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FocusPandas

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Analyze your digital habits, health metrics, and productivity patterns with this data-driven dashboard.

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ZIP 다운로드

- FocusPandas bento.png
- FocusPandas header.png
- example.csv
- extract_db.py
- inspect_db.py
.gitattributes
.gitignore
app.py
brain_rot.py
categories.py
LICENSE
README.md
requirements.txt
roi.py

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jsDelivr

jsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.

버전

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링크

예시

명령어 용어집

이 문서에서 사용된 명령어를 모아봤습니다. 낯선 명령어가 있다면 펼쳐서 확인해보세요.

🔍

`git clone`

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Clone an existing repository.

git clone {{remote_repository_location}} {{path/to/directory}}

Clone an existing repository into a new directory (the default directory is the repository name):

git clone --recursive {{remote_repository_location}}

Clone an existing repository and its submodules:

git clone {{[-n|--no-checkout]}} {{remote_repository_location}}

Clone only the `.git` directory of an existing repository:

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`pip install`

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Install Python packages.

pip install {{package1 package2 ...}}

Install one or more packages:

pip install {{package1 package2 ...}} {{[-U|--upgrade]}}

Upgrade all specified packages to the latest version, installing any that are not already present:

pip install {{package}}=={{version}}

Install a specific version of a package:

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`python3`

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이 명령은 `python`의 별칭입니다.

tldr python

자세한 내용은 원본 명령을 참고하세요:

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`streamlit`

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Python에서 상호작용하는 데이터 기반 웹 앱을 만드는 프레임워크.
더 많은 정보: <https://docs.streamlit.io/develop/api-reference/cli>.

streamlit hello

Streamlit 설치 여부 확인:

streamlit run {{project_name}}

Streamlit 애플리케이션 실행:

streamlit --help

도움말 표시:

# 프로젝트 배지

FocusPandas Header
FocusPandas Bento

// repository documentation

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**FocusPandas** is a data visualization and analysis tool that helps you explore and understand your digital habits, health metrics, and productivity patterns. Using Apple Health and Screen Time data, FocusPandas provides insights into how your daily habits influence your productivity, health, and overall well-being.

## Features
- **Screen Time Analysis**: Explore app usage trends, peak usage times, and detailed app statistics. Use this tool to preserve your Screen Time data, ensuring it remains available for long-term analysis, even if your system resets it.
- **Heart Rate Insights**: Correlate heart rate data with screen time and productivity.
- **Sleep Analysis**: Explore the relationship between sleep patterns, productivity, and screen time.
- **Productivity Metrics**: Calculate daily productivity ratios and highlight trends.
- **Additional Insights**: Gain insights into audio exposure, digital habits, and more.

---

## Getting Started

Follow these steps to set up and run the application:

### 1. Clone the Repository
```bash
git clone https://github.com/AndreasInk/FocusPandas.git
cd focuspandas
```

### 2. Set Up a Virtual Environment
Create and activate a virtual environment to manage dependencies:
```bash
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

### 3. Install Dependencies
Install the required Python libraries using `requirements.txt`:
```bash
pip install -r requirements.txt
```

### 4. Extract Screen Time Data
Run the following command to extract Screen Time data into the `data/` directory:
```bash
sudo python3 extract_db.py
```
This step fetches the Screen Time data from macOS's database and saves it locally, ensuring you have a backup of your digital habits. This is especially useful as macOS only retains Screen Time data for up to 2 months or may reset data during OS updates.

### 5. Export Apple Health Data
Export your Apple Health data from the **Apple Health** app:
1. Open the Apple Health app on iOS.
2. Go to your profile (top right of the app on iOS 18+).
3. Tap **Export All Health Data** (at the bottom of the view).
4. Place the resulting XML file in the `data/` directory.

### 6. Run the Streamlit App
Launch the app using Streamlit:
```bash
streamlit run app.py
```
The app will open in your default web browser. If not, visit [http://localhost:8501](http://localhost:8501).

---

## Application Overview

The app is organized into six main sections, accessible from the sidebar:
1. **Screen Time Analysis**  
   Explore app usage trends, peak usage times, and detailed app statistics.
2. **Heart Rate Analysis**  
   Analyze how heart rate correlates with screen time and digital habits.
3. **Sleep Analysis**  
   Visualize sleep duration trends and how sleep affects productivity and screen usage.
4. **Productivity Analysis**  
   Understand the relationship between productivity, sleep, and audio exposure.
5. **Productivity Metrics**  
   View daily productivity metrics, including usage hours and productivity ratios.
6. **Additional Insights**  
   Gain insights into daily screen time patterns, productivity vs. audio exposure, and more.

---

## Data Sources
- **Screen Time Data**: Extracted directly from your system database.
- **Apple Health Data**: Exported via Apple Health and processed to extract heart rate, sleep, and audio exposure metrics.

---

## Dependencies

The project uses the following Python libraries:
- `streamlit` for creating the interactive web app.
- `pandas` for data processing and analysis.
- `matplotlib` for data visualization.
- `pydantic` for managing structured data.

---

## License

This project is licensed under the MIT License. See the `LICENSE` file for details.

---

## Contributing

Contributions would be great! Please feel free to submit a pull request or open an issue.