I developed an Android app such that each scan is set to 1 Minute, and during this time the sensor collects many many readings randomly. I want to plot one sensor data of one scan only as follows:
The time of the scan is put manually in seconds for only 1 minute (from 1:60 sec) in the x-axis. While the vector of random readings collected from the sensor (sometimes reach hundreds of values) in the y-axis.
How I can do this in Matlab?
I tried using this code but gives me an error. "Vectors must be the same length."
This is my code:
x1 = linspace(0,60);
plot(x1,vector1,'o-r',x1,vector2,'+-k','LineWidth',lw,'MarkerSize',msz);
xlabel('Time (s)');
ylabel('sensor readings')
In order to match the amount of values you have to modify the input for linspace:
x1 = linspace(0,60,length(vector1));
This way you will automatically get the right amount of entries for your x-axis vector.
You basically tell linspace to create a vector that ranges from 0 to 60 with length(vector1) entries, so that it matches the length of your data set.
Note that if your second data set has a different amount of entries as your first, you will need to create a different x-axis vector that respectively matches its amount of values.
Related
I have some raw spreadsheet data that's in a format, like:
12/7/2016 3:07:00, 88.05,
12/7/2016 3:08:00, 89.10,
12/7/2016 3:13:00, 87.00,
etc
These data points are not sampled at a regular interval, but are randomly collected throughout the day.
Using Google Sheets I'm able to graph this easily onto a Timeline chart. This puts the values at the correct position on the timeline and takes the uneven sampling intervals into account.
I would like to generate a histogram of the timeline data while taking into account the timestamps and calculate an average value over a timeframe. I believe if I simply run this through the built-in histogram chart or select my data values and run it through an averaging function, it will be skewed by the uneven sampling intervals.
What's the easiest way to quantize the sampling intervals (ideally within Google Sheets) for generating my histogram and averaging?
or
Is there a built-in method to generate histograms/averaging of values while taking timestamp data into account, eliminating the need for quantized data?
You can calculate the appropriate average as follows (assuming your data is in the range A2:B50)
=sum(arrayformula((A3:A50-A2:A49)*(B3:B50+B2:B49)/2))/(A50-A2)
This formula implements the Trapezoidal rule: the value assigned to each time interval is the average of observed values at the ends of that interval.
There isn't a built-in "weighted histogram" tool, so it appears that needs re-sampling to create a representative histogram. Here is one way to resample. Let's say you want 20 samples; then in C2 enter
=arrayformula(A2+(row(1:20)-1)*(A50-A2)/19)
to get 20 uniformly distributed time values. (Division by 19 because of the fence-post distinction.) Then in D2,
=arrayformula(vlookup(C2:C21, A2:B50, 2))
will lookup a value for each sample time. Then you can build a histogram from column D.
I have a graph like this and I want to determine the number of peaks. Since it's a wave function the whole graph has many peaks that is why I was unsuccefull in finding the number of peaks using functions such as findpeaks for the graph below it returns a number of peaks around 3000 whereas I want to have the number 7.
My idea was to do a for or while loop that counts the number of instances where the average is higher than 0.5. So ultimately I want a function that iterates in segments over the graph returns the number of peaks and the range of y index values for which this peak occurs (I think the best way to do this would to save them to a zeros matrix).
link of file data: https://www.dropbox.com/s/vv8vqv28mqzfr9l/Example_data.mat?dl=0
Do you mean you are trying to count the 'on' parts of your data?
You're on the right track using findpeaks. If you scroll to the bottom of the documentation you'll see that you can actually tweak the routine in various ways, for example specifying the minimum distance between peaks, or the minimum difference between a point and its neighbour before it is considered a peak.
By defining the minimum distance between peaks, I detected the following 7 peaks. Code is included below. Alternatively you can play around with the other parameters you can pass into findpeaks.
The only other thing to note is that I took the absolute value of your data.
load('Example_data.mat')
indx = 1:numel(number11);
[pks, locs] = findpeaks(abs(number11), indx, 'MinPeakDistance', 0.25e4);
hold on
plot(number11)
plot(locs,pks, 'rx')
disp(numel(pks))
I have a script that plots wind speed in m/s (measured every second) against time in minutes over a period of 24 hours. I want to make a new plot that instead of plotting wind speed each second, averages the wind speed over a period of 10 minutes and then plots this against the time.
Here is a sample image of my data:
Any ideas of how I can do this?
You can use a Moving Average filter using the smooth function as suggested by m.s. in a comment. This is fairly simple:
y = smooth(x,span);
This uses a symmetric smoothing filter, so the span (i.e. the number of samples it takes for smoothing) must be odd: take the current sample plus n before and n after the current sample. That way you still have one sample for every second, they are just smoothed to damp noise and measurement errors.
If you want to reduce the number of points, such that only one point every 10 minutes exists, you can do the following: You take the first 10min * 60s = 600 samples of the vector and put them in the first column of a new matrix. Then take the next 600 samples and put them in the second column, and so on. Now you can column-wise take the mean of the matrix. That way you have a new vector where every element is the mean of 600 samples.
In MATLAB this is easily possible:
X = reshape(x,600,[]); % create matrix with 600 elements per column
y = mean(X,1); % take column-wise mean
I have two variables in a .mat file here:
https://www.yousendit.com/download/UW13UGhVQXA4NVVQWWNUQw
testz is a vector of cumulative distance (in meters, monotonically and regularly increasing)
testSDT is a vector of integrated (cumulative) sound wave travel time (in milliseconds) generated using the distance vector and a vector of velocities
(there is an intermediate step of creating interval travel times)
Since velocity is a continuously variable function the resulting interval travelt times and also the integrated travel times are non integers and variable in magnitude
What I want is to resample the distance vector at regular time intervals (e.g. 1 ms, 2 ms, ..., n ms)
What makes it difficult is that the maximum travel time, 994.6659, is less than the number of samples in the 2 vectors, therefore it is not straightforward to use interp1.
i.e.:
X=testSDT -> 1680 samples
Y=testz -> 1680 samples
XI=[1:1:994] -> 994 samples
This is the code I've come up with. It is a working code and it is not too bad I think.
%% Initial chores
M=fix(max(testSDT));
L=(1:1:M);
%% Create indices
% this loops finds the samples in the integrated travel time vector
% that are closest to integer milliseconds and their sample number
for i=1:M
[cl(i) ind(i)] = min(abs(testSDT-L(i)));
nearest(i) = testSDT(ind(i));
end
%% Remove duplicates
% this is necessary to remove duplicates in the index vector (happens in this test).
% For example: 2.5 ms would be the closest to both 2 ms and 2 ms
[clsst,ia,ic] = unique(nearest);
idx=(ind(ia));
%% Interpolation
% this uses the index vectors to resample the depth vectors at
% integer times
newz=interp1(clsst,testz(idx),[1:1:length(idx)],'cubic')';
As far as I can see there is one issue with this code:
I rely on the vector idx as my XI for interpolation. Vector idx is 1 sample shorter than vector ind (one duplicate was removed).
Therefore my new times will stop one millisecond short. This is a very small issue, and duplicate are unlikely but I am wondering if anybody can think of a workaround, or of a different way to approach the problem altogether.
Thank you
If I understand you correctly, you want to extrapolate to that extra point.
you can do this is many ways, one is to add that extra point to the interp1 line.
If you have some function you expect to follow your data you can use it by fitting it to the data and then obtaining that extra point or with a tool like fnxtr.
But I have a problem understanding what you want because of the way you used the line. The third argument you use, [1:1:length(idx)], is just the series [1 2 3 ...], usually when interpolating, one uses some vector x_i of points of interest, though I doubt your points of interest happen to be the series of integers 1:length(idx), what you want is just [1:length(idx) xi], where xi is that extra point x-axis value.
EDIT:
Instead of the loop just produce matrix forms out of L and testSDT, then matrix operation is somewhat faster in doing the min(abs(...:
MM=ones(numel(testSDT),1)*L;
TT=testSDT*ones(1,numel(L));
[cl ind]=(min(abs(TT-MM)));
nearest=testSDT(ind);
I have data from a model I am running. However the data is collected at each time step and there are varying numbers of time steps. It works out that although there are varying time steps, it is compensated by the change in time step so that all runs are running for the same time.
However I would think that when I have a vector that is 200 in length and one that is 900 in length, taking the FFT will give me inherently different frequencies. I feel like I should take the FFT with respect to the same time axis of all the samples.
The way I have the data now is just as row vectors were each entry is not associated with a space in time.
Is there a way to take the fft of each vector with respect to their place in a time axis rather than their place in the vector array?
My goal is to write a for loop and take the fft of many data sets, and then plot them to compare of frequency signatures change.
If you collect 200 samples in 1 second (200 Hz), you can resolve input data from 1 Hz (1/(1 sec)) to 100 Hz. If you sample for 1 second collecting 900 samples, you can resolve input from 1 Hz to 450 Hz. So both your samples have the same spacing (sampling in the frequency axis is 1 Hz), but they go up to different maximum frequencies!
If your issue is just about plotting, you can either throw away the high frequencies which are not available in all your plots:
totaltime=1; %# common total time of all datasets, in seconds
minsamplenumber=200;
figure;
hold all;
cutofffreq=((minsamplenumber/2+1)/totaltime);
freqscale=0:(1/totaltime):cutofffreq;
datasetcount=42;
ffts=NaN(minsamplenumber,datasetcount);
for i=1:datasetcount
data{i}=... %# collect your data; to make life easier always collect an even number..
ffts(:,i)=fft(data{i},minsamplenumber);
plot(freqscale,ffts{i}(1:end/2+1));
end
... or live with reality, and plot all data you have:
totaltime=1; %# common total time of all datasets, in seconds
figure;
hold all;
for i=1:42
data{i}=... %# collect your data; to make life easier always collect an even number..
ffts{i}=fft(data{i});
maxfreq(i)=((numel(ffts{i})/2+1)/totaltime);
freqscale{i}=0:(1/totaltime):maxfreq(i);
plot(freqscale{i},ffts{i}(1:end/2+1));
end
You could resample your data (by filtered interpolation) into constant length vectors where the sample rate was the same constant rate in each frame. You may have to overlap your FFT frames as well to get constant frame or window offsets.