semvec/pypi-packages huggingface dataset:
vibegrep 0.3.0
grep, but the search engine is an LLM
Requires Python: >=3.10

# vibegrep

grep, but the search engine is an LLM.

## Install

```bash
pip install vibegrep  # or: uvx vibegrep
```

## Setup

```bash
export VIBEGREP_API_KEY=<your-api-key>
export VIBEGREP_BASE_URL=https://<openai-compatible-api>/v1
export VIBEGREP_MODEL=<a-fast-and-affordable-model>
```

## Usage

```bash
vibegrep "security vulnerabilities" src/
vibegrep "error handling" . -g "*.py"
vibegrep "authentication logic" src/ --depth 2 -j 5
```

### Options

```
QUERY                 search query (required)
PATH                  search path (default: .)
--depth 1|2           directory depth (default: 1)
-j NUM, --threads NUM concurrent requests (default: 10)
-g GLOB, --glob GLOB  file filter (e.g. '*.py')
--model MODEL         override VIBEGREP_MODEL
--dry-run             preview batching without calling API
```

### Output

TTY:
```
src/auth.py
 42│     password = request.form["password"]
 43│     db.execute(f"SELECT * FROM users WHERE pass='{password}'")

 67│     os.system(user_input)
```

Pipe:
```
src/auth.py:42:    password = request.form["password"]
src/auth.py:43:    db.execute(f"SELECT * FROM users WHERE pass='{password}'")
```

## License

MIT
hwcomponents-cacti 1.0.30
A package for estimating the energy and area of memories with CACTI
Requires Python: >=3.12
Keywords: accelerator, hardware, energy, estimation, CACTI

# HWComponents-Cacti

This model connects CACTI to the HWComponents. It provides models for SRAM, DRAM, and
caches. This is adapted from the Accelergy CACTI plug-in.

These models are for use with the HWComponents package, found at
https://accelergy-project.github.io/hwcomponents/.

## Installation

Install from PyPI:

```bash
pip install hwcomponents-cacti

# Check that the installation is successful
hwc --list | grep SRAM
hwc --list | grep DRAM
hwc --list | grep Cache
```

## Citation

If you use this library in your work, please cite the following:

```bibtex
@INPROCEEDINGS{cimloop,
  author={Andrulis, Tanner and Emer, Joel S. and Sze, Vivienne},
  booktitle={2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
  title={CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool},
  year={2024},
  volume={},
  number={},
  pages={10-23},
  doi={10.1109/ISPASS61541.2024.00012}
}
@inproceedings{accelergy,
  author      = {Wu, Yannan Nellie and Emer, Joel S and Sze, Vivienne},
  booktitle   = {2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
  title       = {Accelergy: An architecture-level energy estimation methodology for accelerator designs},
  year        = {2019},
}
@article{shivakumar2001cacti,
  title={Cacti 3.0: An integrated cache timing, power, and area model},
  author={Shivakumar, Premkishore and Jouppi, Norman P},
  year={2001},
  publisher={Technical Report 2001/2, Compaq Computer Corporation}
}
@ARTICLE{wilton1996cacti,
  title={CACTI: an enhanced cache access and cycle time model},
  author={Wilton, S.J.E. and Jouppi, N.P.},
  journal={IEEE Journal of Solid-State Circuits},
  year={1996},
  volume={31},
  number={5},
  pages={677-688},
  keywords={Driver circuits;Costs;Decoding;Analytical models;Stacking;Delay estimation;Computer architecture;Equations;Councils;Wiring},
  doi={10.1109/4.509850}
}
@article{balasubramonian2017cacti,
  author = {Balasubramonian, Rajeev and Kahng, Andrew B. and Muralimanohar, Naveen and Shafiee, Ali and Srinivas, Vaishnav},
  title = {CACTI 7: New Tools for Interconnect Exploration in Innovative Off-Chip Memories},
  year = {2017},
  issue_date = {June 2017},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {14},
  number = {2},
  issn = {1544-3566},
  url = {https://doi.org/10.1145/3085572},
  doi = {10.1145/3085572},
  journal = {ACM Trans. Archit. Code Optim.},
  month = jun,
  articleno = {14},
  numpages = {25},
  keywords = {DRAM, Memory, NVM, interconnects, tools}
}
```
metamath-prelude 0.0.2
Prelude definitions for Metamath projects
Requires Python: >=3.10
Keywords: metamath, proof, proof-scaffold

# metamath-prelude

`metamath-prelude` is a small Python package that exports the “prelude” layer used by ProofScaffold-based Metamath projects.
It provides the core constants, variables, and a minimal set of foundational statements that downstream packages can depend on and link against.

## Versioning

- Package version: `0.0.2`
- ProofScaffold dependency: `proof-scaffold==0.0.5`

## Installation

This package is published on PyPI: https://pypi.org/project/metamath-prelude/

With `uv`:

```bash
uv add metamath-prelude
```

## What this package contains

- A ProofScaffold `build.py` entrypoint that emits the prelude statements as a linkable unit.
- Authoring helpers for the Hilbert-style propositional fragment (rule bundle used by downstream proof scripts).
- A documented alignment with the early part of `set.mm`.

## Verification

This repository uses `uv` for reproducible installs and runs `skfd verify --level 1` as the primary correctness gate.

From the `metamath-prelude/` directory:

```bash
uv sync --locked --dev
uv run --frozen ruff check .
uv run --frozen mypy .
uv run --frozen python -m pytest
uv run --frozen skfd verify --level 1 metamath-prelude
```

`skfd verify` builds the package into a verification monolith (under `target/`) and checks it with the configured verifiers.

## set.mm alignment

- Milestone boundary (within the first ~700 lines): prelude = `set.mm` lines 1–648; propositional logic starts at line 649 (the `ax-mp` block).
- Mapping notes and migrated comments: [`docs/SETMM_PRELUDE_1_648.md`](docs/SETMM_PRELUDE_1_648.md)
inline-snapshot-pandas 0.2.0
pandas integration for inline-snapshot
Requires Python: >=3.9

<!-- -8<- [start:Header] -->


![ci](https://github.com/15r10nk/inline-snapshot-pandas/actions/workflows/ci.yml/badge.svg?branch=main)
[![Docs](https://img.shields.io/badge/docs-mkdocs-green)](https://15r10nk.github.io/inline-snapshot-pandas/)
[![pypi version](https://img.shields.io/pypi/v/inline-snapshot-pandas.svg)](https://pypi.org/project/inline-snapshot-pandas/)
![Python Versions](https://img.shields.io/pypi/pyversions/inline-snapshot-pandas)
![PyPI - Downloads](https://img.shields.io/pypi/dw/inline-snapshot-pandas)
[![coverage](https://img.shields.io/badge/coverage-100%25-blue)](https://15r10nk.github.io/inline-snapshot-pandas/contributing/#coverage)
[![GitHub Sponsors](https://img.shields.io/github/sponsors/15r10nk)](https://github.com/sponsors/15r10nk)

<!-- -8<- [end:Header] -->

## Installation

``` bash
pip install inline-snapshot-pandas
```

## Usage

This packages provides special `assert_(frame|series|index)_equal` implementation which accept a snapshot as second argument.

``` python
from pandas import DataFrame
from inline_snapshot_pandas import assert_frame_equal
from inline_snapshot import snapshot


def test_assert_equal():
    df = DataFrame({"col0": [1, 2]})

    assert_frame_equal(df, snapshot())
```

```bash
pytest --inline-snapshot=create
```

``` python
from pandas import DataFrame
from inline_snapshot_pandas import assert_frame_equal
from inline_snapshot import snapshot


def test_assert_equal():
    df = DataFrame({"col0": [1, 2]})

    assert_frame_equal(
        df,
        snapshot(DataFrame([{"col0": 1}, {"col0": 2}])),
    )
```

Another way to use it is to call `setup()` in `conftest.py`, which replaces the implementation which pandas uses.

``` python
from inline_snapshot_pandas import setup

setup()
```

You can then use implementation from pandas with snapshots.

``` python
from pandas import DataFrame
from pandas.testing import assert_frame_equal
from inline_snapshot import snapshot


def test_assert_equal():
    df = DataFrame({"col0": [1, 2], "col1": [1, 5j], "col3": ["a", "b"]})

    # the second argument can be a snapshot
    assert_frame_equal(
        df,
        snapshot(
            DataFrame(
                [
                    {"col0": 1, "col1": (1 + 0j), "col3": "a"},
                    {"col0": 2, "col1": 5j, "col3": "b"},
                ]
            )
        ),
    )
```

<!-- -8<- [start:Feedback] -->
## Issues

If you encounter any problems, please [report an issue](https://github.com/15r10nk/inline-snapshot-pandas/issues) along with a detailed description.
<!-- -8<- [end:Feedback] -->

## License

Distributed under the terms of the [MIT](http://opensource.org/licenses/MIT) license, "inline-snapshot-pandas" is free and open source software.