the fastest way to insert 4 million rows (16MB of data) would be to use load data infile - http://dev.mysql.com/doc/refman/5.0/en/load-data.html
so if possible generate a csv file then use load data infile..
hope this helps :)
EDIT
So I took one of your original data files rolloff.dat and wro...
no and I would make the user point to a folder, start parsing all the data
then when they 'commit' build the query and insert
perhaps I would even just start doing that already right after they've pointed to the folder (well they might have to click Ok to confirm the right folder first :P)
I still need to tackle that darn animation thing though
my SO question 'looks' answered, but Ignacio's answer doesn't 'solve' it
I'm trying to create a Matplotlib animation of my paw data, where you can see the pressure distribution on the entire pressure plate over time (256x64 sensors for 250 frames).
I found a working example on Matplotlib's own site and managed to get it working on my own data. However the 'animation'...
I'm looking for help from users who want to help breath new life into the Gaming blog! On Super User we got officially blessed by Jeff and I honestly believe that Gaming should be able to deserve one as well.
On Super User we mainly started out with Questions of the Week, which help create a ste...
well, I might be able to lend a hand - but I don't think I'm really part of the community on gaming; just someone who turns up and asks/answers occasionally...
or that ReadyBoost is speeding up the normal apps when not striped
started gathering as much timing data as I can, will switch RB off at some point and retest, see if it is making a difference or if things are just odd
the strange thing is, I think that only 2 things come out of there :S
I haven't seen an alpha :S
> ([[<glumpy.image.Image object at 0x059DE490>, <matplotlib.image.AxesImage object at 0x059DE3B0>, 1.0]], <type 'list'>)
that was print(items, type(item))
oh wait I see, 1.0 at the end
@window.event
def on_idle(dt):
global Z
for image, axis, alpha in items:
for frames in range(248):
image.data[...] = Z[:,:,frames]
#print(sum(sum(Z[:,:,frames])))
image.update()
window.draw()
this works or rather, doesn't crash and Z[:,:,frames] is a different frame all the time
I found Joe Kington's answer that mentioned using Glumpy instead. At first I couldn't get it to work on my own data, but with some help on chat we managed to figure out how to adapt one of the Matplotlib examples that come with Glumpy to work on my data.
import numpy, glumpy
from glumpy.pylab im...
Matlab... the licenses were present when I arrived, I took over some legacy code... never broke out of the habit. But I've heard before that Python would be the way to go... currently its the inertia which is keeping me ;-)
I already noticed that I "Jonas" answering Matlab questions on SO is taken and picked a longer name... even though I like the idea that nickname strings don't need to be unique :-)
Adaptive Optics in Microscopy is already quite established for people "looking you into the eye" (= eye doctors); for other samples it is a hot topic, but only starting to show results
Na Ji's work in Neurobiology is quite exciting in my eyes
(in her (Na Ji's) case, "bad conditions" means to look through several other cell layers)
if you look in through the eyes, you can't see further than the retina. This is interesting, and eye doctors do this all the time. But if you want to understand the cortex (the big outer part of the brain) you won't see it through the eyes
so you are stuck with animal experiments :-/
meaning that you do a small operation to open the brain
or at least make the skull thin enough to become transparent