM with vagrant ssh
  5. Start the app server with ./start.sh&
  6. The application can now be accessed from the host machine at localhost:28080 and the administration pages can be accessed at localhost:28000

Contact:

All members - info@keesaco.com

Contributors:

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