echarts
vega
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echarts | vega | |
---|---|---|
17 | 17 | |
58,801 | 10,834 | |
0.7% | 0.5% | |
8.9 | 8.4 | |
7 days ago | 18 days ago | |
TypeScript | JavaScript | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
echarts
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A web crawler program for crawling Echarts official website examples implemented by Puppeter
import puppeteer from "puppeteer"; import fs from "node:fs"; import { storiesTpl, storiesArgs, generOptions, generOptionsWithFn, } from "./template.mjs"; const ECHARTS_BASE_URL = "https://echarts.apache.org/examples/en/index.html"; function capitalizeFirstLetter(str) { if (!str || str.length === 0) { return ""; } str = str.toLowerCase(); return str.charAt(0).toUpperCase() + str.slice(1); } (async function () { const browser = await puppeteer.launch(); const page = await browser.newPage(); // Navigate the page to a URL await page.goto(ECHARTS_BASE_URL); // Set screen size await page.setViewport({ width: 1080, height: 1024 }); // Type into search box // const examples = await page.type([".example-list-panel"]); const searchResultSelector = ".example-list-panel > div"; const results = await page.$$(searchResultSelector); for (const element of results) { // gener namespace const ele = await element.$(".chart-type-head"); const title = await ele.evaluate((el) => el.textContent); let namespace = title.split(" ").filter(Boolean); namespace = namespace.slice(0, namespace.length - 1); namespace = namespace .map((item) => item.replace("\n", "").replace("/", "")) .filter(Boolean) .join(""); console.log(`${namespace} start`); const instances = await element.$$(".row .example-list-item"); const components = []; for (const instance of instances) { // title const titleElement = await instance.$(".example-title"); const subTitle = await titleElement.evaluate((el) => el.textContent); const titles = subTitle .split(" ") .filter(Boolean) .map((item) => item .replace(/\+/g, "") .replace(/\(/g, "") .replace(/\)/g, "") .replace(/-/g, "") ); const title = titles.map((item) => capitalizeFirstLetter(item)).join(""); const link = await instance.$(".example-link"); const newPagePromise = new Promise((resolve) => { browser.on("targetcreated", async (target) => { if (target.type() === "page") { const targetPage = await target.page(); const url = await targetPage.url(); if (url.includes("editor")) { resolve(targetPage); } } }); }); await link.click(); const newPage = await newPagePromise; await newPage.setViewport({ width: 40000, height: 20000 }); await newPage.waitForSelector(".ace_text-layer"); await new Promise((resolve) => { setTimeout(() => { resolve(); }, 3000); }); let content = await newPage.evaluate( () => document.querySelector(".ace_text-layer").innerText ); content = content .replace(/\[\]/g, "[] as any") .replace(//g, "") .replace(/var/g, "let"); let options; if (content.includes("myChart")) { options = generOptionsWithFn({ options: content }); } else { options = generOptions({ options: content }); } components.push({ options, title }); await newPage.close(); } const args = components .filter(({ options }) => { if (options.includes("$")) return false; return true; }) .map(({ options, title }) => storiesArgs({ options: options, name: title }) ) .join("\r\n"); const scripts = storiesTpl({ namespace: `Charts/${namespace}`, components: args, }); fs.writeFileSync(`./bots/assests/${namespace}.stories.ts`, scripts); console.log(`${namespace} end`); } })();
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Show HN: Paisa – Open-Source Personal Finance Manager
I want to know where my money goes. I like to look at stacked-area (or column) charts of the categories of spending. To make this work I have some software I made ~20 years ago that does double-entry book-keeping. At the end of the month, I import statements from financial service providers (eg: Wells Fargo, Chase, PayPal, Stripe, etc). Lots of stuff is repeat purchases (eg: Shell Gas) and my software automatically categorises. Some transactions I have to categorise manually. Each category / vendor becomes an expense-account and my banks and CCs exist as assets and liabilities.
Once the import and reconciliation is done I pull up a my column chart that shows where the money went -- and can compare over time -- see a full year of movement. I've been through various charting libraries with it and most recently moved to ECharts[0] -- so I'm planning to expand with Treemap and Sankey style visuals.
The import process, which I do monthly takes maybe an hour. I'm importing from like 5 bank accounts, 3 payment processors, 4 CC providers. The part that takes the longest is signing into their slow sites, navigating past pop-up/interstitial, getting to their download page and waiting for it to download. Loads of these sites (WF, Chase) have been "modernised" and have some real bullshit UI/UX going on -- lags, no keyboard, elements jump around, forms can't remember state, ctrl+click won't open in a new page cause that damned link isn't actually a link but some nested monster of DIVs with 19 event listeners on each one -- and somehow still all wrong.
I think the most-best feature would be to have some tool automatically get all my transactions from all these providers into one common format. Gimmee some JSON with like 10 commonly-named fields for the normal stuff and then 52 other BS fields that each provider likes to add (see a PayPal CSV for example). Does that exist and I just don't know?
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Personal Sträva Activity Statistics
Coded mainly in Perl and Gnuplot, recently extended by Python Pandas and JavaScript Tabulator and ECharts
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Build complex SPAs quickly with vue-element-admin
Dashboards have a lot of charts for different forms and data. This is another common requirement. This template recommends Apache ECharts, a powerful, easy-to-use, and flexible JavaScript visualization library.
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Using Apache ECharts in React Native - wrn-echarts
We have developed an open source graphics library for react native APP, which is based on Apache ECharts and uses RNSVG or RNSkia for rendering in a way that is almost identical to using it in the web, and can satisfy most graphics situations. The project source code is available at https://github.com/wuba/wrn-echarts .
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Best practice for UI design in scientific app
apache-echarts for charting system (it has 3d chart anyway)
- [OC] The crude birth rate in European Union from 1960 to 2020
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Use types (which import other types that reference the DOM) inside a web-worker!
How are you importing the definition? Assuming you are using "apache/echarts" and not some other lib named "echarts", you should be able to import DatasetModel directly and let tree shaking trim out what you're not using.
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Pulling and visualizing data from a database client side
ECharts -- open source js lib for enterprise-grade charts
- [OC] U.S. Inflation Reach High in 20 Years
vega
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Show HN: Minard – Generate beautiful charts with natural language
Hi HN – Excited to share a beta for Minard, a new data visualization toolkit we've been working on that lets you generate publication-quality charts with simple natural language (throw away your matplotlib docs and rejoice!).
Upload or import CSVs, Excel, and JSON, give it a spin, and please let us know what you think! (Long format data works best for now)
For those curious, the stack is a simple Django app with HTMX/Alpine and all of the charts are specified and rendered as Vega (https://vega.github.io/vega/). Lots of LLM function calling under the hood as well.
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Plotting XGBoost Models with Elixir
I recently added support for plotting XGBoost models using Vega (https://vega.github.io/vega/) into the XGBoost Elixir API (https://github.com/acalejos/exgboost).
Since EXGBoost supports loading trained models across different APIs, you can even train using the Python API and then plot using this Elixir API if you prefer.
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[OC] Most In-Demand Programming Languages from Jan-2022 to Jun-2023
The Data Source is from devjobsscanner (I am basically the owner, so I have the data) an the tool used to make the chart is Vega
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If you had to pick a library from another language (Rust, JS, etc.) that isn’t currently available in Python and have it instantly converted into Python for you to use, what would it be?
It’s based on Vega https://vega.github.io/vega/ which means it’s an already matured backend. Vega-lite is the Javascript package and Altair is the Python.
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Does anyone know how to get Visio experience while in between jobs?
Site:: https://vega.github.io/vega/
- Ask HN: What do you use for basic data analysis, visuals, and graphing?
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Launched my ADHD productivity app on product hunt - would love your support
Eh, I have no reason to, and not much interest in PHP anymore. Things like Vega seem really cool.
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[OC] Salaries Distribution by Programming Languages in 2022
This chart is make with Vega (and also all the charts from the article). Data source is from devjobsscanner and contains about 10 Million dev jobs offers, which only a small subset contain salary information thought.
- Angular + Line Chart
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Simple plotting/graphing crate suggestions
Not that I'm working on it, but I'd love to see a vega stack in rust.
What are some alternatives?
Chart.js - Simple HTML5 Charts using the <canvas> tag
d3 - Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Highcharts JS - Highcharts JS, the JavaScript charting framework
Frappe Gantt - Open Source Javascript Gantt
c3 - :bar_chart: A D3-based reusable chart library
apexcharts.js - 📊 Interactive JavaScript Charts built on SVG
Altair - Declarative statistical visualization library for Python
jquery.sparkline - A plugin for the jQuery javascript library to generate small sparkline charts directly in the browser
ggplot2 - An implementation of the Grammar of Graphics in R