pysat 0.2.1

Supports science data analysis across measurement platforms

The Python Science Analysis Toolkit (pysat) is a package providing a simple and flexible interface for downloading, loading, cleaning, managing, processing, and analyzing scientific measurements. Though pysat was initially designed for in-situ satellite based measurements it aims to support all instruments in space science.

Main Features

• Single interface for a wide variety of science data sets.

• Single interface to download data for all supported instruments.

• Data model (modified pandas) that supports a combination of 1D, 2D, 3D, and nD data in a single structure

• Instrument independent analysis routines.

• Science data pipeline tasks of identifying files, loading, and cleaning data sets are built into the instrument object.

• Supports the automatic application of arbitray custom functions upon each load. This nano-kernel functionality ensures that any routine that interacts with the instrument object receives properly processed data.

• Supports metadata consistent with the netCDF CF-1.6 standard. Each variable has a name, long name, and units. Note units are informational only.

• Simplifies data management

• Iterates by day/file using the for loop, manual next/prev methods, or any iterative method.

• Iterate through a data set orbit-by-orbit; orbits are calculated on the fly from loaded data and span day/month/year breaks.

• Iterate over custom seasons

• Supports rigorous time-series calculations. Frequently these methods need time to spin up and down to produce accurate analysis. The instrument object appends real data for a buffer on each end of desired data, applies the custom functions, then removes buffer data before presenting output. The time series code does not need to do anything to support this behavior.

• Uses pandas for the underlying underlying data structure; capable of handling the many forms scientific measurements take in a consistent manner.

• pandas has been forked to accomodate the assignment of pandas Series/Dataframes as single elements of a Series/Dataframe.

• Includes helper functions to reduce the barrier to adding new science instruments to pysat

External Requirements

For data in CDF format:

• NASA CDF libraries

• spacepy

For data in netCDF format:

• Unidata netCDF libraries in C

• netCDF4