semvec/pypi-packages huggingface dataset:
ceres-plusplus 0.0.1
An extension to ceres.
Requires Python: >=3.6

# Ceres-plusplus
This package was written as an extension to the CERES reduction pipeline (https://github.com/rabrahm/ceres) in the sense that it takes spectra reduced by it and extracts some activity indicators (CCF FWHM, BIS, CONTRAST) and calculates others (S index, Ha, HeI, NaID1D2)

## Installation
You can try running `pip install cerespp`

If that fails you can clone the repository with 
```bash
$ git clone https://github.com/jvines/Ceres-plusplus
$ cd Ceres-plusplus
$ python setup.py install
```

## Dependencies
Ceres-plusplus depends on the following packages:
- numpy ([https://numpy.org/](https://numpy.org/))
- scipy ([https://www.scipy.org/](https://www.scipy.org/))
- matplotlib (for plotting, but it's optional for the core functionality) ([https://matplotlib.org/](https://matplotlib.org/))
- astropy ([https://www.astropy.org/](https://www.astropy.org/))
- PyAstronomy ([https://pyastronomy.readthedocs.io/en/latest/](https://pyastronomy.readthedocs.io/en/latest/))
- tqdm ([https://tqdm.github.io/](https://tqdm.github.io/))
- termcolor ([https://pypi.org/project/termcolor/](https://pypi.org/project/termcolor/))

## Usage

Usage is simple, start by importing `cerespp`, optionally you can use `glob` to fetch the files. After importing (and having ready your files) the `get_activities` function, grab a coffee, and wait for your indicators!.

Below there's an example script
```python
import cerespp
import glob

files = glob.glob('path/to/fits/files/*.fits')
act, header = cerespp.get_activities(files, 'output/path/filename.dat')
```
Here `files` is a list with the fits files to process, the output `act` and `header` are the activities and the header of the file (i.e. the names of the columns). `cerespp` automatically saves the output in a file in the desired location, but if you need the output on the session you're working in (in a jupyter notebook, for example), that's what `act` and `header` are for ;)

## Plotting
`cerespp` offers some plotting tools to visually check things! The most important ones are plots showing the activity lines and their surroundings. Creating these is easy:
```python
import cerespp
import glob

# These are the available lines. You can choose which ones to plot here 
lines = ['CaHK', 'Ha', 'HeI', 'NaID1D2'] 

files = glob.glob('path/to/fits/files/*.fits')

# This function creates the plots from a fits file directly
cerespp.line_plot_from_file(files[0], lines, 'output/path/', 'starname')
```

There's a notebook exemplifying the usage in the examples folder!

## How it works

`cerespp` first calculates a radial velocity to correct the spectrum to rest-frame, after this has been done it extracts the available data from the fits headers (CCF FWHM, BIS, CONTRAST) and finally it merges the echelle orders and computes the activity indicators!

The radial velocity is computed using the standard cross-correlation function method, and thus you can specify which mask to use with the `mask` keyword in `get_activities`. Available masks are `G2, K0, K5,` and `M2`.
hoymilesdtumi 0.0.3
Hoymiles DTU-MI data wrapper for python.
Requires Python: >=3.6

# HoymilesDtuMi
## Hoymiles DTU-MI data wrapper for python.
### Retrieving pv data from DTU.

1. Example
```
import hoymilesdtumi

json = hoymilesdtumi.dtu_http_request('192.168.0.109')
print(json)
```
arbor-bcumming 0.5
High performance simulation of networks of multicompartment neurons.
Requires Python: >=3.6

# Arbor

The Arbor Python package is a wrapper around the high-performance C++ library Arbor, for
constructing and running simulations multi-compartment neuron models, from single cell models
to large networks.

Documentation is available online at [Read the Docs](https://arbor.readthedocs.io/en/latest/python.html).

[Submit a ticket](https://github.com/arbor-sim/arbor) if you have any questions or want help.