Metadata-Version: 2.1
Name: mkstd
Version: 0.0.5
Summary: Make standards compatible with HDF5/JSON/XML/YAML.
Author-email: Dilan Pathirana <dilan.pathirana@uni-bonn.de>
License: BSD 3-Clause License
        
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Project-URL: Homepage, https://github.com/dilpath/mkstd
Keywords: standard,hdf5,json,xml,yaml
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pydantic>=2.7.4
Requires-Dist: pydantic_numpy
Requires-Dist: h5py>=3.11
Requires-Dist: pyyaml>=6.0.2
Requires-Dist: oyaml>=1.0
Requires-Dist: defusedxml>=0.7.1
Requires-Dist: xmlschema>=3.3.1
Provides-Extra: dev
Requires-Dist: black; extra == "dev"
Requires-Dist: isort; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Requires-Dist: pre-commit>=3.8.0; extra == "dev"
Provides-Extra: numpyv1
Requires-Dist: pydantic_numpy<6; extra == "numpyv1"
Requires-Dist: numpy<2; extra == "numpyv1"

# mkstd
Make standards that can be exported/imported/validated via HDF5/JSON/XML/YAML.

`mkstd` uses pre-existing standards for validation and schema specification, such that files produced using `mkstd` can be used independently of `mkstd`. For example, a tool developer can use `mkstd` to create a standard for their tool's data, but users do not necessarily need `mkstd` installed to use the data. However, `mkstd` also provides importers and exporters, so intended use also involves an `mkstd` installation for convenience.

# Installation
```bash
pip install mkstd

# For HDF5 support
pip install hdfdict@git+https://github.com/SiggiGue/hdfdict
```
For environments requiring `numpy<2`, replace `mkstd` with `mkstd[numpyv1]` above.

# Intended use
`mkstd` is intended to be used at two stages of data management. An example of these stages is provided in [](https://github/dilpath/mkstd/examples/libssr/ssr_data.py).
## Generating a standard
At this stage, the "user" is the person designing the data type and corresponding standard. For example, a tool developer who wants to standardize the data produced by their tool. The steps could look like:
1. (with `mkstd`) Design the data type as a Pydantic data model. Thanks to Pydantic, it behaves like a standard for your data, as a Python object.
2. (with `mkstd`) Export the standard as e.g. XML and JSON schemas.
3. (TODO, with `mkstd`) Generate documentation for the standard, based on the Pydantic data model docstrings.
## Using a standard
At this stage, the "user" is someone who wants to use data generated by the tool, or import their own data into the tool.
1. (with or without `mkstd`) Reformat data to match the standard specified by the e.g. `mkstd`-generated XML or JSON schema.
2. (with or without `mkstd`) Validate the data against the schema.
3. (with or without `mkstd`) Import/export the reformatted data with the tool.

# External validation
As written above, many uses of the standard produced by `mkstd` are intended to be possible without an `mkstd` installation. This is because the generated standards are in standardized schema formats. Below are the different formats supported by `mkstd`, and how to use/validate standards/data independently of `mkstd`.

## XML
The [XSD format](https://en.wikipedia.org/wiki/XML_Schema_%28W3C%29) is used. Search the web for `validate xml data against schema`.

## JSON
The [official JSON schema](https://en.wikipedia.org/wiki/JSON#Metadata_and_schema) format is used. Search the web for `validate json data against schema`.

## YAML
There is no official YAML schema format, so [YAML data is typically validated against JSON schemas](https://json-schema-everywhere.github.io/yaml). `mkstd` takes this approach too. Hence, tools that can validate YAML data against a JSON schema can be used, without an `mkstd` installation.

For example, [the `pajv` tool](https://github.com/json-schema-everywhere/pajv) can be used to validate YAML data against a JSON schema, without `mkstd`.
```bash
pajv validate -s output/mkstd_generated_schema.yaml -d output/data.yaml
```

By default, `mkstd` stores the schemas for YAML standards in YAML too.

## HDF5
There is currently no standard available for the specification of HDF5 schemas. Hence, the HDF5 files produced by `mkstd` can only be validated with `mkstd`.

There is a [format for HDF5 that enables interconversion with JSON](https://github.com/HDFGroup/hdf5-json). This is out-of-scope.
