semvec/pypi-packages huggingface dataset:
sense-table 0.0.1rc19
Smoothly make sense of your large-scale multi-modal tabular data
Requires Python: >=3.9

# SenseTable


![SenseTable](http://stcdn.smoosense.ai/SenseTable-light.svg)


SenseTable is a purpose-built tool that turns repeititive and manual work in ML data exploration into a smooth intuitive flow-experience. Unlike traditional analytics tools, it’s designed for the ambiguous, branching questions that crop up at every stage of the ML lifecycle—from checking raw data quality and uncovering outliers to investigating model failures and monitoring drift. SenseTable automates 80% of repetitive tasks, works seamlessly with formats like CSV, Parquet, JSONL, and Lance, whether stored locally or in the cloud, and offers a flexible, IDE-style interface with powerful visualization, drill-through, and large-scale data handling.

SenseTable empowers you spend less time writing ad-hoc scripts and more time in flow, finding the insights that matter.

## Quick start
For prerequisites, install [uv](https://docs.astral.sh/uv/) (an extremely fast Python package manager):

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
Start a new terminal after you just installed `uv`. Then install or update SenseTable CLI:
```bash
uv tool install -U sense-table
```

![CLI](http://stcdn.smoosense.ai/guide-cli-install.gif)


## Guide
For more details, please visit

<https://smoosenseai.github.io/sense-table/guide/>
drpangloss 0.0.2
Python package to fit interferometric data, accelerated using Jax.
Requires Python: >=3.11
Keywords: python, optics, machine-learning, jax, autodiff, differentiable, diffraction, simulation, telescope, drpangloss

# drpangloss
[![PyPI version](https://badge.fury.io/py/drpangloss.svg)](https://badge.fury.io/py/drpangloss)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![integration](https://github.com/benjaminpope/drpangloss/actions/workflows/tests.yml/badge.svg)](https://github.com/benjaminpope/drpangloss/actions/workflows/tests.yml)
[![Documentation](https://github.com/benjaminpope/drpangloss/actions/workflows/documentation.yml/badge.svg)](https://benjaminpope.github.io/drpangloss/)

the best of all possible interferometry models

Contributors: Dori Blakely, [Benjamin Pope](https://github.com/benjaminpope)

## What is drpangloss?

drpangloss is a package for modelling optical interferometry data in Jax.

## Installation

drpangloss is hosted on PyPI (though this is currently a placeholder): the easiest way to install this is with 

```
pip install drpangloss
```

You can also build from source. To do so, clone the git repo, enter the directory, and run

```
pip install .
```

We encourage the creation of a virtual enironment to run drpangloss to prevent software conflicts as we keep the software up to date with the lastest version of the core packages.


## Use & Documentation

Documentation will be found [here](https://benjaminpope.github.io/drpangloss/), though this is currently a placeholder. 

## Collaboration & Development

We are always looking to collaborate and further develop this software! We have focused on flexibility and ease of development, so if you have a project you want to use drpangloss for, but it currently does not have the required capabilities, don't hesitate to [email me](mailto:b.pope@uq.edu.au) and we can discuss how to implement and merge it! Similarly you can take a look at the `CONTRIBUTING.md` file.

## Name

Why is it called drpangloss?

The leading optical interferometry model fitting code is [CANDID](https://github.com/amerand/CANDID). In Voltaire's *Candide*, Dr Pangloss' belief that we live in the best of all possible worlds is a satire of Leibniz' theodicy. But we *do* live in a world with Jax, so that if we can't optimize the world, at least we can optimize our fits to VLTI data.