semvec/pypi-packages huggingface dataset:
spatial-detector 0.1.0
3D object detection and spatial mapping using webcam
Requires Python: >=3.7

# Spatial Detector

A Python package for 3D object detection and spatial mapping using a webcam.

## Features

- YOLO-based object detection
- MiDaS depth estimation
- 3D spatial mapping and localization
- Top-down view visualization
- Depth calibration for accurate measurements
- Designed for Apple Silicon and CUDA GPUs

## Installation

```bash
pip install spatial-detector
```

Or install from source:

```bash
git clone https://github.com/yourusername/spatial-detector.git
cd spatial-detector
pip install -e .
```

## Usage

### Command Line Interface

```bash
# Basic usage
spatial-detector

# With custom settings
spatial-detector --camera 0 --yolo-model yolov8n.pt --device mps
```

### As a Python Library

```python
from spatial_detector.detection import YOLODetector
from spatial_detector.depth import MiDaSDepthEstimator
from spatial_detector.mapping import SpatialMap, DepthCalibrator

# Initialize components
detector = YOLODetector(model_path="yolov8n.pt")
depth_estimator = MiDaSDepthEstimator()
spatial_map = SpatialMap()

# Process a frame
detections = detector.detect(frame)
depth_map, _ = depth_estimator.estimate_depth(frame)
```

## Controls

- `q`: Quit
- `d`: Toggle depth visualization
- `l`: Toggle labels
- `c`: Enter calibration mode
- `+`/`-`: Adjust calibration distance
- `space`: Set calibration point in calibration mode
- `s`: Save depth calibration

## Calibration

For accurate measurements:

1. Press `c` to enter calibration mode
2. Place an object at a known distance (e.g., 1 meter from camera)
3. Use `+`/`-` to set the correct distance
4. Align the object with the center crosshair
5. Press `space` to calibrate
6. Press `s` to save the calibration

## License

MIT
BizML 0.1
A Python library for predictive analytics and business forecasting.
Requires Python: >=3.6

# BizML

**BizML** is a Python library for predictive analytics and business forecasting. It provides tools for customer churn prediction, sales forecasting, automated business reporting, and interactive dashboards.

---

## Features
- **Customer Churn Prediction**: Predict which customers are likely to stop using your product or service.
- **Sales Forecasting**: Forecast future sales using advanced time series models.
- **Automated Reporting**: Generate business reports in PDF or Excel format.
- **Interactive Dashboards**: Create visualizations for sales and churn analysis.

---

## Installation

You can install BizML using pip:

```bash
pip install BizML
hardspheres-2d 0.1.4
Add your description here
Requires Python: >=3.13

# Hard Spheres in 2d
> Simulating hard spheres in two dimensions using event driven molecular dynamics and visualizing rotational symmetry using psi-6.

![Melting hexagon of hard spheres](./melting-hexagon.gif)

## Quickstart

    uvx --from hardspheres-2d hardspheres2d --help

## Setup to develop

    git clone https://github.com/eschmidt42/hardspheres-2d.git
    cd hardspheres-2d
    uv sync

For the installation of uv see [here](https://docs.astral.sh/uv/getting-started/installation/).

Note: this project uses Rust 1.82. So you may need to [install the rust toolchain](https://www.rust-lang.org/tools/install).

## How to use

### Command Line

To run the event driven molecular dynamics simulation from your command line within the project check out

    uv run hardspheres2d --help


### Notebook

Alternatively, the notebook `./melting-hard-sphere-hexagons.ipynb` contains all the steps to set up and run event driven molecular dynamcis on your machine. As well as some sanity checks to better understand what is going on.

To visualize the dynamics you may want to install [ovito](https://www.ovito.org). The base version is sufficient.

## Developing

During your rust edits you want to update your build artefacts so you can use them from python / a notebook. For you can use

    make update
requre 0.9.1
Library for testing Python code that allows to store output of various objects and use the stored data for testing.
Requires Python: >=3.6
Keywords: persistent_storage, session_storage, testing

[![PyPI version](https://badge.fury.io/py/requre.svg)](https://badge.fury.io/py/requre)

# REQUest REcording

Requre \[rekure\]

Is a library for storing output of various function and methods to
persistent storage and be able to replay the stored output to functions
back

[Documentation](https://requre.readthedocs.io/en/latest/)

## Plan and current status

- Used for testing [packit-service](https://github.com/packit-service) organization projects
  - ogr
  - packit

## Installation

On Fedora:

```
$ dnf install python3-requre
```

Or from PyPI:

```
$ pip3 install --user requre
```

You can also install `requre` from `main` branch, if you are brave enough:

You can use our [`packit/packit-dev` Copr repository](https://copr.fedorainfracloud.org/coprs/packit/packit-dev/):

```
$ dnf copr enable packit/packit-dev
$ dnf install python3-requre
```

Or

```
$ pip3 install --user git+https://github.com/packit/requre.git
```
proteogenomics 0.1.0
A CLI tool for proteogenomics analysis
Requires Python: >=3.6

Proteogenomics Prototype

A CLI-based tool for integrating proteomics and genomics data for biomarker identification.


---

📌 Features

✔ Supports multiple input formats: FASTA, mzML, CSV, TSV, FASTQ, BAM, VCF, JSON, XML.
✔ Automated data parsing, integration, and biomarker identification.
✔ Mutation-based biomarker detection.
✔ Interactive visualization for analysis.
✔ Fully CLI-based and runs on Linux (Ubuntu).


---

📥 Installation

To install the tool from GitHub:

git clone https://github.com/sethuakasanji/Proteogenomics-Prototype.git
cd Proteogenomics-Prototype
pip install -r requirements.txt

Or install it as a package (after packaging it):

pip install .


---

🚀 Usage

To run the CLI:

proteogenomics --help

🔹 Example Commands:

Input Data Processing:

proteogenomics --proteomics /path/to/proteomics_file  --genomics /path/to/genomics_file

Mutation-Based Biomarker Detection:

proteogenomics --mutation-detection

View Available Commands:

proteogenomics --help



---

🛠 Configuration

Ensure Python 3.8+ is installed.

Works on Linux (Ubuntu).



---

📖 Contributing

If you’d like to contribute, fork the repository and create a pull request!


---

📞 Contact : +91 6383824543
Email : masssanjay85@gmail.com

For queries or issues, reach out via GitHub Issues.
drf-orjson-rp 0.3.0
DRF renderer and parser based on orjson
Requires Python: >=3.9
Keywords: drf, orjson, parser, renderer

# DRF orjson renderer and parser

[![CI](https://github.com/ArtemIsmagilov/drf_orjson_rp/actions/workflows/ci.yaml/badge.svg)](https://github.com/ArtemIsmagilov/drf_orjson_rp/actions/workflows/ci.yaml)
[![Upload Python Package](https://github.com/ArtemIsmagilov/drf_orjson_rp/actions/workflows/python-publish.yml/badge.svg)](https://github.com/ArtemIsmagilov/drf_orjson_rp/actions/workflows/python-publish.yml)
[![pypi](https://img.shields.io/pypi/v/drf-orjson-rp.svg)](https://pypi.python.org/pypi/drf-orjson-rp)

Rendering and parsing JSON with orjson.
Made on example drf_orjson_renderer package.

## Installation

- Install package

  ```bash
  pip install drf_orjson_rp
  ```

- Rewrite settings by example.

  ```python
  REST_FRAMEWORK = {
      'DEFAULT_RENDERER_CLASSES': [
          # replace default json on orjson renderer
          'drf_orjson_rp.renderers.ORJSONRenderer',
          # defaults
          'rest_framework.renderers.BrowsableAPIRenderer',
      ],
      'DEFAULT_PARSER_CLASSES': [
          # replace default json on orjson parser
          'drf_orjson_rp.parsers.ORJSONParser',
          # defaults
          'rest_framework.parsers.FormParser',
          'rest_framework.parsers.MultiPartParser'
      ],
      # add orjson renderer option
      'ORJSON_RENDERER_OPTION': orjson.OPT_APPEND_NEWLINE | orjson.OPT_INDENT_2,
  }
  ```

- [Check settings by default in source code DRF. DEFAULTS](https://github.com/encode/django-rest-framework/blob/master/rest_framework/settings.py)
- Renderer and parser made by example from source code DRF.
  - [DRF Renderer](https://github.com/encode/django-rest-framework/blob/master/rest_framework/renderers.py)
  - [DRF Parser](https://github.com/encode/django-rest-framework/blob/master/rest_framework/parsers.py)

## Developing

- Clone your fork and cd into the repo directory

  ```bash
  git clone git@github.com:<your username>/drf_orjson_rp.git
  cd drf_orjson_rp
  ```

- Create a virtual environment

  ```bash
  python3 -m venv .venv
  source .venv/bin/activate
  ```

- Install uv and dev dependencies

  ```bash
  pip install uv
  bash scripts/install.bash
  ```

- Run tests

  ```bash
  bash scripts/test.bash
  ```

- Run linters

  ```bash
  bash scripts/lint.bash
  ```

- Run test coverage

  ```bash
  bash scripts/coverage.bash
  ```

## Links

- [drf_ujson_renderer](https://github.com/gizmag/drf-ujson-renderer)
- [drf_orjson_renderer](https://github.com/brianjbuck/drf_orjson_renderer)
- [DRF stubs](https://github.com/typeddjango/djangorestframework-stubs)
- [pydantic workflows](https://github.com/pydantic/pydantic/blob/main/.github/workflows/ci.yml)
- [fastapi scripts](https://github.com/fastapi/fastapi/tree/master/scripts)