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


**clean, simple and fast access to public hydrology and climatology data**

Project Status

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- 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 are installed, the
most recent release of ulmo can be installed from pypi. Pip is a good way to do

pip install ulmo

To install the bleeding edge development version, grab a copy of the `source
code`_ and run 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 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:
* Repository:

.. _more sophisticated tools:
.. _issue tracker:
.. _Anaconda:
.. _Miniconda:
.. _conda-forge:
.. _scipy:
.. _source code:  
File Type Py Version Uploaded on Size
ulmo- (md5) Source 2016-08-12 69KB