semvec/pypi-packages huggingface dataset:
flekspy 0.7.5
Python utilities for processing FLEKS data
Requires Python: <3.15,>=3.10

# flekspy: FLEKS Python toolkit

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    <img src="https://img.shields.io/badge/license-MIT-blue">
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Python package for processing [FLEKS](https://github.com/SWMFsoftware/FLEKS) (FLexible Exascale Kinetic Simulator) data.

## Installation

```bash
python -m pip install flekspy
```

## Usage

`flekspy` can load files generated from FLEKS.

```python
import flekspy

ds = flekspy.load("sample_data/3*amrex")
```

Plotting is supported via Matplotlib and YT. For more detailed usage and contribution guide, please refer to the [documentation](https://henry2004y.github.io/flekspy/).

### ParaView Plugin

`flekspy` provides a ParaView plugin for visualizing FLEKS data. To use the plugin, follow these steps:

1. Make sure the ParaView version is 5.13 or higher.

2. Install flekspy for pvpython.
  - Go into the ParaView directory and create a virtual environment for Python

```bash
./bin/pvpython -m venv .venv
```

  - Check the Python version of `pvpython`

```bash
./bin/pvpython -c "import sys; print(sys.version)"
```

  - Install flekspy in the directory right in the venv. You may need to adjust the target path if `pvpython` does not detect `flekspy`. Assuming the Python version is 3.10.11, run

```bash
python3 -m pip  install --only-binary=:all: --python-version 3.10.11 --target ./.venv/lib/python3.10/site-packages/ flekspy
```

3. Launch ParaView with the virtual environment

```bash
./bin/paraview --venv .venv
```

4. In Tools > Manage Plugins, load the `BATSRUSReader` plugin.

## License

`flekspy` is licensed under the terms of the MIT license.
cgpu-info 0.3.0
Quick CUDA/GPU status summary for ML engineers
Requires Python: >=3.8
Keywords: cuda, deep-learning, gpu, machine-learning, nvidia, pytorch

# cgpu

Quick CUDA/GPU status summary for ML engineers. One import, one call, all the info you need.

## Installation

```bash
uv pip install cgpu-info
```

Or with pip:
```bash
pip install cgpu-info
```

## Usage

### Python
```python
from cgpu import cgpu

device = cgpu()
# Now use `device` in your code
model.to(device)
```

### Progress bar with GPU metrics
```python
from cgpu import ctqdm

# Drop-in replacement for tqdm - shows GPU temp, VRAM, and utilization
for batch in ctqdm(dataloader, desc="Training"):
    ...
```

Output:
```
Training:  50%|█████     | 50/100 [00:05<00:05] 42°C | 8.2/24.0GB | GPU: 87%
```

Install `nvidia-ml-py` for temperature and utilization. Without it, VRAM usage is still shown via torch.

### CLI
```bash
# Show GPU status
cgpu

# Show version
cgpu --version
```

That's it! You'll see a colorful summary like:

```
═══════════════════════════════════════
          GPU Status Summary
═══════════════════════════════════════
✓ CUDA Available
  Device: cuda
  GPU Count: 1
  [0] NVIDIA GeForce RTX 4090
      VRAM: 24.0 GB
      Allocated: 0.00 GB
      Reserved: 0.00 GB
      Temp: 42°C
      GPU Util: 0%
      Mem Util: 0%
  CUDA Version: 12.1
  cuDNN Version: 8902
  PyTorch: 2.1.0
═══════════════════════════════════════
```

## What it shows

- CUDA availability status
- Device string (`cuda` or `cpu`)
- GPU name and count
- VRAM total and usage
- Temperature (color-coded: green < 50°C, yellow < 70°C, red >= 70°C)
- GPU/Memory utilization
- CUDA, cuDNN, and PyTorch versions

## Installing PyTorch with specific CUDA version

Use the built-in install helper to get PyTorch with your desired CUDA version:
```bash
# Install torch with CUDA 12.1
cgpu install --cuda 12.1

# Install torch with CUDA 12.4
cgpu install --cuda 12.4

# Install torch with CUDA 11.8
cgpu install --cuda 11.8

# Install CPU-only torch
cgpu install --cuda cpu
```

## Optional dependencies

If you want to install additional ML packages along with cgpu:

```bash
# Full ML stack: torch, torchvision, torchaudio, numpy, pandas, matplotlib, seaborn, scikit-learn
uv pip install cgpu-info[full]

# Just PyTorch stack
uv pip install cgpu-info[torch]

# Just data science packages (no torch)
uv pip install cgpu-info[science]
```

## License

MIT
pythonic-fp-splitends 2.0.2
Mutable stack objects that can safely share data.
Requires Python: >=3.13
Keywords: SplitEnd, lifo, stack

Pythonic FP - SplitEnds
=======================

PyPI project
`pythonic-fp.splitends <https://pypi.org/project/pythonic-fp.splitends/>`_.

Python package Implementing a singularly linked LIFO queue called
a ``SplitEnd``. These data structures can safely share data nodes
between themselves.

- each ``SplitEnd`` is a very simple stateful (mutable) LIFO stack
- data can be "extended" to or "snipped" off of the end (tip)
- the "root" value of a ``SplitEnd`` is fixed and cannot be "snipped"
- different mutable split ends can safely share the same "tail"
- each ``SplitEnd`` sees itself as a singularly linked list
- bush-like datastructures can be formed using multiple ``SplitEnds``
- the ``SplitEnd`` copy method and ``len`` are O(1)
- in boolean context returns true if the ``SplitEnd`` is not just a "root"

Part of the
`pythonic-fp
<https://grscheller.github.io/pythonic-fp>`_
PyPI projects.

Documentation
-------------

Documentation for this project is hosted on
`GitHub Pages
<https://grscheller.github.io/pythonic-fp/splitends>`_.

Copyright and License
---------------------

Copyright (c) 2023-2025 Geoffrey R. Scheller. Licensed under the Apache
License, Version 2.0. See the LICENSE file for details.