mat-file

问题 Using SciPy and MATLAB, I'm having trouble reconstructing an array to match what is given from a MATLAB cell array loaded using scipy.io.loadmat(). For example, say I create a cell containing a pair of double arrays in MATLAB and then load it using scipy.io (I'm using SPM to do imaging analyses in conjunction with pynifti and the like) MATLAB >> onsets{1} = [0 30 60 90] >> onsets{2} = [15 45 75 105] Python >>> import scipy.io as scio >>> mat = scio.loadmat('onsets.mat') >>> mat['onsets'][0]

问题 I have a mat-file that I accessed using from scipy import io mat = io.loadmat('example.mat') From matlab, example.mat contains the following struct >> load example.mat >> data1 data1 = LAT: [53x1 double] LON: [53x1 double] TIME: [53x1 double] units: {3x1 cell} >> data2 data2 = LAT: [100x1 double] LON: [100x1 double] TIME: [100x1 double] units: {3x1 cell} In matlab, I can access data as easy as data2.LON, etc.. It's not as trivial in python. It give me several option though like mat.clear mat

问题 I am running a simulation where i generate huge 2d sparse matrices and hence i use FIND function to only store nonzero values with their indices. Now for each iteration of for loop i generate such matrix and because they are all of different length I use cell-array to store these configurations. But for large simulations even squeezed-off format of cell-array crosses its limits of memory and hence i want to write these cell-array while running the code i.e. for each iteration append a new

问题 I have a big .mat file that I want to process, but it is too big to fit in a single load. I thought to load it in parts, each time to access just the important parameters. So I have practically two questions: How can I access the variables names of the mat file without loading it? How can I load only one of them to the workspace? Thanks! 回答1: you can see the list of variables using: vars = whos('-file','name.mat'); and then just load the variable you want, say the first one on the list, by:

问题 I'm currently cross-compiling on the BeagleBone Black in a Visual Studio environment using Armadillo to translate MATLAB code into C++. This is a signal processing project, so I need a way to read and write binary data files, specifically .mat files. Thankfully, the armadillo documentation says that you can load .mat files directly into a matrix using .load() I attempted that at first, but it seems like it's not reading the file correctly, nor is it reading all the entries. My reference file

问题 I need to perform multiplication involving 60000X70000 matrix either in python or matlab. I have a 16GB RAM and am able to load each row of the matrix easily (which is what I require). I am able to create the matrix as a whole in python but not in matlab. Is there anyway I can save the array as .mat file of v7.3 using h5py or scipy so that I can load each row separately? 回答1: For MATLAB v7.3 you can use hdf5storage which requires h5py , download the file here, extract, then type: python setup

问题 i'm trying to load part of array in a matfile like is showed in http://www.mathworks.com/help/matlab/ref/matfile.html however, when i use loadedData = matObj.varName(indices) i keep getting: 'varName' does not exist someone knows what's rong? 回答1: In place of VarName you should use the name of the actual variable you want to retrieve. Suppose you have saved a variable A into myMat : A = rand(10); save('myMat','A','-v7.3') matObj = matfile('myMat'); data = matObj.A(1:2,2); 来源： https:/

问题 I have some variables, which include dictionaries, list of list, and numpy arrays. I save all of them with the following code, where obj=[var1,var2,...,varn]. The variables size is small enough to be loaded in memory. My problem is when I save the corresponding variables in matlab the output file takes a lot less space on the disk than doing it in python. Similarly, loading the variables from the disk takes a lot more time to be loaded in memory in python than matlab. with open(filename, 'wb'

问题 I am working with a very computational expansive code in Matlab. It requires the usage of optimisation techniques and long computations using very big matrixes. I am having the following issue: even if the code run correctly, at the end of the iterations required by the code, Matlab is not storing the biggest cell arrays that I have. I guess that it is due to some memory inefficiency in my code or with my computer (which is probably not sufficiently powerful). However, I followed all the

问题 I have a matlab .mat file with table data type which I want to import in R. I am using 'readMat' for this and R is reading it as a List. After that is there a way to convert the list into either a dataframe or table format in R? When I use as.dataframe I get the following error : Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : arguments imply differing number of rows: 5, 6, 1 A possible workaround I thought of is to export the table as a .csv from matlab