Release 21.01.0:
core:
* Faster routines for jpeg decoding
* Fix reading signatures in encrypted files
* Add white point correction when lcms is used
* JBIG2Stream: Fix byte counting
* Fix potential data loss if we try to fetch a non existing Ref after modifying the document
* Specifically use DeviceGray instead of DefaultGray for softmasks
* Fix various issues handling broken files
utils:
* pdftocairo: Setmode binary for windows
* pdfsig: Add hability to digitally sign files
* pdftoppm: add options to set DeviceGray/DeviceRGB/DeviceCMYK
* pdftops: add options to set DeviceGray/DeviceRGB/DeviceCMYK
* pdfimages: Account for rotation in PPI calculation
qt5:
* Add hability to digitally sign files
qt6:
* Add hability to digitally sign files
build system:
* Enable clang-tidy bugprone-signed-char-misuse
PR: 252377
Exp-run by: antoine
Release notes at <https://github.com/igraph/python-igraph/releases/tag/0.8.3>
- Update WWW: URL
- Set LIB_DEPENDS for math/igraph
- Depends on py-cairocffi
- Let it find an image viewer for FreeBSD
Without that, you cannot run code like:
>>> import igraph as ig
>>> g = ig.Graph.Famous("petersen")
>>> ig.plot(g)
- Add a plist
PR: 252381
Submitted by: /me
Approved by: lwhsu@ (maintainer)
Gibbs sampling for spatially-correlated variance-components
This is a package to estimate spatially-correlated variance components
models/varying intercept models. In addition to a general toolkit to conduct
Gibbs sampling in Python, the package also provides an interface to PyMC3 and
CODA.
WWW: https://github.com/pysal/spvcm
Spatial Regression Models (spreg)
spreg, short for "spatial regression", is a python package to estimate
simultaneous autoregressive spatial regression models. These models are useful
when modeling processes where observations interact with one another.
WWW: https://spreg.readthedocs.io/en/latest/
WWW: https://github.com/pysal/spreg
splot provides PySAL users with a lightweight visualization interface to explore
their data and quickly iterate through static and dynamic visualisations.
splot connects spatial analysis done in PySAL to different popular visualization
toolkits like matplotlib. The splot package allows you to create both static
plots ready for publication and interactive visualizations for quick iteration
and spatial data exploration. The primary goal of splot is to enable you to
visualize popular PySAL objects and gives you different views on your spatial
analysis workflow.
WWW: https://splot.readthedocs.io/en/latest/
WWW: https://github.com/pysal/splot