I am trying to evaluate with iddata (INFO) in matlab, a number of N_E experiments.
I already computed and have as cell arrays of size 1xN_E the outputs and inputs, y and u respectively. Every entry of the cell arrays y and u is a vector of length N=316 (SISO system). For the sake of correctness, period is also a cell array of size 1xN_E, with the period in every entry.
Using the command:
data = iddata(y,u,period);
doesn't produce the expected averaged data-set. Instead, it is handled as a 361x361MIMO system (!).
I've already tried transposing, without results.
data = iddata(y.',u.',period.');
Does someone know why this happens, and how can I produce the desired multi-experiment data-set?
P.S. the documentation I read is for Matlab R2014b, and I am running R2013b. Does someone know if this was not supported in my edition? Or how can I find out?
Actually, the Matlab documentation provides an answer to my question.
The function iddata is very strict regarding how the dimension of output y, input u and period period are defined.
Defining 1xN_experiments cell arrays for y,u and period (note: same size for all!; also N_experimentsx1 won't be recognized by iddata) and then using iddata:
data = iddata(y,u,period);
gives the desired iddata structure.
Note all vectors within y and u must be of same length(!)
Related
I have a matrix (type:double) of size 106 x 103. The matrix represents European gridded temperature data for one timestep (a day).
For every day, I want to calculate the degree days (thermal time) for a species, based on the temperature recorded for each 'cell' (i,j element) in the matrix using a formula that I have coded in Matlab based on a sinewave approach.
So, ultimately, what I want to do is being able to apply a calculation to my matrix, that will provide individual output for each grid cell (i,j element) dependent on the temperature data that is recorded there.
I could do this with a loop, but I have to accumulate these degree days for multiple years, so I would prefer to find a way of applying the calculation to each element in a daily matrix simultaneously (and then looping through the days (matrices)).
From what I have read, you can use a cellfun if your matrix is a cell array (mine is not).
Then I was also looking at the bsxfun option, but it seems like the functions are just standard functions..like mean, max etc.
So now, I'm looking at using arrayfun in conjunction with a function I create from my algorithm to calculate degree days.
I have been trying to write a test function but Matlab keeps throwing up the same error:
I type:
function output=degreedays(x)
and Matlab throws back:
Error: Function definitions are not permitted in this context.
Can someone tell me what I'm doing wrong? Why is it not accepting the declaration of the function name?
MATLAB does not allow you to define named functions like this at the command line. You need to place your function definition in a file. MATLAB then can call that function by the name of the file - so in your case, put your function definition in a file called degreedays.m.
See the doc for more: https://uk.mathworks.com/help/matlab/matlab_prog/create-functions-in-files.html .
I'm interested in understanding the variety of zeroes that a given function produces with the ultimate goal of identifying the what frequencies are passed in high/low pass filters. My idea is that finding the lowest value zero of a filter will identify the passband for a LPF specifically. I'm attempting to use the [hz,hp,ht] = zplane(z,p) function to do so.
The description for that function reads "returns vectors of handles to the zero lines, hz". Could someone help me with what a vector of a handle is and what I do with one to be able to find the various zeros?
For example, a simple 5-point running average filter:
runavh = (1/5) * ones(1,5);
using zplane(runavh) gives an acceptable pole/zero plot, but running the [hz,hp,ht] = zplane(z,p) function results in hz=175.1075. I don't know what this number represents and how to use it.
Many thanks.
Using the get command, you can find out things about the data.
For example, type G=get(hz) to get a list of properties of the zero lines. Then the XData is given by G.XData, i.e. X=G.XData.
Alternatively, you can only pull out the data you want
X=get(hz,'XData')
Hope that helps.
how to use the "from workspace block in simulink" ?
I have tried using the from workspace block by given 10*2 matrix as input. it is appending some extra data along the data I have given .
and I have such 3 such blocks and want to know how I merge them.
Read the documentation. Simulink is time-based so the data in your From Workspace block must be as a function of time. Does your 10 x 2 matrix represent a signal as a function of time? If so, it needs to be as follows:
A two-dimensional matrix:
The first element of each matrix row is a
time stamp.
The rest of each row is a scalar or vector of signal
values.
The leftmost element of each row is the time stamp of the
value(s) in the rest of the row.
10 values isn't very much, it's likely that Simulink will need additional data points at intermediate times, if you have the Interpolate Data check box ticked. If not, "the current output equals the output at the most recent time for which data exists".
I think you may have a misunderstanding of the variables intended to be read by the FromWorkspace block.
The block expects a time series defining the value at various points in the simulation.
The From Workspace block help should point you in the right direction on this. Mathworks Help Documentation
I believe that something like the following would work for you:
>> WorkspaceVar.time=0;
>> WorkspaceVar.signals.values=zeros(10,2)
>> WorkspaceVar.signals.dimensions = [10,2]
In Matlab, is it possible to measure local variation of a signal across an entire signal without using for loops? I.e., can I implement the following:
window_length = <something>
for n = 1:(length_of_signal - window_length/2)
global_variance(n) = var(my_signal(1:window_length))
end
in a vectorized format?
If you have the image processing toolbox, you can use STDFILT:
global_std = stdfilt(my_signal(:),ones(window_length,1));
% square to get the variance
global_variance = global_std.^2;
You could create a 2D array where each row is shifted one w.r.t. to the row above, and with the number of rows equal to the window width; then computing the variance is trivial. This doesn't require any toolboxes. Not sure if it's much faster than the for loop though:
longSignal = repmat(mySignal(:), [1 window_length+1]);
longSignal = reshape(longSignal(1:((length_of_signal+1)*window_length)), [length_of_signal+1, window_length])';
global_variance = sum(longSignal.*longSignal, 2);
global_variance = global_variance(1:length_of_signal-window_length));
Note that the second column is shifted down by one relative to the one above - this means that when we have the blocks of data on which we want to operate in rows, so I take the transpose. After that, the sum operator will sum over the first dimension, which gives you a row vector with the results you want. However, there is a bit of wrapping of data going on, so we have to limit to the number of "good" values.
I don't have matlab handy right now (I'm at home), so I was unable to test the above - but I think the general idea should work. It's vectorized - I can't guarantee it's fast...
Check the "moving window standard deviation" function at Matlab Central. Your code would be:
movingstd(my_signal, window_length, 'forward').^2
There's also moving variance code, but it seems to be broken.
The idea is to use filter function.
What is the difference between hist and imhist functions in Matlab? I have a matrix of color levels values loaded from image with imread and need to count entropy value of the image using histogram.
When using imhist the resulting matrix contains zeros in all places except the last one (lower-right) which contains some high value number (few thousands or so).
Because that output seems to be wrong, I have tried to use hist instead of imhist and the resulting values are much better, the matrix is fulfilled with correct-looking values instead of zeros.
However, according to the docs, imhist should be better in this case and hist should give weird results..
Unfortunately I am not good at Matlab, so I can not provide you with better problem description. I can add some other information in the future, though.
So I will try to better explain my problem..I have an image, for which I should count entropy and few other values (how much bytes it will take to save that image,..). I wrote this function and it works pretty well
function [entropy, bytes_image, bytes_coding] = entropy_single_pixels(im)
im = double(im);
histg = hist(im);
histg(histg==0) = [];
nzhist = histg ./ numel(im);
entropy = -sum(nzhist.*log2(nzhist));
bytes_image = (entropy*(numel(im))/8);
bytes_coding = 2*numel(unique(im));
fprintf('ENTROPY_VALUE:%s\n',num2str(entropy));
fprintf('BYTES_IMAGE:%s\n',num2str(bytes_image));
fprintf('BYTES_CODING:%s\n',num2str(bytes_coding));
end
Then I have to count the same, but I have to make "pairs" from pixels which are below each other. So I have only half the rows and the same count of columns. I need to express every unique pixel pair as a different number, so I multiplied the first one by 1000 and added the second one to it... Subsequently I need to actually apply the same function as in the first example, but that is the time, when I am getting weird numbers from the imhist function. When using hist, it seems to be OK, but I really don't think that behavior is correct, so that must be my error somewhere. I actually understand pretty good, to what I want to do, or at least I hope so, but unfortunately Matlab makes all that kind of hard for me :)
hist- compute histogram(count number of occurance of each pixel) in color image.........
imhist- compute histogram in two dimensional image.
Use im2double instead of double if you want to use imhist. The imhist function expects double or single-precision data to be in the [0,1] data range, which is why you see everything in the last bin of the histogram.