semvec/pypi-packages huggingface dataset:
lambda-explorer 1.0.0
GUI tool for exploring formulas using DearPyGui
Requires Python: >=3.8

# Lambda Explorer

A small GUI application for interacting with symbolic formulas and plotting them using [Dear PyGui](https://github.com/hoffstadt/dearpygui).

The GUI presents every available formula and lets you calculate or plot results
by filling in all but one variable. Formulas are defined with
[SymPy](https://www.sympy.org/) and are automatically discovered when the
application starts.
Displayed formulas are rendered from LaTeX using Matplotlib for clarity.

## Installation

```bash
pip install lambda-explorer
```

To install from source:

```bash
pip install -r requirements.txt
pip install -e .
```

## Usage

After installation you can launch the GUI with the command:

```bash
lambda-explorer
```

This will open the formula browser where you can calculate and visualize formulas.

### Lightweight CLI

If memory usage is a concern you can use a simple command line interface:

```bash
lambda-explorer-cli
```

This avoids the GUI and only loads the selected formula when needed.

## Library Usage

`lambda-explorer` can also be used programmatically. Formula classes are
available in `lambda_explorer.tools.aero_formulas` and provide a convenient
`solve()` method. Exactly one variable must be omitted so that it can be
calculated:

```python
from lambda_explorer.tools.aero_formulas import ReynoldsNumber

eq = ReynoldsNumber()
# Solve for Re while providing the other values
re = eq.solve(rho=1.225, V=50.0, c=0.5, mu=1.8e-5)
print(re)
```

To launch the GUI from Python simply call `lambda_explorer.main()`:

```python
from lambda_explorer import main

main()
```

Default values used inside the GUI can be customised. Use the *Defaults* tab in
any formula window to load or save the `defaults.yaml` file directly, or choose
"Save As" to export the defaults to a custom YAML file. The defaults map
variable names to their stored string values.

The application also remembers your window layout. When you close the GUI, the
current positions and sizes of all windows are written to `layout.ini` and the
list of visible windows is stored in `open_windows.json`. Both the layout and
open windows are restored on the next start.

The *Settings* window lets you adjust the logging level of the application at
runtime. Choose between `DEBUG`, `INFO`, `WARNING` and `ERROR` to control the
amount of information written to the log window.

## Developer Documentation

Developers who want to implement additional formulas can follow the guide in
[`docs/developer_guide.md`](docs/developer_guide.md). It describes the required
class structure and how new equations are automatically integrated into the GUI.

## Development Workflow

Use the provided `Makefile` to streamline common tasks:

```bash
make install  # install dependencies and project in editable mode
make format   # apply code formatting using Black
make lint     # run pre-commit hooks
```

Run `make run` to start the GUI or `make run-cli` for the command line interface.
pomodev 0.1.3
pomodoro timer for devs with git, streak and history
Requires Python: >=3.6

# ⏳ pomodev

**A developer-first CLI Pomodoro tool** with built-in Git integration, streak tracking, session history, and sound alerts. Designed to keep you in the flow while working on projects.

---

## ✨ Features

* ⏱️ Simple Pomodoro work/break timer
* ✅ Auto Git commit prompt after each work session
* 📊 `--history`: View all your sessions in a rich table
* 🔁 `--streak`: See how consistent you’ve been today and this week
* 🔔 Sound alert when timers finish
* 📁 CSV log file (`session_log.csv`) stores all your sessions locally

---

## 📦 Installation

```bash
pip install pomodev
```

Make sure your Python user bin is in your PATH, or use:

```bash
source ~/.zshrc  # or your shell config file
```

---

## 🚀 Usage

```bash
pomodev
```

By default, this starts a 25-minute work session, followed by a 5-minute break, and prompts you to Git commit when done.

---

### 🎠 Options

| Flag           | Description                               |
| -------------- | ----------------------------------------- |
| `--work`       | Set custom work duration (minutes)        |
| `--break-time` | Set custom break duration (minutes)       |
| `--no-commit`  | Skip Git commit prompt                    |
| `--history`    | Show all logged sessions                  |
| `--streak`     | Display your current day/week work streak |

---

## 🧪 Examples

### Regular session (default 25/5)

```bash
pomodev
```

### Deep work (50 minutes) with no break

```bash
pomodev --work 50 --break-time 0 --no-commit
```

### View past sessions

```bash
pomodev --history
```

### See your streak stats

```bash
pomodev --streak
```

---

## 📁 Where data is stored

All sessions are logged to `session_log.csv` in the working directory.

---

## 👨‍💻 Who is it for?

If you:

* Work on side projects and want accountability
* Want lightweight time tracking tied to Git activity
* Like working in the terminal and hate bloated apps

This is for you.

---

## ✅ Roadmap Ideas

* [ ] Preset durations via config
* [ ] Web dashboard (`--export-html`)
* [ ] Live UI with [Textual](https://github.com/Textualize/textual)
* [ ] Daily goal + streak gamification

---

Built with ❤️ by Dhruv
tableau-parser 0.1.0
A Python library for parsing and analyzing Tableau workbook files
Requires Python: >=3.7

# Tableau Parser

A Python library for parsing and analyzing Tableau workbook (.twb) files. This library provides detailed information about Tableau workbooks including datasources, calculations, dashboards, sheets, and more.

## Installation

```bash
pip install tableau-parser
```

## Usage

```python
from tableau_parser import analyze_tableau_workbook

# Analyze a Tableau workbook
result = analyze_tableau_workbook("path/to/your/workbook.twb")

# Access the analysis results
print(result.model_dump_json(indent=4))
```

## Features

- Parse Tableau workbook (.twb) files
- Extract detailed information about:
  - Datasources (published and embedded)
  - Calculations
  - Dashboards
  - Sheets
  - Charts
  - Tables
  - Parameters
  - Filters
  - Joins
  - And more!

## Example Output

The library returns a structured response containing:

- Summary information (counts of various components)
- Detailed information about each component
- Datasource information including filters and row-level security

## Requirements

- Python 3.7 or higher
- pydantic 2.0.0 or higher

## License

MIT License

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.