Interpolations and Spike analysis
on Monday, December 7th, 2015 2:42 | by Christian Rohrsen
This is just a proof of concept how useful the interpolation is for this purpose: and I would say it´s almost useless. If I delete from the raw data (1st graph) some pieces (2nd graph), and I make a spline interpolation (3rd graph) it doesn´t match that well. Linear interpolation was done in previous weeks with not much of a success. Anyway, the result of a linear interpolation can be imagined by eye just by joinning the two ends with a straight line, and this doesn´t occur in the fly behavior as we can see in the raw data (1st graph).
There could be two posibilities for spike detection: one is the one from Ute and the other one is the one from Maye. It seems to me that the one from Maye is more precise. I have run the script but I do not get so many spike detections as he gets. I did try several thresholds for spike detection and doesn´t change very much. So I have to work more on it to see what is really the important factor for a proper spike detection
Category: flight, R code, Spontaneous Behavior, strokelitude, WingStroke | No Comments
Linear interpolation for solving uneven sampling in the platform
on Monday, November 16th, 2015 2:50 | by Christian Rohrsen
This is the prediction of one of the controls (UAS-TNTxWTB) after being interpolated for compensating for the uneven sampling. The y-axis is Correlation coeficient (taking correlation of bins of 100 sampled points) for the next 2000 sampled points (x-axis). There seems to be no decay in the prediction, as if it was a random behavior. Aparently linear interpolation is not suited for this case. I have heard that linear interpolation is fine when sampling rate variation is low (like 5%). For this case, spline interpolation might be better. If this is really worth to do I don´t know, the best case would be for sure to have the hardware to sample according to the necessities.
Category: R code, Spontaneous Behavior | No Comments
Sampling rate in the platform: looking for even sampling times
on Monday, September 28th, 2015 2:28 | by Christian Rohrsen
Homogeneity of the sampling rate in the platform: is improved mainly by stopping any other running programs and the internet, setting priority in the CPU doesn´t improve much. This doesn´t completely solve the problem so the priority was also changed in the code of the software itself and the sampling frequency has been changed to a higher one (because this one isn´t enough for intrapolation from what I have seen) in order to be able to make intrapolation. However we will try to change this to around 62ms sampling because the system somehow tends to measure a this time. In addition I did model several behaviors (white noise random, oscilatory activity with three frequencies, and several chaotic systems: logistic, clifford and lorentz) to see what their parameters and graphs look like, but they all have very incongruent results, so it didn´t bring much info. I have to check if I´m setting the right values for the analysis parameters.
Category: R code, Spontaneous Behavior | No Comments
Analysis for spontaneity in platform
on Monday, September 21st, 2015 2:26 | by Christian Rohrsen
The sampling rate is not homogeneous and this affect a lot the processing of the data for non linearity. What can be done? On the right we see a recurrent plot of one single fly in darkness for 5 min. It seems to be to some extent periodic, not much chaotic (but I need more experience to really guess that) For more information https://www.recurrence-plot.tk/rqa.php.
In the table below we see a low recurrence but high determinism among many other parameters.
The E2 line shows values around 1 the whole time when the sample is random. This is contradicting the above. The detrended fluctuation analysis show a value around 0.5, which means that if these data was modelled as random it would look like white noise.
Category: R code, Spontaneous Behavior | No Comments
automatic uplaod of data. step2
on Sunday, December 2nd, 2012 3:41 | by Julien Colomb
Figure of result, data, raw data and code uploaded automatically. Still need to get a way to get the statistical analysis published along with it.
I will try to make a video to highlight how fast and easy it is once the code is written.
Bjoern, when do you want to present this? Do I have time or should I do it tomorrow night ? :-)
the R code was made public: everyone can access it without login
Category: open science, R code | 3 Comments
Automatic upload of data on figshare: step1
on Wednesday, November 21st, 2012 7:38 | by Julien Colomb
https://figshare.com/articles/PKC53e_putative_mutant_in_self_learning/97792
This was 100% uploaded directly from R.
First step toward automatic upload of data accomplished!
more permanent link, I think: https://dx.doi.org/10.6084/m9.figshare.97792
Category: open science, R code | No Comments