semvec/pypi-packages huggingface dataset:
bcdict 0.5.0
Python dictionary with broadcast support.
License: MIT
Requires Python: >=3.7.1,<4.0.0

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# Broadcast Dictionary


Python dictionary with broadcast support.

Behaves like a regular dictionary.

Allows to apply operations to all its values at once.
Whithout loops, whithout dict comprehension.

## Installation

```bash
pip install bcdict
```

## Usage

```python
from bcdict import BCDict
>>> d = BCDict({"a": "hello", "b": "world!"})
>>> d
{'a': 'hello', 'b': 'world!'}
```


Regular element access:
```python
>>> d['a']
'hello'
```


Regular element assignments
```python
>>> d['a'] = "Hello"
>>> d
{'a': 'Hello', 'b': 'world!'}
```

Calling functions:
```python
>>> d.upper()
{'a': 'HELLO', 'b': 'WORLD!'}
```

Slicing:
```python
>>> d[1:3]
{'a': 'el', 'b': 'or'}
```

Applying functions:
```python
>>> d.pipe(len)
{'a': 5, 'b': 6}
```

When there is a conflict between an attribute in the values and an attribute in
`BCDict`, use the attribute accessor explicitly:

```python
>>> d.a.upper()
{'a': 'HELLO', 'b': 'WORLD!'}
```

Slicing with conflicting keys:
```python
>>> n = BCDict({1:"hello", 2: "world"})
>>> n[1]
'hello'
>>> # Using the attribute accessor:
>>> n.a[1]
{1: 'e', 2: 'o'}
```

## Next steps

See the [introduction notebook](docs/source/examples/introduction.ipynb) and other
[examples](docs/source/examples/examples.md).

Also check out the full documentation on
[bcdict.readthedocs.io](https://bcdict.readthedocs.io/en/latest/).


## Changelog

### v0.5.0
* feature: broadcast attribute and item assignment
* fix: broadcast slicing with `.a` accessor

### v0.4.3
* fix: unpickling causes recursion error

### v0.4.2
* docs: improve the documenation

### v0.4.1
* fix: sphinxcontrib-mermaid gets installed as default dependency, should be dev dependency

### v0.4.0
* new functions `eq()` and `ne()` for equality/inequality with broadcast support

### v0.3.0
* new functions in `bcdict` package:
  * `apply()`
  * `broadcast()`
  * `broadcast_arg()`
  * `broadcast_kwarg()`
* docs: write some documentation and host it on [readthedocs](https://bcdict.readthedocs.io/en/latest/)

### v0.2.0
* remove `item()` function. Use `.a[]` instead.

### v0.1.0
* initial release


Original repository: [https://github.com/mariushelf/bcdict](https://github.com/mariushelf/bcdict)

Author: Marius Helf
([helfsmarius@gmail.com](mailto:helfsmarius@gmail.com))
gpyrn 1.0.1
Gaussian process regression networks for exoplanet detection
License: MIT

<a href="https://iastro-pt.github.io/gpyrn/">
    <img align="left" width="200" height="200" src="https://i.imgur.com/8jqfpoz.png"> 
</a>

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<br>
<br>
<br>

# gpyrn


**Modelling stellar activity with Gaussian process regression networks**

`gpyrn` is a Python package implementing a GPRN framework for the analysis of RV
datasets.  
A GPRN is a model for multi-output regression which exploits the
structural properties of neural networks and the flexibility of Gaussian
processes.

> The GPRN was originally proposed by 
  [Wilson et al. (2012)](https://icml.cc/2012/papers/329.pdf).


### Documentation

Documentation is available [here](https://iastro-pt.github.io/gpyrn).

### Authors

The `gpyrn` package was developed at [IA](https://www.iastro.pt), in the context
of the PhD thesis of João Camacho, with contributions from João Faria and
Pedro Viana.

### Cite

If you use this package in your work, please cite the following publication
(currently under review)

```bibtex
@ARTICLE{gpyrn2022,
    author = {{Camacho}, J.~D. and {Faria}, J.~P. and {Viana}, P.~T.~P.},
        title = "{Modelling stellar activity with Gaussian process regression networks}",
    journal = {arXiv e-prints},
    keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
        year = 2022,
        month = may,
        eid = {arXiv:2205.06627},
        pages = {arXiv:2205.06627},
archivePrefix = {arXiv},
    eprint = {2205.06627},
primaryClass = {astro-ph.EP},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220506627C},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```


### License

Copyright 2022 Institute of Astrophysics and Space Sciences.  
Licensed under the MIT license (see [`LICENSE`](https://github.com/iastro-pt/gpyrn/blob/main/LICENSE)).