semvec/pypi-packages huggingface dataset:
PersistentObjects 0.1.7
JSON-backed attribute-persistent object with namespace support
Requires Python: >=3.10

This is a very simple lib that provides the PersistantObject class, which saves all its attributes to the given json file. Exceptions are attributes suffixed with '_'.
Attributes are saved as soon as they are set.

[!IMPORTANT]
> morphing actions like appending, inserting, extending, etc. will not automaticly save!
> so its recomended to create a temp var, then append to it and at the end set the PersistantObject attr to the temp var.

with namespace(NAME) seperate sub namespaces can be created inside the object. 


## Usage example:
```python

from PersistantObject import PersistantObject

pobject = PersistantObject("save.json")

pobject.first_value = 10
pobject.second_value = "foo"

namespace = pobject.namespace("test_namespace") # creates a new namespace (new subdict in json)
namespace.third_value = [10, 5]


print(pobject.test_namespace.third_value) # alternate way to access namespaces

pobject.temp_ = True # suffixed with '_' so it will not be saved and behaves like a normal attribute.
```
pyterametrics 0.1.1
Python port of TeraMetrics - IaC quality metrics for Terraform HCL files
Requires Python: >=3.8

# PyTerametrics

[![PyPI version](https://badge.fury.io/py/pyterametrics.svg)](https://badge.fury.io/py/pyterametrics)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Versions](https://img.shields.io/pypi/pyversions/pyterametrics.svg)](https://pypi.org/project/pyterametrics/)

Python port of [TerraMetrics](https://github.com/stilab-ets/terametrics) — IaC quality metrics for Terraform HCL files.

Computes 100+ quality metrics at the block level for Terraform `.tf` files, including:
- Cyclomatic complexity (McCabe CC)
- Lines of code / non-code lines
- Function call counts and parameters
- Conditional expressions, loops, binary operators
- Nested blocks, dynamic blocks, heredocs
- Implicit/explicit resource dependencies
- Text entropy, token statistics
- And many more...

## Installation

```bash
pip install pyterametrics
```

## Usage

### Command Line

```bash
# Analyze a single file
pyterametrics --file main.tf -b --target output.json

# Analyze a directory
pyterametrics -l --repo ./terraform-project --target ./results --project my-project

# Pretty-print output
pyterametrics --file main.tf -b --pretty
```

### Python API

```python
from pyterametrics import analyze_file, analyze_directory

# Single file
result = analyze_file("main.tf")
for block in result["blocks"]:
    print(f"{block['block_identifiers']}: {block['numAttrs']} attrs, CC={block['sumMccabeCC']}")

# Directory
result = analyze_directory("./terraform-project", project_name="my-project")
```

## Development

```bash
# Install with dev dependencies
uv sync

# Run tests
uv run pytest tests/ -v
```
spinal-tap 0.3.3
SPINE Reconstruction Visualization Tool
Requires Python: >=3.8

# Spinal Tap

Spinal Tap is a Dash application that provides simple visualization tools for
the Scalable Particle Imaging With Neural Embeddings
([SPINE](https://github.com/DeepLearnPhysics/spine)) package.


## Installation

You can install Spinal Tap and all dependencies (including Dash, Flask, Plotly, and spine) using pip:

```bash
pip install .
```

Or, for editable development mode:

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

## Usage

After installation, launch the app using the provided CLI:

```bash
spinal-tap
```

You can also check the installed version with:

```bash
spinal-tap --version
# or
spinal-tap -v
```

Then open your browser to [http://0.0.0.0:8888/](http://0.0.0.0:8888/).


## Deployment

### Kubernetes

Spinal Tap is deployed on SLAC's S3DF Kubernetes infrastructure and is accessible at:

**[https://spinal-tap.slac.stanford.edu](https://spinal-tap.slac.stanford.edu)**

The Kubernetes configuration files are located in the `k8s/` directory. For deployment instructions and SLAC-specific configuration details, see:
- [`k8s/README.md`](k8s/README.md) - Deployment guide
- [`k8s/SLAC_CONFIG.md`](k8s/SLAC_CONFIG.md) - Detailed SLAC S3DF configuration

### Docker

Docker images are automatically built and published to GitHub Container Registry when version tags are pushed:

```bash
docker pull ghcr.io/deeplearnphysics/spinal-tap:latest
```

To run locally with Docker:

```bash
docker run -p 8888:8888 ghcr.io/deeplearnphysics/spinal-tap:latest
```

## Development & CI/CD

- Code style is enforced with black, isort, and flake8 (pre-commit and CI).
- The GitHub Actions workflow builds and tests on every commit, PR, tag, and release.
- Docker images are built automatically on version tag pushes (e.g., `v0.1.2`).
- Publishing:
  - On tag push: publishes to Test PyPI (requires `TEST_PYPI_API_TOKEN` secret).
  - On GitHub Release: publishes to PyPI (requires `PYPI_API_TOKEN` secret).