clean, simple and fast access to public hydrology and climatology data
- retrieves and parses datasets from the web
- returns simple python data structures that can be easily pulled into more sophisticated tools for analysis
- caches datasets locally and harvests updates as needed
Currently, ulmo supports the following datasets / services:
- California Department of Water Resources Historical Data
- Climate Prediction Center Weekly Drought
- CUAHSI WaterOneFlow
- Lower Colorado River Authority Hydromet and Water Quality Data
- NASA Daymet weather data
- National Climatic Data Center Climate Index Reference Sequential (CIRS)
- National Climatic Data Center Global Historical Climate Network Daily
- National Climatic Data Center Global Summary of the Day
- Texas Weather Connection Daily Keetch-Byram Drought Index (KBDI)
- US Army Corps of Engineers - Tulsa District Water Control
- USGS National Water Information System
- USGS Emergency Data Distribution Network services
- USGS Earth Resources Observation Systems (EROS) services
- USGS National Elevation Dataset (NED) services
Ulmo depends on a lot of libraries from the scientific python stack (namely: numpy, pytables and pandas) and lxml. There are a couple of ways to get these dependencies installed but it can be tricky if doing it by hand. The simplest way to get things up and running is to use a scientific python distribution that will install everything together. A full list is available on the scipy website but Anaconda / Miniconda is recommended as it is the easiest to set up.
If you are using Anaconda/Miniconda then you can install ulmo from the `conda_forge`_ channel with the following command:
conda install -c conda-forge ulmo
Otherwise, follow the instructions below:
Once the requisite scientific python libraries are installed, the most recent release of ulmo can be installed from pypi. Pip is a good way to do that:
pip install ulmo
To install the bleeding edge development version, grab a copy of the source code and run setup.py from the root directory:
To setup a development environment using conda:
conda env create -n myenv --file py2_conda_environment.yml (or py3_conda_environment.yml if you want to work with python 3)
source activate myenv (use 'activate test_environment' on windows)
python setup.py develop
A list of future datasets is kept in on the issue tracker. If there's a dataset you'd like to see added, please open an issue about it.
- Documentation: http://ulmo.readthedocs.org
- Repository: https://github.com/ulmo-dev/ulmo