Splice

splice

Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . KNOTS AND SPLICES. here KNOTS AND SPLICES. 1. Turn used in making up ropes. End tapered for the purpose of passing it readily through a loop. To make this, we unlay the rope for the necessary length, reducing a rope diminishing in diameter towards the end, which is finished by interlacing the ends without cutting them, as it would weaken the work; it is lastly "whipped" with small twine. Tapered end, covered with interlaced cordage for the purpose of making it stronger. This is done with very small twine attached at one end to the small eye, and at the other to the strands of the rope, thus making a strong "webbing" around the end. Double turn used for making rope. Eye splice. The strands of the cable are brought back over themselves, and interlaced with their original turns, as in a splice. 6. Tie for the end of a four-strand rope. The same completed; the strands are tied together, forming loops, laying one over the other. Commencement for making the end by interlacing the strands. Interlacing complete, but not fastened. 10 and 11. Shell in two views used in No. 65, showing the disposition of it at the throat. This joining is advantageous, as it does not strain the cords, and it prevents them from cutting each other; so that the rings pass one into the other and are joined outside the intermediate shell. Interlacing in two directions. Mode of finishing the end by several turns of the twine continued over the cable. Interlacing commenced, in one direction. Interlacing finished, the ends being worked under the strands, as in a splice. 16. Pigtail commenced. 17. Interlacing fastened. 18. Pigtail with the strands taut. 19. Dead eye, shown in two views. 20. Pigtail finished. We pass the ends of the strands, one under the other, in the same way as if we were making a pudding splice: thus bringing it in a line with the rope, to which it is seized fast, and the ends cut off. 21. Scull pigtail; instead of holding the ends by a tie, we interlace them again, as in No. 16, the one under the other. 22. Pigtail, or "lark's nest." We make this to the "pennant" of a cable, which has several strands, by taking the requisite number of turns over the pudding, in such a manner that the strands shall lay under each other. This "pigtail" forms a knot at the end of the rope. It thus draws together two ropes, as shown in No. 32, forming a "shroud" knot. In these two pigtails, the strands are crossed before finishing the ends, so that the button, a, is made with the strands, a, and b, with those of the rope, b. 23. Slip clinch to sailors' knot. 24. Slip clinch, secured. 25. Ordinary knot upon a double rope. 26. Bowline knot for a man to sit in at his work. 27. Called a "short splice," as it is not of great length, and besides, can be made quickly. 30. Long splice. This extends from a to b. We unlay the strands of each of the ropes we intend to join, for about half the length that the splice will be, putting each strand of the one between two strands of the other. 31. Simple fastening on a rope. 32. A "shroud" knot. 33. The ends of the rope are prepared for making the splice (No. 29) in the same manner as for the "shroud" knot in No. 32. When the strands are untwisted, we put the ends of two cords together as close as possible, and place the ends of the one between the strands of the other, above and below alternately, so as to interlace them as in No. 29. This splice is not, however, very strong, and is only used when there is not time to make a long splice, which is much the best. 34 and 35. Marline spikes. Tools made of wood or iron, used to open out a rope to pass the strands of another through it. 36. Shows strands arranged as described in No. 30. 37. Fastening when a lever is used, and is employed when hauling upon large ropes, where the strength of several men are necessary. 38. A "pudding splice." This is commenced, like the others, by placing the rope end to end, the turns of the one being passed between those of the other; having first swelled out the yarns by a "rat's-tail," we put them, two by two, one over the other, twisting them tightly, and opening a way for them with the marlinspike. The inconvenience of this splice is, that it is larger in diameter than the rope itself; but when made sufficiently long, by gradually reducing the size of the strands, it has great strength. 39. This shows two strands, a and b, of the ropes, A B, knotted together, being drawn as tight as possible; we unlay the strand, a', of the rope, A, for half the length of the splice, and twist the strand, b', of the rope, B, strongly in its place, tying a' and b' together tightly. The same process is again gone through on the rope, B, the strand, a", of the rope, A, being knotted to the strand, b", of the rope, B. When all the strands are thus knotted together, we interlace them with the strands of the cable. Thus the strands, a a' a", are interlocked by being passed alternately above and below the turns of the cord, B, the ends being also sometimes "whipped." In the same manner the strands, b b' b", pass alternately over and under the strands of the rope, A, and are in like manner "whipped." It is important that the several interlacings and knots should not meet at one point; we reduce the size of the strands towards the end, so that they loose themselves in the body of the splice, cutting off such parts as may project. This splice is employed for joining the ends of a rope when a chafed part has been cut out, and is quite as strong as the rope itself. 40. Belaying-pin opened to serve as a button; these are used where it is necessary to stop or check velocity. 41. Chain knot, or fastening. 42. Variable or regulating lashing. By laying the piece, a f, horizontally, it can be slipped along the rope, b; by raising or lowering this, we shall raise or depress the weight, c, the cord, b, running over the two pulleys, d, from the piece, a f, in the direction shown in the figure. The friction of the cord, b, passing through the hole, e, sufficiently fixes the piece, a f, and holds the weight, c, securely. 43. Cleet, with three ties. 44. Cleet, showing the mode of belaying the cord. 45. The piece, a f, of No. 42. 46. Fair leader. 47. Cleet to be fixed to a stay. 48. Loop for slipping other lines. 49. A "bend" which is only used for fear of the stoppers snapping. 50. Bastard loop, made on the end of the rope, and whipped with yarns. 51. Tie to pins: a, the pin; b, small cords fixed by a cross tie. 52. Cleet, fixed to the "rail," either with screws or nails, to which the lines are belayed. 53. Waterman's knot. 54. Fair leader. 55. Tie, or bend to pier. 56. Simple fastening to tie. 57. Fastening by a loop. This can be tied or untied without loosening the loop itself. It is made by following, towards the longer loop, the direction as numbered 1, 2, 3, 4, 5, and is terminated by the loop, 6, 7, 6, finally passing it over the head of the post, A. This knot holds itself, the turns being in opposite directions. To untie it, we slack the turns of the cable sufficiently to again pass the loop, 6, 7, 6, over the post, A, and turn the ends in the contrary direction to that in which they were made (as 5, 4, 3, 2, 1). 58. Iron "shell," in two views. 59 and 60. "Wedding" knots; a b, eyelets; c d, the join; e, the fastening. Lark's-head fastening to running knot. A round turn; the cord, a, is passed through the bight of the cord, b, over the button, c, where it is secured by an ordinary knot. Belaying-pin splice. The cord, b, "stops" the pin, e, its end being spliced upon itself, and "served" with yarn; this rope, with its pin, is passed through the spliced eye, f of the line, g. 64. Round button. 65. Joint by a spherical shell, each loop, a and b, being made by ties and splices, and surrounding the shell, c. Belaying-pin, shown separately, before being stoppered. Fastening to shears. Square mooring. When the cable is round the post, A, and the piece, c, without being crossed, it lays in the section 1, 2, 3, 4, 5, 6, 7, and the end is fastened by tying. Crossed fastening. The turns of the cable, passing in front of the post, B, are crossed at the back of C, in the direction 1, 2, 3, 4, 5, 6, 7, 8, the end, 8, being secured to the cable Wooden shell in section. Wooden shell. Double-chain fastening. 73. Lashing for "ram" block, or "dead-eye." The ram blocks, a and b, are strapped by the cords, e, which hold them; the small lanyards, d, pass through the holes to make the connection, and as they are tightened give the requisite tension to the cordage; the ends are fastened to the main rope. Usually one of these dead-eyes is held by an iron strap to the point where it is required to fix and strain the cordage, which is ordinarily a shroud. Chain fastening. 1'. Simple band, showing the upper side. 2'. The same, showing the under side and the knot. 3'. Tie, with crossed ends, commenced; a turn is taken under the strands, to hold the ends of the cord. 4'. The same, completed. 5'. Bend with crossed strands, commenced, the one end being looped over the other. 6'. The same, completed. 7'. Necklace tie, seen on the upper side. 8'. The same, seen underneath. The greater the strain on the cords, the tighter the knot becomes. 9' and 10' are similar splices to 7' and 8' with slight modifications. 11' shows the commencement of 13', the legs in elevation; 12' being a front view. An ordinary band, made by several turns of a small rope, is lapped round them and hauled taut, and then interlaced at the ends. This done, the legs are shifted into the shape of a St. Andrew's cross. Thus the lashing is tightened, and, for further security, we pass the line several times over the tie and between the spars, knotting the ends. 13'. Portuguese knot. This is a lashing for shear legs, and must be tight enough to prevent the spars slipping on each other; the crossing of the two legs gives a means of securing the knot. 14'. For binding timbers; a, knot commenced. Take several turns round the timbers, and fasten the ends by passing them under the turns; b, knot completed. The end of a round stick, m n, termed a packing stick, should be passed under the knob, the cord being slack enough to allow of this. By turning the stick, the turns can be tightened to any extent; when tight, we fasten the longer arm of the lever to some fixed point, by a rope, p q, so that it cannot fly back. Care must be taken not to turn the stick too far, or the rope may be broken. As the timber dries and shrinks, the lever may be used to make all taut again. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

KNOTS AND SPLICES

Splice is a Javascript Array method that changes the content of an array by deleting or replacing an existing element or adding a new element in place. In this guide, I will explain how to use the Javascript Array splice method. Developers at any level can read and understand this article. Understanding the Array Method(splice) If we take a look at the MDN website we would realize that the Javascript array prototype constructor, which allows us to add new methods and properties to the Array() object has a lot of methods and splice is one of them. Array.prototype.splice() If we however tried to use this same method on the string prototype, like the example below; const strings = 'learn' strings.splice() We would however receive, a type error saying: Uncaught TypeError: strings.splice is not a function It is important to know that splice and slice aren’t the same. The slice method doesn’t mutate or change your data but the splice method does and this is why the splice method can’t be used on strings: strings are immutable. How to use the Javascript array splice method Using the splice Javascript array method is easy, but the syntax is sometimes misused. You can use the splice method in these steps arr.splice(start, deleteCount, item1, ..., itemN) The splice() property takes some parameters, these parameters are used to determine the behavior of whatever array the splice method’s been called upon. They are as follows: Start deleteCount Items Start This is just the index at which we want our array to start the change, no element will be removed if the element's index is greater than the array’s length. But it adds an argument to the end of the array if the index is less than the array’s length. deleteCount This is an optional parameter that denotes the number of elements to be removed from the array, to delete or remove an element the start parameter must be present. If you don’t include this parameter, or if it has equal value to the array’s length or is even greater than the array’s length, all the elements from the start parameter to the last item in the array would be removed. Note, this parameter must be included if there’s an item parameter present. Items This is also an optional parameter that tells you the element to be added to the array If we don’t state the elements, the Javascript array’s splice() method will delete elements from the array. This would return an empty array. Use cases Now that we have understood what the parameters are, let’s try to see use cases and examples of how this can be implemented when writing our program. Deleting zero elements before a specified index If we want to add an element without removing any item, we simply specify the index, and it would remove nothing but start before the specified index. Let’s have a look at the example below: const myMusic = ['piano', 'guitar', ‘violin', 'orchestra']; const removedElement = myMusic.splice(2, 0, 'drum'); // myMusic is [piano', 'guitar', ‘drum’, ‘guitar’, ‘violin’, ‘orchestra’] // removedElement is empty  [ ], no elements deleted In the preceding code, we try to remove an element on index 3(mandarin), meaning as soon as it gets to index 3 whatever items it finds would be deleted without replacing it with any element. We can also delete one element and add other elements. const myMusic = ['piano', 'guitar', 'drum', 'violin', 'orchestra'] const removedElement = myMusic.splice(3, 1, ‘sax’); // myMusic is  ['piano', 'guitar', 'drum', 'sax', 'orchestra'] // removedElement is ['violin'] Similarly, as soon as we got to index 3, whatever it finds, in this case, ‘orchestra’, would be deleted, and add newer ones (‘sax’), The code above shows how ‘drum’ was added to the array. Starting from index 2 the zero tells it to remove nothing, if we had 1 there it would remove 1 element and add the item we want, and so on. Deleting one or more elements at a specified index Say we wanted to remove one item or element without adding a new one into the array, we could just write it like the preceding code snippet const myMusic = ['piano', 'guitar', 'drum', 'violin', 'orchestra']; const removedElement = myMusic.splice(3, 1); // myMusic is ['piano', 'guitar', 'drum', 'ochestra'] // removedElement is ['violin'] We can start at any index and remove as many items as we want. Delete all elements beginning from a specified index We can delete all elements in the array starting from whatever index we want. Let’s say, for example, we want to remove all starting from index 1. Once it gets to index 1, every element starting from index 1 would be deleted. const myMusic = ['piano', 'guitar', 'drum', 'violin', 'orchestra'] const removedElement  = myMusic.splice(1); // myMusic is  ['piano', 'guitar'] // removedElement is ['drum', 'sax', 'ochestra'] This code snippet tries to remove all elements starting from index 1. Where index 1 is ‘clown’ in the array. Since counting begins from 0, this would return a new array. that contains just one element which is ‘angel’. This is because during the counting ‘angel’ appears to be on index 0,.and counting begins at index 1. Index 0 would be omitted making it return that result. Takeaways If you need to perform a task on an array, and you don’t want the original array changed, the Javascript array’s splice() method should be avoided because they change your data. const beforeSplice = ['bread','fish','cook'] console.log(beforeSplice) //  ['bread', 'fish', 'cook'] const afterSpliceData = beforeSplice.splice(1) console.log(afterSplice) //  ['fish', 'cook'] In the example above. Before the splice Javascript array’s method was called on the beforeSplice variable. We had 3 elements, but after the method was applied, it returns 2 elements. This method has changed the data. Sometimes this isn’t what we want. But if all you simply want to do is remove elements or add an element, it’s perfect. See you at the next one.

How to Use Splice in JavaScript: Understanding the Array Method

Cómo usar Splice en JavaScript: comprensión del método de matriz

Comment utiliser Splice en JavaScript : Comprendre la méthode Array

जावास्क्रिप्ट में स्प्लिस का उपयोग कैसे करें: ऐरे विधि को समझना

JavaScript で Splice を使用する方法: 配列メソッドを理解する

Como usar Splice em JavaScript: Entendendo o método Array

Cách sử dụng Splice trong JavaScript: Tìm hiểu phương thức mảng

如何在 JavaScript 中使用 Splice：了解数组方法

The following article is based on a talk Juan Pablo Buriticá, VP of Engineering at Splice, gave at our annual Engineering Leadership Conference and a subsequent interview. Watch the full talk here , and see the slides here . “The most shocking thing is that software engineering is one of the least rigorous practices that exists,” says Splice’s VP of Engineering, Juan Pablo Buriticá. After graduating with a degree in Pharmaceutical Chemistry, he eventually taught himself to code and transitioned to a career in software. Juan Pablo was astonished to find software engineering to be a much more fragmented discipline. Few standards exist around even the most common software production methods, like Agile and Continuous Delivery. When managing a small team, the lack of industry standards was rarely an issue for Juan Pablo. Whenever something felt inefficient, he’d get the whole engineering team in a room, identify the source of friction, adjust, and move on. After Juan Pablo scaled his team from 15 to 80 distributed developers, however, all their processes broke. “I had to go back and fix the mess I created by growing the team so fast,” said Juan Pablo. But fixing them wasn’t so easy anymore. So, Juan Pablo turned to engineering measuring tools and the Scientific Method. Experiment 1: Applying Metrics to Engineering Forming a Hypothesis Before experimenting to determine which actions would yield improvement, Juan Pablo and the rest of the management team agreed that they needed to determine what specific outcome they were optimizing for. The team agreed that everything slow— but a gut feeling wasn’t enough to start making changes in the team. felt They wanted to: First, They weren’t willing to settle for a vague anti-goal of “slowness”— they wanted a clear vision of what the team should look like. decide what they were working towards. Second, Within the newly agreed-upon context and direction of the team, they wanted a system to measure how far away they were from their goals. decide how they would measure progress. Thus, their hypothesis was: A Defined Direction + A Focus on Success Metrics = Increased Tempo. The product and engineering leadership spent an offsite deliberating on what was next for Splice. They came up with the following vision for the organization: This meant that engineers were confident enough in their processes and tooling to take risks. And they also felt able to mitigate issues when invariable something did break. Shipping working code is one of the fastest, safest, and most effective ways to learn and to test new ideas. To test how they were doing and how far they had to go, they leveraged engineering measuring tools to investigate three metrics: Time to Merge, Deploy Frequency, and End to End Test Coverage. Combined, the team believed optimizing for these metrics would give their team confidence in the stability and speed of their processes. Conducting the Experiment Juan Pablo and the leadership team communicated this new vision and supporting metrics to the team. They were careful to note that this was an experiment designed to help improve collaborative processes— not a change in response to performance issues. These are the goals they communicated: 100% of Pull Requests should be merged within 36 hours (or 3 days) Time to Merge: All product teams had to deploy once a day Deploy Frequency: 100% of engineers had to write an end-to-end test in an improved testing environment End to End Test Coverage: The specific targets they chose for each metric were a guess. “I picked 36 hours, because why not?” says Juan Pablo. The team was experimenting with metrics for the first time, so they had to start with a number. He predicted that enabling his team to track and measure these metrics alone would be enough to create change. Analyzing the Results After one quarter, Juan Pablo didn’t observe the results he anticipated. Although one engineer did put in work to make staging less of a blocker to production, there were few other changes to how the team worked. Pull Requests were not being merged within 3 days, and product teams were not deploying once a day. These metrics revealed that they hadn’t moved the needle, but didn’t reveal what to do about it. Experiment 2: Applying Actionable* Metrics to Engineering Forming a Hypothesis Juan Pablo had a conviction that their direction was right, but he realized the metrics that they had chosen weren’t actionable. It wasn’t clear what could be done by any individual engineer or manager to improve how the process works. “I knew I needed better metrics and measurements,” Juan Pablo told us. So he scoured the internet for all the reading material he could find. Two sources moved him toward finding more better measurements: which regularly surveys 1,000s of engineers to identify positive work patterns and the best indicators of improvement. State of DevOps reports , in which the authors of the DevOps reports distilled their findings after four years and identified four crucial metrics to measure and improve engineering tempo. Accelerate , These resources were based on research that had been conducted over several years with scientific rigor— exactly what Juan Pablo was looking for. One major pattern that the researchers promoted was to distinguish product design from product delivery. Juan Pablo had been thinking of all of product and engineering as a single entity— but the process could be separated into predictable and unpredictable portions of the workflow. Product design and planning are, by nature, unpredictable. They often involve scoping work that has never been done before, so it often results in imprecise estimation of scope and effort. Delivery, on the other hand, can be made predictable. Engineers can ship changes incrementally, irrespective of the scope of the feature they’re working on. Thus Juan Pablo’s new hypothesis was: The metrics they chose were Cycle Time, Mean Time to Restore and Deploy Frequency. Decoupling Engineering from Product + Actionable Deliverability Metrics = Increased Engineering Tempo. With a new hypothesis and a “plan for the plan,” as Juan Pablo calls it, the engineering team was ready to try again. Conducting the Experiment Decoupling engineering from product would take some heavy lifting, so Juan Pablo put together a Production Engineering team. “Their job was to build the tooling, services, and expertise that enables teams to deliver and operate high quality, production-level software and services,” says Juan Pablo. This team was responsible for owning improvement on key metrics: targeting less than one hour. Cycle Time (commit to production), targeting less than one hour. Mean Time to Restore (MTTR), targeting more than once a day. Deploy Frequency, To be able to track Cycle Time and Deploy Frequency, Juan Pablo found an engineering analytics tool, . Out-of-the-box, it shows three years of historical data, so Juan Pablo could measure how scale impacted the team, and whether they were trending in the right direction. Velocity To start trending in the right direction, they had to work towards giving engineering more ownership over product delivery. Decoupling meant a few major transitions: Engineers were responsible for deploying code, while product would decide when to release features to customers. Testing is shifted left and becomes integrated into the developers’ responsibilities. Developers, therefore, don’t have to wait for a QA team to ship changes. The department invests in more testing and monitoring tooling, so the team can ship with more confidence. Over the next quarter, the Production Engineering team worked with the organization to bring down Cycle Time. Analyzing the Results At the end of that quarter, the results spoke for themselves. On his Velocity dashboard, Juan Pablo saw Cycle Time had decreased by 25%. Even more importantly, however, it had become consistent: The team’s throughput had increased 3x without a significant change in headcount: “We saw results—and we also finally had the language to talk about performance,” said Juan Pablo. The actionable metrics Juan Pablo had discovered monitored within Velocity gave the whole team a means to communicate how far away they were from their goals. When an engineer was blocked for any reason, they could point to the effect it had on Cycle Time. This focus helped them solve the immediate problem of increasing tempo, but also equipped the team with the visibility to solve upcoming problems. Building Scientific Rigor into Continuous Improvement While the metrics and practices in Accelerate aren’t quite industry standards , the researchers have applied a level of scientific rigor that has yielded predictable results for organizations of all sizes. The DevOps report has shown that over the past 4 years, are practicing Continuous Delivery. More of the industry is using engineering measuring tools to look at metrics like Cycle Time and Deploy frequency, and seeing tangible improvements in engineering speed. yet an increasing number of organizations Through these recent studies and his own research, Juan Pablo had the unbiased data to finally approach software engineering like a scientist. Thanks to the hard work of the Splice engineering team and their investment in engineering measuring tools like Velocity, Juan Pablo told us: “We have created a culture of continuous systems and process improvement. We also have a language and a framework to measure this change.” Try Velocity for free to see how your team can benefit from similar visibility.

How Splice Applied the Scientific Method to Unblock Deployment

The haters always trod on PHP for a variety of reasons, but one of the easiest targets is how ugly the code can be. Some people may not agree that code cleanliness and “prettiness” matter, but when it comes to maintaining codebases for the long term, you can save your team a lot of time by having code that is easy to read. While at Apple, I developed an ecosystem of finance applications used by internal stakeholders. Over the years, I was constantly onboarding new developers into the tools to assist with general maintenance or enhancements. One of the major takeaways from that time is how important it is to be able to read your code with natural language. It’s interesting to find that most code is written in such a way that you have to read through a significant chunk of it, then go back to the start to fully understand what is going on. Using more expressive language or APIs while writing code can greatly reduce the onboarding and ramp-up times of new developers. This is where Collections shine. Laravel Collections take some complicated array traversals and make them incredibly easy to read and understand. Your code becomes a chain of commands read like “Filter this array to only words starting with a, then map the cost amount to the row, and lastly reduce this to a sum of all costs.” It is very easy to undersell the importance of writing your code with more expressive language, but once you’ve made it a habit, it’s hard to imagine how anything else is acceptable. Of course, readability cannot come at the expense of performance so it’s important to ensure that we’re always making sound choices and only prioritizing readability where it’s appropriate. We should always understand what is happening behind the scenes and be fully aware of what tradeoffs we are encountering with the way that we approach our solutions.  It does no good for our code to be readable if it takes a full second longer than a less readable solution. Let’s dive in and have an in-depth look at PHP arrays vs. Collections and compare performance between the two. Readability Laravel Collections One of the biggest advantages of using Laravel Collections is their fluent syntax, which allows for chaining methods. This results in clean and readable code, as you can perform multiple operations in a sequence without needing temporary variables or complex nested function calls. Let’s compare some of the commonly used array manipulation functions below to see how PHP compares to Collections in a very simple example. Filter PHP array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); Collections $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); Search PHP array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); Collections $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); Map PHP array_map(function($row) {
    return "test";
}, $data); Collections $data->map(function($row) {
    return "test";
}); Sort PHP sort($data); Collections $data->sort(); Each PHP foreach($loop as $item) {
    $doSomething = true;
} Collections $data->each(function($row) {
    return "test";
}); Reduce PHP array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); Collections $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Splice PHP array_splice($data, count($data)/2); Collections $data->splice(count($data)/2); All Together (PHP) $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); All Together (Collections) $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Comparison With an approach this simple, there doesn’t seem to be a huge tradeoff in readability for each individual function, although when you consider the example where you need them all to be applied to a single array, you can clearly see that it is more concise and easier to read when using the chained-methods in a collection. Instead of constantly having to overwrite your variable and then set a new variable for output at the end, we can simply chain every command until we get to our desired output. Collections are definitely easier to read the more complex your code becomes. Performance I took the above examples and generated some test data to attempt to determine if collections were more or less performant than standard PHP functions. Each array had 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. The final results are below: ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= Conclusion As you can clearly see, though we gain a large amount of readability from Collections, we lose a significant amount of performance in some key areas. Filter, Map, Foreach, and Reduce are all faster with the standard PHP functions. Foreach and Reduce are actually incredibly significant differences. Search, Sort, and Splice all show Collections as the winner, and Sort is actually a massive time saver. It also is important to note that you would have to create each collection out of a built array which adds a tiny bit of overhead to setting up the collection to begin with, but even with that small amount of extra work and time, the results are pretty clear. In my opinion (and it is just an opinion based on these results), if performance is a huge concern, I would stick with standard PHP functionality for Foreach loops for sure and likely the same for any Reduce needs. If you need to do any sorting on large datasets, clearly Collections is the right way to go. The rest are so close that it really feels like a personal preference. At that point, I’d say collections are always easier to read and maintain. Obviously, you should take this information and make your own informed decision, however, if you’re like me, I think you’ll find yourself slipping collections in for many of these functions above. But I do think I’ll dial back my use of →each and →reduce where appropriate going forward! The haters always trod on PHP for a variety of reasons, but one of the easiest targets is how ugly the code can be. Some people may not agree that code cleanliness and “prettiness” matter, but when it comes to maintaining codebases for the long term, you can save your team a lot of time by having code that is easy to read. While at Apple, I developed an ecosystem of finance applications used by internal stakeholders. Over the years, I was constantly onboarding new developers into the tools to assist with general maintenance or enhancements. One of the major takeaways from that time is how important it is to be able to read your code with natural language. It’s interesting to find that most code is written in such a way that you have to read through a significant chunk of it, then go back to the start to fully understand what is going on. Using more expressive language or APIs while writing code can greatly reduce the onboarding and ramp-up times of new developers. This is where Collections shine. Laravel Collections take some complicated array traversals and make them incredibly easy to read and understand. Your code becomes a chain of commands read like “Filter this array to only words starting with a, then map the cost amount to the row, and lastly reduce this to a sum of all costs.” It is very easy to undersell the importance of writing your code with more expressive language, but once you’ve made it a habit, it’s hard to imagine how anything else is acceptable. Of course, readability cannot come at the expense of performance so it’s important to ensure that we’re always making sound choices and only prioritizing readability where it’s appropriate. We should always understand what is happening behind the scenes and be fully aware of what tradeoffs we are encountering with the way that we approach our solutions.  It does no good for our code to be readable if it takes a full second longer than a less readable solution. Let’s dive in and have an in-depth look at PHP arrays vs. Collections and compare performance between the two. Readability Laravel Collections One of the biggest advantages of using Laravel Collections is their fluent syntax, which allows for chaining methods. This results in clean and readable code, as you can perform multiple operations in a sequence without needing temporary variables or complex nested function calls. Let’s compare some of the commonly used array manipulation functions below to see how PHP compares to Collections in a very simple example. Filter PHP PHP array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
}); Collections Collections $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
}); Search PHP PHP array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data); Collections Collections $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); $data->search(function($row) {
    return substr($row, 0, 1) === "a";
}); Map PHP PHP array_map(function($row) {
    return "test";
}, $data); array_map(function($row) {
    return "test";
}, $data); Collections Collections $data->map(function($row) {
    return "test";
}); $data->map(function($row) {
    return "test";
}); Sort PHP PHP sort($data); sort($data); Collections Collections $data->sort(); $data->sort(); Each PHP PHP foreach($loop as $item) {
    $doSomething = true;
} foreach($loop as $item) {
    $doSomething = true;
} Collections Collections $data->each(function($row) {
    return "test";
}); $data->each(function($row) {
    return "test";
}); Reduce PHP PHP array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); Collections Collections $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); $data->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Splice PHP PHP array_splice($data, count($data)/2); array_splice($data, count($data)/2); Collections Collections $data->splice(count($data)/2); $data->splice(count($data)/2); All Together (PHP) All Together (PHP) $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); $data = array_filter($data, function($row) {
    return substr($row, 0, 1) === "a";
});

$data = array_search(function($row) {
    return substr($row, 0, 1) === "a";
}, $data);

$data = array_map(function($row) {
    return "test";
}, $data);

sort($data);

foreach($loop as $item) {
    $doSomething = true;
}

$sum = array_reduce($data, function($carry, $row) {
    return $carry + strlen($row);
}); All Together (Collections) All Together (Collections) $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); $sum = $data->filter(function($row) {
    return substr($row, 0, 1) === "a";
})->search(function($row) {
    return substr($row, 0, 1) === "a";
})->map(function($row) {
    return "test";
})->sort()
->each(function($row) {
    return "test";
})->reduce(function($carry, $row) {
    return $carry + strlen($row);
}); Comparison Comparison With an approach this simple, there doesn’t seem to be a huge tradeoff in readability for each individual function, although when you consider the example where you need them all to be applied to a single array, you can clearly see that it is more concise and easier to read when using the chained-methods in a collection. Instead of constantly having to overwrite your variable and then set a new variable for output at the end, we can simply chain every command until we get to our desired output. Collections are definitely easier to read the more complex your code becomes. Performance I took the above examples and generated some test data to attempt to determine if collections were more or less performant than standard PHP functions. Each array had 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. 100,000 random strings as items, and I ran each function 100 times. In the end, we calculated the average of all of the response times. The final results are below: ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= ========== Tests Complete (ms) ==========
php      filter:  5.07
collect  filter:  6.49
=======================
php      search:  0.79
collect  search:  0
=======================
php      map:  3.45
collect  map:  4.18
=======================
php      sort:  25.27
collect  sort:  11.18
=======================
php      each:  1.03
collect  each:  6.96
=======================
php      reduce:  2.78
collect  reduce:  7.75
=======================
php      splice:  1
collect  splice:  0.74
======================= Conclusion As you can clearly see, though we gain a large amount of readability from Collections, we lose a significant amount of performance in some key areas. Filter , Map , Foreach , and Reduce are all faster with the standard PHP functions. Foreach and Reduce are actually incredibly significant differences. Search , Sort , and Splice all show Collections as the winner, and Sort is actually a massive time saver. Filter Map Foreach Reduce Foreach Reduce Search Sort Splice Sort It also is important to note that you would have to create each collection out of a built array which adds a tiny bit of overhead to setting up the collection to begin with, but even with that small amount of extra work and time, the results are pretty clear. In my opinion (and it is just an opinion based on these results), if performance is a huge concern, I would stick with standard PHP functionality for Foreach loops for sure and likely the same for any Reduce needs. If you need to do any sorting on large datasets, clearly Collections is the right way to go. The rest are so close that it really feels like a personal preference. Foreach Reduce At that point, I’d say collections are always easier to read and maintain. Obviously, you should take this information and make your own informed decision, however, if you’re like me, I think you’ll find yourself slipping collections in for many of these functions above. But I do think I’ll dial back my use of →each and →reduce where appropriate going forward! →each →reduce

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Authors:
(1) Tamay Aykut, Sureel, Palo Alto, California, USA;
(2) Markus Hofbauer, Technical University of Munich, Germany;
(3) Christopher Kuhn, Technical University of Munich, Germany;
(4) Eckehard Steinbach, Technical University of Munich, Germany;
(5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Related Works Virtual Audience Framework Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Authors: Authors: (1) Tamay Aykut, Sureel, Palo Alto, California, USA; (2) Markus Hofbauer, Technical University of Munich, Germany; (3) Christopher Kuhn, Technical University of Munich, Germany; (4) Eckehard Steinbach, Technical University of Munich, Germany; (5) Bernd Girod, Stanford University, Stanford, California, USA. Table of Links Abstract & Introduction Abstract & Introduction Related Works Related Works Virtual Audience Framework Virtual Audience Framework Conclusions & References Conclusions & References II. RELATED WORK In this section, we summarize techniques for synthesizing acoustic audience feedback. Then, we give an overview of how integrating audience feedback in virtual events is handled currently. A. Sound Synthesis A. Sound Synthesis Traditional sound synthesis techniques can be separated into five categories: sample-based, physical modeling, signal modeling,abstract synthesis [9], and learning-based synthesis. More recently, deep-learning-based synthesis approaches have redefined the possibilities in sound synthesis [8]. In sample-based synthesis, audio recordings are cut and spliced together to produce new sounds. The most common example of this is granular synthesis [10]. A sound grain is generally a small element or component of a sound, typically between 10 ms to 200 ms in length. Concatenative synthesis is a subset of granular synthesis [11]. The goal is to select and recombine the grains in a way that avoids perceivable discontinuities. Instead of using prerecorded audio data, physical modeling synthesis aims to model the underlying physical process of a sound system. Physical models require solving partial differential equations for each sample [12]. The resulting models are computationally intensive, requiring significant GPU resources to run in real time [13], [14]. In signal modeling synthesis, sounds are created based on an analysis of real-world sounds. The analyzed waveform is then used for synthesis. The most common method of signal modeling synthesis is Spectral Modeling Synthesis [15]. Spectral modeling can be approached by analyzing the original audio file, selecting a series of sine waves to be used for synthesis, and then combining it with a residual noise shape to produce the original sound [16]. In abstract synthesis, sounds are obtained using abstract methods and algorithms, typically to create entirely new sounds. An example is Frequency Modulation (FM) synthesis [17]. Two sine waves are multiplied together to create a more complex, richer sound that might not exist in the natural world. Early video game sounds were often based on FM synthesis. These sounds can be created and controlled in realtime due to the low complexity of the required process. Finally, in deep-learning-based synthesis, large amounts of recordings are used to obtain a sound synthesis model in a data-driven way [18], [19]. Autoencoders have shown great promise for this task [20], both for music [21] and speech synthesis [22]. Architectures such as WaveNet [3] allow to learn a generative synthesis model directly from real-world recordings, generating significantly more natural sounds than parametric systems. While such models are complex and computationally expensive, recent architectures have increased the inference speed [23], [24], [25]. In 2023, Meta released Audiocraft, which includes text-to-sound systems such as AudioGen [8] or MusicGen [26]. These models allow turning natural text into arbitrary sound, or into music. For the proposed framework, this flexible language-based approach allows easily turning abstract audience feedback data into sound by turning the abstract data into a text prompt first. B. Acoustic Feedback Synthesis B. Acoustic Feedback Synthesis Next, we address specific implementations of sound synthesis for creating the most common acoustic feedback sounds of clapping, whistling, booing, and laughter. Since the physical mechanism of Clapping is straightforward, synthesizing clapping sounds can be approached using physical modeling [27]. Whistling can be approached using abstract FM synthesis [28]. Booing can be generated using both abstract or sample-based synthesis [29]. The most complex and challenging sound to synthesize in a virtual audience is Laughter. Since an individual laughter is already a complex sound and additionally varies significantly from person to person, the most promising approaches for laughter synthesis are based on deep learning. Mori et al. [30] used WaveNet [3] to generate synthetic laughter that outperformed laughter synthesized using Hidden Markov Models (HMMs). They used the Online Gaming Voice Chat Corpus [31] to condition WaveNet, allowing them to control the amplitude envelope of the synthesized laughter. Despite the improved naturalness, the resulting laughter was still largely perceived as noisy and echoic. Another approach used transfer learning from models trained with speech data to circumvent the problem of lack of laughter training data [6]. First, text-to-speech is trained and then fine-tuned with smiled speech and laughter data. MelGAN [32] is used to obtain the output waveform. Finally, generative artificial intelligence methods such as Generative Spoken Language Modeling [33] or AudioGen [8] can be used to generate laughter from text prompt. Fine-tuning these models specifically for laughter is a promising direction for natural-sounding laughter synthesis. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Related Works on Acoustic Audience Feedback in Large Virtual Events

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