ulmo

Clean, simple and fast access to public hydrology and climatology data

Project Status

Features

• retrieves and parses datasets from the web
• returns simple python data structures that can be easily pulled into more sophisticated tools such as Pandas for analysis
• caches datasets locally and harvests updates as needed

Datasets

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

Installation

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 using pip:

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 conda_environment.yml

# use 'activate myenv' on windows

source activate myenv

pip install -e .

Links

• Documentation: http://ulmo.readthedocs.org
• Repository: https://github.com/ulmo-dev/ulmo