I want to identify when data values ordered as a sine waveform.
For example in the picture
Until now I have worked with features like STD, RMS etc, on the data to identify waveforms. Now I am looking for a feature which will have such functionality although I am opened for any ideas to achieve such identification.
You could, for example, compute the Fourier transform and see if it has a noticeable peak at one of the frequencies.
Related
i read the heatmap might crash on many bins and cannot find the feature to put out bitmaps/images of frequently changing data. It suffices if i could shift the image to the left and append another column of pixels on the right for the t+1 timestamp.
Sure most data reasoned about in aggregated fashion, but i try to get as close to the original 'picture' as possible. Does grafana have a pixel panel to quickly draw/update image data? example:
What other tools would you suggest? to visualize serverside generated data at the client in high resolution.
Is there a way to divide an already- existing road network into a 2-D grid of equal sized cells?
I need to extract some information such as vehicle density, average speed,...etc. from each cell.
are there any libraries, tools, APIs or tutorials?
I am new to SUMO so any help would be appreciated.
The easiest way is probably to generate an fcd output (sumo --fcd-output) which gives coordinates for every vehicle and then aggregate the values in a simple script. Depending on the precision needed and the data volume you expect, you can also aggregate the output to edges (and to time intervals) in SUMO using the mean data output but then you will need to handle the case of edges which are in multiple cells yourself. There is some help in parsing a sumo network and sumo outputs in sumolib.
I am new to labview and I need help.
I am using myrio with gyroscope, and when I display the gyroscope values I get noise.
My question is: How can I implement lowpass filter to reduce the noise in X , Y and Z rates of the gyroscope?
I searched a lot, but I did not understand how can I know what is the sampling frequency, the low and the high cutoff frequency.
Thank you so much.
If you're data is noisy you should try to fix the problem before you digitize the data. If a physical low-pass filter will do the trick, install one. The better the signal before the DAQ the better the data will be once it's digitized.
Some other signal conditioning considerations: make sure to reduce the length of wire from the gyroscope to the DAQ to only what's necessary, if possible eliminate any sources of noise from the environment (like any large rotating magnets--seriously I once helped someone who was complaining about noise when they were using an unshielded wire next to an MRI machine), and if you're going to add any signal conditioning try to amplify close to your sensor.
If you still would like to filter in software, there's an example included with LabVIEW that demonstrates both the point-by-point VIs and the array based VIs. It's called PtByBp and Array Based Filter.vi and can be found in the Example Finder under Analysis, Signal Processing and Mathematics >> Filtering and Conditioning
Please install this FREE toolkit from ni.com: http://sine.ni.com/nips/cds/view/p/lang/en/nid/212733
There are examples and good ready to use application how to use myRIO gyroscope and how to do proper DSP.
Sampling frequency is how fast you sample. Look for this value in the ADC settings. Low and high cutoffs - play with those values. Doing an FFT on your signal may help you to determine spectral frequency density, and decide where to cut.
I have a sequence of gps values each containing: timestamp, latitude, longitude, n_sats, gps_speed, gps_direction, ... (some subset of NMEA data). I'm not sure of what quality the direction and speed values are. Further, I cannot expect the sequence to be evenly spaced w.r.t. the timestamp. I want to get a smooth trajectory at an even time step.
I've read the Kalman Filter is the tool of choice for such tasks. Is this indeed the case?
I've found some implementations of the Kalman Filter for Python:
http://www.scipy.org/Cookbook/KalmanFiltering
http://ascratchpad.blogspot.de/2010/03/kalman-filter-in-python.html
These however appear to assume regularly spaced data, i.e. iterations.
What would it take to integrate support of irregularly spaced observations?
One thing I could imagine is to repeat/adapt the prediction step to a time-based model. Can you recommend such a model for this application? Would it need to take into account the NMEA speed values?
Having looked all over for an understandable resource on Kalman filters, I'd highly recommend this one: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
To your particular question regarding irregularly spaced observations: Look at Chapter 8 in the reference above, and under the heading "Nonstationary Processes". To summarize, you'll need to use a different state transition function and process noise covariance for each iteration. Those are the only things you'll need to change at each iteration, since they're the only components dependent on delta t.
You could also try kinematic interpolation to see if the results fit to what you expect.
Here's a Python implementation of one of these algorithms: https://gist.github.com/talespaiva/128980e3608f9bc5083b
I'm writing a program that has, as one facet, a wave filtration/resolution routine. The more data I collect, the bigger the files stored to the device get. I'm collecting data at discrete time steps, and in the interest of accuracy I'm doing this pretty frequently. However, I noticed that the overall wave form tends to be wide enough that I could be collecting data at about half the rate I am and still be able to draw an accurate-enough-for-my-purposes waveform over the data.
So the question: is there a way to, from this data, create a continuous mathematic description of the curve? I haven't been able to find anything. My data is float inside of NSNumbers contained by an NSArray.
The two things I would like to be able to do are get intersections points for a threshold and find local maximums. The ability to do either one of these would be sufficient.
-EDIT-
If anyone knows a good objective-c FFT method for 1-dimensional real arrays I would love to hear it.
Apple includes an FFT in the Accelerate framework.
Using Fourier Transforms
Example: FFT Sample
Also: Using the Apple FFT and Accelerate Framework