semvec/pypi-packages huggingface dataset:
gradio-canvaworkflow 0.0.2
A custom component to build flows and graphs
Requires Python: >=3.8
Keywords: gradio-custom-component, gradio-template-canvaworkflow

---
tags: [gradio-custom-component, SimpleTextbox, gradio-custom-component, gradio-template-CanvaWorkflow]
title: gradio_canvaworkflow
short_description: A custom component to build flows and graphs
colorFrom: blue
colorTo: yellow
sdk: gradio
pinned: false
app_file: space.py
---

# gradio_canvaworkflow

You can auto-generate documentation for your custom component with the `gradio cc docs` command.
You can also edit this file however you like.


gradio cc dev --gradio-path "C:\Users\k.mace\neo-simple-front\neo_env\Scripts\gradio.exe" --python-path "C:\Users\k.mace\neo-simple-front\neo_env\Scripts\python.exe"
molbridge 0.0.1a0
Interface between rdkit and ASE
Requires Python: >=3.10

[![zincware](https://img.shields.io/badge/Powered%20by-zincware-darkcyan)](https://github.com/zincware)
[![codecov](https://codecov.io/gh/zincware/rdkit2ase/graph/badge.svg?token=Q0VIN03185)](https://codecov.io/gh/zincware/rdkit2ase)
[![PyPI version](https://badge.fury.io/py/rdkit2ase.svg)](https://badge.fury.io/py/rdkit2ase)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15423477.svg)](https://doi.org/10.5281/zenodo.15423477)
[![Docs](https://github.com/zincware/rdkit2ase/actions/workflows/pages.yaml/badge.svg)](https://zincware.github.io/rdkit2ase/)
[![status](https://joss.theoj.org/papers/4c1ec2a0b66ed6baaf3fe93066696d5f/status.svg)](https://joss.theoj.org/papers/4c1ec2a0b66ed6baaf3fe93066696d5f)

# rdkit2ase - Interface between the rdkit and ASE package.

Installation via `pip install rdkit2ase`. For more information please visit the
[documentation](https://zincware.github.io/rdkit2ase/).

A common use case is to create 3D structures from SMILES strings. This can be
achieved using the `rdkit2ase.rdkit2ase` function.

```py
import ase
from rdkit import Chem
from rdkit2ase import rdkit2ase, ase2rdkit

mol = Chem.MolFromSmiles("O")
atoms: ase.Atoms = rdkit2ase(mol)
mol = ase2rdkit(atoms)
```

Because this is such a common use case, there is a convenience function
`rdkit2ase.smiles2atoms` that combines the two steps.

```py
import ase
from rdkit2ase import smiles2atoms

atoms: ase.Atoms = smiles2atoms("O")

print(atoms)
>>> Atoms(symbols='OH2', pbc=False)
```

## Packmol Interface

Given the molecular units, you can build periodic boxes with a given density
using the `rdkit2ase.pack` function.

If you have [packmol](https://github.com/m3g/packmol) (at least `v20.15.0`) you
can use the rdkit2ase interface as follows:

```py
from rdkit2ase import pack, smiles2conformers

water = smiles2conformers("O", 2)
ethanol = smiles2conformers("CCO", 5)
density = 1000  # kg/m^3
box = pack([water, ethanol], [7, 5], density)
print(box)
>>> Atoms(symbols='C10H44O12', pbc=True, cell=[8.4, 8.4, 8.4])
```

Many additional features are described in the
[documentation](https://zincware.github.io/rdkit2ase/).
opencode-cli 0.1.1
CLI tool for opencode server API
Requires Python: >=3.10
Keywords: opencode, ai, agent, cli

# opencode-cli

CLI tool for [opencode](https://github.com/sst/opencode) server API. Built on top of the official [opencode-ai](https://pypi.org/project/opencode-ai/) Python SDK.

## Installation

```bash
pip install opencode-cli
```

## Usage

The `oc` command provides easy access to opencode server operations:

```bash
# List all sessions
oc sessions

# List sessions as JSON
oc sessions --json

# Get session info (by ID or title)
oc info headless-1
oc info ses_abc123

# List messages in a session
oc messages headless-1

# Send a message to a session
oc send headless-1 "hello world"

# Create a new session
oc create --title "My Session"
```

## Features

- **Title resolution**: Use session titles instead of IDs (e.g., `headless-1` instead of `ses_abc123`)
- **Rich formatting**: Beautiful tables and colored output
- **JSON output**: Use `--json` flag for machine-readable output
- **Built on official SDK**: Uses the official `opencode-ai` package under the hood

## Environment Variables

- `OPENCODE_SERVER` - Override default server URL (default: `http://localhost:36000`)

## For Python SDK

If you need the full Python SDK (not just CLI), use the official package:

```bash
pip install opencode-ai
```

See [official SDK docs](https://github.com/sst/opencode-sdk-python) for Python API usage.

## License

MIT
benson 0.1.5
A multiverse data cleaning tool for agent pickup.
Requires Python: >=3.10

# Benson

Benson is the powerhouse behind `Phil`, the advanced imputation engine designed to intelligently handle missing data in complex datasets. Whether you're dealing with high-dimensional gaps, inconsistent encodings, or stubborn anomalies, Benson ensures your data gets the cleanup it deserves—efficiently and at scale.

🔥 **Capabilities**

• Phil 🧩: _PHIL: a Progressive High-Dimensional Imputation Lab._

👉 Phil is an advanced data imputation tool that combines scikit-learn's IterativeImputer with topological methods to generate and analyze multiple versions of a dataset. It allows users to impute missing data using various techniques, generate representations of imputed datasets, and democratically select a representative version. It also comes equipped with a transformer, `PhilTransformer` that is designed to seamlessly integrate with scikit-learn pipelines. By implementing the transformer API, it allows users to incorporate advanced imputation techniques directly into their machine learning workflows, ensuring a smooth and efficient data preprocessing experience.

📦 **Installation**
```
pip install benson
```


🚀 **Coming Soon**

• Bob 🛠️: A structured data repair module that cleans, normalizes, and reconciles inconsistencies in your datasets.

• AgentBenson 🤖: A seamless integration layer for popular agentic frameworks, enabling automated data cleaning and repair with minimal intervention.

Stay tuned for more updates as Benson continues to evolve!
spark3d-batch 0.1.7
Launch SPARK3D simulations by batch; list of powers or accelerating fields.
Requires Python: >=3.9
Keywords: post treatment, multipactor

# Spark3DBatch
Python library to launch (batch) Spark3D simulations.
`SPARK3D` versions `> 2016` should be supported, but last tests were done on
`SPARK3D 2025 SP4`.

## Installation
1. Create a dedicated Python environment, activate it.
2. Run `pip install spark3d-batch`.

> [!NOTE]
> If you are completely new to Python and these instructions are unclear, check [this tutorial](https://python-guide.readthedocs.io/en/latest/).
> In particular, you will want to:
> 1. [Install Python](https://python-guide.readthedocs.io/en/latest/starting/installation/) 3.9 or higher.
> 2. [Learn to use Python environments](https://python-guide.readthedocs.io/en/latest/dev/virtualenvs/), `pipenv` or `virtualenv`.
> 3. [Install a Python IDE](https://python-guide.readthedocs.io/en/latest/dev/env/#ides) such as Spyder or VSCode.

3. Set the environment variable `$SPARK3DPATH` to point to the folder containing the SPARK3D executable.
   In general, adding the following line to your `.bashrc` will do the trick:
   ```bash
   export SPARK3DPATH="/opt/cst/CST_Studio_Suite_20XX/SPARK3D/"
   ```

> [!IMPORTANT]
> Not tested on Windows.

## Usage

Two options:

1. Provide a `SPKX` file,
2. Provide a `XML` file and a field map.
   - `XML` can be generated by unzipping a `SPKX` file, allowing you to modify
   the simulation parameters with a script.
   - Field maps can be `F3E`, `DSP` or `MFE`.

## To-do

### Important
- [x] Allow for control over accelerating field instead of power
- [x] Change how the path to SPARK3D is declared. Currently not robust at all.

### Secondary
- [x] Package test data

### Tertiary
- [x] Complete installation instructions
- [ ] Static typing to detect allowed keys in `XML`
- [ ] CLI interface
- [x] On `pip`
- [ ] Tests
- [x] Use `logging` instead of `printc`
- [x] Add `release.py`