semvec/pypi-packages huggingface dataset:
kepler-apertures 0.1.0
Tools to compute PRF models from Kepler's FFI and use them to do aperture photometry and compile light curves of the EXBA masks. 
License: MIT
Requires Python: >=3.8,<4.0
Keywords: NASA, Kepler, Astronomy

# Kepler Apertures

Take me to the [documentation](https://jorgemarpa.github.io/kepler-apertures/).
Paper coming soon!

Tools to create aperture mask for Kepler sources using PRF models build from Kepler's
Full Frame Images.

# PRF models

First we create PRF models using Kepler's FFI which contains ~10k Gaia EDR3 sources per Kepler's channel.

The following figure shows the PRF models in the focal plane. Channels at the border shows PRFs with very distorted shapes, while in the center these are round and smooth.

![PRF Models](https://github.com/jorgemarpa/kepler-apertures/blob/main/docs/focal_plane_prf_model.png)

Later this PRF models are used to compute apertures photometry.

# Kepler's EXBA masks

The EXBA masks are custom apertures observed by Kepler's first mission, they cover relatively dark regions of the Kepler field and were observed continuously between quarters 4 and 17. The scientific motivation to collect these data was to obtain an unbiased characterization of the eclipsing binary occurrence fraction in the Kepler field.

Here an example of the full EXBA mask observed in quarter 5 with channel 48

![exba_ch48](https://github.com/jorgemarpa/kepler-apertures/blob/main/docs/EXBA_img_q5_ch48.png)

# Dependencies
* numpy
* scipy
* astropy
* matplotlib
* photutils
* pandas
* tqdm
* patsy
* pyia
* lightkurve
truecase 0.0.14
A library to restore capitalization for text
License: MIT

# TrueCase


![Main](https://github.com/daltonfury42/truecase/workflows/Main/badge.svg) ![Publish PyPI](https://github.com/daltonfury42/truecase/workflows/Publish%20Python%20distributions%20to%20PyPI/badge.svg)

A language independent, statistical, language modeling
based tool in Python that restores case information for text.

The model was inspired by the paper of [Lucian Vlad Lita  et al., tRuEcasIng](https://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf) but with some simplifications.


A model trained on NLTK English corpus comes with the package by default, 
and for other languages, a script is provided to create the model. This model is 
not perfect, train the system on a large and recent dataset to achieve 
the best results (e.g. on a recent dump of Wikipedia).

### Prerequisites

- Python 3

The project uses NLTK. Find install instructions [here](https://www.nltk.org/install.html).

### Installing

```bash
pip3 install truecase
```

## Usage

Simple usecase:

```python
>>> import truecase
>>> truecase.get_true_case('hey, what is the weather in new york?')
'Hey, what is the weather in New York?''
```

## Training your own model

TODO. For now refer to Trainer.py

## Contributing

I see a lot of space for improvement. Feel free to fork and improve. Do sent a pull request.

## Authors

* **Dalton Fury** - *Initial work* - [daltonfury42](https://github.com/daltonfury42)

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE) file for details

## Acknowledgments

* [Lucian Vlad Lita  et al., tRuEcasIng](https://www.cs.cmu.edu/~llita/papers/lita.truecasing-acl2003.pdf)
* Borrowed a lot of code, and the idea from [truecaser](https://github.com/nreimers/truecaser/blob/master/README.md) by [nreimers](https://github.com/nreimers)
tribi 1.0.4
Prettier error handling for Python
License: MIT

# Tri/bi
This package provides wrappers around functions that nicely handle errors. Reducing code clutter and allowing better responses to uses. This package is inspired by [tri-fp](https://www.npmjs.com/package/tri-fp)

[![](https://img.shields.io/pypi/v/tribi.svg)](https://pypi.org/project/tribi/)
[![](https://img.shields.io/pypi/implementation/tribi.svg)](https://pypi.org/project/tribi/)

## Tri
Tri still lets native exceptions (usually more severe errors) throw, but catches any other errors
```python
>>> from tri import tri, bi
>>> def my_function():
...    return 1 / 0 # Zero division error!

>>> # This function raises a "native" exception
>>> tri(my_function)()
Traceback (most recent call last):
   ...
ZeroDivisionError: division by zero

>>> def my_other_function():
...    raise Exception

>>> # This only raises a standard exception, so doesn't fail
>>> tri(my_other_function)()
(Exception(), None)
```


### Bi
Bi, on the contrary, handles all errors blindly. This should only be used if you know what you're doing.
```python
>>> bi(my_function)()
(ZeroDivisionError('division by zero'), None)
```


## Async
Both `tri` and `bi` support async.

### Tri
```python
>>> # Assuming running in async
>>> from tri import atri, abi
>>> async def divide(a, b):
...     return a / b

>>> # Let's try to divide by zero
>>> await atri(divide(1, 0))
Traceback (most recent call last):
   ...
ZeroDivisionError: division by zero

>>> # And with abi?
>>> await abi(divide(1, 0))
(ZeroDivisionError('division by zero'), None)

>>> # If the function takes no params, we can just pass the function name
>>> async def func():
...     return "hello"

>>> await atri(func)
(None, "hello")
```


## Real life example
Some examples include...

### Safer JSON loading
```python
from tri import bi

safer_loads = bi(json.loads)
error, result = safer_loads(data)

if error:
   # Error handling JSON
   print("Invalid JSON")
```
pyruler 0.12.0
Simple and powerful rule engine to generate complex data validations on an easy way
License: MIT
Requires Python: >=3.8,<4.0
Keywords: rule,engine,ruler,package

[![codecov](https://codecov.io/gh/danteay/pyruler/branch/master/graph/badge.svg?token=WZ9QXIJ3Z7)](https://codecov.io/gh/danteay/pyruler)

# Pyruler

Simple and powerful rule engine to generate complex data validations on an easy way.

## Requirements

- Python >= 3.6

### Usage

#### Installation

```bash
pip3 install pyruler
```

### Documentation

[Readthedocs](https://pyruler.readthedocs.io)