What’s new in IOSACal#

The release changelog of IOSACal.

Release 0.6 (2022-10-01)#

IOSACal 0.6 brings new features for simulating radiocarbon dates and obtaining quantiles, added respectively by Hythem Sidky and Roger Creel, who both contributed to IOSACal for the first time.

The documentation has been updated substantially, and some pages are now generated directly from Jupyter notebooks, enabling a more direct approach for newcomers who can download the notebooks and start creating their own research notebooks.

The demo web application was discontinued, and usage with MyBinder or Google Colab is now encouraged.

The development process is now more solid, thanks to continuous integration based on Codeberg CI.

  • Fixed minor plotting issues (contributed by Hythem Sidky)

  • Add ability to simulate determinations (contributed by Hythem Sidky)

  • Introduce pre-commit (contributed by Stefano Costa)

  • Make the Python package PEP-517 and PEP-518 compatible, aka pyproject.toml (contributed by Stefano Costa)

  • Added quantiles method to CalAge (contributed by Roger Creel)

  • Apply Black formatting (contributed by Stefano Costa)

  • Introduce Continuous Integration (CI) based on Codeberg CI and tox (contributed by Stefano Costa)

  • Switch documentation theme to furo (contributed by Stefano Costa)

  • Convert some documentation pages to MyST-NB Jupyter notebooks (contributed by Stefano Costa)

Release 0.5.3 (2021-09-14)#

A bugfix release.

  • fix a TabError that was introduced in the previous release (contributed by Stefano Costa and Karl Håkansson)

Release 0.5.2 (2021-08-01)#

A bugfix release.

  • make the calibration curve data available in the distribution packages

  • fixed problem with AD/BC plot missing the calibration curve (contributed by Karl Håkansson)

Version 0.5.1 was tagged without any changes from 0.5 described below.

Release 0.5 (2021-07-28)#

This release brings the new IntCal20 calibration data and several improvements for different use cases, plus one important bug fix.

  • the project has moved to Codeberg for source code hosting and issue tracking. The new Git repository is at https://codeberg.org/steko/iosacal with a default branch name of main

  • there is an official Code of Conduct that all contributors (including the maintainter) will need to follow

  • the documentation has seen some improvements, in particular in the Contributing section. Overall, making contributions easier from both expert and novice users is a major theme in this release.

  • interactive use in Jupyter notebooks is made easier with CalibrationCurve that can be created in many ways (such as loading from an arbitrary file, or from a standard calibration curve called by shorthand)

  • fixed a bug that made plots with AD/CE setting incorrect (contributed by Karl Håkansson)

  • fixed a bug that caused a wrong plot density function for dates 80 BP to 0 BP (contributed by Karl Håkansson)

  • add IntCal20 calibration data (contributed by Wesley Weatherbee)

On the technical side:

  • the command line interface is now based on the Click library

  • most code is now covered by tests, based on pytest

  • Python 3.6 or above required

  • requires Numpy 1.18 and Matplotlib 3.0

Release 0.4 (released 2018-05-08)#

The main highlight of this release are the new classes for summed probability distributions (SPD) and paleodemography, contributed by Mario Gutiérrez-Roig as part of his work for the PALEODEM project at IPHES.

On the technical side:

  • requires NumPy 1.14, SciPy 1.1 and Matplotlib 2.2

  • removed dependencies on obsolete functions

  • improved the command line interface

Release 0.3 (released 2016-04-15)#

  • use genfromtxt to import calibration curves

  • improved documentation

  • intervals as a well-defined type

  • restore AD/BC dates in both text and graphic output

Release 0.2 (released 2014-02-14)#

Main highlights:

  • new function to combine multiple determinations (Ward & Wilson 1978)

  • a simple and straightforward set of commands to get started

  • amazing interactive mode with IPython Notebook

  • plotting multiple dates in a stacked plot actually works

  • added several older calibration curves (useful to check published data)

On the technical side:

  • works with Python 3 only, dropped compatibility with Python 2

  • requires NumPy 1.8 and Matplotlib 1.3

  • calibration curves and calibrated ages are ndarray objects, super-easy to work with

Known issues:

  • AD/BC dates in output are not available, all dates are given as CalBP