semvec/pypi-packages huggingface dataset:
report-of-monaco-2018 0.0.12
Monaco 2018 Q1 lap-time report generator
Requires Python: >=3.9
Keywords: F1, Monaco, lap-time, report

# 🏎️ Application to create Formula 1 Report
   This tool can build a Formula 1 report. It works from CLI and you from import in your code.
The app reads and analyzes some files from a passing directory in CLI (read data by default package),
   or you can import it in your code and pass your own files  

---
## Installation 

Report-of-monaco-2018 can be installed by running
``` bash $
pip install report-of-monaco-2018==0.0.12
```


## 💻 Run from CLI 
You can use this package to run it from CLI and receive a report by default this command:
 
`report-monaco` - you can pass optional ` --asc | --desc ` to sort it;

or you can choose some driver and receive a result by him:

`report-monaco --file --driver 'Sebastian Vettel'` 

## 💻 Import in your project
You can import this package in your code and pass your own files:

```
import report_of_monaco_2018

report_of_monaco_2018.race_report()
```
After import, you should pass a path to directory with files to read, to cli



## Options flag:
 - `--file` - directory with files to read
 - `--desc` or `--asc` - sort report
 - `--driver`  - show info for a specific driver


## 📁 Requirements to directory
`abbreviations.txt`: contains driver abbreviations, full names, and team names.
`start.log`: contains timestamps of when each driver's start race.
`end.log`: contains timestamps of when each driver's end race.

### Example entry for start.log and end.log files:
SVF2018-05-24_12:02:58.917

- `SVF`: Driver abbreviation  
- `2018-05-24`: Date  
- `12:02:58.917`: Start or end time (used for lap duration calculation)

---
### Example entry for abbreviations.txt:
DRR_Daniel Riccardo_RED BULL RACING TAG HEUER

- `DRR`:  Driver abbreviation 
- `Daniel Riccardo`: Driver name
- `RED BULL RACING TAG HEUER`: Driver team

## 🏁 Report print Example

After parsing and calculating lap times, the output will look like:

```
1. Daniel Ricciardo      | RED BULL RACING TAG HEUER     | 1:12.013

2. Sebastian Vettel      | FERRARI                       | 1:12.415

3. ...

------------------------------------------------------------------------

16. Brendon Hartley   | SCUDERIA TORO ROSSO HONDA         | 1:13.179

17. Marcus Ericsson  | SAUBER FERRARI                     | 1:13.265

```



### License
MIT
overhang 0.1.8
Compendium of overhangs
Keywords: biology, overhang, dna

Compendium of overhangs
=======================

The Edinburgh Genome Foundry's compendium of overhangs.
This Python package generates a description of an input set of DNA overhangs and outputs a PDF file.
Additionally, it evaluates suitability of an input set of overhangs for Golden Gate DNA assembly.



**Install:**

.. code:: bash

    pip install overhang


**Web documentation:** `<https://edinburgh-genome-foundry.github.io/Overhang/>`_


**Github page:** `<https://github.com/Edinburgh-Genome-Foundry/Overhang>`_


**License:** MIT

Copyright 2021 Edinburgh Genome Foundry, University of Edinburgh


More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

Overhang is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_ synthetic biology software suite for DNA design, manufacturing and validation.
automata-practical-mohamedaz 0.1
Automata Practical Exam - DFA and Turing Machine

# Automata Practical Exam – Section 5

##  Student Info
- **Section Number:** 5  
- **Tasks Solved:**  
  1. DFA that accepts binary strings containing the substring `101`  
  2. Turing Machine that recognizes binary numbers divisible by 3  

---

## Project Structure

```text
automata_practical_exam_<4549>/
│
├── automata/                    #  Python package root
│   ├── __init__.py              # makes this a package
│   ├── dfa.py                   # DFA implementation
│   └── turing_machine.py        # Turing Machine implementation
│
├── tests/                       #  Unit tests
│   ├── test_dfa.py
│   └── test_turing_machine.py
│
├── README.md                    #  this file
└── setup.py                     #  package metadata


-------------------------------------------------
>>>Installation
# from project root
pip install -e .
Installs the package in “editable” mode so you can modify code and see changes immediately.

>>>Usage
1. Import and run the DFA
from automata.dfa import DFA

dfa = DFA()
print(dfa.accepts("010101"))   # True
print(dfa.accepts("0100"))     # False


------------------------------------------------

2. Import and run the Turing Machine
from automata.turing_machine import TuringMachine

tm = TuringMachine("110")      # binary 6
print(tm.run())                # True (6 divisible by 3)

tm2 = TuringMachine("101")     # binary 5
print(tm2.run())               # False (5 not divisible by 3)


------------------------------------------------
 >>>>>Running Tests
 pip install pytest
pytest tests/


Or run individual test files:
python -m pytest tests/test_dfa.py
python -m pytest tests/test_turing_machine.py
torch-bench-gpu 0.1.3
Tiny PyTorch GPU-benchmark CLI
Requires Python: >=3.8

# Torch Bench GPU

Torch Bench GPU is a benchmarking tool for evaluating the performance of a GPU under pytorch. 

It helps you measure the actual GFLOPS of your GPU in different regime. 

you can run it without installing it by using the following command:

```bash
uvx --from torch-bench-gpu --extra-index-url https://download.pytorch.org/whl/nightly/cu126 torch_bench_gpu
```


# To add the right torch

```bash
uv add --pre torch --default-index https://pypi.org/simple --index https://download.pytorch.org/whl/nightly/cu126
```
minotaor 0.2.3
Amino acid sequence annotator
Keywords: biology

Minotaor
========

Minotaor is an amino acid sequence annotator for quickly identifying common protein tags
and linkers in an ORF. Additionally, it can flag peptide sequences or patterns that are
known to cause problems during translation.


**Install:**

.. code:: bash

  pip install minotaor


**Web documentation:** `<https://edinburgh-genome-foundry.github.io/Minotaor/>`_


**Github page:** `<https://github.com/Edinburgh-Genome-Foundry/Minotaor/>`_


**License:** MIT

Copyright 2021, 2025 Edinburgh Genome Foundry, University of Edinburgh

----

More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

Minotaor is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_
engineering biology software suite for DNA design, manufacturing and validation.
refstis 0.8.4
Pipeline to create STIS CCD superdarks and superbiases
Keywords: astronomy

STIS superdark and superbias reference file creation routines
-------------------------------------------------------------

Contribute
----------

This library primarily serves the internal needs of the STIS instrument team at
STScI, and as such not all requested changes will be able to be accommodated.  
Still, we'd love to know what issues and modifications are out there, so please
don't be shy!

For bugs, issues, suggestions, please use the github issue tracker.
For code, please *fork* the repo, make your changes, and issue a pull request.

Build Status and things
-----------------------

[![build](https://github.com/spacetelescope/refstis/actions/workflows/build.yml/badge.svg)](https://github.com/spacetelescope/refstis/actions/workflows/build.yml)

Documentation: [![Documentation Status](https://readthedocs.org/projects/refstis/badge/?version=latest)](http://refstis.readthedocs.io/en/latest/?badge=latest)

Conda install: [![Anaconda-Server Badge](https://anaconda.org/justincely/refstis/badges/installer/conda.svg)](https://conda.anaconda.org/justincely)
face-vet 1.2.0
A package to detect fake or low-quality images using OpenCV and Tesseract
Requires Python: >=3.6

# **face-vet**

`face-vet` is a comprehensive Python library designed to validate the authenticity and quality of images. It performs multiple checks, including face detection, image quality assessment, depth analysis, and tampering detection (e.g., overlayed text such as timestamps or watermarks). This package is particularly useful for fraud detection, identity verification, and ensuring image authenticity across various applications.

## 🚀 Key Features

- **Face Detection**: Identifies whether an image contains a face, aiding in the detection of manipulated or irrelevant images.
- **Image Quality Check**: Analyzes the sharpness and overall quality of the image, flagging blurry or poorly captured images.
- **Text Detection**: Uses Optical Character Recognition (OCR) to detect overlaid text, such as timestamps, watermarks, or branding.
- **Tampering Detection**: Combines multiple checks to assess the authenticity of an image and detect potential signs of tampering.
- **Depth Detection (Eye-Nose Distance)**: Leverages facial landmarks to detect unusual eye-to-nose distances, which can indicate manipulation or fake images.

## 📦 Installation

To install `face-vet`, simply use `pip`:

```bash
pip install face-vet