On 10/25/2010 09:37 PM, Gabor Grothendieck wrote:
On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningham<flym...@gmail.com> wrote:
I have time-series data from a pair of inexpensive self-logging 3-axis
accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
sure of the vibration/shock spectrum I'm measuring, for my initial sensor
characterization run the units were mounted together with the sample rate
set to the maximum of 640 samples/sec.
Unfortunately, at this sample rate there are significant data dropouts at
various scales (a phenomenon not present at data rates of 160 Hz and below):
1. Approximately every 20ms, a few samples are dropped (believed to be due
to internal buffer wrapping).
2. Approximately every 200ms, about 50 samples are dropped (believed to be
due to flash write times).
3. At seemingly random intervals, a sample will appear with an out-of-order
timestamp (vendor is diagnosing).
Initially, I'm trying to answer the following questions:
A. How well do the 2 units compare? (Calibration, time-base drift, etc.)
B. Can I use a lower sample rate? (What is the observed spectrum?)
I started attacking the problem in Python (numpy/scipy), where I've done
lots of prior time-series sensor data analysis. Unfortunately, the gaps
have made direct use of the data futile, and I found I was spending all my
time manipulating Python lists and numpy vectors rather than finding
answers.
I hope R can help calm my sea of unruly data. I'm presently working my way
through the abundant R references (tutorials, wiki, etc.), but I was hoping
to find pointers here to help me become productive sooner rather than later.
Here's my present brute-force plan of attack:
- Load both data sets (in CSV format). Each data element is a timestamp +
3-axis acceleration.
- Determine timebase offset: The unit clocks don't match perfectly, and the
units were started at slightly different times, so I expect to correlate
common events in the data.
- Find all overlapping data clusters (between superset of gaps).
- See if I have enough data to perform spectral analysis. I'd like to
analyze all clusters together, but I suspect I may have to analyze them
independently, then combine the results.
Thoughts? Hints?
You can use read.zoo in the zoo package to create a zoo time series
from a csv file. The zoo merge method can merge two or more series
together and na.locf, na.approx or na.spline, also in zoo, could be
used to fill in the NAs. There are three vignettes (pdf documents)
that come with the zoo package that will get you up to speed.
Wow, Zoo looks great! (It also looks to be a great source of future
questions from me...)
Many, many thanks,
-BobC
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