semvec/pypi-packages huggingface dataset:
pycfgcut 0.3.2
Python bindings for cfgcut, the deterministic configuration slicer
Requires Python: >=3.9
Keywords: cfgcut, networking, automation

# pycfgcut

Python bindings for [`cfgcut`](https://cfgcut.bedecarroll.com), the deterministic configuration slicer for network engineers.

## Installation

```bash
pip install pycfgcut
```

`pycfgcut` ships prebuilt wheels for CPython 3.9 and newer on Linux and macOS. If a wheel is unavailable for your platform `pip` falls back to building from source; make sure a compatible Rust toolchain is installed (`rustup` recommended).

## Usage

```python
from pathlib import Path

from pycfgcut import run_cfg

fixture = Path("sample.conf")
result = run_cfg(["interfaces|>>|"], [str(fixture)], anonymize=True, tokens=True)

if result["matched"]:
    print(result["stdout"])
```

Refer to the [cfgcut documentation](https://cfgcut.bedecarroll.com) for matcher semantics, anonymisation behaviour, and CLI parity guarantees.

## License

`pycfgcut` is distributed under the terms of the MIT License. See the top-level `LICENSE` file in this repository for the full text.
horillactl 1.0.1
CLI tool to build and manage Horilla projects
Requires Python: >=3.8

# HorillaCTL

**HorillaCTL** is a command-line tool for managing the setup, dependencies, and upgrades of Horilla projects. It streamlines project initialization, package installation, and database migrations for Django-based Horilla applications.

---

## Features

* Clone and initialize a new Horilla project.
* Install Python dependencies from `requirements.txt`.
* Apply Django database migrations automatically.
* Upgrade an existing Horilla project to the latest version.
* User-friendly CLI interface with clear step-by-step guidance.

---

## Installation

You can install `horillactl` using `pip` (assuming itâ€™s published on PyPI):

```bash
pip install horillactl
```

---

## Usage

`horillactl` provides two main commands: `build` and `upgrade`.

### Build

The `build` command is used to set up a new Horilla project or manage individual tasks.

```bash
horillactl build project
```

* Clones the Horilla repository into the current directory.
* Moves files into the project folder.
* Installs dependencies and applies database migrations.

Other subcommands:

```bash
horillactl build packages   # Install Python dependencies
horillactl build migrate    # Apply Django database migrations
```

### Upgrade

The `upgrade` command updates an existing Horilla project.

```bash
horillactl upgrade project
```

* Pulls the latest changes from the Horilla Git repository.
* Ensures your project is up-to-date.

---

## Requirements

* Python 3.8+
* Git installed and available in your system PATH
* Virtual environment (optional but recommended)

---

## Example Workflow

1. **Create a new project**

```bash
mkdir horilla_project
cd horilla_project
horillactl build project
```

2. **Install dependencies**

```bash
horillactl build packages
```

3. **Apply migrations**

```bash
horillactl build migrate
```

4. **Run the server**

```bash
python manage.py runserver
```

5. **Upgrade project in the future**

```bash
horillactl upgrade project
```

---

## Notes

* Ensure you are in an empty directory (or one containing only a virtual environment) when running `build project`.
* If you already have a Git repository in the folder, use `upgrade project` instead.
* Works on Windows, macOS, and Linux.

---

## License

This project is open-source and licensed under the LGPL License.
chatgpt_hj3415 0.4.1
chatgpt-hj3415 is the program using chatgpt api.

# chatgpt-hj3415

chatgpt-hj3415 is the program using chatgpt api.

## Installation

```bash
pip install chatgpt-hj3415
```

## 사전 준비
	1.	https://platform.openai.com/account/api-keys 에서 API 키를 발급받습니다.
	2.	아래 명령어로 라이브러리를 설치합니다:

`pip install openai`
easyibkr 0.1.1
Helper functions for easier IBKR trade automation using ib_insync.
Requires Python: >=3.8

#EasyIBKR 

A helper module which makes automating trades using IBKR and Python MUCH easier through helper functions 
which sort through the confusing syntax elements.

Note: This build is based on ib_insync, though this may be updated to the newer maintained async version


HOW TO USE:

first, run the connect() function. By default it uses the params TWS API sets for you (make sure to set up your TWS to work with API!!), however you can change this by calling the function with params;

connect(ip,port,clientId)

-------Getting Information-------

getLiquidity() -> returns your account summary 
getValue() -> returns your total account value (cash + positions)

getCurrentBasics(ticker) -> returns a list of [bid,ask,last,open] for the specified ticker 

getHistoricals(ticker,duration,size,toShow,endDateTime) 
duration is how far back to go 
size is the bar size 
toShow is what point in the bar to show (open,close,high,low,midpoint) 
endDateTime is the last date to get data for, by default it goes until the latest it can 


-------Placing Trades--------

placeLimit(ticker,quantity,short,limit)
short -> a boolean, if True then places a short trade, if False, long 
limit -> limit price 

placeBracket(ticker,qty,short,limit,stopLoss,takeProfit) 
same as Limit but we can specify stopLoss and takeProfit limit prices, orders are One-Cancels-Order 


## Installation

```Bash
pip install easyIBKR
iterwrap 0.4.6.post1
Wrapper on iterable for automatic caching, resuming, retrying, and multiprocessing
Requires Python: >=3.10

Wrapper on an iterable to support interruption & auto resume, retrying and multiprocessing.

The code is tested on Linux.

# APIs

Please refer to the signature of function.iterate_wrapper or generator.IterateWrapper for usage.

# Examples

## iterate_wrapper

```python
from typing import IO
from time import sleep

from iterwrap import iterate_wrapper

def square(f_io: IO, item: int, fn: Callable[[float], float]):
    result = fn(item)
    f_io.write(f"{result}\n")

data = list(range(10))
num_workers = 3
iterate_wrapper(
    square,
    data,
    output="output.txt",
    num_workers=num_workers,
    fn=lambda x: x * x,
)

with open("output.txt") as f:
    print(f.read()) # [0, 1, 4, 9, ..., 81]
```

## IterateWrapper

Just the same as `tqdm.tqdm`.

```python
from iterwrap import IterateWrapper

data = [1, 2, 3]
results = []
for i in IterateWrapper(data):
    results.append(i * i)
print(results) # [1, 4, 9]
```
wbgt-aqi-status 0.1.0
Heat Index + AQI → OK / Modify / Cancel status for practice-day safety.
Requires Python: >=3.10
Keywords: AQI, heat index, practice status, sports safety, student athletes

# wbgt-aqi-status

**What it does:** Looks up today's **Heat Index (HI)** and **PM2.5 AQI**, then reports a practice-day status: **OK / Modify / Cancel** based on configurable thresholds. Designed for coaches/ADs.

**Defaults:**
- HI: OK < 95°F, Modify 95–102°F, Cancel ≥ 103°F
- AQI (US): OK ≤ 100, Modify 101–150, Cancel ≥ 151
- Rule: the stricter of HI vs AQI sets the status.

> Decision-support only. Coaches/trainers make final decisions per district policy.

## Install
```bash
pip install wbgt-aqi-status
dgraph-flex 0.1.11
A package for working with directed graphs
Requires Python: >=3.10

# dgraph_flex

Package to support flexible storage of directed graphs, specifically for the support of
directed graphs and causal structure analysis.

Changed edges from a list to a dict using the edge name src --> tar
to make it easier to address, query and set

## sample usage

```
from dgraph_flex import DgraphFlex

# create the graph object
obj = DgraphFlex()
# add edges to graph object
obj.add_edge('A', '-->', 'B', color='green', strength=-0.5, pvalue=0.01)
obj.add_edge('B', '-->', 'C', color='red', strength=-.5, pvalue=0.001)
obj.add_edge('C', 'o->', 'E', color='green', strength=0.5, pvalue=0.005)
obj.add_edge('B', 'o-o', 'D')
obj.add_edge('F', '<->', 'B')

# to modify an existing edge
obj.modify_existing_edge('A', 'B', color='green', strength=0.2, pvalue=0.0001)

# Save the graph to a file 'sample_graph' with a default format of png and  resolution of 300
# The format suffix is automatically added to the provided filename.  A graphviz source 'dot'
# file is also generated. This can be edited and rerendered with the 'dot' command.

obj.save_graph('sample_graph')
```

Here is the generated graph

![Example Graph](https://github.com/kelvinlim/dgraph_flex/blob/main/dgraph_flex/dgflex2.png)

## for use in jupyter notebook

```
from dgraph_flex import DgraphFlex

obj = DgraphFlex()
# add edges to graph object
obj.add_edge('A', '-->', 'B', color='green', strength=-0.5, pvalue=0.01)
obj.add_edge('B', '-->', 'C', color='red', strength=-.5, pvalue=0.001)
obj.add_edge('C', 'o->', 'E', color='green', strength=0.5, pvalue=0.005)
obj.add_edge('D', 'o-o', 'B')
obj.add_edge('F', '<->', 'B')

# render to window
obj.show_graph()

```

## sample yaml file

Here is a sample yaml file describing a graph

```yaml

GENERAL:
  version: 2.0
  framework: dgraph_flex
  gvinit:  # global graphviz initialization
    nodes:
      shape: oval
      color: black

GRAPH:
  edges: # Use indentation for the dictionary under 'edges'
    "A --> B": # Key for the first edge
      properties: # Indent for the 'properties' dictionary
        strength: 0.5
        pvalue: 0.01
      gvprops: # Indent for the 'gvprops' dictionary
        color: green
    "B --> C": # Key for the second edge
      properties: # Indent for 'properties'
        strength: -0.5
        pvalue: 0.001
      gvprops: # Indent for 'gvprops'
        color: red
    "C o-> E": # Key for the third edge
      properties: # Indent for 'properties'
        strength: 0.5
        pvalue: 0.0005
      gvprops: # Indent for 'gvprops'
        color: green
    "B o-o D": # Key for the fourth edge
      properties: # Indent for 'properties'
        strength: 0.5
        pvalue: 0.0005
      gvprops: # Indent for 'gvprops'
        color: black

```

Here is python code that reads in the graph and outputs a png

```python

from dgraph_flex import DgraphFlex

obj = DgraphFlex(yamlpath='graph_sample.yaml')
obj.read_yaml(self.config['yamlpath'])
obj.load_graph()



```

## unit tests

```
python -m unittest tests/*.py
```

## build

```
python -m build

# upload
twine upload dist/*

```