semvec/pypi-packages huggingface dataset:
jiraA 0.0.19
Lib created based on documentaion https://docs.atlassian.com/DAC/rest/jira/6.1.html

# JiraX
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=jiraX_SEON&metric=alert_status)](https://sonarcloud.io/dashboard?id=jiraX_SEON)[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=jiraX_SEON&metric=coverage)](https://sonarcloud.io/dashboard?id=jiraX_SEON)[![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=jiraX_SEON&metric=code_smells)](https://sonarcloud.io/dashboard?id=jiraX_SEON)

## General Information
* **Software**:Jira
* **Author**:Paulo Sérgio dos Santos Júnior
* **Author's e-mail**:paulossjunior@gmail.com
* **Source Repository**: [https://gitlab.com/integration_seon/libs/application/jira](https://gitlab.com/integration_seon/libs/application/jira)  

## Goal
Lib created based on documentaion https://docs.atlassian.com/DAC/rest/jira/6.1.html

## Documentation
The Documentation can be found in this [link](./docs/documentation.md)

## Generate documentation

To create the code documentation:
```bash
pdoc --html --force jiraX/ --output docs

```
### Acess code documentation	

To accesss the documenation, go to folder docs/jiraX and open index.html 

To run documentation server: pdoc jiraX --http localhost:8000 

## Instalation
To install jiraX, run this command in your terminal:
	
```bash
pip install jiraX
```

## Usage

```python
from jira import JIRA
from pprint import pprint
from jiraX import factories as factory

user = "x@x.com"
apikey = "Apikey"
server =  'https://xyz.atlassian.net/'

project_apl = factory.ProjectFactory(user=user,apikey=apikey,server=server)
issue_apl = factory.IssueFactory(user=user,apikey=apikey,server=server)
comment_apl = factory.CommentFactory(user=user,apikey=apikey,server=server)

projects = project_apl.find_all()

for project in projects:
    pprint ("Project Data:"+project.name)
    issues_in_proj = project_apl.find_issue(project.key)
    for issue in issues_in_proj:
        pprint ("x")
        pprint ("Project Issue:")
        issueX = issue_apl.find_by_id(issue.id)
        pprint (issueX.fields.summary)
        pprint (issueX.fields.issuetype.name)
        pprint (issueX.fields.creator.accountId)
        pprint (issueX.fields.creator.displayName)
        pprint (issueX.fields.creator.emailAddress)

        
        comments = comment_apl.find_by_issue(issueX)
        pprint ("Comments:")
        for comment in comments:
            pprint ("Comment: "+str(comment.id))
            pprint (comment.body)
            pprint (comment.created)
            pprint (comment.author.displayName)
            pprint (comment.author.emailAddress)
```

## Copyright
This lib was PowerRight by [SEON Application Lib Generator](https://gitlab.com/mdd_seon/application)
Emr-Password-Manager 1.0.0
A simple password manager for generating and validating passwords.
Requires Python: >=3.6

# Python Password Manager Library

This is a Python library that generates random passwords with a specified length, complexity, and character set. Also gives a score of given password.

## Usage

```bash
from password_manager import generate_password , password_strength # Import the library

password = generate_password(length=12, use_letters=True, use_digits=False, use_symbols=False, exclude_chars="a") # Generate a password
print(password) # Print the password

print(password_strength("Emr")) # Check the password strength. Result is like: 3 / 10
```
reflex-plot 0.1.0
Plot your dataframe with reflex!
Requires Python: >=3.9
Keywords: dashboard, pandas, plotting, reflex, reflex-plot

# reflex-plot
Seamlessly plot dataframes in _Reflex_ just like you would do with Matplotlib.

![](./static/screenshot.png)

You can import `plot` from `reflex_plot` to get a Rechart component.

```python
import random
from typing import Literal

import pandas as pd
import reflex as rx
from reflex_plot import plot

def plot_data(kind: Literal["line", "area", "bar"]) -> rx.Component:
    df = pd.DataFrame(
        {
            "category": list(range(20)),
            "value": [random.randint(0, 1000) for _ in range(20)],
        }
    )
    return plot(
        df,
        kind=kind,
        x="category",
        y="value",
        grid=True,
        tool_tip=True,
    )
```

## Pandas plotting backend

You can also set `reflex_plot` as the default backend for pandas and use `DataFrame.plot` as you would do with Matplotlib


```python
pd.set_option("plotting.backend", "reflex_plot")


def plot_data(kind: Literal["line", "area", "bar"]) -> rx.Component:
    df = pd.DataFrame(
        {
            "category": list(range(20)),
            "value": [random.randint(0, 1000) for _ in range(20)],
        }
    )
    return df.plot(
        kind=kind,
        x="category",
        y="value",
        grid=True,
        tool_tip=True,
    )

```

## How to install
```
pip install reflex-plot
```

## Charts type covered
- [x] Line
- [x] Area
- [x] Bar
correl 0.1.22
Library for w2dynamics (DMFT) postprocessing and for solving correlated electron problems.
Requires Python: >=3.8

# correl
Library for [w2dynamics](https://github.com/w2dynamics/) (DMFT) postprocessing and for solving correlated electron problems.

### Developer Installation

_Below are instructions for an editable install of the package. The c++ extension uses the [nanobind](https://nanobind.readthedocs.io/en/latest/index.html) package._

1. Clone the repo
   ```sh
   git clone https://gitlab.tuwien.ac.at/e138/e138-02/software/correl.git
   ```
3. Install nanobing package in your conda/virtual env
   ```sh
   python -m pip install nanobind
   ```
   or
   ```sh
   conda install -c conda-forge nanobind
   ```
4. In root directory run editable install
   ```sh
   pip install --no-build-isolation --config-settings=editable.rebuild=true -Cbuild-dir=build -ve.
   ``` 

<p align="right">(<a href="#readme-top">back to top</a>)</p>
betterlogging-pxnity 0.1.0
A custom logging package with color support
Requires Python: >=3.7

# BetterLogging

BetterLogging is a custom logging package with color support, designed to enhance the readability of log messages in your Python applications.

## Features

- Colored log messages for better readability
- Easy configuration through a JSON file
- Compatible with Python 3.7 and above

## Installation

You can install BetterLogging using pip:

```bash
pip install betterlogging
```