semvec/pypi-packages huggingface dataset:
binigogeo 0.0.5
2D and 3D geometry in Python.
License: MIT
Requires Python: >=3.10

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# KilleenGeo

<a href="https://pepy.tech/project/killeengeo">
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<a href="https://pypi.org/project/killeengeo/">
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</div>

2D and 3D geometry in Python. This library was originally a part of the
[DeepDRR](https://github.com/arcadelab/deepdrr) package, but has been split out into its own package
for easier use in other projects. It contains a lot of custom implementations with no overarching
design philosophy, other than getting me through my PhD.

The naming conventions and general approach to coordinate transforms adheres to [this](https://benjamindkilleen/files/frame_transforms.pdf) tutorial.

It is written solely in Python, and so is not particularly fast. It is also not particularly
well-tested, so use at your own risk.

## Installation

```bash
pip install killeengeo
```

## Usage

```python
import killeengeo as kg

# Define some points in a world coordinate frame
a = kg.point(1, 2, 3) # a point in 3D
b = kg.point(4, 5, 6) # another point in 3D
v = kg.vector(7, 8, 9) # vector
l = kg.line(a, b) # line connecting a and b
pl = kg.plane(a, v) # plane containing a, b, and normal v
# Also supported: line segments, rays

# Define some coordinate frames
world_from_A = kg.FrameTransform.from_translation(a) # coordinate frame with origin at a 
world_from_B = kg.FrameTransform.from_translation(b) # coordinate frame with origin at b

# Transforms to obtain points in those frames
B_from_world = world_from_B.inverse() # transform from B to world
A_from_world = world_from_A.inverse() # transform to A from world

# Transform between the new frames
A_from_B = A_from_world @ world_from_B # transform to B from A

# Objects in the new frame
a_in_A = A_from_world @ a
v_in_A = A_from_world @ v
l_in_A = A_from_world @ l
pl_in_A = A_from_world @ pl
```

Most of the time `kg` objects can be treated like numpy arrays, and they are converted to the expected form.

```python

import numpy as np

points = np.array([a, b]) # [2, 3] array where each row is a point
B2A = np.array(A_from_B) # [4, 4] array representing the transform from B to A
```

Above is the only use of the `X2Y` naming convention for frame transformations present here. For more information on why this variable naming convention is awful, see [this](https://benjamindkilleen/files/frame_transforms.pdf) tutorial.

## Documentation

Documentation is (maybe) available at [killeengeo.readthedocs.io](https://killeengeo.readthedocs.io/en/latest/).
natswork-server 0.0.1
Server library for NatsWork job processing system
License: MIT
Requires Python: >=3.8
Keywords: distributed, jobs, nats, queue, worker

# NatsWork Server (Python)

Server library for the NatsWork job processing system.

## Installation

```bash
pip install -e .
```

## Development

```bash
# Install with development dependencies
pip install -e ".[dev,test]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=src --cov-report=html

# Format code
black src tests

# Lint code
ruff src tests

# Type check
mypy src
```

## Status

⚠️ **Under Development** - Implementation pending (see docs/python/001-010.md)

This package provides the foundation for:
- Job processing workers
- Queue management
- Multi-threaded job execution
- Health monitoring and metrics
- Framework integration (Django, Flask, FastAPI)

## Usage

```python
from natswork_server import job, NatsWorkServer

# Define a job
@job(queue="default", retries=3)
class MyJob:
    def perform(self, x, y):
        return x + y

# Start server
server = NatsWorkServer("nats://localhost:4222")
# Coming in Tasks 004-006
```

## CLI

```bash
# Start worker server
natswork-server start --queues default --concurrency 10
```
natswork-client 0.0.1
Client library for NatsWork job processing system
License: MIT
Requires Python: >=3.8
Keywords: distributed, jobs, nats, queue, worker

# NatsWork Client (Python)

Client library for the NatsWork job processing system.

## Installation

```bash
pip install -e .
```

## Development

```bash
# Install with development dependencies
pip install -e ".[dev,test]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=src --cov-report=html

# Format code
black src tests

# Lint code
ruff src tests

# Type check
mypy src
```

## Status

⚠️ **Under Development** - Implementation pending (see docs/python/001-010.md)

This package provides the foundation for:
- Job dispatching to NATS queues
- Synchronous and asynchronous job execution
- Result handling and status tracking
- Cross-language protocol compatibility

## Usage

```python
from natswork_client import NatsWorkClient

# Coming in Task 007 - Client Interface
client = NatsWorkClient("nats://localhost:4222")
```
cmap-tool 1.0
C-MAP: Cleavage Motif Analysis Pipeline
License: MIT

### C-MAP: a flexible tool for cleavage enrichment analysis

C-MAP is released under the MIT License.
The MEROPS database, used by C-MAP, is distributed under the GNU Library General Public License (LGPL). Users of C-MAP must also comply with the terms of the MEROPS license when redistributing or modifying database content.

### Usage
All Information about usage can be found in the thesis.