semvec/pypi-packages huggingface dataset:
dynamic-forest 1.0.1
A dynamic forest data structure.
Requires Python: >=3.8

# Dynamic Forest Python Library
An implementation of a dynamic tree data structure in Python.

## Reference

If you find this software useful, we would appreciate if you could cite the following work, for which this implementation
was developed.

```
Shin, S., Shomorony, I., and Macgregor, P. (2025). Dynamic DBSCAN with Euler Tour Sequences. In 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025). PMLR.
```

If you use bibtex, you can copy the following into your .bib file.

```
@inproceedings{shin2025dynamic,
  title = {Dynamic DBSCAN with Euler Tour Sequences},
  booktitle = {28th {{International Conference on Artificial Intelligence and Statistics}} ({{AISTATS}}'25)},
  author = {Seiyun Shin and Ilan Shomorony and Peter Macgregor},
  year = {2025}
}
```
flux-kontext-mcp 0.0.4
Flux.1 Kontext MCP Server
Requires Python: >=3.10

# Flux.1 Kontext MCP Server

MCP server configuration:

```json
{
    "mcpServers": {
        "flux_kontext_mcp": {
            "command": "uvx",
            "args": ["flux-kontext-mcp"],
            "env": {
                "REPLICATE_API_TOKEN": "your_replicate_api_token"
            }
        }
    }
}
```

## MCP tools

- flux_kontext_generate
- flux_kontext_async_generate
- flux_kontext_get_prediction
- flux_kontext_list_predictions
- flux_kontext_cancel_prediction
- flux_kontext_wait_for_prediction
- flux_kontext_get_model_info
NSKeyedUnArchiver 1.5.2
Decodes Apple's NSKeyedArchiver that were archived into text or binary plist files and returns a usable Python dict
Requires Python: >=3.8

# NSKeyedUnArchiver

Unserializes any binary|text|file|memory plist data and returns a usable Python dict.

Plist files are very common in Apple platforms (macOS, iOS etc). Plist files are used to store data much like XML and JSON files, but can be pure text or binary (convertible with plutil command). Some Plist files were generated by Apple NSKeyedArchiver and contain a serialized version of objects stored in an intricate and complex schema of nested objects. NSKeyedUnArchiver converts it back to an hierarchical form as a Python dict.

Uses only Python 3.8 plistlib, no other dependencies.

Automatically converts well known data types to Python-equivalent data types:

- `NSArray` ➔ `list`
- `NSMutableDictionary`, `NSDictionary` ➔ `dict`
- `NSMutableString`, `NSString` ➔ unwrap the `string`
- `NSMutableData`, `NSData` ➔ unwrap the data
- `NSDate` ➔ `datetime`

Check [pypi](https://pypi.org/project/NSKeyedUnArchiver/) for packages.

See package name for your system package manager at [Repology](https://repology.org/project/python:nskeyedunarchiver/information).

License is LGPL 3.

## Sample usage

```python
import NSKeyedUnArchiver

file='/path/to/file.plist'
my_dict=NSKeyedUnArchiver.unserializeNSKeyedArchiver(file)


data=b'bplist00\xd4\x01\.........'
my_dict=NSKeyedUnArchiver.unserializeNSKeyedArchiver(data)


data='<plist version="1.0">......'
my_dict=NSKeyedUnArchiver.unserializeNSKeyedArchiver(data)


file=PurePath('/path/to/file.plist')
my_dict=NSKeyedUnArchiver.unserializeNSKeyedArchiver(file)
```
yt-xarray 0.4.1
interface between yt and xarray
Requires Python: >=3.10

# yt_xarray

[![PyPI version](https://badge.fury.io/py/yt_xarray.svg)](https://badge.fury.io/py/yt_xarray)
[![Python Version](https://img.shields.io/pypi/pyversions/yt_xarray.svg?color=green)](https://python.org)
[![Tests](https://github.com/data-exp-lab/yt_xarray/actions/workflows/run-pytest-tests.yml/badge.svg)](https://github.com/data-exp-lab/yt_xarray/actions/workflows/run-pytest-tests.yml)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/data-exp-lab/yt_xarray/main.svg)](https://results.pre-commit.ci/latest/github/data-exp-lab/yt_xarray/main)

An interface between yt and xarray

## Overview

yt_xarray streamlines communication between
[xarray](https://docs.xarray.dev/en/stable/#) and [yt](https://yt-project.org),
making it easier use yt's visualization and analysis methods with data loaded
via xarray.

Presently, yt_xarray primarily adds methods to generate a yt dataset from a
subset of xarray dataset fields, without making copies of data when possible.

For a more detailed description, check out the full documentation at
[https://yt-xarray.readthedocs.io](https://yt-xarray.readthedocs.io/en/latest/).

## Quick Start

### Installation

The latest stable version can be installed using `pip` with:

```commandline
$ pip install yt_xarray
```

This will install both xarray and yt if you are missing one or the other.

### Usage

The main access point is the `YtAccessor` object for xarray. To
use it, simply import `yt_xarray` and the `.yt` object will be available to
xarray datasets. For example, to convert the xarray dataset into a full-fledged
yt dataset:

```python
import xarray as xr
import yt_xarray

ds = xr.open_dataset(...)
yt_ds = ds.yt.load_grid()
```
In this example, `ds.yt.grid()` returns a yt dataset using all of the fields in
the xarray dataset, ready to use with any yt command. This will, however, only
work if all of your data is defined on the same grid using coordinate names that
yt understands. So for more complex cases, see the [example notebooks](https://yt-xarray.readthedocs.io/en/latest/examples.html)
and the [FAQ](https://yt-xarray.readthedocs.io/en/latest/examples.html) on how
to handle those cases.

## Examples

Check out the [example notebooks](https://yt-xarray.readthedocs.io/en/latest/examples.html)
and the [FAQ](https://yt-xarray.readthedocs.io/en/latest/examples.html) for examples and
descriptions of common issues.

## Getting Help

Bug reports and questions are welcome via [github issues](https://github.com/data-exp-lab/yt_xarray/issues).
You can also reach out via the yt slack channel
([see here for how to join](https://yt-project.org/community.html)) by messaging
Chris Havlin directly or posting in help (though you should tag @Chris Havlin in
your post to get a faster response.)

## Contributions

Contributions are welcome, see [CONTRIBUTING.md](CONTRIBUTING.md) for details.
django-cityloc-pkg-Charles-Battle 0.0.1
A Django app about city locations
Requires Python: >=3.8

==============
City Locations
==============

City Locations is a Django app that can be installed in an existing Django project
Documentation: https://toddpy-django-cityloc-pkg.readthedocs.io/en/latest/

Installation
------------

1. Add "citylocations" to your INSTALLED_APPS setting in settings.py:
    INSTALLED_APPS = [
        ...
        
        'citylocations',

    ]

2. Include the citylocations URLconf in your project urls.py like this::
    path('', include('citylocations.urls')),

3. Start the development server and visit http://127.0.0.1:8000/
errorex 0.1.0
A Python library for explaining and debugging errors in ML/data pipelines with variable snapshots and LLM-ready prompts.
Requires Python: >=3.7

# errorex

**Explain errors better.**

`errorex` is a Python library that automatically explains and logs runtime errors in your code by capturing:
- Code traceback (excluding library noise)
- Local variable snapshots
- Suggestions for common errors
- LLM-ready GPT prompts

## 💡 Example

```python
from errorex import explain_errors

...

@explain_errors(log=True, suggest=True)
def run():
    df = pd.read_csv("somefile.csv")
    model.fit(df[['feature']], df['label'])  # Will fail if NaNs or mismatch

run()

🛠 Features
📍 User-focused Tracebacks: Filters out noisy frames from libraries.

🧠 Local Variable Snapshot: Captures all in-scope variables at the error line.

💡 Error Suggestions: Auto-suggests common fixes (e.g., NaNs, shape mismatches).

🤖 LLM Debug Prompt: Structured error message ready to paste into ChatGPT.

📝 Markdown Report Logging: Saves error reports to logs/error_report_<timestamp>.md.


📦 Installation
bash
Copy
Edit
pip install errorex
(Or, if you're developing locally)

bash
Copy
Edit
git clone https://github.com/minalbansal14/errorex.git
cd errorex
pip install -e .
🧪 Tests
bash
Copy
Edit
pytest tests/
🧠 Example Output (Markdown Log)
markdown
Copy
Edit
## ⚠️ Exception:
ValueError: Input y contains NaN

## 📍 Traceback:
- File: `simple_pipeline.py`, Line: 21
  ```python
  model.fit(X, y)
🧠 Local Variables:
y: 0.0, 1.0, NaN

💡 Suggestions:
It looks like your data contains missing values. Try using df.dropna().

🤖 GPT Prompt:
mathematica
Copy
Edit
Here’s the error and variables. How can I fix it?
👤 Author
Created by Minal Bansal
Contributions welcome via issues or PRs.
py-allrecipes 1.0.0
Python API to access the Allrecipes platform.
Requires Python: >=3.12

# Py Allrecipes

Py Allrecipes is a Python package for searching and extracting structured recipe data from Allrecipes.com.

- Search for recipes by keyword
- Extract ingredients, steps, times, servings, and yield from any recipe page
- Output is robust and structured for easy use in your own projects

## Features
- `search_recipes(query)`: Search Allrecipes and get a list of recipe titles, URLs, and ratings
- `get_recipe(url)`: Fetch and parse a recipe page for all details (ingredients, steps, times, servings, yield)

## Quickstart
```python
import py_allrecipes
results = py_allrecipes.search_recipes('chocolate cake')
url = results[0]['url']
recipe = py_allrecipes.get_recipe(url)
print(recipe['title'])
print(recipe['ingredients'])
print(recipe['steps'])
print(recipe['details'])
```

## Documentation
See the [full documentation](https://zacharyj12.github.io/py-allrecipes/) for more details and usage examples.

## License
MIT