I have a fairly simple issue and I just want to know if there's an easy way to do it in MATLAB (i.e. a function to do this rather than writing out loops or something myself).
Let's say I have a timeseries where Time is 1:1:1000 and Data is 2 * (1:1:1000) and I want to expand the array by making the Time and Data vector finer. Let's say that I want Time to be 1:0.1:1000 and Data to be 2 * (1:0.1:1000). Is there an easy way to tell MATLAB that to repeat the values of each vector 10 times (from 1 / 0.1 = 10) so that I can have something like this?:
Time: [1, 2, 3, 4, ...]
Data: [2, 4, 6, 8, ...]
to:
Time: [1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, ...]
Data: [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, ...]
You can use combination of reshape() and repmat() as follow:
Data = [2, 4, 6, 8, ...] % As stated in the question.
Data = reshape(repmat(Data, 10, 1), 1, []);
This is more time-efficient than the others like kron() or combination of sort() and repmat().
Two simulations were done and the results are shown in the following figures.
First: Simulation time vs. length of Data. Here I used N=100 instead of 10.
Second: Simulation time vs. repetition factor. Length of Data is 10000.
So you can select the best one according to the simulation results.
As seb proposed, you can use the function repmat. Here what I would do:
Data = [2, 4, 6, 8, ...];
Data = sort(repmat(Data,1,10));
You can use repmat
interval_size = 10;
Data = 2*(1:1:1000);
out_data = repmat(Data,interval_size,1);
out_data = out_data(:)';
Example Data:
time=1:50
data=2:2:100
t2=1:.1:50.9
For time=1:n this is very simple:
data(:,floor(t2))
If your original data has another time scale, use this:
[a,b]=ismember(floor(t2),time)
data(:,b)
Related
I have the following problem in Matlab:
I have a time series which looks like this:
size(ts) = (n,2); % with n being the number of samples, the first column is the time, the second the value.
Let's say I have:
ts(:,1) = [0, 10, 20, 30, 40];
ts(:,2) = [1, 3, 10, 6, 11];
I would like to resample the signal above to get the interpolated values at different times. Say:
ts(:,1) = [0, 1, 3, 15, 40];
ts(:,2) = ???
I had a look at the Matlab functions for signal processing but they are all only relevant for regular sampling at various frequencies.
Is there a built in function which would give me the above, or do I have to compute the linear interpolation for each new desired time manually? If so, do you have a recommendation to do this efficiently using vecotrized code (just started Matlab a month ago so still 100% at ease with this and relying on for loops a lot still).
For a bit of context, I'm using a finite difference scheme in series to investigate a problem. The output of one FD scheme is fed into the following. Due to the nature of my problem, I have to change the time stepping from one FD to the next, and my time steps can be irregular.
Thanks.
Since your data are 1-D you can use interp1 to perform the interpolation. The code would work as follow:
ts = [0, 10, 20, 30, 40; % Time/step number
1, 3, 10, 6, 11]; % Values
resampled_steps = [0, 1, 3, 15, 40]; % Time for which we want resample
resampled_values = interp1(ts(1, :), ts(2, :), resampled_step);
% Put everything in an array to match initial format
ts_resampled = [resampled_steps; resampled_values];
Or you can alternatively, following the same idea:
ts = [0, 10, 20, 30, 40; % Time/step number
1, 3, 10, 6, 11]; % Values
% Create resample array
ts_resampled = zeros(size(ts));
ts_resampled(1, :) = [0, 1, 3, 15, 40];
% Interpolate
ts_resampled(2, :) = interp1(ts(1, :), ts(2, :), ts_resampled(1, :));
You can even choose the interpolation method you want, by passing a string to the interp1 function. The methods are listed here
Note that this only work if you re-sample with time stamps within your original scope. If you want them outside you have to tell the function how to extrapolate using the key word 'extrap'. Detail here
(To anyone who reads this, just to not waste your time, I wrote up this question and then came up with a solution to it right after I wrote it. I am posting this here just to help out anyone who happened to also be thinking about something like this.)
I have a vector with elements that I would like to sum up. The elements that I would like to add up are elements that share the same "triggerNumber". For example:
vector = [0, 1, 1, 1, 1]
triggerNumber = [1, 1, 1, 2, 2]
I will sum up the numbers that share a triggerNumber of 1 (so 0+1+1 =2) and share a triggerNumber of 2 (so 1+1+1 = 3). Therefore my desiredOutput is the array [2, 2].
accumarray accomplishes this task, and if I give it those two inputs:
output = accumarray(triggerNumber.',vector.').'
which returns [2, 2]. But, while my "triggerNumbers" are always increasing, they are not necessarily always increasing by one. So for example I might have the following situation:
vector = [0, 1, 1, 1, 1]
triggerNumber = [4, 4, 4, 6, 6]
output = accumarray(triggerNumber.',vector.').'
But now this returns the output:
output = [0, 0, 0, 2, 0, 2]
Which is not what I want. I want to just sum up elements with the same trigger number (in order), so the desired output is still [2, 2]. Naively I thought that just deleting the zeros would be sufficient, but then that messes up the situation with the inputs:
vector = [0, 0, 0, 1, 1]
triggerNumber = [4, 4, 4, 6, 6]
which if I deleted the zeroes would return just [2] instead of the desired [0, 2].
Any ideas for how I can accomplish this task (in a vectorized way of course)?
I just needed to turn things like [4, 4, 4, 6, 6] into [1, 1, 1, 2, 2], which can be done with a combination of cumsum and diff.
vector = [0, 0, 0, 1, 1];
triggerNumber = [4, 4, 4, 6, 6];
vec1 = cumsum(diff(triggerNumber)>0);
append1 = [0, vec1];
magic = append1+1;
output = accumarray(magic.',vector.').'
which returns [2, 2]....and hopefully my method works for all cases.
I have an array of positive numbers and there are some duplicates. I want to find the largest index of the minimum value.
For example, if a=[2, 3, 1, 1, 4, 1, 3, 2, 1, 5, 5] then [i, v] = min(a) returns i=3, however I want i=9.
Using find and min.
A = [2, 3, 1, 1, 4, 1, 3, 2, 1, 5, 5];
minA = min(A);
maxIndex = max(find(A==minA));
min get the minimun value, and find return de index of values that meet the condition A==minA. max return de maximun index.
Here's a different idea, which only requires one function, sort:
[~,y] = sort(a,'descend');
i = y(end)
ans =
9
You can use imreginalmin as well with time complexity O(n):
largestMinIndex = find(imregionalmin(A),1,'last');
I'm trying to program a function (or even better it it already exists) in scilab that calculates a regular timed samples of values.
IE: I have a vector 'values' which contains the value of a signal at different times. This times are in the vector 'times'. So at time times(N), the signal has value values(N).
At the moment the times are not regular, so the variable 'times' and 'values' can look like:
times = [0, 2, 6, 8, 14]
values= [5, 9, 10, 1, 6]
This represents that the signal had value 5 from second 0 to second 2. Value 9 from second 2 to second 6, etc.
Therefore, if I want to calculate the signal average value I can not just calculate the average of vector 'values'. This is because for example the signal can be for a long time with the same value, but there will be only one value in the vector.
One option is to take the deltaT to calculate the media, but I will also need to perform other calculations:average, etc.
Other option is to create a function that given a deltaT, samples the time and values vectors to produce an equally spaced time vector and corresponding values. For example, with deltaT=2 and the previous vectors,
[sampledTime, sampledValues] = regularSample(times, values, 2)
sampledTime = [0, 2, 4, 6, 8, 10, 12, 14]
sampledValues = [5, 9, 9, 10, 1, 1, 1, 6]
This is easy if deltaT is small enough to fit exactly with all the times. If the deltaT is bigger, then the average of values or some approximation must be done...
Is there anything already done in Scilab?
How can this function be programmed?
Thanks a lot!
PS: I don't know if this is the correct forum to post scilab questions, so any pointer would also be useful.
If you like to implement it yourself, you can use a weighted sum.
times = [0, 2, 6, 8, 14]
values = [5, 9, 10, 1, 6]
weightedSum = 0
highestIndex = length(times)
for i=1:(highestIndex-1)
// Get the amount of time a certain value contributed
deltaTime = times(i+1) - times(i);
// Add the weighted amount to the total weighted sum
weightedSum = weightedSum + deltaTime * values(i);
end
totalTimeDelta = times($) - times(1);
average = weightedSum / totalTimeDelta
printf( "Result is %f", average )
Or If you want to use functionally the same, but less readable code
timeDeltas = diff(times)
sum(timeDeltas.*values(1:$-1))/sum(timeDeltas)
This question already has answers here:
Reshaping of Array in MATLAB
(3 answers)
Closed 7 years ago.
Here is what I have:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
And here is what I want to get:
[
1, 2, 3, 4,
5, 6, 7, 8,
9, 10, 11, 12
]
The number of rows and columns (3 and 4 in example) is already known.
How would I do that?
reshape
b = reshape(a, 4, 3)' will would work for your example. Elements are taken from the original and inserted into the new matrix column-wise.
Furthermore, reshape is a built-in MATLAB function. There exists other solutions such as vec2mat that require the communications toolbox.
This guide says
mat = vec2mat(vec,matcol) converts the vector vec into a matrix with matcol columns, creating one row at a time. If the length of vec is not a multiple of matcol, then extra zeros are placed in the last row of mat. The matrix mat has ceil(length(vec)/matcol) rows.