semvec/pypi-packages huggingface dataset:
pyzelda 1.4
Zernike wavefront sensor analysis and simulation tools
License: MIT
Keywords: zernike wavefront sensor zelda

pyZELDA
=======

<img src="https://img.shields.io/badge/Python->3.5-yellow.svg?style=flat"> [<img src="https://img.shields.io/pypi/v/pyzelda?color=brightgreen">](https://pypi.org/project/pyzelda/) [<img src="https://img.shields.io/github/v/release/avigan/pyZELDA?color=red">](https://github.com/avigan/pyZELDA/releases/latest/) [<img src="https://img.shields.io/github/release-date/avigan/pyZELDA">](https://github.com/avigan/pyZELDA/releases/latest/) [<img src="https://img.shields.io/github/license/avigan/pyZELDA?color=blue">](https://github.com/avigan/pyZELDA/blob/master/LICENSE) [<img src="https://img.shields.io/badge/ascl-1806.003-blue.svg?colorB=262255" alt="ascl:1806.003" />](https://ascl.net/1806.003) [<img src="https://img.shields.io/badge/ADS%20reference-Vigan%20(2018)-blueviolet">](https://ui.adsabs.harvard.edu/abs/2018ascl.soft06003V/abstract) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6564082.svg)](https://doi.org/10.5281/zenodo.6564082)


Introduction
------------

This repository provides analysis code for Zernike wavefront sensors in high-contrast imaging applications. ZELDA stands for *Zernike sensor for Extremely Low-level Differential Aberration* ([N'Diaye et al. 2013](https://ui.adsabs.harvard.edu/#abs/2013A&A...555A..94N/abstract)). It is also the name of the Zernike wavefront sensor implemented in the VLT/SPHERE ([Beuzit et al. 2019](https://ui.adsabs.harvard.edu/abs/2019arXiv190204080B/abstract)) instrument.

The formalism of the Zernike wavefront sensor and the demonstration of its capabilities in the VLT/SPHERE instrument are presented in the following papers:

- [N'Diaye, Dohlen, Fusco & Paul, 2013, A&A, 555, A94](https://ui.adsabs.harvard.edu/#abs/2013A&A...555A..94N/abstract)
- [N'Diaye, Vigan, Dohlen et al., 2016, A&A, 592, A79](https://ui.adsabs.harvard.edu/#abs/2016A&A...592A..79N/abstract)
- [Vigan, N'Diaye, Dohlen et al., 2019, A&A, 629, A11](https://ui.adsabs.harvard.edu/abs/2019A%26A...629A..11V/abstract)

Requirements
------------

The package relies on usual packages for data science and astronomy: [numpy](https://numpy.org/), [scipy](https://www.scipy.org/), [matplotlib](https://matplotlib.org/) and [astropy](https://www.astropy.org/).

Installation
------------

The easiest is to install `pyzelda` using `pip`:

```sh
pip install pyzelda
```

Otherwise your can download the current repository and install the package manually:

```sh
cd pyZELDA-master/
python setup.py install
```

Citation
--------

If you use this software, or part it, please reference the code from the Astrophysics Source Code Library ([ASCL](http://ascl.net/)):

- [Vigan & N'Diaye, 2018, ascl:1806.003](https://ui.adsabs.harvard.edu/abs/2018ascl.soft06003V/abstract)

Authors and contributors
------------------------

- Arthur Vigan, [arthur.vigan@lam.fr](mailto:arthur.vigan@lam.fr)
- Mamadou N'Diaye, [mamadou.ndiaye@oca.eu](mailto:mamadou.ndiaye@oca.eu)
- Raphaël Pourcelot, [raphael.pourcelot@oca.eu](mailto:raphael.pourcelot@oca.eu)
register3d 0.0.0
An efficient binary value 3d volume registration algorithm based on Cupy.
License: MIT
Requires Python: <3.13,>=3.10

# register3d

An efficient binary value 3d volume registration algorithm based on Cupy.

## Installation
```bash
pip install register3d
```

## Usage
```python
import register3d
import cupy as cp

# full_image3d and image_part3d are binary value 3d array
# value 1.0 means inside the surface
# value 0.0 means outside the surface
full_image3d = cp.array([ ... ]) # Your large image
image_part3d = cp.array([ ... ]) # Your small image

# match_3d_data_rotate will try to rotate image_part3d to match in full_image3d
# registered_image_part is of the same size as full_image3d
# less the score, better the match
# rotation_matrix is a rotation matrix not far from cp.eye(3)
registered_image_part, score, rotation_matrix = register3d.match_3d_data_rotate(full_image3d, image_part3d)
```
bigpyx 0.0.2
A small, easy-to-use wrapper around Python's Decimal with configurable precision and rounding.
License: MIT
Requires Python: >=3.7

# bigpyx — Arbitrary-Precision Decimal Numbers for Python

`bigpyx` is a tiny wrapper around Python’s built-in [`decimal.Decimal`](https://docs.python.org/3/library/decimal.html) type.

It gives you:

- Easy, consistent handling of **big numbers** (money, crypto, prices, etc.)
- **Configurable precision** (`DP`) and **rounding mode** (`RM`)

Perfect when `float` is too imprecise and you want something safer but still easy to use.

---

## Features

- ✅ Arbitrary-precision decimal arithmetic  
- ✅ Global defaults for decimal places (`DP`) and rounding mode (`RM`)  
- ✅ Minimal, focused API  
- ✅ Works with `str`, `int`, `float`, `Decimal`, and `Big` values  

---

## Installation

If you’ve published this as a package:

```bash
pip install bigpyx
```

## Quick Start

```python
from bigpyx import Big

# Basic construction
a = Big("0.1")
b = Big("0.2")

# Addition
c = a + b
print(c)           # -> 0.3

# Or using methods, if you added them:
# c = a.add(b)

# Multiplication
d = Big("1.23") * Big("4")
print(d)           # -> 4.92
```

Because Big uses Decimal under the hood, you don’t get the usual floating-point surprises:

```python
from bigpyx import Big

print(0.1 + 0.2)        # 0.30000000000000004 (float)
print(Big("0.1") + Big("0.2"))  # 0.3 (Big)
```

## Configuration

`Big` exposes two important global settings:

```python
from bigpyx import Big

# Maximum decimal places for division, sqrt, etc.
Big.DP = 20    # default: 20

# Rounding mode (like big.js)
# 0: ROUND_DOWN
# 1: ROUND_HALF_UP      (default)
# 2: ROUND_HALF_EVEN
# 3: ROUND_UP
Big.RM = 1
```

Internally, these map to decimal’s rounding modes via _ROUND_MAP.

You can also use decimal.localcontext() for temporary precision overrides inside your own code, but for most use cases, setting Big.DP and Big.RM is enough.

## Creating Big Numbers

```python
from bigpyx import Big
from decimal import Decimal

Big("1.2345")         # from string
Big(12345)            # from int
Big(1.2345)           # from float (not recommended for exact finance values)
Big(Decimal("1.2345"))# from Decimal
Big(Big("1.2345"))    # from another Big
```

Under the hood, the constructor converts the value to a Decimal and stores it as self._d.

## Arithmetic

The exact API here depends on how you implemented `Big`.

Below is a typical pattern using operator overloading.

### Addition

```python
x = Big("1.1")
y = Big("2.2")
print(x + y)      # -> 3.3
```

### Subtraction

```python
x = Big("5")
y = Big("2.5")
print(x - y)      # -> 2.5
```

### Multiplication

```python
x = Big("1.5")
y = Big("3")
print(x * y)      # -> 4.5
```

### Division

Division respects Big.DP and Big.RM:

```python
from bigpyx import Big

Big.DP = 10
Big.RM = 1  # ROUND_HALF_UP

x = Big("1")
y = Big("3")

print(x / y)  # -> 0.3333333333  (10 decimal places)
```

## License

MIT License

Copyright (c) 2025 Matt Henry
mseep-predictive-maintenance-mcp 0.3.0
Proof of Concept: AI-Powered Predictive Maintenance & Fault Diagnosis MCP Server - Industrial machinery condition monitoring, vibration analysis, bearing diagnostics, and ML-based anomaly detection through Model Context Protocol
License: MIT
Requires Python: >=3.11
Keywords: signal-processing, iiot, predictive-analytics, anomaly-detection, asset-management, envelope-analysis, predictive-maintenance, condition-monitoring, machine-failure, bearing-fault-diagnosis, industrial-iot, fault-diagnosis, vibration-analysis, claude-ai, industry-4-0, model-context-protocol, mcp-server, fastmcp, iso10816, claude-agents

Package managed by MseeP.ai
tlab 0.1.12
Internal use within tsuchimochi lab
License: MIT
Requires Python: >=3.8

# tlab

土持研内部共有プログラムパッケージ

# インストール
```bash
pip install tlab
```
もしくは
```bash
git clone https://github.com/tsuchimoc/tlab.git
pip install -e .
```

# 使い方
esa参照
https://tsuchimochi.esa.io/posts/160