| Submitting KML content to Google Earth -- not as easy as Flickr 02/15/2009 20:15 | |||||
| There are a few rather esoteric steps to getting your KML files recognized by the Google Earth search engine. I think I have it set up, but we will see. -ben | |||||
| New Design Ideas for Tide Tools 02/10/2009 00:59 | |||||
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Well, I found my bug in my lowpass filter. I had two variables, with very
similar names, being used for the same thing. But one wasn't recalculated
as it should have been, the result was that I had figured weighting numbers
based on, say a window of 15 points, but only summing 11. That filter looks
pretty good now.
I believe that it was a mistake to put the interpolator inside of the
data convertor program. I need to destroy outliers BEFORE I do any
interpolation.
Also, I'm not convinced that straight-line interpolation will suit our
needs. I'm thinking more in the lines of a circular arc fit.
So now, the path might be:
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| Don't program when you mind isn't clear. 02/07/2009 21:05 | |||||
I finally found my last problem with the interpolation.
There were two. But last one was the most vexing.
It was my calculation of
fp seconds from datetime.timedelta. I had put this off in its only routine
and had written: dt.seconds + dt.microseconds * exp(-6). Obvious when you
see it: exp is exponent of base e not base 10. The correct expression
is: dt.seconds + dt.microseconds * 10**(-6).
I really like the Python matplotlib plotting routines and interface for
viewing, As you can see, I was able to pinpoint my problems thanks to the
ease of this interface and its ability to create images.
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| Still having problems with my interpolation on tideconvert.py 02/07/2009 12:13 | |||||
I went ahead and hacked my tidePlot.py which will do comparisons of
time based data (using the timedate class). Using it to visually analyze my
interpolation (added as an option to tideconvert), I see that it still needs
work. The slope isn't right, and it doesn't catch all gaps in the data (right
side of image).
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| Youtube video 02/05/2009 00:11 | |||||
| I have embedded a youtube video of Mother of Perl in the general pages under Videos. | |||||
| From and To python datetime objects an UNIX time 02/04/2009 02:29 | |||||
from http://seehuhn.de/pages/pdate
Conversion between Unix times and datetimeTo convert a Unix time stamp to datetime use the fromtimestamp constructor:>>> from datetime import datetime >>> datetime.fromtimestamp(1172969203.1) datetime.datetime(2007, 3, 4, 0, 46, 43, 100000)To convert a datetime object into a Unix time stamp, one has to first convert it into a struct_time: >>> from datetime import datetime >>> from time import mktime >>> t=datetime.now() >>> mktime(t.timetuple())+1e-6*t.microsecond 1172970859.472672My example: In [26]: d1 = datetime.datetime.now() In [27]: d1 Out[27]: datetime.datetime(2009, 2, 3, 21, 25, 4, 588232) In [28]: time.time() Out[28]: 1233714322.753165 In [29]: time.mktime(d1.timetuple()) + 1e-6 * d1.microsecond Out[29]: 1233714304.588232 In [30]: |
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| First task = Interpolating while converting 02/03/2009 18:51 | |||||
My simplistic interpolation scheme was to count the number of
contiguous bad source data lines and interpolate that many
points over the gap. I got what I planned, but more often than not,
the bad source spans many more sample points than bad lines caught.
Now it looks as though I need to do a little analysis on the incoming
data to find an average sample rate, and find any spans that are above
+/- 1 second of that rate. Then interpolate over the gap, and sample
the interpolation at the computed sample interval.
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| The Current Design of the Tides Processing Package 01/30/2009 14:31 | |||||
The current process design is:
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| She Sits on Her Mooring 01/30/2009 08:18 | |||||
Well, Mother of Perl is happy sitting on her mooring off the
New Castle pier. 
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| The Tides Project - the begining 01/25/2009 18:45 | |||||
CCOM operates several tide stations. The three that are most likely to
be recording data are:
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