semvec/pypi-packages huggingface dataset:
ms-file-reader 0.1.1
Reads a few common file formats for processed mass spectra and generates consistent objects from them.
Requires Python: >=3.9
Keywords: mass spectrometry, jcamp, massbank, msp

[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)

This is a small library intended to read different types of mass spectrometry files, store them as (somewhat) standardized objects, and run some exploratory checks on the spectra as a whole.  The focus is on open, text-based file formats for already-processed spectra.  Functionality has been added for reading MSP, JCAMP-DX, and MassBank EU-styled files.

I wrote this because of a project that grabbed collections of mass spectra from a variety of sources.  A number of these sources had  inconsistencies within their libraries -- sometimes due to fields that don't appear in all spectra, sometimes becuase the field does always appear but it has some sort of null value in the field, and so on.  Some exploration of the data was usually necessary, and much of it was repetetive.  This library was written to streamline some of that work for anyone else in the same position.

NumPy is the only dependency for this library.

Feedback on other real-world edge cases is welcome.

# Usage

Install from PyPI:

```
pip install ms-file-reader
```

To import the individual readers:

```
from ms_file_reader.jcamp import JCAMPFileReader
from ms_file_reader.massbank import MassBankFileReader
from ms_file_reader.msp import MSPFileReader
```

The individual readers -- mostly the ones for JCAMP-DX and MSP -- come with options for trying to deal with any non-standardness of files; see the docstrings for argument details.  Processing is done by a `process_file()` method associated with each class.  The method acts on text objects instead of file handles or paths, so the content of a file has to be read in first.

A basic example:

```
from ms_file_reader.msp import MSPFileReader
with open("test.msp", "r", encoding="utf-8") as f:
    file_text = f.read()

reader = MSPFileReader(keep_empty_fields=False, max_intensity=100)
spectrum_library = reader.process_file(file_text)
```
daagy-ai 1.0.1
Daagy is a terminal-based AI assistant that follows natural language instructions to perform system tasks, answer questions, and much more.
Requires Python: >=3.7

# Daagy - Your AI Terminal Assistant

**Daagy** is a terminal-based AI assistant that follows natural language instructions to perform system tasks, answer questions, and much more.


## 🚀 Features

- Command-line based natural language interface  
- Capable of performing a vast range of tasks – from system operations to smart utilities and beyond
- Handles general knowledge questions without running system commands  
- Best experience on **Linux**

## 🖥️ Usage

Just type in your terminal:

```bash
$ daagy 'your task here'
```

or use `ques` to ask general questions that do not require running a command:

```bash
$ daagy ques 'your question here'
```

## 🛠️ Installation

```bash
$ pip install daagy-ai
```

## 📦 Requirements

- Python 3.x  
- Gemini API Key  
- Required Python package:

```bash
$ pip install -q -U google-genai
```

## 🔐 Gemini API Key Setup

On first run, Daagy will prompt you to enter your [Gemini API key](https://aistudio.google.com/app/apikey).  
This is a **one-time setup** — the key will be saved locally for future use.

## ⚠️ Notes

- Optimized for **Linux**  
- Limited support on **Windows**

## 🧪 Sample Commands

``` bash

$ daagy make a node server to handle login

$ daagy make a file named xyz.txt and write hello world 10 times in it

$ daagy delete a folder named useless_folder

$ daagy install a calendar

$ daagy ques who is the PM of INDIA
```
...and so much more.

Daagy understands natural language — just tell it what you want.


### 🛠️ Note for Linux Users

If you get `daagy: command not found`, run the following command and try again. 

```bash
export PATH="$HOME/.local/bin:$PATH"
```


## 😏 Just Saying...
> ⚠️ Curious minds beware: commands like `daagy do something nasty` will do *something* — but you might not want to find out what.


## 📄 License

MIT
pyterp 0.3.1
A package for performing optimized k-NN IDW interpolation using C++.
Requires Python: >=3.10
Keywords: interpolation, k-NN, IDW, C++, parallel, numpy, performance

# PyTerp

A 3D interpolator for **Python** designed for maximum speed on large datasets. It accelerates the IDW algorithm with a parallelized C++ core (`OpenMP`) and optimized k-NN searches (`nanoflann`).

## Theoretical Summary

The interpolation is performed in a two-step process that combines the k-NN and IDW algorithms.

1. **Neighbor Selection (k-NN)**: For each point where a value is to be estimated, the _k-Nearest Neighbors_ algorithm first finds the k closest known source points in space. The efficiency of this search is ensured by an optimized data structure (`k-d tree`).

2. **Value Calculation (IDW)**: Next, the _Inverse Distance Weighting_ method calculates the final value as a weighted average of the k found neighbors. The weight of each neighbor is inversely proportional to its distance (weight = 1/distanceᵖ, where `p` is a power parameter), causing closer points to have a much greater influence on the result.

## Prerequisites

Before you begin, ensure you have the following software installed:

* **Python 3.10+**
* **Git**
* **A C++ compiler**: This package contains C++ code that needs to be compiled during installation.
    * **Windows**: Install Visual Studio Build Tools (select the "Desktop development with C++" workload).
    * **Linux (Debian/Ubuntu)**: Install build-essential with: sudo apt-get install build-essential.

## Installation

### PyPI

#### Install the package:

```bash
pip install pyterp
```

---

### GitHub

#### 1. Clone the repository:

```bash
git clone https://github.com/jgmotta98/PyTerp.git
cd PyTerp
```

#### 2. Create and activate a virtual environment:

```bash
# Create the environment
python -m venv .venv

# Activate the environment
# On Windows (cmd.exe):
.venv\Scripts\activate
# On macOS/Linux (bash/zsh):
source .venv/bin/activate
```

#### 3. Install the requirements:

```bash
pip install -r requirements.txt
```

#### 4. Install the package:

```bash
pip install .
```

## Usage Example

For a complete and runnable example, including the creation and preparation of input data, please see the scripts in the [examples](examples/basic_usage.py) folder.

## Acknowledgements

This project uses `nanoflann`, a high-performance C++ library for the _k-Nearest Neighbors_ algorithm. The efficiency of nanoflann's k-d tree implementation is fundamental to this interpolator's performance.

* **Official Repository:** [Nanoflann](https://github.com/jlblancoc/nanoflann)
jiso-droid 1.1.0.dev9
JISO-DROID - Keystore Generator & APK Signing Tool
Requires Python: >=3.9

## JISO-DROID 
        
    This tool helps you:
        1. Generate new keystores for APK signing
        2. Sign APK files with existing keystores
        3. Supports comprehensive configuration including:
           - Custom distinguished names (DN)
           - Multiple cryptographic algorithms (RSA/EC)
           - Flexible validity periods
           - V1/V2/V3/V4 signature schemes
mcp-postgres 1.0.2
Uma Ferramenta de Consulta de Bases de Dados PostgreSQL usando Linguagem Natural
Requires Python: >=3.8

# MCP Postgres SPS

Agente inteligente que responde perguntas em linguagem natural sobre o banco PostgreSQL usando Django e LangChain.

## ðŸ”§ InstalaÃ§Ã£o

```bash
git clone https://seurepo.com/mcp_postgres_sps.git
cd mcp_postgres_sps
docker build -t mcp-postgres .
docker run -p 8000:8000 mcp-postgres
```

Como usar
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â”œâ”€â”€ README.md

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ost-photometry 0.3.4
Phtometry reduction and analysis package for the OST observatory
Requires Python: >=3.10

# OST Photometry Package
Photometry reduction and analysis package for the 
[OST Observatory](https://polaris.astro.physik.uni-potsdam.de/) of 
the University of Potsdam.

The package aims to provide easy-to-use data reduction and analysis 
functionality for photometric observations made with the observatory's 
telescopes and instruments. It is mainly used in the astrophysics laboratory 
courses of the university and provides on-the-fly data reduction for the 
data archive of the observatory. It is designed with these applications in 
mind. 

## Requirements

The following Python packages provide the core functionality of this 
package:
   
   * [ccdproc](https://github.com/astropy/ccdproc)
   * [photutils](https://github.com/astropy/photutils)
   * [astropy](https://github.com/astropy/astropy)
   * [astroquery](https://github.com/astropy/astroquery)
   * [numpy](https://github.com/numpy/numpy)
   * [scipy](https://github.com/scipy/scipy)
   * [mathplotlib](https://github.com/matplotlib/matplotlib)

For astrometric solutions a local installation of the 
[astronomy.net](https://nova.astrometry.net/) software is required.