Ben-
You *really* need to look at the rcols() routine: pdldoc rcols
I don't know much about your data as it seems a bit irregular
and too large to verify the format but I was able to slurp it into
a containing 2D PDL:
pdl> $data = rcols 'sample.s321p', [], { EXCLUDE=>'/^[#\!]/' };
Reading data into piddles of type: [ Double ]
Read in 468018 elements.
pdl> ?vars
PDL variables in package main::
Name Type Dimension Flow State Mem
----------------------------------------------------------------
$data Double D [52002,9] -C 0.00KB
pdl> p cat($data->statsover)->mv(-1,0) # the cat()->mv() makes the
stats line up vertically
[
[ 2326.8359 375207.06 1.5208731e-06 -0.97827545
61000000 4653.4882 375203.45]
[-0.00028558513 0.0058766133 4.8714989e-06 -0.92295157
0.021193039 0.00058756778 0.0058765568]
[ 0.0023870574 0.055326859 1.7099293e-06 -0.97826891
0.99688132 0.01008911 0.055326327]
[-0.00025093421 0.0014797211 5.2744513e-06 -0.044927633
0.07611833 0.00054094668 0.0014797069]
[ 0.0023014344 0.055315241 1.7512188e-06 -0.97827491
0.98798882 0.010026523 0.055314709]
[-0.00024539816 0.0016970776 6.2292964e-06 -0.091357195
0.075922572 0.00054341477 0.0016970613]
[ 0.0022908007 0.055309355 1.3068813e-06 -0.97826886
0.98751119 0.0098599264 0.055308823]
[-0.00024407073 0.0017278177 6.06088e-06 -0.091357195
0.07590755 0.0005446816 0.0017278011]
[-3.2840509e-07 5.2955969e-05 0
-0.0086078672 0 6.5678493e-07 5.2955459e-05]
]
pdl> p $data->stats
258.537987240325 125070.089699174 1.190347227888e-06 -0.9782754458665
61000000 517.071919985122 125069.956082351
pdl> ?vars
PDL variables in package main::
Name Type Dimension Flow State Mem
----------------------------------------------------------------
$data Double D [52002,9] P 3.57MB
Which is less than 4MB data. You could possibly read all the
100+ array data into one 2D PDL and then use slicing operations
and reshape to extract the matrix forms. Try things out in the
pdl2 or perldl shell to see what works.
Cheers,
Chris
On 7/5/2015 22:25, Benjamin Silva wrote:
Hi Chris,
I had just posted the code snippet with the hopes someone would look
at it and see something obviously wrong. The actual subroutine that
parses the input file is a bit more complex since the file I am
parsing isn't all just matrix data; there is a header and some other
stuff that I pull out, so there are a bunch of other loops and such
going on in there. That said, I've included a working copy of the
script as well as a sample file that it operates on. Any idea why
it's so slow? Please forgive my awful code.
link to my code:
http://pastebin.com/JS9gTs6t
link to the sample file it operates on:
http://www.5plates.com/sample.zip
Note that this example looks relatively fast (4 seconds), but this
script is reading just a single matrix from a file that houses just 2
matrices. I will be using this script on a file that has 100+
instances of these matrices. That means I'll have to read each of
these 100 matrices which makes 4 seconds turn into several minutes.
Please let me know if this helps, or if you can't read the files linked.
Thanks!
-Ben
On Sun, Jul 5, 2015 at 1:11 PM, Chris Marshall <[email protected]
<mailto:[email protected]>>wrote:
Hi Ben-
It helps if you include a small working code example rather than
an out-of-context snippet---something we can run. Some thoughts:
- rcols and wcols are useful reading an writing 2D piddles
- $zmatrix_pre seems to be a perl array object with N x M piddle
elements
- PDL is optimized for large data operations
- Reading in complex values one-by-one is going to be very slow
when
you have 350x350 elements.
- You should be reading all the values in one operation and
create the
PDL from that.
I can't help more since your code doesn't even set $row or $col but
maybe the above thoughts will give you an idea. You can use wcols()
to write out a 2D piddle and rcols() to read it back. Something like
this might be applicable from a pdl2 session:
pdl> $im = sequence(5,5)/25;
pdl> $re = random(5,5);
pdl> use PDL::Complex
pdl> p i
0 +1i
pdl> $c = $re + i * $im;
pdl> p $c
[
[0.971898 +0i 0.754039+0.04i 0.50257+0.08i 0.190826+0.12i
0.613931+0.16i]
[0.132726 +0.2i 0.327291+0.24i 0.251733+0.28i 0.184122+0.32i
0.787163+0.36i]
[0.103273 +0.4i 0.793739+0.44i 0.286722+0.48i 0.71684+0.52i
0.939528+0.56i]
[0.114506 +0.6i 0.750494+0.64i 0.757878+0.68i 0.761478+0.72i
0.827088+0.76i]
[ 0.69723 +0.8i 0.438457+0.84i 0.177937+0.88i 0.321631+0.92i
0.750218+0.96i]
]
I recommend trying out small cases in the pdl2 or perldl shells to
see how things
work. Once you see the patterns it is easier to apply to bigger
data in a program
or script.
Cheers,
Chris
On 7/5/2015 14:57, Benjamin Silva wrote:
Hello,
I recently recoded some of my old scripts to use the PDL
libraries instead of some subroutines I had written myself.
These scripts were for doing matrix inversion and matrix
manipulation for medium sized matrices of complex numbers
(~350x350 max matrix size). These matrices are housed in plain
text files, and I have a parser that goes through and builds a
piddle out of the data in the file. My old subroutines were slow
to do the processing, but were extremely fast for reading in the
file and creating the matrix. The new subroutine, using PDL, is
extremely fast to do the processing, but now it is crazy slow for
reading in the file and creating the initial PDL. My method for
creating the PDL is shown below. Can anyone please let me know
if there's a faster way to do this? Almost all of the time
savings I've achieved by going to PDL have been consumed by the
slower file parsing and PDL building. I've run the code through
a profiler, and it's definitely wasting a lot of cycles on the
3rd line in the while loop where I'm creating the cplx data
structure.
open (FILE, "$input_file") or die;
while($inline1=<FILE>){
chomp $inline1;
@data = split(/\s+/, $inline1);
$zmatrix_pre[$row][$column] = cplx($data[0]+$data[1]*i);
}
Thanks for any help!
-Ben
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