Web3.js Tutorial for Beginners - web3.eth.Iban

web3.eth.Iban

The web3.eth.Iban function converts Ethereum addresses from and to IBAN and BBAN.

Iban instance

This instance of Iban.

> Iban { _iban: 'XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS' }

Iban contructor

new web3.eth.Iban(ibanAddress)

Generates a iban object with conversion methods and validity checks.

Also has singleton functions for conversion like:

Parameters

String: the IBAN address to instantiate an Iban instance from.

Returns

Object - The Iban instance.

Example

var iban = new web3.eth.Iban("XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS");
> Iban { _iban: 'XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS' }

toAddress

Static function.

web3.eth.Iban.toAddress(ibanAddress)

Singleton: Converts a direct IBAN address into an Ethereum address.

Note

This method also exists on the IBAN instance.

Parameters

String: the IBAN address to convert.

Returns

String - The Ethereum address.

Example

web3.eth.Iban.toAddress("XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS");
> "0x00c5496aEe77C1bA1f0854206A26DdA82a81D6D8"

toIban

Static function.

web3.eth.Iban.toIban(address)

Singleton: Converts an Ethereum address to a direct IBAN address.

Parameters

String: the Ethereum address to convert.

Returns

String - The IBAN address.

Example

web3.eth.Iban.toIban("0x00c5496aEe77C1bA1f0854206A26DdA82a81D6D8");
> "XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS"

fromAddress

Static function, returns IBAN instance.

web3.eth.Iban.fromAddress(address)

Singleton: Converts an Ethereum address to a direct IBAN instance.

Parameters

String: the Ethereum address to convert.

Returns

Object - The IBAN instance.

Example

web3.eth.Iban.fromAddress("0x00c5496aEe77C1bA1f0854206A26DdA82a81D6D8");
> Iban {_iban: "XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS"}

static function, return IBAN instance

fromBban

web3.eth.Iban.fromBban(bbanAddress)

Singleton: Converts an BBAN address to a direct IBAN instance.

Parameters

String: the BBAN address to convert.

Returns

Object - The IBAN instance.

Example

web3.eth.Iban.fromBban('ETHXREGGAVOFYORK');
> Iban {_iban: "XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS"}

static function, return IBAN instance

createIndirect

web3.eth.Iban.createIndirect(options)

Singleton: Creates an indirect IBAN address from an institution and identifier.

Parameters

Object: the options object as follows:

institution - String: the institution to be assigned.
identifier - String: the identifier to be assigned.

Returns

Object - The IBAN instance.

Example

web3.eth.Iban.createIndirect({
    institution: "XREG",
    identifier: "GAVOFYORK"
});
> Iban {_iban: "XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS"}

Static function, returns boolean.

isValid

web3.eth.Iban.isValid(ibanAddress)

Singleton: Checks if an IBAN address is valid.

Note

This method also exists on the IBAN instance.

Parameters

String: the IBAN address to check.

Returns

Boolean

Example

web3.eth.Iban.isValid("XE81ETHXREGGAVOFYORK");
> true

web3.eth.Iban.isValid("XE82ETHXREGGAVOFYORK");
> false // because the checksum is incorrect

prototype.isValid

Method of Iban instance.

web3.eth.Iban.prototype.isValid()

Singleton: Checks if an IBAN address is valid.

Note

This method also exists on the IBAN instance.

Parameters

String: the IBAN address to check.

Returns

Boolean

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.isValid();
> true

prototype.isDirect

Method of Iban instance.

web3.eth.Iban.prototype.isDirect()

Checks if the IBAN instance is direct.

Parameters

none

Returns

Boolean

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.isDirect();
> false

prototype.isIndirect

Method of Iban instance.

web3.eth.Iban.prototype.isIndirect()

Checks if the IBAN instance is indirect.

Parameters

none

Returns

Boolean

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.isIndirect();
> true

prototype.checksum

Method of Iban instance.

web3.eth.Iban.prototype.checksum()

Returns the checksum of the IBAN instance.

Parameters

none

Returns

String: The checksum of the IBAN

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.checksum();
> "81"

prototype.institution

Method of Iban instance.

web3.eth.Iban.prototype.institution()

Returns the institution of the IBAN instance.

Parameters

none

Returns

String: The institution of the IBAN

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.institution();
> 'XREG'

prototype.client

Method of Iban instance.

web3.eth.Iban.prototype.client()

Returns the client of the IBAN instance.

Parameters

none

Returns

String: The client of the IBAN

Example

var iban = new web3.eth.Iban("XE81ETHXREGGAVOFYORK");
iban.client();
> 'GAVOFYORK'

prototype.toAddress

Method of Iban instance.

web3.eth.Iban.prototype.toString()

Returns the Ethereum address of the IBAN instance.

Parameters

none

Returns

String: The Ethereum address of the IBAN

Example

var iban = new web3.eth.Iban('XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS');
iban.toAddress();
> '0x00c5496aEe77C1bA1f0854206A26DdA82a81D6D8'

prototype.toString

Method of Iban instance.

web3.eth.Iban.prototype.toString()

Returns the IBAN address of the IBAN instance.

Parameters

none

Returns

String: The IBAN address.

Example

var iban = new web3.eth.Iban('XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS');
iban.toString();
> 'XE7338O073KYGTWWZN0F2WZ0R8PX5ZPPZS'

Original article source at https://web3js.readthedocs.io

#web3 #blockchain #javascript #ethereum

NBB: Ad-hoc CLJS Scripting on Node.js

Nbb

Not babashka. Node.js babashka!?

Ad-hoc CLJS scripting on Node.js.

Status

Experimental. Please report issues here.

Goals and features

Nbb's main goal is to make it easy to get started with ad hoc CLJS scripting on Node.js.

Additional goals and features are:

Fast startup without relying on a custom version of Node.js.
Small artifact (current size is around 1.2MB).
First class macros.
Support building small TUI apps using Reagent.
Complement babashka with libraries from the Node.js ecosystem.

Requirements

Nbb requires Node.js v12 or newer.

How does this tool work?

CLJS code is evaluated through SCI, the same interpreter that powers babashka. Because SCI works with advanced compilation, the bundle size, especially when combined with other dependencies, is smaller than what you get with self-hosted CLJS. That makes startup faster. The trade-off is that execution is less performant and that only a subset of CLJS is available (e.g. no deftype, yet).

Usage

Install nbb from NPM:

$ npm install nbb -g

Omit -g for a local install.

Try out an expression:

$ nbb -e '(+ 1 2 3)'
6

And then install some other NPM libraries to use in the script. E.g.:

$ npm install csv-parse shelljs zx

Create a script which uses the NPM libraries:

(ns script
  (:require ["csv-parse/lib/sync$default" :as csv-parse]
            ["fs" :as fs]
            ["path" :as path]
            ["shelljs$default" :as sh]
            ["term-size$default" :as term-size]
            ["zx$default" :as zx]
            ["zx$fs" :as zxfs]
            [nbb.core :refer [*file*]]))

(prn (path/resolve "."))

(prn (term-size))

(println (count (str (fs/readFileSync *file*))))

(prn (sh/ls "."))

(prn (csv-parse "foo,bar"))

(prn (zxfs/existsSync *file*))

(zx/$ #js ["ls"])

Call the script:

$ nbb script.cljs
"/private/tmp/test-script"
#js {:columns 216, :rows 47}
510
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
true
$ ls
node_modules
package-lock.json
package.json
script.cljs

Macros

Nbb has first class support for macros: you can define them right inside your .cljs file, like you are used to from JVM Clojure. Consider the plet macro to make working with promises more palatable:

(defmacro plet
  [bindings & body]
  (let [binding-pairs (reverse (partition 2 bindings))
        body (cons 'do body)]
    (reduce (fn [body [sym expr]]
              (let [expr (list '.resolve 'js/Promise expr)]
                (list '.then expr (list 'clojure.core/fn (vector sym)
                                        body))))
            body
            binding-pairs)))

Using this macro we can look async code more like sync code. Consider this puppeteer example:

(-> (.launch puppeteer)
      (.then (fn [browser]
               (-> (.newPage browser)
                   (.then (fn [page]
                            (-> (.goto page "https://clojure.org")
                                (.then #(.screenshot page #js{:path "screenshot.png"}))
                                (.catch #(js/console.log %))
                                (.then #(.close browser)))))))))

Using plet this becomes:

(plet [browser (.launch puppeteer)
       page (.newPage browser)
       _ (.goto page "https://clojure.org")
       _ (-> (.screenshot page #js{:path "screenshot.png"})
             (.catch #(js/console.log %)))]
      (.close browser))

See the puppeteer example for the full code.

Since v0.0.36, nbb includes promesa which is a library to deal with promises. The above plet macro is similar to promesa.core/let.

Startup time

$ time nbb -e '(+ 1 2 3)'
6
nbb -e '(+ 1 2 3)'   0.17s  user 0.02s system 109% cpu 0.168 total

The baseline startup time for a script is about 170ms seconds on my laptop. When invoked via npx this adds another 300ms or so, so for faster startup, either use a globally installed nbb or use $(npm bin)/nbb script.cljs to bypass npx.

Dependencies

NPM dependencies

Nbb does not depend on any NPM dependencies. All NPM libraries loaded by a script are resolved relative to that script. When using the Reagent module, React is resolved in the same way as any other NPM library.

Classpath

To load .cljs files from local paths or dependencies, you can use the --classpath argument. The current dir is added to the classpath automatically. So if there is a file foo/bar.cljs relative to your current dir, then you can load it via (:require [foo.bar :as fb]). Note that nbb uses the same naming conventions for namespaces and directories as other Clojure tools: foo-bar in the namespace name becomes foo_bar in the directory name.

To load dependencies from the Clojure ecosystem, you can use the Clojure CLI or babashka to download them and produce a classpath:

$ classpath="$(clojure -A:nbb -Spath -Sdeps '{:aliases {:nbb {:replace-deps {com.github.seancorfield/honeysql {:git/tag "v2.0.0-rc5" :git/sha "01c3a55"}}}}}')"

and then feed it to the --classpath argument:

$ nbb --classpath "$classpath" -e "(require '[honey.sql :as sql]) (sql/format {:select :foo :from :bar :where [:= :baz 2]})"
["SELECT foo FROM bar WHERE baz = ?" 2]

Currently nbb only reads from directories, not jar files, so you are encouraged to use git libs. Support for .jar files will be added later.

Current file

The name of the file that is currently being executed is available via nbb.core/*file* or on the metadata of vars:

(ns foo
  (:require [nbb.core :refer [*file*]]))

(prn *file*) ;; "/private/tmp/foo.cljs"

(defn f [])
(prn (:file (meta #'f))) ;; "/private/tmp/foo.cljs"

Reagent

Nbb includes reagent.core which will be lazily loaded when required. You can use this together with ink to create a TUI application:

$ npm install ink

ink-demo.cljs:

(ns ink-demo
  (:require ["ink" :refer [render Text]]
            [reagent.core :as r]))

(defonce state (r/atom 0))

(doseq [n (range 1 11)]
  (js/setTimeout #(swap! state inc) (* n 500)))

(defn hello []
  [:> Text {:color "green"} "Hello, world! " @state])

(render (r/as-element [hello]))

Promesa

Working with callbacks and promises can become tedious. Since nbb v0.0.36 the promesa.core namespace is included with the let and do! macros. An example:

(ns prom
  (:require [promesa.core :as p]))

(defn sleep [ms]
  (js/Promise.
   (fn [resolve _]
     (js/setTimeout resolve ms))))

(defn do-stuff
  []
  (p/do!
   (println "Doing stuff which takes a while")
   (sleep 1000)
   1))

(p/let [a (do-stuff)
        b (inc a)
        c (do-stuff)
        d (+ b c)]
  (prn d))

$ nbb prom.cljs
Doing stuff which takes a while
Doing stuff which takes a while
3

Also see API docs.

Js-interop

Since nbb v0.0.75 applied-science/js-interop is available:

(ns example
  (:require [applied-science.js-interop :as j]))

(def o (j/lit {:a 1 :b 2 :c {:d 1}}))

(prn (j/select-keys o [:a :b])) ;; #js {:a 1, :b 2}
(prn (j/get-in o [:c :d])) ;; 1

Most of this library is supported in nbb, except the following:

destructuring using :syms
property access using .-x notation. In nbb, you must use keywords.

See the example of what is currently supported.

Examples

See the examples directory for small examples.

Also check out these projects built with nbb:

c64core: retrocompute aesthetics twitter bot
gallery.cljs: script to download walpapers from windowsonearth.org.
nbb-action-example: example of a Github action built with nbb.

API

See API documentation.

Migrating to shadow-cljs

See this gist on how to convert an nbb script or project to shadow-cljs.

Build

Prequisites:

babashka >= 0.4.0
Clojure CLI >= 1.10.3.933
Node.js 16.5.0 (lower version may work, but this is the one I used to build)

To build:

Clone and cd into this repo
bb release

Run bb tasks for more project-related tasks.

Download Details:
Author: borkdude
Download Link: Download The Source Code
Official Website: https://github.com/borkdude/nbb
License: EPL-1.0

#node #javascript

Jeromy Lowe

1599097440

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Willie Beier

1596728880

Tutorial: Getting Started with R and RStudio

In this tutorial we’ll learn how to begin programming with R using RStudio. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. We’ll learn the key RStudio features in order to start programming in R on our own.

If you already know how to use RStudio and want to learn some tips, tricks, and shortcuts, check out this Dataquest blog post.

1. Install R
2. Install RStudio
3. First Look at RStudio
4. The Console
5. The Global Environment
6. Install the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb26184b) Packages
7. Load the [tidyverse](https://www.dataquest.io/blog/tutorial-getting-started-with-r-and-rstudio/#tve-jump-173bb264c2b) Packages into Memory
8. Identify Loaded Packages
9. Get Help on a Package
10. Get Help on a Function
11. RStudio Projects
12. Save Your “Real” Work. Delete the Rest.
13. R Scripts
14. Run Code
15. Access Built-in Datasets
16. Style
17. Reproducible Reports with R Markdown
18. Use RStudio Cloud
19. Get Your Hands Dirty!
Additional Resources
Bonus: Cheatsheets

#data science tutorials #beginner #r tutorial #r tutorials #rstats #tutorial #tutorials

Rowland Gutkowski

1596513720

Tutorial: Loading and Cleaning Data with R and the tidyverse

1. Characteristics of Clean Data and Messy Data

What exactly is clean data? Clean data is accurate, complete, and in a format that is ready to analyze. Characteristics of clean data include data that are:

Free of duplicate rows/values
Error-free (e.g. free of misspellings)
Relevant (e.g. free of special characters)
The appropriate data type for analysis
Free of outliers (or only contain outliers have been identified/understood), and
Follows a “tidy data” structure

Common symptoms of messy data include data that contain:

Special characters (e.g. commas in numeric values)
Numeric values stored as text/character data types
Duplicate rows
Misspellings
Inaccuracies
White space
Missing data
Zeros instead of null values

2. Motivation

In this blog post, we will work with five property-sales datasets that are publicly available on the New York City Department of Finance Rolling Sales Data website. We encourage you to download the datasets and follow along! Each file contains one year of real estate sales data for one of New York City’s five boroughs. We will work with the following Microsoft Excel files:

rollingsales_bronx.xls
rollingsales_brooklyn.xls
rollingsales_manhattan.xls
rollingsales_queens.xls
rollingsales_statenisland.xls

As we work through this blog post, imagine that you are helping a friend launch their home-inspection business in New York City. You offer to help them by analyzing the data to better understand the real-estate market. But you realize that before you can analyze the data in R, you will need to diagnose and clean it first. And before you can diagnose the data, you will need to load it into R!

3. Load Data into R with readxl

Benefits of using tidyverse tools are often evident in the data-loading process. In many cases, the tidyverse package readxl will clean some data for you as Microsoft Excel data is loaded into R. If you are working with CSV data, the tidyverse readr package function read_csv() is the function to use (we’ll cover that later).

Let’s look at an example. Here’s how the Excel file for the Brooklyn borough looks:

The Brooklyn Excel file

Now let’s load the Brooklyn dataset into R from an Excel file. We’ll use the readxlpackage. We specify the function argument skip = 4 because the row that we want to use as the header (i.e. column names) is actually row 5. We can ignore the first four rows entirely and load the data into R beginning at row 5. Here’s the code:

library(readxl) # Load Excel files
brooklyn <- read_excel("rollingsales_brooklyn.xls", skip = 4)

Note we saved this dataset with the variable name brooklyn for future use.

4. View the Data with tidyr::glimpse()

The tidyverse offers a user-friendly way to view this data with the glimpse() function that is part of the tibble package. To use this package, we will need to load it for use in our current session. But rather than loading this package alone, we can load many of the tidyverse packages at one time. If you do not have the tidyverse collection of packages, install it on your machine using the following command in your R or R Studio session:

install.packages("tidyverse")

Once the package is installed, load it to memory:

library(tidyverse)

Now that tidyverse is loaded into memory, take a “glimpse” of the Brooklyn dataset:

glimpse(brooklyn)
## Observations: 20,185
## Variables: 21
## $ BOROUGH <chr> "3", "3", "3", "3", "3", "3", "…
## $ NEIGHBORHOOD <chr> "BATH BEACH", "BATH BEACH", "BA…
## $ `BUILDING CLASS CATEGORY` <chr> "01 ONE FAMILY DWELLINGS", "01 …
## $ `TAX CLASS AT PRESENT` <chr> "1", "1", "1", "1", "1", "1", "…
## $ BLOCK <dbl> 6359, 6360, 6364, 6367, 6371, 6…
## $ LOT <dbl> 70, 48, 74, 24, 19, 32, 65, 20,…
## $ `EASE-MENT` <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `BUILDING CLASS AT PRESENT` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ ADDRESS <chr> "8684 15TH AVENUE", "14 BAY 10T…
## $ `APARTMENT NUMBER` <chr> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `ZIP CODE` <dbl> 11228, 11228, 11214, 11214, 112…
## $ `RESIDENTIAL UNITS` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `COMMERCIAL UNITS` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `TOTAL UNITS` <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1…
## $ `LAND SQUARE FEET` <dbl> 1933, 2513, 2492, 1571, 2320, 3…
## $ `GROSS SQUARE FEET` <dbl> 4080, 1428, 972, 1456, 1566, 22…
## $ `YEAR BUILT` <dbl> 1930, 1930, 1950, 1935, 1930, 1…
## $ `TAX CLASS AT TIME OF SALE` <chr> "1", "1", "1", "1", "1", "1", "…
## $ `BUILDING CLASS AT TIME OF SALE` <chr> "S1", "A5", "A5", "A9", "A9", "…
## $ `SALE PRICE` <dbl> 1300000, 849000, 0, 830000, 0, …
## $ `SALE DATE` <dttm> 2020-04-28, 2020-03-18, 2019-0…

The glimpse() function provides a user-friendly way to view the column names and data types for all columns, or variables, in the data frame. With this function, we are also able to view the first few observations in the data frame. This data frame has 20,185 observations, or property sales records. And there are 21 variables, or columns.

#data science tutorials #beginner #r #r tutorial #r tutorials #rstats #tidyverse #tutorial #tutorials

I am Developer

1615346451

Laravel 8 VUE JS Example | Laravel 8 VUE JS Tutorial For Beginners

Recommended:-Laravel 8 Vue JS Live Search Example

Recommended:-Laravel 8 Vue JS CRUD App

#laravel 8 vue js #laravel vue js #laravel vue js tutorial #laravel 8 vue js tutorial

Web3.js Tutorial for Beginners - web3.eth.Iban

Iban instance

Iban contructor

Parameters

Returns

Example

toAddress

Parameters

Returns

Example

toIban

Parameters

Returns

Example

fromAddress

Parameters

Returns

Example

fromBban

Parameters

Returns

Example

createIndirect

Parameters

Returns

Example

isValid

Parameters

Returns

Example

prototype.isValid

Parameters

Returns

Example

prototype.isDirect

Parameters

Returns

Example

prototype.isIndirect

Parameters

Returns

Example

prototype.checksum

Parameters

Returns

Example

prototype.institution

Parameters

Returns

Example

prototype.client

Parameters

Returns

Example

prototype.toAddress

Parameters

Returns

Example

prototype.toString

Parameters

Returns

Example

What is GEEK

Buddha Community

NBB: Ad-hoc CLJS Scripting on Node.js

Nbb

Status

Goals and features

Requirements

How does this tool work?

Usage

Macros

Startup time

Dependencies

NPM dependencies

Classpath

Current file

Reagent

Promesa

Js-interop