Juana  Torphy

Juana Torphy


Learn Git and GitHub in 20 Minutes

Learn Git and GitHub in 20 Minutes

Learn version control and how to use git and GitHub through this follow-along tutorial on creating a git repository and pushing it to GitHUB, a web-based repository that supports version control.

#git #github #webdevelopment #programming #versioncontrol

What is GEEK

Buddha Community

Learn Git and GitHub in 20 Minutes
Vern  Greenholt

Vern Greenholt


Learn terminal GIT commands in 3 minutes.

In this post we’ll learn about Git — What is git and its terminal command in 3 minutes. So let’s start with What is Git.

What is GIT?

In 2005, Linus Torwalds created a GIT.

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

Git is easy to learn and has a tiny footprint with lightning fast performance. It outclasses SCM tools like Subversion, CVS, Perforce, and ClearCase with features like cheap local branching, convenient staging areas, and multiple workflows.



$ brew install git


$ yum -y install git

Find the version of Git

$ git --version

Create repository from scratch

  1. go to folder of the project (which you want to make repository)
  2. run $ git init


  1. run $ git init [project-name]

It will create new local repository with specific name.

Clone existing git repository

  1. goto folder where you want repository folder.
  2. run $ git clone [url]

Make Changes

Check the status of GIT repository

$ git status

This command list all new or modified files

Add files to GIT repository

$ git add [file-name]


If you want to add more than one file

$ git add .

Commit file (Permanently Store in GIT)

$ git commit -m "Message"

Messages are good way to comment on changes.

#github #begineers #programming #git #learning #deep learning

Monty  Boehm

Monty Boehm


Git vs Github: Difference Between Git and Github

IT is in no way different from any other sector when it comes to naming. You would see some systems being named based on their origin while others are named keeping in mind their features or functionality. Then there are some whose names have nothing in common with their origin, features, or anything else related to them.

It is these inconsistencies in naming conventions that make people confused about what a system is all about, what it does, and what benefits it offers. For instance, there are a lot of people out there who still get puzzled when asked about Git and GitHub and whether or not there is a difference between the two.

The similarity in their names has nothing to do with what they really are. They are two altogether different things. But at the same time, you can say that they still have a thing or two in common. Before we talk about Git and GitHub, let us first shed some light on  version control systems (VCSs) and why are they so important.

What is version control?

In simple terms, version control is nothing but a system that keeps track of the changes made to source code or files. With a version control system, you can look back at the changes made to a particular file, either by you or another person, by accessing the version control database. This system gives you the ability to compare different versions of a file, thus allowing you to stay informed about the changes that have happened over a period of time.

The version control system can be referred to as a database that stores snapshots of different files in a project. These snapshots are taken every time a file is modified. It maintains all the records of the different versions of a file. In addition to comparing different versions of a file, VCSs also allows you to switch between them. VCSs can either be distributed or centralized. Let us see how these two types differ.

Centralized version control systems use a single, centralized server to store all the different versions of a file. Users can access these files by gaining access to this centralized server. Now, there is a disadvantage associated with this type of VCS. If the central server fails to function due to any reason, the entire history stored on its will be gone and no one will be able to recover any version/versions of the lost files.

Distributed version control systems have an edge over their centralized counterparts. These VCSs store file versions in two locations – the centralized server and your local machine. So, the disadvantage that we discussed centralized systems doesn’t exist in distributed systems.

Even if the server undergoes failure, you can retrieve all the different versions of your files from your local machine. Suppose you have a file, which is called VersionControl1. Now you made several changes to this file and saved the changes on each occasion. All the changes that you made to this file will be stored in the VCS, which will have all those versions of this file when you made changes to it.

#full stack development #git vs github #git #github

RobustStats.jl: A Collection Of Robust Statistical Tests in Julia


This package contains a variety of functions from the field robust statistical methods. Many are estimators of location or dispersion; others estimate the standard error or the confidence intervals for the location or dispresion estimators, generally computed by the bootstrap method.

Many functions in this package are based on the R package WRS (an R-Forge repository) by Rand Wilcox. Others were contributed by users as needed. References to the statistics literature can be found below.

This package requires Compat, Rmath, Dataframes, and Distributions. They can be installed automatically, or by invoking Pkg.add("packagename").


Location estimators:

  • tmean(x, tr=0.2) - Trimmed mean: mean of data with the lowest and highest fraction tr of values omitted.
  • winmean(x, tr=0.2)- Winsorized mean: mean of data with the lowest and highest fraction tr of values squashed to the 20%ile or 80%ile value, respectively.
  • tauloc(x) - Tau measure of location by Yohai and Zamar.
  • onestep(x) - One-step M-estimator of location using Huber's ψ
  • mom(x) - Modified one-step M-estimator of location (MOM)
  • bisquareWM(x) - Mean with weights given by the bisquare rho function.
  • huberWM(x) - Mean with weights given by Huber's rho function.
  • trimean(x) - Tukey's trimean, the average of the median and the midhinge.

Dispersion estimators:

  • winvar(x, tr=0.2) - Winsorized variance.
  • wincov(x, y, tr=0.2) - Winsorized covariance.
  • pbvar(x) - Percentage bend midvariance.
  • bivar(x) - Biweight midvariance.
  • tauvar(x) - Tau measure of scale by Yohai and Zamar.
  • iqrn(x) - Normalized inter-quartile range (normalized to equal σ for Gaussians).
  • shorthrange(x) - Length of the shortest closed interval containing at least half the data.
  • scaleQ(x) - Normalized Rousseeuw & Croux Q statistic, from the 25%ile of all 2-point distances.
  • scaleS(x) - Normalized Rousseeuw & Croux S statistic, from the median of the median of all 2-point distances.
  • shorthrange!(x), scaleQ!(x), and scaleS!(x) are non-copying (that is, x-modifying) forms of the above.

Confidence interval or standard error estimates:

  • trimse(x) - Standard error of the trimmed mean.
  • trimci(x) - Confidence interval for the trimmed mean.
  • msmedse(x) - Standard error of the median.
  • binomci(s,n) - Binomial confidence interval (Pratt's method).
  • acbinomci(s,n) - Binomial confidence interval (Agresti-Coull method).
  • sint(x) - Confidence interval for the median (with optional p-value).
  • momci(x) - Confidence interval of the modified one-step M-estimator of location (MOM).
  • trimpb(x) - Confidence interval for trimmed mean.
  • pcorb(x) - Confidence intervale for Pearson's correlation coefficient.
  • yuend - Compare the trimmed means of two dependent random variables.
  • bootstrapci(x, est=f) - Compute a confidence interval for estimator f(x) by bootstrap methods.
  • bootstrapse(x, est=f) - Compute a standard error of estimator f(x) by bootstrap methods.

Utility functions:

  • winval(x, tr=0.2) - Return a Winsorized copy of the data.
  • idealf(x) - Ideal fourths, interpolated 1st and 3rd quartiles.
  • outbox(x) - Outlier detection.
  • hpsi(x) - Huber's ψ function.
  • contam_randn - Contaminated normal distribution (generates random deviates).
  • _weightedhighmedian(x) - Weighted median (breaks ties by rounding up). Used in scaleQ.


For location, consider the bisquareWM with k=3.9σ, if you can make any reasonable guess as to the "Gaussian-like width" σ (see dispersion estimators for this). If not, trimean is a good second choice, though less efficient. Also, though the author personally has no experience with them, tauloc, onestep, and mom might be useful.

For dispersion, the scaleS is a good general choice, though scaleQ is very efficient for nearly Gaussian data. The MAD is the most robust though less efficient. If scaleS doesn't work, then shorthrange is a good second choice.

The first reference on scaleQ and scaleS (below) is a lengthy discussion of the tradeoffs among scaleQ, scaleS, shortest half, and median absolute deviation (MAD, see BaseStats.mad for Julia implementation). All four have the virtue of having the maximum possible breakdown point, 50%. This means that replacing up to 50% of the data with unbounded bad values leaves the statistic still bounded. The efficiency of Q is better than S and S is better than MAD (for Gaussian distributions), and the influence of a single bad point and the bias due to a fraction of bad points is only slightly larger on Q or S than on MAD. Unlike MAD, the other three do not implicitly assume a symmetric distribution.

To choose between Q and S, the authors note that Q has higher statistical efficiency, but S is typically twice as fast to compute and has lower gross-error sensitivity. An interesting advantage of Q over the others is that its influence function is continuous. For a rough idea about the efficiency, the large-N limit of the standardized variance of each quantity is 2.722 for MAD, 1.714 for S, and 1.216 for Q, relative to 1.000 for the standard deviation (given Gaussian data). The paper gives the ratios for Cauchy and exponential distributions, too; the efficiency advantages of Q are less for Cauchy than for the other distributions.


#Set up a sample dataset:
x=[1.672064, 0.7876588, 0.317322, 0.9721646, 0.4004206, 1.665123, 3.059971, 0.09459603, 1.27424, 3.522148,
   0.8211308, 1.328767, 2.825956, 0.1102891, 0.06314285, 2.59152, 8.624108, 0.6516885, 5.770285, 0.5154299]

julia> mean(x)     #the mean of this dataset

tmean: trimmed mean

julia> tmean(x)            #20% trimming by default

julia> tmean(x, tr=0)      #no trimming; the same as the output of mean()

julia> tmean(x, tr=0.3)    #30% trimming

julia> tmean(x, tr=0.5)    #50% trimming, which gives you the median of the dataset.

winval: winsorize data

That is, return a copy of the input array, with the extreme low or high values replaced by the lowest or highest non-extreme value, repectively. The fraction considered extreme can be between 0 and 0.5, with 0.2 as the default.

julia> winval(x)           #20% winsorization; can be changed via the named argument `tr`.
20-element Any Array:

winmean, winvar, wincov: winsorized mean, variance, and covariance

julia> winmean(x)          #20% winsorization; can be changed via the named argument `tr`.
julia> winvar(x)
julia> wincov(x, x)
julia> wincov(x, x.^2)

trimse: estimated standard error of the trimmed mean

julia> trimse(x)           #20% winsorization; can be changed via the named argument `tr`.

trimci: (1-α) confidence interval for the trimmed mean

Can be used for paired groups if x consists of the difference scores of two paired groups.

julia> trimci(x)                 #20% winsorization; can be changed via the named argument `tr`.
(1-α) confidence interval for the trimmed mean

Degrees of freedom:   11
Estimate:             1.292180
Statistic:            3.469611
Confidence interval:  0.472472       2.111889
p value:              0.005244

idealf: the ideal fourths:

Returns (q1,q3), the 1st and 3rd quartiles. These will be a weighted sum of the values that bracket the exact quartiles, analogous to how we handle the median of an even-length array.

julia> idealf(x)

pbvar: percentage bend midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> pbvar(x)

bivar: biweight midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> bivar(x)

tauloc, tauvar: tau measure of location and scale

Robust estimators of location and scale, with breakdown points of 50%.

See Yohai and Zamar JASA, vol 83 (1988), pp 406-413 and Maronna and Zamar Technometrics, vol 44 (2002), pp. 307-317.

julia> tauloc(x)       #the named argument `cval` is 4.5 by default.
julia> tauvar(x)

outbox: outlier detection

Use a modified boxplot rule based on the ideal fourths; when the named argument mbox is set to true, a modification of the boxplot rule suggested by Carling (2000) is used.

julia> outbox(x)
Outlier detection method using
the ideal-fourths based boxplot rule

Outlier ID:         17
Outlier value:      8.62411
Number of outliers: 1
Non-outlier ID:     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20

msmedse: Standard error of the median

Return the standard error of the median, computed through the method recommended by McKean and Schrader (1984).

julia> msmedse(x)

binomci(), acbinomci(): Binomial confidence interval

Compute the (1-α) confidence interval for p, the binomial probability of success, given s successes in n trials. Instead of s and n, can use a vector x whose values are all 0 and 1, recording failure/success one trial at a time. Returns an object.

binomci uses Pratt's method; acbinomci uses a generalization of the Agresti-Coull method that was studied by Brown, Cai, & DasGupta.

julia> binomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0274   0.5562
Sample size          10

julia> trials=[1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0]
julia> binomci(trials, alpha=0.01)    #trial results are provided in array form consisting of 1's and 0's.
 p_hat:               0.5000
 confidence interval: 0.1768   0.8495
 Sample size          12

julia> acbinomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0459   0.5206
Sample size          10


Compute the confidence interval for the median. Optionally, uses the Hettmansperger-Sheather interpolation method to also estimate a p-value.

julia> sint(x)
Confidence interval for the median

 Confidence interval:  0.547483       2.375232

julia> sint(x, 0.6)
Confidence interval for the median with p-val

 Confidence interval:  0.547483       2.375232
 p value:              0.071861


Compute Huber's ψ. The default bending constant is 1.28.

julia> hpsi(x)
20-element Array{Float64,1}:


Compute one-step M-estimator of location using Huber's ψ. The default bending constant is 1.28.

julia> onestep(x)

bootstrapci, bootstrapse

Compute a bootstrap, (1-α) confidence interval (bootstrapci) or a standard error (bootstrapse) for the measure of location corresponding to the argument est. By default, the median is used. Default α=0.05.

julia> ci = bootstrapci(x, est=onestep, nullvalue=0.6)
 Estimate:             1.305811
 Confidence interval:  0.687723       2.259071
 p value:              0.026000

julia> se = bootstrapse(x, est=onestep)

mom and mom!

Returns a modified one-step M-estimator of location (MOM), which is the unweighted mean of all values not more than (bend times the mad(x)) away from the data median.

julia> mom(x)


Compute the bootstrap (1-α) confidence interval for the MOM-estimator of location based on Huber's ψ. Default α=0.05.

julia> momci(x, seed=2, nboot=2000, nullvalue=0.6)
Estimate:             1.259646
Confidence interval:  0.504223       2.120979
p value:              0.131000


Create contaminated normal distributions. Most values will by from a N(0,1) zero-mean unit-variance normal distribution. A fraction epsilon of all values will have k times the standard devation of the others. Default: epsilon=0.1 and k=10.

julia> srand(1);
julia> std(contam_randn(2000))


Compute a (1-α) confidence interval for a trimmed mean by bootstrap methods.

julia> trimpb(x, nullvalue=0.75)
 Estimate:             1.292180
 Confidence interval:  0.690539       2.196381
 p value:              0.086000


Compute a .95 confidence interval for Pearson's correlation coefficient. This function uses an adjusted percentile bootstrap method that gives good results when the error term is heteroscedastic.

julia> pcorb(x, x.^5)
 Estimate:             0.802639
 Confidence interval:  0.683700       0.963478


Compare the trimmed means of two dependent random variables using the data in x and y. The default amount of trimming is 20%.

julia> srand(3)
julia> y2 = randn(20)+3;
julia> yuend(x, y2)

Comparing the trimmed means of two dependent variables.

Sample size:          20
Degrees of freedom:   11
Estimate:            -1.547776
Standard error:       0.460304
Statistic:           -3.362507
Confidence interval: -2.560898      -0.534653
p value:              0.006336

Unmaintained functions

See UNMAINTAINED.md for information about functions that the maintainers have not yet understood but also not yet deleted entirely.


Percentage bend and related estimators come from L.H. Shoemaker and T.P. Hettmansperger "Robust estimates and tests for the one- and two-sample scale models" in Biometrika Vol 69 (1982) pp. 47-53.

Tau measures of location and scale are from V.J. Yohai and R.H. Zamar "High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale" in J. American Statistical Assoc. vol 83 (1988) pp. 406-413.

The outbox(..., mbox=true) modification was suggested in K. Carling, "Resistant outlier rules and the non-Gaussian case" in Computational Statistics and Data Analysis vol 33 (2000), pp. 249-258. doi:10.1016/S0167-9473(99)00057-2

The estimate of the standard error of the median, msmedse(x), is computed by the method of J.W. McKean and R.M. Schrader, "A comparison of methods for studentizing the sample median" in Communications in Statistics: Simulation and Computation vol 13 (1984) pp. 751-773. doi:10.1080/03610918408812413

For Pratt's method of computing binomial confidence intervals, see J.W. Pratt (1968) "A normal approximation for binomial, F, Beta, and other common, related tail probabilities, II" J. American Statistical Assoc., vol 63, pp. 1457- 1483, doi:10.1080/01621459.1968.10480939. Also R.G. Newcombe "Confidence Intervals for a binomial proportion" Stat. in Medicine vol 13 (1994) pp 1283-1285, doi:10.1002/sim.4780131209.

For the Agresti-Coull method of computing binomial confidence intervals, see L.D. Brown, T.T. Cai, & A. DasGupta "Confidence Intervals for a Binomial Proportion and Asymptotic Expansions" in Annals of Statistics, vol 30 (2002), pp. 160-201.

Shortest Half-range comes from P.J. Rousseeuw and A.M. Leroy, "A Robust Scale Estimator Based on the Shortest Half" in Statistica Neerlandica Vol 42 (1988), pp. 103-116. doi:10.1111/j.1467-9574.1988.tb01224.x . See also R.D. Martin and R. H. Zamar, "Bias-Robust Estimation of Scale" in Annals of Statistics Vol 21 (1993) pp. 991-1017. doi:10.1214/aoe/1176349161

Scale-Q and Scale-S statistics are described in P.J. Rousseeuw and C. Croux "Alternatives to the Median Absolute Deviation" in J. American Statistical Assoc. Vo 88 (1993) pp 1273-1283. The time-efficient algorithms for computing them appear in C. Croux and P.J. Rousseeuw, "Time-Efficient Algorithms for Two Highly Robust Estimators of Scale" in Computational Statistics, Vol I (1992), Y. Dodge and J. Whittaker editors, Heidelberg, Physica-Verlag, pp 411-428. If link fails, see ftp://ftp.win.ua.ac.be/pub/preprints/92/Timeff92.pdf

Download Details:

Author: Mrxiaohe
Source Code: https://github.com/mrxiaohe/RobustStats.jl 
License: MIT license

#julia #statistical #tests 

Madyson  Reilly

Madyson Reilly


Best Practices for Using Git

Git has become ubiquitous as the preferred version control system (VCS) used by developers. Using Git adds immense value especially for engineering teams where several developers work together since it becomes critical to have a system of integrating everyone’s code reliably.

But with every powerful tool, especially one that involves collaboration with others, it is better to establish conventions to follow lest we shoot ourselves in the foot.

At DeepSource, we’ve put together some guiding principles for our own team that make working with a VCS like Git easier. Here are 5 simple rules you can follow:

1. Make Clean, Single-Purpose Commits

Oftentimes programmers working on something get sidetracked into doing too many things when working on one particular thing — like when you are trying to fix one particular bug and you spot another one, and you can’t resist the urge to fix that as well. And another one. Soon, it snowballs and you end up with so many changes all going together in one commit.

This is problematic, and it is better to keep commits as small and focused as possible for many reasons, including:

  • It makes it easier for other people in the team to look at your change, making code reviews more efficient.
  • If the commit has to be rolled back completely, it’s far easier to do so.
  • It’s straightforward to track these changes with your ticketing system.

Additionally, it helps you mentally parse changes you’ve made using git log.

#open source #git #git basics #git tools #git best practices #git tutorials #git commit

Loma  Baumbach

Loma Baumbach


Mirroring Git Changes From One Server to Another Server


Hello all, nowadays most of the development teams using GIT version control, some of you may have a requirement of mirroring your team’s git changes from one server to another Git server. This article will help you to achieve the Git mirroring between one server to another server.

Business Case

I got one assignment wherein there will be 2 Git Servers, development will happen in one Git server and the changes should be synchronized to another Git server at regular intervals. But in my case, the complexity is both the servers are in different restricted network. So I have done the small experiment and it worked. And I am sharing the steps to you all in this article.

The Experiment Performed Using Below 2 GIT Servers

Main GIT Server: Let’s take our main git server is located in our office and can be accessed only in-office network.

**Mirror GIT Server: **The mirror server is located at the vendor/client-side, which can be accessible in a normal internet connection but not with our office network. Since the office proxy will block the outside URL’s.

#devops #git #git and github #git best practices #git cloning #git server