ODM2 Python API
A Python-based application programmer's interface for the Observations Data Model 2 (ODM2).
List of current and planned functions included in the API
Installation
The easiest and most reliable way to install the ODM2 Python API (odm2api
) is using the Conda package management system via either Anaconda or Miniconda. To start using conda (if it's not your system default), add conda to the PATH; on MacOSX and Linux, it's something like export PATH=$HOME/miniconda/bin:$PATH
, but the exact path may vary.
To activate a conda environment, say, "myenv":
activate myenv # On Windows
source activate myenv # On MacOSX or Linux
Note: odm2api
currently is only tested on Python 2.7. Some changes have been made to support Python 3.x, but they haven't been tested thoroughly.
Latest release, from ODM2 anaconda.org channel
The latest odm2api
release is available on the ODM2 anaconda.org channel for all major OS paltforms (linux, OSX, win32/win64). To install it on an existing conda environment:
conda install -c odm2 odm2api
All dependencies are installed, including Pandas and its dependencies (numpy, etc).
To create a new environment "myenv" with the odm2api
package:
conda create -n myenv -c odm2 python=2.7 odm2api
master
branch on github
Installing the development version from the Note from 4/26/2016: These instructions may be slightly outdated. Follow these directions for installing the bleeding edge github master branch, mainly for development and testing purposes.
To create a new environment "myenv" with odm2api
, first download the conda environment file condaenvironment_1.yml. Go to the directory where condaenvironment_1.yml
was downloaded. Then, on a terminal shell:
conda env create -n myenv --file py2_conda_environment.yml
Activate the new environment, then install odm2api
into the environment:
activate myenv # On Windows
source activate myenv # On MacOSX or Linux
pip install --process-dependency-links git+https://github.com/ODM2/ODM2PythonAPI.git
Credits
This work was supported by National Science Foundation Grants EAR-1224638 and ACI-1339834. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.