semvec/pypi-packages huggingface dataset:
Pydvault 0.0.1
A Python package for synthetic data augmentation
Requires Python: >=3.7

# Installation 
pip install Pydvault

# Use
import pandas as pd
from Pydbox import Syndbox

df=pd.read_excel('/content/filename.xlsx')
df1=Syndbox.TESI(df, Equation='Euc_p1', 
                    start=0.01,
                    augmentation_factor=10)
                              
df1.pd_frame(save=True)
df1.arr()

# Output
Pd_Dataframe/numpy.array

# Parameters 
start: (float), default=0.01, The starting value of the angle sequence. 
stop: (int), default=1, The end value of the angle's sines vector. Should be always 1 because the angle's max sine is 1. 
augmentation_factor: (int), default (0.01), the number of times the data is augmented. should be from 0 to infinity.
Equation: (default='Euc_p1'), Either the Euclidian or the following transformed Euclidian formulas. 

'Euc_p1': The linear polynomial.
'Euc_p2': The quadratic polynomial. 
'Euc_p3': The cubic polynomial.
'Euc_Log': The log-transformed euclidian formula. 

# Documentation
The TESI was developed in a study context, the study paper was published in Elsivier. 
A detailed description behind the logical of the TESI can be found in the paper.
NOTE: PLEASE CITE THE PAPER. 

Publisher name: Elsevier
Journal: Computers and Electronics in Agriculture
Volume: 206
Issue: March 2023
Pages: 107646
DOI: https://doi.org/10.1016/j.compag.2023.107646
Pydbox 0.0.1
A Python package for synthetic data augmentation
Requires Python: >=3.7

# Installation 
pip install Pydbox

# Use
import pandas as pd
from Pydbox import TESI

df=pd.read_excel('/content/filename.xlsx')
df1=TESI.TESI(df, Equation='Euc_p1', 
                    start=0.01,
                    augmentation_factor=10)
                              
df1.pd_frame(save=True)
df1.arr()

# Output
Pd_Dataframe/numpy.array

# Parameters 
start: (float), default=0.01, The starting value of the angle sequence. 
stop: (int), default=1, The end value of the angle's sines vector. Should be always 1 because the angle's max sine is 1. 
augmentation_factor: (int), default (0.01), the number of times the data is augmented. should be from 0 to infinity.
Equation: (default='Euc_p1'), Either the Euclidian or the following transformed Euclidian formulas. 

'Euc_p1': The linear polynomial.
'Euc_p2': The quadratic polynomial. 
'Euc_p3': The cubic polynomial.
'Euc_Log': The log-transformed euclidian formula. 

# Documentation
The TESI was developed in a study context, the study paper was published in Elsivier. 
A detailed description behind the logical of the TESI can be found in the paper.
NOTE: PLEASE CITE THE PAPER. 

Publisher name: Elsevier
Journal: Computers and Electronics in Agriculture
Volume: 206
Issue: March 2023
Pages: 107646
DOI: https://doi.org/10.1016/j.compag.2023.107646


# License
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. 
Contributors provide an express grant of patent rights. Licensed works, modifications, 
and larger works may be distributed under different terms and without source code.

Permissions	               Conditions	                       Limitations
 Commercial use            License and copyright notice        Liability
 Distribution              State changes                       Trademark use
 Modification                                                  Warranty
 Patent use
 Private use
bearing-python 1.0.4
README.md

# Introduction

The **bearing-python** is package which include common bearing fault diagnosis dataset implement in numpy, CWRU[1], Paderborn[2] and Jnu[3], etc., and will include more in the future. This package can be used to **Deep Learning**, **Transfer Learning** and **Semi-Supervised Learning** (domain adaptation), etc.. Original data comes from the official bearing vibration. The data is based on the original vibration data without repeated segmentation (the sampling window length is equal to hop length) and is provided in numpy.Array format.

Although the original '*. mat' file of CWRU can be downloaded automatically at the first call , we still recommend you, for possible time consumption and transmission interruption, manually download the dataset file through the link below:

CWRU:
Download Link：https://pan.xunlei.com/s/VNNBE0GiGHD5r5_l4BsYOTP-A1?pwd=3syk# 
Extraction Code：3syk

Paderborn:
Download Link: https://www.aliyundrive.com/s/AanRmmBNZna  
Extraction Code: 4qq9

Jnu:
Download Link: https://pan.xunlei.com/s/VNNVrpOoaEAPPdIODQaJQQcQA1?pwd=jnri# 
Extraction Code: jnri

You may need to reorganize dir if you download CWRU from the official file by following format:

```html
Cwru
	12DriveEndFault
		1797
			12DriveEndFault
				1797	
					0.007-Ball.mat
					..........
			NormalBaseline
				1797
					.........
	12FanEndFault
		......
	48DriveEndFault
```

# install package 
```shell script
pip install bearing-py
```

# Example
## datasets
```python
import bearing
dataset = bearing.datasets.Cwru(exp='12DriveEndFault', rpm='1797', length=1024, root=r'.')
print(dataset.x.shape, dataset.y.shape)
print(type(dataset.x))
cout >>:
 (2013, 1024) (2013,)
 <class 'numpy.ndarray'>
```
--------

## transform
```python
import torch
import bearing.transform as T
transform = T.Compose(
    T.Normalization()
)
x = torch.randn(1, 1, 1024)
x_transform = transform(x)
```

# Declaration
Our code refers to [[Litchiware/cwru (github.com)](https://github.com/Litchiware/cwru)] and modifies a bit.

for more details about each dataset please visit the following link:
[1] CWRU: https://engineering.case.edu/bearingdatacenter/download-data-file.
[2] Paderborn: https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/
[3] Jnu:
texplotlibx 1.1.2
A plot library for creating graphics using tikz/pgfplots
Requires Python: >=3.6

# texplotlibx

A python plotting library inspired by matplotlib using pgfplots/tikz to create graphics.

## Description

This is a library for creating plots using tikz and pgfplots as a backend. One can create `.tex` files which can then be compiled manually using an arbitrary latex compiler (e.g. pdflatex) or `.tikz` files which are essentially only the graphics part of a tex file. The latter can then simply be included in another latex file to create the plot in this document. There is also the option to create a `.pdf` plot directly, but this requires pdflatex to be installed and prolongs the runtime of the python file as the .tex files are compiled at runtime. Creation of text files is OS independent; Compilation to .pdf files is currently written using bash commands (i.e. only available on Linux with a bash (or bash compatible) shell).

## Usage

texplotlibx is written in a way that makes it very compatible to [matplotlib](https://matplotlib.org/). Some commands are altered to better suit the needs of the author, but generally the underlying syntax is very similar.

TODO: Examples

## Availability / Installation

This python package is available on [PyPI](https://pypi.org/project/texplotlibx/) and can be installed via pip:

```sh
python3 -m pip install texplotlibx
```

The source code is available on [gitlab](https://gitlab.com/d_hir/texplotlib).

## License

This package is licensed under the GNU General Public License v3 (GNU GPLv3).

See the LICENSE file ([local](LICENSE), [gitlab](https://gitlab.com/d_hir/texplotlib/-/blob/main/LICENSE)) for more information.

## Project Status

This project is currently in the very early stages of development. Everything written so far is probably subject to change.
dominant-image-color 1.0.1
Get the most frequent color from an image

# Dominant Image Color

A Python package to extract the dominant color of an image. Given the path to an image file, the `get_image_color` function returns the hex code of the most frequent color in the image.

## Installation

To install the package, simply run the following command:

```
pip install dominant_image_color
```

## Usage

Here's an example of how to use the `get_image_color` function:

```python
from dominant_image_color import get_image_color

hex_code = get_image_color("path/to/image.jpg")
print(hex_code)
```

## Dependencies

These dependencies will be automatically installed when you install the package using pip.

- Pillow

## Contributions

Contributions to this package are welcome. If you have any suggestions or bug reports, feel free to open an issue or a pull request on Github.
[https://github.com/itsmdsameerkhan/dominant-image-color](https://github.com/itsmdsameerkhan/dominant-image-color)
image-frequent-color 0.1.1
Get the most frequent color from an image

# Image Color Picker

The `image_color_picker` package provides a function to extract the most frequent color from an image.

## Installation

To install the package, run the following command:

`pip install image_color_picker`

## Usage

To use the `get_image_color` function, you'll need to import it from the `image_color_picker` package:

`from image_color_picker import get_image_color`

The `get_image_color` function takes one argument:

- `path_to_image_file`: The path to the image file you want to extract the color from.

Here's an example of how to use the `get_image_color` function:

```
path_to_image_file = '/path/to/image.jpg'
hex_code = get_image_color(path_to_image_file)
print(hex_code)
```

The `get_image_color` function returns the hex code of the most frequent color in the image.

## Dependencies

The `image_color_picker` package depends on the `Pillow` library. This library will be automatically installed when you install the `image_color_picker` package.

## Contributions

Contributions to the `image_color_picker` package are welcome! If you'd like to contribute, please create a pull request or open an issue on the project's GitHub repository.
rosepy 0.0.1
A Python package for synthetic data augmentation
Requires Python: >=3.7

# Documentation:
The TESI was developed in a study context, the study paper was published in Elsivier. 
A detailed description behind the logical of the TESI can be found in the paper.
NOTE: PLEASE CITE THE PAPER. 

Publisher name: Elsevier
Journal: Computers and Electronics in Agriculture
Volume: 206
Issue: March 2023
Pages: 107646
DOI: https://doi.org/10.1016/j.compag.2023.107646


# License: 
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. 
Contributors provide an express grant of patent rights. Licensed works, modifications, 
and larger works may be distributed under different terms and without source code.

Permissions	               Conditions	                       Limitations
 Commercial use            License and copyright notice        Liability
 Distribution              State changes                       Trademark use
 Modification                                                  Warranty
 Patent use
 Private use
docker-parser 0.0.8
package parsing docker files
Requires Python: >=3.7

# Docker parser

Docker attempts to simplify machine processing of dockerfiles. This moduel is not intended to extend docker SDK or build images, instead it attempts to parse instructions into simply manipulatable python objects, deduce state of environment in which commands are executed and allow modification of resulting dockerfile.

# Instalation

```
pip3 install docker-parser
```


# Usage


Print all executed RUN commands
```python
from docker_parser import parser

with open("dockerfile") as src:
    dockerfile = parser.Parser.loads(src.read())

for cmd in dockerfile.run():
    print(cmd.arg)
```

dockerfile:
```
FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive
RUN apt update
RUN apt install -y gcc make
COPY ./src /var/tmp/
WORKDIR /var/tmp/src
RUN make
```

output:
```
apt update
apt install -y gcc make
make
```

for more examples refer to [examples](examples/)


# Contributing

To contribute:
1. Increment version in [pyproject.toml](pyproject.toml)
2. Create merge request to `preprod` branch
3. Once your changes are reviewed and merged, pipeline publishes new version to testpypi
4. After verifying that changes don't break rest of the code and don't justify major release, changes will be pushed to `main` branch and pushed to production pypi