Johns Hopkins Turbulence Databases

Using JHTDB with Python

Download

Python code: directly from here or https://github.com/idies/pyJHTDB

This downloads a directory with sample IPython Notebook code in the examples folder that illustrate the basic functionality of the interface. Choose one of the following to use the pyJHTDB package.
Please see the README file for more information.

Use through SciServer (RECOMMENDED)

The SciServer is a cloud-based data-driven cluster, of The Institute for Data Intensive Engineering and Science (IDIES) at Johns Hopkins University. Users get the advantages of more reliable and faster data access since the SciServer is directly connected to JHTDB through a 10 Gigabit ethernet connection. SciServer provides docker containers with the pyJHTDB library pre-installed.

To use pyJHTDB through Sciserver:

Login to SciServer http://www.sciserver.org (may need to create a new account first).
Click on Compute and then Create container (You could also run jobs in batch mode, by selecting Compute Jobs).
Type in Container name, select JH Turbulence DB in Compute Image and then click on Create.
Click on the container you just created, then you could start using pyJHTDB with Python or IPython Notebook.

Examples of using pyJHTDB could be found at https://github.com/idies/pyJHTDB. Please go to http://www.sciserver.org for more information on SciServer as well as the help on Sciserver.

Use on local computers

Installing pypi version

If you have pip, you can simply do this:

pip install pyJHTDB

If you're running unix (i.e. some MacOS or GNU/Linux variant), you will probably need to have a sudo in front of the pip command. If you don't have pip on your system, it is quite easy to get it following the instructions at http://pip.readthedocs.org/en/latest/installing.html.

Installing from source

In terminal:

cd /path/to/your/folder/
git clone https://github.com/idies/pyJHTDB.git
cd pyJHTDB
python update_turblib.py
pip install --upgrade ./

Note that doing this should update pyJHTDB and all the required packages, including numpy, scipy, sympy, h5py and matplotlib.