semvec/pypi-packages huggingface dataset:
translatio-delineationis-recens 1.4.1b1
A comprehensive Python library for reading and writing DXF files
Requires Python: >=3.8
Keywords: DXF, CAD, drawing, geometry, engineering

# Translatio Delineationis Recens (formerly EzDXF) Library

A Python library to create and modify DXF drawings, compatible with Python 3.7 and above.

## Overview

Translatio Delineationis Recens (formerly EzDXF) is an interface library for the DXF file format. The package is designed to facilitate the creation and manipulation of DXF documents, with compatibility across various DXF versions. It empowers users to seamlessly load and edit DXF files while preserving all content, except for comments.

Any unfamiliar DXF tags encountered in the document are gracefully ignored but retained for future modifications. This feature enables the processing of DXF documents containing data from third-party applications without any loss of valuable information.

## Installation

```bash
pip install translatio-delineationis-recens
```

Or install from source:

```bash
git clone https://github.com/yourusername/translatio-delineationis-recens.git
cd translatio-delineationis-recens
pip install -e .
```

## Basic Usage

```python
import translatio_delineationis_recens as tdr

# Create a new DXF document
doc = tdr.new('R2010')  # or R12, R2000, R2004, R2007, R2013, R2018

# Add new entities to modelspace
msp = doc.modelspace()
msp.add_line((0, 0), (10, 10))
msp.add_circle((0, 0), radius=5)

# Save the document
doc.saveas('example.dxf')

# Open an existing DXF document
doc = tdr.readfile('example.dxf')

# Iterate through all entities in modelspace
msp = doc.modelspace()
for entity in msp:
    if entity.dxftype() == 'LINE':
        print(f"Line from {entity.dxf.start} to {entity.dxf.end}")
    elif entity.dxftype() == 'CIRCLE':
        print(f"Circle at {entity.dxf.center} with radius {entity.dxf.radius}")
```

## Features

- Create new DXF documents
- Read and modify existing DXF files
- Support for various DXF versions (R12, R2000, R2004, R2007, R2010, R2013, R2018)
- Comprehensive entity support (lines, circles, arcs, text, dimensions, etc.)
- Block management
- Layer management
- Viewport configuration
- Extended entity data (XDATA)
- Object attribute access via Python properties

## Requirements

- Python 3.7 or higher

## License

MIT License

## Documentation

For more detailed documentation, see the [Docs](./Docs) directory.
flet-open-file 0.1.3
FletOpenFile control for Flet
Requires Python: >=3.10

# flet-open-file
FletOpenFile control for Flet

## Installation

Add dependency to `pyproject.toml` of your Flet app:

* **Git dependency**

Link to git repository:

```
dependencies = [
  "flet-open-file @ git+https://github.com/creeper19472/flet-open-file",
  "flet>=0.70.0.dev6214",
]
```

* **PyPi dependency**  

If the package is published on pypi.org:

```
dependencies = [
  "flet-open-file",
  "flet>=0.70.0.dev6214",
]
```

Build your app:
```
flet build macos -v
```

## Documentation

[Link to documentation](https://github.com/creeper19472/flet-open-file)
pytconf 0.1.21
Configuration, command line and injection framework for python
Requires Python: >=3.12
Keywords: argparse, click, command-line-parser, configuration, optparse

# *pytconf* project by Mark Veltzer

description: Configuration, command line and injection framework for python

project website: https://veltzer.github.io/pytconf

author: Mark Veltzer

version: 0.1.21

![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

## github

![License](https://img.shields.io/github/license/veltzer/pytconf)

## build

![build](https://github.com/veltzer/pytconf/workflows/build/badge.svg)

## pypi

![PyPI - Status](https://img.shields.io/pypi/status/pytconf)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pytconf)
![PyPI - License](https://img.shields.io/pypi/l/pytconf)
![PyPI - Package Name](https://img.shields.io/pypi/v/pytconf)
![PyPI - Format](https://img.shields.io/pypi/format/pytconf)

## pypi download

![PyPI - Downloads](https://img.shields.io/pypi/dd/pytconf)
![PyPI - Downloads](https://img.shields.io/pypi/dw/pytconf)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pytconf)



## contact me
[mailto](mailto:mark.veltzer@gmail.com)
![gitter](https://img.shields.io/gitter/room/veltzer/mark.veltzer)
![discord](https://img.shields.io/discord/719336281624281119)
![discord](https://img.shields.io/discord/719336282194444302)

Mark Veltzer, Copyright © 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
dsiter 0.1.3
A library for iterating over aggregated datasets from HuggingFace and local CSV files
Requires Python: >=3.9

# dsiter

ds-iter (ds is short for "dataset" and iter for "iteration") is an opinionated, declarative dataset preparation/iteration tool that helps you create an single iterator over many datasets, useful for ML/data engineering tasks.

# Problem

During the training of large-scale machine learning models (especially language models or NLP tasks), we're often working with many different datasets from various sources, with very different shapes and dimensions, sometimes reaching gigabytes of data.

Failure in implementing efficient memory usage within training or data preparation scripts often leads to operating system errors, such as _OOM killed_.

ML/data engineers tend to write custom scripts, often with bespoke pre-processing/post-processing mutations, and sometimes entirely different implementations of the data preparation step across projects. This can lead to reproducibility issues, as different team members using slightly different scripts produce inconsistent results. Additionally, debugging becomes difficult because OOM errors or preprocessing bugs are often non-deterministic and hard to trace.


## Usage

### step one: create yaml configuration file

create a yaml file (by default the library looks for `datasets.yaml`) and start adding your Hugginface 🤗 datasets using their `repoId` like below:

> **Example**: See `examples/datasets.yml` for a complete configuration file with various dataset types.

```yaml
datasets:
  - path: facebook/recycling_the_web
```

you can add as many Hugginface 🤗 datasets as you want:

```yaml
datasets:
  - path: facebook/recycling_the_web
  - path: Lk123/InfoSeek
  - path: ...
  - path: ...
```

you can also load your own custom dataset files, supported formats are:

- csv or tsv
- parquet

just pass the relative/absolute path of the dataset with `path` key.

```yaml
datasets:
  - path: ./files/dataset_dump.csv
  - path: ./files/other_dataset.tsv
  - path: ./files/other_other_dataset_0001.parquet
```

for each dataset you can target an array of specific columns to be returned within iterator:

```yaml
datasets:
  - path: MathLLMs/MathVision
    columns:
      - questions

  - path: community-datasets/farsi_news
    columns:
      - title
      - summary
```

### step two: python scripting

install the library using pip
```bash
pip install dsiter
```

and then:

> **Example**: See `examples/example.py` for a complete working example.

```python
from dsiter import DSIterCollection

collection = DSIterCollection()

for row in collection.iter_rows():
    print(row)

```

Calling iter_rows() lazily streams the dataset's rows through a generator, enabling efficient iteration and processing without loading the entire dataset into memory.