semvec/pypi-packages huggingface dataset:
repler 1.0.1
The tool keeps you project envieronment clean by delete or archive files with specified extensions.
Requires Python: >=3.7

# repler: report handler


The tool keeps you project envieronment clean by delete or archive files with specified extensions.
Every time your script run, it produces logs, reports and other auxiliary files
that relevant only for current execution.
Also you may want to save some logs for the future compare\analysis.
And obviously you will not think about to explode your hard with enormous bulk of old logs.
The idea is to define clean (the cleaner) and archive (archivator) policy on folders.
Running report handler at start of each test will clean unwanted files and
archive desired files for future use.

The tool keeps you project environment clean by delete or archive files with specified extensions.

# Installation:
pip install repler

# Usage:

# Create report handler with logging enable.
rh = ReportHandler(log_enable=True)

# Apply clean policiy (still not execute) on project folder:
# to delete all files with extentions txt and log.
# an age of deleted files should be greter then 0.
rh.set_cleaner(folder_path="\working directory",
               age_before_del_hour=0,
               ext_to_del=["log","txt"])

# Apply archive policiy (still not execute) on log folder:
# to archive all files with extention log.
rh.set_archivator(sourse_folder="\working directory\Logs",
                  archive_folder="\working directory\Logs\Archive",
                  archive_prefix="MyTest",
                  ext_to_arch=["log"],
                  delete_archived_files=True)

# Apply clean policiy (still not execute) on Archive folder:
# to delete all archives older then 24 hours.
rh.set_cleaner(folder_path="\working directory\Logs\Archive",
               age_before_del_hour=24,
               ext_to_del=["zip"])

# Prints all defined cleaners
rh.show_cleaner()

# Prints all defined archivators
rh.show_archivator()

# Run cleaning. Activate all defined cleaners.
rh.clean()

# Run archivation, Activate all defined archives.
rh.archive()

# Run both archive and clean. Activate all defined archives and cleaners.
rh.arcive_and_clean()

# Enable report handler logging, if initially it was defined with out the log enable.
rh.set_log_on()
vix_utils 0.1.3
Provide VIX Cash and Futures Term Structure as Pandas dataframes
Requires Python: >=3.11
Keywords: vix,volatility,pandas,vix term structure,cboe

# VIX Utils
## Overview

*vix_utils* provides some tools for preparing data for analysing  the VIX Futures and Cash Term structures.

The futures can also contain a 30 day continuous maturity weighting of front two months of vix futures.

VIX Futures Data downloaded from [CBOE Futures Historical Data](https://www.cboe.com/us/futures/market_statistics/historical_data/).

Vix Cash Data are downloaded from [CBOE Historical Volatility Indexes](https://www.cboe.com/tradable_products/vix/vix_historical_data/).


There is an API for Python to load the data into Pandas DataFrames.  If you do your analysis in Python, use the API.

Since there is no documentation yet, look at the examples in the src/vix_utils/examples folder.

*Important note for Juypter notebooks.*  
You must use  async_get_vix_index_histories and async_load_vix_term_structure 
rather than get_vix_index_histories and load_vix_term_structure.  There is an example Jupyter notebook "vix_utils use in Jupyter.ipynb" in the src/vix_utils/examples folder. 
 
If you do your analysis in other tools such as R or excel, you can use the command line tool vixutil.

`vixutil -h` will give the help.  The data are availble in record and wide formats.  Just run it and look at the excel or csv output to see what they look like.

 


## Installation

You will need a Python 3.11 or later instalation.

### Install from the Python Packaging Index
 
Install using pip from [The Python Package Index ](https://www.pypi.org):

`pip install vix_utils`

If you want to run the samples, install like this:
`pip install vix_utils[examples]`

The sample to load all the various data frames can be run as:
'vix_sample_load_data'

The sample to plot the history of futures and cash term structures:
`vix_sample_plots`

To load the sample Jupyter notebook, run vix_sample_load_data to figure out where the examples folder is. Browse there with Jupyter and open a notebook.   


### Development 

Clone from  [github repository](https://github.com/dougransom/vix_utils).

 
`pip install -e .[test,examples]` will:
- install vix_utils into your python environment, including any command line scripts. 
- install the necessary prequisites for running any 
tests in the `test` folder, and for running the programs in the `src/vixutils/examples` folder.

#### Testing

The tests directory contains a few tests.  This project wasn't developed with 
[Test Driven Development](https://www.agilealliance.org/glossary/tdd/), unit tests have been added to isolate
and fix defects. 

However, new features and bug fixes should be developed with [Test Driven Development](https://www.agilealliance.org/glossary/tdd/) practices when practical.


## Examples
Source is in `src/vix_utils/examples`
 
~~~
## Developing
https://numpydoc.readthedocs.io/en/latest/format.html