semvec/pypi-packages huggingface dataset:
cqh-tail 0.0.10
tail -F
Requires Python: >=3.6

cqh_tail
=============================================

python tail

copy from `activestate <http://code.activestate.com/recipes/577968-log-watcher-tail-f-log/>`_


Usage
-------------------------------------------------

watch dir
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

.. code-block::

    cqh_tail --pattern=~/**/*.log

watch dir and filter
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

.. code-block::

    cqh_tail --pattern=~/**/*.log --line-filter="\.general/"




problems
---------------------------------------------

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x82 in position 0: invalid start byte
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

.. code-block::


    Traceback (most recent call last):
    File "/home/deploy/envs/default/bin/cqh_tail", line 8, in <module>
        sys.exit(main())
    File "/home/deploy/envs/default/lib/python3.6/site-packages/cqh_tail/run.py", line 318, in main
        w = LogWatcher(pattern, tail_lines=count, callback=echo)
    File "/home/deploy/envs/default/lib/python3.6/site-packages/cqh_tail/run.py", line 66, in __init__
        self._callback(file.name, lines)
    File "/home/deploy/envs/default/lib/python3.6/site-packages/cqh_tail/run.py", line 313, in echo
        line = line.decode(convert_args.encode)
    UnicodeDecodeError: 'utf-8' codec can't decode byte 0x82 in position 0: invalid start byte
ThreshIt 0.0.1
Python package containing various thresholding algorithms
Keywords: python,first package

# ThreshIt

## Project Description
---

__threshIt__ is a python package containing different Thresholding algorithms. 

## Installation
---

Run the following command in the terminal to install threshIt:

```
pip install threshIt
```

## Thresholding
Thresholding is the binarization of an image. It is used to convert a grayscale image to a binary image, where the pixels are either 0 or 255.

### Simple Thresholding
A simple thresholding would select a pixel value p, and then setting all pixel intensities less than p to zero, and all pixel values greater than p to 255. In this way we create a binary representation of the image.

### Adaptive Thresholding
Adaptive thresholding  considers small neighbors of pixels and the finds an optimal threshold value T for each neighbors. This allow us to handle cases where there may be ranges of pixel intensities and the optimal value T may change for different parts of the image.

### Otsu and Riddler-Calvard
In Otsu and Riddler-Calvard, we can automatically compute the threshold value T. It assumes there are two peaks in the grayscale 
histogram of the image. It then finds an optimal value to separate these two peaks i.e the value T. 

## Example
---

The below example shows the use of __threshIt__:

<pre>
import threshIt as thrsh
thrsh.adaptive(image)</pre>

### Output

<img src="Thresholding.png?raw=true" alt="Threshold Output" style="max-width: 500px;">




The following thresholding methods can be used:
* threshIt.simple(image) → Simple Thresholding
* threshIt.adaptive(image) → Adaptive Thresholding
* threshIt.otsu_rc(image) → Otsu-Riddler Calvard Thresholding