semvec/pypi-packages huggingface dataset:
hieroskopia-pkg-simetrik 0.0.1
Description
Requires Python: >=3.6

# hieroskopia Package

The hiereskopia package its a library to infer properties 
from dates and numeric data types like formats or separators from samples through regular patterns 
in a pandas series.

## Support 
###Date-times:
- Support to dates and datetime format
- This library receive a series as input and try to return
 a dictionary with the format found in the series Based on the 1989 C 
 Standard date time  format code

### Numeric:
- This library receive a series as input and try to return
 a dictionary with the three digit and decimal character separator

## Usage
Usage doc based on a similar library called `dateinfer` from `@jeffreystarr`

````Python
>>> import hieroskopia
>>> hierokopia.infer_datetime(['Mon Jan 13 09:52:52 MST 2014', 'Tue Jan 21 15:30:00 EST 2014'])
>>> {'format': '%a %b %d %H:%M:%S %Z %Y'}
````

The above method works with a best guess approach to detect a format in a series and try 
to return a `datetime.strftime`/`strptime` format that will cover or parse the majority
of the samples.

````Python
>>> import hieroskopia
>>> hierokopia.infer_number(['300.231,3', '200,3'])
>>> {'three_digit_separator': '.', 'decimal_separator': ',' }
````

The above method will try to detect and return certain properties in a series
like `three_digit_separator` or `deciamal_separator` character that will cover 
the majority of the samples.


## To do:
- Specify another output standard format to support java date format, snowflake date characters definition etc.
- Feed more regular expressions
- Develop multiple algorithms to get a better precision.
astroPImage-AroLeaf 0.1.1
Makes it easy to process and locate astrophotography images.
Requires Python: >=3.6

# AstroPImage

### A python package that makes processing astrophotography data a piece of PI.
## Installation
### PIP
```
Coming soon
```
### manual
Download the package from my [GitHub](https://www.github.com/AroLeaf/astroPImage) and put it in your project or packages folder.
## Example usage
```python
from astroPImage.image import Image
from PIL import Image as pillow
import numpy as np

def ondone():
  print('done')

image = Image('light/file.fits') \
        .createBias('bias/') \
        .createDark('dark/') \
        .createFlat('flat/') \
        .process() \
        .filter() \

pillow.fromarray(np.asarray(image.binary*255, dtype=np.uint8), mode='L').save('binary.png')
```
## Documentation

```python
image = Image(path, exposure='EXPOSURE')
```
Creates a new instance of `Image`

`path`: the path of the fits file to create this image with.\
`exposure`: the name of the header to use for the exposure value, defaults to `EXPOSURE`.
- - -
```python
Image.createBias(path)
```
Creates a master bias file from all fits files in the given directory

`path`: the path of the directory to use fits files from.
- - -
```python
Image.createDark(path, exposure='EXPOSURE')
```
Creates a master dark file from all fits files in the given directory. If  `createBias()` was called on before this method, the bias file will be applied to the dark files.

`path`: the path of the directory to use fits files from.\
`exposure`: the name of the header to use for the exposure value, defaults to `EXPOSURE`.
- - -
```python
Image.createFlat(path, exposure='EXPOSURE')
```
Creates a master dark file from all fits files in the given directory. If a `createBias()` or `createDark()` were called before this method, the bias and dark files will be applied to the flat files

`path`: the path of the directory to use fits files from.
`exposure`: the name of the header to use for the exposure value, defaults to `EXPOSURE`.</p>
- - -
```python
Image.process()
```
Applies `Image.bias`, `Image.dark` and `Image.flat` to `Image.image` if they exist.
- - -
```python
Image.filter()
```
Filters almost all noise out of both `Image.binary` and `Image.processed`. Calls `Image.process()` if `Image.processed` does not exist yet.
- - -
```python
Image.locate(key, callback=None
```
Tries to find out what part of the sky the image contains, using nova.astrometry.net.

`key`: your API key for nova.astrometry.net
`callback`: a function to call after the locating process has finished.