semvec/pypi-packages huggingface dataset:
elp-mpp02 0.0.6
Accurate Moon positions using the Lunar solution ELP/MPP02 in Python
Keywords: Moon, astronomy, ephemeris

# ELP_MPP02: accurate Moon positions using the lunar solution ELP/MPP02 in Python #

Compute accurate Moon positions using the semi-analytical lunar solution ELP2000/MPP02 by Chapront & Francou
(2003) in Python.


## Installation ##

This package can be installed using `pip install elp-mpp02`.  You will need to manually download the six data
files `ELP_MAIN/PERT.S1/2/3` from
[ftp://cyrano-se.obspm.fr/pub/2_lunar_solutions/2_elpmpp02](ftp://cyrano-se.obspm.fr/pub/2_lunar_solutions/2_elpmpp02)
and save them in a directory of your choice.


## Using the package ##

You can import the package as follows:
```python
from elp_mpp02 import mpp02 as mpp
```

Then, make sure you define the directory where the data files are located (if not in the current dir). 
For the subdir `data/` of the current directory, do:
```python
mpp.dataDir = 'data'  # Set the dir where the ELP_*.S* data files can be found
```

Choose whether to run the code in LLR (`mode=0`; default) or DE405 (`mode=1`; 'historical') mode, select a
Julian day and compute the Moon position for that instance:
```python
mode = 1  # Historical mode
jd = 2451545
lon,lat,dist = mpp.compute_lbr(jd, mode)  
print('jd =',jd, ':   lon =',lon,'rad,  lat =',lat, 'rad,  dist =',dist,'km.')
```
The result should be
```
jd = 2451545 :   lon = -2.385534575256455 rad,  lat = 0.09024868423130429 rad,  dist = 402448.6385830673 km
```

The ecliptical longitude and latitude are expressed in radians, the distance is in kilometres.  The
coordinates are valid for the mean equinox of J2000.  Note that on the first call, the constants must be
initialised and the data files need to be read, which can take ~10s.  If `mode` is changed between calls, the
data must be reinitialised.


## Copyright and licence ##

* Copyright:  2019-2025 Marc van der Sluys, Department of Physics, Utrecht University and Nikhef (institute for high-energy physics and gravitational waves), Amsterdam (NL)
* Contact:    https://marc.vandersluys.nl
* Website:    [Github](https://github.com/MarcvdSluys/ELP-MPP02), [PyPI](https://pypi.org/project/elp_mpp02/)
* Licence:    [EUPL 1.2](https://www.eupl.eu/1.2/en/)


## Acknowledgements ##

* Thanks to A. Vähäkangas for bug reports!


## References ##

* [Chapront & Francou (2003)](https://ui.adsabs.harvard.edu/abs/2003A%26A...404..735C/abstract)
* [FTP data files](ftp://cyrano-se.obspm.fr/pub/2_lunar_solutions/2_elpmpp02) &mdash; in case [FTP urls don't work in Markdown](https://github.com/gollum/gollum/issues/759): ftp://cyrano-se.obspm.fr/pub/2_lunar_solutions/2_elpmpp02
* This Python code is adapted from the Fortran implementation in [libTheSky](https://libthesky.sourceforge.net/)
histastro 0.0.8
A Python package for historical-astronomy calculations of Sun, Moon and planets
Keywords: astronomy, history, ephemeris, sun, moon, planets

# HistAstro #

A Python package for historical-astronomy calculations of Sun, Moon and planets.


## Installation ##

This package can be installed using `pip install histastro`.  This should automatically install the
dependency packages `numpy` and `fortranformat` if they haven't been installed already.  If you are installing
by hand, you have to ensure these packages are installed as well.


### Data files ###

You will need to manually download the eight data files
[`VSOP87D.xxx`](http://cdsarc.u-strasbg.fr/viz-bin/Cat?cat=VI/81) (click on FTP), one for each of the planets,
and save them in a directory of your choice (e.g. the subdirectory `data/` of your working directory).  To
compute the Moon position, the file `moonposMeeus.csv` is needed, which can be downloaded from this page:
http://astro.ru.nl/~sluys/HistAstro/


## HistAstro pages ##

* [Radboud University](http://astro.ru.nl/~sluys/HistAstro/): HistAstro homepage: data files and code documentation
* [Pypi](https://pypi.org/project/histastro/): HistAstro Python package
* [GitHub](https://github.com/MarcvdSluys/HistAstro): HistAstro source code


## Author and licence ##

* Author: Marc van der Sluys
* Contact: http://astro.ru.nl/~sluys/
* Licence: [GPLv3+](https://www.gnu.org/licenses/gpl.html)


## References ##

* Meeus, [Astronomical algorithms](https://www.willbell.com/math/MC1.HTM), 2nd Ed.
* VSOP87 planet theory: [Bretagnon & Francou, 1988A+A...202..309B](https://ui.adsabs.harvard.edu/abs/1988A%26A...202..309B/)
* [VSOP87 data files](http://cdsarc.u-strasbg.fr/viz-bin/Cat?cat=VI/81)
* This Python code is adapted from the Fortran implementation in [libTheSky](http://libthesky.sourceforge.net/)
* [Celestial mechanics in a nutshell (CMiaNS)](https://cmians.sourceforge.io/)
kubejyg 0.3.17
kubejyg: Kubernetes Resource Extraction with Namespace Grouping built for filtering with jq, yq and grep.
Requires Python: >=3.8

# Kubejyg
##### _Kubernetes Resource Extraction with Namespace Grouping._

## How it works

`kubejyg` extracts `Kubernetes` resources across multiple namespaces, grouping them by `namespace`. The output is generated in either `JSON` or `YAML`, ready to be piped into well known processors like `jq`, `yq`, and `grep`.

**Examples**

Fetch all deployment manifests from all namespaces in YAML.

```bash
kubejyg | yq ".Namespaces.[].[].[].Deployments" -C
```

Fetch all deployment manifests across all namespaces in YAML and filter nulls.

```bash
kubejyg | yq ".Namespaces[].[].[].Deployments | select ( . != null ) | .[]" -C
```

Fetch all the annotations from all the Services across all Namespaces in JSON.

```bash
kubejyg -o json | jq ".Namespaces[].[].[].Services | select ( . != null) | .[].metadata.annotations" -C
```

## Output structure

```json
{
    "Namespaces": [
        {
            "ns-1": [
                {
                    "Deployments": [
                        {
                            deployment-manifest-1
                        }
                        ...
                    ]
                    "Services": [
                        {
                            service-manifest-1
                        }
                        ...
                    ]
                    "Ingress": [
                        {
                            ingress-manifest-1
                        }
                        ...
                    ]
                }
            ]
        ...
        }
    ]
}
```

## Installation

```bash
pip install kubejyg
```

## Features

**Kubernetes resources**:
- Deployments
- Services
- Ingresses

**Output**:
- YAML
- JSON

## Naming

**kubejyg** is named after the following utilities:
1. `kubectl`
2. `jq`
3. `yq`
4. `grep`

## Gotchas

`jq: error (at <stdin>:1): Cannot iterate over null (null)`.

Some namespaces might not contain resources of all kinds. If we apply filters over empty arrays, `jq` and `yq` will replaces those arrays with `null` values. Applying filters over them will error out. Use `select ( . != null)` to filter `null` expressions, **see examples for details**.


## Contributing

1. Create an issue
2. Fork
3. Change
4. Open PR
5. Profit!
preaudio 0.1.0
A small audio visualization library built from Jupyter Notebook code.
Requires Python: >=3.7

# preaudio

`preaudio` is a simple Python library for audio loading, trimming, and visualization built on top of `librosa`, `matplotlib`, and `seaborn`.

## Features

- Load and trim `.wav` files
- Plot waveform and zoomed views
- Generate and display standard and mel spectrograms

## Installation

```bash
pip install -e .