semvec/pypi-packages huggingface dataset:
alpaka-re 0.3.0
A tool for mapping classes between different versions of the same APK.
Requires Python: >=3.7
Keywords: apk, android, mapping, reverse-engineering, deobfuscation

# Alpaka

[![PyPI - Version](https://img.shields.io/pypi/v/alpaka-re.svg)](https://pypi.org/project/alpaka-re)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/alpaka-re.svg)](https://pypi.org/project/alpaka-re)

-----

Alpaka is a command-line tool designed for matching classes across different
versions of the same APK. This is particularly useful for reverse engineering,
allowing you to track class renames and changes across different builds, and
preserving manual deobfuscation efforts.

## Features

* Class Matching: Generates a JSON matching of classes from one APK version to another.

* Deobfuscation Migration: Migrate existing Enigma deobfuscation
  mapping files to new APK versions, helping maintain your renaming efforts
  in tools like Jadx.

## Installation

```console
git clone https://github.com/DanielFi/alpaka
pip install ./alpaka
```

## Usage

### Basic Class Matching

Output a JSON matching from `A.apk` (older version) to `B.apk` (newer version).
This will show how classes in `A.apk` correspond to classes in `B.apk`.

```console
> alpaka match A.apk B.apk1
{
    "LX/003;": "LX/003;",
    "LX/004;": "LX/004;",
    "LX/005;": "LX/005;",
    "LX/006;": "LX/006;",
...
```

### Migrating Deobfuscation Mappings Across Versions

If you have an Enigma mapping file (e.g., `A.mapping`) for `A.apk`, you can use
Alpaka to generate a new mapping file (`B.mapping`) that applies your existing
renames to `B.apk`. This is incredibly useful for maintaining your manual
deobfuscation work when a new version of the APK is released.

The generated `B.mapping` file can then be imported into tools like Jadx to preserve your manual renamings!

```console
> alpaka match --deobfuscation A.mapping --only-obfuscated A.apk B.apk1 > B.mapping
```

### Advanced Options

The `match` command supports several options to fine-tune its behavior:

* `--only-obfuscated`: Prevents Alpaka from creating unnecessary matches
  between unobfuscated classes.
* `--no-propagation`: Only run the first analysis stage. Usually leads to more
  accurate results but fewer matches overall.

## License

Alpaka is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
rshf 0.1.12
RS pretrained models in huggingface style
Requires Python: >=3.9
Keywords: Remote Sensing, Huggingface

# rshf
[![PyPI - Version](https://img.shields.io/pypi/v/rshf)](https://pypi.org/project/rshf/)
[![PyPI Downloads](https://static.pepy.tech/badge/rshf)](https://pypistats.org/packages/rshf)
[![PyPI Docs](https://img.shields.io/readthedocs/rshf)](https://rshf-docs.readthedocs.io/en/latest/)


### Remote sensing pretrained models easy loading using huggingface -- PyTorch (for fast benchmarking)

### Installation:
```bash
pip install rshf
```

### Example:
```python
from rshf.satmae import SatMAE
model = SatMAE.from_pretrained("MVRL/satmae-vitlarge-fmow-pretrain-800")
input = model.transform(torch.randint(0, 256, (224, 224, 3)).float().numpy(), 224).unsqueeze(0)
print(model.forward_encoder(input, mask_ratio=0.0)[0].shape)
```

### TODO:
- [ ] Add transforms for each model
- [ ] Add Documentation (https://rshf-docs.readthedocs.io/en/latest/)
- [x] Add initial set of models

### Citations

|Model Type|Venue|Citation|
|----------|-----|--------|
|BioCLIP|CVPR'24|[link](./rshf/bioclip/README.md)
|Climplicit|ICLRW'25|[link](./rshf/climplicit/README.md)
|CLIP|ICML'21|[link](./rshf/clip/README.md)
|CROMA|NeurIPS'23|[link](./rshf/croma/README.md)
|GeoCLAP|BMVC'23|[link](./rshf/geoclap/README.md)
|GeoCLIP|NeurIPS'23|[link](./rshf/geoclip/README.md)
|Presto||[link](./rshf/presto/README.md)
|Prithvi||[link](./rshf/prithvi/README.md)
|RCME|ICCV'25|[link](./rshf/rcme/README.md)
|RemoteCLIP|TGRS'23|[link](./rshf/remoteclip/README.md)
|RVSA|TGRS'22|[link](./rshf/rvsa/README.md)
|Sat2Cap|EarthVision'24|[link](./rshf/sat2cap/README.md)
|SatClip|AAAI'25|[link](./rshf/satclip/README.md)
|SatMAE|NeurIPS'22|[link](./rshf/satmae/README.md)
|SatMAE++|CVPR'24|[link](./rshf/satmaepp/README.md)
|ScaleMAE|ICCV'23|[link](./rshf/scalemae/README.md)
|SenCLIP|WACV'25|[link](./rshf/senclip/README.md)
|SINR|ICML'23|[link](./rshf/sinr/README.md)
|StreetCLIP||[link](./rshf/streetclip/README.md)
|TaxaBind|WACV'25|[link](./rshf/taxabind/README.md)
### List of models available here: [Link](https://huggingface.co/collections/MVRL/remote-sensing-foundation-models-664e8fcd67d8ca8c03f42d00)
spyreapi 0.0.9
A robust and extensible Python client for interacting with the [Spire Business Software API](https://developer.spiresystems.com/reference). This client provides an object-oriented interface to get, create, update, delete, query, filter, sort, and manage various Spire modules such as Sales Orders, Invoices, Inventory Items, and more.
Requires Python: >=3.9

# Spire API Python Client

A robust and extensible Python client for interacting with the [Spire Business Software API](https://developer.spiresystems.com/reference). This client provides an object-oriented interface to get, create, update, delete, query, filter, sort, and manage various Spire modules such as Sales Orders, Invoices, Inventory Items, and more.

---

## Documentation

https://sanjid-sharaf.github.io/spyre/

## ✨ Features

- ✅ Object-oriented resource wrappers for each module (e.g., `salesOrder`, `invoice`, `item`)
- 🔍 Full-text search via `q` parameter
- 🔁 Pagination with `start` and `limit` support
- 🧾 JSON-based advanced filtering (supports `$gt`, `$lt`, `$in`, `$or`, etc.)
- ↕️ Multi-field sorting with ascending/descending control
- 🔧 Clean abstraction layer for API endpoints
- 📦 Powered by `pydantic` models for validation

---

## 📦 Installation

```bash
pip install spyreapi
```

---

## ⚙️ Configuration

- How to set up your spire client

### Find your Spire URL

The base URL for the Spire API is the same url provided by Spire that you use to access Spire server and uses port 10880 as default:
Replace {spire-url} with the url provided by Spire.

- https://{spire-url}:10880/api/v2/
  
Spire Cloud
If you are using Spire cloud you do not need to specify a port. The base URL for API for Spire Cloud customers would be:

- https://{spire-cloud-url}/api/v2/

### Set up your client with your credentials

```python
from spyre import Spire
# host is your spire url and the port if applicable
client = Spire(host = 'your-spire-host', company = 'comapany-name' , username = 'username' , password = 'password' )

```

## Example : Updating the status of an inventory item 
```python
item = client.inventory.items.get_item(1101)    # Gets item with id 1101
item.status = 1                                 # Use either item. or item.model. . item.model. will bring up all attributes
item.update()
```
piperx-env-vars 0.1.0
Add your description here
Requires Python: >=3.13

# piperx-env-vars

A Python package for managing environment variables for the PiperX platform. This package centralizes configuration for services, storage, external clients, server settings, and block prompts using environment variables and `.env` files.

## Features

- Loads environment variables for different PiperX components.
- Supports configuration for authentication, storage, external clients, and more.
- Uses [`python-dotenv`](https://pypi.org/project/python-dotenv/) for `.env` file support.

## Directory Structure

- **blocks_vars.py**: Prompt and LLM configuration variables.
- **external_client_vars.py**: External client connection settings.
- **internal_storage_vars.py**: Internal storage and database configuration.
- **server_vars.py**: Server-level environment variables.
- **services_vars.py**: Service integrations and API keys.

## Usage

Install dependencies (requires Python 3.13+):

```sh
uv sync

from piperx_env_vars import APP_NAME, PG_USER_NAME, OPENAI_API_KEY
plot-boundary-extractor 0.3.3
Plot boundary extractor using segment anything model
Requires Python: <3.12,>=3.10

# `plot-boundary-extractor` module

This module is to extract field experiment plot boundary for agricultural applications.

---

*Developed by [Hansae Kim](https://gdsl.org/team/hansae-kim/) and [Jinha Jung](https://gdsl.org/team/jinha-jung/)*

## `plot-boundary-extractor` installation

### Set up a virtual environment

First, we need to install `plot-boundary-extractor` on the machine you would like to run this example tutorial. We will set up a new Python Virtual Environment called `pbe`. 

```bash
$ conda create -n pbe -c conda-forge gdal python=3.10 -y
```

We need to activate the newly created virtual environment before proceeding.

```bash
$ conda activate pbe
```

### Install `plot-boundary-extractor` module

Now we need to install `plot-boundary-extractor` module using `pip`.

```bash
$ pip install plot-boundary-extractor
```

### Install torch

The `plot-boundary-extractor` module uses `torch` module, so we need to install it on the machine you would like to run this example tutorial. 

#### CPU version

If your machine is not equipoped with NVIDIA GPU, then you will have to install the `torch` without GPU support.

```bash
$ pip install torch torchvision
```

#### GPU version

If your machine is equipped with NVIDIA GPU, then you will have to install the `torch` with GPU support.

```bash
$ pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
```

### Install `SAM`

The `plot-boundary-extractor` uses `SAM` to detect plot boundary automatically, so you have to install `SAM` using the below command.

```bash
$ pip install git+https://github.com/facebookresearch/segment-anything.git
```

You also have to download a pre-trained model using the below command. Please make a note where you're downloading the pre-trained model. We will need this path information in the later tutorial.

```bash
$ wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
```

Now the installation is done, and you're ready to extract plot boundaries using the `plot-boundary-extractor` module. An example [Jupyter Notebook](./plot_boundary_extractor_demo.ipynb) is available for your reference.


## Citation

If you use this repository in your research, please cite the following paper:

> Kim, H.S., Olaniyi, I., Chang, A., & Jung, J. (2025).  
> *Developing a segment anything model-based framework for automated plot extraction*.  
> Precision Agriculture, 26(3), 53. Springer.  

```bibtex
@article{kim2025developing,
  title={Developing a segment anything model-based framework for automated plot extraction},
  author={Kim, Han Sae and Olaniyi, Ismail and Chang, Anjin and Jung, Jinha},
  journal={Precision Agriculture},
  volume={26},
  number={3},
  pages={53},
  year={2025},
  publisher={Springer}
}