11/14/2023 0 Comments Matlab textscan into matrixM dlmread (filename,delimiter) reads data from the file using the specified delimiter and treats repeated delimiter characters as separate delimiters. The dlmread function detects the delimiter from the file and treats repeated white spaces as a single delimiter. To show you the serial number still represent the right time: > datestr(time,'yyyy-mm-dd HH:MM:SS. M dlmread (filename) reads an ASCII-delimited numeric data file into matrix M. For instance, if the first 5 lines of the files are the header, just add the option 'headerlines', 5 to the function call. This will give you an array time with a Matlab time serial number (often easier to use than strings). textscan is of course capable of handling headerlines and even comments within the file. In a case with your sample data, it looks like this: %% // read file Pass 2: Process your irregular column according to its specificities.However, you must define a format specification to use this function. A value of 1 or true instructs textscan to concatenate consecutive columns of the same class in the output cell array. Putting them into one cell array would just make the syntax to read and manipulate them harder with no benefit other than combinign them. The output is a matrix containing these results: DLMOutput 15 25 30 18 29 33 21 35 41 Using textscan() The textscan() function can read both strings and numbers in the same data set. I may want to look at a different set of 6889 lines within the text file. You might want to keep these in a structure: data.num num data.txt txt This will keep them in one container, and let you refer to them as more intuitive names. ) and read the non constant part in a separate column which will be processed in second pass. I am interested in blocks of 6889 lines where each set of 83 lines needs to write to a row of a matrix (83x83) - a different matrix for each column. The important point here is that when textscan encounters a row that doesn't contain numbers it returns an empty matrix so when you concatenate the resulting vectors you just get the ones that come from non-string rows. Pass 1: Read all the columns with a constant format according to their type (string, integer, etc.In this case they are all the same, but you can customize them however you want.There may be a way to do that in one single pass, but for me these kind of problems are easier to sort with a 2 pass approach. So we exclude that.Ĭonvdict is a dictionary where each key is a column number and each value is the function to convert that column to a number. Since there is a delimiter at the end of each line, this counts as an empty columns. I would like to read in the file as a string array of seperated strings so I can index the elements in the array. Mycols is just the numbers of the columns you want to keep. Hello, I have a text file that contains one column of words. Myconv is an anonymous function that takes a value (say 'field1=1'), splits it on the '=', symbol (making ), takes the last result ('1' ), the converts that to a float (1.`). Hmm but this solution reads data into a matrix, while I need to read it into a cell array and also using fid identifier (which textscan does). Using a value of Na positive integer (read format N times) does work the same as in Matlab. For instance, the N-1 option (repeat reading format until end of string) is not implemented in Octave 3.4.0. So in my tests this works: myconv = lambda x: int(x.split(b'='))ĭata = np.loadtxt('temp.txt', delimiter=' ', usecols=mycols, converters=convdict) strread and textscan in Octave 3.4.0 are not fully compatible with their implementations in Matlab 2009b (and probably later versions as well). You can provide a function that, you just need to provide it a function to strip out the unneeded text. textscan attempts to match the data in the file to the conversion specifier in formatSpec. There is an argument, converters, that lets you provide functions that do the actual conversion from text to a number. This works for your example: data = np.fromregex('temp.txt', r'field1=(\d+) field2=(\d+) field3=(\d+) ', dtype='int') It lets you read in based on a regular expression, with groups (parts surrounded by ()) as the values. Numpy's fromregex function is basically the same as textscan.
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