I need to compare two matrix in two different .mat files, I mean that i have two different files: file1.mat and file2.mat, In each file I have 3 matrix:
File1.mat =(M11, M12, M13)
Fileé.mat =(M21, M22, M23)
I need to compare M11 and M21:
function [Matrice_Result]= difference ()
R1=importdata('file1.mat')
R2=importdata('file2.mat')
Matrice_Result= R1== R2
endfunction
The error that i found is:
error: binary operator '==' not implemented for 'scalar struct' by 'scalar struct' operations
error: called from differences at line 6 column 9
I would be very grateful if you could help me.
The easiest / most appropriate way to load data from a .mat file onto the workspace is via the load command. It allows you to import only a single variable (whose name you know) into the workspace.
You can do this by simply running the load command, without assigning to a variable:
>> load ('file1.mat', 'M11');
>> load ('file2.mat', 'M21');
>> whos
Variables in the current scope:
Attr Name Size Bytes Class
==== ==== ==== ===== =====
M11 1x3 24 double
M21 1x3 24 double
Total is 6 elements using 48 bytes
>> isequal (M11, M21)
ans = 1
However, if you do collect into a variable, this variable becomes a struct, whose fieldnames correspond to the names of the variables you imported, e.g.
>> S1 = load ('file1.mat', 'M11');
>> S2 = load ('file2.mat', 'M21');
>> isequal (S1.M11, S2.M21)
ans = 1
Related
I've got a little MatLab script, which I try to understand. It doesn't do very much. It only reads a text from a file and encode and decode it with the Huffman-functions.
But it throws an error while decoding:
"error: out of memory or dimension too large for Octave's index type
error: called from huffmandeco>dict2tree at line 95 column 19"
I don't know why, because I debugged it and don't see a large index type.
I added the part which calculates p from the input text.
%text is a random input text file in ASCII
%calculate the relative frequency of every Symbol
for i=0:127
nlet=length(find(text==i));
p(i+1)=nlet/length(text);
end
symb = 0:127;
dict = huffmandict(symb,p); % Create dictionary
compdata = huffmanenco(fdata,dict); % Encode the data
dsig = huffmandeco(compdata,dict); % Decode the Huffman code
I can oly use octave instead of MatLab. I don't know, if there is an unexpected error. I use the Octave Version 6.2.0 on Win10. I tried the version for large data, it didn't change anything.
Maybe anyone knows the error in this context?
EDIT:
I debugged the code again. In the function huffmandeco I found the following function:
function tree = dict2tree (dict)
L = length (dict);
lengths = zeros (1, L);
## the depth of the tree is limited by the maximum word length.
for i = 1:L
lengths(i) = length (dict{i});
endfor
m = max (lengths);
tree = zeros (1, 2^(m+1)-1)-1;
for i = 1:L
pointer = 1;
word = dict{i};
for bit = word
pointer = 2 * pointer + bit;
endfor
tree(pointer) = i;
endfor
endfunction
The maximum length m in this case is 82. So the function calculates:
tree = zeros (1, 2^(82+1)-1)-1.
So it's obvious why the error called a too large index type.
But there must be a solution or another error, because the code is tested before.
I haven't weeded through the code enough to know why yet, but huffmandict is not ignoring zero-probability symbols the way it claims to. Nor have I been able to find a bug report on Savannah, but again I haven't searched thoroughly.
A workaround is to limit the symbol list and their probabilities to only the symbols that actually occur. Using containers.Map would be ideal, but in Octave you can do that with a couple of the outputs from unique:
% Create a symbol table of the unique characters in the input string
% and the indices into the table for each character in the string.
[symbols, ~, inds] = unique(textstr);
inds = inds.'; % just make it easier to read
For the string
textstr = 'Random String Input.';
the result is:
>> symbols
symbols = .IRSadgimnoprtu
>> inds
inds =
Columns 1 through 19:
4 6 11 7 12 10 1 5 15 14 9 11 8 1 3 11 13 16 15
Column 20:
2
So the first symbol in the input string is symbols(4), the second is symbols(6), and so on.
From there, you just use symbols and inds to create the dictionary and encode/decode the signal. Here's a quick demo script:
textstr = 'Random String Input.';
fprintf("Starting string: %s\n", textstr);
% Create a symbol table of the unique characters in the input string
% and the indices into the table for each character in the string.
[symbols, ~, inds] = unique(textstr);
inds = inds.'; % just make it easier to read
% Calculate the frequency of each symbol in table
% max(inds) == numel(symbols)
p = histc(inds, 1:max(inds))/numel(inds);
dict = huffmandict(symbols, p);
compdata = huffmanenco(inds, dict);
dsig = huffmandeco(compdata, dict);
fprintf("Decoded string: %s\n", symbols(dsig));
And the output:
Starting string: Random String Input.
Decoded string: Random String Input.
To encode strings other than the original input string, you would have to map the characters to symbol indices (ensuring that all symbols in the string are actually present in the symbol table, obviously):
>> [~, s_idx] = ismember('trogdor', symbols)
s_idx =
15 14 12 8 7 12 14
>> compdata = huffmanenco(s_idx, dict);
>> dsig = huffmandeco(compdata, dict);
>> fprintf("Decoded string: %s\n", symbols(dsig));
Decoded string: trogdor
I want to run a MATLAB script M-file to reconstruct a point cloud in Octave. Therefore I had to rewrite some parts of the code to make it compatible with Octave. Actually the M-file works fine in Octave (I don't get any errors) and also the plotted point cloud looks good at first glance, but it seems that the variables are only half the size of the original MATLAB variables. In the attached screenshots you can see what I mean.
Octave:
MATLAB:
You can see that the dimension of e.g. M in Octave is 1311114x3 but in MATLAB it is 2622227x3. The actual number of rows in my raw file is 2622227 as well.
Here you can see an extract of the raw file (original data) that I use.
Rotation angle Measured distance
-0,090 26,295
-0,342 26,294
-0,594 26,294
-0,846 26,295
-1,098 26,294
-1,368 26,296
-1,620 26,296
-1,872 26,296
In MATLAB I created my output variable as follows.
data = table;
data.Rotationangle = cell2mat(raw(:, 1));
data.Measureddistance = cell2mat(raw(:, 2));
As there is no table function in Octave I wrote
data = cellfun(#(x)str2num(x), strrep(raw, ',', '.'))
instead.
Octave also has no struct2array function, so I had to replace it as well.
In MATLAB I wrote.
data = table2array(data);
In Octave this was a bit more difficult to do. I had to create a struct2array function, which I did by means of this bug report.
%% Create a struct2array function
function retval = struct2array (input_struct)
%input check
if (~isstruct (input_struct) || (nargin ~= 1))
print_usage;
endif
%convert to cell array and flatten/concatenate output.
retval = [ (struct2cell (input_struct)){:}];
endfunction
clear b;
b.a = data;
data = struct2array(b);
Did I make a mistake somewhere and could someone help me to solve this problem?
edit:
Here's the part of my script where I'm using raw.
delimiter = '\t';
startRow = 5;
formatSpec = '%s%s%[^\n\r]';
fileID = fopen(filename,'r');
dataArray = textscan(fileID, formatSpec, 'Delimiter', delimiter, 'HeaderLines' ,startRow-1, 'ReturnOnError', false, 'EndOfLine', '\r\n');
fclose(fileID);
%% Convert the contents of columns containing numeric text to numbers.
% Replace non-numeric text with NaN.
raw = repmat({''},length(dataArray{1}),length(dataArray)-1);
for col=1:length(dataArray)-1
raw(1:length(dataArray{col}),col) = mat2cell(dataArray{col}, ones(length(dataArray{col}), 1));
end
numericData = NaN(size(dataArray{1},1),size(dataArray,2));
for col=[1,2]
% Converts text in the input cell array to numbers. Replaced non-numeric
% text with NaN.
rawData = dataArray{col};
for row=1:size(rawData, 1)
% Create a regular expression to detect and remove non-numeric prefixes and
% suffixes.
regexstr = '(?<prefix>.*?)(?<numbers>([-]*(\d+[\.]*)+[\,]{0,1}\d*[eEdD]{0,1}[-+]*\d*[i]{0,1})|([-]*(\d+[\.]*)*[\,]{1,1}\d+[eEdD]{0,1}[-+]*\d*[i]{0,1}))(?<suffix>.*)';
try
result = regexp(rawData(row), regexstr, 'names');
numbers = result.numbers;
% Detected commas in non-thousand locations.
invalidThousandsSeparator = false;
if numbers.contains('.')
thousandsRegExp = '^\d+?(\.\d{3})*\,{0,1}\d*$';
if isempty(regexp(numbers, thousandsRegExp, 'once'))
numbers = NaN;
invalidThousandsSeparator = true;
end
end
% Convert numeric text to numbers.
if ~invalidThousandsSeparator
numbers = strrep(numbers, '.', '');
numbers = strrep(numbers, ',', '.');
numbers = textscan(char(numbers), '%f');
numericData(row, col) = numbers{1};
raw{row, col} = numbers{1};
end
catch
raw{row, col} = rawData{row};
end
end
end
You don't see any raw in my workspaces because I clear all temporary variables before I reconstruct my point cloud.
Also my original data in row 1311114 and 1311115 look normal.
edit 2:
As suggested here is a small example table to clarify what I want and what MATLAB does with the table2array function in my case.
data =
-0.0900 26.2950
-0.3420 26.2940
-0.5940 26.2940
-0.8460 26.2950
-1.0980 26.2940
-1.3680 26.2960
-1.6200 26.2960
-1.8720 26.2960
With the struct2array function I used in Octave I get the following array.
data =
-0.090000 26.295000
-0.594000 26.294000
-1.098000 26.294000
-1.620000 26.296000
-2.124000 26.295000
-2.646000 26.293000
-3.150000 26.294000
-3.654000 26.294000
If you compare the Octave array with my original data, you can see that every second row is skipped. This seems to be the reason for 1311114 instead of 2622227 rows.
edit 3:
I tried to solve my problem with the suggestions of #Tasos Papastylianou, which unfortunately was not successful.
First I did the variant with a struct.
data = struct();
data.Rotationangle = [raw(:,1)];
data.Measureddistance = [raw(:,2)];
data = cell2mat( struct2cell (data ).' )
But this leads to the following structure in my script. (Unfortunately the result is not what I would like to have as shown in edit 2. Don't be surprised, I only used a small part of my raw file to accelerate the run of my script, so here are only 769 lines.)
[766,1] = -357,966
[767,1] = -358,506
[768,1] = -359,010
[769,1] = -359,514
[1,2] = 26,295
[2,2] = 26,294
[3,2] = 26,294
[4,2] = 26,296
Furthermore I get the following error.
error: unary operator '-' not implemented for 'cell' operands
error: called from
Cloud_reconstruction at line 137 column 11
Also the approach with the dataframe octave package didn't work. When I run the following code it leads to the error you can see below.
dataframe2array = #(df) cell2mat( struct(df).x_data );
pkg load dataframe;
data = dataframe();
data.Rotationangle = [raw(:, 1)];
data.Measureddistance = [raw(:, 2)];
dataframe2array(data)
error:
warning: Trying to overwrite colum names
warning: called from
df_matassign at line 147 column 13
subsasgn at line 172 column 14
Cloud_reconstruction at line 106 column 20
warning: Trying to overwrite colum names
warning: called from
df_matassign at line 176 column 13
subsasgn at line 172 column 14
Cloud_reconstruction at line 106 column 20
warning: Trying to overwrite colum names
warning: called from
df_matassign at line 147 column 13
subsasgn at line 172 column 14
Cloud_reconstruction at line 107 column 23
warning: Trying to overwrite colum names
warning: called from
df_matassign at line 176 column 13
subsasgn at line 172 column 14
Cloud_reconstruction at line 107 column 23
error: RHS(_,2): but RHS has size 768x1
error: called from
df_matassign at line 179 column 11
subsasgn at line 172 column 14
Cloud_reconstruction at line 107 column 23
Both error messages refer to the following part of my script where I'm doing the reconstruction of the point cloud in cylindrical coordinates.
distLaserCenter = 47; % Distance between the pipe centerline and the blind zone in mm
m = size(data,1); % Find the length of the first dimension of data
zincr = 0.4/360; % z increment in mm per deg
data(:,1) = -data(:,1);
for i = 1:m
data(i,2) = data(i,2) + distLaserCenter;
if i == 1
data(i,3) = 0;
elseif abs(data(i,1)-data(i-1)) < 100
data(i,3) = data(i-1,3) + zincr*(data(i,1)-data(i-1));
else abs(data(i,1)-data(i-1)) > 100;
data(i,3) = data(i-1,3) + zincr*(data(i,1)-(data(i-1)-360));
end
end
To give some background information for a better understanding. The script is used to reconstruct a pipe as a point cloud. The surface of the pipe was scanned from inside with a laser and the laser measured several points (distance from laser to the inner wall of the pipe) at each deg of rotation. I hope this helps to understand what I want to do with my script.
Not sure exactly what you're trying to do, but here's a toy example of how a struct could be used in an equivalent manner to a table:
matlab:
data = table;
data.A = [1;2;3;4;5];
data.B = [10;20;30;40;50];
table2array(data)
octave:
data = struct();
data.A = [1;2;3;4;5];
data.B = [10;20;30;40;50];
cell2mat( struct2cell (data ).' )
Note the transposition operation (.') before passing the result to cell2mat, since in a table, the 'fieldnames' are arranged horizontally in columns, whereas the struct2cell ends up arranging what used to be the 'fieldnames' as rows.
You might also be interested in the dataframe octave package, which performs similar functions to matlab's table (or in fact, R's dataframe object): https://octave.sourceforge.io/dataframe/ (you can install this by typing pkg install -forge dataframe in your console)
Unfortunately, the way to display the data as an array is still not ideal (see: https://stackoverflow.com/a/55417141/4183191), but you can easily convert that into a tiny function, e.g.
dataframe2array = #(df) cell2mat( struct(df).x_data );
Your code can then become:
pkg load dataframe;
data = dataframe();
data.A = [1;2;3;4;5];
data.B = [10;20;30;40;50];
dataframe2array(data)
I have a question regarding octave or matlab data post processing.
I have files exported from fluent like below:
"Surface Integral Report"
Mass-Weighted Average
Static Temperature (k)
crossplane-x-0.001 1242.9402
crossplane-x-0.025 1243.0017
crossplane-x-0.050 1243.2036
crossplane-x-0.075 1243.5321
crossplane-x-0.100 1243.9176
And I want to use octave/matlab for post processing.
If I read first line by line, and save only the lines with "crossplane-x-" into a new file, or directly save the data in those lines into a matrix. Since I have many similar files, I can make plots by just calling their titles.
But I go trouble on identify lines which contain the char "crossplane-x-". I am trying to do things like this:
clear, clean, clc;
% open a file and read line by line
fid = fopen ("h20H22_alongHGpath_temp.dat");
% save full lines into a new file if only chars inside
txtread = fgetl (fid)
num_of_lines = fskipl(fid, Inf);
char = 'crossplane-x-'
for i=1:num_of_lines,
if char in fgetl(fid)
[x, nx] = fscanf(fid);
print x
endif
endfor
fclose (fid);
Would anybody shed some light on this issue ? Am I using the right function ? Thank you.
Here's a quick way for your specific file:
>> S = fileread("myfile.dat"); % collect file contents into string
>> C = strsplit(S, "crossplane-x-"); % first cell is the header, rest is data
>> M = str2num (strcat (C{2:end})) % concatenate datastrings, convert to numbers
M =
1.0000e-03 1.2429e+03
2.5000e-02 1.2430e+03
5.0000e-02 1.2432e+03
7.5000e-02 1.2435e+03
1.0000e-01 1.2439e+03
given is a function f(a,b,x,y) in gnuplot, where we got a 3D-space with x,y,z (using splot).
Also given is a csv file (without any header) of the following structure:
2 4
1 9
6 7
...
Is there a way to read out all the values of the first column and assign them to the variable a? Implicitly it should create something like:
a = [2,1,6]
b = [4,9,7]
The idea is to plot the function f(a,b,x,y) having iterated for all a,b tuples.
I've read through other posts where I hoped it would be related to it such as e.g. Reading dataset value into a gnuplot variable (start of X series). However I could not make any progres.
Is there a way to go through all rows of a csv file with two columns, using the each column value of a row as the parameter of a function?
Say you have the following data file called data:
1 4
2 5
3 6
You can load the 1st and 2nd column values to variables a and b easily using an awk system call (you can also do this using plot preprocessing with gnuplot but it's more complicated that way):
a=system("awk '{print $1}' data")
b=system("awk '{print $2}' data")
f(a,b,x,y)=a*x+b*y # Example function
set yrange [-1:1]
set xrange [-1:1]
splot for [i in a] for [j in b] f(i,j,x,y)
This is a gnuplot-only solution without the need for a system call:
a=""
b=""
splot "data" u (a=sprintf(" %s %f", a, $1), b=sprintf(" %s %f", b, \
$2)):(1/0):(1/0) not, for [i in a] for [j in b] f(i,j,x,y)
I'm currently doing Cox Proportional Hazards Modeling using Rpy2 - I imagine my question will cover other functions and the results from calling them as well though.
After I run the function, I have a variable which contains the results from the function, in the form of a vector. I have tried explicitly converting this to a DataFrame (resultsDataFrame = DataFrame(resultVector)). There are no errors returned when doing this. However, when I do resultsDataFrame.to_csvfile(filename) I get the following error:
Traceback (most recent call last):
File "<pyshell#171>", line 1, in <module>
modelFrame.to_csvfile('/Users/fortylashes/Documents/Matthews_Research/Cox_PH/ResultOutput_Exp1.csv')
File "/Library/Python/2.7/site-packages/rpy2/robjects/vectors.py", line 1031, in to_csvfile
'col.names': col_names, 'qmethod': qmethod, 'append': append})
RRuntimeError: Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) :
cannot coerce class ""coxph"" to a data.frame
Furthermore, when I simply do:
for result in resultVector:
print (result)
I get an extremely long list of results- including information on each entry in the dataset used in the model, for each variable (so 9,000 records x 9 variables = 81,000 unneeded results). The results I really need are at the bottom of this vector and look like this:
coef exp(coef) se(coef) z p
age_age6574 -0.057775 0.944 0.05469 -1.056 2.9e-01
age_age75plus -0.020795 0.979 0.04891 -0.425 6.7e-01
sex_female -0.005304 0.995 0.03961 -0.134 8.9e-01
stage_late -0.261609 0.770 0.04527 -5.779 7.5e-09
access -0.000494 1.000 0.00069 -0.715 4.7e-01
Likelihood ratio test=36.6 on 5 df, p=7.31e-07 n= 9752, number of events= 2601
*NOTE: There were several more variables for which data was reported in the initial results (the 9,000 x 9 that I was talking about) but weren't actually used in the model.
I was wondering if there was a way to explicitly get this data, put it in one long ordered row, and then output it to a csv file?
::::UPDATE::::
When I call theModel.names I get a list of the various measures which can be called by numerical index:
[1] "coefficients" "var" "loglik"
[4] "score" "iter" "linear.predictors"
[7] "residuals" "means" "concordance"
[10] "method" "n" "nevent"
[13] "terms" "assign" "wald.test"
[16] "y" "formula" "call"
From this I can get the coefficients, which can then be exponentiated. I have not found, however, the p-value, the z score or the likelihood test ratio, which I will need.