Tag: Python

Exploring csig_onp-cfgdump: A Tool for Configuration Dumping

In the ever-evolving landscape of software development, the need for efficient tools that simplify complex tasks is paramount. The csig_onp-cfgdump project stands as a testament to this need, providing a streamlined solution for configuration dumping. This project was initiated in response to the growing demand for better management and analysis of configuration files, which are critical in various software environments.

The csig_onp-cfgdump project was started in 2021, marking the beginning of a journey aimed at addressing the challenges faced by developers and system administrators in handling configuration dumps. The significance of this tool lies in its ability to extract and present configuration data in a user-friendly manner, making it easier for users to understand and manage their configurations.

Project Overview

The primary purpose of the csig_onp-cfgdump project is to provide a robust tool for dumping configuration files from various sources. This tool is particularly aimed at developers, system administrators, and IT professionals who require a reliable way to manage and analyze configuration settings.

Utilizing technologies such as Python, the project leverages powerful libraries and frameworks to ensure efficiency and reliability. The tool is designed to handle a variety of configuration formats, making it versatile and adaptable to different environments.

Key Features

  • Multi-Format Support: The tool can handle multiple configuration file formats, ensuring that users can work with their preferred setups.
  • User-Friendly Interface: A clean and intuitive interface allows users to easily navigate through the configuration data.
  • Efficient Data Extraction: The tool is optimized for fast and accurate extraction of configuration settings, saving users valuable time.
  • Comprehensive Documentation: Well-structured documentation is provided, making it easy for users to get started and make the most of the tool’s features.

Current State and Future Plans

As of now, the csig_onp-cfgdump project is actively maintained, with ongoing improvements being made to enhance its functionality and user experience. The developer, dmzoneill, is committed to incorporating user feedback and addressing any emerging needs within the community.

Looking ahead, there are plans to introduce additional features that will further streamline the configuration management process. The goal is to make csig_onp-cfgdump an indispensable tool for anyone dealing with configuration files.

Conclusion

The csig_onp-cfgdump project exemplifies the spirit of innovation and problem-solving in the tech community. By providing a dedicated tool for configuration dumping, it addresses a critical need and empowers users to manage their configurations more effectively. Whether you are a developer looking to simplify your workflow or a system administrator seeking better control over your configurations, csig_onp-cfgdump is worth exploring.

To learn more about the project and get involved, visit the GitHub repository today!


Exploring the BMRADashboard: A Comprehensive Tool for Data Visualization

In the ever-evolving landscape of data analytics, the BMRADashboard stands out as a significant project that began its journey in 2020. This repository was created in response to the growing need for effective data visualization tools that can help users make sense of complex datasets. The project aims to provide a user-friendly interface that simplifies the process of data analysis and visualization, making it accessible to a wider audience.

The BMRADashboard is designed to serve data analysts, researchers, and anyone interested in visualizing data trends and patterns. It leverages modern web technologies to create an interactive dashboard that allows users to explore their data intuitively. The project utilizes technologies such as HTML, CSS, JavaScript, and various data visualization libraries to deliver an engaging user experience.

Key Features and Unique Aspects

One of the standout features of the BMRADashboard is its ability to integrate with various data sources, allowing users to import data seamlessly. The dashboard provides a range of visualization options, including graphs, charts, and tables, enabling users to present their data in the most effective format. Additionally, the project emphasizes responsiveness, ensuring that the dashboard functions well on both desktop and mobile devices.

Moreover, the BMRADashboard is designed with user experience in mind. It includes interactive elements that allow users to filter and manipulate data in real-time, making the analysis process both efficient and insightful. The project also encourages community contributions, inviting developers to enhance its capabilities further and adapt it to their specific needs.

Current State and Future Plans

As of now, the BMRADashboard is actively maintained, with ongoing developments aimed at expanding its features and improving user experience. The project has garnered attention from the data analytics community, and there are plans to introduce new visualization types and enhance existing functionalities based on user feedback.

In conclusion, the BMRADashboard represents a significant step forward in the realm of data visualization tools. Its commitment to accessibility, interactivity, and community involvement makes it a valuable resource for anyone looking to harness the power of data. Whether you’re a seasoned data analyst or a newcomer to the field, the BMRADashboard offers a robust platform to explore and visualize your data effectively.

For more information and to explore the project, visit the BMRADashboard GitHub repository.


Streamlining Reimbursement Processes with AA Concur Reimbursement Internet

In the world of financial management and expense reporting, efficiency is key. The AA Concur Reimbursement Internet project was initiated in response to the growing need for a streamlined solution to manage reimbursements effectively. Launched in 2019, this project has evolved significantly, addressing various challenges faced by organizations in handling expense reports.

This project was started in 2019, marking a pivotal moment in the integration of technology into financial processes. The aim was to simplify the reimbursement workflow, making it more user-friendly and accessible. As organizations increasingly transitioned to digital solutions, the need for an intuitive reimbursement system became apparent, and AA Concur stepped up to fill this gap.

Project Overview

The AA Concur Reimbursement Internet project is designed to facilitate the reimbursement process for employees and employers alike. It provides a platform where users can submit their expenses, track their reimbursement status, and manage their financial records with ease. This project is particularly beneficial for finance departments looking to reduce the time and resources spent on processing reimbursements.

Targeted towards businesses of all sizes, the project leverages modern web technologies to create a seamless user experience. Built using JavaScript, HTML, and CSS, the application is both responsive and intuitive, ensuring that users can navigate through their financial tasks without hassle.

Key Features

  • User-Friendly Interface: The design focuses on simplicity, allowing users to submit expenses quickly and efficiently.
  • Status Tracking: Users can easily track the status of their reimbursements, providing transparency throughout the process.
  • Integration Capabilities: The project is built to integrate seamlessly with existing financial systems, enhancing its utility.
  • Mobile Compatibility: The application is optimized for mobile use, making it accessible on various devices.

Current Developments and Future Plans

As of now, the AA Concur Reimbursement Internet project is actively maintained, with ongoing improvements and updates being implemented to enhance its functionality. The development team is focused on incorporating user feedback to refine features and expand integration options with other financial tools. Future plans include the introduction of advanced analytics to help organizations better understand their spending patterns and optimize their reimbursement processes.

In conclusion, the AA Concur Reimbursement Internet project represents a significant step forward in the realm of expense management. By addressing the common pain points associated with reimbursement processes, it not only saves time and resources but also empowers users to take control of their financial activities. As the project continues to grow and evolve, it stands as a testament to the importance of innovation in financial management.

For more information and to contribute to the project, visit the GitHub repository.


Exploring Jackett-Indexarr: A Powerful Tool for Torrent Indexing

In the world of torrenting, having the right tools can make all the difference. Jackett-Indexarr is a project that emerged to address the challenges faced by users seeking a seamless experience in managing torrent indexers. This project was initiated in 2018, marking its inception as a response to the growing need for better integration and management of various torrent indexers within the Jackett ecosystem.

Jackett-Indexarr serves as a bridge between Jackett and Indexarr, allowing users to efficiently manage their torrent indexers and automate the process of searching for content across multiple sources. The primary goal of this project is to simplify the user experience by providing a unified interface for managing different indexers, thus solving the problem of fragmented access to torrent content.

This project is particularly beneficial for avid torrent users and developers who are looking to streamline their downloading process. By utilizing Jackett-Indexarr, users can easily configure and manage their indexers, enhancing their overall torrenting experience.

Key Features and Technologies

  • Integration with Jackett: Jackett-Indexarr works seamlessly with Jackett, allowing users to leverage the power of multiple torrent indexers.
  • User-Friendly Interface: The project provides a straightforward interface that simplifies the management of indexers.
  • Automation: Users can automate their searches, saving time and effort in finding the content they desire.
  • Open Source: As an open-source project, it invites contributions from the community, fostering collaboration and continuous improvement.

Throughout its development, Jackett-Indexarr has seen several milestones, including various updates that have enhanced its functionality and user experience. The project is actively maintained, with ongoing developments aimed at improving performance and adding new features based on user feedback.

As we look to the future, the team behind Jackett-Indexarr is excited about the potential for further enhancements and integrations. The project stands as a testament to the power of community-driven development and the importance of providing users with the tools they need to navigate the complex world of torrenting.

For those interested in exploring Jackett-Indexarr further, you can find the repository on GitHub. Join the community, contribute to the project, and enhance your torrenting experience today!

Jackett-Indexarr Overview


Distributed FFMPEG using Google App Engine

I’ve developed a distributed ffmpeg google app engine solution.

The solution uses a combination of publish subscribe and redis queues for distributed communication.
https://github.com/dmzoneill/appengine-ffmpeg

Its composed of 2 services which scale horizontally (default and worker).

Coordinator (default service and human interface)

#!/usr/bin/python
from flask import Flask
import os
import sys
import glob
import string
import random
import redis
import logging
from gcloud import storage, pubsub
from google.cloud import logging

PROJECT_ID = 'transcode-159215'
TOPIC = 'projects/{}/topics/message'.format(PROJECT_ID)

logclient = logging.Client()
logger = logclient.logger( "ffmpeg-pool" )

app = Flask(__name__)
app.config[ "SECRET_KEY" ] = "test"
app.debug = True


def publish( msg ):
    pubsub_client = pubsub.Client( PROJECT_ID )
    topic = pubsub_client.topic( "ffmpeg-pool" )

    if not topic.exists():
        topic.create()

    topic.publish( msg )


@app.route( "/readlog" )
def readLog():
    msg = ""

    try: 
	for entry in logger.list_entries():
            msg = msg + entry.payload + "
" logger.delete() except: msg = "" return msg @app.route( "/cleantopic" ) def cleanTopics(): client = pubsub.Client( PROJECT_ID ) topic = client.topic( "ffmpeg-pool" ) topic.delete() topic.create() return "Cleaned topic" @app.route( "/split" ) def split(): publish( "split" ) return "File queued for spliting" @app.route( "/transcode" ) def transcode(): publish( "transcode" ) return "Job queued for transcoding" @app.route( "/combine" ) def combine(): publish( "combine" ) return "Job queued for combining" @app.route( "/" ) def home(): return "/split | /transcode | /combine | /cleantopic | /readlog" if __name__ == '__main__': app.run(host='127.0.0.1', port=8080, debug=True)

Worker

import os
from gcloud import storage, pubsub, logging
import sys
import socket
import time
import redis
import glob
from google.cloud import logging


logclient = logging.Client()
logger = logclient.logger( "ffmpeg-pool" )

PROJECT_ID = 'transcode-159215'
TOPIC = 'projects/{}/topics/message'.format(PROJECT_ID)
psclient = None
pstopic = None
pssub = None

class RedisQueue(object):
    def __init__( self, name, namespace = 'queue' ):
       self.__db = redis.Redis( host = "redis-11670.c10.us-east-1-4.ec2.cloud.redislabs.com", port=11670 )
       self.key = '%s:%s' %(namespace, name)

    def qsize( self ):
        return self.__db.llen( self.key )

    def empty( self ):
        return self.qsize() == 0

    def put( self, item ):
        self.__db.rpush( self.key, item )

    def get( self, block=True, timeout=None ):
        if block:
            item = self.__db.blpop( self.key, timeout=timeout )
        else:
            item = self.__db.lpop( self.key )

        if item:
            item = item[1]
        return item

    def get_nowait( self ):
        return self.get( False )


def download( rfile ):
    client = storage.Client( PROJECT_ID )
    bucket = client.bucket( PROJECT_ID + ".appspot.com" )
    blob = bucket.blob( rfile )

    with open( "/tmp/" + rfile, 'w' ) as f:
        blob.download_to_file( f )
        logger.log_text( "Worker: Downloaded: /tmp/" + rfile )


def upload( rfile ):
    client = storage.Client( PROJECT_ID )
    bucket = client.bucket( PROJECT_ID + ".appspot.com" )
    blob = bucket.blob( rfile )
    
    blob = bucket.blob( rfile )
    blob.upload_from_file( open( "/tmp/" + rfile ) )

    logger.log_text( "Worker: Uploaded /tmp/" + rfile )


def transcode( rfile ):
    download( rfile )
    
    os.system( "rm /tmp/output*" )
    ret = os.system( "ffmpeg -i /tmp/" + rfile + " -c:v libx265 -preset medium -crf 28 -c:a aac -b:a 128k -strict -2 /tmp/output-" + rfile + ".mkv" )    
    
    if ret:
        logger.log_text( "Worker: convert failed : " + rfile + " - " + str( ret ).encode( 'utf-8' ) )
        return

    upload( "output-" + rfile + ".mkv" ) 


def split():
    rqueue = RedisQueue( "test" )
    download( "sample.mp4" )

    os.system( "rm -f /tmp/chunk*" )
    ret = os.system( "ffmpeg -i /tmp/sample.mp4 -map 0:a -map 0:v -codec copy -f segment -segment_time 10 -segment_format matroska -v error '/tmp/chunk-%03d.orig'" )

    if ret:
        return "Failed"

    for rfile in glob.glob( "/tmp/chunk*" ):
        basename = os.path.basename( rfile )
        upload( basename )
        rqueue.put( basename )


def combine():
    client = storage.Client( PROJECT_ID )
    bucket = client.bucket( PROJECT_ID + ".appspot.com" )
    blobs = bucket.list_blobs()

    os.system( "rm /tmp/*" )
    
    names = []
    
    for blob in blobs:
        if "output" in blob.name:
            names.append( blob.name.encode( 'utf-8' ) )

    names.sort()

    with open( '/tmp/combine.lst', 'w' ) as f1:
        for name in names:
            f1.write( "file '/tmp/" + name + "'\n" )
            download( name )

    logger.log_text( "Worker: created combine list: /tmp/combine.lst" )

    ret = os.system( "ffmpeg -f concat -safe 0 -i  /tmp/combine.lst -c copy /tmp/combined.mkv" )    
    
    if ret:
        logger.log_text( "Worker: combine failed: /tmp/combine.mkv - " + str(ret).encode( 'utf-8' ) )
        return

    upload( "combined.mkv" )


def subscribe():
    global psclient, pstopic, pssub

    psclient = pubsub.Client( PROJECT_ID )
    pstopic = psclient.topic( "ffmpeg-pool" )

    if not pstopic.exists():
        pstopic.create()
    
    pssub = pstopic.subscription( "ffmpeg-worker-" + socket.gethostname() )
    
    if not pssub.exists():
        pssub.create()
    

def handlemessages():
    global psclient, pstopic, pssub
    
    rqueue = RedisQueue( 'test' )
    subscribe()

    while True:
        messages = pssub.pull( return_immediately=False, max_messages=110 )

        for ack_id, message in messages:
            payload = message.data.encode( 'utf-8' ).replace( u"\u2018", "'" ).replace( u"\u2019", "'" )
            logger.log_text( "Worker: Received message: " + payload )
 
            try:
                pssub.acknowledge( [ack_id] )
                if payload == "combine":
                    combine()
                elif payload  == "split":
                    split()
                else:
                    rfile = rqueue.get()
                    basename = os.path.basename( rfile )
                    logger.log_text( "Worker: Redis popped: " + basename )

                    while basename != "None":
                        transcode( basename )
                        rfile = rqueue.get()
                        basename = os.path.basename( rfile )
                        logger.log_text( "Worker: Redis popped: " + rfile )

            except Exception as e:
                logger.log_text( "Worker: Error: " + e.message )
                sys.stderr.write( e.message )

        subscribe()
        time.sleep( 1 )


if __name__ == '__main__':
    handlemessages()