semvec/pypi-packages huggingface dataset:
iceberg-cli 0.1.0
A CLI for Iceberg REST Catalogs leveraging PyIceberg and DataFusion
Requires Python: >=3.8

# Iceberg CLI

A powerful, unified command-line interface for Apache Iceberg REST catalogs. This tool extends the capabilities of [PyIceberg](https://py.iceberg.apache.org/) with profile management, DataFusion SQL querying, and robust administrative tools.

## Documentation

**[-> View Full Documentation](./docs/README.md)**

## Installation

```bash
pip install iceberg-cli
```

### Storage Extras

```bash
pip install "iceberg-cli[s3]"   # AWS S3
pip install "iceberg-cli[adls]" # Azure Data Lake
pip install "iceberg-cli[gcs]"  # Google Cloud Storage
pip install "iceberg-cli[all]"  # All backends
```

## Quick Reference

| Feature | Command |
| :--- | :--- |
| **Profiles** | `iceberg profile list`, `add`, `update`, `rename`, `remove` |
| **Catalog** | `iceberg list`, `create`, `drop` |
| **Data** | `iceberg query`, `upload`, `files` |
| **Metadata** | `iceberg describe`, `metadata`, `maintenance` |
| **Shell** | `iceberg shell`, `completion` |

Run `iceberg docs` or `iceberg <command> --help` for details.
numtype 2.4.0.0a1
Experimental Typing Stubs for NumPy
Requires Python: >=3.11
Keywords: numpy, typing, stubs

<h1 align="center">
    <img
        src="https://raw.githubusercontent.com/numpy/numtype/main/docs/theme/img/logo-numtype.svg"
        width="384"
        alt="NumType"
    />
</h1>

NumType provides experimental typing stubs for <a href="https://github.com/numpy/numpy">NumPy</a>.
See the <a href="https://numpy.org/numtype/">documentation</a> for more information.

> [!WARNING]
> Under development — use at your own risk.
depman-app 2.1.0
DepMan - micro, single-file general-purpose version manager.
Requires Python: >=3.13

# Depman, version 2.1.0

Micro, single-file general-purpose version manager

## Repository

Upstream repository is <https://gitlab.com/xgqt/xgqt-python-app-depman/>.

## Package

Download from PYPI: <https://pypi.org/project/depman-app/>.

## License

### Code

Code in this project is licensed under the MPL, version 2.0.

    This Source Code Form is subject to the terms of the Mozilla Public
    License, v. 2.0. If a copy of the MPL was not distributed with this
    file, You can obtain one at http://mozilla.org/MPL/2.0/.
bucketcloner 0.3.0
Clone all repositories from a Bitbucket workspace.
Requires Python: >=3.8

# Bucketcloner
Simple tool to list all your bitbucket workspaces and clone (all) repositories associated with these workspaces.

## Requirements
1. You need to know your Bitbucket account email. Can be found at https://bitbucket.org/account/settings/email/.
2. You need to create an API token here https://id.atlassian.com/manage-profile/security/api-tokens with read permissions for *account*, *workspace membership* and *repositories*.


| Command | Scopes |
|---|---|
| workspace | `read:user:bitbucket`, `read:workspace:bitbucket` |
| clone | `read:user:bitbucket`, `read:workspace:bitbucket`, `read:repository:bitbucket` |
| project | `read:user:bitbucket`, `read:workspace:bitbucket`, `read:project:bitbucket` |


Install bucketcloner either by cloning this repository and running `pip install .` or by installing it via `pip install bucketcloner`. Minimum required python version is 3.9.

Alternatively, you can run bucketcloner using `uvx`: `uvx bucketcloner --help`

Authentication for cloning the repository can be done via `https` and the API token or using an SSH key. IMPORTANT: When using `ssh` make sure you have added the bitbucket server ssh fingerprint to the known hosts, otherwise the command will fail. Run `git clone git@bitbucket.org:...` once manually to make sure the key is added.

You can pass your ssh key with `--ssh-key`, otherwise the users default ssh key will be used.

## List all workspaces
```bash
bucketcloner -e <email> -t <api_token> workspace
```
This lists all workspaces, including your personal workspace, where you have access.

## Clone workspace(s)
You can clone all repositories of all workspaces by simply calling
```bash
bucketcloner -e <email> -t <api_token> clone
```
This clones all repositories of all workspaces into the folders `workspace/repository` relative to the current directory.

To select specific workspace(s), add the `-w` option with workspace slug names separated by commas
```bash
bucketcloner -e <email> -t <api_token> -w workspace1,workspace2 clone
```

All existing repositories in the folders will be deleted and cloned again. To just skip existing repositories, add `--skip-existing` flag.
```bash
bucketcloner -e <email> -t <api_token> -w workspace1,workspace2 --skip-existing clone
```

Clone all repos from all workspaces with project PRO1 into /tmp with folder structure workspace/project/repo.
```bash
bucketcloner -e <email> -t <api_token> --project PRO1 --base-folder /tmp --project-folder clone
```

Clone all repos using an ssh key
```bash
bucketcloner -e <email> -t <api_token> --base-folder /tmp  --auth-mode ssh --ssh-key "/my/id_rsa" clone
```

List all projects in all workspaces
```bash
bucketcloner -e <email> -t <api_token> project
```

## Python example
The `example.ipynb` includes an example how to read the workspaces and download the repositories from within python.
scarf-sdk 0.2.1
Python bindings for Scarf telemetry
Requires Python: >=3.7

# scarf-py

![PyPI - Version](https://img.shields.io/pypi/v/scarf-sdk)
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=337d6c89-63ff-4430-865b-2b0dd211eb0c" />

A Python client for sending telemetry events to Scarf using JSON payloads.

## Installation

```bash
pip install scarf-sdk
```

## Usage

```python
from scarf import ScarfEventLogger

# Initialize with required endpoint URL
logger = ScarfEventLogger(
    endpoint_url="https://your-scarf-endpoint.com",
    timeout=5.0  # Optional: Set default timeout in seconds (default: 3.0)
)

# Send an event with properties
success = logger.log_event({
    "event": "package_download",
    "package": "scarf",
    "details": {
        "version": "1.0.0",
        "platforms": ["linux", "macos"]
    }
})

# Send an event with a custom timeout
success = logger.log_event(
    properties={"event": "custom_event"},
    timeout=1.0  # Override default timeout for this call
)

# Empty properties are allowed
success = logger.log_event({})
```

## Configuration

The client can be configured through environment variables:

- `DO_NOT_TRACK=1`: Disable analytics
- `SCARF_NO_ANALYTICS=1`: Disable analytics (alternative)
- `SCARF_VERBOSE=1`: Enable verbose logging

## Features

- Simple API for sending telemetry events
- JSON payloads (supports nested data)
- Environment variable configuration
- Configurable timeouts (default: 3 seconds)
- Respects user Do Not Track settings
- Verbose logging mode for debugging

## Development

1. Clone the repository
2. Install development dependencies:
   ```bash
   pip install -e ".[dev]"
   ```
3. Run tests:
   ```bash
   pytest
   ```

## Publishing

To publish a new version:

1. Update version in `setup.py` and `pyproject.toml`
2. Create and push a new tag:
   ```bash
   git tag v0.1.0
   git push origin v0.1.0
   ```

The CI workflow will automatically build and publish to PyPI when a new version tag is pushed.

## License

Apache 2.0
pydantic-extra-types 2.11.0
Extra Pydantic types.
Requires Python: >=3.9

# Pydantic Extra Types

[![CI](https://github.com/pydantic/pydantic-extra-types/actions/workflows/ci.yml/badge.svg)](https://github.com/pydantic/pydantic-extra-types/actions/workflows/ci.yml)
[![Coverage](https://codecov.io/gh/pydantic/pydantic-extra-types/branch/main/graph/badge.svg)](https://codecov.io/gh/pydantic/pydantic-extra-types)
[![pypi](https://img.shields.io/pypi/v/pydantic-extra-types.svg)](https://pypi.python.org/pypi/pydantic-extra-types)
[![license](https://img.shields.io/github/license/pydantic/pydantic-extra-types.svg)](https://github.com/pydantic/pydantic-extra-types/blob/main/LICENSE)

A place for pydantic types that probably shouldn't exist in the main pydantic lib.

See [pydantic/pydantic#5012](https://github.com/pydantic/pydantic/issues/5012) for more info.

## Installation

Install this library with the desired extras dependencies as listed in [project.optional-dependencies](./pyproject.toml).

For example, if pendulum support was desired:

```shell
# via uv
$ uv add "pydantic-extra-types[pendulum]"

# via pip
$ pip install -U "pydantic-extra-types[pendulum]"
```
django-htmx-tools 1.0.2
An assortment of Django mixins and middleware for working with HTMX
Requires Python: >=3.10
Keywords: django, htmx

# django-htmx-tools

[![PyPI version](https://badge.fury.io/py/django-htmx-tools.svg)](https://badge.fury.io/py/django-htmx-tools)
[![Python versions](https://img.shields.io/pypi/pyversions/django-htmx-tools.svg)](https://pypi.org/project/django-htmx-tools/)
[![Django versions](https://img.shields.io/pypi/djversions/django-htmx-tools.svg)](https://pypi.org/project/django-htmx-tools/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://readthedocs.org/projects/django-htmx-tools/badge/?version=latest)](https://django-htmx-tools.readthedocs.io/en/latest/?badge=latest)

An assortment of Django mixins and middleware for working with HTMX.

django-htmx-tools provides a collection of utilities to make it easier to build
HTMX-powered Django applications. It includes middleware for proper caching and
authentication handling, as well as mixins and decorators for protecting views.

## Features

- **IsHtmxRequestMixin** - Class-based view mixin for HTMX-only endpoints
- **htmx_only_request** - Function decorator for HTMX-only views
- **is_htmx** - Utility function to check if a request is from HTMX
- **htmx_vary_middleware** - Proper caching headers for HTMX requests
- **htmx_auth_middleware** - Authentication redirect handling for HTMX

## References

- [HTMX Caching](https://htmx.org/docs/#caching)
- [How to Handle Django Login Redirects with HTMX](https://www.caktusgroup.com/blog/2022/11/11/how-handle-django-login-redirects-htmx/)

## Documentation

Please visit [https://django-htmx-tools.readthedocs.io](https://django-htmx-tools.readthedocs.io)
pyquantumclifford 0.10.1
Thin Python wrapper for the Julia library QuantumClifford.jl and QuantumClifford.ECC. Supports extremely fast Stabilizer state simulations, weakly non-Clifford dynamics, and generation of many modern quantum error correcting codes.
Requires Python: >=3.11

# pyquantumclifford

Thin Python wrapper for [`QuantumClifford.jl`](https://qc.quantumsavory.org/stable/) and [`QuantumClifford.ECC`](https://qc.quantumsavory.org/stable/ECC_API/) using the JuliaPy stack.
Supports extremely fast Stabilizer state simulations, weakly non-Clifford dynamics, and generation of many modern quantum error correcting codes.

## Instalation

```
pip install pyquantumclifford
```

or

```
uv add pyquantumclifford
```

On first `import` the Julia runtime will be downloaded and QuantumClifford.jl will be compiled (optimized for your hardware). This is a slow process, might take around 5 minutes.

Optional: Having [`juliaup`](https://github.com/JuliaLang/juliaup) installed in your system (and available in your PATH) makes managing Julia versions much simpler behind the scenes. Consider manually installing `juliaup` before installing `pyquantumclifford`. The install command is `curl -fsSL https://install.julialang.org | sh` for a selfcontained install in `~/.julia` that you can delete at any time.

## Usage

```python
import numpy as np
from pyquantumclifford import QuantumClifford, ECC

# Optional, for calling arbitrary Julia code
from juliacall import Main as jl

# Simple ECC code
code = ECC.Shor9()

# Access parity checks and matrices
ECC.parity_checks(ECC.Shor9())
ECC.parity_matrix(ECC.Shor9())
np.array(ECC.parity_matrix(ECC.Shor9()))
```

## Notes

- Windows is not supported due to the Oscar computer algebra system being unavailable on it. Use WSL on Windows.
- `juliacall` provides access to Julia modules from Python.
qphase-viz 1.0.0
Visualization plugin for QPhase: Phase Portraits and PSD Analysis
Requires Python: >=3.10

# qphase-viz

**Plotting Utilities for QPhase**

`qphase-viz` provides a set of plotting tools for visualizing data from quantum phase-space simulations. It is designed to work with `qphase` but can also be used independently to generate figures.

## Features

- **Phase Portraits**:
    - **Re-Im Plane**: Visualize distributions in complex phase space.
    - **Abs-Abs Plane**: Analyze amplitude correlations.
    - **Marginal Distributions**: Project dynamics onto axes.
- **Spectral Analysis**:
    - **Power Spectral Density (PSD)**: Compute spectra with windowing.
    - **Log/Linear Scaling**: Flexible axis options.
- **Time Series**:
    - **Trajectory Evolution**: Plot stochastic paths and averages.
    - **Confidence Intervals**: Visualize variance/std-dev.

## Installation

```bash
pip install qphase-viz
```

## Usage

### As a QPhase Plugin
Add a visualization job to your `qphase` configuration:

```yaml
jobs:
  - name: "plot_phase"
    type: "viz"
    dependencies: ["my_simulation"]
    config:
      plots:
        - kind: "phase_plane"
          mode: 0
          style: "density"
        - kind: "spectrum"
          source: "output.npy"
```

### Standalone Usage
```python
from qphase_viz.engine import VizEngine
from qphase_viz.config import PhasePlaneConfig

# Configure and run
config = PhasePlaneConfig(mode=0)
engine = VizEngine()
engine.plot(data, config)
```

## License

MIT License
kxspy 1.0.5b0
Kxspy is a async python client for KxsClient Network.
Keywords: kxsclient, surviv, kxspy, kxs, kxsnetwork

<div align="center">
  <img 
    src="https://github.com/lavecat/Kxspy/blob/main/assets/kxspy_banner.png" 
    alt="kxspy Banner" 
    style="border-radius: 5px;">
  <h1>Kxspy</h1>
  <h3>Kxspy is a async python client for KxsClient Network</h3>
  <div>&nbsp;</div>
</div>

> [!WARNING]
> Kxspy is in beta version, so some features may not work as expected.
> Please report any issues you encounter on the GitHub repository.


## Features
- [x] **Auto-Reconnect**
- [x] **Full websocket & RestAPI**
- [x] **Easy to use**
- [x] **Example**
- [x] ***And more...***

## Getting Started
```shell
pip install kxspy
```
**You can now choose an example or do it yourself, and you can consult the documentation to help you.**


## Need help ?
- Discord server : https://discord.wf/kxsclient ( mention @lirus_12345 for help )
- Documentation : https://kxspy.readthedocs.io/
arcfish 1.0.1
Accurate and robust 3D genome feature discovery from chromatin tracing data
Requires Python: >=3.12

ArcFISH: accurate and robust 3D genome feature discovery from chromatin tracing data
====================================================================================

Please visit [https://hyuyu104.github.io/ArcFISH](https://hyuyu104.github.io/ArcFISH) for full documentation.