semvec/pypi-packages huggingface dataset:
ltfatpy 1.1.2
The Large Time-Frequency Toolbox (LTFAT) in Python
Requires Python: >=3.4

The **ltfatpy** package is a partial Python port of the
`Large Time/Frequency Analysis Toolbox (LTFAT)
<http://ltfat.sourceforge.net/>`_, a MATLAB®/Octave toolbox for working with
time-frequency analysis and synthesis.

It is intended both as an educational and a computational tool.

The package provides a large number of linear transforms including Gabor
transforms along with routines for constructing windows (filter prototypes)
and routines for manipulating coefficients.

The original LTFAT Toolbox for MATLAB®/Octave is developed at
`CAHR <http://www.dtu.dk/centre/cahr/English.aspx>`_, Technical
University of Denmark, `ARI <http://www.kfs.oeaw.ac.at>`_, Austrian Academy
of Sciences and `I2M <http://www.i2m.univ-amu.fr>`__, Aix-Marseille Université.

The Python port is developed at
`LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_, as a
`LIF <http://www.lif.univ-mrs.fr/>`_ (now `LIS <http://www.lis-lab.fr/>`_)
and I2M project, Aix-Marseille Université.

This package, as well as the original LTFAT toolbox, is Free software, released
under the GNU General Public License (GPLv3).

The latest version of **ltfatpy** can be downloaded from the following
`PyPI page <https://pypi.python.org/pypi/ltfatpy>`_.

The **documentation** is available 
`here <http://dev.pages.lis-lab.fr/ltfatpy/>`_.

The development is done in this
`GitLab project <https://gitlab.lis-lab.fr/dev/ltfatpy>`_, which provides
the git repository managing the **source code** and where issues can be
reported.
gc-data-storage 3.0.0
for managing data storage between local environments and Google Cloud Storage (GCS) bucketss.
Requires Python: >=3.6



`gc-data-storage` provides a unified interface for saving, reading, and managing various data types including DataFrames, plots, images, and generic files.

 * Save data from your development environment to the bucket.
 * Read data from the bucket into your development environment, with the option to keep a copy in the persistent disk.
 * Copy data between different directories within the bucket or between two different buckets owned by the user.
 * Obtain a list of data saved in the bucket or the persistent disk.
 * Delete data saved in the bucket or the persistent disk.
 * Obtain information on a file, using the full path or a partial string

# Features:

 * Universal GCP Compatibility: Works across all GCP environments including All of Us Researcher Workbench, Google Colab, Vertex AI Workbench, and local development
 * Auto-detection: Automatically detects bucket names and project IDs from environment variables
 * Multi-format Support: Handles DataFrames, plots, images, Excel workbooks, and generic files
 * Robust Error Handling: Comprehensive logging and error management
 * Flexible Path Management: Supports both relative and absolute GCS paths
 * Batch Operations: Copy, list, and delete operations for file management

More information, including code examples, at https://github.com/AymoneKouame/google-cloud-data-storage.
AgIcons 0.1.1
A Python icons module.
Requires Python: >=3.6

# AgIcons

A Python icons module.
## Installation

```bash
pip install AgIcons
```

## Usage

```python
from AgIcons import *

icon = Icon('camera', 'outline')
if icon:
    print(f"Icon found: {icon}")
else:
    print("Icon not found.")
```
go-polars 1.0.0
A high-performance DataFrame library for Python powered by Go
Requires Python: >=3.7

# go-polars

A high-performance DataFrame library for Python powered by Go. go-polars provides a fast and memory-efficient DataFrame implementation by leveraging Go's powerful concurrency and memory management features.

## Features

- Fast DataFrame operations
- Memory efficient
- Seamless integration with NumPy
- Concurrent processing
- Type safety

## Performance

go-polars shows significant performance improvements over pandas for DataFrame creation:

```
Size | Columns | go-polars (s) | Pandas (s) | Ratio
-----|---------|--------------|------------|-------
1K   |    9    |    0.0012    |   0.0034   | 0.35
10K  |    9    |    0.0089    |   0.0312   | 0.29
100K |    9    |    0.0892    |   0.3012   | 0.30
1M   |    9    |    0.8923    |   3.0123   | 0.30
```

## Installation

```bash
pip install go-polars
```

## Usage

```python
import go_polars as gp

# Create a DataFrame
data = {
    'A': [1, 2, 3, 4, 5],
    'B': [10.0, 20.0, 30.0, 40.0, 50.0],
    'C': [True, False, True, False, True]
}
df = gp.DataFrame.from_dict(data)
```

## Development

To build from source:

```bash
git clone https://github.com/manaschopra/go-polars
cd go-polars
pip install -e .
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
this-is-my-test-app 0.0.2
Demo FastAPI Standalone Application
Requires Python: <4.0,>=3.12

# FastAPI Demo Application

Dieses Projekt ist eine Demoanwendung mit FastAPI, die einen GET- und einen POST-Endpunkt bereitstellt. Die API-Dokumentation ist automatisch über SwaggerUI verfügbar.

## Endpunkte
- **GET /items/{item_id}**: Gibt ein Item mit der angegebenen ID zurück.
- **POST /items/**: Erstellt ein neues Item (Name erforderlich).

## Entwicklung & Tests
- Abhängigkeiten werden mit [Poetry](https://python-poetry.org/) verwaltet.
- Starte den Server mit:
  ```bash
  poetry run uvicorn app.main:app --reload
  ```
- Tests ausführen mit:
  ```bash
  poetry run pytest
  ```

## API Dokumentation
- SwaggerUI: [http://localhost:8000/docs](http://localhost:8000/docs)
- Redoc: [http://localhost:8000/redoc](http://localhost:8000/redoc)