semvec/pypi-packages huggingface dataset:
git-credential-op 0.1.0
A simple `git credential` helper for 1password CLI (op)
Requires Python: >=3.13

# git-credential-op

[![Changelog](https://img.shields.io/pypi/v/git-credential-op)](https://git.sr.ht/~nwgh/git-credential-op/log)
[![Tests](https://builds.sr.ht/~nwgh/git-credential-op.svg)](https://builds.sr.ht/~nwgh/git-credential-op?)
[![License](https://img.shields.io/badge/license-CC0%201.0-blue.svg)](https://git.sr.ht/~nwgh/git-credential-op/tree/main/item/LICENSE)

A simple `git credential` helper for 1password CLI (op)

## Installation

Install this tool using uv:

```bash
uv tool install git-credential-op
```

## Usage

For help, run:

```bash
git-credential-op --help
```

## Development

To contribute to this tool, first install `uv`. See [the uv documentation](https://docs.astral.sh/uv/getting-started/installation/) for how.

Next, checkout the code

```bash
git clone https://git.sr.ht/nwgh/git-credential-op
```

Then create a new virtual environment and sync the dependencies:

```bash
cd git-credential-op
uv sync
```

To run the tests:

```bash
uv run python -m pytest
```
streamlit-pdf 2.0.1
A Streamlit component for viewing PDF files
Requires Python: >=3.9
Keywords: streamlit, pdf, viewer, react, component

# streamlit-pdf

A lightweight **Streamlit** feature that seamlessly displays **PDF** files in your apps, allowing for simple, responsive, and clean document viewing with minimal effort.

---

## 📦 Installation

```bash
pip install streamlit[pdf]
```

Ensure you have **Streamlit** installed:

---

## 💡 Usage

Here's how to display a PDF file in your Streamlit app:

```python
import streamlit as st

# Display PDF from URL
st.pdf("https://example.com/document.pdf")

# Display with custom height
st.pdf("path/to/document.pdf", height=600)

# Display uploaded file
uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
if uploaded_file is not None:
    st.pdf(uploaded_file)
```

---

## ⚙️ API Reference

### `st.pdf(data, height=600)`

#### Parameters:

- **`data`** (_str | Path | bytes | BytesIO_): The PDF file to show. This can be one of the following:

  - A URL (string) for a hosted PDF file (`"https://example.com/doc.pdf"`)
  - A path to a local PDF file (`"./documents/sample.pdf"`)
  - A file-like object, e.g. a file opened with `open` or an `UploadedFile` returned by `st.file_uploader`
  - Raw bytes data

- **`height`** (_int_, optional): Height of the PDF viewer in pixels. Default: 600

---

## 🖼️ Examples

### Basic PDF Display

```python
import streamlit as st

st.title("PDF Viewer Example")

# Display a PDF from URL
st.pdf("https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf")
```

### File Upload with PDF Display

```python
import streamlit as st

st.title("Upload and View PDF")

uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
if uploaded_file is not None:
    st.pdf(uploaded_file)
```

### Custom Height

```python
import streamlit as st

# Display PDF with specific height
st.pdf("document.pdf", height=800)

# Display PDF with default height
st.pdf("document.pdf")
```

### Reading from Local File

```python
import streamlit as st

with open("document.pdf", "rb") as file:
    st.pdf(file.read(), height=700)
```

---

## 📝 Contributing

Feel free to file issues in [our Streamlit Repository](https://github.com/streamlit/streamlit/issues/new/choose).

Contributions are welcome 🚀, however, please inform us before building a feature.

### Development flow

Prerequisites:

```bash
uv venv .venv
source .venv/bin/activate
uv pip install -e '.[with-streamlit]' .
cd streamlit_pdf/frontend && npm install
```

- Build the frontend once:
  ```bash
  npm run build
  ```
- Or run the frontend in watch/development mode:
  ```bash
  npm run dev
  ```

---
aasp 0.1.1
Add your description here
Requires Python: >=3.13.7

# MLM25: MaveDB Amino Acid Substitution Prediction
## Summary
Understanding the functional consequences of genetic variants is a cornerstone of modern genomics. Multiplexed Assays of Variant Effect (MAVEs) provide high-throughput measurements of how thousands of variants impact gene function, and MaveDB is a growing repository of these datasets, but it is still expensive and time-consuming to experimentally test every variant. In this challenge, your task is to develop a machine learning model that can predict the functional outcomes of amino acid substitutions.

# Sources/Citations

[MaveDB](https://mavedb.org/)
> Rubin, A.F., Stone, J., Bianchi, A.H. et al. MaveDB 2024: a curated community database with over seven million variant effects from multiplexed functional assays. Genome Biol 26, 13 (2025). https://doi.org/10.1186/s13059-025-03476-y

[ESM C](https://www.evolutionaryscale.ai/blog/esm-cambrian)
> ESM Team. "ESM Cambrian: Revealing the mysteries of proteins with unsupervised learning." EvolutionaryScale, 2024, https://evolutionaryscale.ai/blog/esm-cambrian.
czi2ometiff 0.1.0
Convert czi to OME-TIFF
Requires Python: >=3.6

# czi2ometiff

`czi2ometiff` is a Python tool for converting **Zeiss CZI** microscopy files into **OME-TIFF**, a widely used open standard for bioimaging data.  
It provides a simple, reliable interface for extracting image data, channel names, and pixel sizes from CZI files and exporting them into high-quality OME-TIFF files.

## Features

- **Easy to use** — convert a CZI file to OME-TIFF with a single function call.
- **Automatic metadata extraction**:
  - Channel names (from CZI metadata if available)
  - Pixel sizes in X, Y, and Z (converted to micrometers)
- **Flexible output** — works with multi-channel and multi-Z images.
- **OME-TIFF pyramids** — optional downsampled pyramid levels for fast viewing in tools like QuPath, OMERO, and napari.
- **Built on numpy2ometiff** — leverages robust OME-TIFF writing from the `numpy2ometiff` library.

## Installation

Install directly via pip:

```bash
pip install czi2ometiff
```

## Example Usage

```python
from czi2ometiff import convert_czi_to_ometiff

# Input CZI file
input_file = "example_image.czi"

# Optional: specify output path
output_file = "example_image.ome.tiff"

# Convert to OME-TIFF
output_path = convert_czi_to_ometiff(
    input_path=input_file,
    output_path=output_file,   # Optional—defaults to input filename with .ome.tiff
    downsample_count=8          # Number of pyramid levels
)

print("Saved OME-TIFF:", output_path)
```

## How It Works

`czi2ometiff` automatically:

1. **Reads the CZI file** using `czifile`
2. **Extracts image data** as a NumPy array  
3. **Detects channel names** from CZI metadata  
4. **Extracts pixel sizes** (stored in meters in CZI; converted to micrometers)
5. **Reformats data** into the (Z, C, Y, X) layout expected by OME-TIFF
6. **Writes the final OME-TIFF** using the `numpy2ometiff.write_ome_tiff()` function

All required metadata is set for compatibility with standard bioimaging software.

## Contributing

Contributions are welcome!  
If you'd like to add features, improve metadata parsing, or enhance compatibility with other CZI variants:

1. Fork the repository  
2. Create a feature branch  
3. Submit a pull request  

For major changes, please open an issue first to discuss your ideas.

## License

This project is licensed under the BSD 3-Clause License — see the [LICENSE](LICENSE) file for details.