semvec/pypi-packages huggingface dataset:
PyJPDF 0.1.4
A simple library to extract text from PDF files.
Requires Python: >=3.6

# PyJPDF

PyJPDF is a Python library for extracting information from PDF files.

## Author

Jasson Carvalho

## Version

0.1.4

## Installation

```bash
pip install pyjpdf
```

## Usage

```bash
import pyjpdf as PyJPDF

p = PyJPDF.PyjPDFExtract()

## From URL
text = p.from_url('http://www.example.com/teste.pdf')

## From File
text = p.from_file('teste.pdf')

## From Stream
text = p.from_stream(open('teste.pdf', 'rb'))
```
entitytocodepy-official 0.1.1
Formula Package for Mathmatical/Physics/Chemistry/economical/(+) calulations
Requires Python: ~=3.12

<h1>Entity to code py</h1>
<h2>!!!WARNING!!! mathetc FUNCTIONS NOT AVAILIBLE</h2>

Version | 0.0.1 | 0.1.0  
--- | --- | --- 
Stable | yes | NO 


<h2>New feats</h2>
<h3>0.1.1 </h3>
<p><b>SO FILE BUG DETECTION, AS OF NOW, MODULE WITH VERSIONS 0.1.0 - Latest is UNUSABLE</b></p>
<h3>0.1.0 </h3>
<p><b>Binomials and Quadratic Calculations</b></p>
<h3>0.0.1 </h3>
<p><b>First with add_one()</b></p>

<h2>Usage</h2>
<h3>1) Installation</h3>

    pip install entitytocodepy_official==x.y.z
    pip install entitytocodepy_official==0.1.0


<h3>2) Importing to your python script</h3>

    from entitytocodepy import main

<h3>3) Using, here's a sample function</h3>

    main.add_one(1)

Output

    2

<h2>Methods</h2>
<h3>Quadratics (0.1.0 +)</h3>

Import

    from entitytocodepy import mathetc
Use

    mathetc.Roots(1,3,2)


<h3>Factorials (0.1.0 +)</h3>

Import

    from entitytocodepy import mathetc
Use

    mathetc.Factorial(4)


<h3>Binomials  (0.1.0 +)</h3>

Note: This is for finding singular terms in an expansion of (1+x)^n, eg: find the term of x^5 in (1+x)^7, here R (2nd param) == 5 and N = 7

Import

    from entitytocodepy import mathetc
Use

    mathetc.Binomialterm(7,5)

<h3>add_one (0.0.1 +)</h3>

Import

    from entitytocodepy import main
Use

    main.add_one(1)
mflag 1.7.3
put/remove flags for files and folders

# mflag: powerful Python toolkit for mark/unmark files and directories 
[![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/mflag/)

## What is it?

**mflag** is a Python package that provides marking/unmarking files and directores. For years, dealing with processing files, we understood that we need to put flag for files/directories that are already processed. This package provides the ability to mark files/directories and avoid any unnecessary repeatitive processing of same files/directories.
It also can manage serveral processes that willing to write on/in same files/directories. For example, we want to write to a parquet file, the whole processing generate part of the parquet file managed by a separate parallel processes. Using **mflag** avoid writing errors that happen at same time.

## Where to get it
The source code is currently hosted on GitHub at:
https://github.com/MosesDastmard/mflag

Binary installers for the latest released version are available at the [Python
Package Index (PyPI)](https://pypi.org/project/mflag/)


```python
# !pip install flagify
from mflag import Flag
import pandas as pd
import os
```


```python
# lets make a simple csv file
data = {'name':['mflag', 'pandas', 'numpy'],
        'toolkit':['marking', 'data analysis', 'data computing']}

pd.DataFrame(data).to_csv('data.csv', index=False)

class Process(Flag):
    def __init__(self, process_name):
        self.process_name = process_name
        Flag.__init__(self, process_name)
        
    def run(self, csv_path, parquet_path):
        if not self.isFlagged(csv_path):
            pd.read_csv(csv_path).to_parquet(parquet_path)
            self.putFlag(csv_path)
        else:
            print(f"the file {csv_path} is already processed")
```


```python
Process(process_name='convert_csv_to_parquet').run('data.csv', 'data.parquet')
```


```python
Process(process_name='convert_csv_to_parquet').run('data.csv', 'data.parquet')
```

    the file data.csv is already processed
gdrive-bot 1.0.6
upload files to gdrive with access token
Requires Python: >=3
Keywords: gdrive,g-bot,gdrive-api,gdrive-api-bot,gdrive-file-upload,gdrive-upload

# gdrive-bot

Upload files to your gdrive with gdrive-bot without using google-api-python-client.

 [![Publish package](https://github.com/jakbin/gdrive-bot/actions/workflows/publish.yml/badge.svg)](https://github.com/jakbin/gdrive-bot/actions/workflows/publish.yml)
 [![PyPI version](https://badge.fury.io/py/gdrive-bot.svg)](https://pypi.org/project/gdrive-bot/)
 [![Downloads](https://pepy.tech/badge/gdrive-bot/month)](https://pepy.tech/project/gdrive-bot)
 [![Downloads](https://static.pepy.tech/personalized-badge/gdrive-bot?period=total&units=international_system&left_color=green&right_color=blue&left_text=Total%20Downloads)](https://pepy.tech/project/gdrive-bot)
 ![GitHub Contributors](https://img.shields.io/github/contributors/jakbin/gdrive-bot)
 ![GitHub commit activity](https://img.shields.io/github/commit-activity/m/jakbin/gdrive-bot)
 ![GitHub last commit](https://img.shields.io/github/last-commit/jakbin/gdrive-bot)
 ![Python 3.6](https://img.shields.io/badge/python-3.6-yellow.svg)


## Features
- Progress bar


## Installation

```sh
pip3 install -U gdrive-bot
```

## Usage 
```sh
g-bot setup               # setup your gdrive credentials
g-bot reset               # reset to default your gdrive credentials
g-bot up {file_name}      # upload gdrive channel or group
g-bot d {url}             # download and upload file to your gdrive
```

# API

The g-bot client is also usable through an API (for test integration, automation, etc)

### g_bot.main.upload_file(access_token:str, filename:str, filedirectory:str, folder_id: str = None)

```py
from g_bot.main import upload_file

upload_file(access_token:str, filename:str, filedirectory:str, folder_id: str = None)
```
mjms 1.2.1
Python client for mailsrv.marcusj.org

# MJMS - A client for  https://mailsrv.marcusj.org

**Usage**

```python3
from mjms import MJMS

mjms = MJMS('API_KEY')

mjms.send_mail('example@email.com', 'This is the subject!', html='<h1>Hello world</h1>') # the first argument can also be a list of emails. text is also a valid kwarg

res = mjms.verify_email('example@email.com') 
# {'ok': True, 'token': 'XXXXXXX'} or {'ok': False, 'error': '...'}
print(mjms.check_verified(res['token']))
```

API keys can be obtained by visiting https://mailsrv.marcusjt.tech and signing in with [replit](https://replit.com).
nebari-plugin-metrics-server 0.0.1
MetroStar Onyx - Metrics Server plugin for Nebari platform
Requires Python: >=3.8


# Nebari Plugin - Metrics Server

[![PyPI - Version](https://img.shields.io/pypi/v/nebari-plugin-metrics-server.svg)](https://pypi.org/project/nebari-plugin-metrics-server)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nebari-plugin-metrics-server.svg)](https://pypi.org/project/nebari-plugin-metrics-server)

-----

## Overview
This plugin integrates Metrics Server into the Nebari platform, allowing container resource metrics for Kubernetes built-in autoscaling pipelines within Nebari. Utilizing Python, Terraform, and Helm, the plugin provides a configurable deployment.

## Design and Architecture
The plugin follows a modular design, leveraging Terraform to define the deployment of Metrics Server within a Kubernetes cluster. Key component is:
**Terraform Configuration**: Defines variables and Helm release for deployment.

## Installation Instructions


```console
pip install nebari-plugin-metrics-server
```


## Usage Instructions
**Configurations**: Various configurations are available, including name, namespace, and affinity settings.

## Configuration Details

### Public
Configuration of the Metrics Server plugin is controlled through the `metrics_server` section of the `nebari-config.yaml` for the environment.

``` yaml
metrics_server:
  # helm release name - default metrics-server
  name: metrics-server
  # target namespace - default kube-system
  namespace: kube-system
  # configure default affinity/selector for chart components
  affinity:
    enabled: true # default
    selector: general # default
    # -- or --
    selector:
      default: general
      worker: worker
      db: general
      auth: general
  # helm chart values overrides
  values: {}
```

### Internal
The following configuration values apply to the internally managed terraform module and are indirectly controlled through related values in `nebari-config.yaml`.

- `name`: Chart name for Helm release.
- `namespace`: Kubernetes namespace configuration.
- `affinity`: Affinity configuration for Helm release.
- `overrides`: Map for overriding default configurations.

## License

`nebari-plugin-metrics-server` is distributed under the terms of the [Apache](./LICENSE.md) license.