conda-forge / xmlschema-feedstock

A conda-smithy repository for xmlschema.

About xmlschema-feedstock

Feedstock license: BSD-3-Clause

Home: https://github.com/sissaschool/xmlschema

Package license: MIT

Summary: An XML Schema validator and decoder

Development: https://github.com/sissaschool/xmlschema

This is an implementation of XML Schema for Python 3.8+.

This library arises from the needs of a solid Python layer for processing XML Schema based files for MaX (Materials design at the Exascale) European project. A significant problem is the encoding and the decoding of the XML data files produced by different simulation software. Another important requirement is the XML data validation, in order to put the produced data under control. The lack of a suitable alternative for Python in the schema-based decoding of XML data has led to build this library. Obviously this library can be useful for other cases related to XML Schema based processing, not only for the original scope.

Current build status

All platforms:

Current release info

Name	Downloads	Version	Platforms

Installing xmlschema

Installing xmlschema from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge
conda config --set channel_priority strict

Once the conda-forge channel has been enabled, xmlschema can be installed with conda:

conda install xmlschema

or with mamba:

mamba install xmlschema

It is possible to list all of the versions of xmlschema available on your platform with conda:

conda search xmlschema --channel conda-forge

or with mamba:

mamba search xmlschema --channel conda-forge

Alternatively, mamba repoquery may provide more information:

# Search all versions available on your platform:
mamba repoquery search xmlschema --channel conda-forge

# List packages depending on `xmlschema`:
mamba repoquery whoneeds xmlschema --channel conda-forge

# List dependencies of `xmlschema`:
mamba repoquery depends xmlschema --channel conda-forge

About conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information please check the conda-forge documentation.

Terminology

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

Updating xmlschema-feedstock

If you would like to improve the xmlschema recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/xmlschema-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

• If the version of a package is not being increased, please add or increase the build/number.
• If the version of a package is being increased, please remember to return the build/number back to 0.

Feedstock Maintainers

• @ccwang002

xmlschema-feedstock's Issues

xmlschema installed to wrong location

When using miniconda3-4.2.12 (python 3.5) and installing xmlschema from conda-forge, the package gets installed to a 'site-packages' directory directly under the env root:

$ wget https://repo.continuum.io/miniconda/Miniconda3-4.2.12-MacOSX-x86_64.sh
$ bash Miniconda3-4.2.12-MacOSX-x86_64.sh -b -p miniconda/4.2.12
$ PATH=miniconda/4.2.12/bin:$PATH conda install -c conda-forge xmlschema
$ find miniconda/4.2.12 -type d -name xmlschema
miniconda/4.2.12/**site-packages/xmlschema**
miniconda/4.2.12/pkgs/xmlschema-0.9.18-py_0/site-packages/xmlschema
$ PATH=miniconda/4.2.12/bin:$PATH python -c "import xmlschema"
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ImportError: No module named 'xmlschema'

This site-packages directory is not in the python path. Trying this with Miniconda3-4.3.11 (python 3.6) shows that xmlschema is correctly installed to the lib/python3.6/site-packages directory:

$ wget https://repo.continuum.io/miniconda/Miniconda3-4.3.11-MacOSX-x86_64.sh
$ bash Miniconda3-4.3.11-MacOSX-x86_64.sh -b -p miniconda/4.3.11
$ PATH=miniconda/4.3.11/bin:$PATH conda install -c conda-forge xmlschema
$ find miniconda/4.3.11 -type d -name xmlschema
miniconda/4.3.11/**lib/python3.6/site-packages/xmlschema**
miniconda/4.3.11/pkgs/xmlschema-0.9.18-py_0/site-packages/xmlschema
$ PATH=miniconda/4.3.11/bin:$PATH python -c "import xmlschema"
<no errors emitted>

I raised this issue on the main conda repo but I was pointed in this direction as it was suggested it was a packaging issue.