semvec/pypi-packages huggingface dataset:
dummynet 10.2.0
A tool for creating dummy networks using network namespaces.
Requires Python: >=3.11
Keywords: dummynet, network, namespace

============
Introduction
============

|PyPi| |Waf Python Tests| |Black| |Pip Install|

.. |PyPi| image:: https://badge.fury.io/py/dummynet.svg
    :target: https://badge.fury.io/py/dummynet

.. |Waf Python Tests| image:: https://github.com/steinwurf/dummynet-python/actions/workflows/python-waf.yml/badge.svg
   :target: https://github.com/steinwurf/dummynet-python/actions/workflows/python-waf.yml

.. |Black| image:: https://github.com/steinwurf/dummynet-python/actions/workflows/black.yml/badge.svg
      :target: https://github.com/steinwurf/dummynet-python/actions/workflows/black.yml

.. |Pip Install| image:: https://github.com/steinwurf/dummynet-python/actions/workflows/pip.yml/badge.svg
      :target: https://github.com/steinwurf/dummynet-python/actions/workflows/pip.yml


DummyNet is a tool for creating local test networks in Python on Linux.

Find documentation for DummyNet here:

https://dummynet.pages.dev/latest/
water-packer 0.2.6
Pack water molecules into atomic systems for molecular simulations
Requires Python: >=3.9

# Water Packer

Pack water molecules into atomic systems for molecular simulations. High-performance, O(1) collision detection using spatial hashing.

## Installation

```bash
pip install water-packer
```

For hyobj support:
```bash
pip install "water-packer[hyobj]"
```

## Quick Start

```python
from water_packer import WaterPacker
from molecular_builder import create_bulk_crystal

# Create a MgO slab
mgo = create_bulk_crystal("periclase", [15.0, 15.0, 15.0])

# Create packer and pack water
packer = WaterPacker(
    water_density=1.0,  # target density in g/cm³
    pairwise_distances={('O', 'Mg'): 2.0},
    seed=42,
)

result = packer.pack(mgo)
print(f"Added {(len(result) - len(mgo))//3} water molecules")
```

![Solvated Periclase](https://raw.githubusercontent.com/henriasv/water-packer/main/docs/_static/periclase_solvated.png)

## Features

- **Multiple input formats**: ASE Atoms, hyobj PeriodicSystem, or raw arrays
- **Species-specific distances**: Different minimum distances per atom pair
- **Automatic density calculation**: Computes number of waters from target density
- **Volume-aware packing**: Accounts for substrate exclusion volume
- **Physics Safeguards**: Warns user if requested density exceeds physical packing limits
- **Hybrid Relaxation**: Supports optional MC/minimization steps for fitting water into tight surface features

## API

### WaterPacker

```python
WaterPacker(
    min_distance=2.0,        # Default minimum distance (Å)
    pairwise_distances=None, # Species-specific: {('O', 'Mg'): 2.0}
    water_density=1.0,       # Target density (g/cm³)
    seed=None,               # Random seed
)
```

### pack_water (convenience function)

```python
from water_packer import pack_water

# Option A: Pack exactly 10 waters
result = pack_water(system, n_waters=10)

# Option B: Pack using target density (mutually exclusive with n_waters)
result = pack_water(system, water_density=1.0)
```
ncplot 0.3.13
Interactive viewing of NetCDF data
Requires Python: >=3.6.1



# ncplot - Easy interactive viewing of NetCDF files in Python 



[![pypi](https://img.shields.io/pypi/v/ncplot.svg)](https://pypi.python.org/pypi/ncplot/)
[![Documentation Status](https://readthedocs.org/projects/ncplot/badge/?version=latest)](https://ncplot.readthedocs.io/en/latest/?badge=latest)


An easy to use Python (3.6 and above) package for quickly plotting the contents of NetCDF files or xarray datasets in Python or on the command line. 

This package is designed to work with [CF](https://cfconventions.org/) compliant NetCDF data.

## Installation

To install from conda:
```sh
conda install -c conda-forge ncplot

```

To install from pypi:
```sh
pip install ncplot 
```

Install the development version using using pip:
```sh
pip install git+https://github.com/pmlmodelling/ncplot.git
```



## How to use in Python



The package is made up of a simple and easy to use function: view. To visualize everything in a file:

```sh
from ncplot import view

view("example.nc")

```

or to visualize a specific variable or list of variables:

```sh
from ncplot import view

view("example.nc", vars)

```

where vars is either a string representing a variable or a list of variables.


If you want to plot xarray datasets dataarrays, first import the xarray accessor, then plot in a similar way to xarray's built in plotting method:


```sh
import ncplot.xarray
import xarray as xr

ds = xr.open_dataset("example.nc") 
ds.ncplot.view()

```




## How to use on the command line

Command line usage is as simple as:


```sh


ncplot example.nc

```












# Reference and tutorials

A full API reference, and a how-to guide are available at [readthedocs](https://ncplot.readthedocs.io/en/latest/).
sqv 0.4.1
A fast, lightweight SQLite database viewer TUI
Requires Python: >=3.11

# sqv - SQLite Viewer TUI

A fast, lightweight SQLite database viewer TUI built with Python and Textual.

## Features

- **Database Structure Tab**: Browse tables, indices, views, and triggers with their SQL definitions
- **Data Tab**: View table data with sorting and filtering
- **Execute SQL Query Tab**: Execute arbitrary SQL queries

## Screenshots

![sqv structure screenshot](https://raw.githubusercontent.com/RhetTbull/sqv/main/screenshots/structure_view.png)

![sqv data screenshot](https://raw.githubusercontent.com/RhetTbull/sqv/main/screenshots/data_view.png)

![sqv sql screenshot](https://raw.githubusercontent.com/RhetTbull/sqv/main/screenshots/sql_view.png)

![sqv sql screenshot](https://raw.githubusercontent.com/RhetTbull/sqv/main/screenshots/cell_view.png)

## Installation

I recommend installation with [uv](https://docs.astral.sh/uv/).

```bash
uv tool install sqv
```

Or to run without installing:

```bash
uv tool run sqv <database.db>
```

To install from source:

```bash
git clone git@github.com:RhetTbull/sqv.git
uv sync
```

## Usage

```bash
sqv <database.db>
```

## Key Bindings

| Key | Action |
|-----|--------|
| d | Go to Database Structure tab |
| b | Go to Browse Data tab |
| e | Go to Execute SQL tab |
| Ctrl+q | Quit |
| Ctrl+Enter | Execute SQL (in SQL tab) |
| ? | Show help |

## See Also

sqv is not intended to be a full SQL IDE. I built it to do exactly the things I wanted and nothing else. If you find sqv doesn't suit your needs, you might find one of these more full featured alternatives does:

* [harlequin](https://github.com/tconbeer/harlequin): The SQL IDE for your terminal
* [sqlit](https://github.com/Maxteabag/sqlit): A user friendly TUI for SQL databases. Written in python. Supports SQL server, Mysql, PostreSQL, SQLite, Turso and more.
* [litecli](https://github.com/dbcli/litecli): CLI for SQLite Databases with auto-completion and syntax highlighting