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JRONoise.py
199 lines | 5.2 KiB | text/x-python | PythonLexer
/ schainpy / old / Processing / JRONoise.py
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Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107 import numpy
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 def hildebrand_sekhon(data, navg):
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107 """
This method is for the objective determination of de noise level in Doppler spectra. This
implementation technique is based on the fact that the standard deviation of the spectral
densities is equal to the mean spectral density for white Gaussian noise
Inputs:
Data : heights
navg : numbers of averages
Return:
-1 : any error
anoise : noise's level
"""
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 dataflat = data.reshape(-1)
dataflat.sort()
npts = dataflat.size #numbers of points of the data
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107
if npts < 32:
print "error in noise - requires at least 32 points"
return -1.0
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 dataflat2 = numpy.power(dataflat,2)
cs = numpy.cumsum(dataflat)
cs2 = numpy.cumsum(dataflat2)
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 # data sorted in ascending order
nmin = int((npts + 7.)/8)
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107
for i in range(nmin, npts):
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 s = cs[i]
s2 = cs2[i]
p = s / float(i);
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107 p2 = p**2;
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 q = s2 / float(i) - p2;
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107 leftc = p2;
rightc = q * float(navg);
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 R2 = leftc/rightc
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107 # Signal detect: R2 < 1 (R2 = leftc/rightc)
Miguel Valdez
Cambio en la forma de generacion de graficos espectrales
 r112 if R2 < 1:
npts_noise = i
break
anoise = numpy.average(dataflat[0:npts_noise])
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107
return anoise;
def sorting_bruce(Data, navg):
sortdata = numpy.sort(Data)
lenOfData = len(Data)
nums_min = lenOfData/10
if (lenOfData/10) > 0:
nums_min = lenOfData/10
else:
nums_min = 0
rtest = 1.0 + 1.0/navg
sum = 0.
sumq = 0.
j = 0
cont = 1
while((cont==1)and(j<lenOfData)):
sum += sortdata[j]
sumq += sortdata[j]**2
j += 1
if j > nums_min:
if ((sumq*j) <= (rtest*sum**2)):
lnoise = sum / j
else:
j = j - 1
sum = sum - sordata[j]
sumq = sumq - sordata[j]**2
cont = 0
if j == nums_min:
lnoise = sum /j
return lnoise
class Noise:
"""
Clase que implementa los metodos necesarios para deternimar el nivel de ruido en un Spectro Doppler
"""
data = None
noise = None
dim = None
def __init__(self, data=None):
"""
Inicializador de la clase Noise para la la determinacion del nivel de ruido en un Spectro Doppler.
Inputs:
data: Numpy array de la forma nChan x nHeis x nProfiles
Affected:
self.noise
Return:
None
"""
self.data = data
self.dim = None
self.nChannels = None
self.noise = None
def setNoise(self, data):
"""
Inicializador de la clase Noise para la la determinacion del nivel de ruido en un Spectro Doppler.
Inputs:
data: Numpy array de la forma nChan x nHeis x nProfiles
Affected:
self.noise
Return:
None
"""
if data == None:
Daniel Valdez
Getnoise se agrega al metodo getFft de SpectraProcessor.
 r111 raise ValueError, "The data value is not defined"
Daniel Valdez
Changing the Model for the input Objects in Processing package...
 r107
shape = data.shape
self.dim = len(shape)
if self.dim == 3:
nChan, nProfiles, nHeis = shape
elif self.dim == 2:
nChan, nHeis = shape
else:
raise ValueError, ""
self.nChannels = nChan
self.data = data.copy()
self.noise = numpy.zeros(nChan)
return 1
def byHildebrand(self, navg=1):
"""
Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
Return:
noiselevel
"""
daux = None
for channel in range(self.nChannels):
daux = self.data[channel,:,:]
self.noise[channel] = hildebrand_sekhon(daux, navg)
return self.noise
def byWindow(self, heiIndexMin, heiIndexMax, freqIndexMin, freqIndexMax):
"""
Determina el ruido del canal utilizando la ventana indicada con las coordenadas:
(heiIndexMIn, freqIndexMin) hasta (heiIndexMax, freqIndexMAx)
Inputs:
heiIndexMin: Limite inferior del eje de alturas
heiIndexMax: Limite superior del eje de alturas
freqIndexMin: Limite inferior del eje de frecuencia
freqIndexMax: Limite supoerior del eje de frecuencia
"""
data = self.data[:, heiIndexMin:heiIndexMax, freqIndexMin:freqIndexMax]
for channel in range(self.nChannels):
daux = data[channel,:,:]
self.noise[channel] = numpy.average(daux)
return self.noise
def bySort(self,navg = 1):
daux = None
for channel in range(self.nChannels):
daux = self.data[channel,:,:]
self.noise[channel] = sorting_bruce(daux, navg)
return self.noise