semvec/pypi-packages huggingface dataset:
yte 1.9.4
A YAML template engine with Python expressions
Requires Python: <4.0,>=3.8

# YTE - A YAML template engine with Python expressions

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YTE is a template engine for YAML format that utilizes the YAML structure in combination with Python expressions for enabling to dynamically build YAML documents.

The key idea of YTE is to rely on the YAML structure to enable conditionals, loops and other arbitrary Python expressions to dynamically render YAML files.
Python expressions are thereby declared by prepending them with a `?` anywhere in the YAML.
Any such value will be automatically evaluated by YTE, yielding plain YAML as a result.
Importantly, YTE templates are still valid YAML files (for YAML, the `?` expressions are just strings).

Documentation of YTE can be found at https://yte-template-engine.github.io.

## Comparison with other engines

Lots of template engines are available, for example the famous generic [jinja2](https://jinja.palletsprojects.com).
The reasons to generate a YAML specific engine are

1. The YAML syntax can be exploited to simplify template expression syntax, and make it feel less foreign (i.e. fewer special characters for control flow needed) while increasing human readability.
2. Whitespace handling (which is important with YAML since it has a semantic there) becomes unnecessary (e.g. with jinja2, some [tuning](https://radeksprta.eu/posts/control-whitespace-in-ansible-templates) is required to obtain proper YAML rendering).

Of course, YTE is not the first YAML specific template engine.
Others include

* [Yglu](https://yglu.io)
* [Emrichen](https://github.com/con2/emrichen)

The main difference between YTE and these two is that YTE extends YAML with plain Python syntax instead of introducing another specialized language.
Of course, the choice is also a matter of taste.
kyupy 0.0.6
High-performance processing and analysis of non-hierarchical VLSI designs
Requires Python: <3.14,>=3.12

KyuPy - Pythonic Processing of VLSI Circuits
============================================

KyuPy is a Python package for processing and analysis of non-hierarchical gate-level VLSI designs.
It contains fundamental building blocks for research software in the fields of VLSI test, diagnosis and reliability:

* Efficient data structures for gate-level circuits and related design data.
* Partial [lark](https://github.com/lark-parser/lark) parsers for common design files like
  bench, gate-level Verilog, standard delay format (SDF), standard test interface language (STIL), design exchange format (DEF).
* Bit-parallel gate-level 2-, 4-, and 8-valued logic simulation.
* GPU-accelerated high-throughput gate-level timing simulation.
* High-performance through the use of [numpy](https://numpy.org) and [numba](https://numba.pydata.org).


Getting Started
---------------

KyuPy is available in [PyPI](https://pypi.org/project/kyupy).
It requires Python 3.10 or newer, [lark](https://pypi.org/project/lark), and [numpy](https://numpy.org).
Although optional, [numba](https://numba.pydata.org) should be installed for best performance. [numba-cuda](https://nvidia.github.io/numba-cuda/) is required for GPU/CUDA acceleration.
If numba is not available, KyuPy will automatically fall back to slow, pure Python execution.

The Jupyter Notebook [Introduction.ipynb](https://github.com/s-holst/kyupy/blob/main/examples/Introduction.ipynb) contains some useful examples to get familiar with the API.


Development
-----------

To work with the latest pre-release source code, clone the [KyuPy GitHub repository](https://github.com/s-holst/kyupy).

* Using ``pip``: Run ``pip install -e .`` within your local checkout to make the package available in your Python environment. The source code comes with tests that can be run with ``pytest``.
* Using ``uv``: Run ``uv run pytest`` within your local checkout to get started.