Google Colab: TypeError in bsoid_assign() (Too many bodyparts?) about b-soid HOT 5 CLOSED

Hi,

Thanks for your issue. You guessed correctly, it was because we hard-coded in 6 body-parts (we've done HCA based on all kinds of features using those 6 body parts so we are only confident in 6 at the moment), so 9 will cause an issue.

Implementation wise: For yours, if you could just use ['nose','left shoulder','right shoulder','left hip','right hip', 'tail root'], it should work. In other words, by removing the columns before b-soid_assign, it should run as expected. The number of rows and data file size should not be of concern.

Your data specifically: Obviously, 9 data points will provide more detailed information for b-soid to parse, and likely resulting in finer behavioral differences (though in my computation of the 6 points, I do estimate points to get around that). We can talk more about incorporating "neck" or "centroid" if you like. By having these additional 3 points, you can analyze a barrage of kinematics that 6 points are lacking, but does not necessarily add weight to extracting new behaviors.

Note that the python notebook is in beta and I have only tested it on my open-field data so far. I am currently still testing it on other experiments.

Please keep me posted on any new issues using Google Colab! I am eager to fully implement the python version of this algorithm (pip install bsoid will be great for python users as opposed to running it on the cloud!).

Alex

from b-soid.

Hi Alex,

thanks for the quick reply. I will try out slicing the dataframe to 6 bodyparts!
Currently I am running open field data (from my DeepLabStream experiments) and would be happy to share any results/issues I will find on my way.

from b-soid.

Changing the number of bodyparts to 6 did not solve the issue.

Screenshot

Followup question

is the number of csv files that are imported important?

I imported 2 csv files (6 bodyparts, no multiindex etc.; see the print(data[0].shape, data[1].shape) statement in the screenshot.

from b-soid.

Hi,

I've fixed it. It was a panda version difference that they recently updated on Colab (1.03).
The new copy of the colab notebook is located here
What I changed (in the boxcar_center function):
.
.
moving_avg = np.array(a1.rolling(window = n,min_periods=1,center = True).mean())
.

Let me know if this solves this issue.

Alex

from b-soid.

This solves the issue. It continues to run smoothly.
Thank you for the quick resolution!

from b-soid.