semvec/pypi-packages huggingface dataset:
griblet 0.1.0
Distill the optimal computation tree from a field of possibilities.
Requires Python: >=3.8

# griblet

**Carve computation trees from a burl of dependencies.**

Griblet is a dynamic, cache-aware calculation engine for building and evaluating omputation trees from a flexible graph of possible dependencies and recipes.

- Supports alternative computation paths for each result
- Dynamic costs, including cache- and disk-aware lazy evaluation
- Clean separation of recipes, cache, loader, and evaluation logic
- Suitable for scientific postprocessing, engineering workflows, and flexible data rocessing pipelines

## Quickstart

Clone and install in editable mode:

```bash
git clone https://github.com/svaberg/griblet.git
cd griblet
pip install -e .
```

## Example

See [`examples/demo_griblet.py`](examples/demo_griblet.py) for a demonstration.

---
sprintforecast 0.2.9
Hierarchical Bayesian sprint forecasting and intake planning
Requires Python: >=3.10

# SprintForecast

A hierarchical Bayesian sprint‑forecast model that converts each ticket’s PERT triad into a scaled‑Beta prior, layers skew‑Student‑t execution noise with queue‑based review and capacity terms, and finally Monte‑Carlo‑simulates the Dev → Review → Done flow to yield the sprint‑completion probability and expected carry‑over.

## TODO
- [ ] Implement the hierarchical Bayesian model
- [ ] Add unit tests for the model components
- [ ] Prioritise dependencies of issues in plan
- [ ] Implement the Monte Carlo simulation for sprint completion
- [ ] Visualise the sprint forecast results and carry-over and interpretation of the results
- [ ] Move class and function definitions to separate files for better organisation
- [ ] Refactor cli.py using SOLID principles for better maintainability

*NOTE* that this is a work in progress, and the model is not yet implemented. The code is provided for educational purposes only.
pyinspirationquotes 1.0.5
A simple and fun Python package that provides random inspirational quotes.
Requires Python: >=3.6

# pyinspirationquotes
🎯 This is simple and fun Python package that provides random inspirational quotes from a collection of 900+ motivational quotes.

# 📦 Installation
You can install the package directly using `pip`:

pip install pyinspirationquotes

## 🚀 Usage
from pyinspirationquotes import get_inspiration

print(get_inspiration())

Each time you call `get_inspiration()`, it returns a random motivational quote from a preloaded list.

or 

Jot pyinspirationquotes directly in the terminal to get inspirational quote in your terminal directly🔥.


## 📁 Example Output
Push yourself because no one else is going to do it for you.

## 📚 How It Works
The package reads quotes from an internal `quotes.txt` file and selects one at random.

## 🔧 Development
To install and run locally for development:

git clone https://github.com/akankshasoni024/pyinspirationquotes.git
cd pyinspirationquotes
pip install -e .


To test locally:

python
from pyinspirationquotes import get_quote.
get_inspiration() .


## 📄 Verification
This project is Verified By Pypi.

## 📩 Connect with me 
Made with ❤️ by [Akanksha Soni].

Github : https://github.com/akankshasoni024 
Gmail: aakankshasoni024@gmail.com 
Linkedin: https://www.linkedin.com/in/akankshasoni024/ 

Happy to Connect!
sampah 0.1.2
Short description of your package
Requires Python: >=3.6

# 🧭 S.A.M.P.A.H.

**Spatial Annotation Mapping Platform for Alignment & Heading**  
*A general-purpose image annotation tool with support for directional labeling.*

---

## 📝 Overview

**S.A.M.P.A.H.** is a lightweight image labeling application built for annotating not just the presence of objects — but their **orientation**. It enables annotators to label an object's **heading direction** using an **8D compass-style system**, in addition to bounding boxes or segmentation.

Whether you're labeling vehicles, humans, animals, or other directional objects, **S.A.M.P.A.H.** provides an intuitive interface to mark both **position** and **direction** with precision.

---

## 🧭 8D Compass Annotation

S.A.M.P.A.H. introduces a novel directional labeling system based on **8 compass directions**:

```
          N
       NW   NE
     W    ●    E
       SW   SE
          S
```

Each object annotation can be tagged with one of the following directions:

- **N** (North) back B
- **NE** (Northeast) Back-Right BR
- **E** (East) right R
- **SE** (Southeast) front-right FR
- **S** (South) front F
- **SW** (Southwest) front-left FL
- **W** (West) left L
- **NW** (Northwest) back-left BL

This is ideal for tasks involving:
- **Object orientation** (e.g., which way a person or vehicle is facing)
- **Motion prediction**
- **Pose estimation (simplified)**

---

## 🚀 Features

- 📦 Label images with bounding boxes or masks
- 🧭 Add an 8-direction compass heading to each object
- 🧠 Supports class-based object labeling
- 💾 Saves annotations in **Pascal VOC** format (XML), with added direction tag
- 🌐 Simple Flask + HTML interface — run locally with no installation overhead

---

## 📂 Annotation Format

S.A.M.P.A.H. saves annotations in a modified **Pascal VOC XML** format, with an extra `<direction>` tag inside each object block:
```xml
<object>
    <name>front</name>
    <bndbox>
        <xmin>123</xmin>
        <ymin>85</ymin>
        <xmax>200</xmax>
        <ymax>160</ymax>
    </bndbox>
</object>
```
---

## 🛠️ Getting Started git

```bash
git clone https://github.com/yourname/sampah
cd sampah
pip install -r requirements.txt
python app.py
```

Then, open your browser to `http://127.0.0.1:5000`.


## 🛠️ Getting Started via PIP
pip install sampah
sampah

Then, open your browser to `http://127.0.0.1:5000`.

---

## 🔮 Coming Soon

- Multi-object tracking
- Custom compass (16D ~ Deg)
- Export to COCO format with direction support
- Integrated model-assisted annotation (YOLO/ViT)

---
## 🧑‍💻 Author

Built by [Your Name]  
For questions, email [you@example.com]
wwttoolbox 0.17.1
WaterWebTool toolbox for common tasks in hydrology and water resources management

# wwt-toolbox

# Sub-packages

## nc - work with NetCDF files

An module with NetCDF files. The module should be used in conjunction with:

- [netcdf4](https://unidata.github.io/netcdf4-python/)
- [NetCDF CF Conventions](https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html)

**No support for coordinates which are function of multiple dimensions**

E.g.:

Dimensions:

- time
- depth

Variables:

- time (time)
- depth (time, depth)

## conversion - convert between different units

### unit conversion

### time conversion

## transformers - transform data to different formats

### WET

### SWAT

- Write station data files like `.tmp`, `.hmd`
- Write `.sim` file
- Write `.cli` files

## yaml - edit yaml files

## QAQC

To use qaqc modules, the 'qaqc' optional dependency must be installed with `pip install wwtttoolbox[qaqc]`.

To use the qaqa validation module following must also be installed: `pip install wwtttoolbox[qaqc-validation]`.