3d Max 8 Keygen Generator 12
Wan Cabiness
The function* declaration creates a binding of a new generator function to a given name. A generator function can be exited and later re-entered, with its context (variable bindings) saved across re-entrances.
A function* declaration creates a GeneratorFunction object. Each time when a generator function is called, it returns a new Generator object, which conforms to the iterator protocol. When the iterator's next() method is called, the generator function's body is executed until the first yield expression, which specifies the value to be returned from the iterator or, with yield*, delegates to another generator function. The next() method returns an object with a value property containing the yielded value and a done property which indicates whether the generator has yielded its last value, as a boolean. Calling the next() method with an argument will resume the generator function execution, replacing the yield expression where an execution was paused with the argument from next().
\n A function* declaration creates a GeneratorFunction object. Each time when a generator function is called, it returns a new Generator object, which conforms to the iterator protocol. When the iterator's next()\n method is called, the generator function's body is executed until the first\n yield expression, which specifies the value to be\n returned from the iterator or, with yield*, delegates\n to another generator function. The next() method returns an object with a\n value property containing the yielded value and a done\n property which indicates whether the generator has yielded its last value, as a boolean.\n Calling the next() method with an argument will resume the generator\n function execution, replacing the yield expression where an execution was\n paused with the argument from next().\n
What is a generator and why would you use it? Without quoting any books, obviously (unless you can find a decent, simplistic answer direct from a book). Perhaps with examples, if you're feeling generous!
A generator is simply a function which returns an object on which you can call next, such that for every call it returns some value, until it raises a StopIteration exception, signaling that all values have been generated. Such an object is called an iterator.
Normal functions return a single value using return, just like in Java. In Python, however, there is an alternative, called yield. Using yield anywhere in a function makes it a generator. Observe this code:
Observe that a generator object is generated once, but its code is not run all at once. Only calls to next actually execute (part of) the code. Execution of the code in a generator stops once a yield statement has been reached, upon which it returns a value. The next call to next then causes execution to continue in the state in which the generator was left after the last yield. This is a fundamental difference with regular functions: those always start execution at the "top" and discard their state upon returning a value.
There are more things to be said about this subject. It is e.g. possible to send data back into a generator (reference). But that is something I suggest you do not look into until you understand the basic concept of a generator.
This code uses itertools.islice to take a finite number of elements from an infinite stream. You are advised to have a good look at the functions in the itertools module, as they are essential tools for writing advanced generators with great ease.
and so on. The (or one) benefit of generators is that because they deal with data one piece at a time, you can deal with large amounts of data; with lists, excessive memory requirements could become a problem. Generators, just like lists, are iterable, so they can be used in the same ways:
First of all, the term generator originally was somewhat ill-defined in Python, leading to lots of confusion. You probably mean iterators and iterables (see here). Then in Python there are also generator functions (which return a generator object), generator objects (which are iterators) and generator expressions (which are evaluated to a generator object).
According to the glossary entry for generator it seems that the official terminology is now that generator is short for "generator function". In the past the documentation defined the terms inconsistently, but fortunately this has been fixed.
As many other answers are showing, there are different ways to create a generator. You can use the parentheses syntax as in my example above, or you can use yield. Another interesting feature is that generators can be "infinite" -- iterators that don't stop:
When a function is a generator, it can return a value without the stack frame being discarded, using the yield statement. The values of local variables and the program counter within the function are preserved. This allows the generator to be resumed at a later time, with execution continuing from the yield statement, and it can execute more code and return another value.
Before Python 2.5 this was all generators did. Python 2.5 added the ability to pass values back in to the generator as well. In doing so, the passed-in value is available as an expression resulting from the yield statement which had temporarily returned control (and a value) from the generator.
The key advantage to generators is that the "state" of the function is preserved, unlike with regular functions where each time the stack frame is discarded, you lose all that "state". A secondary advantage is that some of the function call overhead (creating and deleting stack frames) is avoided, though this is a usually a minor advantage.
The only thing I can add to Stephan202's answer is a recommendation that you take a look at David Beazley's PyCon '08 presentation "Generator Tricks for Systems Programmers," which is the best single explanation of the how and why of generators that I've seen anywhere. This is the thing that took me from "Python looks kind of fun" to "This is what I've been looking for." It's at
yield behaves like return in the sense that values that are yielded get "returned" by the generator. Unlike return, however, the next time the generator gets asked for a value, the generator's function, foo, resumes where it left off -- after the last yield statement -- and continues to run until it hits another yield statement.
The yield statement is used in conjuction with Python generators. It saves the state of the function and returns the yeilded value. The next time you call the next() function on the generator, it will continue where the yield left off.
After I ran this that the memory is almost exactly the same and that's because the generator hasn't actually done anything yet it's not holding those million values in memory it's waiting for me to grab the next one.
As well and you can still use all of the comprehensions and this generator expression here so you don't lose anything in that area. So those are a few reasons why you would use generators and also some of the advantages that come along with that.
A function which returns an asynchronous generator iterator. Itlooks like a coroutine function defined with async def exceptthat it contains yield expressions for producing a series ofvalues usable in an async for loop.
This is an asynchronous iterator which when called using the__anext__() method returns an awaitable object which will executethe body of the asynchronous generator function until the nextyield expression.
Each yield temporarily suspends processing, remembering thelocation execution state (including local variables and pendingtry-statements). When the asynchronous generator iterator effectivelyresumes with another awaitable returned by __anext__(), itpicks up where it left off. See PEP 492 and PEP 525.
A function which returns a generator iterator. It looks like anormal function except that it contains yield expressionsfor producing a series of values usable in a for-loop or that can beretrieved one at a time with the next() function.
Each yield temporarily suspends processing, remembering thelocation execution state (including local variables and pendingtry-statements). When the generator iterator resumes, it picks up whereit left off (in contrast to functions which start fresh on everyinvocation).
In electricity generation, a generator[1] is a device that converts motion-based power (potential and kinetic energy) or fuel-based power (chemical energy) into electric power for use in an external circuit. Sources of mechanical energy include steam turbines, gas turbines, water turbines, internal combustion engines, wind turbines and even hand cranks. The first electromagnetic generator, the Faraday disk, was invented in 1831 by British scientist Michael Faraday. Generators provide nearly all the power for electrical grids.
The reverse conversion of electrical energy into mechanical energy is done by an electric motor, and motors and generators are very similar. Many motors can generate electricity from mechanical energy.
Before the connection between magnetism and electricity was discovered, electrostatic generators were invented. They operated on electrostatic principles, by using moving electrically charged belts, plates and disks that carried charge to a high potential electrode. The charge was generated using either of two mechanisms: electrostatic induction or the triboelectric effect. Such generators generated very high voltage and low current. Because of their inefficiency and the difficulty of insulating machines that produced very high voltages, electrostatic generators had low power ratings, and were never used for generation of commercially significant quantities of electric power. Their only practical applications were to power early X-ray tubes, and later in some atomic particle accelerators.
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