Help reading and cleaning data from text file

Question

jnaumann 2013-8-30

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/85987-help-reading-and-cleaning-data-from-text-file

采纳的回答： dpb

Hello everyone,

I am currently having difficulties trying to read a text file in Matlab. A small example is shown below.

My aim is to;

-Open the file

-Ignore the header (first n lines)

-Replace the Bad entries with NaN

-Replace any corrupted entries with NaN (as entry (3,1))

----

I tried the following code with which I had some success if I removed the header first but I am struggling to get it all to work together.

buffer=fileread('C:\Users\Jack\Desktop\ContourGT\13.07.2013_header_removed.txt);

buffer = strrep(buffer, 'Bad', 'NaN') ;

surface=sscanf(buffer, '%f',[width length]);

---

Wyko ASCII Data File Format 0 0 1

X Size 640

Y Size 3015

Block Name Type Length Value

Wavelength 7 4 200.746124

Bad Bad Bad Bad Bad Bad

Bad -15216.897461 Bad Bad Bad Bad

-15s83.3t93555 Bad Bad Bad Bad Bad

Bad Bad -15169.881836 Bad Bad Bad

Bad Bad Bad Bad Bad Bad

---

Any ideas? Any thoughts would be gratefully appreciated.

Thanks in advance

Jack

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Answer 1

dpb 2013-8-30

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/85987-help-reading-and-cleaning-data-from-text-file#answer_95528

在 MATLAB Online 中打开

>> fmt=repmat('%s',1,6);
>> c=textscan(fid,fmt,'delimiter',' ','headerlines',5,'collectoutput',true);
>> fid=fclose(fid);
>> strrep(c{:},'Bad','NaN')
ans = 
'NaN'            'NaN'           'NaN'           'NaN'    'NaN'    'NaN'
'NaN'            '-15216.897461' 'NaN'           'NaN'    'NaN'    'NaN'
'-15s83.3t93555' 'NaN'           'NaN'           'NaN'    'NaN'    'NaN'
'NaN'            'NaN'           '-15169.881836' 'NaN'    'NaN'    'NaN'
'NaN'            'NaN'           'NaN'           'NaN'    'NaN'    'NaN'
'NaN'            'NaN'           'NaN'           'NaN'    'NaN'    'NaN'
>>

Now either use regexp or cellfun w/ a pattern on the cell content to replace any string that isn't all numeric w/ 'NaN' as well. Then you can use str2num()

The only assumption is that the corruption is limited as your sample so that there are a consistent number of fields per record.