Skip to main content

codebase to query the Johns Hopkins Turbulence Database (JHTDB) datasets

Project description

giverny

Python (version 3.9+) codebase for querying the JHU Turbulence Database Cluster library.

Use giverny 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 containers with the "giverny" library pre-installed.

Demo notebooks for the SciServer compute environment are provided at the JHU Turbulence github.

To use giverny through Sciserver:

Login to [SciServer](https://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 *SciServer Essentials (Test)* in *Compute Image*, mark *Turbulence (filedb)* in *Data volumes*, and then click on *Create*.
Click on the container you just created, then you could start using giverny with Python or IPython Notebook.

Please go to SciServer for more information on SciServer as well as the help on SciServer.

Prerequisites: numpy>=1.23.4, scipy>=1.9.3, sympy>=1.12, h5py>=3.7.0, matplotlib>=3.6.2, wurlitzer>=3.0.3, morton-py>=1.3, dill>=0.3.6, zarr>=2.13.3, bokeh>=2.4.3, dask>=2022.11.0, pandas>=1.5.1, xarray>=2022.11.0, tqdm>=4.64.1, tenacity>=8.1.0, plotly>=5.11.0, attrs>=23.2.0, jsonschema>=4.23.0, jsonschema-specifications>=2023.12.1, nbformat>=5.10.4, referencing>=0.35.1, rpds-py>=0.19.1, jupyter-core>=5.7.2, pyJHTDB>=20210108.0, SciServer>=2.1.0

Use giverny on local computers

Demo notebooks for the local compute environment are provided at the JHU Turbulence github.

If you have pip, you can simply do this:

pip install givernylocal

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/installation.

Prerequisites: numpy>=1.23.4, matplotlib>=3.6.2, pandas>=1.5.1, requests>=2.31.0, tenacity>=8.1.0, plotly>=5.11.0, attrs>=23.2.0, jsonschema>=4.23.0, jsonschema-specifications>=2023.12.1, nbformat>=5.10.4, referencing>=0.35.1, rpds-py>=0.19.1, jupyter-core>=5.7.2

Configuration

While our service is open to anyone, we would like to keep track of who is using the service, and how. To this end, we would like each user or site to obtain an authorization token from us: http://turbulence.pha.jhu.edu/help/authtoken.aspx

For simple experimentation, the default token included in the package should be valid.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

givernylocal-2.3.11.tar.gz (42.9 kB view details)

Uploaded Source

Built Distribution

givernylocal-2.3.11-py3-none-any.whl (44.1 kB view details)

Uploaded Python 3

File details

Details for the file givernylocal-2.3.11.tar.gz.

File metadata

  • Download URL: givernylocal-2.3.11.tar.gz
  • Upload date:
  • Size: 42.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.9.13 Windows/10

File hashes

Hashes for givernylocal-2.3.11.tar.gz
Algorithm Hash digest
SHA256 ea299699dca911da138979397c1a9d058a606c06434065ea8c4f0de7f758fe69
MD5 d063c43656919a0ed7b905505d88ff7b
BLAKE2b-256 af5ecd69736370c223bfb0f1a8d45c6e27fad374749edd2133986cec9ef682f9

See more details on using hashes here.

File details

Details for the file givernylocal-2.3.11-py3-none-any.whl.

File metadata

  • Download URL: givernylocal-2.3.11-py3-none-any.whl
  • Upload date:
  • Size: 44.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.2 CPython/3.9.13 Windows/10

File hashes

Hashes for givernylocal-2.3.11-py3-none-any.whl
Algorithm Hash digest
SHA256 f8392e86ff7cd9351c90ea06ca397363c890552e018bb17be604c605f86fc2d3
MD5 4baa3138b4ce29823bdd485a039aa253
BLAKE2b-256 1b453196dee126ee24473779cba4d56187bd4074e5fbbf718b75b438d9eb26a6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page