I accidentally nuked my laptop's 80G hard drive this morning (D'OH!) while shuffling around partitions. Supposedly there's a valid windows (XP) installation on there still that's about 20G. I'd love to repair it so I can use it today while I'm in the confocal room, but I don't have an Ubuntu CD, Windows CD, or any CD for that matter! I looked around, but I guess blank CD-Rs aren't something that's standard in molecular biology laboratories. Anyhow, I wanted to install the new Ubuntu 8.10 Linux distribution, and I've downloaded the ISO, but since I can't find a CD to burn it to I decided to try booting from a USB drive (something I've never done before). I found an AWESOME program which specialized in putting ISO files onto bootable USB drives. It's called UNetBootin and it's free (of course), runs on Linux or Windows, and has some built-in options for various linux distributions. I can repair my PC now! Yay!

⚠️ Warning: This article is obsolete.

Articles typically receive this designation when the technology they describe is no longer relevant, code provided is later deemed to be of poor quality, or the topics discussed are better presented in future articles. Articles like this are retained for the sake of preservation, but their content should be critically assessed.

def smoothListGaussian(list, degree=5):
window = degree*2-1
weight = numpy.array([1.0]*window)
weightGauss = []
for i in range(window):
i = i-degree+1
frac = i/float(window)
gauss = 1/(numpy.exp((4*(frac))**2))
weightGauss.append(gauss)
weight = numpy.array(weightGauss)*weight
smoothed = [0.0]*(len(list)-window)
for i in range(len(smoothed)):
smoothed[i] = sum(numpy.array(list[i:i+window])*weight)/sum(weight)
return smoothed

Provide a list and it will return a smoother version of the data. The Gaussian smoothing function I wrote is leagues better than a moving window average method, for reasons that are obvious when viewing the chart below. Surprisingly, the moving triangle method appears to be very similar to the Gaussian function at low degrees of spread. However, for large numbers of data points, the Gaussian function should perform better.

import pylab
import numpy
def smoothList(list, strippedXs=False, degree=10):
if strippedXs == True:
return Xs[0:-(len(list)-(len(list)-degree+1))]
smoothed = [0]*(len(list)-degree+1)
for i in range(len(smoothed)):
smoothed[i] = sum(list[i:i+degree])/float(degree)
return smoothed
def smoothListTriangle(list, strippedXs=False, degree=5):
weight = []
window = degree*2-1
smoothed = [0.0]*(len(list)-window)
for x in range(1, 2*degree):
weight.append(degree-abs(degree-x))
w = numpy.array(weight)
for i in range(len(smoothed)):
smoothed[i] = sum(numpy.array(list[i:i+window])*w)/float(sum(w))
return smoothed
def smoothListGaussian(list, strippedXs=False, degree=5):
window = degree*2-1
weight = numpy.array([1.0]*window)
weightGauss = []
for i in range(window):
i = i-degree+1
frac = i/float(window)
gauss = 1/(numpy.exp((4*(frac))**2))
weightGauss.append(gauss)
weight = numpy.array(weightGauss)*weight
smoothed = [0.0]*(len(list)-window)
for i in range(len(smoothed)):
smoothed[i] = sum(numpy.array(list[i:i+window])*weight)/sum(weight)
return smoothed
### DUMMY DATA ###
data = [0]*30 # 30 "0"s in a row
data[15] = 1 # the middle one is "1"
### PLOT DIFFERENT SMOOTHING FUNCTIONS ###
pylab.figure(figsize=(550/80, 700/80))
pylab.suptitle('1D Data Smoothing', fontsize=16)
pylab.subplot(4, 1, 1)
p1 = pylab.plot(data, ".k")
p1 = pylab.plot(data, "-k")
a = pylab.axis()
pylab.axis([a[0], a[1], -.1, 1.1])
pylab.text(2, .8, "raw data", fontsize=14)
pylab.subplot(4, 1, 2)
p1 = pylab.plot(smoothList(data), ".k")
p1 = pylab.plot(smoothList(data), "-k")
a = pylab.axis()
pylab.axis([a[0], a[1], -.1, .4])
pylab.text(2, .3, "moving window average", fontsize=14)
pylab.subplot(4, 1, 3)
p1 = pylab.plot(smoothListTriangle(data), ".k")
p1 = pylab.plot(smoothListTriangle(data), "-k")
pylab.axis([a[0], a[1], -.1, .4])
pylab.text(2, .3, "moving triangle", fontsize=14)
pylab.subplot(4, 1, 4)
p1 = pylab.plot(smoothListGaussian(data), ".k")
p1 = pylab.plot(smoothListGaussian(data), "-k")
pylab.axis([a[0], a[1], -.1, .4])
pylab.text(2, .3, "moving gaussian", fontsize=14)
# pylab.show()
pylab.savefig("smooth.png", dpi=80)

This data needs smoothing. Below is a visual representation of the differences in the methods of smoothing.

The degree of window coverage for the moving window average, moving triangle, and Gaussian functions are 10, 5, and 5 respectively. Also note that (due to the handling of the "degree" variable between the different functions) the actual number of data points assessed in these three functions are 10, 9, and 9 respectively. The degree for the last two functions represents "spread" from each point, whereas the first one represents the total number of points to be averaged for the moving average.

I'm posting this because I wanted to charge my Motorola Razor v3 Phone from a USB port on a PC using one of those standard miniUSB cables, and you wouldn't believe how hard it was for me to find out how! I didn't want to do any special software imports or exports or communication with the phone - I just wanted to get my battery charged before it died! It was surprisingly hard to find the software to send power to the USB port for windows. These were the two solutions I came across.

Solution1: Install DriverTool.exe (which will add some drivers) and allow power to be sent to the phone when it's plugged in.

Solution2: Simply use UBUNTU (booted from the LiveCD or an installation) and plug in the cord. It automatically sends power to the phone, no drivers needed!

yeah, that's right. Finally! Off my crappy internet connection! Woohoo!

I'm not 100% sure I'm going to be using this company... but things are really slick so far. I feel pretty good about these guys =o) So far, they've been treating me really well and been great people to work with.... more info later.

It'll be at least a day or two before I can move my whole website over to this new host... I have a TON of server-specific code I wrote since I never dreamed I'd move to a real host... (never would have imagined my site would get so big). So, gimme a few days, see what happens...