Crime and Justice Statistics for the EU

I have obtained a dataset from the Central Statistics Office of Ireland ( http://www.cso.ie ) which describes the levels of crime by type
for each country within the EU.
For the purpose of this project I have focused on the smaller countries with populations of less than 10 million people.
The reason for this is that it would not make sense to compare the crime statistics of the smaller countries against the bigger countries.
E.G. Northern Ireland Vs Germany

The dataset also contains details on the criminal justice system for each country such as Policing and Prison Population etc

The data range is between the years 2008 and 2014

Python (Flask),
MongoDB,
D3.js,
DC.js,
Crossfilter.js,
JQuery,
BootStrap,
CSS3,
HTML5

1. Clone the REPO to local git clone https://github.com/cormacio100/eu_crime_stats.git

2. Import the eu_crime_stats.json file from the db folder into a MongoDB collection based on the below settings

   MONGODB_HOST = ‘localhost’,
   MONGODB_PORT = 27017,
   DBS_NAME = ‘projectModule2’,
   COLLECTION_NAME = ‘eu_crime_stats’

3. Run the project through program Pycharm and then access the site through a browser at address http://127.0.0.1:5000/

HTML – https://validator.w3.org/
CSS – https://jigsaw.w3.org/css-validator/

Central Statistics Office of Ireland

Statistics for countries with population of greater than 10 Million people

Generate an ATD file from a JSON Schema / OpenAPI document.

The package is available on opam.

opam install jsonschema2atd

If you wish to install the development version you can do so with:

make install

Generate an ATD file from a JSON Schema:

jsonschema2atd ../path-to-jsonschema.json

Generate an ATD file from an OpenAPI document:

jsonschema2atd --format openapi ../path-to-openapi.json

You can call jsonschema2atd and atdgen in your dune file to generate OCaml types and JSON serializers/deserializers from your JSON Schema or OpenAPI document:

; Add jsonschema2atd.runtime to have access to the oneOf serialization adapter (for variant unboxing).
(library
 ...
 (libraries ... jsonschema2atd.runtime))

; Generate dashboard_gen.atd from the dashboard_types_gen.json OpenAPI document with jsonschema2atd.
(rule
 (target dashboard_gen.atd)
 ; Store the generated .atd file in the code. 
 (mode promote)
 (deps ../grok/dashboard_types_gen.json)
 (action
  (with-stdout-to
   %{target}
   (run
    %{bin:jsonschema2atd} -f openapi
    %{deps}))))

; Generate dashboard_gen_t.mli, dashboard_gen_t.ml, dashboard_gen_j.mli, and dashboard_gen_j.ml from dashboard_gen.atd with atdgen.
(rule
 (targets
  dashboard_gen_t.mli
  dashboard_gen_t.ml
  dashboard_gen_j.mli
  dashboard_gen_j.ml)
 (deps dashboard_gen.atd)
 (action
  (progn
   (run %{bin:atdgen} -j -j-std -j-defaults %{deps})
   (run %{bin:atdgen} -t %{deps}))))

Other options can be used to control the output:

• --json-ocaml-type KEYWORD:MODULE.PATH:TYPE-NAME to control the defitiion of
  the json type used as default/fallback.
• --only-matching REGEXP to limit the JSONSchema types to convert, when used
  together with --avoid-dangling-refs, missing types are replaced with json.

See also jsonschema2atd --help.

• Base types
• Records
• Nullable
• String enums
• Integer enums
• Other primitive enums
• Refs (OpenAPI format)
• OneOf (Only serialization is supported)
• not
• anyOf
• allOf

This C project, developed during my first semester in college, is a simple restaurant billing system. It allows users to create invoices, view all previous invoices, search for invoices by customer name, and save the invoices to a file.

• Create Invoice: Users can create invoices by entering customer details, items purchased, quantity, and unit price. The invoice includes the date, customer name, items purchased, quantity, total price, discounts, and grand total.
• Show All Invoices: Users can view all previous invoices stored in the file.
• Search Invoice: Users can search for a specific invoice by entering the customer’s name.
• Save Invoice: Users have the option to save the invoice to a file.

To run the program, compile the source code using any C compiler. For example, if you’re using GCC, you can compile the program with the following command:

gcc -o restaurant_billing restaurant_billing.c

After compilation, execute the program:

./restaurant_billing

Follow the on-screen prompts to navigate through the menu options:

1. Create Invoice: Enter customer details, items purchased, quantity, and unit price.
2. Show All Invoices: View all previous invoices.
3. Search Invoice: Enter the customer’s name to search for a specific invoice.
4. Exit: Exit the program.

The program stores the invoices in a file named Restaurant.dat. Make sure to handle file permissions and ensure that the file is accessible to the program.

Feel free to explore and modify the source code to enhance the functionality or customize it according to your requirements.

希望收藏爱好者能发现有用的东西，也欢迎你推荐你正在使用的 「利器」！

其他分享：

• 微信 ：乐于交志同道合的朋友，欢迎扫一扫，备注来源哈 💯
• 公众号：Coder魔法院 ：因为懒，「不持续性」输出一些干货内容，技术、工具等 🎯

在线阅读

因为本人是程序猿一枚，因此，本页面收集的开发相关的书签也比较多！

将这个排在第一个分享的主要原因就是，搜索技能是解决问题的必备技能！很多人搜索问题时常常输入一段话，其实，有时候效率不一定有提取关键词语搜索到的内容很准确！

• 虫部落-快搜 整合了 Google 等其他搜索引擎，推荐！
• 学术搜索 学术搜索，科研利器，国内可访问
• 谷歌趋势 查看 Google 的搜索趋势
• 设计师导航
• 考拉新媒体导航 导航 新媒体人必备运营神器
• SymbolHound 符号搜索
• java-api-examples 顾名思义

• Tool在线工具 很全的在线工具集合，例如 URL 编码、JSON 格式化等都有
• 站长工具 > UrlEncode编码/解码
• 菜鸟工具 开发相关的大而全的工具集合
• 开源中国-在线工具
• ATool 集成了开发者工具、站长工具、便民工具

• sourcegraph 配合 Github 使用，真是利器，有 Chrome 扩展
• Git History 一篇文章介绍了如何使用它，但是我看了官网，貌似只需要将 GitHub 仓库的 「github.com」网址替换为「github-history.xyz.com」，则可以动态显示文件的提交历史，也有对应的扩展
• BitHubLab 搜索所有主要 Git 平台上的项目，包括 GitHub，GitLab 和 BitBucket 等的项目
• gitignore.io 在线给开发项目生成 .gitignore 文件
• Octodex Github 的章鱼猫图片集
• octoverse Github 技术趋势概览，比如，可以看到最新的开发语言排行等信息

• 正则可视化工具
• regexr
• JS 正则

• commandlinefu 一个命令行欢迎排行的网站
• Man-Linux Linux 命令行帮助查询，国外的，页面很简洁
• ManKier Linux man pages
• ExplainShell 顾名思义，输入 Shell 语句，它会帮你分析

• Dig GUI

• HTML color codes and names
• paletton
• Button Generator 生成CSS按钮
• codepen 代码在线编辑分享

• 占位图-极客标签
• 图标搜索-easyicon app 图标、ico 图标
• Iconfont-阿里巴巴矢量图标库
• 图标-Font Awesome
• 奥森字体图标
• Instant Logo Search 企业 Logo

• locust Python 实现的压测工具，参考作为程序员的你，常用的工具软件有哪些？

• 廖雪峰-在线跑SQL的SQL教程

• devdocs 在线的开发文档，包含各个语言

• codelf 变量取名、函数取名
• httpbin httpbin 是一个 HTTP 测试库，你可以拿它来测试 HTTP 请求，源码地址
• API-POI搜索服务 经纬度行政区域查询 任意提交一个经纬度坐标和一个关键词（比如美食）获得周边相关服务的位置和简介信息
• pythontutor 可以看到 Python 执行的步骤

• leetcode-cn 力扣，算法练习网站，leetcode 英文版
• PythonTip 挑战 Python 算法练习
• algorithm-visualizer 一个算法可视化的在线演示网站
• sorting 排序算法可视化展示

• 华为开源镜像站 Maven 源、Manjaro 源、Elasticsearch 等都有，非常全
• 阿里镜像 有 Homebrew 的镜像源
• USTC中科大镜像源
• 清华大学开源镜像站
• 腾讯软件源

• 金老师的新教学网站

• 盲打学习课程
• Oeasy 这个站长传了很多教程视频，关于视频制作、PPT、WORD 等；
• Crash Course 中文字幕组
• 黑果小兵

• B 站 厚脸皮放一个自己的主页，但是不常更新

• IT大咖说 技术视频分享
• odecasts 从高质量的视频中学习 Web 开发技术，虽然关于 PHP 框架的多，但是还是能学到东西的
• 中国大学MOOC
• 学堂在线

• 奈菲影视
• RARBG 视频下载，国外资源
• 字幕库 搜索字幕
• 人人字幕组 搜索字幕
• SubHD 字幕搜索

• 汉典
• 阮一峰-每周分享
• Inoreader 在线的 RSS 阅读平台，遇到好的博客，通常都会订阅一下，RSS 发现来源：feedx、RSSHub 能够发现很多 RSS 地址
• 钛媒体 一些科技新闻质量还不错
• SlideShare PPT 分享的网站，推荐

中文资源：

• 看云 有很多中文 IT wiki
• 扫地僧的橱柜 「每周一书」推荐的书籍评分也都不错
• 鸠摩-电子书搜索 推荐，电子书搜索引擎,可以按照mobi, epub, pdf等格式分类
• 熊猫搜书 电子书搜索
• IT 熊猫 IT 电子书很多，质量很高
• 逍遥云 博主收集了很多电子书！
• 博客-我的小书屋
• Kindle伴侣
• 三秋书屋
• EBOOK
• 好读

英文资源：

• SaltTiger 一位博主整理的最新出版的技术类书籍，全部为英文原版，PDF、EPUB、MOBI、AZW3等多种格式，推荐
• Library Genesis1M 英文书籍搜索引擎
• pythonbooks Python 英文技术图书
• Book see
• allitebooks
• Itbook

• DockerOne 容器技术社区
• DaoCloud 社区
• 增长黑盒 一个网络安全的咨询网站
• TesterHome 一个测试开发者社区，中文的
• V2EX
• elastic 中文社区
• 黑客与画家的社区 以前 CSDN 上的称为「D大」的技术爱好者创建的社区
• 并发编程网 质量较高的 Java 开发社区

• 利器 利器分享的网站，估计创始人和我一样喜欢“利器”，哈哈😜
• 书格 数字古籍图书馆
• Paper Airplane Designs 一个教你折纸飞机的网站🛫
• Rainy Mood 下雨的声音好像能让人“想静静”
• Emojipedia emoji表情搜索
• 火柴人
• hitokoto 一言

• PDF 工具 万能 PDF 工具集合
• Word PDF 等格式转换
• 文件转换器

• md.aclickall 为了能在知乎利用markdown发文章，发现了这个工具，类似的还有：online-markdown、mdnice
• 壹伴 微信公众号高效地排版、修图、找素材

• ProcessOn 自己也在付费使用的一个款在线绘图工具，流程图、思维导图、UMP 图等，都可以绘制，力荐！！！
• 小画桌 白板绘图
• Cloudcraft 绘制网络设备架构图，很赞
• 幕布
• 百度脑图

• 毛笔字体控制 生成毛笔字在线工具
• 识字体 根据你上传图片的文字，识别字体
• 字体转换器

• TinyPng 在线图片压缩网站
• Trianglify 自己动手生成三角立体背景图片
• vectormagic 将图片转换成矢量图片
• logoly.pro 可以自定义生成类似 porxhub 风格的图片，我的 wiki logo 就是这么做的
• shields Github 项目的徽章就可以利用这个工具生成！
• remove.bg 图片处理 专注于人物抠图的神器
• carbon 生成比较精美的代码图片

• YEELOGO LOGO 图标制作
• 图怪兽 海报、logo 等作图
• 可画 海报、公众号配图等制作
• 创客贴 极简的设计工具

• Pixabay 免费图片，不可商用，可搜中文
• Pexels 免费图片，不可商用
• Unsplash 免费图片，不可商用
• colorhub 高清无版权图片，个人和商业用途免费

• Bing 壁纸
• wallhaven 壁纸网站
• wallpapershome 壁纸
• ZOL桌面壁纸
• justinmaller 立体图案的壁纸
• dynamicwallpaper MacOS 动态壁纸下载网站，壁纸会随着一天里面的时间不同，自动变换

• 图鱼 图片纹理素材
• LogoSearch LOGO 商标图片搜索
• Instant Logo Search LOGO 商标图片搜索

• 图壳

• chrome插件下载
• Chrome扩展收藏列表

• 稿定设计 一篇文章 介绍了这个作图超方便的网站！打算制作个公众号 logo 试试~
• nipponcolors 日本的一个配色网站
• flatuicolors 配色网站，复制颜色

• 冷熊简历 MD 简历生成 PDF
• 天眼查

• musclewiki 点击肌肉块，会有对应教程动画

• 数字帝国 函数绘制、不定积分计算等功能
• desmos 数学函数绘制
• 油管-Essence of linear algebra，B站有人搬运过来，并加了字幕 【部分双字】线性代数的本质（几何意义）Essence of linear algebra

• 全历史 很酷炫的网站，方便查看各个历史时期的成果！强烈推荐！

• A SOFT MURMUR 白噪音网站，可以组合模拟出各个场景，工作时听听，静静心
• Pandownload 网盘下载器
• 谷歌访问助手
• speedtest 测网速
• 在线涂鸦
• linkmoji linkmoji 🍕💩-网址转换为表情短链接
• oreilly-generator 图灵书籍封面制作

• msdn.itellyou
• 大眼仔
• ZD423
• 软件缘
• NoCmd
• 果核剥壳

• RPM 软件包搜索

awesome-wiki 系列将会持续更新，目前为止有如下可供阅读的 wiki：

• awesome-wiki-books 收集高质量值得一读的书籍 📚
• awesome-wiki-bookmarks 书签「利器」收集 🍪🍿
• awesome-wiki-IT IT 基础知识 基本素养 💻☕️
• awesome-wiki-java Java 技术栈的知识点索引

可以采用如下方法推荐推荐你的「利器」🎯：

• Fork 仓库 awesome-wiki-bookmarks 提交 PR，推荐方式
• 在下方留言
• 加我微信推荐

如果推荐被收纳，将在下方列出贡献者!

码字不易，赏个茶叶蛋吧 👇

This project aims to create a collection of recipes using HTML. The project is divided into iterations, with each iteration adding new features and improving the overall structure of the recipe collection.

In this iteration, an initial structure is set up for the recipe collection. An index.html file is created with basic HTML boilerplate code. The file includes an <h1> heading with the title “Odin Recipes.”

The second iteration focuses on creating a recipe page template. A new directory named /recipes is created within the project directory. Inside this directory, an HTML file is created for each recipe, named after the dish it contains. For example, lasagna.html can be used as a template for a lasagna recipe.

The index.html file is updated to include links to the recipe pages. Each recipe link is added under the “Odin Recipes” heading using the <a> tag.

In this iteration, the recipe pages are enhanced with more content. Each recipe page should include the following:

• An image of the finished dish.
• A “Description” heading followed by a paragraph or two describing the recipe.
• An “Ingredients” heading followed by an unordered list of the ingredients needed for the recipe.
• A “Steps” heading followed by an ordered list of the steps needed to make the dish.

The final iteration involves adding two more recipes to the collection. These recipes should follow the same page structure as the existing recipe page. The index.html file should be updated to include links to the newly added recipes. Consider using an unordered list to organize the recipe links and prevent them from appearing on a single line.

Once the project is completed, the following skills will be demonstrated:

• Creating a basic HTML structure.
• Linking between pages using HTML.
• Adding and displaying images.
• Structuring content with headings and paragraphs.
• Creating lists for ingredients and steps.
• Organizing content within directories.

This project provides a foundation for building a more advanced recipe collection, with the possibility of adding additional features such as search functionality, responsive design, and interactive elements.

A Python implementation that uses AWS Rekognition to detect personal protective equipment (PPE) in images stored in S3 buckets. The script provides detailed analysis of face covers, hand covers, and head covers for each person detected in the image.

• Python 3.8.8
• AWS Account with Rekognition access
• AWS Access Key and Secret Key
• S3 bucket containing images
• boto3 library

pip install boto3

import boto3

aws_accesskey = "Your Access Key"
aws_secretaccess = "Your Secret Access Key"
myregion = "your-region"

def Detect_PPE(aws_access, aws_secret, aws_region, Image, Bucket_Name):
    """
    Detects PPE equipment in images stored in S3.
    
    Args:
        aws_access: AWS access key
        aws_secret: AWS secret key
        aws_region: AWS region name
        Image: Name of image file in S3
        Bucket_Name: S3 bucket name
    
    Returns:
        int: Number of persons detected in the image
    """

Default configuration:

• Minimum confidence threshold: 80%
• Required equipment types:
  • FACE_COVER
  • HAND_COVER
  • HEAD_COVER

person_count = Detect_PPE(
    aws_accesskey,
    aws_secretaccess,
    "us-west-2",
    "image.jpg",
    "my-bucket"
)

The analysis provides detailed information about:

1. Person Detection:

   • Unique person IDs
   • Body parts detected
   • Confidence scores

2. PPE Details:

   • Type of equipment detected
   • Coverage assessment
   • Confidence scores
   • Bounding box coordinates

3. Summary Statistics:

   • Persons with required equipment
   • Persons without required equipment
   • Indeterminate cases

Detected PPE for people in image [image_name]

Detected people
---------------
Person ID: 0
Body Parts
----------
    FACE
        Confidence: 99.69
        Detected PPE
        ------------
        FACE_COVER
            Confidence: 99.70
            Covers body part: True
            Confidence: 99.49

1. Body Parts:

   • FACE
   • HEAD
   • LEFT_HAND
   • RIGHT_HAND

2. PPE Types:

   • FACE_COVER (masks, respirators)
   • HAND_COVER (gloves)
   • HEAD_COVER (helmets, hard hats)

1. Image Quality:

   • Use clear, well-lit images
   • Ensure subjects are clearly visible
   • Consider image resolution requirements

2. Performance:

   • Batch process multiple images when possible
   • Consider S3 transfer times
   • Monitor API usage limits

3. Security:

   • Secure credential storage
   • Use appropriate S3 bucket permissions
   • Implement access controls

The implementation handles:

• S3 access errors
• Image processing failures
• Invalid parameters
• Service limits

• Works only with images stored in S3
• Requires minimum confidence threshold
• Subject to AWS Rekognition service limits
• May have reduced accuracy in poor lighting conditions

1. IAM Permissions:

   {
       "Version": "2012-10-17",
       "Statement": [
           {
               "Effect": "Allow",
               "Action": [
                   "rekognition:DetectProtectiveEquipment",
                   "s3:GetObject"
               ],
               "Resource": "*"
           }
       ]
   }

2. S3 Bucket Requirements:

   • Appropriate bucket permissions
   • Supported image formats
   • Size limits

For AWS specific issues, refer to:

• AWS Rekognition PPE Documentation
• AWS S3 Documentation

A Python implementation that uses AWS Rekognition to detect personal protective equipment (PPE) in images stored in S3 buckets. The script provides detailed analysis of face covers, hand covers, and head covers for each person detected in the image.

• Python 3.8.8
• AWS Account with Rekognition access
• AWS Access Key and Secret Key
• S3 bucket containing images
• boto3 library

pip install boto3

import boto3

aws_accesskey = "Your Access Key"
aws_secretaccess = "Your Secret Access Key"
myregion = "your-region"

def Detect_PPE(aws_access, aws_secret, aws_region, Image, Bucket_Name):
    """
    Detects PPE equipment in images stored in S3.
    
    Args:
        aws_access: AWS access key
        aws_secret: AWS secret key
        aws_region: AWS region name
        Image: Name of image file in S3
        Bucket_Name: S3 bucket name
    
    Returns:
        int: Number of persons detected in the image
    """

Default configuration:

• Minimum confidence threshold: 80%
• Required equipment types:
  • FACE_COVER
  • HAND_COVER
  • HEAD_COVER

person_count = Detect_PPE(
    aws_accesskey,
    aws_secretaccess,
    "us-west-2",
    "image.jpg",
    "my-bucket"
)

The analysis provides detailed information about:

1. Person Detection:

   • Unique person IDs
   • Body parts detected
   • Confidence scores

2. PPE Details:

   • Type of equipment detected
   • Coverage assessment
   • Confidence scores
   • Bounding box coordinates

3. Summary Statistics:

   • Persons with required equipment
   • Persons without required equipment
   • Indeterminate cases

Detected PPE for people in image [image_name]

Detected people
---------------
Person ID: 0
Body Parts
----------
    FACE
        Confidence: 99.69
        Detected PPE
        ------------
        FACE_COVER
            Confidence: 99.70
            Covers body part: True
            Confidence: 99.49

1. Body Parts:

   • FACE
   • HEAD
   • LEFT_HAND
   • RIGHT_HAND

2. PPE Types:

   • FACE_COVER (masks, respirators)
   • HAND_COVER (gloves)
   • HEAD_COVER (helmets, hard hats)

1. Image Quality:

   • Use clear, well-lit images
   • Ensure subjects are clearly visible
   • Consider image resolution requirements

2. Performance:

   • Batch process multiple images when possible
   • Consider S3 transfer times
   • Monitor API usage limits

3. Security:

   • Secure credential storage
   • Use appropriate S3 bucket permissions
   • Implement access controls

The implementation handles:

• S3 access errors
• Image processing failures
• Invalid parameters
• Service limits

• Works only with images stored in S3
• Requires minimum confidence threshold
• Subject to AWS Rekognition service limits
• May have reduced accuracy in poor lighting conditions

1. IAM Permissions:

   {
       "Version": "2012-10-17",
       "Statement": [
           {
               "Effect": "Allow",
               "Action": [
                   "rekognition:DetectProtectiveEquipment",
                   "s3:GetObject"
               ],
               "Resource": "*"
           }
       ]
   }

2. S3 Bucket Requirements:

   • Appropriate bucket permissions
   • Supported image formats
   • Size limits

For AWS specific issues, refer to:

• AWS Rekognition PPE Documentation
• AWS S3 Documentation

In-progress Game Gear emulator written in Rust.

It has a mostly complete Z80 emulator, that passes z80test (1.0 only), zexall, and FUSE tests.

• supports the game gear/master system SEGA rom banking
• minimal CPU interrupt support (mode 1)
• a VDP implementation, able to display some games
• Small “UI” relying on winit+pixels+cpal+gilrs crates that supports input with keyboard and gamepad (both hardcoded bindings)
• A test suite for the VDP with some ROM frames to prevent regressions
• WASM target for in-browser emulation.

• Finish VDP details
  • still missing horizontal interrupt testing (H counter, line completion)
• Polish the PSG (sound)
  • there is no filtering or downsampling strategy. A low-pass filter should do.
• WASM is incomplete: lacks a complete UI (e.g configurable keybindings), just like the desktop version.
• Support more game gear games. Many work, but there might be bugs.
• It’s fast enough but there is a margin for improvement.
• Master system support at some point because some game gear cartridges actually shipped the SMS version. It will also be useful to enjoy the wider screen in some infamously hard games on Game Gear (Sonic 2 for example).

The web version of the gears emulator is available online here. It should always be the latest version.

Over the course of writing this emulator, I took a pause to reflect on some intricacies of the Z80, and gave a talk on Z80’s “last secrets” at FOSDEM 2022. It’s not exhaustive, and there are many more such secrets that have been discovered in the past 10 years by the Z80 emulation community.

I also gave two talks at FOSEDM 2024, one about WASM on the web with Rust, using gears as an example. And another about my advice on how to start to write an emulator.

I also wrote the following updates:

• Talks and emulation: with some emulator progress
• December 2022: rendering bug(s), missing features
• January 2023: fixing the map bug, VDP writes, tilesets and regions
• February to April 2023: interrupts, VDP palette, and audio

1. Implementig A-Bench

• Requirements
• Tech

2. Getting started

• 1. Step
• 2. Step
• 3. Step
• 4. Step

3. Additional information

This is my master thesis project. The main goal is to make installing, setting up and implementing the Big Data Benchmark A-Bench easier and to automate the process as much as possible. Using HTML, python3, flask, pandas and some other tools I created a WebUI management console for easier control over the setup of the infrastructure, running the benchmark and visualizing the results in a few charts which gives information about some metrics like CPU, memory and file system usage.

• Iternet connection
• Ubuntu 18.04 LTS (clean install)
• Modern web browser like Chromium or Mozilla Firefox

The ABench management console uses a number of open source projects to work properly:

• python3 – Python Programming Language version 3.6
• Flask – Flask is a microframework for Python based on Werkzeug, Jinja 2 and good intentions
• pandas – pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language
• chart.js – Simple yet flexible JavaScript charting for designers & developers

1. Step:

• Download the repository into home/user/ in order to work properly
• Go go project folder “scripts”, make it executable and run install_requirements.sh as root to install missing tools if any and download a GitHub repository for creating A-Bench infrastructure. An action from the user is required during the installation [Press ENTER to continue]

$ chmod +x install_requirements.sh
$ sudo ./install_requirements.sh

• To start the main WebUI run the python script in terminal as root:

$ sudo python3 abench-management-console.py

• Verify the deployment by navigating to your server address in your preferred browser

http://127.0.0.1:5000

2. Step:

• On the homepage there are three columns of buttons to the left and text box with the output from running different commands inside the page to the right
• First set of buttons under the “Setup” are used to check software pre-requirements, if everything needed is installed, to deploy A-Bench infrastructure using A-Bench infrastructure and to monitor the infrastructure using Grafana/Kubernetes dashboards after successful deployment
• Second set of buttons under “Run” are used to configure which queries to be run and to run a sample ABench experiments after going to “Configuration” page and selecting the queries
• Third set of buttons under “Analyse” are used to load the results ONLY AFTER running a sample experiment described in 3.Step.

3. Step:

• When you click the button “Configuration” under “Run” you will be forwarded to a new page
• There are shown all 30 queries with explanation that can be run as an experiment as a check boxes
• After selecting the desired one click “Save config” and under the field with all queries the chosen one will be shown
• An environment variable will be created and after clicking “Run SRE with HIVE”https://github.com/”Run SRE with SPARK” this variable would be used

4. Step:

• After successfully running an experiment the results will be saved in:

$ ~/wd/abench/a-bench/results/

• On the homepage under “Run” by clicking on “Load results” a file explorer will open, navigate to ~/wd/abench/a-bench/results/, choose the experiment_tag_sample_qXX.zip file name to load the results and analyze them using density charts
• If you want to load new results from different experiment repeat the previous step

• In the folder “all_executed_exp” will be stored all results of all executed experiments
• In the folder “experiment_results” will be saved as a .csv tables the results from the experiment needed for the charts
• In the folder “outputs” are two .txt files used for the output from all executed commands to be shown on the homepage
• In the folder “scripts” are all necessary scripts for deploying and running the infrastructure
• In the folder “templates” are all html pages
• In the folder “static” are located all .css files for the styling of the pages
• In folder “~/wd” will be downloaded everything necessary for the infrastructure from GitHub repository https://github.com/FutureApp/a-bench from Michael Czaja

by Prajjwal Datir
using Python3, Flask, bs4

Problem Statement

$ export FLASK_APP=app.py
$ flask run

 * Running on http://127.0.0.1:5000/

C:\path\to\app>set FLASK_APP=app.py
And on PowerShell:
PS C:\path\to\app> $env:FLASK_APP = "app.py"
Alternatively you can use python -m flask:

$ export FLASK_APP=app.py
$ python -m flask run
 * Running on http://127.0.0.1:5000/

This launches a very simple builtin server, which is good enough for testing but probably not what you want to use in production. For deployment options see Deployment Options.

for more info visit here