Textscan with very large .dat files, Matlab keeps crashing

2 vues (au cours des 30 derniers jours)
mashtine le 24 Avr 2014
Modifié(e) : Jeremy Hughes le 13 Mar 2017
Hey everyone,
I am using R2013b to read in some very large files, 35 of them but I am running into memory problems and matlab usually crashes before loading the files in. I am using textscan but I was hoping someone could help me edit the code so that it will load in the data a block at a time or at least make it less memory intensive. I need all the years in one large cell array.
Any ideas?
Many thanks!
HWFiles = {'midas_wind_197901-197912.txt','midas_wind_198001-198012.txt', ..........(up to 2013)};
HWData = cell(1,numel(HWFiles));
for i=1:numel(HWFiles);
fid = fopen(HWFiles{i}, 'r');
tmp = textscan(fid,'%s %*s %*f %*f %s %*f %f %*f %f %f %f %f %f %*f %*f %*f %*f %*f %*s %*f %*f %*s %*f %*f', 'Delimiter',',');
HWData{i} = tmp ;

Réponse acceptée

Walter Roberson
Walter Roberson le 24 Avr 2014
Run through the files, reading them with textscan(), but instead of storing the results all in memory, use the matFile class to append the new data to the end of a variable in a .mat file.
Once that is done, you can start a new MATLAB session and load() the .mat file to get the combined cell array.
  1 commentaire
mashtine le 24 Avr 2014
Hey Walter, that definitely sounds like it will do the trick. I am unfamiliar with the script for appending with matfile though (just the basics of it), how would that look roughly using any example in a loop.

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Plus de réponses (3)

per isakson
per isakson le 24 Avr 2014
Modifié(e) : per isakson le 24 Avr 2014
  • "load in the data a block at a time" . Block is that a part of a file?
  • converting from double to single saves on memory
  • your format specifier shows that you already skip many columns, "%*f"
  • "very large files" . Do these files contain hourly weather data? How large are they?
With textscan you can read N lines at a time
C = textscan( fileID, formatSpec, N )
But that will probably not help.
I imagine there are many possibilities to decrease the requirement for memory. However,
  • what data do you need to have simultaneously in memory to do the calculations?
  • could the files be downloaded from the net? Or could you attach a file to the question?
  8 commentaires
per isakson
per isakson le 25 Avr 2014
Modifié(e) : per isakson le 27 Avr 2014
"designed to handle large amounts of data" . HDF5 complies to that definition. HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections.
To discuss data storage, we need a better description of the use case than the one provided by OP.
I did an evaluation regarding storing time series from building automation systems and I settled on HDF5. And I'm happy with that choice.

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Justin le 24 Avr 2014
Modifié(e) : Justin le 24 Avr 2014
One thing that might help is increasing the Java Heap Memory. Go into the Home tab > Preferences > General > Java Heap Memory
The default is 128, try something conservative first such as 256. If you set it too high you will have to manually edit some config files before Matlab can start again so increment it slowly.
Another option is doing your analysis on each file separately or pulling out only the needed data from each file one at a time so the entire contents of all the files do not need to remain in memory.
For different file reading options you could also use readtable or if you have the statistics toolbox there is dataset.

Jeremy Hughes
Jeremy Hughes le 13 Mar 2017
Modifié(e) : Jeremy Hughes le 13 Mar 2017
Hi, If you can access R2014b or later, I'd recommend using DATASTORE to manage your import. It automatically breaks up files into blocks and manages multiple files.
ds = datastore(folder)
% List the names you want to import. e.g. ds.SelectedVariableNames = ds.VariableNames([1 3 5]);
ds.SelectedVariableNames = ...;
while hasdata(ds)
t = read(DS);% returns a table with the data for the current block.
% do stuff
This should do what you need. https://www.mathworks.com/help/matlab/datastore.html

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