scipp - Multi-dimensional data arrays with labeled dimensions¶
scipp is heavily inspired by xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays can be combined into datasets. While for many applications xarray is certainly more suitable (and definitely much more matured) than scipp, there is a number of features missing in other situations. If your use case requires one or several of the items on the following list, using scipp may be worth considering:
Physical units are stored with each data or coord array and are handled in arithmetic operations.
Propagation of uncertainties.
Support for histograms, i.e., bin-edge axes, which are by 1 longer than the data extent.
Support for scattered data and non-destructive binning. This includes first and foremost event data, a particular form of sparse data with arrays of random-length lists, with very small list entries.
Support for masks stored with data.
Written in C++ for better performance (for certain applications), in combination with Python bindings.
Generic functionality of scipp is provided in the scipp Python package. In addition, more specific functionality is made available in other packages. Currently the only example for this is scippneutron for handling data from neutron-scattering experiments.
News¶
Scipp is moving from GPLv3 to the more permissive BSD-3 license which fits better into the Python eco system.
Looking for
scipp.neutron? This submodule has been moved into its own package, scippneutron.[March 2021] scipp-0.6 has been released. The What’s new notebook provides an overview of the highlights and major changes.
[Janunary 2021] scipp-0.5 has been released. The What’s new notebook provides an overview of the highlights and major changes.
Documentation¶
Getting Started
User Guide
- Data Structures
- Slicing
- Computation
- Masking
- Binned Data
- GroupBy
- Reading and Writing Files
- How to…
- Tips, tricks, and anti-patterns
Visualization
- Representations and Tables
- Plotting Overview
- Plotting 1-D data
- Plotting 2-D data
- Plotting N-D data
- Customizing figures
- Datetime handling
Reference
Developer Documentation
- Getting Started
- Tooling
- Dependencies
- Coding conventions
- How To…
- Transforming data
- C++ API for constructing variables
- Implementation of class Variable
- Customizing Scipp
- API Reference
- Concepts
- Architecture Decision Records
- ADR 0001: Remove dataset masking support
- ADR 0002: Remove instrument view 2D projection options
- ADR 0003: Refactor plotting code to use Model-View-Controller design pattern
- ADR 0004: Use the ipympl backend for Matplotlib figures in Jupyter
- ADR 0005: Remove need for
binsproperty when callingbinorhistogram
- Deployment