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Sequences 3-4-5-7.zip



NumPy offers more indexing facilities than regular Python sequences. Inaddition to indexing by integers and slices, as we saw before, arrayscan be indexed by arrays of integers and arrays of booleans.




Sequences 3-4-5-7.zip


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DataFrame.from_dict() takes a dict of dicts or a dict of array-like sequencesand returns a DataFrame. It operates like the DataFrame constructor exceptfor the orient parameter which is 'columns' by default, but which can beset to 'index' in order to use the dict keys as row labels.


Beyond the colon operator, there are several functions for creating more general sequences. The seq function is the most general, and allows you to specify sequences in many different ways. In practice, though, you should never need to call it, since there are three other specialist sequence functions that are faster and easier to use, covering specific use cases.


A name file contains two columns. The firstcolumn contains the name of a reference sequence that is in a distancematrix and the second column contains the names of the sequences(separated by commas) that the reference sequence represents. The listof names in the second column should always contain at least thereference sequence name.


There are several reasons to be interested in providing a name file withyour distance matrix. First, as sequencing collections increase in size,the number of duplicate sequences is increasing. This is especially thecase with sequences generated via pyrosequencing. Sogin and colleagues1 found that less than50% of their sequences were unique. Because the alignments and distancesfor the duplicate sequences are the same, re-processing each duplicatesequence takes a considerable amount of computing time and memory.


A count or name file is not required (unless you are using the column=option), but depending on the data set to be analyzed, couldsignificantly accelerate the processing time of downstream calculations.In this simple example, the final dataset contains 51474 sequences. Thedistance matrix in the file final.phylip.dist is a lower triangle matrixfor the 3772 unique sequences. While you could just read the matrix inand analyze the set of 3772 unqiue sequences, this would give aconsiderably different analysis than if you used the entire 51474sequence data set. Considering the frequency of sequences is criticalfor pretty much every analysis in mothur, we want to use the name orcount file to artificially inflate the matrix to its full size. In thiscase we use the namefile option:


mothur remembers that the distances for the reference sequence alsoapply to all of the sequences listed in the second column. Using a namefile can considerably accelerate the amount of processing time requiredto analyze some data sets.


The initialize parameter allows to select the initial randomization forthe opticluster method. Options are singleton, meaning each sequence israndomly assigned to its own OTU, or oneotu meaning all sequences areassigned to one otu. We have found initialize=singleton to producebetter clustering in less time. Default=singleton.


In this segmentation problem, the input to the LSTM network is an ECG signal and the output is a sequence or mask of labels with the same length as the input signal. The network task is to label each signal sample with the name of the region it belongs to. For this reason, it is necessary to transform the region labels on the dataset to sequences containing one label per signal sample. Use a transformed datastore and the getmask helper function to transform the region labels. The getmask function adds a label category, "n/a", to label samples that do not belong to any region of interest.


We find all the matches witch Matches method.From the match collection, we get all the words into a list.var res = words .GroupBy(m => m) .OrderByDescending(g => g.Count()) .Select(x => new word = x.Key, Count = x.Count() ) .Take(10);The words are grouped and ordered by frequency in descending order. We takethe first top words.$ dotnet runthe 62103and 38848of 34478to 13400And 12846that 12576in 12331shall 9760he 9665unto 8942C# LINQ joinThe join clause joins sequences.Program.csstring[] basketA = "coin", "book", "fork", "cord", "needle" ;string[] basketB = "watches", "coin", "pen", "book", "pencil" ;var res = from item1 in basketA join item2 in basketB on item1 equals item2 select item1;foreach (var item in res) Console.WriteLine(item);We have two arrays in the example. With the join clause, we findall items that are present in both arrays.$ dotnet runcoinbookThe coin and book words are included in both arrays.C# LINQ partitioningThe Skip method skips the specified number of elements from thestart of the sequence and returns the remaining elements. TheSkipLast method returns the sequence elements with the specifiedlast number of elements omitted. The SkipWhile skips elements in asequence as long as the specified condition is true and then returns theremaining elements.The Takemethod returns the specified number of contiguous elements from the start of asequence. The TakeLast method omits all but the specified number ofthe last elements. The TakeWhile method returns elements from asequence as long as a specified condition is true, and then skips the remainingelements.Note: The SkipWhile and TakeWhilemethods stop at the first non-matching element.Program.csint[] vals = 1, 2, 7, 8, 5, 6, 3, 4, 9, 10 ;var res1 = vals.Skip(3);Console.WriteLine(string.Join(", ", res1));var res2 = vals.SkipLast(3);Console.WriteLine(string.Join(", ", res2));var res3 = vals.SkipWhile(e => e e


Other Non-GRE Pulse Sequences. Techniques using fast (turbo) spin echo have been investigated for fMRI in that it offers high signal-to-noise and reduced geometric distortion. However, FSE/TSE sequences produce T2-weighting, and some modification (such as introducing echo shifting delays or echo parity corrections) must be introduced to impart the needed T2* sensitivity required for fMRI at 3T and below. The hybrid GRASE method (with alternating gradient- and spin-echoes may offer a solution, but has not been widely explored at present. At 7T and above the need for T2*-weighting may not be so great, as pure T2-changes in the fMRI signal may better reflect the locus of neuronal activity. At ultra-high fields, therefore, spin echo and SSFP methods may be used and enjoy certain advantages.


The zip() function returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument sequences. In this example, we unzip our array using * and then zip it to get the transpose. 041b061a72


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