I have 2 matlab programs : prog1.m and prog2.m
I have to use a 2-D matrix M in both programs.
I have loaded the matrix in prog1.m (manually from a TEXT FILE).
I have to run both the programs ~100 times (for different matrix each time) . So now i have ran prog1.m 100 times , every time with different matrix .
Now is turn of prog2.m but i don't want to load matrix manually again.
I have the saved (100 copies of) prog1.m which contains M to be used in prog2.m also . Is there a method to load M from prog2.m to prog1.m ?
I want to add some code in prog2.m which automatically loads M from prog1.m .
PS: I am very new to MATLAB
If prog1 and prog2 are not already functions, rewrite them as functions. For example, the first line in them may look like:
function [out1 out2] = prog1(M)
function out = prog2(M)
Then, write a third function which, given a filename, loads the data, calls prog1 and prog2 and optionally saves the data in an appropriate form (you can use fileparts and fullfile to automatically create a new filename based on the input - e.g. given data101.txt, return data101_proc.mat. Bare bones of such a function would be along these lines:
function M = prog3(fname)
M = load(fname); % or whatever method is required for loading this data
%call prog1 and prog2
[out1 out2] = prog1(M);
out3 = prog2(M);
%make new filename
[fpath,fname2,ext] = fileparts(fname);
fname_out = fullfile(fpath, [fname2,'_out','.mat']);
%save data - depends on what outputs are and what you need to do with them later
save(fname_out, 'out1','out2','out3','M');
end
Finally, as babaea mentioned, you can use ls or dir, make up a list of the files you want to use, and create a loop which calls the above function on each file in turn.
The most efficient way of doing what you want to do is to read directly from the text file using textscan:
http://www.mathworks.co.uk/help/matlab/ref/textscan.html
If the formatting in the text files are the same, you can read from one file at a time, do your process then change the name and run again.
You can make the process more automated by changing the name of the file from which data is read dynamically in a loop around your main program. But the way to do this depends on the name of the text files.
Related
I have multiple small *.mat files, each containing 4 input images (template{1:4} and a second channel template2{1:4}) and 4 output images (region_of_interests{1:4}), a binarized ('mask') image to train a deep neural network.
I basically followed an example on Mathworks and it suggests to use a function (in this example #matreader) to read in custom file formats.
However ...
It seems impossible to load multiple images from one *.mat file using any load function as it only allows one output, and imageDatastore doen't seem to allow loading data from workspace. How could this be achieved?
Similarly, it seems impossible to load a pixelLabelDatastore from a workspace variable. As a workaround I ended up saving the contents of my *.mat file to an image (using imwrite, saving to save_dir), and re-loading it from there (in this case, the function doesn't even allow to load *.mat files.). (How) can this be achieved without re-saving the file as image?
Here my failed attempt to do so:
%main script
image_dir = pwd; %location of *.mat files
save_dir = [pwd '/a/']; %location of saved output masks
imds = imageDatastore(image_dir,'FileExtensions','.mat','ReadFcn',#matreader); %load template (input) images
pxds = pixelLabelDatastore(save_dir,{'nothing','something'},[0 255]);%load region_of_interests (output) image
%etc, etc, go on to train network
%matreader function, save as separate file
function data=matreader(filename)
in=1; %give up the 3 other images stored in template{1:4}
load(filename); %loads template and template2, containing 4x input images each
data=cat(3,template{in},template2{in}); %concatinate 2 template input images in 3rd dimension
end
%generate example data for this question, will save into a file 'example.mat' in workspace
for ind=1:4
template{ind}=rand([200,400]);
template2{ind}=rand([200,400]);
region_of_interests{ind}=rand([200,400])>.5;
end
save('example','template','template2','output')
You should be able to achieve this using the standard load and save function. Have a look at this code:
image_dir = pwd;
save_dir = pwd;
imds = imageDatastore(image_dir,'FileExtensions',{'.jpg','.tif'});
pxds = pixelLabelDatastore(save_dir,{'nothing','something'},[0 255]);
save('images.mat','imds', 'pxds')
clear
load('images.mat') % gives you the variable "imds" and "pxds" directly -> might override previous variables
tmp = load('images.mat'); % saves all variables in a struct, access it via tmp.imds and tmp.pxds
If you only want to select the variables you want to load use:
load('images.mat','imds') % loads "imds" variable
load('images.mat','pxds') % loads "pxds" variable
load('images.mat','imds','pxds') % loads both variables
EDIT
Now I get the problem, but I fear this is not how it is going to work. The Idea behind the Datastore objects is, that it is used if the data is too big to fit in memory as a whole, but every little piece is small enough to fit in memory. You can use the Datastore object than to easily process and read multiple files on a disk.
This means for you: Simply save your images not as one big *mat file but as multiple small *.mat files that only contain one image.
EDIT 2
Is it strictly necessary to use an imageDatastore for this task? If not you can use something like the following:
image_dir = pwd;
matFiles = dir([image_dir '*.mat']);
for i=1:length(matFiles)
data = load(matFiles(i).name);
img = convertMatToImage(data); % write custom function which converts the mat input to your image
% or something like this:
% for j=1:4
% img(:,:,j) = cat(3,template{j},template2{j});
% end
% process image
end
another alternative would be to create a "image" in your 'matreader' which does not only have 2 bands but to simply put all bands (all templates) on top of each other providing a "datacube" and then in an second step after iterating over all small mat files and reading them splitting the single images out of the one bigger datacube.
would look something like this:
function data=matreader(filename)
load(filename);
for in=1:4
data=cat(3,template{in},template2{in});
end
end
and in your main file, you have to simply split the data into 4 pieces.
I have never tested it but maybe it is possible to return a cell instead of a matrix?
function data=matreader(filename)
load(filename);
data = cell(1,4)
for in=1:4
data{in}=cat(3,template{in},template2{in});
end
end
Not sure if this would work.
However, the right way to go forward from here really depends on how you plan to use the images from imds and if it is really necessary to use a imageDatastore.
new MATLAB user here so apologies if this seems like a silly question. I have the following list of variables (doubles) in my workspace:
E1_01Strain E1_06Strain E1_07Strain E1_08Strain E1_09Strain E1_10Strain
E1_01Stress E1_06Stress E1_07Stress E1_08Stress E1_09Stress E1_10Stress
These are lists of numbers. I would like to remove the last n elements from each variable. I can do it with the command
E1_01Strain = E1_01Strain(1:end-100)
but it's impractical because later I'm going to have to do it on many, many more similar variables. Therefore I wanted to write a function that accepts as inputs a list of the workspace variables (as in, I highlight the variables I want and drag and drop into the function input) and removes from each one n elements.
I understand that I can write a function like this:
function [X1, X2, X3, X4] = Remove_n_elements[n, X1, X2, X3, X4]
X1 = X1(1:end-100);
X2 = X2(1:end-100);
X3 = X3(1:end-100);
X4= X4(1:end-100);
end
but that would mean that I would have to change the number of inputs, outputs, and the lines of code in the function every time. I'm sure there's a better way to do it but I can't figure it out.
I keep thinking that there might be a way to do it by looping over all the inputs but I can't get it to work since (as far as I know) I need to create a list of the inputs and then the operation is performed only on the elements of that list, not the inputs themselves.
I was looking at Passing A Variable Number of Arguments into a Function and from that using inputParser from https://www.mathworks.com/help/matlab/matlab_prog/parse-function-inputs.html but since I'm new to MATLAB I'm not sure how to use it for my case.
I used the code provided by il_raffa for a bit but followed his advice and went back and reconsidered how the script functions. After some more digging I wrote the following script that does exactly what I need. This script extracts the columns des_cols from all .csv files in a folder and plots them together. It then makes another plot of the averages.
files = dir('*.csv'); % navigate to the folder that you want to run the script on in MATLAB
avgStress = [];
avgStrain = [];
set(groot, 'DefaultLegendInterpreter', 'none') % the names of my .csv files have underscores that I want to see in the legend, if you don't want this then comment this line
hold on; %comment this and hold off further down if you want separate plots for every .csv
for file = files'
csv = xlsread(file.name);
[n,s,r] = xlsread(file.name);
des_cols = {'Stress','Ext.1(Strain)'}; % type here the names of the columns you want to extract
colhdrs = s(2,:);
[~,ia] = intersect(colhdrs, des_cols);
colnrs = flipud(ia);
file.name = n(:, colnrs);
file.name = file.name(1:end-600,:); % I wanted to remove the last 600 rows but if you want them all, remove the -600
plot(file.name(:,2),file.name(:,1),'DisplayName',s{1,1});
avgStress = [avgStress file.name(1:1500,1)]; % calculates the average stress for the first 1500 points, you can change it to whatever you want
avgStrain = [avgStrain file.name(1:1500,2)];
end
ylabel({'Stress (MPa)'}); % y-axis label
xlabel({'Strain (%)'}); %x-axis label
title({'E_2'}); % title of the plot
legend('show');
hold off; % commment this if you want different plots for all .csv files
avgStress = mean(avgStress,2);
avgStrain = mean(avgStrain,2);
plot(avgStrain,avgStress);
This creates two plots, one with all the raw data and another with just the averages. I hope this helps anyone that might have a similar issue.
The best thing you can do is to review the architecture of your SW in order to avoid the needs to perform such operations on the Workspace variables.
That is: how those variables are created? Are these variables loaded from a ".mat" file? etc.
Anyway, in order to avoid using the eval function and given your situation, a possible approach could be:
identify the names of the varailbes by using the function who. You can specify in the call to who the root name of the varaibles and use the * as, for example, who('E1*'). Make sure it fit wiht the desired variables. You can also use regexp to better refine the selection of the variables
save these varaibles in a temporary .mat file: the name (including the path of the temporary file can be created with the function tempname
load the temporary .mat file: this will create a struct in the Workspace whose fields are the variables you want to midify
call the function to remove the undesired elements form the fields of the struct. The function have to return the updated struct
save the updated struct in the temporary file
load again the temporary file by specifying the option -struct which allows loading the content of the file as single varaibles
The function to remove the undesired elements can be made as follows:
get the nams of the struct's fields by using the function fieldnames
loop over the filed of the struct by using the dynamic field names property
remove the undesired elements form the fields
return the updated struct
A possible implementatin could be:
Code "before" the call to the function
% Get the names of the variables
list_var=who('E1*')
% Define the name of a temporary ".mat" file
tmp_file=tempname
% Save the variables in the temporary ".mat" file
save(tmp_file,list_var{:});
% Load the variables in a struct
sel_vars=load(tmp_file);
% Call the function to remove the elements
out_str=Remove_n_elements(8,sel_vars)
Function to remove the undesired elements
function sel_vars=Remove_n_elements(n,sel_vars)
% Get the names of the fields of the struct
var_names=fieldnames(sel_vars)
% Loop over the fields and remove the undesired elements
for i=1:length(var_names)
sel_vars.(var_names{i})=sel_vars.(var_names{i})(1:end-n)
end
Code "after" the call to the function
% Save the updated struct in the temporary ".mat" file
save(tmp_file,'-struct','out_str')
% Load the updated struct as separate variables
load(tmp_file)
I have a function which takes multi-page tiff images and finds the average of the maximum pixel from each page. That function is working fine, but it takes a variable number of input pathways using the varargin function.
The inputs would have a format like 'C:\Users\me\desktop\thefolder\theimage.tif', the function takes all of these inputs and gives me the averages. I need help automating this process a bit more.
Up until now I have just been manually writing out the path for each individual image, but as the number of multipage tiff goes up it becomes time consuming to write everything out...
How do I write a function that will easily find all of the images, then give me char variables in the workspace corresponding to all of the image paths that I can then feed to the main function?
The dir function can take wildcards to return a structure array of all matching files in a single folder.
E.g.
>> files = dir('C:\Users\me\desktop\thefolder\*.tif');
files =
3x1 struct array with fields:
name
date
bytes
isdir
datenum
You could loop through each element of the array, or you can create a cell array in the workspace as follows:
>> tif_filenames = {files.name}
tif_filenames =
'theimage.tif' 'another.tif' 'and_another.tif'
However if your processing function uses varargin, then you can simple call it as follows:
>> find_max_average_pixel(files.name)
and MATLAB maps each filename to an entry in varargin, so from inside your function it appears as if it's been called with multiple strings.
If you wish to pull files from a tree of sub-folders, then it needs a bit more thought.
I have 100 data files in a folder called "Experiment1", and I need to take all of the data from them and put them into a single matrix. Each data file contains 15 columns and 40 rows of data.
The order in which the files are in the folder is arbitrary. It doesn't matter in what order they get put into the combined matrix.
I've written some code using dlmread that will do the job:
for i = 1:100
%% Read in the relevant file.
filename = ['File_' int2str(i) '.dat']
Data = dlmread(fullfile(pwd, 'Experiment1',filename));
%% Put all the data I need in a separate matrix
NeededData(1+((i-1)*40):i+((i-1)*40)-i+40,1:15) = Data(:,1:15);
end
However, there are two things I don't like about my code.
The files have random names at present, and I'd need to manually change all their names to "File_1.dat", "File_2.dat", etc.
The code is cumbersome and hard to read.
How could I do things better?
Since you've fixed the problem of defining the name of the files to be read with dir, you can improve the way you add the read data (Data) to the output matrix (NeededData).
You can sumultaneously read the input files and add the data to the output matrix by inserting the call to dlmread directly in the assignment statement:
files=dir('*.dat');
n_files=length(files)
% Initialize the output matrix as empty
NeededData_0=[]
for i=1:n_files
% Simultaneously read input file and assign data to the output matrinx
NeededData_0=[NeededData_0;dlmread(files(i).name)]
end
In case you prefer working with the inides (as in your origina approach), since you know in advance that all the files have the same (40) number of rows) you can simplify the notation as follows:
files=dir('*.dat');
n_files=length(files)
% Define the number of rows in each inout file
n_rows=40;
% Define the number of colums in each inout file
n_col=15;
NeededData_2=nan(n_rows*n_files,n_col)
% Define the sequence of rows
r_list=1:n_rows:n_rows*n_files
for i=1:3
Data=dlmread(files(i).name)
NeededData_2(r_list(i):r_list(i)+n_rows-1,:)=Data
end
Hope this helps.
Using the suggestion to use dir present in the answers I have made the following code, which is clearly an improvement on my earlier effort. I would welcome further improvements, or suggestions for alternative approaches.
files = dir('*.dat');
for i = 1:length({files.name})
%% Read in the relevant file.
Data = dlmread(files(i).name);
%% Put all the data I need in a separate matrix
NeededData(1+((i-1)*40):i+((i-1)*40)-i+40,1:15) = Data(:,1:15);
end
I am quite new to data analysis, so if this is a rookie question, I'm sorry, I am learning as I go.
I have just started doing some work in variable star astronomy. I have about 100 files for every night of observation that all contain the same basic information (star coordinates, magnitude, etc.). I am loading all of the files into my workspace as arrays using a for-loop
files = dir('*.out');
for i=1:length(files)
eval(['load ' files(i).name ' -ascii']);
end
I'm only really interested in two columns in each file. Is there a way to extract a column and set it to a vector while this for-loop is running? I'm sure that it's possible, but the actual syntax for it is escaping me.
try using load as a function and save it's output to a variable
files = dir('*.out');
twoCols = {};
for ii=1:length(files)
data = load( files(ii).name, '-ascii' ); % load file into "data"
twoCols{ii} = data(:,1:2); % take only two columns
end
Now variable twoCols holds the two columns of each file in a different cell.
You have to assign the load result to a new variable. Then if lets say your variable is starsInfo you can use
onlyTwoFirst = starsInfo(:,1:2)
That means take all the rows, but only columns 1 and 2.