Sample Serial Numbers

[Nella Mcnairy]

For integers, there is uniform selection from a range. For sequences, there isuniform selection of a random element, a function to generate a randompermutation of a list in-place, and a function for random sampling withoutreplacement.

On the real line, there are functions to compute uniform, normal (Gaussian),lognormal, negative exponential, gamma, and beta distributions. For generatingdistributions of angles, the von Mises distribution is available.

If a is omitted or None, the current system time is used. Ifrandomness sources are provided by the operating system, they are usedinstead of the system time (see the os.urandom() function for detailson availability).

Returns a non-negative Python integer with k random bits. This methodis supplied with the Mersenne Twister generator and some other generatorsmay also provide it as an optional part of the API. When available,getrandbits() enables randrange() to handle arbitrarily largeranges.

If a weights sequence is specified, selections are made according to therelative weights. Alternatively, if a cum_weights sequence is given, theselections are made according to the cumulative weights (perhaps computedusing itertools.accumulate()). For example, the relative weights[10, 5, 30, 5] are equivalent to the cumulative weights[10, 15, 45, 50]. Internally, the relative weights are converted tocumulative weights before making selections, so supplying the cumulativeweights saves work.

If neither weights nor cum_weights are specified, selections are madewith equal probability. If a weights sequence is supplied, it must bethe same length as the population sequence. It is a TypeErrorto specify both weights and cum_weights.

The weights or cum_weights can use any numeric type that interoperateswith the float values returned by random() (that includesintegers, floats, and fractions but excludes decimals). Weights are assumedto be non-negative and finite. A ValueError is raised if allweights are zero.

For a given seed, the choices() function with equal weightingtypically produces a different sequence than repeated calls tochoice(). The algorithm used by choices() uses floating-pointarithmetic for internal consistency and speed. The algorithm usedby choice() defaults to integer arithmetic with repeated selectionsto avoid small biases from round-off error.

Note that even for small len(x), the total number of permutations of xcan quickly grow larger than the period of most random number generators.This implies that most permutations of a long sequence can never begenerated. For example, a sequence of length 2080 is the largest thatcan fit within the period of the Mersenne Twister random number generator.

Returns a new list containing elements from the population while leaving theoriginal population unchanged. The resulting list is in selection order so thatall sub-slices will also be valid random samples. This allows raffle winners(the sample) to be partitioned into grand prize and second place winners (thesubslices).

Multithreading note: When two threads call this functionsimultaneously, it is possible that they will receive thesame return value. This can be avoided in three ways.1) Have each thread use a different instance of the randomnumber generator. 2) Put locks around all calls. 3) Use theslower, but thread-safe normalvariate() function instead.

mu is the mean angle, expressed in radians between 0 and 2*pi, and kappais the concentration parameter, which must be greater than or equal to zero. Ifkappa is equal to zero, this distribution reduces to a uniform random angleover the range 0 to 2*pi.

Class that uses the os.urandom() function for generating random numbersfrom sources provided by the operating system. Not available on all systems.Does not rely on software state, and sequences are not reproducible. Accordingly,the seed() method has no effect and is ignored.The getstate() and setstate() methods raiseNotImplementedError if called.

Sometimes it is useful to be able to reproduce the sequences given by apseudo-random number generator. By reusing a seed value, the same sequence should bereproducible from run to run as long as multiple threads are not running.

Economics Simulationa simulation of a marketplace byPeter Norvig that shows effectiveuse of many of the tools and distributions provided by this module(gauss, uniform, sample, betavariate, choice, triangular, and randrange).

After I import an audio file into Audacity it displays time or length of track above the waveform. How do I make it display the number of samples instead? I need to do this specifically for wav files. Thank you.

no
I have a pure speech sample and the same sample mixed with various standard noise files
I need to see what kind on change a particular type of noise has on the speech waveform
If I could have a fine grid over the waveform display then it would e easier for me to measure and get an estimate of how much the amplitude of speech is changing due to particular type of noise
And in the code I am working with in Matlab, I do everything in terms if samples, but Audacity displays it in terms of time duration
If I could display it in term of samples in Audacity as well it would be easier for me to compare between my Matlab code result and the Audacity result

But if k is large relative to N, this algorithm could lead to lots of collisions and could be pretty slow. We can do better by guaranteeing that we can add one element on each insertion (brought to you by Robert Floyd):

It works by randomly generating K numbers and adding them to a set. If a generated number happens to already exist in the set, it places the value of a counter instead which is guaranteed to have not been seen yet. Thus it is guaranteed to run in linear time and does not require a large intermediate structure. It still has pretty good random distribution properties.

As pointed out in Yksisarvinen's answer, C++17 provides std::sample in that should be useful. Unfortunately its use of iterators makes working directly with integers awkward, i.e. not building a large temporary array/vector, and the only way I've got it working usefully was with lots of boilerplate code:

Some notes: std::shuffle always shuffles the whole range, but when you only need k items you can stop with the fisher-yates shuffle at the kth element, making it the fastest method when the set to be sampled from already exists.

I added an optional binary search for the location on where to insert the newlygenerated random member, but after attempting to benchmark its execution over large ranges(N)/and sets (K) (done on codeinterview.io/), I have not found any significant benefit to doing so, over just linearly traversing and exiting early.

I believe this is about the platform because when I just re-run without changing anything or re-extraction, read count can jump from 2k to 100k. Plus, phred scores are getting worse and worse with each run.

You can have a wide range on a sequencing run. Keep in mind that there are multiple stochastic processes in 16s rRNA sequencing that affect read depth. Extraction efficiency is one, but you also have PCR efficiency and flow cell adherence efficiency. These can all result in varying read depths for multiple samples in the same run. Re-extraction may save some of the samples, but some just have lower counts.

You can also use objective methods to determine a minimum acceptable read depth. The alpha and beta rarefaction methods allow you to determine the effects of rarefaction on alpha and beta diversity, which can be used as guides for your analysis. Minimum acceptable depth will depend on the diversity present in a sample, so (compared to using a rule-of-thumb approach) these methods can allow you to select acceptably lower read depths in low diversity samples and prevent you from going too low in high diversity samples.

The sample boat registration documents below are the registration document and the registration renewal notice. The samples show the location of the boat registration number and the first three letters of the last name of the registrant.

The sample snowmobile registration document below is the registration document. The sample shows the location of the snowmobile registration number and the first three letters of the last name of the registrant.

This page presents an annotated sample GenBank record (accessionnumber U49845) in its GenBank Flat File format. You can see thecorresponding live record for U49845, and seeexamples of other records that show a range ofbiological features.

The locus name was originally designed to help group entries withsimilar sequences: the first three characters usually designated theorganism; the fourth and fifth characters were used to show othergroup designations, such as gene product; for segmented entries, thelast character was one of a series of sequential integers. (SeeGenBank release notessection 3.4.4 for more info.)

However, the 10 characters in the locus name are no longer sufficientto represent the amount of information originally intended to becontained in the locus name. The only rule now applied in assigning alocus name is that it must be unique. For example, for GenBank recordsthat have 6-character accessions (e.g., U12345), the locus name isusually the first letter of the genus and species names, followed bythe accession number. For 8-character character accessions (e.g.,AF123456), the locus name is just the accession number.

The RefSeq database of reference sequences assigns formallocus names to each record, based on gene symbol. RefSeq is separatefrom the GenBank database, but contains cross-references tocorresponding GenBank records.

Entrez Search Field: Accession Number [ACCN] Search Tip : It isbetter to search for the actual accession number rather than the locusname, because the accessions are stable and locus names can change.

Entrez Search Field : Sequence Length [SLEN] Search Tips : (1) Toretrieve records within a range of lengths, use the colon as the rangeoperator, e.g., 2500:2600[SLEN]. (2) To retrieve all sequencesshorter than a certain number, use 2 as the lower bound, e.g.,2:100[SLEN]. (3) To retrieve all sequences longer than a certainnumber, use a series of 9's as the upper bound, e.g.,325000:99999999[SLEN].

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