Max von der LindeView Profile
This post shows the latest day of testing and its results.
All with small hooks:
All with big hooks:
All on front of plarform:
All on back of platform:
-> big hooks and front of platform seem like promising factors, let’s pool them!
Front and big hooks:
Front and small hooks (worse):
last problem: If compared to manual stimulation, opposite signals are found. Wrong manual stimulation?
The following Link shows the results of the latest tests with Wildtype flys, evaluated with the latest version of the HTML_DTS_project.R programm.
The histograms unfortunately show very little difference between the state of left turning arena and right turning arena.
What else should be integrated?
The fair took place from 30th September – 2nd Oktober (Sunday to Tuesday). I attended at Monday and Tuesday. The first two days were located at the Messegelände Ost Munich while for the last day the “Schottenhammel” Tent at the Oktoberfest had been reserved.
Visit of the “Corporate Stage” which showcased Interviews:
– The guest came from Adidias but worked independently first- before Adidas she used to make statistical interpretations on her own and present them to companies-> inefficient, it makes more sence to first inquire what the company is interested in- more and more Big Data with high tech factories- example of practical use of statistics: spike in sales as Kanye West fashioned a certain shoe
2. Divide and conquer debugging: break code up smaller and smaller until you reach the line with the problem
Is the program doing its job? Manual stimulation of the platform:
-> yes it is
Why frequency histograms? I want to compare my data to the data from “Can a fly ride a bicycle?” (Wolf 1992):
example fly E7:
Histogram of 3 flys (periods of idividual flys have been merged -> one odd and one even period per fly):
Histogram of platform without fly:
Histogram of 11 flys (periods of idividual flys have been merged -> one odd and one even period per fly):
Histogram of 11 flys (no arenaturn):
Fly 3 (discussion model)
-> merging makes little sence if the distribution is completely different areas for each fly!
The different stages of data processing:
1. single fly dyplot with all data present
2. Plot of all the even / odd periods of one fly
3. Plot of all even / odd Periods of one fly merged
4. Plot of the even/odd periods from all tested flys
Another example (first fly tested):
Table with the generated fly test data:
|Test||Description||Average right||Average left||Differece||general|
Text data file with all the rawdata generated by my new programme: Rawdata OptMoto
R programme which generates the plot:
rawdata <- read.csv(“KHZtext.txt”, header = FALSE, sep = ” “)
flydata <- rawdata[c(3, 1)]
#dygraph(flydata) %>% dyRangeSelector()
arenadata <- rawdata[c(3, 2)]
rawdata <- rawdata[c(3, 1, 2)]
dygraph(rawdata, main = “test graph”) %>%
# dySeries(“V1”, name = “fly”) %>%
# dySeries(“V2”, name = “arena”) %>%
# dyAxis(“y”, label = “Voltage (V)”) %>%