semvec/pypi-packages huggingface dataset:
java-stubs-converted-strings 0.1.0
The JPype java.* type definitions with automatic Java string -> Python string conversions enabled
Requires Python: >=3.11

================
jpype-java-stubs
================

The `JPype <https://jpype.readthedocs.io/en/latest/index.html>`_ ``java.*`` type definitions.
They cover imports like ``from java.util import ArrayList``.

There are two mutually exclusive variants distributed under different names:

1. ``java-stubs`` (no automated Java string to Python string conversions)
2. ``java-stubs-converted-strings`` (with the conversions)

Some details from `JPype string conversions documentation
<https://jpype.readthedocs.io/en/latest/userguide.html#string-conversions>`_:

    The ``convertStrings`` argument controls whether Java strings are automatically
    converted to Python strings. By default, this behavior is disabled
    (``convertStrings=False``) to preserve Java string methods and avoid unnecessary conversions.

    If enabled (``convertStrings=True``), Java strings are returned as Python strings, but this
    can impact performance and chaining of Java string methods. This option is consisted a legacy
    option as it will result in unncessary calls to str() every time a String is passed from Java.

    Best practice: Set ``convertStrings=False`` unless your application explicitly requires
    automatic conversion.

Which version you need depends on the ``convertStrings`` value used when starting the JVM.

Both of these distributions provide the ``java-stubs`` package providing type definitions
for the whole ``java.*`` namespace.

The stubs are produced with `CERN's stubgenj <https://gitlab.cern.ch/scripting-tools/stubgenj>`_.

There's a related `JPype discussion <https://github.com/jpype-project/jpype/issues/714>`_.

Known limitations
-----------------

* The types are tied to a specific Java version (currently OpenJDK 25).
* The JPype version used when generating the types may affect them, currently
  only one version is used.
* A few Java -> Python ``stubgenj`` translation problems have been reported, some are
  very difficult to resolve in a way that will satisfy all users (more:
  https://gitlab.cern.ch/scripting-tools/stubgenj/-/issues).

Internal documentation
----------------------

Run ``./generate`` to regenerate the stubs.
java-stubs 0.1.0
The JPype java.* type definitions (the default, no automated Java string -> Python string conversions)
Requires Python: >=3.11

================
jpype-java-stubs
================

The `JPype <https://jpype.readthedocs.io/en/latest/index.html>`_ ``java.*`` type definitions.
They cover imports like ``from java.util import ArrayList``.

There are two mutually exclusive variants distributed under different names:

1. ``java-stubs`` (no automated Java string to Python string conversions)
2. ``java-stubs-converted-strings`` (with the conversions)

Some details from `JPype string conversions documentation
<https://jpype.readthedocs.io/en/latest/userguide.html#string-conversions>`_:

    The ``convertStrings`` argument controls whether Java strings are automatically
    converted to Python strings. By default, this behavior is disabled
    (``convertStrings=False``) to preserve Java string methods and avoid unnecessary conversions.

    If enabled (``convertStrings=True``), Java strings are returned as Python strings, but this
    can impact performance and chaining of Java string methods. This option is consisted a legacy
    option as it will result in unncessary calls to str() every time a String is passed from Java.

    Best practice: Set ``convertStrings=False`` unless your application explicitly requires
    automatic conversion.

Which version you need depends on the ``convertStrings`` value used when starting the JVM.

Both of these distributions provide the ``java-stubs`` package providing type definitions
for the whole ``java.*`` namespace.

The stubs are produced with `CERN's stubgenj <https://gitlab.cern.ch/scripting-tools/stubgenj>`_.

There's a related `JPype discussion <https://github.com/jpype-project/jpype/issues/714>`_.

Known limitations
-----------------

* The types are tied to a specific Java version (currently OpenJDK 25).
* The JPype version used when generating the types may affect them, currently
  only one version is used.
* A few Java -> Python ``stubgenj`` translation problems have been reported, some are
  very difficult to resolve in a way that will satisfy all users (more:
  https://gitlab.cern.ch/scripting-tools/stubgenj/-/issues).

Internal documentation
----------------------

Run ``./generate`` to regenerate the stubs.
gemini-unwatermark 0.1.1
Remove watermarks from Gemini-generated images using LaMa inpainting
Requires Python: <3.14,>=3.10
Keywords: ai, gemini, image-processing, inpainting, lama, watermark

# gemini-unwatermark

Fast CLI to remove watermarks from Google Gemini-generated images using AI inpainting.

## Installation

```bash
pip install gemini-unwatermark
```

Or run directly without installing:
```bash
uvx gemini-unwatermark image.png
```

For GPU acceleration (CUDA):
```bash
pip install "gemini-unwatermark[gpu]"
```

**Requirements:** Python 3.10-3.13

## Usage

```bash
# Single image
gemini-unwatermark image.png

# Custom output
gemini-unwatermark image.png -o clean.png

# Batch processing
gemini-unwatermark *.png -d ./cleaned/

# Parallel (8 workers)
gemini-unwatermark photos/*.jpg -d ./output -w 8
```

## Options

| Option | Description |
|--------|-------------|
| `-o, --output` | Output path (single image only) |
| `-d, --output-dir` | Output directory for batch |
| `-f, --force` | Overwrite existing files |
| `-q, --quiet` | Suppress progress output |
| `-w, --workers` | Parallel workers (1-8, default: 4) |
| `--model-path` | Custom ONNX model path |

## How it works

Uses [LaMa](https://github.com/advimman/lama) (Large Mask Inpainting) to intelligently fill the watermark region in the bottom-right corner of Gemini images.

The model (~200MB) auto-downloads on first use to `~/.cache/gemini-unwatermark/`.

## Disclaimer

This tool only removes the **visible** watermark. It does **not** remove [SynthID](https://deepmind.google/technologies/synthid/), Google's invisible watermarking technology embedded in the image data.

## Credits

- Original Chrome extension: [dinoBOLT/Gemini-Watermark-Remover](https://github.com/dinoBOLT/Gemini-Watermark-Remover)
- LaMa ONNX model: [Carve/LaMa-ONNX](https://huggingface.co/Carve/LaMa-ONNX)

## License

Apache-2.0
yieldthought-agents 0.1.0
Automation for TTNN model bringup.
Requires Python: >=3.10

# Automod agents

Automation for TTNN model bringup using GitHub issues/projects and codexapi tasks.

## Requirements
- `gh`, `git`, `python3`, `codex` CLI
- `tt-smi` (or equivalent TT reset tool)
- `codexapi` Python package

Install Python deps:

```bash
pip install -r requirements.txt
```

Install as a package (adds `yt-bringup-worker`):

```bash
pip install -e .
```

## Environment variables

Required:
- `YT_SYSTEM` = `n150` | `n300` | `lb`
- `YT_OWNER` (default: `yieldthought`)
- `YT_REPO_MODELS` (default: `ttnn_models`)
- `YT_WORKER_NAME` (default: hostname)

Project config:
- `YT_PROJECT_NUMBER` (preferred) or `YT_PROJECT_TITLE`

HF + Codex:
- `HF_TOKEN` (required for gated models)
- `CODEX_BIN` (optional)
- `CODEX_*` env vars required by your codex install

Policy defaults:
- `YT_TOP1_MIN` (default `0.90`)
- `YT_TOP5_MIN` (default `0.97`)
- `YT_MAX_ATTEMPTS` (default `10`)
- `YT_SLEEP_SECS` (default `20`)
- `YT_TMP_ROOT` (optional temp root)

## Run the worker

```bash
python scripts/run_worker.py
```

Run once and exit:

```bash
python scripts/run_worker.py --once
```

Dry run (prints actions only):

```bash
python scripts/run_worker.py --dry-run
```

Dry run with a specific issue number in the output:

```bash
python scripts/run_worker.py --dry-run --issue 123
```

## Bootstrap a machine

```bash
scripts/bootstrap_machine.sh
```

## Tests

```bash
pytest -q
```
psi-continuum-statespace 0.1.0
Macroscopic state-space diagnostics for late-time cosmic expansion
Requires Python: >=3.10
Keywords: cosmology, state-space diagnostics, late-time acceleration, dark energy, LCDM, phenomenological cosmology, observational cosmology

# Psi–Continuum State-Space Diagnostics

This repository hosts diagnostic tools for the Psi–Continuum cosmology
framework, providing a macroscopic state-space representation of
late-time cosmic expansion.

The state-space construction is implemented as a derived diagnostic
based on a fixed background-level implementation,
consistent with Psi–Continuum Cosmology v2.

---

## Scope

This package implements **diagnostic state-space analysis only**.

It does not introduce new cosmological models, modify gravitational
dynamics, alter the Psi–Continuum v2 API, or perform parameter fitting.
All results are derived exclusively from background expansion histories
provided by the frozen v2 implementation.

The package is designed to support transparent and fully reproducible
state-space diagnostics at the background level.

---

## Current status

- Core state-space diagnostic framework implemented
- Interactive command-line interface available
- Reproducible numerical metrics and publication-ready figures supported

---

## Repository Structure

```text
psi-continuum-statespace
├── CITATION.cff
├── LICENSE
├── psi_continuum_statespace
│   ├── cli.py
│   ├── consistency.py
│   ├── diagnostics.py
│   ├── __init__.py
│   ├── plotting.py
│   └── psi.py
├── publication
│   ├── fig1_state_space.png
│   └── README.md
├── pyproject.toml
├── README.md
└── requirements.txt
```

## Usage

The package provides a command-line interface for exploratory and
diagnostic use. All files generated by the CLI are runtime artefacts and
do not represent published results.

Example usage (interactive shell):

```bash
psi-statespace interactive
```

---

### Installation from source (clone repository)

```bash
git clone https://github.com/dmitrylife/psi-continuum-statespace.git
cd psi-continuum-statespace
python3 -m venv sci_venv
source sci_venv/bin/activate
```

### (Optional) Install the package as editable

```bash
pip install -e .
```

---

## Relation to the publication

This repository serves as a companion diagnostic implementation for the
article:

“Psi–Continuum Cosmology v5:
A Macroscopic State–Space Response Framework for Late–Time Cosmic Expansion”

The finalized figures used in the article are provided separately in the
`publication/` directory for reference.

## License

This project is licensed under the terms of the MIT License.
See the **LICENSE** file for details.

---

## Citation

If you use this software or the associated state-space diagnostics in
academic work, please cite the corresponding article:


```
Dmitry Vasilevich Klimov (2025).
Psi–Continuum Cosmology v5:
A Macroscopic State–Space Response Framework
for Late–Time Cosmic Expansion.
Zenodo preprint. DOI: 10.5281/zenodo.18088720
```

---

## Contact

 - Email: d.klimov.psi@gmail.com
 - Website: https://psi-continuum.org