semvec/pypi-packages huggingface dataset:
metaPTCM 0.0.1
metaPTCM pipeline & dataset
Requires Python: >=3.10.0
Keywords: deep learning

# metaPTCM pipeline & dataset

# Install the pipeline

```bash
conda create -n metaPTCM python=3.10
conda activate metaPTCM
pip install -e .
```

# install the qc dashboard

Install node js and npm on your system. [node js](https://nodejs.org/en/download/)

```bash
npm install
npm run dev
```
turktoken 0.1.0
urkish-optimized BPE tokenizer with GPT-4 style pre-tokenization. Train, encode, decode with custom special tokens.
Requires Python: >=3.8
Keywords: bpe, byte-pair-encoding, nlp, tokenizer, transformers, turkish

# TurkToken

A Turkish-optimized Byte Pair Encoding (BPE) tokenizer with GPT-4 style pre-tokenization patterns.

## Installation

```bash
pip install turktoken
```

## Quick Start

```python
from turktoken import TurkishBPETokenizer

tokenizer = TurkishBPETokenizer()
```

## Training

Train a tokenizer on your own corpus:

```python
from turktoken import TurkishBPETokenizer

tokenizer = TurkishBPETokenizer()

# Load your training data
with open("corpus.txt", "r", encoding="utf-8") as f:
    text = f.read()

# Train with desired vocabulary size
tokenizer.train(text, vocab_size=8000)

# Save the trained tokenizer
tokenizer.save("./my_tokenizer")
```

## Encoding and Decoding

```python
from turktoken import TurkishBPETokenizer

# Load a trained tokenizer
tokenizer = TurkishBPETokenizer()
tokenizer.load("./my_tokenizer")

# Encode text to token IDs
text = "Merhaba dünya! Bu bir test cümlesidir."
ids = tokenizer.encode(text)
print(ids)  # [256, 312, 445, 78, ...]

# Decode token IDs back to text
decoded_text = tokenizer.decode(ids)
print(decoded_text)  # "Merhaba dünya! Bu bir test cümlesidir."
```

## Special Tokens

Add custom special tokens for your use case:

```python
from turktoken import TurkishBPETokenizer

tokenizer = TurkishBPETokenizer()
tokenizer.train(training_text, vocab_size=4096)

# Add special tokens
tokenizer.add_special_tokens([
    "
",      # beginning of sequence
    "
",      # end of sequence
    "
",      # padding
    "
",      # unknown token
    "
"       # separator
])

tokenizer.save("./my_tokenizer")
```

Using special tokens during encoding:

```python
tokenizer = TurkishBPETokenizer()
tokenizer.load("./my_tokenizer")

# Special tokens are recognized during encoding
ids = tokenizer.encode("
Merhaba dünya!
")
text = tokenizer.decode(ids)
```

## API Reference

### TurkishBPETokenizer

| Method | Description |
|--------|-------------|
| `train(text, vocab_size=512)` | Train the tokenizer on the given text corpus |
| `encode(text)` | Convert text to a list of token IDs |
| `decode(ids)` | Convert a list of token IDs back to text |
| `add_special_tokens(tokens)` | Add a list of special tokens to the vocabulary |
| `save(directory)` | Save the tokenizer to a directory |
| `load(directory)` | Load a tokenizer from a directory |

## Features

- Unicode-aware pre-tokenization optimized for Turkish text
- Byte Pair Encoding for efficient subword tokenization
- Support for custom special tokens
- Save and load trained tokenizers
- Minimal dependencies (only `regex`)

## License

MIT
precice-profiling 2.1.0
A tool for post-processing and analyzing preCICE profiling data.
Requires Python: >=3.10
Keywords: preCICE, visualization

# preCICE profiling tools

## Workflow

1. _Optional_ Enable `<profiling mode="all"/>` in the preCICE configuration of your case to get the profiling information.
2. Run the preCICE simulation.
3. Run `precice-profiling-merge` to combine all emitted profiling files of the simulation into a single `profiling.db` file.
4. Analyze the solvers:
    * Use `precice-profiling-analyze` to get a breakdown of an individual solver.
    * Use `precice-profiling-trace` to visualize the data.
    * Use `precice-profiling-export` to export the data as CSV to import in other software.


## Tools

### merge

Merges profiling files emitted by each rank of each participant into a single easily portable file.
Creates a `profiling.db` by default.

### export

Transforms all events to a tabular format and exports the result as a CSV format.
The columns `Participant`, and `Rank` can be used to filter the dataset to extract individual participants or specific ranks.

Reads `profiling.db` and creates `profiling.csv` by default.

### trace

Transforms all events to the [Google Trace Format](https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU) which can be visualized by tools such as `about::tracing` in Chromium based browsers or [perfetto.dev](https://ui.perfetto.dev/).

Reads `profiling.db` and creates `trace.json` by default.

### analyze

Analyzes a given solver and returns a table of all timings including some statistics based on their duration.

Reads `profiling.db` by default.

## HPC users

HPC users or users of locked down clusters without easy access to pip can download the [`merge` command](https://raw.githubusercontent.com/precice/profiling/refs/heads/main/preciceprofiling/merge.py) as a standalone file and run it without installing additional dependencies.

```console
wget -O precice-profiling-merge https://raw.githubusercontent.com/precice/profiling/refs/heads/main/preciceprofiling/merge.py
chmod +x precice-profiling-merge
```

## Licensing

This repository contains modified part of the the `precice-profiling` script, which is part of the preCICE library (precice/precice `tools/profiling/precice-profiling`) and licensed under the LGPv3 license.
The two copyright holders David Schneider (@davidscn david.schneider@ipvs.uni-stuttgart.de) and Frédéric Simonis (@fsimonis frederic.simonis@ipvs.uni-stuttgart.de) decided on 23. June 2025 15:00 CET to rerelease the content of the `precice-profiling` script in this repository under the MIT license.
ljscode 0.0.3
Utilities for ljs project
Requires Python: >=3.8

# code

[![PyPI - Version](https://img.shields.io/pypi/v/code.svg)](https://pypi.org/project/code)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/code.svg)](https://pypi.org/project/code)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install ljscode
```

## License

`code` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.