semvec/pypi-packages huggingface dataset:
snekuity 0.1.1
Pythonic API for GnuCash
Requires Python: >=3.11

# snekuity

snekuity (pronounced like “equity”) is a Pythonic API for interacting
with GnuCash.

## Installation

### Installing from PyPI

To install snekuity from PyPI, open a shell and run:

```shell
pip install snekuity
```

If that doesn’t work, try:

```shell
python3 -m pip install snekuity
```

## Usage

See
[`USAGE.md`](https://github.com/claui/snekuity/blob/main/USAGE.md)
or `man snekuity` for details.

## Contributing to snekuity

See [`CONTRIBUTING.md`](https://github.com/claui/snekuity/blob/main/CONTRIBUTING.md).

## License

Copyright (c) 2025 Claudia Pellegrino

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
For a copy of the License, see [LICENSE](https://raw.githubusercontent.com/claui/snekuity/refs/heads/main/LICENSE).
oculus-python 0.0.2a1
A python library for importing and exporting Blueprint Subsea Oculus imaging sonar log files.
Requires Python: >=3.10

# README

`oculus-python`, a python library for importing and exporting [Blueprint Subsea Oculus imaging sonar](https://www.blueprintsubsea.com/oculus/) log files.

## Supported log import/export types

| Type          | Import | Export |
|---------------|--------|--------|
| v1 .oculus    | X | X      |
| v2 .oculus    | X | X\*    |
| [APL ROS1](https://github.com/apl-ocean-engineering/sonar_image_proc.git)  | X | -      |
| netCDF4       | - | X      |
| HEVC .mp4     | - | X      |
| Lossless .avi | - | X      |

\* Experimental.

## Installation

`pip install oculus-python`

## CLI Usage

For these examples, assume you have a .oculus log file called "test.oculus"

Convert to NETCDF4:

`bps_oculus_io test.oculus --output netcdf4`

Convert to lossy video:

`bps_oculus_io test.oculus --output hevc --param color=True`

Convert to lossless video:

`bps_oculus_io test.oculus --output lossless --param color=True`

## Python Usage

See the [examples](https://gitlab.gbar.dtu.dk/fletho/oculus-python/-/tree/main/examples) folder for some simple usages.
lightstream 1.0.6
A Pytorch lightning framework for streaming CNN's
Requires Python: >=3.10

# Lightstream

Lightstream is a Pytorch library to train CNN's with large input images. Parsing large inputs is achieved through a combination of 
gradient checkpointing and tiling the input image. For a full overview of the streaming algorithm, please read the article:

[1] H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images," in IEEE Transactions on Pattern Analysis and Machine Intelligence, [doi: 10.1109/TPAMI.2020.3019563](https://ieeexplore.ieee.org/abstract/document/9178453)


![](docs/images/ddh_08_06_2022.gif)

## Installation
The lightstream repository can be installed using pip, or you can alternatively clone the git repository and build the wheel.

```python
pip install lightstream
```

We also recommend to install the albumentationsxl package, which is an albumentations fork with a pyvips backend to preprocess large images

```python
pip install albumentationsxl
```


### Requirements
The lightstream package requires PyTorch version 2.0 or greater to be installed, along with Pytorch lightning version 2.0.0 or greater. 
- PyTorch 2.0.0 or greater
- Pytorch Lightning 2.0.0 or greater
- Albumentationsxl (recommended)
Furthermore, we recommend a GPU with at least 10 GB of VRAM, and a system with at least 32 GB of RAM.


## Using lightstream with pre-trained networks
lightstream offers several out-of-the-box streaming equivalents of ImageNet classifiers. Currently ResNet and ConvNext architectures are supported

```python
import torch.nn
from lightstream.models.resnet.resnet import StreamingResNet

model = StreamingResNet(model_name="resnet18", tile_size=2880, loss_fn=torch.nn.CrossEntropyLoss(), train_streaming_layers=True)
```

## Documentation
The documentation can be found at https://diagnijmegen.github.io/lightstream/

Alternatively the documentation can be generated locally using 

```
make docs
```
##