semvec/pypi-packages huggingface dataset:
erton 1.0.0
Erton Security — unified defensive game security kit (telemetry analyzers, watchlist, signed policy, runtime guard, GUI helpers). No hooking/injection.
Requires Python: >=3.9


# Erton Security — Unified Kit (v1.0.0)


# Made By DeathAmir

```bash
pip install -e .

pip install '.[gui]'

pip install '.[lua]'
```


```bash
erton-serve --host 127.0.0.1 --port 8765 --policy config/policy.json --guard
```


- EXE (PyInstaller): `tools/build_exe.bat`
- DLL/PYD (Nuitka module): `tools/build_dll.bat`
orca-studio 0.2.17
A Python toolkit for parsing, analyzing, and visualizing ORCA quantum chemistry calculations
Requires Python: >=3.11

# ORCA Studio

## Installation

For the command-line interface:

```bash
uv tool install orca-studio
```

Access the modules in your own Python code:

```bash
uv add orca-studio
```

Or if you're not using `uv`:

```bash
pip install orca-studio
```

## Modules

### ORCA Parse

reading and interpreting data from ORCA output files

Data is made available either directly as values (int, float, bool, ..),
custom dataclasses for structured, non-tabular data (e.g. AILFT data etc.),
and polars DataFrames for tabular data such as absorption spectra.

Each parsing task is handeled in a separate file in `src/orca_parse`.
The module exposes a central `OrcaOutput` class that bundles all parseable attributes
and provides introspective access via properties.


### ORCA Render

creating and rendering 3D visualizations of molecular structures and density isosurfaces

The molecule and isosurfaces are added as meshes to a plotly Figure object.
The module exposes a single user-facing `Renderer` class.

Creating densities (i.e. cube files) requires `orca_plot` to be available in the `$PATH`.


### ORCA Studio

high-level data analysis and visualization with Marimo GUI applications and CLI interface

This central module utilizes the lower-level `orca_parse` and `orca_render` modules to
craft powerful tools for analyzing ORCA calculations.
Raw data from `orca_parse` is refined to provide insight-oriented summaries.
amazme_platform 0.23.0.dev202509191217
Python SDK for AmazMe Platfom services
Requires Python: <3.12,>=3.8

⚠️ If you are looking for `platformsdk_deployment_tool` take a look at [amazme-cd](https://gitlab.amazmetest.ru/platform/amazme-cd) - new repo for deployment tool.

⚠️ If you have some unmerged changes for tool please cherry-pick is to new repo.

# Platform Python SDK

Library for interacting with AmazMe Platform services.

## Features
- Interaction with platform configuration ([examples](./examples/configuration_example.ipynb)):
  - create scenario
  - create artifact
  - get scenario_url to scenario_id mapping
  - get metric_codename to metric_id mapping
- Uploading data to AmazeMe Platform:
  - upload business metrics ([example](./examples/uploading_example.ipynb))
  - managing the recommendations(personalized/default) upload task ([example](./examples/tasking_example.ipynb))
- Interacting with the AmazMe Platform deployment API ([examples](./examples/deployment_example.ipynb))


## Installation

### Pip Command

```
pip install amazme-platform --extra-index-url https://__token__:<your_personal_token>@gitlab.amazmetest.ru/api/v4/projects/160/packages/pypi/simple
```

You will need a [personal access token](https://gitlab.amazmetest.ru/help/user/profile/personal_access_tokens).

### Registry setup

If you haven't already done so, you will need to add the below to your `.pypirc` file.

```
[gitlab]
repository = https://gitlab.amazmetest.ru/api/v4/projects/160/packages/pypi
username = __token__
password = <your personal access token>
```

For more information on the PyPi
registry, [see the documentation](https://gitlab.amazmetest.ru/help/user/packages/pypi_repository/index).

## Release process

[https://sberworks.ru/wiki/pages/viewpage.action?pageId=442091254](https://sberworks.ru/wiki/pages/viewpage.action?pageId=442091254)