semvec/pypi-packages huggingface dataset:
helpit 1.0.0
Ask OpenAI-compatible models about a Python object with runtime metadata and optional help() snippets.
Requires Python: >=3.9

# helpit

[![PyPI](https://img.shields.io/pypi/v/helpit.svg)](https://pypi.org/project/helpit/)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

**Python's `help()` is great… until it isn’t.**

`helpit` lets you ask questions about `help()` output and get answers tailored to the **exact object in front of you**.

<video src="assets/helpit_gif.mp4" width="720" controls muted loop></video>

---

## Install

```bash
pip install helpit
```

---

## Quickstart

```python
from openai import OpenAI
from helpit import helpit, set_default_client

set_default_client(OpenAI())  

import torch

x = torch.randn(1, 32, 1)
helpit(
    x.squeeze,
    "How do I remove the last dimension and keep the leading dim?",
)
```

---

## Examples

```python
import pandas as pd
from helpit import helpit

df = pd.DataFrame({"city": [None, "ZRH", None], "sales": [3, 10, 2]})

helpit(df, "Group by city, keep NaNs, and sum sales—what's the right dropna setting?")
```


```python
from helpit import helpit

def stream():
    for i in range(1_000_000):
        yield i

it = stream()
helpit(it, "How do I safely consume only the first 10 items without exhausting the iterator?")
```

```python
from sklearn.ensemble import RandomForestClassifier
from helpit import helpit

rf = RandomForestClassifier(
    n_estimators=300,
    max_depth=None,
    min_samples_split=2,
    min_samples_leaf=1,
    max_features="sqrt",
    random_state=0,
)

helpit(rf, "Which hyperparameters matter most for overfitting here? Show how to adjust them.")
```


---

## When docs matter 📚 (grounded answers)

`helpit` can attach the most relevant `help()` snippets when `add_documentation=True`, keeping answers grounded without dumping full docs.

```python
from transformers import pipeline
from helpit import helpit

pipe = pipeline("sentiment-analysis")

helpit(pipe, "How does this pipeline work?", add_documentation=True)
```

---

## Run locally 🏠 (fast + private)

`helpit` talks to the OpenAI **Responses API**. Any compatible local server works.

```python
from openai import OpenAI
from helpit import helpit

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",   # any token works
)

helpit(
    len,
    "How does len behave on nested lists?",
    model="llama3.2",
    openai_client=client,
)
```

---

## Tests

```bash
python3 -m unittest discover -s tests -v
```
generic-request-signer 2.0.3
A python library for signing http requests.

I simple utility to sign http requests given some private key

To run the python tests

1. pip install -r requirements/test.txt
2. nosetests

To run the example using flask

1. pip install -r requirements/example.txt
2. python app.py
3. open a browser and point to http://localhost:5000/

After you invoke the GET request above you should see a request in the terminal

GET /verify?__client_id=example&username=foo&password=bar&__signature=somestrangestuffhere

This shows the client in action but at this time our example does not truly verify the signed request
django-request-signer 5.1.4
A python library for signing http requests.

.. _django_request_signer:

Version 5 is compatible with Python 3 and Django 1.8+

*********************
Django Request Signer
*********************

Version 5 removes the AuthorizedClients model and does not look there for client ids or private keys.

Client ids and private keys must be in API_KEYS in settings.

```
API_KEYS = {'client_id_X': 'private_key_X'}
```

No data are removed or tables dropped. You can make the migrations that will do such.
airtable-db-export 0.4.2
Download data from Airtable to a SQL database.
Requires Python: >=3.12

# Airtable DB Export (ADBE)

Export Airtable tables to a SQL database.

## Background

[Airtable](https://airtable.com/) is a low-code platform that allows individuals, teams, and
businesses to build database-backed applications. As a low-code tool Airtable is optimized for
non-technical users to be able to create new tables, forms and interfaces quickly.

An individual or organization using Airtable may find that after building a quick solution
or prototype in Airtable, they want to export this data into a structure that is optimized
for use in tranditional developement or for further analysis.

[Airtable DB Export](https://github.com/sivy/airtable-db-export) enables developers to download
tables of data from Airtable as JSON files, configure the mapping of Airtable tables and fields
to SQL tables and columns, and (currently) create and load a DuckDB SQL database with downloaded
data.



## Documentation

Read more on [ReadTheDocs](https://airtable-db-export.readthedocs.io/en/latest/).

### Using pip

`pip install airtable-db-export`

### Using uv

`uv add airtable-db-export`

## Usage

```
Usage: adbe [OPTIONS] COMMAND [ARGS]...

  Main entry point for the CLI.

Options:
  -c, --config TEXT
  --base-dir TEXT
  --schemas-file TEXT
  --data-dir TEXT
  --sql-dir TEXT
  --db-file TEXT
  --help               Show this message and exit.

Commands:
  all
  create-db            Generate database definitions
  create-sql           Generate database definitions
  download-json
  generate-schema-map
  load-db
```

## Configuration

## Similar  projects

- Simon WIllison's [Airtable Export](https://github.com/simonw/airtable-export)
torchfsm 0.0.6
Fourier Spectral Method with PyTorch

<h1 align="center">
  <br>TorchFSM<br>
</h1>
<h4 align="center">Fourier Spectral Method with PyTorch</h4>
<p align="center">
  [<a href="https://qiauil.github.io/torchfsm/"> Documentation & Examples</a>]
</p>

## TL;DR
`TorchFSM` is a PyTorch-based library for solving PDEs using Fourier spectral method. It is designed for physics-based deep learning and differentiable simulations.

## Feature

* **Modular by design**: TorchFSM offers a modular architecture with essential mathematical operators—like divergence, gradient, and convection—so you can build custom solvers like stacking building blocks, quickly and intuitively.

* **GPU-accelerated**: TorchFSM leverages GPU computing to speed up simulations dramatically. Run complex 3D PDEs in minutes, not hours, with seamless hardware acceleration.

* **Batched simulation support**: Built on PyTorch, TorchFSM enables batched simulations with varied initial conditions—ideal for parameter sweeps, uncertainty quantification, or ensemble analysis.

* **Differentiable and ML-ready**: Fully differentiable by design, TorchFSM integrates naturally with machine learning workflows—for residual operators, differentiable physics, or dataset generation.

## Documentations

Check 👉 [here](https://qiauil.github.io/torchfsm/) for more details.
python3-cyberfusion-nodechup 1.4
Use nodechup to install multiple Node.js versions.

# python3-cyberfusion-nodechup

Use nodechup to install multiple Node.js versions.

Need multiple Node.js versions on your system? Installing multiple Node.js Debian packages is not supported. nodechup allows you to install multiple Node.js versions with a Python API and a CLI.

# Install

## PyPI

Run the following command to install the package from PyPI:

    pip3 install python3-cyberfusion-nodechup

## Debian

Run the following commands to build a Debian package:

    mk-build-deps -i -t 'apt -o Debug::pkgProblemResolver=yes --no-install-recommends -y'
    dpkg-buildpackage -us -uc

# Configure

No configuration is supported.

# Usage

## Concepts

* Base directory: contains one or more Node.js installations.
* Node.js installation: represents Node.js version.

### Notes about point release

#### Omit point release

Node.js versions must contain major/minor release. Point release is optional.

If the point release is omitted, the most recent point release for the specified major/minor release is used.

#### Release symlink

If 'update default version' is true, nodechup creates a symlink in the base directory path from the major/minor release to the installed point release.

For example:

    /usr/local/lib/nodejs/14.19 -> /usr/local/lib/nodejs/14.19.1

### Notes about base directory

* The [Node.js wiki](https://github.com/nodejs/help/wiki/Installation) recommends using the base directory `/usr/local/lib/nodejs`.
* The base directory is created if it doesn't exist yet.

## Python API

Use the Python API as follows:

```python
import os

from cyberfusion.Nodechup import BaseDirectory, Installation

Installation(base_directory=BaseDirectory(path="/usr/local/lib/nodejs/"), version="14.19.1").download(
    update_default_version=True)
```

## CLI

Run the `nodechup` command without any arguments for help.
snras 1.0.0
A robust metric for exoplanet transit vetting in heteroscedastic light curves
Requires Python: >=3.7

# SNRAS-Lib: Signal-to-Noise Ratio with Adjusted Stability

Developed by **Ahmed Sattar Jabbar**, this library provides a robust statistical metric for 
vetting exoplanet candidates in heteroscedastic light curves.

## Installation
```bash
pip install snras
turtlelab 0.1.0
An interactive graphical interface to make turtle programming easier & better
Requires Python: >=3.11
Keywords: turtle, turtleide, interactiveturtle

# ✨Interactive Turtle Lab 🐢🔬

A simple Graphical Interface for executing Python turtle graphics from external files interactively, to make turtle programming easier & better.

## Features
- Browse and load `.py` files with turtle code
- A small built-in integrated code editor inside
- Embedded turtle canvas (no external windows)
- Built-in sandboxed execution environment

## Usage

```cli
turtlelab  # Execute from CLI
```

1. Click **Browse...** to select a Python file
2. Review the code from the editor
3. Click **Run** to execute (Ctrl+Enter). It will automatically sync from the file loaded as well before running. So you can edit the file in your personal Editor and just click 'Run' and it will auto-fetch code from the file.
4. Click **Clear** to reset the canvas

## Important Rules

- Use provided `t` (turtle) and `screen` objects directly
- Do NOT use `import turtle` in your code

## Example Files

Check the `examples/` folder (inside ..Python/Lib/site-packages/turtlelab ) for sample scripts:

- `square.py` - Draw a square
- `star.py` - Draw a star
- `spiral.py` - Draw a spiral

## API

Your code has access to:
- `t` - RawTurtle object with full turtle graphics API
- `screen` - TurtleScreen for the canvas

**More features will be coming soon!**