Learn Testing with the new Flutter Sample | GSoC’20 Work Product

A quick introduction to GSoC

Google Summer of Code(GSoC) is a 3-month program sponsored by Google in which students work with an open source organization. With the help of mentors, the students complete a project during their summer break.

This year, the Dart team participated for the first time as a mentoring organization in GSoC. Five Dart project proposals were accepted by GSoC, of which two featured Flutter.

This blog shows the work I did for my GSoC project. All of the resulting source code is in GitHub repositories, and you can see how the work progressed by looking at individual pull requests (PRs).

Project details: Testing sample app

Out of my two submitted proposals, the Testing sample app was accepted. Testing is the most common thing skipped by students when learning any new technology.

#gsoc #flutter

What is GEEK

Buddha Community

Google's Flutter 1.20 stable announced with new features - Navoki

Flutter Google cross-platform UI framework has released a new version 1.20 stable.

Flutter is Google’s UI framework to make apps for Android, iOS, Web, Windows, Mac, Linux, and Fuchsia OS. Since the last 2 years, the flutter Framework has already achieved popularity among mobile developers to develop Android and iOS apps. In the last few releases, Flutter also added the support of making web applications and desktop applications.

Last month they introduced the support of the Linux desktop app that can be distributed through Canonical Snap Store(Snapcraft), this enables the developers to publish there Linux desktop app for their users and publish on Snap Store.  If you want to learn how to Publish Flutter Desktop app in Snap Store that here is the tutorial.

Flutter 1.20 Framework is built on Google’s made Dart programming language that is a cross-platform language providing native performance, new UI widgets, and other more features for the developer usage.

Here are the few key points of this release:

Performance improvements for Flutter and Dart

In this release, they have got multiple performance improvements in the Dart language itself. A new improvement is to reduce the app size in the release versions of the app. Another performance improvement is to reduce junk in the display of app animation by using the warm-up phase.

If your app is junk information during the first run then the Skia Shading Language shader provides for pre-compilation as part of your app’s build. This can speed it up by more than 2x.

Added a better support of mouse cursors for web and desktop flutter app,. Now many widgets will show cursor on top of them or you can specify the type of supported cursor you want.

Autofill for mobile text fields

Autofill was already supported in native applications now its been added to the Flutter SDK. Now prefilled information stored by your OS can be used for autofill in the application. This feature will be available soon on the flutter web.

A new widget for interaction

InteractiveViewer is a new widget design for common interactions in your app like pan, zoom drag and drop for resizing the widget. Informations on this you can check more on this API documentation where you can try this widget on the DartPad. In this release, drag-drop has more features added like you can know precisely where the drop happened and get the position.

Updated Material Slider, RangeSlider, TimePicker, and DatePicker

In this new release, there are many pre-existing widgets that were updated to match the latest material guidelines, these updates include better interaction with Slider and RangeSlider, DatePicker with support for date range and time picker with the new style.

New pubspec.yaml format

Other than these widget updates there is some update within the project also like in pubspec.yaml file format. If you are a flutter plugin publisher then your old pubspec.yaml  is no longer supported to publish a plugin as the older format does not specify for which platform plugin you are making. All existing plugin will continue to work with flutter apps but you should make a plugin update as soon as possible.

Preview of embedded Dart DevTools in Visual Studio Code

Visual Studio code flutter extension got an update in this release. You get a preview of new features where you can analyze that Dev tools in your coding workspace. Enable this feature in your vs code by _dart.previewEmbeddedDevTools_setting. Dart DevTools menu you can choose your favorite page embed on your code workspace.

Network tracking

The updated the Dev tools comes with the network page that enables network profiling. You can track the timings and other information like status and content type of your** network calls** within your app. You can also monitor gRPC traffic.

Generate type-safe platform channels for platform interop

Pigeon is a command-line tool that will generate types of safe platform channels without adding additional dependencies. With this instead of manually matching method strings on platform channel and serializing arguments, you can invoke native class and pass nonprimitive data objects by directly calling the Dartmethod.

There is still a long list of updates in the new version of Flutter 1.2 that we cannot cover in this blog. You can get more details you can visit the official site to know more. Also, you can subscribe to the Navoki newsletter to get updates on these features and upcoming new updates and lessons. In upcoming new versions, we might see more new features and improvements.

You can get more free Flutter tutorials you can follow these courses:

• Setup Flutter Desktop on Windows, Linux, MacOS using VSCode and Build – Flutter Tutorial
• Flutter Desktop Linux app | Build and Publish on Snapcraft Store
• Firebase Local Emulator Suite in Flutter

#dart #developers #flutter #app developed #dart devtools in visual studio code #firebase local emulator suite in flutter #flutter autofill #flutter date picker #flutter desktop linux app build and publish on snapcraft store #flutter pigeon #flutter range slider #flutter slider #flutter time picker #flutter tutorial #flutter widget #google flutter #linux #navoki #pubspec format #setup flutter desktop on windows

What is Flutter and why you should learn it?

Flutter is an open-source UI toolkit for mobile developers, so they can use it to build native-looking** Android and iOS** applications from the same code base for both platforms. Flutter is also working to make Flutter apps for Web, PWA (progressive Web-App) and Desktop platform (Windows,macOS,Linux).

Flutter was officially released in December 2018. Since then, it has gone a much stronger flutter community.

There has been much increase in flutter developers, flutter packages, youtube tutorials, blogs, flutter examples apps, official and private events, and more. Flutter is now on top software repos based and trending on GitHub.

Flutter meaning?

What is Flutter? this question comes to many new developer’s mind.

Flutter means flying wings quickly, and lightly but obviously, this doesn’t apply in our SDK.

So Flutter was one of the companies that were acquired by **Google **for around $40 million. That company was based on providing gesture detection and recognition from a standard webcam. But later when the Flutter was going to release in alpha version for developer it’s name was Sky, but since Google already owned Flutter name, so they rename it to Flutter.

Where Flutter is used?

Flutter is used in many startup companies nowadays, and even some MNCs are also adopting Flutter as a mobile development framework. Many top famous companies are using their apps in Flutter. Some of them here are

Dream11

NuBank

Reflectly app

Abbey Road Studios

and many more other apps. Mobile development companies also adopted Flutter as a service for their clients. Even I was one of them who developed flutter apps as a freelancer and later as an IT company for mobile apps.

#dart #flutter #uncategorized #flutter framework #flutter jobs #flutter language #flutter meaning #flutter meaning in hindi #google flutter #how does flutter work #what is flutter

Learn Testing with the new Flutter Sample | GSoC’20 Work Product

A quick introduction to GSoC

Google Summer of Code(GSoC) is a 3-month program sponsored by Google in which students work with an open source organization. With the help of mentors, the students complete a project during their summer break.

This year, the Dart team participated for the first time as a mentoring organization in GSoC. Five Dart project proposals were accepted by GSoC, of which two featured Flutter.

This blog shows the work I did for my GSoC project. All of the resulting source code is in GitHub repositories, and you can see how the work progressed by looking at individual pull requests (PRs).

Project details: Testing sample app

Out of my two submitted proposals, the Testing sample app was accepted. Testing is the most common thing skipped by students when learning any new technology.

#gsoc #flutter

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

RobustStats

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").

Estimators

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.

Recommendations:

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.

Examples

#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
1.853401259

tmean: trimmed mean

julia> tmean(x)            #20% trimming by default
1.2921802666666669

julia> tmean(x, tr=0)      #no trimming; the same as the output of mean()
1.853401259

julia> tmean(x, tr=0.3)    #30% trimming
1.1466045875000002

julia> tmean(x, tr=0.5)    #50% trimming, which gives you the median of the dataset.
1.1232023

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:
 1.67206
 0.787659
 0.400421
 0.972165
 ...
 0.651689
 2.82596
 0.51543

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

julia> winmean(x)          #20% winsorization; can be changed via the named argument `tr`.
1.4205834800000001
julia> winvar(x)
0.998659015947531
julia> wincov(x, x)
0.998659015947531
julia> wincov(x, x.^2)
3.2819238397424004

trimse: estimated standard error of the trimmed mean

julia> trimse(x)           #20% winsorization; can be changed via the named argument `tr`.
0.3724280347984342

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)
(0.4483411416666667,2.7282743333333332)

pbvar: percentage bend midvariance

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

julia> pbvar(x)
2.0009575278957623

bivar: biweight midvariance

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

julia> bivar(x)
1.5885279811329132

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.
1.2696652567510853
julia> tauvar(x)
1.53008203090696

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)
0.4708261134886094

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

sint()

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

hpsi

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

julia> hpsi(x)
20-element Array{Float64,1}:
1.28
0.787659
0.317322
0.972165
0.400421
...

onestep

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

julia> onestep(x)
1.3058109021286803

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)
0.41956761772722817

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)
1.2596462322222222

momci

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

contam_randn

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))
3.516722458797104

trimpb

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

pcorb

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

yuend

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.

References

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 

Flutter Hotel Booking UI - Book your Stay At A New Hotel With Flutter - Ep1

https://youtu.be/-tHUmjIkGJ4
Flutter Hotel Booking UI - Book your Stay At A New Hotel With Flutter - Ep1
#flutter #fluttertravelapp #hotelbookingui #flutter ui design 

In this video, I'm going to show you how to make a Cool Hotel Booking App using Flutter and visual studio code. 

In this tutorial, you will learn how to create a Splash Screen and Introduction Screen, how to implement a SmoothPageIndicator in Flutter. 

🚀 Nice, clean and modern Hotel Booking #App #UI made in #Flutter 

⚠️ IMPORTANT: If you want to learn, I strongly advise you to watch the video at a slow speed and try to follow the code and understand what is done, without having to copy the code, and then download it from GitHub. 

► Social Media 

    GitHub: https://github.com/Punithraaj 

    LinkedIn: https://www.linkedin.com/in/roaring-r...

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I hope you liked it, and don't forget to like,comment, subscribe, share this video with your friends, and star the repository on GitHub! 

⭐️ Thanks for watching the video and for more updates don't forget to click on the notification.⭐️Please comment your suggestion for my improvement. ⭐️Remember to like, subscribe, share this video, and star the repo on Github :)Hope you enjoyed this video! If you loved it, you can Buy me a coffee : https://www.buymeacoffee.com/roaringraaj

#flutter riverpod #flutter travel app #appointment app flutter #morioh