Fancorico  Hunt

Fancorico Hunt

1596094529

How to Use Google Sheets with D3.js and Google Visualization

The D3.js visualization library can be used for creating beautiful graphs and visualizations using data from external sources including CSV files and JSON data.

To give you an example, this D3.js animation inside the Google Sheets associated with the COVID-19 tracker project visualizes the growth of Coronavirus cases in India over time. It uses the Google Visualization API, D3.js and the very-awesome Bar Chart Race component built by Mike Bostock, the creator of D3.js.

Google Sheets and D3.js

This guide explains how you can use data in your Google Spreadsheets to create charts with D3.js using the Visualization API. The data is fetched in real-time so if the data in your Google Sheets is updated, it is reflected in the graph as well.

#google sheets #google apps script #d3.js #javascript

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How to Use Google Sheets with D3.js and Google Visualization

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:

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

Fancorico  Hunt

Fancorico Hunt

1596094529

How to Use Google Sheets with D3.js and Google Visualization

The D3.js visualization library can be used for creating beautiful graphs and visualizations using data from external sources including CSV files and JSON data.

To give you an example, this D3.js animation inside the Google Sheets associated with the COVID-19 tracker project visualizes the growth of Coronavirus cases in India over time. It uses the Google Visualization API, D3.js and the very-awesome Bar Chart Race component built by Mike Bostock, the creator of D3.js.

Google Sheets and D3.js

This guide explains how you can use data in your Google Spreadsheets to create charts with D3.js using the Visualization API. The data is fetched in real-time so if the data in your Google Sheets is updated, it is reflected in the graph as well.

#google sheets #google apps script #d3.js #javascript

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Eva  Murphy

Eva Murphy

1625674200

Google analytics Setup with Next JS, React JS using Router Events - 14

In this video, we are going to implement Google Analytics to our Next JS application. Tracking page views of an application is very important.

Google analytics will allow us to track analytics information.

Frontend: https://github.com/amitavroy/video-reviews
API: https://github.com/amitavdevzone/video-review-api
App link: https://video-reviews.vercel.app

You can find me on:
Twitter: https://twitter.com/amitavroy7​
Discord: https://discord.gg/Em4nuvQk

#next js #js #react js #react #next #google analytics

Fannie  Zemlak

Fannie Zemlak

1597932000

D3.js Examples for Advanced Uses - Custom Visualization

In the previous article D3-Force Directed Graph Layout Optimization in Nebula Graph Studio, we have discussed the advantages that D3.js has over other open source visualization libraries in custom graph and the flexible operations on the document object model (DOM) with D3 js. Given the customizability of the D3.js, is it possible to achieve whatever I want by using it? In this article, I will show you how to take full advantage of the flexibility of D3.js to realize on-demand functions which are not supported by D3.js itself.

Building the D3-Force Directed Graph

Here I won’t elaborate on the principle of the particle physical movement module of the d3-force. You can refer to our previous post if you are interested in this topic. Instead, I will focus on the practice of visualization in this article.

Now let me show you how I developed some new functions with the help of D3.js to better analyze the graph databases. Firstly, let’s build a simple relationship network with the d3-force directed graph.

Shell

1

this.force = d3

2

        .forceSimulation()

3

        // Allocate coordinates for the vertices

4

        .nodes(data.vertexes)

5

        // Link

6

        .force('link', linkForce)

7

        // For setting the center of gravity of the system

8

        .force('center', d3.forceCenter(width / 2, height / 2))

9

        // The gravity

10

        .force('charge', d3.forceManyBody().strength(-20))

11

        // The collision force, for preventing the vertices from overlapping

12

        .force('collide',d3.forceCollide().radius(60).iterations(2));

We can get the following vertices and relationships graph with the preceding code.

vertices and relationships

The preceding figure is a screenshot of the exploration tab of the graph visualization tool, Nebula Graph Studio. There you can select a certain vertex as the starting point of exploration by finding other vertices that are associated with it. For example, in this figure, vertex 100 and vertex 200 are connected with a single directed follow relation.

The problem is, if I have a super vertex which have thousands of edges, or if I want to display multi-hop query results, then the visual graph needs to display vertices and edges in huge density. It is also difficult to locate a specific vertex. Chances are users want to analyze only part of the data in the graph instead of the whole graph. Deleting the selected data will be a great solution to this scenario. You just delete the unwanted data and keep what you want.

Deleting the Selected

Before introducing how this function is implemented, let me begin with the native APIs provided by D3.js. Yes, I mean the enter() mentioned in the previous post and exit() I didn’t cover last time. Here are some descriptions from the documentation:

_When binding data, it’s likely that the array has more (or less) elements than the DOM elements. Fortunately D3 can help in adding and removing DOM elements using the _.enter_ and __.exit_. _If the array is longer than the selection there’s a shortfall of DOM elements and we need to add elements with __enter_. _If the array is shorter than the selection there’s a surplus of DOM elements and we need to remove elements with __exit_There are three cases in data binding:

• A shortfall of DOM elements

• A surplus of DOM elements

• The array is equal with the DOM elements

According to the documentation, it seems simple to implement the “deleting the selected” function. I was so optimistic that I thought simply operating on the data level was enough. Thus I deleted some vertices directly from the vertices data, then removed the extra element with the d3.select(this.nodeRef).exit().remove() API. Now let’s check the result of this operation:

Operation results

The targeted vertices are deleted as expected. However, other vertices are messed up because the colors and properties of the vertices are inconsistent with the current DOM vertices. Why? I checked the documentation more carefully and was inspired by an idea. Why not print the exit().remove() vertices out to see which vertices are removed?

Sure enough my guess has been confirmed. It’s the length change of the listening element that triggers the enter() and exit() function. That is to say, if two elements are taken out, the exit() will be triggered. But it won’t process the data you want to delete. Instead, it processes the last two vertices of the current data. In another word, enter() and exit() are triggered by the data length. However, take exit() as example, when D3.js detects any data length changes, say N, it will cut all the elements between the last N and the last element. On the contrary, enter() will add N pieces of data after the last element in the array.

Therefore, although the vertices are deleted from the previously returned data (apparently they are not the last element in the current array), the d3.select(this.nodeRef).exit() will locate the last element in the existing graph. This is totally a mess. So how to deal with this issue?

The D3.js recommends that you solve this problem with the officially provided merge function. But in our case, since we know the IDs of the vertices to be deleted, we operated directly on the DOM.

Here’s my simple yet effective solution. Since the exit() API can’t meet our requirements, I will process the vertices to be deleted separately. First I need to locate the DOM where the deleting operation is actually performed. To achieve this, I need to bind an ID to each vertex when rendering. Then I traverse. Find the corresponding DOM of the deleted vertices based on their ID. Following is my code:

#data visualization #d3.js #graph visualization #nebula graph