semvec/pypi-packages huggingface dataset:
Memorix 0.0.2
A modern way to give your AI Memory
Requires Python: >=3.10
Keywords: AI, Memory

﻿
---

# Memorix – Coming Soon!

---

## License

This project is licensed under the [Apache License, Version 2.0](LICENSE).
Copyright 2025 Tristan McBride Sr.

---

## Acknowledgements

Project by:
- Tristan McBride Sr.
- Sybil

---
nuanced 0.1.9
Nuanced Python
Requires Python: >=3.10

## nuanced

nuanced uses static analysis to generate enriched function call graphs of Python packages, providing agents and coding assistants with deeper understanding of code behavior.

Docs: https://docs.nuanced.dev

### CLI Quick Start

**Install nuanced**

```bash
uv tool install nuanced
```

**Initialize a graph**

```bash
cd my_project
nuanced init path/to/my_package
```

**Enrich and add context**

```bash
nuanced enrich path/to/my_package/file.py some_function_name > some_function_name_subgraph.json
```

```bash
echo "test_some_function_name_succeeds is failing. Can you use the call graph in
some_function_name_subgraph.json to debug and update the relevant code to make
the test pass?" > agent_prompt.txt
```

### Contributing

#### Setup

1. Set up and activate virtualenv

```bash
% cd nuanced
% uv venv
% source .venv/bin/activate
```

2. Install dependencies

```bash
% uv sync
```

#### Running tests

```bash
% pytest
```

#### Releasing new versions

https://docs.nuanced.dev/versioning#release-process
apitest-cli 0.1.0
A CLI tool for testing HTTP APIs with request saving, templates, and sentiment analysis.
Requires Python: >=3.6

# API Test CLI

A command-line tool for testing HTTP APIs. Send requests, save them, create templates, search response content, and optionally analyze response sentiment.

## Installation

```bash
pip install apitest-cli
```

## Usage

Run `apitest --help` for a list of commands. Below are examples:

### Send a GET Request
```bash
apitest get https://jsonplaceholder.typicode.com/posts
```

### Search in Response
```bash
apitest get https://jsonplaceholder.typicode.com/posts --search "title"
```

### Analyze Sentiment
```bash
apitest get https://jsonplaceholder.typicode.com/posts --analyze
```

### Send a POST Request
```bash
apitest post https://jsonplaceholder.typicode.com/posts --data '{"title": "Hello", "body": "Test"}' --header "Content-Type: application/json"
```

### Save a Request
```bash
apitest save myrequest
```

### Replay a Saved Request
```bash
apitest replay myrequest
```

### Create a Template
```bash
apitest template mytemplate --url https://jsonplaceholder.typicode.com/posts --method GET --header "Content-Type: application/json"
```

### Use a Template
```bash
apitest use-template mytemplate
```

## Features
- Send GET/POST requests with custom headers and JSON data.
- Save and replay requests.
- Create and use request templates.
- Search keywords in responses with highlighted results.
- Optional sentiment analysis of response text (English only).
- Responses saved as JSON files in `responses/` directory.

## Requirements
- Python 3.8+
- Install dependencies: `pip install click requests rich textblob`
- Download TextBlob corpora (optional): `python -m textblob.download_corpora`

## License
MIT
jarviscg 0.1.0rc7
Call graph generator for Python forked from pythonJaRvis/pythonJaRvis.github.io
Requires Python: >=3.8

## jarviscg

Nuanced's fork of the [pythonJaRvis](https://github.com/pythonJaRvis/pythonJaRvis.github.io) Python call graph generator.

Note: The benchmarking and evaluation scripts in the `evaluation` directory have not been updated to run on this fork.

Prerequisites:

* Python >= 3.8
* uv

## Usage

jarviscg command-line usage:

```bash
% uv tool install git+https://github.com/nuanced-dev/jarviscg
% jarviscg -h
usage: jarviscg [-h] [--package PACKAGE] [--decy] [--precision] [--entry_point [ENTRY_POINT ...]] [-o OUTPUT] [module ...]

positional arguments:
  module                Modules to be processed

options:
  -h, --help            show this help message and exit
  --package PACKAGE     Package containing the code to be analyzed
  --decy                whether iterate the dependency
  --precision           whether flow-sensitive
  --entry_point [ENTRY_POINT ...]
                        Entry Points to be processed
  -o OUTPUT, --output OUTPUT
                        Output path
```

## Setup

```bash
% cd jarviscg
% uv venv
% source .venv/bin/activate
% uv sync
```

## Running pytest tests

```bash
% source .venv/bin/activate
% pytest tests/
```
jaxkineticmodel 0.0.4
A tool for building, training, and simulating metabolic kinetic models in Jax/Diffrax
Requires Python: >=3.9



# JaxKineticModel
Python package to simulate, build, and train kinetic models.

### Installation

```
git clone https://github.com/AbeelLab/jaxkineticmodel.git
python -m pip install .
```


### Documentation package 
Documentation on jaxkineticmodel can be found [here](https://abeellab.github.io/jaxkineticmodel/)

### Running experiments as reported in paper
- Datasets generated from SBML models can be found in [datasets](datasets/)
- Glucose pulse datasets can be found [here](datasets/VanHeerden_Glucose_Pulse/)
- Experiments as reported in [1] can be found in [scripts](scripts/). Results from these experiments can found in the 
[4tu-repository](https://data.4tu.nl/private_datasets/o-HY8kDJhoCXyNOijO9Eaylje7E2dU-ex-edboPBDZ8)

### Source code
All data, source code, scripts, and results can be found in the [4tu-repository](https://data.4tu.nl/private_datasets/o-HY8kDJhoCXyNOijO9Eaylje7E2dU-ex-edboPBDZ8)

### Reference
If you use this package in your work, please reference:

[1] Lent, P. V., Bunkova, O., Planken, L., Schmitz, J., & Abeel, T. (2024). 
Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models. bioRxiv, 2024-12.