Synchrony.jl: Analysis Of Synchronous Signals

Analysis of synchronous signals

This package implements efficient multitaper and continuous wavelet transforms, along with the following transform statistics most of which operate on pairs of signals:

  • Power spectral density (PowerSpectrum)
  • Power spectral density variance (PowerSpectrumVariance)
  • Cross spectrum (CrossSpectrum)
  • Coherence (Coherence for the absolute value, Coherency for the complex value)
  • Phase locking value, a.k.a. the mean resultant vector length or R̄ (PLV)
  • Pairwise phase consistency, a.k.a. the unbiased estimator of R̄^2 (PPC)
  • Phase lag index (PLI)
  • Unbiased squared phase lang index (PLI2Unbiased)
  • Weighted phase lag index (WPLI)
  • Debiased squared weighted phase lag index (WPLI2Debiased)
  • Jammalamadaka circular correlation coefficient (JCircularCorrelation)
  • Jupp-Mardia squared circular correlation coefficient (JMCircularCorrelation)
  • Hurtado et al. modulation index (phase-amplitude coupling) (HurtadoModulationIndex)

Additionally, the following point-field measures are implemented:

  • Point-field coherence (pfcoherence)
  • Point-field PLV (pfplv)
  • Point-field PPC, variants 0, 1, and 2 (pfppc0, pfppc1, pfppc2)

And the following point-point measures:

  • Point-point cross correlation (pfxcorr)

All measures except for the point-field measures have corresponding unit tests. Documentation is forthcoming.

Download Details:

Author: Simonster
Source Code: https://github.com/simonster/Synchrony.jl 
License: View license

#julia #signal #analysis 

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Synchrony.jl: Analysis Of Synchronous Signals

Synchrony.jl: Analysis Of Synchronous Signals

Analysis of synchronous signals

This package implements efficient multitaper and continuous wavelet transforms, along with the following transform statistics most of which operate on pairs of signals:

  • Power spectral density (PowerSpectrum)
  • Power spectral density variance (PowerSpectrumVariance)
  • Cross spectrum (CrossSpectrum)
  • Coherence (Coherence for the absolute value, Coherency for the complex value)
  • Phase locking value, a.k.a. the mean resultant vector length or R̄ (PLV)
  • Pairwise phase consistency, a.k.a. the unbiased estimator of R̄^2 (PPC)
  • Phase lag index (PLI)
  • Unbiased squared phase lang index (PLI2Unbiased)
  • Weighted phase lag index (WPLI)
  • Debiased squared weighted phase lag index (WPLI2Debiased)
  • Jammalamadaka circular correlation coefficient (JCircularCorrelation)
  • Jupp-Mardia squared circular correlation coefficient (JMCircularCorrelation)
  • Hurtado et al. modulation index (phase-amplitude coupling) (HurtadoModulationIndex)

Additionally, the following point-field measures are implemented:

  • Point-field coherence (pfcoherence)
  • Point-field PLV (pfplv)
  • Point-field PPC, variants 0, 1, and 2 (pfppc0, pfppc1, pfppc2)

And the following point-point measures:

  • Point-point cross correlation (pfxcorr)

All measures except for the point-field measures have corresponding unit tests. Documentation is forthcoming.

Download Details:

Author: Simonster
Source Code: https://github.com/simonster/Synchrony.jl 
License: View license

#julia #signal #analysis 

William Hill

1618837564

Signal Clone: How to Create a Messaging App Like Signal in 2021?

It is known that Signal is a free instant messaging application that is available on Android, iOS, and desktop platforms. It offers encrypted voice & video calls and text messaging features. Even though it was founded in 2010, it is named Signal in 2014. Since 2014, it has been an open-source messaging platform.

This blog is for you if you plan to enter into the mobile app industry with the Signal clone app. It will explain why you should invest in the Signal clone app and the basic features to concentrate on while developing the Signal like app

Read on to get insightful information.

Reasons to develop a real-time messaging app like Signal in 2021

Since 2020, the Signal app has become more familiar to people and it has witnessed many downloads significantly. Last year, WhatsApp came up with a new privacy policy and many concerns were raised. 

People need an alternative app to WhatsApp that should be a highly reliable and secure instant messaging app. At that time, Elon Musk (the CEO of Tesla) wrote “Use Signal” on Twitter in January 2021. These are the major reasons many people shifted towards the Signal app. 

Notably, the Signal app’s encryption protocol hides the metadata (messages) and reduces the probability of hacking. In fact, there is no chance of data leakage. Also, it conceals the location details of the users. 

Since 2016, Signal's source code is open, so it is easy to develop & launch a similar app in the market. To get your app noticed by target users, you have to focus on a business model, smooth user experience, and marketing strategy. 

Concerning data privacy, the demand for a secure messaging app is skyrocketing in 2021. Therefore, it is the right time to launch an excellent real-time messaging app. 

Essential features to include in the Signal clone app

Signal Private Messenger is known for its data privacy. Therefore, consider including the features that are incorporated in Signal. Below are the must-have features to consider while developing a modern messaging app.

  • Push notification

This feature notifies the user about important information like incoming messages, received files, missed voice/video calls. Thereby, it helps to improve user engagement. 

  • Encryption

End-to-end encryption is a vital feature for a real-time instant messaging application. This ensures that the messages (metadata) are safe and secure. However, hackers find it difficult to hack data. 

  • Geolocation

The Geolocation feature enables the users to share their current location with their friends or family members when they are outside of their homes. 

  • Temporary messages

This feature will allow the users to delete the message when the receiver has seen the message. Like, setting a time limit for the messages. It is up to the users choice to enable or disable this feature.

Apart from these, you can add the voice/video call feature. Including this feature in the messaging app is a big plus. Notably, the user base of your app increases if you incorporate sticker and emoji features. The mentioned features are the basics for the messaging app. You must try some new features if you want to stand out from your app competitors.

Bottom line

We all know that the demand for Signal arises to a great extent when WhatsApp has updated its privacy policy. So, there is no wonder that users are moving to a secure and reliable real-time messaging app. 

Signal being the privacy-focused messaging app, you can consider developing a similar app and launch it in 2021 as demand for such apps is high now. 

Signal clone script is a real-time app solution that empowers entrepreneurs to launch their own app instantly. It is a 100% customizable solution with the white-labeling ability that makes it modifiable depending on the business needs. Go ahead with the Signal clone app development

#signal clone #signal clone app #develop an app like signal #signal clone app development #signal like app

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