Fast & Non-regex Based Lexical Analysis for Deno

Lightweight, fast and non-regex based tokeniser for Deno.

features

  • Fast compared to regex based lexers.
  • Clever ident lookup.
  • Works on Deno, Node and the browser.

usage

let lexer = new Lexer("1 + 1 - 1");
lexer.use("1", (ch) => { 
  return { type: "one", value: ch };
});

lexer.use(["+", "-"], (ch) => { 
  return { type: "operator", value: ch };
});

const tokens = lexer.lex();

benchmarks

Benchmark code can be found in benchmarks/benchmark.ts. Below results are for a math expression lexer implemented in lexy and regexp. Input length: 400001 characters.

iters lexy (ms) regexp (ms)
1 0.461942 196.247439
10 0.587031 1526.35242
100 0.61392 14445.481962
1000 1.158556 156252.4421

Less is better

why

Lexical analysis is a common step towards building great parsers & analysers. Many libraries use regex for building lexers although that can impact performance in the long run.

In real programming languages, lexers are generally hand-written instead of regex. One of the main reason is performance.

This article compares hand-written lexers to regex based.

Download Details:

Author: littledivy

Source Code: https://github.com/littledivy/lexy

#deno #nodejs #node #javascript

What is GEEK

Buddha Community

Fast & Non-regex Based Lexical Analysis for Deno

Fast & Non-regex Based Lexical Analysis for Deno

Lightweight, fast and non-regex based tokeniser for Deno.

features

  • Fast compared to regex based lexers.
  • Clever ident lookup.
  • Works on Deno, Node and the browser.

usage

let lexer = new Lexer("1 + 1 - 1");
lexer.use("1", (ch) => { 
  return { type: "one", value: ch };
});

lexer.use(["+", "-"], (ch) => { 
  return { type: "operator", value: ch };
});

const tokens = lexer.lex();

benchmarks

Benchmark code can be found in benchmarks/benchmark.ts. Below results are for a math expression lexer implemented in lexy and regexp. Input length: 400001 characters.

iters lexy (ms) regexp (ms)
1 0.461942 196.247439
10 0.587031 1526.35242
100 0.61392 14445.481962
1000 1.158556 156252.4421

Less is better

why

Lexical analysis is a common step towards building great parsers & analysers. Many libraries use regex for building lexers although that can impact performance in the long run.

In real programming languages, lexers are generally hand-written instead of regex. One of the main reason is performance.

This article compares hand-written lexers to regex based.

Download Details:

Author: littledivy

Source Code: https://github.com/littledivy/lexy

#deno #nodejs #node #javascript

Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

What is Base Protocol (BASE) | What is BASE token

Base Protocol (BASE) is a token whose price is pegged to the total market cap of all cryptocurrencies at a ratio of 1:1 trillion. BASE allows traders to speculate on the entire crypto industry with one token. The Base Protocol is built on the Ethereum blockchain, integrates a (Chainlink) oracle, and is launching on ((Uniswap)

As cryptocurrency enthusiasts, we’re sometimes divided on which digital assets to buy — bullish on certain projects and bearish on others.

But we all agree on one thing, which is that the overall cryptocurrency industry will achieve long-term growth and future adoption.

The Base Protocol makes it possible to invest with this consensus. BASE allows traders to speculate on the entire industry with one token.

Image for post

The Base Protocol is the world’s first and only tokenized cryptocurrency market tracker. By holding BASE tokens, users can get exposure to the performance of the entire cryptocurrency market. Unlike the index trackers currently operating in the traditional markets, there is no entry or exit fee or brokerage charges.

Index funds have consistently outperformed actively managed mutual funds. Until the launch of BASE, there was no real cryptocurrency market tracker that tracked the performance of the entire digital asset market. BASE will be useful for institutional investors and traders to diversify and hedge their crypto portfolios. BASE will also help new and existing retail investors to take out the guesswork and get exposed to the growth of all current and future digital assets entering the market.

The BASE token’s underlying protocol creates several additional use cases in DeFi, trading, venture capital, hedge funds and many other business sectors.

The Base Protocol mission is simple — to make it easy for everyone to benefit from the performance of the entire cryptocurrency market in a secure, decentralized and future-proof way.

Why BASE?

It’s no doubt that a crypto industry ETF would be a valuable product for investors. But it is very challenging to create such a product through traditional means, as it would be almost impossible to manage portfolio ownership of 5,000+ assets. How would the portfolio manager weigh ownership of each asset as market cap dominance changes? How would they account for newly entering/exiting assets? Who would take on all the associated transaction and custodial fees? There are also various legal limitations that restrict the formation of such an instrument in many countries — and even if it could be formed, it would be a highly centralized product.

By simply pegging price to the total market capitalization of all cryptocurrencies, the Base Protocol cuts through all of these problems. BASE gives holders the same function as a traditional industry ETF without all of the centralized challenges that make such an ETF impossible.

BASE will offer new value for investors in the cryptocurrency ecosystem through an elegantly simple protocol — so valuable and so simple that you might be asking yourself:

How has this not been done before?

The answer is that it wasn’t possible until recently. This project couldn’t be achieved without a robust decentralized blockchain, proven oracle integrations, and new developments in the DeFi space. We founded the Base Protocol to build on these innovations and create BASE; one tokenized asset that represents speculation on all cryptocurrencies.

Vision

We’ve seen that there are many individuals who want to invest in cryptocurrencies, but don’t necessarily understand how they work. While the overview for each different crypto asset can be difficult to understand for a new user, the pitch for BASE is simple: it’s the way to invest in all of those crypto assets simultaneously. In this way, the Base Protocol can become an instrumental force in driving new adoption in the blockchain space.

We’ve also noticed that institutional investors have been introducing cryptocurrency investments to their portfolios. These institutions typically invest at a high level with great diversification covering multiple industries. Their cryptocurrency holdings are usually composed of just Bitcoin, or some handful mix of “blue chip” digital assets. By holding BASE, these institutions will gain exposure to the entire cryptocurrency industry — an objectively more diversified alternative.

In the same way that Bitcoin is the household name of cryptocurrencies, the Base Protocol aims to become the household name for general cryptocurrency investing. BASE’s vision is to become the primary channel of investment for new/existing cryptocurrency traders and institutional investors.

Would you like to earn token right now! ☞ CLICK HERE

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#blockchain #bitcoin #crypto #base protocol #base