The introduction to Reactive Programming you've been missing · Git. Hub. The introduction to Reactive Programming you've been missing(by @andrestaltz)This tutorial as a series of videos. If you prefer to watch video tutorials with live- coding, then check out this series I recorded with the same contents as in this article: Egghead. Introduction to Reactive Programming. Convert Activity Diagram To Sequence Diagram TutorialsSo you're curious in learning this new thing called Reactive Programming, particularly its variant comprising of Rx, Bacon. RAC, and others. Learning it is hard, even harder by the lack of good material. When I started, I tried looking for tutorials. I found only a handful of practical guides, but they just scratched the surface and never tackled the challenge of building the whole architecture around it. Library documentations often don't help when you're trying to understand some function. I mean, honestly, look at this: Rx. Convert Activity Diagram To Sequence Diagram Tutorial PptObservable. prototype. Map. Latest(selector, [this. Arg])Projects each element of an observable sequence into a new sequence of observable sequences by incorporating the element's index and then transforms an observable sequence of observable sequences into an observable sequence producing values only from the most recent observable sequence. Holy cow. I've read two books, one just painted the big picture, while the other dived into how to use the Reactive library. Data Flow Diagram tutorial with an example. Learn how to draw context and level 1 DFD with the DFD tool. I ended up learning Reactive Programming the hard way: figuring it out while building with it. At my work in Futurice I got to use it in a real project, and had the support of some colleagues when I ran into troubles. The hardest part of the learning journey is thinking in Reactive. It's a lot about letting go of old imperative and stateful habits of typical programming, and forcing your brain to work in a different paradigm. I haven't found any guide on the internet in this aspect, and I think the world deserves a practical tutorial on how to think in Reactive, so that you can get started. Library documentation can light your way after that. I hope this helps you."What is Reactive Programming?"There are plenty of bad explanations and definitions out there on the internet. Wikipedia is too generic and theoretical as usual. Stackoverflow's canonical answer is obviously not suitable for newcomers. Reactive Manifesto sounds like the kind of thing you show to your project manager or the businessmen at your company. Microsoft's Rx terminology "Rx = Observables + LINQ + Schedulers" is so heavy and Microsoftish that most of us are left confused. Terms like "reactive" and "propagation of change" don't convey anything specifically different to what your typical MV* and favorite language already does. Of course my framework views react to the models. Of course change is propagated.
If it wouldn't, nothing would be rendered. So let's cut the bullshit. Reactive programming is programming with asynchronous data streams. In a way, this isn't anything new. Event buses or your typical click events are really an asynchronous event stream, on which you can observe and do some side effects. Reactive is that idea on steroids. You are able to create data streams of anything, not just from click and hover events. Streams are cheap and ubiquitous, anything can be a stream: variables, user inputs, properties, caches, data structures, etc. For example, imagine your Twitter feed would be a data stream in the same fashion that click events are. You can listen to that stream and react accordingly. On top of that, you are given an amazing toolbox of functions to combine, create and filter any of those streams. That's where the "functional" magic kicks in. A stream can be used as an input to another one. Even multiple streams can be used as inputs to another stream. You can merge two streams. You can filter a stream to get another one that has only those events you are interested in. You can map data values from one stream to another new one. If streams are so central to Reactive, let's take a careful look at them, starting with our familiar "clicks on a button" event stream. A stream is a sequence of ongoing events ordered in time. It can emit three different things: a value (of some type), an error, or a "completed" signal. Consider that the "completed" takes place, for instance, when the current window or view containing that button is closed. We capture these emitted events only asynchronously, by defining a function that will execute when a value is emitted, another function when an error is emitted, and another function when 'completed' is emitted. Sometimes these last two can be omitted and you can just focus on defining the function for values. The "listening" to the stream is called subscribing. The functions we are defining are observers. The stream is the subject (or "observable") being observed. This is precisely the Observer Design Pattern. An alternative way of drawing that diagram is with ASCII, which we will use in some parts of this tutorial: -- a- -- b- c- -- d- -- X- -- |- >. Since this feels so familiar already, and I don't want you to get bored, let's do something new: we are going to create new click event streams transformed out of the original click event stream. First, let's make a counter stream that indicates how many times a button was clicked. In common Reactive libraries, each stream has many functions attached to it, such as map, filter, scan, etc. When you call one of these functions, such as click. Stream. map(f), it returns a new stream based on the click stream. It does not modify the original click stream in any way. This is a property called immutability, and it goes together with Reactive streams just like pancakes are good with syrup. That allows us to chain functions like click. Stream. map(f). scan(g): click. Stream: - -- c- -- -c- -c- -- -c- -- -- -c- ->. Stream: - -- 1- -- -2- -3- -- -4- -- -- -5- ->. The map(f) function replaces (into the new stream) each emitted value according to a function f you provide. In our case, we mapped to the number 1 on each click. The scan(g) function aggregates all previous values on the stream, producing value x = g(accumulated, current), where g was simply the add function in this example. Then, counter. Stream emits the total number of clicks whenever a click happens. To show the real power of Reactive, let's just say that you want to have a stream of "double click" events. To make it even more interesting, let's say we want the new stream to consider triple clicks as double clicks, or in general, multiple clicks (two or more). Take a deep breath and imagine how you would do that in a traditional imperative and stateful fashion. I bet it sounds fairly nasty and involves some variables to keep state and some fiddling with time intervals. Well, in Reactive it's pretty simple. In fact, the logic is just 4 lines of code. But let's ignore code for now. Thinking in diagrams is the best way to understand and build streams, whether you're a beginner or an expert. Grey boxes are functions transforming one stream into another. First we accumulate clicks in lists, whenever 2. Don't worry about understanding the details at this point, we are just demoing Reactive for now). The result is a stream of lists, from which we apply map() to map each list to an integer matching the length of that list. Finally, we ignore 1 integers using the filter(x > = 2) function. That's it: 3 operations to produce our intended stream. We can then subscribe ("listen") to it to react accordingly how we wish. I hope you enjoy the beauty of this approach. This example is just the tip of the iceberg: you can apply the same operations on different kinds of streams, for instance, on a stream of API responses; on the other hand, there are many other functions available."Why should I consider adopting RP?"Reactive Programming raises the level of abstraction of your code so you can focus on the interdependence of events that define the business logic, rather than having to constantly fiddle with a large amount of implementation details. Code in RP will likely be more concise. The benefit is more evident in modern webapps and mobile apps that are highly interactive with a multitude of UI events related to data events. Apps have evolved to be more real- time: modifying a single form field can automatically trigger a save to the backend, "likes" to some content can be reflected in real time to other connected users, and so forth. Apps nowadays have an abundancy of real- time events of every kind that enable a highly interactive experience to the user. The Database of Short Genetic Variation (db. SNP) - The NCBI Handbook. Scope. Sequence variation is of scientific interest to population geneticists, genetic mappers, and those investigating relationships among variation and phenotype. These variations can be of several types, from simple substitutions that do not affect sequence length, to those that result in minor length differences, to those that affect multiple genes and multiple chromosomes. Variations can also be categorized with respect to their frequency within a population, from a variation with a single allele to a variation that is highly polymorphic. Although SNP is the abbreviation for “single nucleotide polymorphism,” db. SNP is a public archive of all short sequence variation, not just single nucleotide substitutions that occur frequently enough in a population to be termed polymorphic. SNP includes a broad collection of simple genetic variations such as single- base nucleotide substitutions, small- scale multi- base deletions or insertions, and microsatellite repeats. Data submitted to db. SNP can be from any organism, from any part of a genome, and can include genotype and allele frequency data if those data are available. SNP accepts submissions for all classes of simple sequence variation, and provides access to variations of germline or somatic origin that are clinically significant. In order to emphasize the comprehensive nature of db. SNP’s content, the full name of the database was changed from “database of Single Nucleotide Polymorphism” to the more inclusive “database of Short Genetic Variation” in July of 2. The acronym that represents the database will remain “db. SNP” to avoid any confusion that might arise from a complete name change. Each record in db. SNP includes the sequence context of the variant, the frequency of the polymorphism in a population if available, its zygosity if available, and the experimental method(s), protocols, and conditions used to assay the variation by each submitter. Individual submissions are clustered into db. SNP reference records (rs#) that contain summary data which may include clinical significance from Clin. Var, association with phenotype from db. Ga. P, variation false positive status, allele origin (germline or somatic), and submitter attributes. The db. SNP has been designed to support submissions and research into a broad range of biological problems that include the identification of genotype- phenotype relationships, genetic and physical mapping, functional analysis, pharmacogenomics, and association studies. Medical Genetics. Advances in next- generation sequencing technologies allow researchers to generate massive amounts of sequence data. When clinical samples are sequenced using these technologies, novel variants that have causative roles in disease may be identified. SNP’s role is to manage information on the location and type of these novel variants, while Clin. Var manages the current interpretation of the variants' clinical phenotype. SNP integrates clinical attribute data from Clin. Var (i. e., clinical assertions and asserted allele origin) into new and existing human ref. SNP records, as well as into additional curated attribute data that includes minor allele frequency and variation false positive status. VCF files generated from db. SNP’s curated records can be used to filter (subtract) known variants from a set of variant calls to identify novel variants or narrow a list of potential causative variants that might warrant further evaluation. Genome Mapping. Variations are used as positional markers in genetic and physical mapping of nucleotide sequences when they map to a unique location in a genome. In other words, the variations represented by db. SNP records can serve as stable landmarks in the genome even if the variation is fixed for one allele in a sample. When multiple alleles are observed in a sample pedigree, pedigree members can be tested for variation genotypes as in traditional genetic mapping studies. To aid in such mapping efforts, db. SNP updates variation mapping and annotation for each organism with the release of every new genome assembly. Molecular and Functional Consequences. Variations that occur in functional regions of genes or in conserved non- coding regions might affect transcription, post- transcriptional processing, or a protein product. SNP computes the molecular consequence of any sequence change, based on NCBI’s annotation of the genome. Functional consequences may be reported from submitters. Association Studiesdb. SNP annotates variations with significant association to phenotype from Genome Wide Association Studies (GWAS) as reported by db. GAP and provides a detailed catalog of common variations. SNP’s GWAS annotations and common variation catalog are used to inform the design of GWAS studies, the creation of variation arrays used in GWAS studies, and the interpretation of GWAS study results. History. Creation and Growthdb. SNP was established in September,1. Initially, db. SNP was composed of small- scale locus specific submissions defined by flanking invariant sequence. Following the advent of high- throughput sequencing and the availability of complete genome assemblies for many organisms, however, db. SNP now receives a greater number of variants defined by sequence change at asserted locations on a reference sequence. Evolution in Submitted Content. Because db. SNP was developed before a human reference assembly was available, initial submissions were primarily from human and defined a variant sequence in the context of flanking sequence. There was often little supporting evidence or validation data. As sequencing and other discovery technologies have changed, db. SNP has grown apace, and now includes data from over 3. To meet community needs for a centralized variation database, in the spring of 2. SNP began accepting clinical assertions for new and existing variations as well as asserted locations for variation placement. The Clin. Var database, now has the role of accepting variation clinical assertion data, and following its own accession process, will route novel variation positions to db. SNP for the assignment of ss (submitted SNP) and rs (ref. SNP) numbers. In addition to integrating clinical assertions into ref. SNP records, db. SNP has introduced other curated attributes into ref. SNP records, such as minor allele frequency, asserted allele origin, and potential false positive status. It also uses the curated records to generate VCF files that can be employed to filter variation calls for the presence of novel variations and identify potential causative variants. Usage Evolution. Originally, the data in db. SNP was used only to populate sequence maps since polymorphic marker density was too low to allow further application of the data. By 2. 00. 7, however, marker density had increased enough to allow for the use of variation data in association studies, high resolution mapping, and a host of other applications, including population evolution and phylogeny studies that continue to further our understanding of genetic relationships and the genomic basis of traits. SNP’s current integration of clinical information into db. SNP records will allow greater application of db. SNP data to the fields of molecular medicine and pharmacogenetics, as well as emerging fields study such as pharmacometabolomics and precision medicine. Data Model. The db. SNP data model will evolve to capture new content, but currently has two major classes of data. The first class is submitted data, namely original observations of sequence variation, which are accessioned using a “submitted SNP” (ss) identifier. The second class is generated during the db. SNP build cycle (Figure 1) by aggregating data from multiple submissions as well as data from other sources and is identified with a “reference SNP” (rs) number. The rs identifier represents aggregation by type of sequence change and location on the genome if an assembled genome is available, or aggregation by common sequence if a genome is not available. SNP Build Cycle. The db. SNP build cycle starts with close of data for new submissions. SNP calculates summary attributes and provide submitter asserted summary attributes for each ref.
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