spacetimepy 0.1.6

A toolkit for working with spatiotemporal data

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Spacetime: A user-friendly tool for working with spatiotemporal data.

About spacetimepy

Spacetimepy is the python version of the larger spacetime project. The main objective of spacetime is to make tasks like loading, rescaling, merging, and conducting mathmatical operations on spatiotemporal (or other D-dimensional data sets) easier for the user by providing a set of concise yet powerful functions. spacetime opperations utilize a cube-like structure for all data sets that makes storing and manipulating large D-dimensional datasets more efficient. For scientists working with spatiotemporal data (such as climate or weather data sets) spacetimepy is an ideal platform that allows the user to focus on the science rather than the coding.

Main Functionality

Spacetime is in the beta stage and additional functionality will be added on a regular basis. The current functionality of spacetimepy is below:

• Read in common raster data types
• Rescale files to a common or specified SRS and grid size
• Trim datasets to the same bounding box/shape
• Select and rescale data along the time dimension
• Mathematical and function-based cube operations
• Return cube using a tabular format (dataframe)
• Plot cube spatially or temporally

Documentation

The full documentation for this library may be found at Spacetime Lab Notebook

Dependancies

• spacetimepy relies on the Python bindings of GDAL for most of its geographic rescalling functionality.
• spacetimepy ustilizes the netCDF4 library for storing and passing data sets between functions.

Installation

The source code may be found on Github (https://github.com/alexburn17/spacetime_python)

The latest version of spacetimepy may be installed from the Python Package Index (PyPI).

pip install spacetimepy

Funding

Spacetime is a product of Barracuda, a collaboration between the University of Vermont and the University of Maine. Barracuda’s mission is to harness diverse current and historic data and new mechanistic models across the continental United States to help us better predict and adapt to climate change impacts on biodiversity and rural communities. This project is open source and funded by a National Science Foundation EPSCoR grant.

Authors

• P. Alexander Burnham
• Brian McGill
• Nicholas Gotelli
• Matt Dube