def convert(size, srcunit, trgunit):
value = size
if srcunit != trgunit:
sizeinbytes = size * Factor[srcunit]
trgfact = Factor[trgunit]
value = round(float(sizeinbytes) / trgfact, int(log10(trgfact)))
return value
def getHumanUnit(size, unit="B"):
size = convert(size, unit, "B")
hu = "B"
for u in Factor.keys():
if size > 10**(int(log10(Factor[u]))):
if Factor[u] > Factor[hu]:
hu = u
return hu
"""Load Nicolet BMSI data."""
if tmin < 0: tmin = 0
fh = file(filename, 'rb')
indmin = Fs * tmin
numsamples = os.path.getsize(filename) / (channels * 2)
indmax = min(numsamples, Fs * tmax)
byte0 = int(indmin * channels * 2)
numbytes = int((indmax - indmin) * channels * 2)
fh.seek(byte0)
data = fromstring(fh.read(numbytes), Int16).astype(Float)
data.shape = -1, channels
t = (1 / Fs) * arange(indmin, indmax)
return t, data
t, data = read_nicolet(0, 10)
x = data[:, 5]
Pxx, freqs, t = specgram(x, NFFT=512, Fs=Fs, noverlap=412)
T, F = meshgrid(t, freqs)
pcolor(T, F, 10 * log10(Pxx), shading='flat')
set(gca(), 'ylim', [0, 100])
#print Pxx.shape, freqs.shape, t.shape
show()
indmax = min(numsamples, Fs*tmax)
byte0 = int(indmin*channels*2)
numbytes = int( (indmax-indmin)*channels*2 )
fh.seek(byte0)
data = fromstring(fh.read(numbytes), Int16).astype(Float)
data.shape = -1, channels
t = (1/Fs)*arange(indmin, indmax)
return t, data
t, data = read_nicolet(0,10)
x = data[:,5]
Pxx, freqs, t = specgram(x, NFFT=512, Fs=Fs, noverlap=412)
T, F = meshgrid(t,freqs)
pcolor(T, F, 10*log10(Pxx), shading='flat')
set(gca(), 'ylim', [0,100])
#print Pxx.shape, freqs.shape, t.shape
show()
def log10Image(self): if self.fname == '': return from Numeric import log10 data = self.data da = log10(data) self.createImage(da)
def log10Image(self):
if self.fname == '': return
from Numeric import log10
data = self.data
da = log10(data)
self.createImage(da)
