I've recently started a new job and having never used MATLAB before, I'm a little stuck. Any help would be much appreciated.
Here is the story. I have been given the velocities of fragmentation pellets at time intervals of 0.001 milliseconds. There are 28 gauges (i.e 28 sets of data) and each gauge has 20,000 readings. I have created a matrix consisting of all this data.
My objective to take that matrix and create 2 more matrices with the corresponding displacement and acceleration values of each reading. The next step is to export the time and acceleration values to an excel spreadsheet.
I am at a loss as to how to do this. I have tried to integrate and differentiate but I cant seem to get it right. Is it possible to create a function that takes the velocity data and automatically calculates acceleration/displacement? (This would make things easier as people in the future could use that same code)
Any help on how to solve any part of this problem would be much appreciated. I've only been using the software 3 days.
Many thanks.
Related
I ran a computer simulation for my Pendulum, to measure time taken to reach the lowest point, for every velocity and every angle.
As you can imagine there is a lot of data, thousands of lines for all angles and velocity.
On every frame, I will be measuring the velocity and angle of the pendulum, and will look for the closest data in my Excel spreadsheet.
How can I go about this to make sure it's not too CPU-intensive?
Should I create a massive array where every element corresponds to a certain angle: for example, myArray[30] will be for all velocities and times for all my data between 30.0 degrees and 30.999. (That way it will be avoid lots of if statements)
Or should I keep everything in my Excel spreadsheet?
Any suggestion?
The best approach in my opinion would be dividing your data into intervals based on distribution since you have to access that data in every frame. Then when you measure the velocity and angle you can go look for the interval and access only that part of your data.
I would find maximum and minimum of your data points while importing to Unity and then divide that part based on (maximum - minimum) / NumOfIntervals. Lets say your interval size is 5 for each Angle. When you got an angle of 17 you can do (int)15/5 = 3(Assuming indexes start from zero) and go for third item in your structure. This can be a dictionary or Array of an Arbitrary class instances based on your data.
I can try to help further if you can share the structure of your data. But in my opinion evenly distribution of data to every interval is important.
I need help regarding the time series in Tableau. So far Here is what I can do.
Connect to TabPY
Call / Run scripts on TabPy
My current issue is that tableau doesn't seem to allow more output than input elements. Say I want to use the last 100 data points to predict the coming 10 points. Input of the data to python isn't a problem. The problem comes when I want to return a list with 110 elements. I've also tried returning the 10 elements and it complaints that it expects 100 elements list.
Thanks for reading
I've found a work around. You can see the post here for more information. Basically you shift the original values by the prediction amount and then have the prediction return the same amount as the shifted original
I have some data which is time-stamped by a NMEA GPS string that I decode in order to obtain the single data point Year, Month, Day, etcetera.
The problem is is that in few occasions the GPS (probably due to some signal loss) goes boinks and it spits out very very wrong stuff. This generates spikes in the time-stamp data as you can see from the attached picture which plots the vector of Days as outputted by the GPS.
As you can see, the GPS data are generally well behaved, and the days go between 1 and 30/31 each month before falling back to 1 at the next month. In certain moments though, the GPS spits out a random day.
I tried all the standard MATLAB functions for despiking (such as medfilt1 and findpeaks), but either they are not suited to the task, either I do not know how to set them up properly.
My other idea was to loop over differences between adjacent elements, but the vector is so big that the computer cannot really handle it.
Is there any vectorized way to go down such a road and detect those spikes?
Thanks so much!
you need to filter your data using a simple low pass to get rid of the outliers:
windowSize = 5;
b = (1/windowSize)*ones(1,windowSize);
a = 1;
FILTERED_DATA = filter(b,a,YOUR_DATA);
just play a bit with the windowSize until you get the smoothness you want.
Good morning,
I have a question about the time execution of a script on Matlab. Is it possible to know previously how long spend the execution of a script before running it (an estimated time, for example)? I know that with tic and toc command, among others, is it possible to know the time at the end but I don't know if it's possible to know it before.
Thanks in advance,
It is not too hard to make an estimate of how long your calculation will take.
You already know how to record calculation times with tic and toc, so now you can do this:
Start with a small scale test (example, n=1) and record the calculation time
Multiply n with a constant k (I usually choose 2 or 10 for easy calculations), record the calculation time
Keep multiplying with n untill you find a consistent relation: 'If I multiply my input size with k, my calculation time changes like so ...'
Now you can extrapolate your estimated calculation time by:
calculating how many times you need to multiply input size of the biggest small scale example to get your real data size
Applying the consistent relation that you found exactly that many times to the calculation time of your biggest small scale example
Of course this combines well with some common sense, like if you do certain things t times they will take about t times as long. This can easily be used when you have to perform a certain calculation a million times. Just interrupt the loop after a minute or so, if it is still in the first ten calculations you may want to give up!
I have a set of data for the past 5 years. Approx 7000 rows of data with features that are binary {yes/no} or are multi-classed {product A, B, C} A total of about 20+ features.
I am trying to make a program (or one time analysis project) to determine (predict) the product shipdate(shipping delay days) based on this historical data. I have 2 columns that indicate when a product was planned to be shipped and another column of when it was actually shipped! Currently.
I'm wondering how I can make a prediction program that determines based on the historic data when new data input of a product will expect to ship. I don't care about a getting a specific date but even just a program that can tell me number of delay days to add...
I took an ML class a while back and I wasn't sure how to start something like this. Any advice? Plus the closest thing to this I can think of is an image recognition assignment using NN. but that was too easy here I have to deal with a date instead of pixel white/black.... I used Matlab back in the day (I still know how to use it) but I just downloaded Weka data mining tool.
I was thinking of a neural network but I'm not sure how to set it up to have my program give me a the expected delay time (# of days/month) from the inputed ship date.
Basically,
I want to input (size = 5, prod = A, ....,expected ship date = jan 1st)
and the program returns the number of days to add as a delay onto my expected ship date given the historical trends...
Would appreciate any any help on how start something like this the correct/easiest/best way... Thanks in advance.
If you use weka, then get your input/label data into the arff format and then you try out all the different regressors (this is a regression problem after all). To avoid having to do too much programming quite yet (if you are just in an exploratory phase), use the weka experimenter which has a GUI for trying out a whole bunch of regressors on your dataset.
Then when you find one that does something expected and you want to do some more data analysis using MATLAB, then you can use a weka/matlab interface.