semvec/pypi-packages huggingface dataset:
ipyvue-flatsurf 0.6.4
Visualizations for Translations Surfaces in Jupyter Notebooks and JupyterLab
Keywords: ipython, jupyter, widgets

<p align="center">
    <img alt="logo" src="https://github.com/flatsurf/ipyvue-flatsurf/raw/master/logo.svg?sanitize=true" width="300px">
</p>

<h1><p align="center">ipyvue-flatsurf</p></h1>

<p align="center">
  <img src="https://img.shields.io/badge/License-GPL_3.0_or_later-blue.svg" alt="License: GPL 3.0 or later">
  <a href="https://github.com/flatsurf/ipyvue-flatsurf/actions/workflows/test.yml"><img src="https://github.com/flatsurf/ipyvue-flatsurf/actions/workflows/test.yml/badge.svg" alt="Test"></a>
  <a href="https://doi.org/10.5281/zenodo.5208761"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.5208761.svg" alt="DOI 10.5281/zenodo.5208761"></a>
</p>

<p align="center">Jupyter Widgets for flatsurf</p>
<hr>

This project provides Jupyter Widgets for the [flatsurf suite](https://flatsurf.github.io). In particular it provides interactive widgets for surfaces from [sage-flatsurf](https://flatsurf.github.io/sage-flatsurf) and their quotients and flow decompositions. Behind the scenes, this project provides the glue between the mathematical objects of [sage-flatsurf](https://flatsurf.github.io/sage-flatsurf) and their visualization in the browser which is implemented in [vue-flatsurf](https://github.com/flatsurf/vue-flatsurf).

Note that this project is in an alpha stage. The interface is mostly a proof-of-concept and very likely to change substantially in the future.

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

If you already have [sage-flatsurf](https://github.com/flatsurf/sage-flatsurf) and [pyflatsurf](https://github.com/flatsurf/flatsurf), you can install ipyvue-flatsurf with pip:

    pip install ipyvue-flatsurf

Development
-----------

Install a local copy of this package:

    git clone https://github.com/flatsurf/ipyvue-flatsurf.git
    cd ipyvue-flatsurf
    pixi run jupyter lab  # to explore the examples/

To use the [Vue.js
devtools](https://addons.mozilla.org/en-US/firefox/addon/vue-js-devtools/), you
should also install [this
hack](https://addons.mozilla.org/en-US/firefox/addon/vue-js-devtools/) since
otherwise, Vue won't be detected properly.

How to Cite this Project
------------------------

If you have used this project in the preparation of a publication, please cite it as described on our [Zenodo page](https://doi.org/10.5281/zenodo.5208761).

Acknowledgements
----------------

* Julian Rüth's contributions to this project have been supported by the Simons Foundation Investigator grant of Alex Eskin.

Maintainers
-----------

* [@saraedum](https://github.com/saraedum)
mymlscratch 0.1.0
A library that returns ML algorithm source code implemented from scratch
Requires Python: >=3.7

# MyMLScratch

A Python library that returns the complete source code for various machine learning algorithms implemented from scratch using pure Python and NumPy.

## Installation
```bash
pip install mymlscratch
```

## Usage
```python
import mymlscratch

# Get Linear Regression code
code = mymlscratch.get_linear_regression_code()
print(code)

# Save to a file
with open('linear_regression.py', 'w') as f:
    f.write(code)

# Execute the code
exec(code)

# List all available algorithms
algorithms = mymlscratch.list_available_algorithms()
print(algorithms)
```

## Available Algorithms

- **Linear Regression** - `get_linear_regression_code()`
- **Logistic Regression** - `get_logistic_regression_code()`
- **K-Nearest Neighbors** - `get_knn_code()`
- **Decision Tree** - `get_decision_tree_code()` *(coming soon)*
- **K-Means Clustering** - `get_kmeans_code()` *(coming soon)*

## Features

- ✅ Pure Python/NumPy implementations
- ✅ Complete, runnable source code
- ✅ Educational comments and docstrings
- ✅ Example usage included in each algorithm
- ✅ No dependencies except NumPy

## Use Cases

- **Learning**: Study how ML algorithms work under the hood
- **Teaching**: Use in educational materials and courses
- **Customization**: Get base code to modify for specific needs
- **Prototyping**: Quick algorithm implementations for experiments

## Example
```python
import mymlscratch
import numpy as np

# Get the code
code = mymlscratch.get_linear_regression_code()

# Execute it
exec(code)

# Now you can use the LinearRegression class
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

model = LinearRegression(learning_rate=0.01, n_iterations=1000)
model.fit(X, y)
predictions = model.predict(X)

print("Predictions:", predictions)
```

## License

MIT License - see LICENSE file for details

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
ctpnr 1.0.1
Command-line utility to check Indian Railways PNR status using ConfirmTKT API
Requires Python: >=3.8

# ctpnr

A lightweight command-line tool to fetch **Indian Railways PNR Status** using the ConfirmTKT API.

---

## 📦 Installation

Install locally from the project folder:

```bash
pip install ctpnr
````

---

## 🚀 Usage

### Basic Usage

```bash
ctpnr 6261386648
```

### Raw JSON Output

```bash
ctpnr 6261386648 --json
```

---

## ⭐ Example Output

### Command:

```bash
ctpnr 6261386648
```

### Result:

```
===== PNR STATUS =====
PNR: 6261386648
Train: 12792 - SECUNDERABAD EX
From: DNR → To: NGP
Date of Journey: 24-11-2025
Class: SL
Chart Prepared: False

--- Passenger Status ---
Passenger 1:
  Booking Status : PQWL 65
  Current Status : PQWL 44
  Prediction     : 60% Chance

Passenger 2:
  Booking Status : PQWL 66
  Current Status : PQWL 45
  Prediction     : 59% Chance

Departure Time     : 12:15
Arrival Time       : 11:00
Expected Platform  : 4
=======================
```

---

## 📚 Features

* ✔ Fast CLI tool
* ✔ JSON output mode
* ✔ Accurate prediction (from ConfirmTKT API)
* ✔ Cross-platform (Windows, macOS, Linux)

---

## 🔮 Upcoming Improvements (Optional)

I can add more features if you want:

* 🎨 **Colored Terminal Output**
* 💾 **Caching** (faster next runs)
* 🔁 **Auto retry on failed API**
* 🖥 **GUI Version**
* 📦 **Publish to PyPI**

Just say:
**“add colors”**
or **“publish to PyPI”**
or **“make GUI version”**

---

## 📄 License

MIT License © 2025

```

---
tinyfan 0.2.9
Tiny Argo Workflows python devkit
Requires Python: >=3.12
Keywords: argo-workflow, pipeline, tinyfan, workflow

# Tinyfan

Tinyfan: Minimalist Data Pipeline Kit - Generate Argo Workflows with Python

# Features

* Generate Argo Workflows manifests from Python data pipeline definitions.
* Ease of Use and highly extendable
* Intuitive data model abstraction: let Argo handle orchestration—we focus on the data.
* Argo Workflows is notably lightweight and powerful – and so are we!
* Enhanced DevOps Experience: easly testable, Cloud Native and GitOps-ready.

# Our Goal

* **Minimize mental overhead** when building data pipelines.

# Not Our Goal

* **Full-featured orchestration framework:** We don't aim to be a battery-powered, comprehensive data pipeline orchestration solution.  
  No databases, web servers, or controllers—just a data pipeline compiler. Let's Algo Workflows handle all the complexity.

# Installation

```
# Requires Python 3.10+
pipx install tinyfan
```

# Tiny Example

```python
# main.py

# Asset definitions

from tinyfan import asset

@asset(schedule="*/3 * * * *")
def world() -> str:
    return "world"

@asset()
def greeting(world: str):
    print("hello " + world)
```

```shell
# Apply the changes to argo workflow

tinyfan main.py | kubectl apply -f -
```

# Real World Example (still tiny though!)

Comming soon



# License

This project is licensed under the MIT License.
sat-utils 1.7.6
Contains a collection of shared utility functions
Requires Python: >=3.9

# SAT Utilities

This repository contains a collection of shared utility functions.

- Slack: A class to upload files to our Slack workspace
- SATLogger: A standard logger for SAT projects

## Installation

```shell
# Install the package from private PyPI (CLI)
$ pip install sat-utils
```

## Usage

### Slack

```python
from sat.slack import Slack

message = Slack(token="abc123-8dkhnna-97hasdj-xyz")
message.upload_file(channel="support", file_path="C:/files", file_name="support.pdf", file_type="", title="Support manual v3.2", initial_comment="Woot!")
```

### SATLogger

```python
from sat.logs import SATLogger
logger = SATLogger(__name__)

...
logger.info("Hello, world!")
```

### Gravity Forms

Three environment variables are required to authenticate with the Gravity Forms API.

- GRAVITY_FORMS_CONSUMER_KEY
- GRAVITY_FORMS_CONSUMER_SECRET
- GRAVITY_FORMS_BASE_URL

Alternatively, these values can be passed into the GravityForms initialization as parameters.

```python
from sat.gravity_forms import GravityForms


gravity = GravityForms()
cards_requested = gravity.get("/forms/3/entries")
```

## Development

### Setup

Ensure you are in a virtual environment with Python 3.9.6 or higher.

```shell
> make setup
```

### Add dependencies

#### Updating Requirements

This project uses `pip-tools` to manage requirements. To update the requirements add your requirement
to the `pyproject.toml` file.

For dependencies required to run the app in production, add them to the `pyproject.toml` file under the `[project]` section.

```toml
[project]
...
dependencies = [
    "fastapi>=0.95.1, <1.0.0",
    "pyjwt>=2.6.0, <3.0.0",
    "...",
    "<YOUR NEW REQUIREMENT HERE>",
    "...",
]
```

For developer dependencies required or nice to have for development, add them to the `pyproject.toml` file under the `[project.optional-dependencies]` section.

```toml
[project.optional-dependencies]
dev = [
    "pytest>=6.2.5, <7.0.0",
    "...",
    "<YOUR NEW DEV REQUIREMENT HERE>",
    "...",
]
```

When you have add the dependency run:

```shell
> make update-requirements
```

## Build and Publish

Update the version in `pyproject.toml` before building.

### Build

```shell
> flit build
```

### Publish

As long as your PyPI credentials are set up correctly, you can publish to PyPI with the following command:

```shell
> flit publish
```