home:illuusio:python:3.11

Let's admit Python 3.6 is long gone and there is new sheriff in town.. Don't fight adapt

As Leap 15.4 brought Python 3.11 and 15.5 rised it as equal partner with 3.6 it's major pain to support 3.6 and it's holding back everything. This project tries to support newer version of: offlineimap3, borg, Canbus and canopen tools https://download.opensuse.org/repositories/home:/illuusio:/python:/3.11/openSUSE_Slowroll/ openSUSE:Slowroll

Slowroll

https://en.opensuse.org/openSUSE:Slowroll = a derivative codestream of openSUSE:Factory that uses automated rules to limit the rate of change to the codebase Release manager: bmwiedemann https://www.zq1.de/~bernhard/linux/opensuse/slowroll/slowroll-vs-tumbleweed-updates.svg https://download.opensuse.org/update/slowroll/repo/oss/ openSUSE:Slowroll:Base

base snapshot from Tumbleweed

This provides an easy-to-use redirect for both sources and binaries in openSUSE:Slowroll:Base:N, switching every cycle (every month) https://download.opensuse.org/repositories/openSUSE:/Slowroll:/Base/standard/ openSUSE:Slowroll:Base:2

staging area for new Base snapshot from Tumbleweed

Before a version bump, I collect i586 and bootstrap binaries here. These are missing in released Tumbleweed snapshots, but are needed for building packages. Also binaries required for DVD builds, because product-builder cannot use those from download-on-demand (dod) https://download.opensuse.org/repositories/openSUSE:/Slowroll:/Base:/2/standard/ openSUSE:Tumbleweed

Tumbleweed

Tumbleweed is the openSUSE Rolling Release This OBS Project represents the content of the currently published snapshot. The newer repository for next publish can be found in openSUSE:Factory standard repository. https://download.opensuse.org/repositories/openSUSE:/Tumbleweed/slowroll-next/ python-pyogrio

Vectorized spatial vector file format I/O using GDAL/OGR

Pyogrio provides a GeoPandas-oriented API to OGR vector data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources have geometries, such as points, lines, or polygons, and associated records with potentially many columns worth of data. Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and from OGR vector data sources in order to give you faster interoperability. It uses pre-compiled bindings for GDAL/OGR so that the performance is primarily limited by the underlying I/O speed of data source drivers in GDAL/OGR rather than multiple steps of converting to and from Python data types within Python.