From: Anthony Scopatz <scop...@gmail.com<mailto:scop...@gmail.com>>
Reply-To: Discussion list for PyTables
<pytables-users@lists.sourceforge.net<mailto:pytables-users@lists.sourceforge.net>>
Date: Wednesday, August 15, 2012 2:47 PM
To: Discussion list for PyTables
<pytables-users@lists.sourceforge.net<mailto:pytables-users@lists.sourceforge.net>>
Subject: Re: [Pytables-users] In-kernal for subset?
On Wed, Aug 15, 2012 at 12:33 PM, Adam Dershowitz
<adershow...@exponent.com<mailto:adershow...@exponent.com>> wrote:
I am trying to find all cases where a value transitions above a threshold. So,
my code first does a getwherelist to find values that are above the threshold,
then it uses that list to find immediately prior values that are below. The
code is working, but the second part, searching through just a smaller subset
is much slower (First search is on the order of 1 second, while the second is a
minute).
Is there any way to get this second part of the search in-kernal? Or any more
general way to do a search for values above a threshold, where the prior value
is below?
Essentially, what I am looking for is a way to speed up that second search for
"all rows in a prior defined list, where a condition is applied to the table"
My table is just seconds and values, in chronological order.
Here is the code that I am using now:
h5data = tb.openFile("AllData.h5","r")
table1 = h5data.root.table1
#Find all values above threshold:
thelist= table1.getWhereList("""Value > 150""")
#From the above list find all values where the immediately prior value is below:
transition=[]
for i in thelist:
if (table1[i-1]['Value'] < 150) and (i != 0) :
transition.append(i)
Hey Adam,
Sorry for taking a while to respond. Assuming you don't mind one of these
being <= or >=, you don't really need the second loop with a little index
arithmetic:
import numpy as np
inds = np.array(thelist)
dinds = inds[1:] - inds[:-1]
transition = dinds[(1 < dinds)]
This should get you an array of all of the transition indices since wherever
the difference in indices is greater than 1 the Value must have dropped below
the threshold and then returned back up.
Be Well
Anthony
Thanks much for the response. At first it didn't work, but it gave me the
right idea, and now I got it working. There were two problems above. 1) I
believe that you had a typo and the last line should have been "inds[(1 < …"
and not "dinds[(1<…" Otherwise you just get back the deltas instead of the
actual index values.
But, that still returned an array that wasn't working. Turns out, after
thinking some, that it was actually offset by one. So by prepending a value
into dinds (greater then 1, since the first value greater than the threshold,
must always be a transition or the first table entry) it seems to solve the
problem. Here is the code that seems to work:
import numpy as np
inds = np.array(thelist)
dinds=np.append([2],inds[1:] - inds[:-1])
trans=inds[(1<dinds)]
Now, I am still curious, more for academic reasons, since the code now works,
if there would be a way to speed up the second loop above? It seems like there
are other examples, where index arithmetic might not work, so is there a way to
do an in-kernal search through just a subset of a table?
Again, thanks for the help!
Best,
--Adam
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