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schain-jc
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Noise.py:...
Victor Sarmiento -
r93:1e5ff7f4bb15
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@@ -1,9 +1,7
1 1 import numpy
2 from Model.Spectra import Spectra
2 3
3 from Model.JROHeader import *
4 from Model.Voltage import Voltage
5
6 def hildebrand_sekhon(Data, navg=1 ):
4 def hildebrand_sekhon(Data, navg=1):
7 5 """
8 6 This method is for the objective determination of de noise level in Doppler spectra. This
9 7 implementation technique is based on the fact that the standard deviation of the spectral
@@ -73,7 +71,7 class Noise():
73 71 m_DataObj = None
74 72
75 73
76 def __init__(self, m_Voltage=None):
74 def __init__(self, m_Spectra=None):
77 75 """
78 76 Inicializador de la clase Noise para la la determinacion del nivel de ruido en un Spectro Doppler.
79 77
@@ -83,13 +81,13 class Noise():
83 81 Return:
84 82 None
85 83 """
86 # if m_Voltage == None:
87 # m_Voltage = Voltage()
88 #
89 # if not(isinstance(m_Voltage, Voltage)):
90 # raise ValueError, "in Noise class, m_Voltage must be an Voltage class object"
84 if m_Spectra == None:
85 m_Spectra = Spectra()
86
87 if not(isinstance(m_Spectra, Spectra)):
88 raise ValueError, "in Noise class, m_Spectra must be an Spectra class object"
91 89
92 self.m_DataObj = m_Voltage
90 self.m_DataObj = m_Spectra
93 91
94 92
95 93 def getNoiseLevelByHildebrandSekhon(self):
@@ -99,10 +97,16 class Noise():
99 97 Return:
100 98 noise level
101 99 """
102 data = self.m_DataObj.data # heights x perfiles
103 #heights = numpy.transpose( Data, (2,0,1) ) # channel x profile x height
104 #data = Data[0,0,:]
100 data = self.m_DataObj.data_spc
101 daux = None
105 102
106 noiselevel = hildebrand_sekhon(data)
103 for channel in range(self.m_DataObj.nChannels):
104 daux = data[channel,:,:]
105 noiselevel = hildebrand_sekhon(daux)
106 print noiselevel
107 107
108 print noiselevel
108
109 for pair in range(self.m_DataObj.nPairs):
110 daux = data[pair,:,:]
111 noiselevel = hildebrand_sekhon(daux)
112 print noiselevel
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