semvec/pypi-packages huggingface dataset:
pymablock 2.2.0
Numerical and symbolic implementation of quasi-degenerate perturbation theory
Requires Python: >=3.11

# Pymablock: quasi-degenerate perturbation theory in Python

`Pymablock` (Python matrix block-diagonalization) is a Python package that constructs
effective models using quasi-degenerate perturbation theory.
It handles both numerical and symbolic inputs, and it efficiently
block-diagonalizes Hamiltonians with multivariate perturbations to arbitrary
order.

Building an effective model using Pymablock is a three step process:

* Define a Hamiltonian
* Call `pymablock.block_diagonalize`
* Request the desired order of the effective Hamiltonian

```python
from pymablock import block_diagonalize

# Define perturbation theory
H_tilde, *_ = block_diagonalize([h_0, h_p], subspace_eigenvectors=[vecs_A, vecs_B])

# Request correction to the effective Hamiltonian
H_AA_4 = H_tilde[0, 0, 4]
```

Here is why you should use Pymablock:

* Do not reinvent the wheel

  Pymablock provides a tested reference implementation

* Apply to any problem

  Pymablock supports `numpy` arrays, `scipy` sparse arrays, `sympy` matrices and
  quantum operators

* Speed up your code

  Due to several optimizations, Pymablock can reliably handle both higher orders
  and large Hamiltonians

For more details see the Pymablock [documentation](https://pymablock.readthedocs.io/en/latest/).
python-client-generator 1.3.1
Python package to generate an httpx-based client off an OpenAPI spec
Requires Python: <4.0,>=3.8

# python-client-generator

Python package to generate an [httpx](https://github.com/encode/httpx)- and
[pydantic](https://github.com/pydantic/pydantic)-based async (or sync) client off an OpenAPI spec >= 3.0 (partial specification support).

```mermaid
flowchart LR
    generator["python-client-generator"]
    app["REST API App"]
    package["App HTTP client"]

    app -- "OpenAPI JSON" --> generator
    generator -- "Generates" --> package
```

> :warning: **Currently does not support OpenAPI 2.x** (PRs for < 3.0 support are welcome).

> :exclamation: **Only partial OpenAPI specification support, if you encounter unsupported spec. please feel free to contribute to the project.**

## Using the generator

Install the package (see on [PyPi](https://pypi.org/project/python-client-generator/)):

```bash
pip install python-client-generator
```

Run:

```bash
python -m python_client_generator --open-api openapi.json --package-name foo_bar --project-name foo-bar --outdir clients
```

This will produce a Python package with the following structure:

```bash
clients
├── foo_bar
│   ├── __init__.py
│   ├── apis.py
│   ├── base_client.py
│   └── models.py
└── pyproject.toml
```

### Using PATCH functions from the generator

When calling one of the generated update functions that uses an HTTP `PATCH` method, you'll
probably want to pass the additional argument `body_serializer_args={"exclude_unset": True}`. This
will ensure that only the fields that are set in the update object are sent to the API. Example:

```python
await api_client.update_contact_v1_contacts__contact_id__patch(
    body=patch_body,
    contact_id=contact.id,
    tenant=tenant,
    body_serializer_args={"exclude_unset": True}
)
```

## Contributing

Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md).
CopaData 15.0.0
Read metadata and online data from Report Engine Server
Requires Python: >=3.7

## Package description
This package contains data connectivity modules developed by COPA-DATA.

## Included modules
- pyZAN

## pyZAN module description
pyZAN is a module to connect with a Report Engine metadata database. It provides:
- read access to metadata tables
- access to connector functions for reading historic variable values, alarms, events, lots, shifts and context lists
- option to execute SQL Stored Procedures and SQL Userdefined Functions

Data is returned as pandas DataFrames and can be used in all common data science libraries.

## Prerequisite modules
- pyodbc (requires Microsoft Visual C++ Build Tools)
- pandas

## Prerequisites to retrieve data
- Report Engine 10 or higher OR zenon Analyzer 3.30 or higher
- Report Engine metadada database in current structure version including at least one project
- valid connection to the Report Engine metadata database
- valid configuration to receive data from at least one of the following connectors:
  - SCADA Service Engine Connector
  - SCADA SQL Connector
  - 3rd Party Database Connector
  - IIoT Services Data Storage Connector

## Tutorials
https://github.com/COPA-DATA/pyZAN
markdown-to-png 1.1.5
A Python package designed to convert Markdown files into beautifully styled PNG images.
Requires Python: >=3.8

# Markdown To PNG

**markdown-to-png** is a Python package designed to convert Markdown files into beautifully styled PNG images. Whether you want to create visually appealing graphics from text or simply share Markdown content in an image format, this tool has you covered.

This package is designed to be simple to use yet flexible enough to accommodate future enhancements and features.

---

## Features
- Convert Markdown files to PNG images effortlessly.
- Support for custom CSS styling to match your desired visual design.
- Lightweight and easy to integrate into your projects.

---

## Installation
You can install the package using `pip` (once published to PyPI):
```bash
pip install markdown-to-png
```

If you're cloning the repository for development:
```bash
git clone https://github.com/richiepagard/markdown-to-png.git
cd markdown-to-png
pip install -e .
```

---

## Usage
Here’s a basic example of how to use the package:
```python
from markdown_to_png import convertor

# convert a markdown file to png
convertor.markdown_to_png("path/to/markdown_file.md", "path/to/output.png")
```

### Optional Parameters
- **CSS File**: Customize the style by specifying a custom CSS file.
```python
convertor.markdown_to_png("path/to/markdown_file.md", "path/to/output.png", css_path="path/to/custom_style.css")
```

---

## Future Plans
This project is a work in progress, and more features are planned for future versions:
- Add support for multiple output formats (e.g., PDF, JPEG).
- Command-line interface for easier use.
- Enhanced error handling and logging.
- Predefined themes for styling.
- Integration with Markdown extensions like tables and footnotes.

Stay tuned for updates!

---

## Contributing
Contributions are welcome! If you have ideas, suggestions, or bug reports, please feel free to open an issue or submit a pull request on [GitHub](https://github.com/richiepagard/markdown-to-png).

---

## License
This project is licensed under the [MIT License](LICENSE). You are free to use, modify, and distribute this package as per the terms of the license.

---

## Contact
If you have any questions or feedback, feel free to reach out:
- GitHub: [richiepagard](https://github.com/richiepagard) (Richie)
- Email: richiepagard@gmail.com
gwas-assoc-tools 0.1.2
GWAS Association Tools
Requires Python: <4.0,>=3.10

# GWAS Association Tools

A command-line tool for genomic-wide association studies (GWAS) data validation and analysis.

## Features

- Validate:
  - Input: File path of GWAS Submission template that contains top associations
  - Output: Validation report of top associations

## Installation

Install from [PyPI](https://pypi.org/project/gwas-assoc-tools/):

```bash
pip install gwas-assoc-tools
```

## Usage

```bash
# Basic validation command
gwas-assoc validate <template excel file path>

# For help
gwas-assoc --help
gwas-assoc validate --help
```

## Development

### Setup

```bash
# Install Poetry (if not already installed)
curl -sSL https://install.python-poetry.org | python3 -

# Install dependencies
poetry install

# Activate virtual environment
poetry shell
```

### Building and Installation

```bash
# Build the package (creates .whl and .tar.gz in dist/)
poetry build

# Install your new package in development mode
poetry install
```

### Testing

TODO

### Running with Real Files

```bash
# Run the CLI command through Poetry
# For failed test file
poetry run gwas-assoc validate ./tests/test_fail.xlsx   
# For successful test file
poetry run gwas-assoc validate ./tests/test_success.xlsx   


# Or after activating the shell
poetry shell
# For failed test file
gwas-assoc validate ./tests/test_fail.xlsx   
# For successful test file
gwas-assoc validate ./tests/test_success.xlsx   

```

### Dependency Management

```bash
# Add a new dependency
poetry add new-package

# Add a development dependency
poetry add --group dev pytest-mock

# Check for dependency updates
poetry update --dry-run

# Update dependencies
poetry update
```

### Code Quality

```bash
# Run formatting
poetry run black .
poetry run isort .

# Run linting
poetry run ruff check .

# Run type checking
poetry run mypy src

# Run all formatting checks
poetry run pre-commit run --all-files
```

## Documentation
Please find the Confluence page here: https://embl.atlassian.net/wiki/spaces/GOCI/pages/57606184/GWAS+Associations+Tools

## License

[License](LICENSE)