semvec/pypi-packages huggingface dataset:
LaneQ 0.3.1
LaneQ: A Python package for lane line detection and classification
Requires Python: >=3.9

<h1 align="center">
  <a href="https://github.com/dfenny/LaneQ/tree/main">
    <img src="https://raw.githubusercontent.com/dfenny/LaneQ/refs/heads/main/images/LaneQ_background.png" alt="LaneQ logo" width="450" height="187">
  </a>
</h1>

Install:

`python3 -m pip install laneq`

Get started easily with:

```python
from laneq import DegradationDetector

dt = DegradationDetector("output")
dt.predict("path/to/dashcam/image.jpg")
```

Or to run the GUI:

`python -m laneq`

![LaneQ Architecture](https://raw.githubusercontent.com/dfenny/LaneQ/refs/heads/main/images/architecture_with_example.png)
img2pdfify 0.2.5
Convert images to a single PDF via command line
Requires Python: >=3.7

# 🖼️ img2pdfify

A simple command-line tool to convert images (PNG, JPG, JPEG) into a single PDF file. Each image gets its own page in the output PDF.

---

## 🚀 Features

- 📂 Accepts either a single image or a folder of images.
- 🔁 Recursive folder search (explicit `true` or `false`).
- ✍️ Option to overwrite output PDF (explicit `true` or `false`).
- 🔒 Strict CLI usage — you must specify all options clearly.
- 🧠 Uses Pillow for reliable image handling.

---

## 📦 Installation

### 1. Clone the repo

```bash
git clone https://github.com/c2p-cmd/img2pdfify.git
cd img2pdfify
```

### 2. Install locally
```bash
pip install -e .
```

---

## 🧪 Usage
```bash
img2pdfify <input_path> <output_file.pdf> --recursive true|false --overwrite true|false
```

---

## 📌 Examples
Convert images from a folder (non-recursive, no overwrite):
```bash
img2pdfify ./images myoutput.pdf --recursive false --overwrite false
```

Convert a single image and allow overwriting the output:
```bash
img2pdfify image.jpg output.pdf --recursive false --overwrite true
```

---

## 🧰 Dependencies
- Python 3.7+
- [Pillow](https://python-pillow.org)
```bash
pip install -r requirements.txt
```

---

## 🙌 Contributing

Open an issue or PR — happy to accept contributions or improvements!
malloctag-tools 1.5.4
Utility to postprocess malloc-tag JSON snapshots
Requires Python: >=3.6

# malloc-tag tools

This README describes the Python-based tools to postprocess malloc-tag JSON snapshots:

* mtag-json2dot
* mtag-postprocess

## How to install

Installing these utilities from [pypi](https://pypi.org/project/malloctag-tools/) is really simple:

```
pip3 install --upgrade malloctag-tools
```

## Rendering

A basic step to enable the visual inspection of the results of a malloc-tag instrumented application is to use 
the `mtag-json2dot` utility on the output JSON snapshots:

```
mtag-json2dot --output nice-picture.svg  <malloc-tag-snapshot-file.json>
```

Check the top-level README for example of generated SVG output.
Note that despite the name `mtag-json2dot` can produce a variety of output file, supported by the Graphviz engine: .dot, .svg, .svgz, .jpeg, .png, etc.


## Postprocess 

It might happen that inside your C/C++ application you have several threads running the same code.
In this case the pure snapshot provided by the malloc-tag framework (where 1 thread means 1 tree) can be hard to read/follow.
In such cases you might want to aggregate together all memory operations done by hese identical threads.

The tool `mtag-postprocess` makes this very simple. An example usage follows.

Let's assume you need to aggregate the memory alloc/frees done by all threads having the prefix `MyThreadPrefix`.
First of all create a suitable config `agg_config.json`:

```
{
    "comment": "This is a simple example of postprocessing rule",
    "rule0": {
        "aggregate_trees": {
            "matching_prefix": "MyThreadPrefix/*"
        }
    }
}
```

Then launch the postprocessing utility:

```
mtag-postprocess  --output post-process-out.json --config agg_config.json    <malloc-tag-snapshot-file.json>
```

The output file is a JSON file with the same identical format used by malloc-tag C++ library.
This allows to easily chain and combine different post-processing steps.

The post-processed JSON file can then be used as input of the `mtag-json2dot` utility.
As an example check this picture: 

![multithread_aggregated_example_svg](../examples/multithread/multithread_stats.aggregated.svg?raw=true "Malloc-tag aggregated output")

And compare it against the non-aggregated picture:

![multithread_example_svg](../examples/multithread/multithread_stats.dot.svg?raw=true "Malloc-tag output for MULTITHREAD example")

You can see the configuration file used to produce such picture with the `mtag-postprocess` tool [here](../examples/multithread/postprocess_agg_rules.json).
imbue-desktop 0.0.9
A thin client for the Imbue AI coding tool.
Requires Python: >=3.11

# Imbue Desktop

A thin client for the Imbue AI coding tool. (More details to come.)

## Quickstart

```
uvx imbue-desktop <local_path_to_working_repo> <server_url> --api-key <api_key>
```
py-sodas-sdk 1.1.0
Official Python SDK for the SODAS Framework
Requires Python: >=3.7

# py-sodas-sdk

SODAS 프레임워크를 위한 공식 Python SDK입니다.
이 SDK를 통해 데이터셋의 메타데이터를 효율적으로 관리하고 표준화된 방식으로 처리할 수 있습니다.
DCAT(Data Catalog Vocabulary) 표준을 기반으로 하여 데이터셋의 메타데이터를 체계적으로 관리할 수 있습니다.

## 설치 방법

```bash
pip install py-sodas-sdk
```

## 주요 기능

- 데이터셋 메타데이터 생성 및 관리 (DCAT 기반)
- 다국어 메타데이터 지원 (제목, 설명, 키워드 등)
- 프로파일 기반의 메타데이터 스키마 관리
- 리소스 설명자를 통한 데이터 리소스 관리
- 버전 관리 및 이력 추적

## 핵심 컴포넌트

### API 초기화

SDK를 사용하기 전에 반드시 API URL을 초기화해야 합니다:

```python
from sodas_sdk import configure_api_url

DATAHUB_API_URL = "http://sodas-profile.example.com"
GOVERNANCE_API_URL = "http://api.example.com"

configure_api_url(DATAHUB_API_URL, GOVERNANCE_API_URL)
```

### Dataset

데이터셋의 메타데이터를 관리하는 핵심 클래스입니다.

```python
from sodas_sdk import Dataset,Distribution

dataset = Dataset()
# 기본 메타데이터 설정
dataset.set_title("데이터셋 제목")
dataset.set_description("데이터셋 설명")

# 다국어 지원
dataset.set_title("English Title", "en")
dataset.set_description("English Description", "en")

# 기타 메타데이터
dataset.type = "http://purl.org/dc/dcmitype/Dataset"
dataset.access_rights = "http://purl.org/eprint/accessRights/OpenAccess"
dataset.lincense = "http://creativecommons.org/licenses/by/4.0/"\

# 디스트리뷰션
distribution = dataset.create_distribution()
distribution.set_title("디스트리뷰션 제목")
distribution.set_description("디스트리뷰션 설명")
# 파일을 업로드하는 경우
if file exists:
    distribution.set_uploading_data("상대경로")
# download_url이 존재하는 경우
if downlod url exists:
    distribution.download_url = "https://DOWNLOAD_URL"

#만약 버킷 정보를 입력하고 싶으면 다음과 같이 합니다.
Distribution.configure_bucket_name("bucket")
# dataset에서 db_record api를 호출하면 distribution은 자동호출됩니다.
await dataset.create_db_record()
# dataset과 distribution은 백엔드와 통신 후 정보를 업데이트합니다.
# 파일 설정시 파일을 업로드하고 download_url이 생깁니다.
downnload_url = distribution.download_url
```

### DatasetSeries

데이터셋들의 집합을 관리합니다.
데이터셋은 단 하나의 데이터셋 시리즈에만 들어갈 수 있으며(1:N),
현재는 데이터셋 시리즈는 데이터셋 시리즈에 들어가지 못합니다.

```python
from sodas_sdk import DatasetSeries
# 처음 데이터 활용
creating_series = DatasetSeries()

dataset1 = await Dataset.get_db_record(dataset1_ID)
dataset2 = await Dataset.get_db_record(dataset2_ID)

creating_series.append_series_member(dataset1)
creating_series.append_series_member(dataset2)

await creating_series.create_db_record()

# 이후 만들어진 데이터셋 시리즈 활용. create_db_record 이후 똑같은 프로세스로 활용 가능합니다.
updating_series = await DatasetSeries.get_db_record(existing_series.id)
appending_dataset = await Dataset.get_db_record(exsiting_dataset.id)
updating_series.append_series_member(appending_dataset)

await updating_series.update_db_record()
```

### Profile

```python
from sodas_sdk import Profile,Dataset
# 처음 데이터 활용
existing_dataset = await Dataset.get_db_record(existing_dataset_id)

dcatProfile = await Profile.get_db_record(exsiting_dataset.profile_iri)
dataProfile = await Profile.get_db_record(existing_dataset.conforms_to)

mapping_value = dataProfile.get_template_descriptor_value_of_role(ResourceDescriptorRole.MAPPING)
schema_value = dataProfile.get_template_descriptor_value_of_role(ResourceDescriptorRole.SCHEMA)
```

### ResourceDescriptor

데이터 리소스의 구조와 특성을 기술하는 클래스입니다.