# SpacePy Capabilities

This page lists some capabilities of SpacePy and, in some cases, of other packages that might be of interest to SpacePy users. It is organized by topic; searching within this page is recommended. See the module reference for every class/function available in SpacePy, organized by module.

- Array manipulation

- Coordinate Transforms

- File I/O

- Modeling

- Statistics

- Time conversions

- Time series analysis and correlations

- Visualization

## Array manipulation

Various `toolbox` functions are useful in manipulating NumPy arrays. `datamanager` also contains many functions for indexing arrays and manipulating them in ways that do not depend on the interpretation of their contents.

## Coordinate Transforms

`coordinates` provides a class for transforming among most coordinate systems used in Earth magnetospheric and ionospheric physics.

It also provides generalized coordinate transforms via quaternions.

## File I/O

`pycdf` provides reading and writing of NASA CDF files, with additional functionality for those with ISTP-compliant metadata.

`datamodel` provides easy reading and writing of HDF5 and most netCDF files. It also supports reading and writing ASCII-based data files with rich JSON metadata, supported by tools such as Autoplot.

`numpy.loadtxt()` and related functions are helpful for reading various “plain-text” files into numerical arrays.

`scipy.io.readsav()` reads IDL savesets.

`astropy.io.fits` supports FITS files.

## Modeling

`ae9ap9` supports import and visualization of data from the AE9/AP9 empirical radiation belt model.

`empiricals` implements several simple empirical and/or analytic models for magnetospheric and solar wind phenomena, including the plasmapause location, the Shue magnetopause model, and solar wind temperature.

`pybats` supports output analysis and visualization of many models compatible with the Space Weather Modeling Framework, including the BATS-R-US global MHD model and the RAM-SCB ring current model.

`omni` provides ready access to the OMNI near-Earth solar wind dataset, useful for model inputs.

## Statistics

`poppy` supports determining confidence intervals on

  population metrics using the non-parametric bootstrap method.

## Time conversions

`time` contains a class that easily allows time to be represented in, and converted among, many representations, including Python datetimes, ISO time strings, GPS time, TAI, etc.

## Time series analysis and correlations

`poppy` implements association analysis to determine the relationship between point-in-time events.

`seapy` implements superposed epoch analysis, the statistical evaluation of the time evolution of a system relative to a set of starting epochs.

## Visualization

`plot` provides tools useful in making publication-quality plots with the matplotlib toolkit.