I need to run a simulation with sample time of
tsample = 0.01 ; % seconds
I have a table such as below
I need to resample each column in input table such that value of the input Time vector gets equally spaced based on tsample values.
For the [Time] column I achieved this by following code
simTime = max(tests.(test_names{i}).Times); % Seconds
% Interpolate the time and frequency values as per sample time
numSteps = simTime/tsample;
time = tsample * [0:(numSteps-1)]';
What I need to do now is resize the frequency (f) values such that it shall be filled with previous values until a new value is found in column;
Time
f
0
50
....
50
4.99
50
5.00
49.65
....
49.65
19.99
49.65
20.00
49.80
I am confused whether I should use fillmissing or resample or interp1.
The examples I am following for these seem kind of different than what I wish to achieve here.
Any help would be really appreciated.
Thank you.
Ok,
I tried experimenting with more examples for interp1 and this solved my issue.
freq = interp1(tests.Times, tests.fHz, time, 'previous');
I was earlier unaware of the 'previous' option
Should have searched the documentation more extensively.
Related
I generate a time series from a normal distribution and then I try to plot the autocorrelation by using the following code snippet:
ts1 = normrnd(0,0.25,1,100);
autocorrelation_ts1 = xcorr(ts1);
I was expecting that the autocorrelation would show 1 for x=0 and almost 0 for the rest of values, instead I get value 6 at axis position 100.
I think the question applies both to Matlab and Octave but I am not sure.
First thing is that your second line of code is wrong. I think you meant to put
autocorrelation_ts1 = xcorr(ts1);
Other than this, I think your solution is correct. The reason the max value is at 100 and not 0 is because a temporal shift of 0 in the autocorrelation actually happens on the 100th iteration of the correlation function. In other words, the numbers on the X axis don't correspond to time.
To get time on the X axis change your code to
[autocorrelation_ts1, shifts] = xcorr(ts1);
Then
plot(shifts, autocorrelation_ts1)
With regard to the max value, matlab documentation for xcorr indicates that 1 is not the maximum output value of the function when called without the normalization argument. If you want to normalize such that all values are 1 or less, use
[autocorrelation_ts1, shifts] = xcorr(ts1, 'normalized');
Just as complementary reference to Scott's answer, this is the complete code snippet, including stem chart scaling to show up to 20 shifts/lags.
[auto_ts1, lags] = xcorr(ts1);
ts_begin = ceil(size(lags,2)/2);
ts_end = ts_begin + 20;
stem(lags(ts_begin:ts_end),auto_ts1(ts_begin:ts_end)/max(auto_ts1), 'linewidth', 4.0, 'filled')
I'm trying to plot some data with respect to minutes instead of seconds in Matlab as formatted time, i.e. min.sec.
I have real time data streaming in where with every sample received, its time in seconds is also sent. I then plot them with respect to time. Now, since my session is around 15 minutes long, I can't be plotting with respect to time. Therefore I wanted to plot it with respect to time (min.sec). I tried dividing the received time by 60 but this gives me minutes with 100 subdivisions instead of 60 (the minutes increment after 0.9999 instead of 0.59). How do I convert it so that I'm able to plot with respect to time in minutes?
Here is what I mean by 0.99 fractions of a minute instead of 0.59. A normal minute has 60 divisions not 100.
EDIT:
I tried m7913d's suggestions and here is what I got.
first I plot the signal with respect to time in seconds without changing the ticks ( A normal plot(t,v))
The I added datetick('x', 'mm:ss'); to the plot (Xtick format is not supported in Matlab 2015b)
Here is a screenshot of the results
The time in seconds was up to 80 seconds, when translated into minutes, it should give me 1 minutes and 20 seconds as the maximum x axis limit. But this is not the case. I tried to construct a t vector (i.e like t=0:seconds(3):minutes(3)) but I couldn't link it to my seconds vector which will be constantly updating as new samples are received from the serial port.
Thanks
You can use xtickformat to specify the desired format of your x labels as follows:
% generate a random signal (in seconds)
t = 0:5:15*60;
y = rand(size(t));
plot(seconds(t),y) % plot your signal, making it explicit that the t is expressed in seconds
xtickformat('mm:ss') % specify the desired format of the x labels
Note that I used the seconds methods, which returns a duration object, to indicate to Matlab that t is expressed in seconds.
The output of the above script is (the right image is a zoomed version of the left image):
Pre R2016b
One can use datetime instead of xtickformat as follows:
datetimes = datetime(0,0,0,0,0,t); % convert seconds to datetime
plot(datetimes,y)
datetick('x', 'MM:SS'); % set the x tick format (Note that you should now use capital M and S in the format string
xlim([min(datetimes) max(datetimes)])
I have voltage and current signals from multiple days. The time vector is in seconds of the day (SOD), and the voltage and current vectors are in volts and amps respectively. However, the vector data from each day is different lengths. For example Mondays data might be 1x100000 for both time and voltage/current, and Tuesdays might be 1x50000 for both time and voltage/current. I was asked to plot the different days of data on the same figure for comparison purposes. I have tried using the plot(x1,y1,x2,y2) method but that obviously didn't work due to different vector lengths. I tried interpolating to the larger data set, but then realized that I will get all NaNs on the result since there is no overlap in time. I ran out of ideas and am desperately in need of help.
EDIT:
I guess I forgot to mention that somehow I would like to overlay them one on top of the other in the same figure and not using a subplot.
It sounds like you want a data vector of length n to span, I'm guessing, 24 hours = 86400 seconds, for any n (e.g. n=100000 or n=50000). Assuming the original data is uniformly sampled, this should do the trick:
x1=linspace(0,86400,length(x1));
x2=linspace(0,86400,length(x2));
plot(x1,y1,'r-',x2,y2,'b-');
If it is not uniformly sampled, we can still make it work:
t1=linspace(0,86400,length(x1));
t2=linspace(0,86400,length(x2));
newy1 = spline(x1,y1,t1);
newy2 = spline(x2,y2,t2);
plot(t1,newy1,'r-',t2,newy2,'b-');
I have an EMG signal of a subject walking on a treadmill.
We used footswitches to be able to see when the subject is placing his foot, so we can see how many periods (steps) there are in time.
We would like to cut the signal in periods (steps) and get one average signal (100% step cycle).
I tried the reshape function but it does not work
when I count 38 steps:
nwaves = 38;
sig2 = reshape(sig,[numel(sig)/nwaves nwaves])';
avgSig = mean(sig2,1);
plot(avgSig);
the error displayed is this: Size arguments must be real integers.
Can anyone help me with this? Thanks!
First of all, reshaping the array is a bad approach to the problem. In real world one cannot assume that the person on the treadmill will step rhythmically with millisecond-precision (i.e. for the same amount of samples).
A more realistic approach is to use the footswitch signal: assume is really a switch on a single foot (1=foot on, 0=foot off), and its actions are filtered to avoid noise (Schmidt trigger, for example), you can get the samples index when the foot is removed from the treadmill with:
foot_off = find(diff(footswitch) < 0);
then you can transform your signal in a cell array (variable lengths) of vectors of data between consecutive steps:
step_len = diff([0, foot_off, numel(footswitch)]);
sig2 = mat2cell(sig(:), step_len, 1);
The problem now is you can't apply mean() to the signal slices in order to get an "average step": you must process each step first, then average the results.
It's probably because numel(sig)/nwaves isn't an integer. You need to round it to the nearest integer with round(numel(sig)/nwaves).
EDIT based on comments:
Your problem is you can't divide 51116 by 38 (it's 1345.2), so you can't reshape your signal in chunks of 38 long. You need a signal whose length is exactly a multiple of 38 if you want to be able to reshape it in chunks of 38. Either that, or remove the last (or first) 6 values from your signal to have an exact multiple of 38 (1345 * 38 = 51110):
nwaves = 38;
n_chunks = round(numel(sig)/nwaves);
max_sig_length = n_chunks * nwaves;
sig2 = reshape(sig(1:max_sig_length),[n_chunks nwaves])';
avgSig = mean(sig2,1);
plot(avgSig);
I am processing 1Hz timestamps (variable 'timestamp_1hz') from a logger which doesn't log exactly at the same time every second (the difference varies from 0.984 to 1.094, but sometimes 0.5 or several seconds if the logger burps). The 1Hz dataset is used to build a 10 minute averaged dataset, and each 10 minute interval must have 600 records. Because the logger doesn't log exactly at the same time every second, the timestamp slowly drifts through the 1 second mark. Issues come up when the timestamp cross the 0 mark, as well as the 0.5 mark.
I have tried various ways to pre-process the timestamps. The timestamps with around 1 second between them should be considered valid. A few examples include:
% simple
% this screws up around half second and full second values
rawseconds = raw_1hz_new(:,6)+(raw_1hz_new(:,7)./1000);
rawsecondstest = rawseconds;
rawsecondstest(:,1) = floor(rawseconds(:,1))+ rawseconds(1,1);
% more complicated
% this screws up if there is missing data, then the issue compounds because k+1 timestamp is dependent on k timestamp
rawseconds = raw_1hz_new(:,6)+(raw_1hz_new(:,7)./1000);
A = diff(rawseconds);
numcheck = rawseconds(1,1);
integ = floor(numcheck);
fract = numcheck-integ;
if fract>0.5
rawseconds(1,1) = rawseconds(1,1)-0.5;
end
for k=2:length(rawseconds)
rawsecondstest(k,1) = rawsecondstest(k-1,1)+round(A(k-1,1));
end
I would like to pre-process the timestamps then compare it to a contiguous 1Hz timestamp using 'intersect' in order to find the missing, repeating, etc data such as this:
% pull out the time stamp (round to 1hz and convert to serial number)
timestamp_1hz=round((datenum(raw_1hz_new(:,[1:6])))*86400)/86400;
% calculate new start time and end time to find contig time
starttime=min(timestamp_1hz);
endtime=max(timestamp_1hz);
% determine the contig time
contigtime=round([floor(mean([starttime endtime])):1/86400:ceil(mean([starttime endtime]))-1/86400]'*86400)/86400;
% find indices where logger time stamp matches real time and puts
% the indices of a and b
clear Ia Ib Ic Id
[~,Ia,Ib]=intersect(timestamp_1hz,contigtime);
% find indices where there is a value in real time that is not in
% logger time
[~,Ic] = setdiff(contigtime,timestamp_1hz);
% finds the indices that are unique
[~,Id] = unique(timestamp_1hz);
You can download 10 days of the raw_1hz_new timestamps here. Any help or tips would be much appreciated!
The problem you have is that you can't simply match these stamps up to a list of times, because you could be expecting a set of datapoints at seconds = 1000, 1001, 1002, but if there was an earlier blip you could have entirely legitimate data at 1000.5, 1001.5, 1002.5 instead.
If all you want is a list of valid times/their location in your series, why not just something like (times in seconds):
A = diff(times); % difference between times
n = find(abs(A-1)<0.1) % change 0.1 to whatever your tolerance is
times2 = times(n+1);
times2 should then be a list of all your timestamps where the previous timestamp was approximately 1 second ago - works on a small set of fake data I constructed, didn't try it on yours. (For future reference: it would be more help to provide a small subset of your data, e.g. just a few minutes worth, that you know contains a blip).
I would then take the list of valid timestamps and split it up into 10 minute sections for averaging, counting how many valid timestamps were obtained in each section. If it's working, you should end up with no more than 600 - but not much less if the blips are occasional.