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import numpy
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import numpy
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import math
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import math
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from scipy import optimize, interpolate, signal, stats, ndimage
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from scipy import optimize, interpolate, signal, stats, ndimage
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import scipy
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import scipy
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import re
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import re
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import datetime
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import datetime
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import copy
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import copy
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import sys
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import sys
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import importlib
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import importlib
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import itertools
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import itertools
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from multiprocessing import Pool, TimeoutError
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from multiprocessing import Pool, TimeoutError
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from multiprocessing.pool import ThreadPool
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from multiprocessing.pool import ThreadPool
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import copy_reg
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import copy_reg
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import cPickle
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import cPickle
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import types
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import types
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from functools import partial
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from functools import partial
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import time
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import time
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#from sklearn.cluster import KMeans
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#from sklearn.cluster import KMeans
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from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
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from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
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from jroproc_base import ProcessingUnit, Operation
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from jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
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from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
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from scipy import asarray as ar,exp
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from scipy import asarray as ar,exp
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from scipy.optimize import curve_fit
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from scipy.optimize import curve_fit
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import warnings
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import warnings
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from numpy import NaN
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from numpy import NaN
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from scipy.optimize.optimize import OptimizeWarning
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from scipy.optimize.optimize import OptimizeWarning
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warnings.filterwarnings('ignore')
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warnings.filterwarnings('ignore')
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SPEED_OF_LIGHT = 299792458
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SPEED_OF_LIGHT = 299792458
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'''solving pickling issue'''
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'''solving pickling issue'''
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def _pickle_method(method):
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def _pickle_method(method):
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func_name = method.im_func.__name__
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func_name = method.im_func.__name__
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obj = method.im_self
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obj = method.im_self
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cls = method.im_class
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cls = method.im_class
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return _unpickle_method, (func_name, obj, cls)
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return _unpickle_method, (func_name, obj, cls)
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def _unpickle_method(func_name, obj, cls):
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def _unpickle_method(func_name, obj, cls):
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for cls in cls.mro():
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for cls in cls.mro():
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try:
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try:
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func = cls.__dict__[func_name]
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func = cls.__dict__[func_name]
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except KeyError:
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except KeyError:
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pass
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pass
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else:
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else:
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break
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break
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return func.__get__(obj, cls)
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return func.__get__(obj, cls)
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class ParametersProc(ProcessingUnit):
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class ParametersProc(ProcessingUnit):
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nSeconds = None
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nSeconds = None
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def __init__(self):
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def __init__(self):
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ProcessingUnit.__init__(self)
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ProcessingUnit.__init__(self)
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# self.objectDict = {}
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# self.objectDict = {}
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self.buffer = None
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self.buffer = None
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self.firstdatatime = None
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self.firstdatatime = None
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self.profIndex = 0
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self.profIndex = 0
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self.dataOut = Parameters()
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self.dataOut = Parameters()
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def __updateObjFromInput(self):
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def __updateObjFromInput(self):
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self.dataOut.inputUnit = self.dataIn.type
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self.dataOut.inputUnit = self.dataIn.type
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self.dataOut.timeZone = self.dataIn.timeZone
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self.dataOut.timeZone = self.dataIn.timeZone
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self.dataOut.dstFlag = self.dataIn.dstFlag
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self.dataOut.dstFlag = self.dataIn.dstFlag
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self.dataOut.errorCount = self.dataIn.errorCount
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self.dataOut.errorCount = self.dataIn.errorCount
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self.dataOut.useLocalTime = self.dataIn.useLocalTime
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self.dataOut.useLocalTime = self.dataIn.useLocalTime
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self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
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self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
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self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
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self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
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self.dataOut.channelList = self.dataIn.channelList
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self.dataOut.channelList = self.dataIn.channelList
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self.dataOut.heightList = self.dataIn.heightList
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self.dataOut.heightList = self.dataIn.heightList
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self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
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self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
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# self.dataOut.nHeights = self.dataIn.nHeights
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# self.dataOut.nHeights = self.dataIn.nHeights
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# self.dataOut.nChannels = self.dataIn.nChannels
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# self.dataOut.nChannels = self.dataIn.nChannels
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self.dataOut.nBaud = self.dataIn.nBaud
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self.dataOut.nBaud = self.dataIn.nBaud
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self.dataOut.nCode = self.dataIn.nCode
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self.dataOut.nCode = self.dataIn.nCode
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self.dataOut.code = self.dataIn.code
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self.dataOut.code = self.dataIn.code
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# self.dataOut.nProfiles = self.dataOut.nFFTPoints
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# self.dataOut.nProfiles = self.dataOut.nFFTPoints
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self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
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self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
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# self.dataOut.utctime = self.firstdatatime
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# self.dataOut.utctime = self.firstdatatime
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self.dataOut.utctime = self.dataIn.utctime
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self.dataOut.utctime = self.dataIn.utctime
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self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
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self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
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self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
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self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
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self.dataOut.nCohInt = self.dataIn.nCohInt
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self.dataOut.nCohInt = self.dataIn.nCohInt
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# self.dataOut.nIncohInt = 1
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# self.dataOut.nIncohInt = 1
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
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# self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
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self.dataOut.timeInterval1 = self.dataIn.timeInterval
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self.dataOut.timeInterval1 = self.dataIn.timeInterval
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self.dataOut.heightList = self.dataIn.getHeiRange()
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self.dataOut.heightList = self.dataIn.getHeiRange()
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self.dataOut.frequency = self.dataIn.frequency
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self.dataOut.frequency = self.dataIn.frequency
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# self.dataOut.noise = self.dataIn.noise
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# self.dataOut.noise = self.dataIn.noise
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def run(self):
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def run(self):
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#---------------------- Voltage Data ---------------------------
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#---------------------- Voltage Data ---------------------------
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if self.dataIn.type == "Voltage":
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if self.dataIn.type == "Voltage":
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self.__updateObjFromInput()
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self.__updateObjFromInput()
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self.dataOut.data_pre = self.dataIn.data.copy()
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self.dataOut.data_pre = self.dataIn.data.copy()
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self.dataOut.flagNoData = False
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self.dataOut.flagNoData = False
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
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self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
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return
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return
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#---------------------- Spectra Data ---------------------------
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#---------------------- Spectra Data ---------------------------
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if self.dataIn.type == "Spectra":
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if self.dataIn.type == "Spectra":
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self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc)
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self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc)
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self.dataOut.data_spc = self.dataIn.data_spc
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self.dataOut.data_spc = self.dataIn.data_spc
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self.dataOut.data_cspc = self.dataIn.data_cspc
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self.dataOut.data_cspc = self.dataIn.data_cspc
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self.dataOut.nProfiles = self.dataIn.nProfiles
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self.dataOut.nProfiles = self.dataIn.nProfiles
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self.dataOut.nIncohInt = self.dataIn.nIncohInt
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self.dataOut.nIncohInt = self.dataIn.nIncohInt
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self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
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self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
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self.dataOut.ippFactor = self.dataIn.ippFactor
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self.dataOut.ippFactor = self.dataIn.ippFactor
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self.dataOut.abscissaList = self.dataIn.getVelRange(1)
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self.dataOut.abscissaList = self.dataIn.getVelRange(1)
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self.dataOut.spc_noise = self.dataIn.getNoise()
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self.dataOut.spc_noise = self.dataIn.getNoise()
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self.dataOut.spc_range = (self.dataIn.getFreqRange(1)/1000. , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
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self.dataOut.spc_range = (self.dataIn.getFreqRange(1)/1000. , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
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self.dataOut.pairsList = self.dataIn.pairsList
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self.dataOut.pairsList = self.dataIn.pairsList
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self.dataOut.groupList = self.dataIn.pairsList
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self.dataOut.groupList = self.dataIn.pairsList
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self.dataOut.flagNoData = False
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self.dataOut.flagNoData = False
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if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
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if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
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self.dataOut.ChanDist = self.dataIn.ChanDist
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self.dataOut.ChanDist = self.dataIn.ChanDist
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else: self.dataOut.ChanDist = None
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else: self.dataOut.ChanDist = None
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if hasattr(self.dataIn, 'VelRange'): #Velocities range
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if hasattr(self.dataIn, 'VelRange'): #Velocities range
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self.dataOut.VelRange = self.dataIn.VelRange
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self.dataOut.VelRange = self.dataIn.VelRange
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else: self.dataOut.VelRange = None
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else: self.dataOut.VelRange = None
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if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
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if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
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self.dataOut.RadarConst = self.dataIn.RadarConst
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self.dataOut.RadarConst = self.dataIn.RadarConst
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if hasattr(self.dataIn, 'NPW'): #NPW
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if hasattr(self.dataIn, 'NPW'): #NPW
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self.dataOut.NPW = self.dataIn.NPW
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self.dataOut.NPW = self.dataIn.NPW
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if hasattr(self.dataIn, 'COFA'): #COFA
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if hasattr(self.dataIn, 'COFA'): #COFA
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self.dataOut.COFA = self.dataIn.COFA
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self.dataOut.COFA = self.dataIn.COFA
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#---------------------- Correlation Data ---------------------------
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#---------------------- Correlation Data ---------------------------
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if self.dataIn.type == "Correlation":
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if self.dataIn.type == "Correlation":
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acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
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acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
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self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
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self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
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self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
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self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
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self.dataOut.groupList = (acf_pairs, ccf_pairs)
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self.dataOut.groupList = (acf_pairs, ccf_pairs)
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self.dataOut.abscissaList = self.dataIn.lagRange
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self.dataOut.abscissaList = self.dataIn.lagRange
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self.dataOut.noise = self.dataIn.noise
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self.dataOut.noise = self.dataIn.noise
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self.dataOut.data_SNR = self.dataIn.SNR
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self.dataOut.data_SNR = self.dataIn.SNR
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self.dataOut.flagNoData = False
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self.dataOut.flagNoData = False
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self.dataOut.nAvg = self.dataIn.nAvg
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self.dataOut.nAvg = self.dataIn.nAvg
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#---------------------- Parameters Data ---------------------------
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#---------------------- Parameters Data ---------------------------
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if self.dataIn.type == "Parameters":
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if self.dataIn.type == "Parameters":
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self.dataOut.copy(self.dataIn)
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self.dataOut.copy(self.dataIn)
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self.dataOut.flagNoData = False
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self.dataOut.flagNoData = False
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return True
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return True
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self.__updateObjFromInput()
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self.__updateObjFromInput()
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.utctimeInit = self.dataIn.utctime
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self.dataOut.paramInterval = self.dataIn.timeInterval
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self.dataOut.paramInterval = self.dataIn.timeInterval
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return
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return
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def target(tups):
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def target(tups):
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obj, args = tups
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obj, args = tups
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#print 'TARGETTT', obj, args
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#print 'TARGETTT', obj, args
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return obj.FitGau(args)
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return obj.FitGau(args)
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class GaussianFit(Operation):
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class GaussianFit(Operation):
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'''
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'''
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Function that fit of one and two generalized gaussians (gg) based
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Function that fit of one and two generalized gaussians (gg) based
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on the PSD shape across an "power band" identified from a cumsum of
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on the PSD shape across an "power band" identified from a cumsum of
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the measured spectrum - noise.
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the measured spectrum - noise.
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Input:
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Input:
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self.dataOut.data_pre : SelfSpectra
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self.dataOut.data_pre : SelfSpectra
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196
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Output:
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Output:
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self.dataOut.GauSPC : SPC_ch1, SPC_ch2
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self.dataOut.GauSPC : SPC_ch1, SPC_ch2
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199
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'''
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'''
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def __init__(self, **kwargs):
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def __init__(self, **kwargs):
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Operation.__init__(self, **kwargs)
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Operation.__init__(self, **kwargs)
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self.i=0
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203
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self.i=0
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204
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204
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205
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205
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206
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def run(self, dataOut, num_intg=7, pnoise=1., vel_arr=None, SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
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206
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def run(self, dataOut, num_intg=7, pnoise=1., vel_arr=None, SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
|
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207
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"""This routine will find a couple of generalized Gaussians to a power spectrum
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207
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"""This routine will find a couple of generalized Gaussians to a power spectrum
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208
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input: spc
|
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208
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input: spc
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209
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output:
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209
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output:
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210
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Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise
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210
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Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise
|
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211
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"""
|
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211
|
"""
|
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212
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212
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213
|
self.spc = dataOut.data_pre[0].copy()
|
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213
|
self.spc = dataOut.data_pre[0].copy()
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214
|
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214
|
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215
|
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215
|
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216
|
print 'SelfSpectra Shape', numpy.asarray(self.spc).shape
|
|
216
|
print 'SelfSpectra Shape', numpy.asarray(self.spc).shape
|
|
|
217
|
|
|
217
|
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218
|
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218
|
|
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219
|
#plt.figure(50)
|
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219
|
#plt.figure(50)
|
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220
|
#plt.subplot(121)
|
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220
|
#plt.subplot(121)
|
|
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221
|
#plt.plot(self.spc,'k',label='spc(66)')
|
|
221
|
#plt.plot(self.spc,'k',label='spc(66)')
|
|
|
222
|
#plt.plot(xFrec,ySamples[1],'g',label='Ch1')
|
|
222
|
#plt.plot(xFrec,ySamples[1],'g',label='Ch1')
|
|
|
223
|
#plt.plot(xFrec,ySamples[2],'r',label='Ch2')
|
|
223
|
#plt.plot(xFrec,ySamples[2],'r',label='Ch2')
|
|
|
224
|
#plt.plot(xFrec,FitGauss,'yo:',label='fit')
|
|
224
|
#plt.plot(xFrec,FitGauss,'yo:',label='fit')
|
|
|
225
|
#plt.legend()
|
|
225
|
#plt.legend()
|
|
|
226
|
#plt.title('DATOS A ALTURA DE 7500 METROS')
|
|
226
|
#plt.title('DATOS A ALTURA DE 7500 METROS')
|
|
|
227
|
#plt.show()
|
|
227
|
#plt.show()
|
|
|
228
|
|
|
228
|
|
|
|
229
|
self.Num_Hei = self.spc.shape[2]
|
|
229
|
self.Num_Hei = self.spc.shape[2]
|
|
|
230
|
#self.Num_Bin = len(self.spc)
|
|
230
|
#self.Num_Bin = len(self.spc)
|
|
|
231
|
self.Num_Bin = self.spc.shape[1]
|
|
231
|
self.Num_Bin = self.spc.shape[1]
|
|
|
232
|
self.Num_Chn = self.spc.shape[0]
|
|
232
|
self.Num_Chn = self.spc.shape[0]
|
|
|
233
|
|
|
233
|
|
|
|
234
|
Vrange = dataOut.abscissaList
|
|
234
|
Vrange = dataOut.abscissaList
|
|
|
235
|
|
|
235
|
|
|
|
236
|
#print 'self.spc2', numpy.asarray(self.spc).shape
|
|
236
|
#print 'self.spc2', numpy.asarray(self.spc).shape
|
|
|
237
|
|
|
237
|
|
|
|
238
|
GauSPC = numpy.empty([2,self.Num_Bin,self.Num_Hei])
|
|
238
|
GauSPC = numpy.empty([2,self.Num_Bin,self.Num_Hei])
|
|
|
239
|
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
239
|
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
|
240
|
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
240
|
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
|
241
|
SPC_ch1[:] = numpy.NaN
|
|
241
|
SPC_ch1[:] = numpy.NaN
|
|
|
242
|
SPC_ch2[:] = numpy.NaN
|
|
242
|
SPC_ch2[:] = numpy.NaN
|
|
|
243
|
|
|
243
|
|
|
|
244
|
|
|
244
|
|
|
|
245
|
start_time = time.time()
|
|
245
|
start_time = time.time()
|
|
|
246
|
|
|
246
|
|
|
|
247
|
noise_ = dataOut.spc_noise[0].copy()
|
|
247
|
noise_ = dataOut.spc_noise[0].copy()
|
|
|
248
|
|
|
248
|
|
|
|
249
|
|
|
249
|
|
|
|
250
|
|
|
250
|
|
|
|
251
|
pool = Pool(processes=self.Num_Chn)
|
|
251
|
pool = Pool(processes=self.Num_Chn)
|
|
|
252
|
args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)]
|
|
252
|
args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)]
|
|
|
253
|
objs = [self for __ in range(self.Num_Chn)]
|
|
253
|
objs = [self for __ in range(self.Num_Chn)]
|
|
|
254
|
attrs = zip(objs, args)
|
|
254
|
attrs = zip(objs, args)
|
|
|
255
|
gauSPC = pool.map(target, attrs)
|
|
255
|
gauSPC = pool.map(target, attrs)
|
|
|
256
|
dataOut.GauSPC = numpy.asarray(gauSPC)
|
|
256
|
dataOut.GauSPC = numpy.asarray(gauSPC)
|
|
|
257
|
# ret = []
|
|
257
|
# ret = []
|
|
|
258
|
# for n in range(self.Num_Chn):
|
|
258
|
# for n in range(self.Num_Chn):
|
|
|
259
|
# self.FitGau(args[n])
|
|
259
|
# self.FitGau(args[n])
|
|
|
260
|
# dataOut.GauSPC = ret
|
|
260
|
# dataOut.GauSPC = ret
|
|
|
261
|
|
|
261
|
|
|
|
262
|
|
|
262
|
|
|
|
263
|
|
|
263
|
|
|
|
264
|
# for ch in range(self.Num_Chn):
|
|
264
|
# for ch in range(self.Num_Chn):
|
|
|
265
|
#
|
|
265
|
#
|
|
|
266
|
# for ht in range(self.Num_Hei):
|
|
266
|
# for ht in range(self.Num_Hei):
|
|
|
267
|
# #print (numpy.asarray(self.spc).shape)
|
|
267
|
# #print (numpy.asarray(self.spc).shape)
|
|
|
268
|
# spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
268
|
# spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
|
269
|
#
|
|
269
|
#
|
|
|
270
|
# #############################################
|
|
270
|
# #############################################
|
|
|
271
|
# # normalizing spc and noise
|
|
271
|
# # normalizing spc and noise
|
|
|
272
|
# # This part differs from gg1
|
|
272
|
# # This part differs from gg1
|
|
|
273
|
# spc_norm_max = max(spc)
|
|
273
|
# spc_norm_max = max(spc)
|
|
|
274
|
# spc = spc / spc_norm_max
|
|
274
|
# spc = spc / spc_norm_max
|
|
|
275
|
# pnoise = pnoise / spc_norm_max
|
|
275
|
# pnoise = pnoise / spc_norm_max
|
|
|
276
|
# #############################################
|
|
276
|
# #############################################
|
|
|
277
|
#
|
|
277
|
#
|
|
|
278
|
# if abs(vel_arr[0])<15.0: # this switch is for spectra collected with different length IPP's
|
|
278
|
# if abs(vel_arr[0])<15.0: # this switch is for spectra collected with different length IPP's
|
|
|
279
|
# fatspectra=1.0
|
|
279
|
# fatspectra=1.0
|
|
|
280
|
# else:
|
|
280
|
# else:
|
|
|
281
|
# fatspectra=0.5
|
|
281
|
# fatspectra=0.5
|
|
|
282
|
#
|
|
282
|
#
|
|
|
283
|
# wnoise = noise_ / spc_norm_max
|
|
283
|
# wnoise = noise_ / spc_norm_max
|
|
|
284
|
# #print 'wnoise', noise_, dataOut.spc_noise[0], wnoise
|
|
284
|
# #print 'wnoise', noise_, dataOut.spc_noise[0], wnoise
|
|
|
285
|
# #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
285
|
# #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
|
286
|
# #if wnoise>1.1*pnoise: # to be tested later
|
|
286
|
# #if wnoise>1.1*pnoise: # to be tested later
|
|
|
287
|
# # wnoise=pnoise
|
|
287
|
# # wnoise=pnoise
|
|
|
288
|
# noisebl=wnoise*0.9; noisebh=wnoise*1.1
|
|
288
|
# noisebl=wnoise*0.9; noisebh=wnoise*1.1
|
|
|
289
|
# spc=spc-wnoise
|
|
289
|
# spc=spc-wnoise
|
|
|
290
|
#
|
|
290
|
#
|
|
|
291
|
# minx=numpy.argmin(spc)
|
|
291
|
# minx=numpy.argmin(spc)
|
|
|
292
|
# spcs=numpy.roll(spc,-minx)
|
|
292
|
# spcs=numpy.roll(spc,-minx)
|
|
|
293
|
# cum=numpy.cumsum(spcs)
|
|
293
|
# cum=numpy.cumsum(spcs)
|
|
|
294
|
# tot_noise=wnoise * self.Num_Bin #64;
|
|
294
|
# tot_noise=wnoise * self.Num_Bin #64;
|
|
|
295
|
# #tot_signal=sum(cum[-5:])/5.; ''' How does this line work? '''
|
|
295
|
# #tot_signal=sum(cum[-5:])/5.; ''' How does this line work? '''
|
|
|
296
|
# #snr=tot_signal/tot_noise
|
|
296
|
# #snr=tot_signal/tot_noise
|
|
|
297
|
# #snr=cum[-1]/tot_noise
|
|
297
|
# #snr=cum[-1]/tot_noise
|
|
|
298
|
#
|
|
298
|
#
|
|
|
299
|
# #print 'spc' , spcs[5:8] , 'tot_noise', tot_noise
|
|
299
|
# #print 'spc' , spcs[5:8] , 'tot_noise', tot_noise
|
|
|
300
|
#
|
|
300
|
#
|
|
|
301
|
# snr = sum(spcs)/tot_noise
|
|
301
|
# snr = sum(spcs)/tot_noise
|
|
|
302
|
# snrdB=10.*numpy.log10(snr)
|
|
302
|
# snrdB=10.*numpy.log10(snr)
|
|
|
303
|
#
|
|
303
|
#
|
|
|
304
|
# #if snrdB < -9 :
|
|
304
|
# #if snrdB < -9 :
|
|
|
305
|
# # snrdB = numpy.NaN
|
|
305
|
# # snrdB = numpy.NaN
|
|
|
306
|
# # continue
|
|
306
|
# # continue
|
|
|
307
|
#
|
|
307
|
#
|
|
|
308
|
# #print 'snr',snrdB # , sum(spcs) , tot_noise
|
|
308
|
# #print 'snr',snrdB # , sum(spcs) , tot_noise
|
|
|
309
|
#
|
|
309
|
#
|
|
|
310
|
#
|
|
310
|
#
|
|
|
311
|
# #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
311
|
# #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
|
312
|
# # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
312
|
# # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
313
|
#
|
|
313
|
#
|
|
|
314
|
# cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region
|
|
314
|
# cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region
|
|
|
315
|
# cumlo=cummax*epsi;
|
|
315
|
# cumlo=cummax*epsi;
|
|
|
316
|
# cumhi=cummax*(1-epsi)
|
|
316
|
# cumhi=cummax*(1-epsi)
|
|
|
317
|
# powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
317
|
# powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
|
318
|
#
|
|
318
|
#
|
|
|
319
|
# #if len(powerindex)==1:
|
|
319
|
# #if len(powerindex)==1:
|
|
|
320
|
# ##return [numpy.mod(powerindex[0]+minx,64),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
320
|
# ##return [numpy.mod(powerindex[0]+minx,64),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
321
|
# #return [numpy.mod(powerindex[0]+minx, self.Num_Bin ),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
321
|
# #return [numpy.mod(powerindex[0]+minx, self.Num_Bin ),None,None,None,],[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
322
|
# #elif len(powerindex)<4*fatspectra:
|
|
322
|
# #elif len(powerindex)<4*fatspectra:
|
|
|
323
|
# #return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
323
|
# #return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
324
|
#
|
|
324
|
#
|
|
|
325
|
# if len(powerindex) < 1:# case for powerindex 0
|
|
325
|
# if len(powerindex) < 1:# case for powerindex 0
|
|
|
326
|
# continue
|
|
326
|
# continue
|
|
|
327
|
# powerlo=powerindex[0]
|
|
327
|
# powerlo=powerindex[0]
|
|
|
328
|
# powerhi=powerindex[-1]
|
|
328
|
# powerhi=powerindex[-1]
|
|
|
329
|
# powerwidth=powerhi-powerlo
|
|
329
|
# powerwidth=powerhi-powerlo
|
|
|
330
|
#
|
|
330
|
#
|
|
|
331
|
# firstpeak=powerlo+powerwidth/10.# first gaussian energy location
|
|
331
|
# firstpeak=powerlo+powerwidth/10.# first gaussian energy location
|
|
|
332
|
# secondpeak=powerhi-powerwidth/10.#second gaussian energy location
|
|
332
|
# secondpeak=powerhi-powerwidth/10.#second gaussian energy location
|
|
|
333
|
# midpeak=(firstpeak+secondpeak)/2.
|
|
333
|
# midpeak=(firstpeak+secondpeak)/2.
|
|
|
334
|
# firstamp=spcs[int(firstpeak)]
|
|
334
|
# firstamp=spcs[int(firstpeak)]
|
|
|
335
|
# secondamp=spcs[int(secondpeak)]
|
|
335
|
# secondamp=spcs[int(secondpeak)]
|
|
|
336
|
# midamp=spcs[int(midpeak)]
|
|
336
|
# midamp=spcs[int(midpeak)]
|
|
|
337
|
# #x=numpy.spc.shape[1]
|
|
337
|
# #x=numpy.spc.shape[1]
|
|
|
338
|
#
|
|
338
|
#
|
|
|
339
|
# #x=numpy.arange(64)
|
|
339
|
# #x=numpy.arange(64)
|
|
|
340
|
# x=numpy.arange( self.Num_Bin )
|
|
340
|
# x=numpy.arange( self.Num_Bin )
|
|
|
341
|
# y_data=spc+wnoise
|
|
341
|
# y_data=spc+wnoise
|
|
|
342
|
#
|
|
342
|
#
|
|
|
343
|
# # single gaussian
|
|
343
|
# # single gaussian
|
|
|
344
|
# #shift0=numpy.mod(midpeak+minx,64)
|
|
344
|
# #shift0=numpy.mod(midpeak+minx,64)
|
|
|
345
|
# shift0=numpy.mod(midpeak+minx, self.Num_Bin )
|
|
345
|
# shift0=numpy.mod(midpeak+minx, self.Num_Bin )
|
|
|
346
|
# width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
346
|
# width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
|
347
|
# power0=2.
|
|
347
|
# power0=2.
|
|
|
348
|
# amplitude0=midamp
|
|
348
|
# amplitude0=midamp
|
|
|
349
|
# state0=[shift0,width0,amplitude0,power0,wnoise]
|
|
349
|
# state0=[shift0,width0,amplitude0,power0,wnoise]
|
|
|
350
|
# #bnds=((0,63),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
350
|
# #bnds=((0,63),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
351
|
# bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
351
|
# bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
352
|
# #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(0.1,0.5))
|
|
352
|
# #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(0.1,0.5))
|
|
|
353
|
# # bnds = range of fft, power width, amplitude, power, noise
|
|
353
|
# # bnds = range of fft, power width, amplitude, power, noise
|
|
|
354
|
# lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
354
|
# lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
|
355
|
#
|
|
355
|
#
|
|
|
356
|
# chiSq1=lsq1[1];
|
|
356
|
# chiSq1=lsq1[1];
|
|
|
357
|
# jack1= self.y_jacobian1(x,lsq1[0])
|
|
357
|
# jack1= self.y_jacobian1(x,lsq1[0])
|
|
|
358
|
#
|
|
358
|
#
|
|
|
359
|
#
|
|
359
|
#
|
|
|
360
|
# try:
|
|
360
|
# try:
|
|
|
361
|
# sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1))))
|
|
361
|
# sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1))))
|
|
|
362
|
# except:
|
|
362
|
# except:
|
|
|
363
|
# std1=32.; sigmas1=numpy.ones(5)
|
|
363
|
# std1=32.; sigmas1=numpy.ones(5)
|
|
|
364
|
# else:
|
|
364
|
# else:
|
|
|
365
|
# std1=sigmas1[0]
|
|
365
|
# std1=sigmas1[0]
|
|
|
366
|
#
|
|
366
|
#
|
|
|
367
|
#
|
|
367
|
#
|
|
|
368
|
# if fatspectra<1.0 and powerwidth<4:
|
|
368
|
# if fatspectra<1.0 and powerwidth<4:
|
|
|
369
|
# choice=0
|
|
369
|
# choice=0
|
|
|
370
|
# Amplitude0=lsq1[0][2]
|
|
370
|
# Amplitude0=lsq1[0][2]
|
|
|
371
|
# shift0=lsq1[0][0]
|
|
371
|
# shift0=lsq1[0][0]
|
|
|
372
|
# width0=lsq1[0][1]
|
|
372
|
# width0=lsq1[0][1]
|
|
|
373
|
# p0=lsq1[0][3]
|
|
373
|
# p0=lsq1[0][3]
|
|
|
374
|
# Amplitude1=0.
|
|
374
|
# Amplitude1=0.
|
|
|
375
|
# shift1=0.
|
|
375
|
# shift1=0.
|
|
|
376
|
# width1=0.
|
|
376
|
# width1=0.
|
|
|
377
|
# p1=0.
|
|
377
|
# p1=0.
|
|
|
378
|
# noise=lsq1[0][4]
|
|
378
|
# noise=lsq1[0][4]
|
|
|
379
|
# #return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
379
|
# #return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
|
380
|
# # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
380
|
# # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
|
381
|
#
|
|
381
|
#
|
|
|
382
|
# # two gaussians
|
|
382
|
# # two gaussians
|
|
|
383
|
# #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
383
|
# #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
|
384
|
# shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
|
|
384
|
# shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
|
|
|
385
|
# shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
385
|
# shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
|
386
|
# width0=powerwidth/6.;
|
|
386
|
# width0=powerwidth/6.;
|
|
|
387
|
# width1=width0
|
|
387
|
# width1=width0
|
|
|
388
|
# power0=2.;
|
|
388
|
# power0=2.;
|
|
|
389
|
# power1=power0
|
|
389
|
# power1=power0
|
|
|
390
|
# amplitude0=firstamp;
|
|
390
|
# amplitude0=firstamp;
|
|
|
391
|
# amplitude1=secondamp
|
|
391
|
# amplitude1=secondamp
|
|
|
392
|
# state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
392
|
# state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
|
393
|
# #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
393
|
# #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
394
|
# bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
394
|
# bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
395
|
# #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
|
|
395
|
# #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
|
|
|
396
|
#
|
|
396
|
#
|
|
|
397
|
# lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
397
|
# lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
|
398
|
#
|
|
398
|
#
|
|
|
399
|
#
|
|
399
|
#
|
|
|
400
|
# chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0])
|
|
400
|
# chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0])
|
|
|
401
|
#
|
|
401
|
#
|
|
|
402
|
#
|
|
402
|
#
|
|
|
403
|
# try:
|
|
403
|
# try:
|
|
|
404
|
# sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2))))
|
|
404
|
# sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2))))
|
|
|
405
|
# except:
|
|
405
|
# except:
|
|
|
406
|
# std2a=32.; std2b=32.; sigmas2=numpy.ones(9)
|
|
406
|
# std2a=32.; std2b=32.; sigmas2=numpy.ones(9)
|
|
|
407
|
# else:
|
|
407
|
# else:
|
|
|
408
|
# std2a=sigmas2[0]; std2b=sigmas2[4]
|
|
408
|
# std2a=sigmas2[0]; std2b=sigmas2[4]
|
|
|
409
|
#
|
|
409
|
#
|
|
|
410
|
#
|
|
410
|
#
|
|
|
411
|
#
|
|
411
|
#
|
|
|
412
|
# oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
|
|
412
|
# oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
|
|
|
413
|
#
|
|
413
|
#
|
|
|
414
|
# if snrdB>-9: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
414
|
# if snrdB>-9: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
|
415
|
# if oneG:
|
|
415
|
# if oneG:
|
|
|
416
|
# choice=0
|
|
416
|
# choice=0
|
|
|
417
|
# else:
|
|
417
|
# else:
|
|
|
418
|
# w1=lsq2[0][1]; w2=lsq2[0][5]
|
|
418
|
# w1=lsq2[0][1]; w2=lsq2[0][5]
|
|
|
419
|
# a1=lsq2[0][2]; a2=lsq2[0][6]
|
|
419
|
# a1=lsq2[0][2]; a2=lsq2[0][6]
|
|
|
420
|
# p1=lsq2[0][3]; p2=lsq2[0][7]
|
|
420
|
# p1=lsq2[0][3]; p2=lsq2[0][7]
|
|
|
421
|
# s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
|
|
421
|
# s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
|
|
|
422
|
# gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
422
|
# gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
|
423
|
#
|
|
423
|
#
|
|
|
424
|
# if gp1>gp2:
|
|
424
|
# if gp1>gp2:
|
|
|
425
|
# if a1>0.7*a2:
|
|
425
|
# if a1>0.7*a2:
|
|
|
426
|
# choice=1
|
|
426
|
# choice=1
|
|
|
427
|
# else:
|
|
427
|
# else:
|
|
|
428
|
# choice=2
|
|
428
|
# choice=2
|
|
|
429
|
# elif gp2>gp1:
|
|
429
|
# elif gp2>gp1:
|
|
|
430
|
# if a2>0.7*a1:
|
|
430
|
# if a2>0.7*a1:
|
|
|
431
|
# choice=2
|
|
431
|
# choice=2
|
|
|
432
|
# else:
|
|
432
|
# else:
|
|
|
433
|
# choice=1
|
|
433
|
# choice=1
|
|
|
434
|
# else:
|
|
434
|
# else:
|
|
|
435
|
# choice=numpy.argmax([a1,a2])+1
|
|
435
|
# choice=numpy.argmax([a1,a2])+1
|
|
|
436
|
# #else:
|
|
436
|
# #else:
|
|
|
437
|
# #choice=argmin([std2a,std2b])+1
|
|
437
|
# #choice=argmin([std2a,std2b])+1
|
|
|
438
|
#
|
|
438
|
#
|
|
|
439
|
# else: # with low SNR go to the most energetic peak
|
|
439
|
# else: # with low SNR go to the most energetic peak
|
|
|
440
|
# choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
440
|
# choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
|
441
|
#
|
|
441
|
#
|
|
|
442
|
# #print 'choice',choice
|
|
442
|
# #print 'choice',choice
|
|
|
443
|
#
|
|
443
|
#
|
|
|
444
|
# if choice==0: # pick the single gaussian fit
|
|
444
|
# if choice==0: # pick the single gaussian fit
|
|
|
445
|
# Amplitude0=lsq1[0][2]
|
|
445
|
# Amplitude0=lsq1[0][2]
|
|
|
446
|
# shift0=lsq1[0][0]
|
|
446
|
# shift0=lsq1[0][0]
|
|
|
447
|
# width0=lsq1[0][1]
|
|
447
|
# width0=lsq1[0][1]
|
|
|
448
|
# p0=lsq1[0][3]
|
|
448
|
# p0=lsq1[0][3]
|
|
|
449
|
# Amplitude1=0.
|
|
449
|
# Amplitude1=0.
|
|
|
450
|
# shift1=0.
|
|
450
|
# shift1=0.
|
|
|
451
|
# width1=0.
|
|
451
|
# width1=0.
|
|
|
452
|
# p1=0.
|
|
452
|
# p1=0.
|
|
|
453
|
# noise=lsq1[0][4]
|
|
453
|
# noise=lsq1[0][4]
|
|
|
454
|
# elif choice==1: # take the first one of the 2 gaussians fitted
|
|
454
|
# elif choice==1: # take the first one of the 2 gaussians fitted
|
|
|
455
|
# Amplitude0 = lsq2[0][2]
|
|
455
|
# Amplitude0 = lsq2[0][2]
|
|
|
456
|
# shift0 = lsq2[0][0]
|
|
456
|
# shift0 = lsq2[0][0]
|
|
|
457
|
# width0 = lsq2[0][1]
|
|
457
|
# width0 = lsq2[0][1]
|
|
|
458
|
# p0 = lsq2[0][3]
|
|
458
|
# p0 = lsq2[0][3]
|
|
|
459
|
# Amplitude1 = lsq2[0][6] # This is 0 in gg1
|
|
459
|
# Amplitude1 = lsq2[0][6] # This is 0 in gg1
|
|
|
460
|
# shift1 = lsq2[0][4] # This is 0 in gg1
|
|
460
|
# shift1 = lsq2[0][4] # This is 0 in gg1
|
|
|
461
|
# width1 = lsq2[0][5] # This is 0 in gg1
|
|
461
|
# width1 = lsq2[0][5] # This is 0 in gg1
|
|
|
462
|
# p1 = lsq2[0][7] # This is 0 in gg1
|
|
462
|
# p1 = lsq2[0][7] # This is 0 in gg1
|
|
|
463
|
# noise = lsq2[0][8]
|
|
463
|
# noise = lsq2[0][8]
|
|
|
464
|
# else: # the second one
|
|
464
|
# else: # the second one
|
|
|
465
|
# Amplitude0 = lsq2[0][6]
|
|
465
|
# Amplitude0 = lsq2[0][6]
|
|
|
466
|
# shift0 = lsq2[0][4]
|
|
466
|
# shift0 = lsq2[0][4]
|
|
|
467
|
# width0 = lsq2[0][5]
|
|
467
|
# width0 = lsq2[0][5]
|
|
|
468
|
# p0 = lsq2[0][7]
|
|
468
|
# p0 = lsq2[0][7]
|
|
|
469
|
# Amplitude1 = lsq2[0][2] # This is 0 in gg1
|
|
469
|
# Amplitude1 = lsq2[0][2] # This is 0 in gg1
|
|
|
470
|
# shift1 = lsq2[0][0] # This is 0 in gg1
|
|
470
|
# shift1 = lsq2[0][0] # This is 0 in gg1
|
|
|
471
|
# width1 = lsq2[0][1] # This is 0 in gg1
|
|
471
|
# width1 = lsq2[0][1] # This is 0 in gg1
|
|
|
472
|
# p1 = lsq2[0][3] # This is 0 in gg1
|
|
472
|
# p1 = lsq2[0][3] # This is 0 in gg1
|
|
|
473
|
# noise = lsq2[0][8]
|
|
473
|
# noise = lsq2[0][8]
|
|
|
474
|
#
|
|
474
|
#
|
|
|
475
|
# #print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0)
|
|
475
|
# #print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0)
|
|
|
476
|
# SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
|
|
476
|
# SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
|
|
|
477
|
# SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
|
|
477
|
# SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
|
|
|
478
|
# #print 'SPC_ch1.shape',SPC_ch1.shape
|
|
478
|
# #print 'SPC_ch1.shape',SPC_ch1.shape
|
|
|
479
|
# #print 'SPC_ch2.shape',SPC_ch2.shape
|
|
479
|
# #print 'SPC_ch2.shape',SPC_ch2.shape
|
|
|
480
|
# #dataOut.data_param = SPC_ch1
|
|
480
|
# #dataOut.data_param = SPC_ch1
|
|
|
481
|
# GauSPC[0] = SPC_ch1
|
|
481
|
# GauSPC[0] = SPC_ch1
|
|
|
482
|
# GauSPC[1] = SPC_ch2
|
|
482
|
# GauSPC[1] = SPC_ch2
|
|
|
483
|
|
|
483
|
|
|
|
484
|
# #plt.gcf().clear()
|
|
484
|
# #plt.gcf().clear()
|
|
|
485
|
# plt.figure(50+self.i)
|
|
485
|
# plt.figure(50+self.i)
|
|
|
486
|
# self.i=self.i+1
|
|
486
|
# self.i=self.i+1
|
|
|
487
|
# #plt.subplot(121)
|
|
487
|
# #plt.subplot(121)
|
|
|
488
|
# plt.plot(self.spc,'k')#,label='spc(66)')
|
|
488
|
# plt.plot(self.spc,'k')#,label='spc(66)')
|
|
|
489
|
# plt.plot(SPC_ch1[ch,ht],'b')#,label='gg1')
|
|
489
|
# plt.plot(SPC_ch1[ch,ht],'b')#,label='gg1')
|
|
|
490
|
# #plt.plot(SPC_ch2,'r')#,label='gg2')
|
|
490
|
# #plt.plot(SPC_ch2,'r')#,label='gg2')
|
|
|
491
|
# #plt.plot(xFrec,ySamples[1],'g',label='Ch1')
|
|
491
|
# #plt.plot(xFrec,ySamples[1],'g',label='Ch1')
|
|
|
492
|
# #plt.plot(xFrec,ySamples[2],'r',label='Ch2')
|
|
492
|
# #plt.plot(xFrec,ySamples[2],'r',label='Ch2')
|
|
|
493
|
# #plt.plot(xFrec,FitGauss,'yo:',label='fit')
|
|
493
|
# #plt.plot(xFrec,FitGauss,'yo:',label='fit')
|
|
|
494
|
# plt.legend()
|
|
494
|
# plt.legend()
|
|
|
495
|
# plt.title('DATOS A ALTURA DE 7500 METROS')
|
|
495
|
# plt.title('DATOS A ALTURA DE 7500 METROS')
|
|
|
496
|
# plt.show()
|
|
496
|
# plt.show()
|
|
|
497
|
# print 'shift0', shift0
|
|
497
|
# print 'shift0', shift0
|
|
|
498
|
# print 'Amplitude0', Amplitude0
|
|
498
|
# print 'Amplitude0', Amplitude0
|
|
|
499
|
# print 'width0', width0
|
|
499
|
# print 'width0', width0
|
|
|
500
|
# print 'p0', p0
|
|
500
|
# print 'p0', p0
|
|
|
501
|
# print '========================'
|
|
501
|
# print '========================'
|
|
|
502
|
# print 'shift1', shift1
|
|
502
|
# print 'shift1', shift1
|
|
|
503
|
# print 'Amplitude1', Amplitude1
|
|
503
|
# print 'Amplitude1', Amplitude1
|
|
|
504
|
# print 'width1', width1
|
|
504
|
# print 'width1', width1
|
|
|
505
|
# print 'p1', p1
|
|
505
|
# print 'p1', p1
|
|
|
506
|
# print 'noise', noise
|
|
506
|
# print 'noise', noise
|
|
|
507
|
# print 's_noise', wnoise
|
|
507
|
# print 's_noise', wnoise
|
|
|
508
|
|
|
508
|
|
|
|
509
|
print '========================================================'
|
|
509
|
print '========================================================'
|
|
|
510
|
print 'total_time: ', time.time()-start_time
|
|
510
|
print 'total_time: ', time.time()-start_time
|
|
|
511
|
|
|
511
|
|
|
|
512
|
# re-normalizing spc and noise
|
|
512
|
# re-normalizing spc and noise
|
|
|
513
|
# This part differs from gg1
|
|
513
|
# This part differs from gg1
|
|
|
514
|
|
|
514
|
|
|
|
515
|
|
|
515
|
|
|
|
516
|
|
|
516
|
|
|
|
517
|
''' Parameters:
|
|
517
|
''' Parameters:
|
|
|
518
|
1. Amplitude
|
|
518
|
1. Amplitude
|
|
|
519
|
2. Shift
|
|
519
|
2. Shift
|
|
|
520
|
3. Width
|
|
520
|
3. Width
|
|
|
521
|
4. Power
|
|
521
|
4. Power
|
|
|
522
|
'''
|
|
522
|
'''
|
|
|
523
|
|
|
523
|
|
|
|
524
|
|
|
524
|
|
|
|
525
|
###############################################################################
|
|
525
|
###############################################################################
|
|
|
526
|
def FitGau(self, X):
|
|
526
|
def FitGau(self, X):
|
|
|
527
|
|
|
527
|
|
|
|
528
|
Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X
|
|
528
|
Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X
|
|
|
529
|
#print 'VARSSSS', ch, pnoise, noise, num_intg
|
|
529
|
#print 'VARSSSS', ch, pnoise, noise, num_intg
|
|
|
530
|
|
|
530
|
|
|
|
531
|
#print 'HEIGHTS', self.Num_Hei
|
|
531
|
#print 'HEIGHTS', self.Num_Hei
|
|
|
532
|
|
|
532
|
|
|
|
533
|
GauSPC = []
|
|
533
|
GauSPC = []
|
|
|
534
|
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
534
|
SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
|
535
|
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
535
|
SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
|
|
|
536
|
SPC_ch1[:] = 0#numpy.NaN
|
|
536
|
SPC_ch1[:] = 0#numpy.NaN
|
|
|
537
|
SPC_ch2[:] = 0#numpy.NaN
|
|
537
|
SPC_ch2[:] = 0#numpy.NaN
|
|
|
538
|
|
|
538
|
|
|
|
539
|
|
|
539
|
|
|
|
540
|
|
|
540
|
|
|
|
541
|
for ht in range(self.Num_Hei):
|
|
541
|
for ht in range(self.Num_Hei):
|
|
|
542
|
#print (numpy.asarray(self.spc).shape)
|
|
542
|
#print (numpy.asarray(self.spc).shape)
|
|
|
543
|
|
|
543
|
|
|
|
544
|
#print 'TTTTT', ch , ht
|
|
544
|
#print 'TTTTT', ch , ht
|
|
|
545
|
#print self.spc.shape
|
|
545
|
#print self.spc.shape
|
|
|
546
|
|
|
546
|
|
|
|
547
|
|
|
547
|
|
|
|
548
|
spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
548
|
spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
|
549
|
|
|
549
|
|
|
|
550
|
#############################################
|
|
550
|
#############################################
|
|
|
551
|
# normalizing spc and noise
|
|
551
|
# normalizing spc and noise
|
|
|
552
|
# This part differs from gg1
|
|
552
|
# This part differs from gg1
|
|
|
553
|
spc_norm_max = max(spc)
|
|
553
|
spc_norm_max = max(spc)
|
|
|
554
|
spc = spc / spc_norm_max
|
|
554
|
spc = spc / spc_norm_max
|
|
|
555
|
pnoise = pnoise / spc_norm_max
|
|
555
|
pnoise = pnoise / spc_norm_max
|
|
|
556
|
#############################################
|
|
556
|
#############################################
|
|
|
557
|
|
|
557
|
|
|
|
558
|
fatspectra=1.0
|
|
558
|
fatspectra=1.0
|
|
|
559
|
|
|
559
|
|
|
|
560
|
wnoise = noise_ / spc_norm_max
|
|
560
|
wnoise = noise_ / spc_norm_max
|
|
|
561
|
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
561
|
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
|
562
|
#if wnoise>1.1*pnoise: # to be tested later
|
|
562
|
#if wnoise>1.1*pnoise: # to be tested later
|
|
|
563
|
# wnoise=pnoise
|
|
563
|
# wnoise=pnoise
|
|
|
564
|
noisebl=wnoise*0.9; noisebh=wnoise*1.1
|
|
564
|
noisebl=wnoise*0.9; noisebh=wnoise*1.1
|
|
|
565
|
spc=spc-wnoise
|
|
565
|
spc=spc-wnoise
|
|
|
566
|
# print 'wnoise', noise_[0], spc_norm_max, wnoise
|
|
566
|
# print 'wnoise', noise_[0], spc_norm_max, wnoise
|
|
|
567
|
minx=numpy.argmin(spc)
|
|
567
|
minx=numpy.argmin(spc)
|
|
|
568
|
spcs=numpy.roll(spc,-minx)
|
|
568
|
spcs=numpy.roll(spc,-minx)
|
|
|
569
|
cum=numpy.cumsum(spcs)
|
|
569
|
cum=numpy.cumsum(spcs)
|
|
|
570
|
tot_noise=wnoise * self.Num_Bin #64;
|
|
570
|
tot_noise=wnoise * self.Num_Bin #64;
|
|
|
571
|
#print 'spc' , spcs[5:8] , 'tot_noise', tot_noise
|
|
571
|
#print 'spc' , spcs[5:8] , 'tot_noise', tot_noise
|
|
|
572
|
#tot_signal=sum(cum[-5:])/5.; ''' How does this line work? '''
|
|
572
|
#tot_signal=sum(cum[-5:])/5.; ''' How does this line work? '''
|
|
|
573
|
#snr=tot_signal/tot_noise
|
|
573
|
#snr=tot_signal/tot_noise
|
|
|
574
|
#snr=cum[-1]/tot_noise
|
|
574
|
#snr=cum[-1]/tot_noise
|
|
|
575
|
snr = sum(spcs)/tot_noise
|
|
575
|
snr = sum(spcs)/tot_noise
|
|
|
576
|
snrdB=10.*numpy.log10(snr)
|
|
576
|
snrdB=10.*numpy.log10(snr)
|
|
|
577
|
|
|
577
|
|
|
|
578
|
if snrdB < SNRlimit :
|
|
578
|
if snrdB < SNRlimit :
|
|
|
579
|
snr = numpy.NaN
|
|
579
|
snr = numpy.NaN
|
|
|
580
|
SPC_ch1[:,ht] = 0#numpy.NaN
|
|
580
|
SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
581
|
SPC_ch1[:,ht] = 0#numpy.NaN
|
|
581
|
SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
582
|
GauSPC = (SPC_ch1,SPC_ch2)
|
|
582
|
GauSPC = (SPC_ch1,SPC_ch2)
|
|
|
583
|
continue
|
|
583
|
continue
|
|
|
584
|
#print 'snr',snrdB #, sum(spcs) , tot_noise
|
|
584
|
#print 'snr',snrdB #, sum(spcs) , tot_noise
|
|
|
585
|
|
|
585
|
|
|
|
586
|
|
|
586
|
|
|
|
587
|
|
|
587
|
|
|
|
588
|
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
588
|
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
|
589
|
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
589
|
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
590
|
|
|
590
|
|
|
|
591
|
cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region
|
|
591
|
cummax=max(cum); epsi=0.08*fatspectra # cumsum to narrow down the energy region
|
|
|
592
|
cumlo=cummax*epsi;
|
|
592
|
cumlo=cummax*epsi;
|
|
|
593
|
cumhi=cummax*(1-epsi)
|
|
593
|
cumhi=cummax*(1-epsi)
|
|
|
594
|
powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
594
|
powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
|
595
|
|
|
595
|
|
|
|
596
|
|
|
596
|
|
|
|
597
|
if len(powerindex) < 1:# case for powerindex 0
|
|
597
|
if len(powerindex) < 1:# case for powerindex 0
|
|
|
598
|
continue
|
|
598
|
continue
|
|
|
599
|
powerlo=powerindex[0]
|
|
599
|
powerlo=powerindex[0]
|
|
|
600
|
powerhi=powerindex[-1]
|
|
600
|
powerhi=powerindex[-1]
|
|
|
601
|
powerwidth=powerhi-powerlo
|
|
601
|
powerwidth=powerhi-powerlo
|
|
|
602
|
|
|
602
|
|
|
|
603
|
firstpeak=powerlo+powerwidth/10.# first gaussian energy location
|
|
603
|
firstpeak=powerlo+powerwidth/10.# first gaussian energy location
|
|
|
604
|
secondpeak=powerhi-powerwidth/10.#second gaussian energy location
|
|
604
|
secondpeak=powerhi-powerwidth/10.#second gaussian energy location
|
|
|
605
|
midpeak=(firstpeak+secondpeak)/2.
|
|
605
|
midpeak=(firstpeak+secondpeak)/2.
|
|
|
606
|
firstamp=spcs[int(firstpeak)]
|
|
606
|
firstamp=spcs[int(firstpeak)]
|
|
|
607
|
secondamp=spcs[int(secondpeak)]
|
|
607
|
secondamp=spcs[int(secondpeak)]
|
|
|
608
|
midamp=spcs[int(midpeak)]
|
|
608
|
midamp=spcs[int(midpeak)]
|
|
|
609
|
|
|
609
|
|
|
|
610
|
x=numpy.arange( self.Num_Bin )
|
|
610
|
x=numpy.arange( self.Num_Bin )
|
|
|
611
|
y_data=spc+wnoise
|
|
611
|
y_data=spc+wnoise
|
|
|
612
|
|
|
612
|
|
|
|
613
|
# single gaussian
|
|
613
|
# single gaussian
|
|
|
614
|
shift0=numpy.mod(midpeak+minx, self.Num_Bin )
|
|
614
|
shift0=numpy.mod(midpeak+minx, self.Num_Bin )
|
|
|
615
|
width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
615
|
width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
|
616
|
power0=2.
|
|
616
|
power0=2.
|
|
|
617
|
amplitude0=midamp
|
|
617
|
amplitude0=midamp
|
|
|
618
|
state0=[shift0,width0,amplitude0,power0,wnoise]
|
|
618
|
state0=[shift0,width0,amplitude0,power0,wnoise]
|
|
|
619
|
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
619
|
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
620
|
lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
620
|
lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
|
621
|
|
|
621
|
|
|
|
622
|
chiSq1=lsq1[1];
|
|
622
|
chiSq1=lsq1[1];
|
|
|
623
|
jack1= self.y_jacobian1(x,lsq1[0])
|
|
623
|
jack1= self.y_jacobian1(x,lsq1[0])
|
|
|
624
|
|
|
624
|
|
|
|
625
|
|
|
625
|
|
|
|
626
|
try:
|
|
626
|
try:
|
|
|
627
|
sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1))))
|
|
627
|
sigmas1=numpy.sqrt(chiSq1*numpy.diag(numpy.linalg.inv(numpy.dot(jack1.T,jack1))))
|
|
|
628
|
except:
|
|
628
|
except:
|
|
|
629
|
std1=32.; sigmas1=numpy.ones(5)
|
|
629
|
std1=32.; sigmas1=numpy.ones(5)
|
|
|
630
|
else:
|
|
630
|
else:
|
|
|
631
|
std1=sigmas1[0]
|
|
631
|
std1=sigmas1[0]
|
|
|
632
|
|
|
632
|
|
|
|
633
|
|
|
633
|
|
|
|
634
|
if fatspectra<1.0 and powerwidth<4:
|
|
634
|
if fatspectra<1.0 and powerwidth<4:
|
|
|
635
|
choice=0
|
|
635
|
choice=0
|
|
|
636
|
Amplitude0=lsq1[0][2]
|
|
636
|
Amplitude0=lsq1[0][2]
|
|
|
637
|
shift0=lsq1[0][0]
|
|
637
|
shift0=lsq1[0][0]
|
|
|
638
|
width0=lsq1[0][1]
|
|
638
|
width0=lsq1[0][1]
|
|
|
639
|
p0=lsq1[0][3]
|
|
639
|
p0=lsq1[0][3]
|
|
|
640
|
Amplitude1=0.
|
|
640
|
Amplitude1=0.
|
|
|
641
|
shift1=0.
|
|
641
|
shift1=0.
|
|
|
642
|
width1=0.
|
|
642
|
width1=0.
|
|
|
643
|
p1=0.
|
|
643
|
p1=0.
|
|
|
644
|
noise=lsq1[0][4]
|
|
644
|
noise=lsq1[0][4]
|
|
|
645
|
#return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
645
|
#return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
|
646
|
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
646
|
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
|
647
|
|
|
647
|
|
|
|
648
|
# two gaussians
|
|
648
|
# two gaussians
|
|
|
649
|
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
649
|
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
|
650
|
shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
|
|
650
|
shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
|
|
|
651
|
shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
651
|
shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
|
652
|
width0=powerwidth/6.;
|
|
652
|
width0=powerwidth/6.;
|
|
|
653
|
width1=width0
|
|
653
|
width1=width0
|
|
|
654
|
power0=2.;
|
|
654
|
power0=2.;
|
|
|
655
|
power1=power0
|
|
655
|
power1=power0
|
|
|
656
|
amplitude0=firstamp;
|
|
656
|
amplitude0=firstamp;
|
|
|
657
|
amplitude1=secondamp
|
|
657
|
amplitude1=secondamp
|
|
|
658
|
state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
658
|
state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
|
659
|
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
659
|
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
660
|
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
660
|
bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
661
|
#bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
|
|
661
|
#bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
|
|
|
662
|
|
|
662
|
|
|
|
663
|
lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
663
|
lsq2=fmin_l_bfgs_b(self.misfit2,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
|
|
|
664
|
|
|
664
|
|
|
|
665
|
|
|
665
|
|
|
|
666
|
chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0])
|
|
666
|
chiSq2=lsq2[1]; jack2=self.y_jacobian2(x,lsq2[0])
|
|
|
667
|
|
|
667
|
|
|
|
668
|
|
|
668
|
|
|
|
669
|
try:
|
|
669
|
try:
|
|
|
670
|
sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2))))
|
|
670
|
sigmas2=numpy.sqrt(chiSq2*numpy.diag(numpy.linalg.inv(numpy.dot(jack2.T,jack2))))
|
|
|
671
|
except:
|
|
671
|
except:
|
|
|
672
|
std2a=32.; std2b=32.; sigmas2=numpy.ones(9)
|
|
672
|
std2a=32.; std2b=32.; sigmas2=numpy.ones(9)
|
|
|
673
|
else:
|
|
673
|
else:
|
|
|
674
|
std2a=sigmas2[0]; std2b=sigmas2[4]
|
|
674
|
std2a=sigmas2[0]; std2b=sigmas2[4]
|
|
|
675
|
|
|
675
|
|
|
|
676
|
|
|
676
|
|
|
|
677
|
|
|
677
|
|
|
|
678
|
oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
|
|
678
|
oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
|
|
|
679
|
|
|
679
|
|
|
|
680
|
if snrdB>-6: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
680
|
if snrdB>-6: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
|
681
|
if oneG:
|
|
681
|
if oneG:
|
|
|
682
|
choice=0
|
|
682
|
choice=0
|
|
|
683
|
else:
|
|
683
|
else:
|
|
|
684
|
w1=lsq2[0][1]; w2=lsq2[0][5]
|
|
684
|
w1=lsq2[0][1]; w2=lsq2[0][5]
|
|
|
685
|
a1=lsq2[0][2]; a2=lsq2[0][6]
|
|
685
|
a1=lsq2[0][2]; a2=lsq2[0][6]
|
|
|
686
|
p1=lsq2[0][3]; p2=lsq2[0][7]
|
|
686
|
p1=lsq2[0][3]; p2=lsq2[0][7]
|
|
|
687
|
s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1;
|
|
687
|
s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1;
|
|
|
688
|
s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
|
|
688
|
s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
|
|
|
689
|
gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
689
|
gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
|
690
|
|
|
690
|
|
|
|
691
|
if gp1>gp2:
|
|
691
|
if gp1>gp2:
|
|
|
692
|
if a1>0.7*a2:
|
|
692
|
if a1>0.7*a2:
|
|
|
693
|
choice=1
|
|
693
|
choice=1
|
|
|
694
|
else:
|
|
694
|
else:
|
|
|
695
|
choice=2
|
|
695
|
choice=2
|
|
|
696
|
elif gp2>gp1:
|
|
696
|
elif gp2>gp1:
|
|
|
697
|
if a2>0.7*a1:
|
|
697
|
if a2>0.7*a1:
|
|
|
698
|
choice=2
|
|
698
|
choice=2
|
|
|
699
|
else:
|
|
699
|
else:
|
|
|
700
|
choice=1
|
|
700
|
choice=1
|
|
|
701
|
else:
|
|
701
|
else:
|
|
|
702
|
choice=numpy.argmax([a1,a2])+1
|
|
702
|
choice=numpy.argmax([a1,a2])+1
|
|
|
703
|
#else:
|
|
703
|
#else:
|
|
|
704
|
#choice=argmin([std2a,std2b])+1
|
|
704
|
#choice=argmin([std2a,std2b])+1
|
|
|
705
|
|
|
705
|
|
|
|
706
|
else: # with low SNR go to the most energetic peak
|
|
706
|
else: # with low SNR go to the most energetic peak
|
|
|
707
|
choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
707
|
choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
|
708
|
|
|
708
|
|
|
|
709
|
|
|
709
|
|
|
|
710
|
shift0=lsq2[0][0]; vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0])
|
|
710
|
shift0=lsq2[0][0]; vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0])
|
|
|
711
|
shift1=lsq2[0][4]; vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0])
|
|
711
|
shift1=lsq2[0][4]; vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0])
|
|
|
712
|
|
|
712
|
|
|
|
713
|
max_vel = 20
|
|
713
|
max_vel = 20
|
|
|
714
|
|
|
714
|
|
|
|
715
|
#first peak will be 0, second peak will be 1
|
|
715
|
#first peak will be 0, second peak will be 1
|
|
|
716
|
if vel0 > 0 and vel0 < max_vel : #first peak is in the correct range
|
|
716
|
if vel0 > 0 and vel0 < max_vel : #first peak is in the correct range
|
|
|
717
|
shift0=lsq2[0][0]
|
|
717
|
shift0=lsq2[0][0]
|
|
|
718
|
width0=lsq2[0][1]
|
|
718
|
width0=lsq2[0][1]
|
|
|
719
|
Amplitude0=lsq2[0][2]
|
|
719
|
Amplitude0=lsq2[0][2]
|
|
|
720
|
p0=lsq2[0][3]
|
|
720
|
p0=lsq2[0][3]
|
|
|
721
|
|
|
721
|
|
|
|
722
|
shift1=lsq2[0][4]
|
|
722
|
shift1=lsq2[0][4]
|
|
|
723
|
width1=lsq2[0][5]
|
|
723
|
width1=lsq2[0][5]
|
|
|
724
|
Amplitude1=lsq2[0][6]
|
|
724
|
Amplitude1=lsq2[0][6]
|
|
|
725
|
p1=lsq2[0][7]
|
|
725
|
p1=lsq2[0][7]
|
|
|
726
|
noise=lsq2[0][8]
|
|
726
|
noise=lsq2[0][8]
|
|
|
727
|
else:
|
|
727
|
else:
|
|
|
728
|
shift1=lsq2[0][0]
|
|
728
|
shift1=lsq2[0][0]
|
|
|
729
|
width1=lsq2[0][1]
|
|
729
|
width1=lsq2[0][1]
|
|
|
730
|
Amplitude1=lsq2[0][2]
|
|
730
|
Amplitude1=lsq2[0][2]
|
|
|
731
|
p1=lsq2[0][3]
|
|
731
|
p1=lsq2[0][3]
|
|
|
732
|
|
|
732
|
|
|
|
733
|
shift0=lsq2[0][4]
|
|
733
|
shift0=lsq2[0][4]
|
|
|
734
|
width0=lsq2[0][5]
|
|
734
|
width0=lsq2[0][5]
|
|
|
735
|
Amplitude0=lsq2[0][6]
|
|
735
|
Amplitude0=lsq2[0][6]
|
|
|
736
|
p0=lsq2[0][7]
|
|
736
|
p0=lsq2[0][7]
|
|
|
737
|
noise=lsq2[0][8]
|
|
737
|
noise=lsq2[0][8]
|
|
|
738
|
|
|
738
|
|
|
|
739
|
if Amplitude0<0.1: # in case the peak is noise
|
|
739
|
if Amplitude0<0.1: # in case the peak is noise
|
|
|
740
|
shift0,width0,Amplitude0,p0 = 4*[numpy.NaN]
|
|
740
|
shift0,width0,Amplitude0,p0 = 4*[numpy.NaN]
|
|
|
741
|
if Amplitude1<0.1:
|
|
741
|
if Amplitude1<0.1:
|
|
|
742
|
shift1,width1,Amplitude1,p1 = 4*[numpy.NaN]
|
|
742
|
shift1,width1,Amplitude1,p1 = 4*[numpy.NaN]
|
|
|
743
|
|
|
743
|
|
|
|
744
|
|
|
744
|
|
|
|
745
|
# if choice==0: # pick the single gaussian fit
|
|
745
|
# if choice==0: # pick the single gaussian fit
|
|
|
746
|
# Amplitude0=lsq1[0][2]
|
|
746
|
# Amplitude0=lsq1[0][2]
|
|
|
747
|
# shift0=lsq1[0][0]
|
|
747
|
# shift0=lsq1[0][0]
|
|
|
748
|
# width0=lsq1[0][1]
|
|
748
|
# width0=lsq1[0][1]
|
|
|
749
|
# p0=lsq1[0][3]
|
|
749
|
# p0=lsq1[0][3]
|
|
|
750
|
# Amplitude1=0.
|
|
750
|
# Amplitude1=0.
|
|
|
751
|
# shift1=0.
|
|
751
|
# shift1=0.
|
|
|
752
|
# width1=0.
|
|
752
|
# width1=0.
|
|
|
753
|
# p1=0.
|
|
753
|
# p1=0.
|
|
|
754
|
# noise=lsq1[0][4]
|
|
754
|
# noise=lsq1[0][4]
|
|
|
755
|
# elif choice==1: # take the first one of the 2 gaussians fitted
|
|
755
|
# elif choice==1: # take the first one of the 2 gaussians fitted
|
|
|
756
|
# Amplitude0 = lsq2[0][2]
|
|
756
|
# Amplitude0 = lsq2[0][2]
|
|
|
757
|
# shift0 = lsq2[0][0]
|
|
757
|
# shift0 = lsq2[0][0]
|
|
|
758
|
# width0 = lsq2[0][1]
|
|
758
|
# width0 = lsq2[0][1]
|
|
|
759
|
# p0 = lsq2[0][3]
|
|
759
|
# p0 = lsq2[0][3]
|
|
|
760
|
# Amplitude1 = lsq2[0][6] # This is 0 in gg1
|
|
760
|
# Amplitude1 = lsq2[0][6] # This is 0 in gg1
|
|
|
761
|
# shift1 = lsq2[0][4] # This is 0 in gg1
|
|
761
|
# shift1 = lsq2[0][4] # This is 0 in gg1
|
|
|
762
|
# width1 = lsq2[0][5] # This is 0 in gg1
|
|
762
|
# width1 = lsq2[0][5] # This is 0 in gg1
|
|
|
763
|
# p1 = lsq2[0][7] # This is 0 in gg1
|
|
763
|
# p1 = lsq2[0][7] # This is 0 in gg1
|
|
|
764
|
# noise = lsq2[0][8]
|
|
764
|
# noise = lsq2[0][8]
|
|
|
765
|
# else: # the second one
|
|
765
|
# else: # the second one
|
|
|
766
|
# Amplitude0 = lsq2[0][6]
|
|
766
|
# Amplitude0 = lsq2[0][6]
|
|
|
767
|
# shift0 = lsq2[0][4]
|
|
767
|
# shift0 = lsq2[0][4]
|
|
|
768
|
# width0 = lsq2[0][5]
|
|
768
|
# width0 = lsq2[0][5]
|
|
|
769
|
# p0 = lsq2[0][7]
|
|
769
|
# p0 = lsq2[0][7]
|
|
|
770
|
# Amplitude1 = lsq2[0][2] # This is 0 in gg1
|
|
770
|
# Amplitude1 = lsq2[0][2] # This is 0 in gg1
|
|
|
771
|
# shift1 = lsq2[0][0] # This is 0 in gg1
|
|
771
|
# shift1 = lsq2[0][0] # This is 0 in gg1
|
|
|
772
|
# width1 = lsq2[0][1] # This is 0 in gg1
|
|
772
|
# width1 = lsq2[0][1] # This is 0 in gg1
|
|
|
773
|
# p1 = lsq2[0][3] # This is 0 in gg1
|
|
773
|
# p1 = lsq2[0][3] # This is 0 in gg1
|
|
|
774
|
# noise = lsq2[0][8]
|
|
774
|
# noise = lsq2[0][8]
|
|
|
775
|
|
|
775
|
|
|
|
776
|
#print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0)
|
|
776
|
#print len(noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0)
|
|
|
777
|
SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
|
|
777
|
SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
|
|
|
778
|
SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
|
|
778
|
SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
|
|
|
779
|
#print 'SPC_ch1.shape',SPC_ch1.shape
|
|
779
|
#print 'SPC_ch1.shape',SPC_ch1.shape
|
|
|
780
|
#print 'SPC_ch2.shape',SPC_ch2.shape
|
|
780
|
#print 'SPC_ch2.shape',SPC_ch2.shape
|
|
|
781
|
#dataOut.data_param = SPC_ch1
|
|
781
|
#dataOut.data_param = SPC_ch1
|
|
|
782
|
GauSPC = (SPC_ch1,SPC_ch2)
|
|
782
|
GauSPC = (SPC_ch1,SPC_ch2)
|
|
|
783
|
#GauSPC[1] = SPC_ch2
|
|
783
|
#GauSPC[1] = SPC_ch2
|
|
|
784
|
|
|
784
|
|
|
|
785
|
# print 'shift0', shift0
|
|
785
|
# print 'shift0', shift0
|
|
|
786
|
# print 'Amplitude0', Amplitude0
|
|
786
|
# print 'Amplitude0', Amplitude0
|
|
|
787
|
# print 'width0', width0
|
|
787
|
# print 'width0', width0
|
|
|
788
|
# print 'p0', p0
|
|
788
|
# print 'p0', p0
|
|
|
789
|
# print '========================'
|
|
789
|
# print '========================'
|
|
|
790
|
# print 'shift1', shift1
|
|
790
|
# print 'shift1', shift1
|
|
|
791
|
# print 'Amplitude1', Amplitude1
|
|
791
|
# print 'Amplitude1', Amplitude1
|
|
|
792
|
# print 'width1', width1
|
|
792
|
# print 'width1', width1
|
|
|
793
|
# print 'p1', p1
|
|
793
|
# print 'p1', p1
|
|
|
794
|
# print 'noise', noise
|
|
794
|
# print 'noise', noise
|
|
|
795
|
# print 's_noise', wnoise
|
|
795
|
# print 's_noise', wnoise
|
|
|
796
|
|
|
796
|
|
|
|
797
|
return GauSPC
|
|
797
|
return GauSPC
|
|
|
798
|
|
|
798
|
|
|
|
799
|
|
|
799
|
|
|
|
800
|
def y_jacobian1(self,x,state): # This function is for further analysis of generalized Gaussians, it is not too importan for the signal discrimination.
|
|
800
|
def y_jacobian1(self,x,state): # This function is for further analysis of generalized Gaussians, it is not too importan for the signal discrimination.
|
|
|
801
|
y_model=self.y_model1(x,state)
|
|
801
|
y_model=self.y_model1(x,state)
|
|
|
802
|
s0,w0,a0,p0,n=state
|
|
802
|
s0,w0,a0,p0,n=state
|
|
|
803
|
e0=((x-s0)/w0)**2;
|
|
803
|
e0=((x-s0)/w0)**2;
|
|
|
804
|
|
|
804
|
|
|
|
805
|
e0u=((x-s0-self.Num_Bin)/w0)**2;
|
|
805
|
e0u=((x-s0-self.Num_Bin)/w0)**2;
|
|
|
806
|
|
|
806
|
|
|
|
807
|
e0d=((x-s0+self.Num_Bin)/w0)**2
|
|
807
|
e0d=((x-s0+self.Num_Bin)/w0)**2
|
|
|
808
|
m0=numpy.exp(-0.5*e0**(p0/2.));
|
|
808
|
m0=numpy.exp(-0.5*e0**(p0/2.));
|
|
|
809
|
m0u=numpy.exp(-0.5*e0u**(p0/2.));
|
|
809
|
m0u=numpy.exp(-0.5*e0u**(p0/2.));
|
|
|
810
|
m0d=numpy.exp(-0.5*e0d**(p0/2.))
|
|
810
|
m0d=numpy.exp(-0.5*e0d**(p0/2.))
|
|
|
811
|
JA=m0+m0u+m0d
|
|
811
|
JA=m0+m0u+m0d
|
|
|
812
|
JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d)
|
|
812
|
JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d)
|
|
|
813
|
|
|
813
|
|
|
|
814
|
JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)
|
|
814
|
JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)
|
|
|
815
|
|
|
815
|
|
|
|
816
|
JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2
|
|
816
|
JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2
|
|
|
817
|
jack1=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,1./y_model])
|
|
817
|
jack1=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,1./y_model])
|
|
|
818
|
return jack1.T
|
|
818
|
return jack1.T
|
|
|
819
|
|
|
819
|
|
|
|
820
|
def y_jacobian2(self,x,state):
|
|
820
|
def y_jacobian2(self,x,state):
|
|
|
821
|
y_model=self.y_model2(x,state)
|
|
821
|
y_model=self.y_model2(x,state)
|
|
|
822
|
s0,w0,a0,p0,s1,w1,a1,p1,n=state
|
|
822
|
s0,w0,a0,p0,s1,w1,a1,p1,n=state
|
|
|
823
|
e0=((x-s0)/w0)**2;
|
|
823
|
e0=((x-s0)/w0)**2;
|
|
|
824
|
|
|
824
|
|
|
|
825
|
e0u=((x-s0- self.Num_Bin )/w0)**2;
|
|
825
|
e0u=((x-s0- self.Num_Bin )/w0)**2;
|
|
|
826
|
|
|
826
|
|
|
|
827
|
e0d=((x-s0+ self.Num_Bin )/w0)**2
|
|
827
|
e0d=((x-s0+ self.Num_Bin )/w0)**2
|
|
|
828
|
e1=((x-s1)/w1)**2;
|
|
828
|
e1=((x-s1)/w1)**2;
|
|
|
829
|
|
|
829
|
|
|
|
830
|
e1u=((x-s1- self.Num_Bin )/w1)**2;
|
|
830
|
e1u=((x-s1- self.Num_Bin )/w1)**2;
|
|
|
831
|
|
|
831
|
|
|
|
832
|
e1d=((x-s1+ self.Num_Bin )/w1)**2
|
|
832
|
e1d=((x-s1+ self.Num_Bin )/w1)**2
|
|
|
833
|
m0=numpy.exp(-0.5*e0**(p0/2.));
|
|
833
|
m0=numpy.exp(-0.5*e0**(p0/2.));
|
|
|
834
|
m0u=numpy.exp(-0.5*e0u**(p0/2.));
|
|
834
|
m0u=numpy.exp(-0.5*e0u**(p0/2.));
|
|
|
835
|
m0d=numpy.exp(-0.5*e0d**(p0/2.))
|
|
835
|
m0d=numpy.exp(-0.5*e0d**(p0/2.))
|
|
|
836
|
m1=numpy.exp(-0.5*e1**(p1/2.));
|
|
836
|
m1=numpy.exp(-0.5*e1**(p1/2.));
|
|
|
837
|
m1u=numpy.exp(-0.5*e1u**(p1/2.));
|
|
837
|
m1u=numpy.exp(-0.5*e1u**(p1/2.));
|
|
|
838
|
m1d=numpy.exp(-0.5*e1d**(p1/2.))
|
|
838
|
m1d=numpy.exp(-0.5*e1d**(p1/2.))
|
|
|
839
|
JA=m0+m0u+m0d
|
|
839
|
JA=m0+m0u+m0d
|
|
|
840
|
JA1=m1+m1u+m1d
|
|
840
|
JA1=m1+m1u+m1d
|
|
|
841
|
JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d)
|
|
841
|
JP=(-1/4.)*a0*m0*e0**(p0/2.)*numpy.log(e0)+(-1/4.)*a0*m0u*e0u**(p0/2.)*numpy.log(e0u)+(-1/4.)*a0*m0d*e0d**(p0/2.)*numpy.log(e0d)
|
|
|
842
|
JP1=(-1/4.)*a1*m1*e1**(p1/2.)*numpy.log(e1)+(-1/4.)*a1*m1u*e1u**(p1/2.)*numpy.log(e1u)+(-1/4.)*a1*m1d*e1d**(p1/2.)*numpy.log(e1d)
|
|
842
|
JP1=(-1/4.)*a1*m1*e1**(p1/2.)*numpy.log(e1)+(-1/4.)*a1*m1u*e1u**(p1/2.)*numpy.log(e1u)+(-1/4.)*a1*m1d*e1d**(p1/2.)*numpy.log(e1d)
|
|
|
843
|
|
|
843
|
|
|
|
844
|
JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)
|
|
844
|
JS=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)
|
|
|
845
|
|
|
845
|
|
|
|
846
|
JS1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1)
|
|
846
|
JS1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1)
|
|
|
847
|
|
|
847
|
|
|
|
848
|
JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2
|
|
848
|
JW=(p0/w0/2.)*a0*m0*e0**(p0/2.-1)*((x-s0)/w0)**2+(p0/w0/2.)*a0*m0u*e0u**(p0/2.-1)*((x-s0- self.Num_Bin )/w0)**2+(p0/w0/2.)*a0*m0d*e0d**(p0/2.-1)*((x-s0+ self.Num_Bin )/w0)**2
|
|
|
849
|
|
|
849
|
|
|
|
850
|
JW1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)**2+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)**2+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1)**2
|
|
850
|
JW1=(p1/w1/2.)*a1*m1*e1**(p1/2.-1)*((x-s1)/w1)**2+(p1/w1/2.)*a1*m1u*e1u**(p1/2.-1)*((x-s1- self.Num_Bin )/w1)**2+(p1/w1/2.)*a1*m1d*e1d**(p1/2.-1)*((x-s1+ self.Num_Bin )/w1)**2
|
|
|
851
|
jack2=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,JS1/y_model,JW1/y_model,JA1/y_model,JP1/y_model,1./y_model])
|
|
851
|
jack2=numpy.sqrt(7)*numpy.array([JS/y_model,JW/y_model,JA/y_model,JP/y_model,JS1/y_model,JW1/y_model,JA1/y_model,JP1/y_model,1./y_model])
|
|
|
852
|
return jack2.T
|
|
852
|
return jack2.T
|
|
|
853
|
|
|
853
|
|
|
|
854
|
def y_model1(self,x,state):
|
|
854
|
def y_model1(self,x,state):
|
|
|
855
|
shift0,width0,amplitude0,power0,noise=state
|
|
855
|
shift0,width0,amplitude0,power0,noise=state
|
|
|
856
|
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
856
|
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
|
857
|
|
|
857
|
|
|
|
858
|
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
|
|
858
|
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
|
|
|
859
|
|
|
859
|
|
|
|
860
|
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
|
|
860
|
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
|
|
|
861
|
return model0+model0u+model0d+noise
|
|
861
|
return model0+model0u+model0d+noise
|
|
|
862
|
|
|
862
|
|
|
|
863
|
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
|
|
863
|
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
|
|
|
864
|
shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state
|
|
864
|
shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state
|
|
|
865
|
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
865
|
model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
|
866
|
|
|
866
|
|
|
|
867
|
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
|
|
867
|
model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
|
|
|
868
|
|
|
868
|
|
|
|
869
|
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
|
|
869
|
model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
|
|
|
870
|
model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1)
|
|
870
|
model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1)
|
|
|
871
|
|
|
871
|
|
|
|
872
|
model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1)
|
|
872
|
model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1)
|
|
|
873
|
|
|
873
|
|
|
|
874
|
model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1)
|
|
874
|
model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1)
|
|
|
875
|
return model0+model0u+model0d+model1+model1u+model1d+noise
|
|
875
|
return model0+model0u+model0d+model1+model1u+model1d+noise
|
|
|
876
|
|
|
876
|
|
|
|
877
|
def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is.
|
|
877
|
def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is.
|
|
|
878
|
|
|
878
|
|
|
|
879
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
|
|
879
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
|
|
|
880
|
|
|
880
|
|
|
|
881
|
def misfit2(self,state,y_data,x,num_intg):
|
|
881
|
def misfit2(self,state,y_data,x,num_intg):
|
|
|
882
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
|
|
882
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
|
|
|
883
|
|
|
883
|
|
|
|
884
|
|
|
884
|
|
|
|
885
|
class PrecipitationProc(Operation):
|
|
885
|
class PrecipitationProc(Operation):
|
|
|
886
|
|
|
886
|
|
|
|
887
|
'''
|
|
887
|
'''
|
|
|
888
|
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
|
|
888
|
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
|
|
|
889
|
|
|
889
|
|
|
|
890
|
Input:
|
|
890
|
Input:
|
|
|
891
|
self.dataOut.data_pre : SelfSpectra
|
|
891
|
self.dataOut.data_pre : SelfSpectra
|
|
|
892
|
|
|
892
|
|
|
|
893
|
Output:
|
|
893
|
Output:
|
|
|
894
|
|
|
894
|
|
|
|
895
|
self.dataOut.data_output : Reflectivity factor, rainfall Rate
|
|
895
|
self.dataOut.data_output : Reflectivity factor, rainfall Rate
|
|
|
896
|
|
|
896
|
|
|
|
897
|
|
|
897
|
|
|
|
898
|
Parameters affected:
|
|
898
|
Parameters affected:
|
|
|
899
|
'''
|
|
899
|
'''
|
|
|
900
|
|
|
900
|
|
|
|
901
|
|
|
901
|
|
|
|
902
|
def run(self, dataOut, radar=None, Pt=None, Gt=None, Gr=None, Lambda=None, aL=None,
|
|
902
|
def run(self, dataOut, radar=None, Pt=None, Gt=None, Gr=None, Lambda=None, aL=None,
|
|
|
903
|
tauW=None, ThetaT=None, ThetaR=None, Km = 0.93, Altitude=None):
|
|
903
|
tauW=None, ThetaT=None, ThetaR=None, Km = 0.93, Altitude=None):
|
|
|
904
|
|
|
904
|
|
|
|
905
|
self.spc = dataOut.data_pre[0].copy()
|
|
905
|
self.spc = dataOut.data_pre[0].copy()
|
|
|
906
|
self.Num_Hei = self.spc.shape[2]
|
|
906
|
self.Num_Hei = self.spc.shape[2]
|
|
|
907
|
self.Num_Bin = self.spc.shape[1]
|
|
907
|
self.Num_Bin = self.spc.shape[1]
|
|
|
908
|
self.Num_Chn = self.spc.shape[0]
|
|
908
|
self.Num_Chn = self.spc.shape[0]
|
|
|
909
|
|
|
909
|
|
|
|
910
|
Velrange = dataOut.abscissaList
|
|
910
|
Velrange = dataOut.abscissaList
|
|
|
911
|
|
|
911
|
|
|
|
912
|
if radar == "MIRA35C" :
|
|
912
|
if radar == "MIRA35C" :
|
|
|
913
|
|
|
913
|
|
|
|
914
|
Ze = self.dBZeMODE2(dataOut)
|
|
914
|
Ze = self.dBZeMODE2(dataOut)
|
|
|
915
|
|
|
915
|
|
|
|
916
|
else:
|
|
916
|
else:
|
|
|
917
|
|
|
917
|
|
|
|
918
|
self.Pt = Pt
|
|
918
|
self.Pt = Pt
|
|
|
919
|
self.Gt = Gt
|
|
919
|
self.Gt = Gt
|
|
|
920
|
self.Gr = Gr
|
|
920
|
self.Gr = Gr
|
|
|
921
|
self.Lambda = Lambda
|
|
921
|
self.Lambda = Lambda
|
|
|
922
|
self.aL = aL
|
|
922
|
self.aL = aL
|
|
|
923
|
self.tauW = tauW
|
|
923
|
self.tauW = tauW
|
|
|
924
|
self.ThetaT = ThetaT
|
|
924
|
self.ThetaT = ThetaT
|
|
|
925
|
self.ThetaR = ThetaR
|
|
925
|
self.ThetaR = ThetaR
|
|
|
926
|
|
|
926
|
|
|
|
927
|
RadarConstant = GetRadarConstant()
|
|
927
|
RadarConstant = GetRadarConstant()
|
|
|
928
|
SPCmean = numpy.mean(self.spc,0)
|
|
928
|
SPCmean = numpy.mean(self.spc,0)
|
|
|
929
|
ETA = numpy.zeros(self.Num_Hei)
|
|
929
|
ETA = numpy.zeros(self.Num_Hei)
|
|
|
930
|
Pr = numpy.sum(SPCmean,0)
|
|
930
|
Pr = numpy.sum(SPCmean,0)
|
|
|
931
|
|
|
931
|
|
|
|
932
|
#for R in range(self.Num_Hei):
|
|
932
|
#for R in range(self.Num_Hei):
|
|
|
933
|
# ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA)
|
|
933
|
# ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA)
|
|
|
934
|
|
|
934
|
|
|
|
935
|
D_range = numpy.zeros(self.Num_Hei)
|
|
935
|
D_range = numpy.zeros(self.Num_Hei)
|
|
|
936
|
EqSec = numpy.zeros(self.Num_Hei)
|
|
936
|
EqSec = numpy.zeros(self.Num_Hei)
|
|
|
937
|
del_V = numpy.zeros(self.Num_Hei)
|
|
937
|
del_V = numpy.zeros(self.Num_Hei)
|
|
|
938
|
|
|
938
|
|
|
|
939
|
for R in range(self.Num_Hei):
|
|
939
|
for R in range(self.Num_Hei):
|
|
|
940
|
ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA)
|
|
940
|
ETA[R] = RadarConstant * Pr[R] * R**2 #Reflectivity (ETA)
|
|
|
941
|
|
|
941
|
|
|
|
942
|
h = R + Altitude #Range from ground to radar pulse altitude
|
|
942
|
h = R + Altitude #Range from ground to radar pulse altitude
|
|
|
943
|
del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity
|
|
943
|
del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity
|
|
|
944
|
|
|
944
|
|
|
|
945
|
D_range[R] = numpy.log( (9.65 - (Velrange[R]/del_V[R])) / 10.3 ) / -0.6 #Range of Diameter of drops related to velocity
|
|
945
|
D_range[R] = numpy.log( (9.65 - (Velrange[R]/del_V[R])) / 10.3 ) / -0.6 #Range of Diameter of drops related to velocity
|
|
|
946
|
SIGMA[R] = numpy.pi**5 / Lambda**4 * Km * D_range[R]**6 #Equivalent Section of drops (sigma)
|
|
946
|
SIGMA[R] = numpy.pi**5 / Lambda**4 * Km * D_range[R]**6 #Equivalent Section of drops (sigma)
|
|
|
947
|
|
|
947
|
|
|
|
948
|
N_dist[R] = ETA[R] / SIGMA[R]
|
|
948
|
N_dist[R] = ETA[R] / SIGMA[R]
|
|
|
949
|
|
|
949
|
|
|
|
950
|
Ze = (ETA * Lambda**4) / (numpy.pi * Km)
|
|
950
|
Ze = (ETA * Lambda**4) / (numpy.pi * Km)
|
|
|
951
|
Z = numpy.sum( N_dist * D_range**6 )
|
|
951
|
Z = numpy.sum( N_dist * D_range**6 )
|
|
|
952
|
RR = 6*10**-4*numpy.pi * numpy.sum( D_range**3 * N_dist * Velrange ) #Rainfall rate
|
|
952
|
RR = 6*10**-4*numpy.pi * numpy.sum( D_range**3 * N_dist * Velrange ) #Rainfall rate
|
|
|
953
|
|
|
953
|
|
|
|
954
|
|
|
954
|
|
|
|
955
|
RR = (Ze/200)**(1/1.6)
|
|
955
|
RR = (Ze/200)**(1/1.6)
|
|
|
956
|
dBRR = 10*numpy.log10(RR)
|
|
956
|
dBRR = 10*numpy.log10(RR)
|
|
|
957
|
|
|
957
|
|
|
|
958
|
dBZe = 10*numpy.log10(Ze)
|
|
958
|
dBZe = 10*numpy.log10(Ze)
|
|
|
959
|
dataOut.data_output = Ze
|
|
959
|
dataOut.data_output = Ze
|
|
|
960
|
dataOut.data_param = numpy.ones([2,self.Num_Hei])
|
|
960
|
dataOut.data_param = numpy.ones([2,self.Num_Hei])
|
|
|
961
|
dataOut.channelList = [0,1]
|
|
961
|
dataOut.channelList = [0,1]
|
|
|
962
|
print 'channelList', dataOut.channelList
|
|
962
|
print 'channelList', dataOut.channelList
|
|
|
963
|
dataOut.data_param[0]=dBZe
|
|
963
|
dataOut.data_param[0]=dBZe
|
|
|
964
|
dataOut.data_param[1]=dBRR
|
|
964
|
dataOut.data_param[1]=dBRR
|
|
|
965
|
print 'RR SHAPE', dBRR.shape
|
|
965
|
print 'RR SHAPE', dBRR.shape
|
|
|
966
|
print 'Ze SHAPE', dBZe.shape
|
|
966
|
print 'Ze SHAPE', dBZe.shape
|
|
|
967
|
print 'dataOut.data_param SHAPE', dataOut.data_param.shape
|
|
967
|
print 'dataOut.data_param SHAPE', dataOut.data_param.shape
|
|
|
968
|
|
|
968
|
|
|
|
969
|
|
|
969
|
|
|
|
970
|
def dBZeMODE2(self, dataOut): # Processing for MIRA35C
|
|
970
|
def dBZeMODE2(self, dataOut): # Processing for MIRA35C
|
|
|
971
|
|
|
971
|
|
|
|
972
|
NPW = dataOut.NPW
|
|
972
|
NPW = dataOut.NPW
|
|
|
973
|
COFA = dataOut.COFA
|
|
973
|
COFA = dataOut.COFA
|
|
|
974
|
|
|
974
|
|
|
|
975
|
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
|
|
975
|
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
|
|
|
976
|
RadarConst = dataOut.RadarConst
|
|
976
|
RadarConst = dataOut.RadarConst
|
|
|
977
|
#frequency = 34.85*10**9
|
|
977
|
#frequency = 34.85*10**9
|
|
|
978
|
|
|
978
|
|
|
|
979
|
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
|
|
979
|
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
|
|
|
980
|
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
|
|
980
|
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
|
|
|
981
|
|
|
981
|
|
|
|
982
|
ETA = numpy.sum(SNR,1)
|
|
982
|
ETA = numpy.sum(SNR,1)
|
|
|
983
|
print 'ETA' , ETA
|
|
983
|
print 'ETA' , ETA
|
|
|
984
|
ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN)
|
|
984
|
ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN)
|
|
|
985
|
|
|
985
|
|
|
|
986
|
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
|
|
986
|
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
|
|
|
987
|
|
|
987
|
|
|
|
988
|
for r in range(self.Num_Hei):
|
|
988
|
for r in range(self.Num_Hei):
|
|
|
989
|
|
|
989
|
|
|
|
990
|
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
|
|
990
|
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
|
|
|
991
|
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
|
|
991
|
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
|
|
|
992
|
|
|
992
|
|
|
|
993
|
return Ze
|
|
993
|
return Ze
|
|
|
994
|
|
|
994
|
|
|
|
995
|
def GetRadarConstant(self):
|
|
995
|
def GetRadarConstant(self):
|
|
|
996
|
|
|
996
|
|
|
|
997
|
"""
|
|
997
|
"""
|
|
|
998
|
Constants:
|
|
998
|
Constants:
|
|
|
999
|
|
|
999
|
|
|
|
1000
|
Pt: Transmission Power dB
|
|
1000
|
Pt: Transmission Power dB
|
|
|
1001
|
Gt: Transmission Gain dB
|
|
1001
|
Gt: Transmission Gain dB
|
|
|
1002
|
Gr: Reception Gain dB
|
|
1002
|
Gr: Reception Gain dB
|
|
|
1003
|
Lambda: Wavelenght m
|
|
1003
|
Lambda: Wavelenght m
|
|
|
1004
|
aL: Attenuation loses dB
|
|
1004
|
aL: Attenuation loses dB
|
|
|
1005
|
tauW: Width of transmission pulse s
|
|
1005
|
tauW: Width of transmission pulse s
|
|
|
1006
|
ThetaT: Transmission antenna bean angle rad
|
|
1006
|
ThetaT: Transmission antenna bean angle rad
|
|
|
1007
|
ThetaR: Reception antenna beam angle rad
|
|
1007
|
ThetaR: Reception antenna beam angle rad
|
|
|
1008
|
|
|
1008
|
|
|
|
1009
|
"""
|
|
1009
|
"""
|
|
|
1010
|
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
1010
|
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
|
1011
|
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
|
|
1011
|
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
|
|
|
1012
|
RadarConstant = Numerator / Denominator
|
|
1012
|
RadarConstant = Numerator / Denominator
|
|
|
1013
|
|
|
1013
|
|
|
|
1014
|
return RadarConstant
|
|
1014
|
return RadarConstant
|
|
|
1015
|
|
|
1015
|
|
|
|
1016
|
|
|
1016
|
|
|
|
1017
|
|
|
1017
|
|
|
|
1018
|
class FullSpectralAnalysis(Operation):
|
|
1018
|
class FullSpectralAnalysis(Operation):
|
|
|
1019
|
|
|
1019
|
|
|
|
1020
|
"""
|
|
1020
|
"""
|
|
|
1021
|
Function that implements Full Spectral Analisys technique.
|
|
1021
|
Function that implements Full Spectral Analisys technique.
|
|
|
1022
|
|
|
1022
|
|
|
|
1023
|
Input:
|
|
1023
|
Input:
|
|
|
1024
|
self.dataOut.data_pre : SelfSpectra and CrossSPectra data
|
|
1024
|
self.dataOut.data_pre : SelfSpectra and CrossSPectra data
|
|
|
1025
|
self.dataOut.groupList : Pairlist of channels
|
|
1025
|
self.dataOut.groupList : Pairlist of channels
|
|
|
1026
|
self.dataOut.ChanDist : Physical distance between receivers
|
|
1026
|
self.dataOut.ChanDist : Physical distance between receivers
|
|
|
1027
|
|
|
1027
|
|
|
|
1028
|
|
|
1028
|
|
|
|
1029
|
Output:
|
|
1029
|
Output:
|
|
|
1030
|
|
|
1030
|
|
|
|
1031
|
self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind
|
|
1031
|
self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind
|
|
|
1032
|
|
|
1032
|
|
|
|
1033
|
|
|
1033
|
|
|
|
1034
|
Parameters affected: Winds, height range, SNR
|
|
1034
|
Parameters affected: Winds, height range, SNR
|
|
|
1035
|
|
|
1035
|
|
|
|
1036
|
"""
|
|
1036
|
"""
|
|
|
1037
|
def run(self, dataOut, E01=None, E02=None, E12=None, N01=None, N02=None, N12=None, SNRlimit=7):
|
|
1037
|
def run(self, dataOut, E01=None, E02=None, E12=None, N01=None, N02=None, N12=None, SNRlimit=7):
|
|
|
1038
|
|
|
1038
|
|
|
|
1039
|
spc = dataOut.data_pre[0].copy()
|
|
1039
|
spc = dataOut.data_pre[0].copy()
|
|
|
1040
|
cspc = dataOut.data_pre[1].copy()
|
|
1040
|
cspc = dataOut.data_pre[1].copy()
|
|
|
1041
|
|
|
1041
|
|
|
|
1042
|
nChannel = spc.shape[0]
|
|
1042
|
nChannel = spc.shape[0]
|
|
|
1043
|
nProfiles = spc.shape[1]
|
|
1043
|
nProfiles = spc.shape[1]
|
|
|
1044
|
nHeights = spc.shape[2]
|
|
1044
|
nHeights = spc.shape[2]
|
|
|
1045
|
|
|
1045
|
|
|
|
1046
|
pairsList = dataOut.groupList
|
|
1046
|
pairsList = dataOut.groupList
|
|
|
1047
|
if dataOut.ChanDist is not None :
|
|
1047
|
if dataOut.ChanDist is not None :
|
|
|
1048
|
ChanDist = dataOut.ChanDist
|
|
1048
|
ChanDist = dataOut.ChanDist
|
|
|
1049
|
else:
|
|
1049
|
else:
|
|
|
1050
|
ChanDist = numpy.array([[E01, N01],[E02,N02],[E12,N12]])
|
|
1050
|
ChanDist = numpy.array([[E01, N01],[E02,N02],[E12,N12]])
|
|
|
1051
|
|
|
1051
|
|
|
|
1052
|
#print 'ChanDist', ChanDist
|
|
1052
|
#print 'ChanDist', ChanDist
|
|
|
1053
|
|
|
1053
|
|
|
|
1054
|
if dataOut.VelRange is not None:
|
|
1054
|
if dataOut.VelRange is not None:
|
|
|
1055
|
VelRange= dataOut.VelRange
|
|
1055
|
VelRange= dataOut.VelRange
|
|
|
1056
|
else:
|
|
1056
|
else:
|
|
|
1057
|
VelRange= dataOut.abscissaList
|
|
1057
|
VelRange= dataOut.abscissaList
|
|
|
1058
|
|
|
1058
|
|
|
|
1059
|
ySamples=numpy.ones([nChannel,nProfiles])
|
|
1059
|
ySamples=numpy.ones([nChannel,nProfiles])
|
|
|
1060
|
phase=numpy.ones([nChannel,nProfiles])
|
|
1060
|
phase=numpy.ones([nChannel,nProfiles])
|
|
|
1061
|
CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_)
|
|
1061
|
CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_)
|
|
|
1062
|
coherence=numpy.ones([nChannel,nProfiles])
|
|
1062
|
coherence=numpy.ones([nChannel,nProfiles])
|
|
|
1063
|
PhaseSlope=numpy.ones(nChannel)
|
|
1063
|
PhaseSlope=numpy.ones(nChannel)
|
|
|
1064
|
PhaseInter=numpy.ones(nChannel)
|
|
1064
|
PhaseInter=numpy.ones(nChannel)
|
|
|
1065
|
dataSNR = dataOut.data_SNR
|
|
1065
|
dataSNR = dataOut.data_SNR
|
|
|
1066
|
|
|
1066
|
|
|
|
1067
|
|
|
1067
|
|
|
|
1068
|
|
|
1068
|
|
|
|
1069
|
data = dataOut.data_pre
|
|
1069
|
data = dataOut.data_pre
|
|
|
1070
|
noise = dataOut.noise
|
|
1070
|
noise = dataOut.noise
|
|
|
1071
|
print 'noise',noise
|
|
1071
|
print 'noise',noise
|
|
|
1072
|
#SNRdB = 10*numpy.log10(dataOut.data_SNR)
|
|
1072
|
#SNRdB = 10*numpy.log10(dataOut.data_SNR)
|
|
|
1073
|
|
|
1073
|
|
|
|
1074
|
FirstMoment = numpy.average(dataOut.data_param[:,1,:],0)
|
|
1074
|
FirstMoment = numpy.average(dataOut.data_param[:,1,:],0)
|
|
|
1075
|
#SNRdBMean = []
|
|
1075
|
#SNRdBMean = []
|
|
|
1076
|
|
|
1076
|
|
|
|
1077
|
|
|
1077
|
|
|
|
1078
|
#for j in range(nHeights):
|
|
1078
|
#for j in range(nHeights):
|
|
|
1079
|
# FirstMoment = numpy.append(FirstMoment,numpy.mean([dataOut.data_param[0,1,j],dataOut.data_param[1,1,j],dataOut.data_param[2,1,j]]))
|
|
1079
|
# FirstMoment = numpy.append(FirstMoment,numpy.mean([dataOut.data_param[0,1,j],dataOut.data_param[1,1,j],dataOut.data_param[2,1,j]]))
|
|
|
1080
|
# SNRdBMean = numpy.append(SNRdBMean,numpy.mean([SNRdB[0,j],SNRdB[1,j],SNRdB[2,j]]))
|
|
1080
|
# SNRdBMean = numpy.append(SNRdBMean,numpy.mean([SNRdB[0,j],SNRdB[1,j],SNRdB[2,j]]))
|
|
|
1081
|
|
|
1081
|
|
|
|
1082
|
data_output=numpy.ones([3,spc.shape[2]])*numpy.NaN
|
|
1082
|
data_output=numpy.ones([3,spc.shape[2]])*numpy.NaN
|
|
|
1083
|
|
|
1083
|
|
|
|
1084
|
velocityX=[]
|
|
1084
|
velocityX=[]
|
|
|
1085
|
velocityY=[]
|
|
1085
|
velocityY=[]
|
|
|
1086
|
velocityV=[]
|
|
1086
|
velocityV=[]
|
|
|
1087
|
|
|
1087
|
|
|
|
1088
|
dbSNR = 10*numpy.log10(dataSNR)
|
|
1088
|
dbSNR = 10*numpy.log10(dataSNR)
|
|
|
1089
|
dbSNR = numpy.average(dbSNR,0)
|
|
1089
|
dbSNR = numpy.average(dbSNR,0)
|
|
|
1090
|
for Height in range(nHeights):
|
|
1090
|
for Height in range(nHeights):
|
|
|
1091
|
|
|
1091
|
|
|
|
1092
|
[Vzon,Vmer,Vver, GaussCenter]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR[Height], SNRlimit)
|
|
1092
|
[Vzon,Vmer,Vver, GaussCenter]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR[Height], SNRlimit)
|
|
|
1093
|
|
|
1093
|
|
|
|
1094
|
if abs(Vzon)<100. and abs(Vzon)> 0.:
|
|
1094
|
if abs(Vzon)<100. and abs(Vzon)> 0.:
|
|
|
1095
|
velocityX=numpy.append(velocityX, Vzon)#Vmag
|
|
1095
|
velocityX=numpy.append(velocityX, Vzon)#Vmag
|
|
|
1096
|
|
|
1096
|
|
|
|
1097
|
else:
|
|
1097
|
else:
|
|
|
1098
|
print 'Vzon',Vzon
|
|
1098
|
print 'Vzon',Vzon
|
|
|
1099
|
velocityX=numpy.append(velocityX, numpy.NaN)
|
|
1099
|
velocityX=numpy.append(velocityX, numpy.NaN)
|
|
|
1100
|
|
|
1100
|
|
|
|
1101
|
if abs(Vmer)<100. and abs(Vmer) > 0.:
|
|
1101
|
if abs(Vmer)<100. and abs(Vmer) > 0.:
|
|
|
1102
|
velocityY=numpy.append(velocityY, Vmer)#Vang
|
|
1102
|
velocityY=numpy.append(velocityY, Vmer)#Vang
|
|
|
1103
|
|
|
1103
|
|
|
|
1104
|
else:
|
|
1104
|
else:
|
|
|
1105
|
print 'Vmer',Vmer
|
|
1105
|
print 'Vmer',Vmer
|
|
|
1106
|
velocityY=numpy.append(velocityY, numpy.NaN)
|
|
1106
|
velocityY=numpy.append(velocityY, numpy.NaN)
|
|
|
1107
|
|
|
1107
|
|
|
|
1108
|
if dbSNR[Height] > SNRlimit:
|
|
1108
|
if dbSNR[Height] > SNRlimit:
|
|
|
1109
|
velocityV=numpy.append(velocityV, FirstMoment[Height])
|
|
1109
|
velocityV=numpy.append(velocityV, FirstMoment[Height])
|
|
|
1110
|
else:
|
|
1110
|
else:
|
|
|
1111
|
velocityV=numpy.append(velocityV, numpy.NaN)
|
|
1111
|
velocityV=numpy.append(velocityV, numpy.NaN)
|
|
|
1112
|
#FirstMoment[Height]= numpy.NaN
|
|
1112
|
#FirstMoment[Height]= numpy.NaN
|
|
|
1113
|
# if SNRdBMean[Height] <12:
|
|
1113
|
# if SNRdBMean[Height] <12:
|
|
|
1114
|
# FirstMoment[Height] = numpy.NaN
|
|
1114
|
# FirstMoment[Height] = numpy.NaN
|
|
|
1115
|
# velocityX[Height] = numpy.NaN
|
|
1115
|
# velocityX[Height] = numpy.NaN
|
|
|
1116
|
# velocityY[Height] = numpy.NaN
|
|
1116
|
# velocityY[Height] = numpy.NaN
|
|
|
1117
|
|
|
1117
|
|
|
|
1118
|
|
|
1118
|
|
|
|
1119
|
data_output[0]=numpy.array(velocityX)
|
|
1119
|
data_output[0]=numpy.array(velocityX)
|
|
|
1120
|
data_output[1]=numpy.array(velocityY)
|
|
1120
|
data_output[1]=numpy.array(velocityY)
|
|
|
1121
|
data_output[2]=-velocityV#FirstMoment
|
|
1121
|
data_output[2]=-velocityV#FirstMoment
|
|
|
1122
|
|
|
1122
|
|
|
|
1123
|
print ' '
|
|
1123
|
print ' '
|
|
|
1124
|
#print 'FirstMoment'
|
|
1124
|
#print 'FirstMoment'
|
|
|
1125
|
#print FirstMoment
|
|
1125
|
#print FirstMoment
|
|
|
1126
|
print 'velocityX',data_output[0]
|
|
1126
|
print 'velocityX',data_output[0]
|
|
|
1127
|
print ' '
|
|
1127
|
print ' '
|
|
|
1128
|
print 'velocityY',data_output[1]
|
|
1128
|
print 'velocityY',data_output[1]
|
|
|
1129
|
#print numpy.array(velocityY)
|
|
1129
|
#print numpy.array(velocityY)
|
|
|
1130
|
print ' '
|
|
1130
|
print ' '
|
|
|
1131
|
#print 'SNR'
|
|
1131
|
#print 'SNR'
|
|
|
1132
|
#print 10*numpy.log10(dataOut.data_SNR)
|
|
1132
|
#print 10*numpy.log10(dataOut.data_SNR)
|
|
|
1133
|
#print numpy.shape(10*numpy.log10(dataOut.data_SNR))
|
|
1133
|
#print numpy.shape(10*numpy.log10(dataOut.data_SNR))
|
|
|
1134
|
print ' '
|
|
1134
|
print ' '
|
|
|
1135
|
|
|
1135
|
|
|
|
1136
|
|
|
1136
|
|
|
|
1137
|
dataOut.data_output=data_output
|
|
1137
|
dataOut.data_output=data_output
|
|
|
1138
|
return
|
|
1138
|
return
|
|
|
1139
|
|
|
1139
|
|
|
|
1140
|
|
|
1140
|
|
|
|
1141
|
def moving_average(self,x, N=2):
|
|
1141
|
def moving_average(self,x, N=2):
|
|
|
1142
|
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
|
|
1142
|
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
|
|
|
1143
|
|
|
1143
|
|
|
|
1144
|
def gaus(self,xSamples,a,x0,sigma):
|
|
1144
|
def gaus(self,xSamples,a,x0,sigma):
|
|
|
1145
|
return a*numpy.exp(-(xSamples-x0)**2/(2*sigma**2))
|
|
1145
|
return a*numpy.exp(-(xSamples-x0)**2/(2*sigma**2))
|
|
|
1146
|
|
|
1146
|
|
|
|
1147
|
def Find(self,x,value):
|
|
1147
|
def Find(self,x,value):
|
|
|
1148
|
for index in range(len(x)):
|
|
1148
|
for index in range(len(x)):
|
|
|
1149
|
if x[index]==value:
|
|
1149
|
if x[index]==value:
|
|
|
1150
|
return index
|
|
1150
|
return index
|
|
|
1151
|
|
|
1151
|
|
|
|
1152
|
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR, SNRlimit):
|
|
1152
|
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, VelRange, dbSNR, SNRlimit):
|
|
|
1153
|
|
|
1153
|
|
|
|
1154
|
ySamples=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
1154
|
ySamples=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
|
1155
|
phase=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
1155
|
phase=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
|
1156
|
CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_)
|
|
1156
|
CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_)
|
|
|
1157
|
coherence=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
1157
|
coherence=numpy.ones([spc.shape[0],spc.shape[1]])
|
|
|
1158
|
PhaseSlope=numpy.ones(spc.shape[0])
|
|
1158
|
PhaseSlope=numpy.ones(spc.shape[0])
|
|
|
1159
|
PhaseInter=numpy.ones(spc.shape[0])
|
|
1159
|
PhaseInter=numpy.ones(spc.shape[0])
|
|
|
1160
|
xFrec=VelRange
|
|
1160
|
xFrec=VelRange
|
|
|
1161
|
|
|
1161
|
|
|
|
1162
|
'''Getting Eij and Nij'''
|
|
1162
|
'''Getting Eij and Nij'''
|
|
|
1163
|
|
|
1163
|
|
|
|
1164
|
E01=ChanDist[0][0]
|
|
1164
|
E01=ChanDist[0][0]
|
|
|
1165
|
N01=ChanDist[0][1]
|
|
1165
|
N01=ChanDist[0][1]
|
|
|
1166
|
|
|
1166
|
|
|
|
1167
|
E02=ChanDist[1][0]
|
|
1167
|
E02=ChanDist[1][0]
|
|
|
1168
|
N02=ChanDist[1][1]
|
|
1168
|
N02=ChanDist[1][1]
|
|
|
1169
|
|
|
1169
|
|
|
|
1170
|
E12=ChanDist[2][0]
|
|
1170
|
E12=ChanDist[2][0]
|
|
|
1171
|
N12=ChanDist[2][1]
|
|
1171
|
N12=ChanDist[2][1]
|
|
|
1172
|
|
|
1172
|
|
|
|
1173
|
z = spc.copy()
|
|
1173
|
z = spc.copy()
|
|
|
1174
|
z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
|
|
1174
|
z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
|
|
|
1175
|
|
|
1175
|
|
|
|
1176
|
for i in range(spc.shape[0]):
|
|
1176
|
for i in range(spc.shape[0]):
|
|
|
1177
|
|
|
1177
|
|
|
|
1178
|
'''****** Line of Data SPC ******'''
|
|
1178
|
'''****** Line of Data SPC ******'''
|
|
|
1179
|
zline=z[i,:,Height]
|
|
1179
|
zline=z[i,:,Height]
|
|
|
1180
|
|
|
1180
|
|
|
|
1181
|
'''****** SPC is normalized ******'''
|
|
1181
|
'''****** SPC is normalized ******'''
|
|
|
1182
|
FactNorm= (zline.copy()-noise[i]) / numpy.sum(zline.copy())
|
|
1182
|
FactNorm= (zline.copy()-noise[i]) / numpy.sum(zline.copy())
|
|
|
1183
|
FactNorm= FactNorm/numpy.sum(FactNorm)
|
|
1183
|
FactNorm= FactNorm/numpy.sum(FactNorm)
|
|
|
1184
|
|
|
1184
|
|
|
|
1185
|
SmoothSPC=self.moving_average(FactNorm,N=3)
|
|
1185
|
SmoothSPC=self.moving_average(FactNorm,N=3)
|
|
|
1186
|
|
|
1186
|
|
|
|
1187
|
xSamples = ar(range(len(SmoothSPC)))
|
|
1187
|
xSamples = ar(range(len(SmoothSPC)))
|
|
|
1188
|
ySamples[i] = SmoothSPC
|
|
1188
|
ySamples[i] = SmoothSPC
|
|
|
1189
|
|
|
1189
|
|
|
|
1190
|
#dbSNR=10*numpy.log10(dataSNR)
|
|
1190
|
#dbSNR=10*numpy.log10(dataSNR)
|
|
|
1191
|
print ' '
|
|
1191
|
print ' '
|
|
|
1192
|
print ' '
|
|
1192
|
print ' '
|
|
|
1193
|
print ' '
|
|
1193
|
print ' '
|
|
|
1194
|
|
|
1194
|
|
|
|
1195
|
#print 'dataSNR', dbSNR.shape, dbSNR[0,40:120]
|
|
1195
|
#print 'dataSNR', dbSNR.shape, dbSNR[0,40:120]
|
|
|
1196
|
print 'SmoothSPC', SmoothSPC.shape, SmoothSPC[0:20]
|
|
1196
|
print 'SmoothSPC', SmoothSPC.shape, SmoothSPC[0:20]
|
|
|
1197
|
print 'noise',noise
|
|
1197
|
print 'noise',noise
|
|
|
1198
|
print 'zline',zline.shape, zline[0:20]
|
|
1198
|
print 'zline',zline.shape, zline[0:20]
|
|
|
1199
|
print 'FactNorm',FactNorm.shape, FactNorm[0:20]
|
|
1199
|
print 'FactNorm',FactNorm.shape, FactNorm[0:20]
|
|
|
1200
|
print 'FactNorm suma', numpy.sum(FactNorm)
|
|
1200
|
print 'FactNorm suma', numpy.sum(FactNorm)
|
|
|
1201
|
|
|
1201
|
|
|
|
1202
|
for i in range(spc.shape[0]):
|
|
1202
|
for i in range(spc.shape[0]):
|
|
|
1203
|
|
|
1203
|
|
|
|
1204
|
'''****** Line of Data CSPC ******'''
|
|
1204
|
'''****** Line of Data CSPC ******'''
|
|
|
1205
|
cspcLine=cspc[i,:,Height].copy()
|
|
1205
|
cspcLine=cspc[i,:,Height].copy()
|
|
|
1206
|
|
|
1206
|
|
|
|
1207
|
'''****** CSPC is normalized ******'''
|
|
1207
|
'''****** CSPC is normalized ******'''
|
|
|
1208
|
chan_index0 = pairsList[i][0]
|
|
1208
|
chan_index0 = pairsList[i][0]
|
|
|
1209
|
chan_index1 = pairsList[i][1]
|
|
1209
|
chan_index1 = pairsList[i][1]
|
|
|
1210
|
CSPCFactor= abs(numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1])) #
|
|
1210
|
CSPCFactor= abs(numpy.sum(ySamples[chan_index0]) * numpy.sum(ySamples[chan_index1])) #
|
|
|
1211
|
|
|
1211
|
|
|
|
1212
|
CSPCNorm = (cspcLine.copy() -noise[i]) / numpy.sqrt(CSPCFactor)
|
|
1212
|
CSPCNorm = (cspcLine.copy() -noise[i]) / numpy.sqrt(CSPCFactor)
|
|
|
1213
|
|
|
1213
|
|
|
|
1214
|
CSPCSamples[i] = CSPCNorm
|
|
1214
|
CSPCSamples[i] = CSPCNorm
|
|
|
1215
|
coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor)
|
|
1215
|
coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor)
|
|
|
1216
|
|
|
1216
|
|
|
|
1217
|
coherence[i]= self.moving_average(coherence[i],N=2)
|
|
1217
|
coherence[i]= self.moving_average(coherence[i],N=2)
|
|
|
1218
|
|
|
1218
|
|
|
|
1219
|
phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi
|
|
1219
|
phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi
|
|
|
1220
|
|
|
1220
|
|
|
|
1221
|
print 'cspcLine', cspcLine.shape, cspcLine[0:20]
|
|
1221
|
print 'cspcLine', cspcLine.shape, cspcLine[0:20]
|
|
|
1222
|
print 'CSPCFactor', CSPCFactor#, CSPCFactor[0:20]
|
|
1222
|
print 'CSPCFactor', CSPCFactor#, CSPCFactor[0:20]
|
|
|
1223
|
print numpy.sum(ySamples[chan_index0]), numpy.sum(ySamples[chan_index1]), -noise[i]
|
|
1223
|
print numpy.sum(ySamples[chan_index0]), numpy.sum(ySamples[chan_index1]), -noise[i]
|
|
|
1224
|
print 'CSPCNorm', CSPCNorm.shape, CSPCNorm[0:20]
|
|
1224
|
print 'CSPCNorm', CSPCNorm.shape, CSPCNorm[0:20]
|
|
|
1225
|
print 'CSPCNorm suma', numpy.sum(CSPCNorm)
|
|
1225
|
print 'CSPCNorm suma', numpy.sum(CSPCNorm)
|
|
|
1226
|
print 'CSPCSamples', CSPCSamples.shape, CSPCSamples[0,0:20]
|
|
1226
|
print 'CSPCSamples', CSPCSamples.shape, CSPCSamples[0,0:20]
|
|
|
1227
|
|
|
1227
|
|
|
|
1228
|
'''****** Getting fij width ******'''
|
|
1228
|
'''****** Getting fij width ******'''
|
|
|
1229
|
|
|
1229
|
|
|
|
1230
|
yMean=[]
|
|
1230
|
yMean=[]
|
|
|
1231
|
yMean2=[]
|
|
1231
|
yMean2=[]
|
|
|
1232
|
|
|
1232
|
|
|
|
1233
|
for j in range(len(ySamples[1])):
|
|
1233
|
for j in range(len(ySamples[1])):
|
|
|
1234
|
yMean=numpy.append(yMean,numpy.mean([ySamples[0,j],ySamples[1,j],ySamples[2,j]]))
|
|
1234
|
yMean=numpy.append(yMean,numpy.mean([ySamples[0,j],ySamples[1,j],ySamples[2,j]]))
|
|
|
1235
|
|
|
1235
|
|
|
|
1236
|
'''******* Getting fitting Gaussian ******'''
|
|
1236
|
'''******* Getting fitting Gaussian ******'''
|
|
|
1237
|
meanGauss=sum(xSamples*yMean) / len(xSamples)
|
|
1237
|
meanGauss=sum(xSamples*yMean) / len(xSamples)
|
|
|
1238
|
sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples)
|
|
1238
|
sigma=sum(yMean*(xSamples-meanGauss)**2) / len(xSamples)
|
|
|
1239
|
|
|
1239
|
|
|
|
1240
|
print '****************************'
|
|
1240
|
print '****************************'
|
|
|
1241
|
print 'len(xSamples): ',len(xSamples)
|
|
1241
|
print 'len(xSamples): ',len(xSamples)
|
|
|
1242
|
print 'yMean: ', yMean.shape, yMean[0:20]
|
|
1242
|
print 'yMean: ', yMean.shape, yMean[0:20]
|
|
|
1243
|
print 'ySamples', ySamples.shape, ySamples[0,0:20]
|
|
1243
|
print 'ySamples', ySamples.shape, ySamples[0,0:20]
|
|
|
1244
|
print 'xSamples: ',xSamples.shape, xSamples[0:20]
|
|
1244
|
print 'xSamples: ',xSamples.shape, xSamples[0:20]
|
|
|
1245
|
|
|
1245
|
|
|
|
1246
|
print 'meanGauss',meanGauss
|
|
1246
|
print 'meanGauss',meanGauss
|
|
|
1247
|
print 'sigma',sigma
|
|
1247
|
print 'sigma',sigma
|
|
|
1248
|
|
|
1248
|
|
|
|
1249
|
#if (abs(meanGauss/sigma**2) > 0.0001) : #0.000000001):
|
|
1249
|
#if (abs(meanGauss/sigma**2) > 0.0001) : #0.000000001):
|
|
|
1250
|
if dbSNR > SNRlimit :
|
|
1250
|
if dbSNR > SNRlimit :
|
|
|
1251
|
try:
|
|
1251
|
try:
|
|
|
1252
|
popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=[1,meanGauss,sigma])
|
|
1252
|
popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=[1,meanGauss,sigma])
|
|
|
1253
|
|
|
1253
|
|
|
|
1254
|
if numpy.amax(popt)>numpy.amax(yMean)*0.3:
|
|
1254
|
if numpy.amax(popt)>numpy.amax(yMean)*0.3:
|
|
|
1255
|
FitGauss=self.gaus(xSamples,*popt)
|
|
1255
|
FitGauss=self.gaus(xSamples,*popt)
|
|
|
1256
|
|
|
1256
|
|
|
|
1257
|
else:
|
|
1257
|
else:
|
|
|
1258
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
1258
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
|
1259
|
print 'Verificador: Dentro', Height
|
|
1259
|
print 'Verificador: Dentro', Height
|
|
|
1260
|
except :#RuntimeError:
|
|
1260
|
except :#RuntimeError:
|
|
|
1261
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
1261
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
|
1262
|
|
|
1262
|
|
|
|
1263
|
|
|
1263
|
|
|
|
1264
|
else:
|
|
1264
|
else:
|
|
|
1265
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
1265
|
FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
|
|
|
1266
|
|
|
1266
|
|
|
|
1267
|
Maximun=numpy.amax(yMean)
|
|
1267
|
Maximun=numpy.amax(yMean)
|
|
|
1268
|
eMinus1=Maximun*numpy.exp(-1)#*0.8
|
|
1268
|
eMinus1=Maximun*numpy.exp(-1)#*0.8
|
|
|
1269
|
|
|
1269
|
|
|
|
1270
|
HWpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1)))
|
|
1270
|
HWpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-eMinus1)))
|
|
|
1271
|
HalfWidth= xFrec[HWpos]
|
|
1271
|
HalfWidth= xFrec[HWpos]
|
|
|
1272
|
GCpos=self.Find(FitGauss, numpy.amax(FitGauss))
|
|
1272
|
GCpos=self.Find(FitGauss, numpy.amax(FitGauss))
|
|
|
1273
|
Vpos=self.Find(FactNorm, numpy.amax(FactNorm))
|
|
1273
|
Vpos=self.Find(FactNorm, numpy.amax(FactNorm))
|
|
|
1274
|
|
|
1274
|
|
|
|
1275
|
#Vpos=FirstMoment[]
|
|
1275
|
#Vpos=FirstMoment[]
|
|
|
1276
|
|
|
1276
|
|
|
|
1277
|
'''****** Getting Fij ******'''
|
|
1277
|
'''****** Getting Fij ******'''
|
|
|
1278
|
|
|
1278
|
|
|
|
1279
|
GaussCenter=xFrec[GCpos]
|
|
1279
|
GaussCenter=xFrec[GCpos]
|
|
|
1280
|
if (GaussCenter<0 and HalfWidth>0) or (GaussCenter>0 and HalfWidth<0):
|
|
1280
|
if (GaussCenter<0 and HalfWidth>0) or (GaussCenter>0 and HalfWidth<0):
|
|
|
1281
|
Fij=abs(GaussCenter)+abs(HalfWidth)+0.0000001
|
|
1281
|
Fij=abs(GaussCenter)+abs(HalfWidth)+0.0000001
|
|
|
1282
|
else:
|
|
1282
|
else:
|
|
|
1283
|
Fij=abs(GaussCenter-HalfWidth)+0.0000001
|
|
1283
|
Fij=abs(GaussCenter-HalfWidth)+0.0000001
|
|
|
1284
|
|
|
1284
|
|
|
|
1285
|
'''****** Getting Frecuency range of significant data ******'''
|
|
1285
|
'''****** Getting Frecuency range of significant data ******'''
|
|
|
1286
|
|
|
1286
|
|
|
|
1287
|
Rangpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.10)))
|
|
1287
|
Rangpos=self.Find(FitGauss,min(FitGauss, key=lambda value:abs(value-Maximun*0.10)))
|
|
|
1288
|
|
|
1288
|
|
|
|
1289
|
if Rangpos<GCpos:
|
|
1289
|
if Rangpos<GCpos:
|
|
|
1290
|
Range=numpy.array([Rangpos,2*GCpos-Rangpos])
|
|
1290
|
Range=numpy.array([Rangpos,2*GCpos-Rangpos])
|
|
|
1291
|
elif Rangpos< ( len(xFrec)- len(xFrec)*0.1):
|
|
1291
|
elif Rangpos< ( len(xFrec)- len(xFrec)*0.1):
|
|
|
1292
|
Range=numpy.array([2*GCpos-Rangpos,Rangpos])
|
|
1292
|
Range=numpy.array([2*GCpos-Rangpos,Rangpos])
|
|
|
1293
|
else:
|
|
1293
|
else:
|
|
|
1294
|
Range = numpy.array([0,0])
|
|
1294
|
Range = numpy.array([0,0])
|
|
|
1295
|
|
|
1295
|
|
|
|
1296
|
print ' '
|
|
1296
|
print ' '
|
|
|
1297
|
print 'GCpos',GCpos, ( len(xFrec)- len(xFrec)*0.1)
|
|
1297
|
print 'GCpos',GCpos, ( len(xFrec)- len(xFrec)*0.1)
|
|
|
1298
|
print 'Rangpos',Rangpos
|
|
1298
|
print 'Rangpos',Rangpos
|
|
|
1299
|
print 'RANGE: ', Range
|
|
1299
|
print 'RANGE: ', Range
|
|
|
1300
|
FrecRange=xFrec[Range[0]:Range[1]]
|
|
1300
|
FrecRange=xFrec[Range[0]:Range[1]]
|
|
|
1301
|
|
|
1301
|
|
|
|
1302
|
'''****** Getting SCPC Slope ******'''
|
|
1302
|
'''****** Getting SCPC Slope ******'''
|
|
|
1303
|
|
|
1303
|
|
|
|
1304
|
for i in range(spc.shape[0]):
|
|
1304
|
for i in range(spc.shape[0]):
|
|
|
1305
|
|
|
1305
|
|
|
|
1306
|
if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.5:
|
|
1306
|
if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.5:
|
|
|
1307
|
PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3)
|
|
1307
|
PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3)
|
|
|
1308
|
|
|
1308
|
|
|
|
1309
|
print 'FrecRange', len(FrecRange) , FrecRange
|
|
1309
|
print 'FrecRange', len(FrecRange) , FrecRange
|
|
|
1310
|
print 'PhaseRange', len(PhaseRange), PhaseRange
|
|
1310
|
print 'PhaseRange', len(PhaseRange), PhaseRange
|
|
|
1311
|
print ' '
|
|
1311
|
print ' '
|
|
|
1312
|
if len(FrecRange) == len(PhaseRange):
|
|
1312
|
if len(FrecRange) == len(PhaseRange):
|
|
|
1313
|
slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange,PhaseRange)
|
|
1313
|
slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange,PhaseRange)
|
|
|
1314
|
PhaseSlope[i]=slope
|
|
1314
|
PhaseSlope[i]=slope
|
|
|
1315
|
PhaseInter[i]=intercept
|
|
1315
|
PhaseInter[i]=intercept
|
|
|
1316
|
else:
|
|
1316
|
else:
|
|
|
1317
|
PhaseSlope[i]=0
|
|
1317
|
PhaseSlope[i]=0
|
|
|
1318
|
PhaseInter[i]=0
|
|
1318
|
PhaseInter[i]=0
|
|
|
1319
|
else:
|
|
1319
|
else:
|
|
|
1320
|
PhaseSlope[i]=0
|
|
1320
|
PhaseSlope[i]=0
|
|
|
1321
|
PhaseInter[i]=0
|
|
1321
|
PhaseInter[i]=0
|
|
|
1322
|
|
|
1322
|
|
|
|
1323
|
'''Getting constant C'''
|
|
1323
|
'''Getting constant C'''
|
|
|
1324
|
cC=(Fij*numpy.pi)**2
|
|
1324
|
cC=(Fij*numpy.pi)**2
|
|
|
1325
|
|
|
1325
|
|
|
|
1326
|
'''****** Getting constants F and G ******'''
|
|
1326
|
'''****** Getting constants F and G ******'''
|
|
|
1327
|
MijEijNij=numpy.array([[E02,N02], [E12,N12]])
|
|
1327
|
MijEijNij=numpy.array([[E02,N02], [E12,N12]])
|
|
|
1328
|
MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi)
|
|
1328
|
MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi)
|
|
|
1329
|
MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi)
|
|
1329
|
MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi)
|
|
|
1330
|
MijResults=numpy.array([MijResult0,MijResult1])
|
|
1330
|
MijResults=numpy.array([MijResult0,MijResult1])
|
|
|
1331
|
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
|
|
1331
|
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
|
|
|
1332
|
|
|
1332
|
|
|
|
1333
|
'''****** Getting constants A, B and H ******'''
|
|
1333
|
'''****** Getting constants A, B and H ******'''
|
|
|
1334
|
W01=numpy.amax(coherence[0])
|
|
1334
|
W01=numpy.amax(coherence[0])
|
|
|
1335
|
W02=numpy.amax(coherence[1])
|
|
1335
|
W02=numpy.amax(coherence[1])
|
|
|
1336
|
W12=numpy.amax(coherence[2])
|
|
1336
|
W12=numpy.amax(coherence[2])
|
|
|
1337
|
|
|
1337
|
|
|
|
1338
|
WijResult0=((cF*E01+cG*N01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC))
|
|
1338
|
WijResult0=((cF*E01+cG*N01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC))
|
|
|
1339
|
WijResult1=((cF*E02+cG*N02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC))
|
|
1339
|
WijResult1=((cF*E02+cG*N02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC))
|
|
|
1340
|
WijResult2=((cF*E12+cG*N12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC))
|
|
1340
|
WijResult2=((cF*E12+cG*N12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC))
|
|
|
1341
|
|
|
1341
|
|
|
|
1342
|
WijResults=numpy.array([WijResult0, WijResult1, WijResult2])
|
|
1342
|
WijResults=numpy.array([WijResult0, WijResult1, WijResult2])
|
|
|
1343
|
|
|
1343
|
|
|
|
1344
|
WijEijNij=numpy.array([ [E01**2, N01**2, 2*E01*N01] , [E02**2, N02**2, 2*E02*N02] , [E12**2, N12**2, 2*E12*N12] ])
|
|
1344
|
WijEijNij=numpy.array([ [E01**2, N01**2, 2*E01*N01] , [E02**2, N02**2, 2*E02*N02] , [E12**2, N12**2, 2*E12*N12] ])
|
|
|
1345
|
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
|
|
1345
|
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
|
|
|
1346
|
|
|
1346
|
|
|
|
1347
|
VxVy=numpy.array([[cA,cH],[cH,cB]])
|
|
1347
|
VxVy=numpy.array([[cA,cH],[cH,cB]])
|
|
|
1348
|
|
|
1348
|
|
|
|
1349
|
VxVyResults=numpy.array([-cF,-cG])
|
|
1349
|
VxVyResults=numpy.array([-cF,-cG])
|
|
|
1350
|
(Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults)
|
|
1350
|
(Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults)
|
|
|
1351
|
|
|
1351
|
|
|
|
1352
|
Vzon = Vy
|
|
1352
|
Vzon = Vy
|
|
|
1353
|
Vmer = Vx
|
|
1353
|
Vmer = Vx
|
|
|
1354
|
Vmag=numpy.sqrt(Vzon**2+Vmer**2)
|
|
1354
|
Vmag=numpy.sqrt(Vzon**2+Vmer**2)
|
|
|
1355
|
Vang=numpy.arctan2(Vmer,Vzon)
|
|
1355
|
Vang=numpy.arctan2(Vmer,Vzon)
|
|
|
1356
|
Vver=xFrec[Vpos]
|
|
1356
|
Vver=xFrec[Vpos]
|
|
|
1357
|
print 'vzon y vmer', Vzon, Vmer
|
|
1357
|
print 'vzon y vmer', Vzon, Vmer
|
|
|
1358
|
return Vzon, Vmer, Vver, GaussCenter
|
|
1358
|
return Vzon, Vmer, Vver, GaussCenter
|
|
|
1359
|
|
|
1359
|
|
|
|
1360
|
class SpectralMoments(Operation):
|
|
1360
|
class SpectralMoments(Operation):
|
|
|
1361
|
|
|
1361
|
|
|
|
1362
|
'''
|
|
1362
|
'''
|
|
|
1363
|
Function SpectralMoments()
|
|
1363
|
Function SpectralMoments()
|
|
|
1364
|
|
|
1364
|
|
|
|
1365
|
Calculates moments (power, mean, standard deviation) and SNR of the signal
|
|
1365
|
Calculates moments (power, mean, standard deviation) and SNR of the signal
|
|
|
1366
|
|
|
1366
|
|
|
|
1367
|
Type of dataIn: Spectra
|
|
1367
|
Type of dataIn: Spectra
|
|
|
1368
|
|
|
1368
|
|
|
|
1369
|
Configuration Parameters:
|
|
1369
|
Configuration Parameters:
|
|
|
1370
|
|
|
1370
|
|
|
|
1371
|
dirCosx : Cosine director in X axis
|
|
1371
|
dirCosx : Cosine director in X axis
|
|
|
1372
|
dirCosy : Cosine director in Y axis
|
|
1372
|
dirCosy : Cosine director in Y axis
|
|
|
1373
|
|
|
1373
|
|
|
|
1374
|
elevation :
|
|
1374
|
elevation :
|
|
|
1375
|
azimuth :
|
|
1375
|
azimuth :
|
|
|
1376
|
|
|
1376
|
|
|
|
1377
|
Input:
|
|
1377
|
Input:
|
|
|
1378
|
channelList : simple channel list to select e.g. [2,3,7]
|
|
1378
|
channelList : simple channel list to select e.g. [2,3,7]
|
|
|
1379
|
self.dataOut.data_pre : Spectral data
|
|
1379
|
self.dataOut.data_pre : Spectral data
|
|
|
1380
|
self.dataOut.abscissaList : List of frequencies
|
|
1380
|
self.dataOut.abscissaList : List of frequencies
|
|
|
1381
|
self.dataOut.noise : Noise level per channel
|
|
1381
|
self.dataOut.noise : Noise level per channel
|
|
|
1382
|
|
|
1382
|
|
|
|
1383
|
Affected:
|
|
1383
|
Affected:
|
|
|
1384
|
self.dataOut.data_param : Parameters per channel
|
|
1384
|
self.dataOut.data_param : Parameters per channel
|
|
|
1385
|
self.dataOut.data_SNR : SNR per channel
|
|
1385
|
self.dataOut.data_SNR : SNR per channel
|
|
|
1386
|
|
|
1386
|
|
|
|
1387
|
'''
|
|
1387
|
'''
|
|
|
1388
|
|
|
1388
|
|
|
|
1389
|
def run(self, dataOut):
|
|
1389
|
def run(self, dataOut):
|
|
|
1390
|
|
|
1390
|
|
|
|
1391
|
#dataOut.data_pre = dataOut.data_pre[0]
|
|
1391
|
#dataOut.data_pre = dataOut.data_pre[0]
|
|
|
1392
|
data = dataOut.data_pre[0]
|
|
1392
|
data = dataOut.data_pre[0]
|
|
|
1393
|
absc = dataOut.abscissaList[:-1]
|
|
1393
|
absc = dataOut.abscissaList[:-1]
|
|
|
1394
|
noise = dataOut.noise
|
|
1394
|
noise = dataOut.noise
|
|
|
1395
|
nChannel = data.shape[0]
|
|
1395
|
nChannel = data.shape[0]
|
|
|
1396
|
data_param = numpy.zeros((nChannel, 4, data.shape[2]))
|
|
1396
|
data_param = numpy.zeros((nChannel, 4, data.shape[2]))
|
|
|
1397
|
|
|
1397
|
|
|
|
1398
|
for ind in range(nChannel):
|
|
1398
|
for ind in range(nChannel):
|
|
|
1399
|
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
|
|
1399
|
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
|
|
|
1400
|
|
|
1400
|
|
|
|
1401
|
dataOut.data_param = data_param[:,1:,:]
|
|
1401
|
dataOut.data_param = data_param[:,1:,:]
|
|
|
1402
|
dataOut.data_SNR = data_param[:,0]
|
|
1402
|
dataOut.data_SNR = data_param[:,0]
|
|
|
1403
|
dataOut.data_DOP = data_param[:,1]
|
|
1403
|
dataOut.data_DOP = data_param[:,1]
|
|
|
1404
|
dataOut.data_MEAN = data_param[:,2]
|
|
1404
|
dataOut.data_MEAN = data_param[:,2]
|
|
|
1405
|
dataOut.data_STD = data_param[:,3]
|
|
1405
|
dataOut.data_STD = data_param[:,3]
|
|
|
1406
|
return
|
|
1406
|
return
|
|
|
1407
|
|
|
1407
|
|
|
|
1408
|
def __calculateMoments(self, oldspec, oldfreq, n0,
|
|
1408
|
def __calculateMoments(self, oldspec, oldfreq, n0,
|
|
|
1409
|
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
|
|
1409
|
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
|
|
|
1410
|
|
|
1410
|
|
|
|
1411
|
if (nicoh == None): nicoh = 1
|
|
1411
|
if (nicoh == None): nicoh = 1
|
|
|
1412
|
if (graph == None): graph = 0
|
|
1412
|
if (graph == None): graph = 0
|
|
|
1413
|
if (smooth == None): smooth = 0
|
|
1413
|
if (smooth == None): smooth = 0
|
|
|
1414
|
elif (self.smooth < 3): smooth = 0
|
|
1414
|
elif (self.smooth < 3): smooth = 0
|
|
|
1415
|
|
|
1415
|
|
|
|
1416
|
if (type1 == None): type1 = 0
|
|
1416
|
if (type1 == None): type1 = 0
|
|
|
1417
|
if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1
|
|
1417
|
if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1
|
|
|
1418
|
if (snrth == None): snrth = -3
|
|
1418
|
if (snrth == None): snrth = -3
|
|
|
1419
|
if (dc == None): dc = 0
|
|
1419
|
if (dc == None): dc = 0
|
|
|
1420
|
if (aliasing == None): aliasing = 0
|
|
1420
|
if (aliasing == None): aliasing = 0
|
|
|
1421
|
if (oldfd == None): oldfd = 0
|
|
1421
|
if (oldfd == None): oldfd = 0
|
|
|
1422
|
if (wwauto == None): wwauto = 0
|
|
1422
|
if (wwauto == None): wwauto = 0
|
|
|
1423
|
|
|
1423
|
|
|
|
1424
|
if (n0 < 1.e-20): n0 = 1.e-20
|
|
1424
|
if (n0 < 1.e-20): n0 = 1.e-20
|
|
|
1425
|
|
|
1425
|
|
|
|
1426
|
freq = oldfreq
|
|
1426
|
freq = oldfreq
|
|
|
1427
|
vec_power = numpy.zeros(oldspec.shape[1])
|
|
1427
|
vec_power = numpy.zeros(oldspec.shape[1])
|
|
|
1428
|
vec_fd = numpy.zeros(oldspec.shape[1])
|
|
1428
|
vec_fd = numpy.zeros(oldspec.shape[1])
|
|
|
1429
|
vec_w = numpy.zeros(oldspec.shape[1])
|
|
1429
|
vec_w = numpy.zeros(oldspec.shape[1])
|
|
|
1430
|
vec_snr = numpy.zeros(oldspec.shape[1])
|
|
1430
|
vec_snr = numpy.zeros(oldspec.shape[1])
|
|
|
1431
|
|
|
1431
|
|
|
|
1432
|
for ind in range(oldspec.shape[1]):
|
|
1432
|
for ind in range(oldspec.shape[1]):
|
|
|
1433
|
|
|
1433
|
|
|
|
1434
|
spec = oldspec[:,ind]
|
|
1434
|
spec = oldspec[:,ind]
|
|
|
1435
|
aux = spec*fwindow
|
|
1435
|
aux = spec*fwindow
|
|
|
1436
|
max_spec = aux.max()
|
|
1436
|
max_spec = aux.max()
|
|
|
1437
|
m = list(aux).index(max_spec)
|
|
1437
|
m = list(aux).index(max_spec)
|
|
|
1438
|
|
|
1438
|
|
|
|
1439
|
#Smooth
|
|
1439
|
#Smooth
|
|
|
1440
|
if (smooth == 0): spec2 = spec
|
|
1440
|
if (smooth == 0): spec2 = spec
|
|
|
1441
|
else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
|
|
1441
|
else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
|
|
|
1442
|
|
|
1442
|
|
|
|
1443
|
# Calculo de Momentos
|
|
1443
|
# Calculo de Momentos
|
|
|
1444
|
bb = spec2[range(m,spec2.size)]
|
|
1444
|
bb = spec2[range(m,spec2.size)]
|
|
|
1445
|
bb = (bb<n0).nonzero()
|
|
1445
|
bb = (bb<n0).nonzero()
|
|
|
1446
|
bb = bb[0]
|
|
1446
|
bb = bb[0]
|
|
|
1447
|
|
|
1447
|
|
|
|
1448
|
ss = spec2[range(0,m + 1)]
|
|
1448
|
ss = spec2[range(0,m + 1)]
|
|
|
1449
|
ss = (ss<n0).nonzero()
|
|
1449
|
ss = (ss<n0).nonzero()
|
|
|
1450
|
ss = ss[0]
|
|
1450
|
ss = ss[0]
|
|
|
1451
|
|
|
1451
|
|
|
|
1452
|
if (bb.size == 0):
|
|
1452
|
if (bb.size == 0):
|
|
|
1453
|
bb0 = spec.size - 1 - m
|
|
1453
|
bb0 = spec.size - 1 - m
|
|
|
1454
|
else:
|
|
1454
|
else:
|
|
|
1455
|
bb0 = bb[0] - 1
|
|
1455
|
bb0 = bb[0] - 1
|
|
|
1456
|
if (bb0 < 0):
|
|
1456
|
if (bb0 < 0):
|
|
|
1457
|
bb0 = 0
|
|
1457
|
bb0 = 0
|
|
|
1458
|
|
|
1458
|
|
|
|
1459
|
if (ss.size == 0): ss1 = 1
|
|
1459
|
if (ss.size == 0): ss1 = 1
|
|
|
1460
|
else: ss1 = max(ss) + 1
|
|
1460
|
else: ss1 = max(ss) + 1
|
|
|
1461
|
|
|
1461
|
|
|
|
1462
|
if (ss1 > m): ss1 = m
|
|
1462
|
if (ss1 > m): ss1 = m
|
|
|
1463
|
|
|
1463
|
|
|
|
1464
|
valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1
|
|
1464
|
valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1
|
|
|
1465
|
power = ((spec2[valid] - n0)*fwindow[valid]).sum()
|
|
1465
|
power = ((spec2[valid] - n0)*fwindow[valid]).sum()
|
|
|
1466
|
fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power
|
|
1466
|
fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power
|
|
|
1467
|
w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power)
|
|
1467
|
w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power)
|
|
|
1468
|
snr = (spec2.mean()-n0)/n0
|
|
1468
|
snr = (spec2.mean()-n0)/n0
|
|
|
1469
|
|
|
1469
|
|
|
|
1470
|
if (snr < 1.e-20) :
|
|
1470
|
if (snr < 1.e-20) :
|
|
|
1471
|
snr = 1.e-20
|
|
1471
|
snr = 1.e-20
|
|
|
1472
|
|
|
1472
|
|
|
|
1473
|
vec_power[ind] = power
|
|
1473
|
vec_power[ind] = power
|
|
|
1474
|
vec_fd[ind] = fd
|
|
1474
|
vec_fd[ind] = fd
|
|
|
1475
|
vec_w[ind] = w
|
|
1475
|
vec_w[ind] = w
|
|
|
1476
|
vec_snr[ind] = snr
|
|
1476
|
vec_snr[ind] = snr
|
|
|
1477
|
|
|
1477
|
|
|
|
1478
|
moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
|
|
1478
|
moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
|
|
|
1479
|
return moments
|
|
1479
|
return moments
|
|
|
1480
|
|
|
1480
|
|
|
|
1481
|
#------------------ Get SA Parameters --------------------------
|
|
1481
|
#------------------ Get SA Parameters --------------------------
|
|
|
1482
|
|
|
1482
|
|
|
|
1483
|
def GetSAParameters(self):
|
|
1483
|
def GetSAParameters(self):
|
|
|
1484
|
#SA en frecuencia
|
|
1484
|
#SA en frecuencia
|
|
|
1485
|
pairslist = self.dataOut.groupList
|
|
1485
|
pairslist = self.dataOut.groupList
|
|
|
1486
|
num_pairs = len(pairslist)
|
|
1486
|
num_pairs = len(pairslist)
|
|
|
1487
|
|
|
1487
|
|
|
|
1488
|
vel = self.dataOut.abscissaList
|
|
1488
|
vel = self.dataOut.abscissaList
|
|
|
1489
|
spectra = self.dataOut.data_pre
|
|
1489
|
spectra = self.dataOut.data_pre
|
|
|
1490
|
cspectra = self.dataIn.data_cspc
|
|
1490
|
cspectra = self.dataIn.data_cspc
|
|
|
1491
|
delta_v = vel[1] - vel[0]
|
|
1491
|
delta_v = vel[1] - vel[0]
|
|
|
1492
|
|
|
1492
|
|
|
|
1493
|
#Calculating the power spectrum
|
|
1493
|
#Calculating the power spectrum
|
|
|
1494
|
spc_pow = numpy.sum(spectra, 3)*delta_v
|
|
1494
|
spc_pow = numpy.sum(spectra, 3)*delta_v
|
|
|
1495
|
#Normalizing Spectra
|
|
1495
|
#Normalizing Spectra
|
|
|
1496
|
norm_spectra = spectra/spc_pow
|
|
1496
|
norm_spectra = spectra/spc_pow
|
|
|
1497
|
#Calculating the norm_spectra at peak
|
|
1497
|
#Calculating the norm_spectra at peak
|
|
|
1498
|
max_spectra = numpy.max(norm_spectra, 3)
|
|
1498
|
max_spectra = numpy.max(norm_spectra, 3)
|
|
|
1499
|
|
|
1499
|
|
|
|
1500
|
#Normalizing Cross Spectra
|
|
1500
|
#Normalizing Cross Spectra
|
|
|
1501
|
norm_cspectra = numpy.zeros(cspectra.shape)
|
|
1501
|
norm_cspectra = numpy.zeros(cspectra.shape)
|
|
|
1502
|
|
|
1502
|
|
|
|
1503
|
for i in range(num_chan):
|
|
1503
|
for i in range(num_chan):
|
|
|
1504
|
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
|
|
1504
|
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
|
|
|
1505
|
|
|
1505
|
|
|
|
1506
|
max_cspectra = numpy.max(norm_cspectra,2)
|
|
1506
|
max_cspectra = numpy.max(norm_cspectra,2)
|
|
|
1507
|
max_cspectra_index = numpy.argmax(norm_cspectra, 2)
|
|
1507
|
max_cspectra_index = numpy.argmax(norm_cspectra, 2)
|
|
|
1508
|
|
|
1508
|
|
|
|
1509
|
for i in range(num_pairs):
|
|
1509
|
for i in range(num_pairs):
|
|
|
1510
|
cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
|
|
1510
|
cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
|
|
|
1511
|
#------------------- Get Lags ----------------------------------
|
|
1511
|
#------------------- Get Lags ----------------------------------
|
|
|
1512
|
|
|
1512
|
|
|
|
1513
|
class SALags(Operation):
|
|
1513
|
class SALags(Operation):
|
|
|
1514
|
'''
|
|
1514
|
'''
|
|
|
1515
|
Function GetMoments()
|
|
1515
|
Function GetMoments()
|
|
|
1516
|
|
|
1516
|
|
|
|
1517
|
Input:
|
|
1517
|
Input:
|
|
|
1518
|
self.dataOut.data_pre
|
|
1518
|
self.dataOut.data_pre
|
|
|
1519
|
self.dataOut.abscissaList
|
|
1519
|
self.dataOut.abscissaList
|
|
|
1520
|
self.dataOut.noise
|
|
1520
|
self.dataOut.noise
|
|
|
1521
|
self.dataOut.normFactor
|
|
1521
|
self.dataOut.normFactor
|
|
|
1522
|
self.dataOut.data_SNR
|
|
1522
|
self.dataOut.data_SNR
|
|
|
1523
|
self.dataOut.groupList
|
|
1523
|
self.dataOut.groupList
|
|
|
1524
|
self.dataOut.nChannels
|
|
1524
|
self.dataOut.nChannels
|
|
|
1525
|
|
|
1525
|
|
|
|
1526
|
Affected:
|
|
1526
|
Affected:
|
|
|
1527
|
self.dataOut.data_param
|
|
1527
|
self.dataOut.data_param
|
|
|
1528
|
|
|
1528
|
|
|
|
1529
|
'''
|
|
1529
|
'''
|
|
|
1530
|
def run(self, dataOut):
|
|
1530
|
def run(self, dataOut):
|
|
|
1531
|
data_acf = dataOut.data_pre[0]
|
|
1531
|
data_acf = dataOut.data_pre[0]
|
|
|
1532
|
data_ccf = dataOut.data_pre[1]
|
|
1532
|
data_ccf = dataOut.data_pre[1]
|
|
|
1533
|
normFactor_acf = dataOut.normFactor[0]
|
|
1533
|
normFactor_acf = dataOut.normFactor[0]
|
|
|
1534
|
normFactor_ccf = dataOut.normFactor[1]
|
|
1534
|
normFactor_ccf = dataOut.normFactor[1]
|
|
|
1535
|
pairs_acf = dataOut.groupList[0]
|
|
1535
|
pairs_acf = dataOut.groupList[0]
|
|
|
1536
|
pairs_ccf = dataOut.groupList[1]
|
|
1536
|
pairs_ccf = dataOut.groupList[1]
|
|
|
1537
|
|
|
1537
|
|
|
|
1538
|
nHeights = dataOut.nHeights
|
|
1538
|
nHeights = dataOut.nHeights
|
|
|
1539
|
absc = dataOut.abscissaList
|
|
1539
|
absc = dataOut.abscissaList
|
|
|
1540
|
noise = dataOut.noise
|
|
1540
|
noise = dataOut.noise
|
|
|
1541
|
SNR = dataOut.data_SNR
|
|
1541
|
SNR = dataOut.data_SNR
|
|
|
1542
|
nChannels = dataOut.nChannels
|
|
1542
|
nChannels = dataOut.nChannels
|
|
|
1543
|
# pairsList = dataOut.groupList
|
|
1543
|
# pairsList = dataOut.groupList
|
|
|
1544
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
|
|
1544
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
|
|
|
1545
|
|
|
1545
|
|
|
|
1546
|
for l in range(len(pairs_acf)):
|
|
1546
|
for l in range(len(pairs_acf)):
|
|
|
1547
|
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
|
|
1547
|
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
|
|
|
1548
|
|
|
1548
|
|
|
|
1549
|
for l in range(len(pairs_ccf)):
|
|
1549
|
for l in range(len(pairs_ccf)):
|
|
|
1550
|
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
|
|
1550
|
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
|
|
|
1551
|
|
|
1551
|
|
|
|
1552
|
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
|
|
1552
|
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
|
|
|
1553
|
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
|
|
1553
|
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
|
|
|
1554
|
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
|
|
1554
|
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
|
|
|
1555
|
return
|
|
1555
|
return
|
|
|
1556
|
|
|
1556
|
|
|
|
1557
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
1557
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
|
1558
|
#
|
|
1558
|
#
|
|
|
1559
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
1559
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
|
1560
|
#
|
|
1560
|
#
|
|
|
1561
|
# for l in range(len(pairsList)):
|
|
1561
|
# for l in range(len(pairsList)):
|
|
|
1562
|
# firstChannel = pairsList[l][0]
|
|
1562
|
# firstChannel = pairsList[l][0]
|
|
|
1563
|
# secondChannel = pairsList[l][1]
|
|
1563
|
# secondChannel = pairsList[l][1]
|
|
|
1564
|
#
|
|
1564
|
#
|
|
|
1565
|
# #Obteniendo pares de Autocorrelacion
|
|
1565
|
# #Obteniendo pares de Autocorrelacion
|
|
|
1566
|
# if firstChannel == secondChannel:
|
|
1566
|
# if firstChannel == secondChannel:
|
|
|
1567
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
1567
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
|
1568
|
#
|
|
1568
|
#
|
|
|
1569
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
1569
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
|
1570
|
#
|
|
1570
|
#
|
|
|
1571
|
# pairsCrossCorr = range(len(pairsList))
|
|
1571
|
# pairsCrossCorr = range(len(pairsList))
|
|
|
1572
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
1572
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
|
1573
|
#
|
|
1573
|
#
|
|
|
1574
|
# return pairsAutoCorr, pairsCrossCorr
|
|
1574
|
# return pairsAutoCorr, pairsCrossCorr
|
|
|
1575
|
|
|
1575
|
|
|
|
1576
|
def __calculateTaus(self, data_acf, data_ccf, lagRange):
|
|
1576
|
def __calculateTaus(self, data_acf, data_ccf, lagRange):
|
|
|
1577
|
|
|
1577
|
|
|
|
1578
|
lag0 = data_acf.shape[1]/2
|
|
1578
|
lag0 = data_acf.shape[1]/2
|
|
|
1579
|
#Funcion de Autocorrelacion
|
|
1579
|
#Funcion de Autocorrelacion
|
|
|
1580
|
mean_acf = stats.nanmean(data_acf, axis = 0)
|
|
1580
|
mean_acf = stats.nanmean(data_acf, axis = 0)
|
|
|
1581
|
|
|
1581
|
|
|
|
1582
|
#Obtencion Indice de TauCross
|
|
1582
|
#Obtencion Indice de TauCross
|
|
|
1583
|
ind_ccf = data_ccf.argmax(axis = 1)
|
|
1583
|
ind_ccf = data_ccf.argmax(axis = 1)
|
|
|
1584
|
#Obtencion Indice de TauAuto
|
|
1584
|
#Obtencion Indice de TauAuto
|
|
|
1585
|
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
|
|
1585
|
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
|
|
|
1586
|
ccf_lag0 = data_ccf[:,lag0,:]
|
|
1586
|
ccf_lag0 = data_ccf[:,lag0,:]
|
|
|
1587
|
|
|
1587
|
|
|
|
1588
|
for i in range(ccf_lag0.shape[0]):
|
|
1588
|
for i in range(ccf_lag0.shape[0]):
|
|
|
1589
|
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
|
|
1589
|
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
|
|
|
1590
|
|
|
1590
|
|
|
|
1591
|
#Obtencion de TauCross y TauAuto
|
|
1591
|
#Obtencion de TauCross y TauAuto
|
|
|
1592
|
tau_ccf = lagRange[ind_ccf]
|
|
1592
|
tau_ccf = lagRange[ind_ccf]
|
|
|
1593
|
tau_acf = lagRange[ind_acf]
|
|
1593
|
tau_acf = lagRange[ind_acf]
|
|
|
1594
|
|
|
1594
|
|
|
|
1595
|
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
|
|
1595
|
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
|
|
|
1596
|
|
|
1596
|
|
|
|
1597
|
tau_ccf[Nan1,Nan2] = numpy.nan
|
|
1597
|
tau_ccf[Nan1,Nan2] = numpy.nan
|
|
|
1598
|
tau_acf[Nan1,Nan2] = numpy.nan
|
|
1598
|
tau_acf[Nan1,Nan2] = numpy.nan
|
|
|
1599
|
tau = numpy.vstack((tau_ccf,tau_acf))
|
|
1599
|
tau = numpy.vstack((tau_ccf,tau_acf))
|
|
|
1600
|
|
|
1600
|
|
|
|
1601
|
return tau
|
|
1601
|
return tau
|
|
|
1602
|
|
|
1602
|
|
|
|
1603
|
def __calculateLag1Phase(self, data, lagTRange):
|
|
1603
|
def __calculateLag1Phase(self, data, lagTRange):
|
|
|
1604
|
data1 = stats.nanmean(data, axis = 0)
|
|
1604
|
data1 = stats.nanmean(data, axis = 0)
|
|
|
1605
|
lag1 = numpy.where(lagTRange == 0)[0][0] + 1
|
|
1605
|
lag1 = numpy.where(lagTRange == 0)[0][0] + 1
|
|
|
1606
|
|
|
1606
|
|
|
|
1607
|
phase = numpy.angle(data1[lag1,:])
|
|
1607
|
phase = numpy.angle(data1[lag1,:])
|
|
|
1608
|
|
|
1608
|
|
|
|
1609
|
return phase
|
|
1609
|
return phase
|
|
|
1610
|
|
|
1610
|
|
|
|
1611
|
class SpectralFitting(Operation):
|
|
1611
|
class SpectralFitting(Operation):
|
|
|
1612
|
'''
|
|
1612
|
'''
|
|
|
1613
|
Function GetMoments()
|
|
1613
|
Function GetMoments()
|
|
|
1614
|
|
|
1614
|
|
|
|
1615
|
Input:
|
|
1615
|
Input:
|
|
|
1616
|
Output:
|
|
1616
|
Output:
|
|
|
1617
|
Variables modified:
|
|
1617
|
Variables modified:
|
|
|
1618
|
'''
|
|
1618
|
'''
|
|
|
1619
|
|
|
1619
|
|
|
|
1620
|
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
|
|
1620
|
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
|
|
|
1621
|
|
|
1621
|
|
|
|
1622
|
|
|
1622
|
|
|
|
1623
|
if path != None:
|
|
1623
|
if path != None:
|
|
|
1624
|
sys.path.append(path)
|
|
1624
|
sys.path.append(path)
|
|
|
1625
|
self.dataOut.library = importlib.import_module(file)
|
|
1625
|
self.dataOut.library = importlib.import_module(file)
|
|
|
1626
|
|
|
1626
|
|
|
|
1627
|
#To be inserted as a parameter
|
|
1627
|
#To be inserted as a parameter
|
|
|
1628
|
groupArray = numpy.array(groupList)
|
|
1628
|
groupArray = numpy.array(groupList)
|
|
|
1629
|
# groupArray = numpy.array([[0,1],[2,3]])
|
|
1629
|
# groupArray = numpy.array([[0,1],[2,3]])
|
|
|
1630
|
self.dataOut.groupList = groupArray
|
|
1630
|
self.dataOut.groupList = groupArray
|
|
|
1631
|
|
|
1631
|
|
|
|
1632
|
nGroups = groupArray.shape[0]
|
|
1632
|
nGroups = groupArray.shape[0]
|
|
|
1633
|
nChannels = self.dataIn.nChannels
|
|
1633
|
nChannels = self.dataIn.nChannels
|
|
|
1634
|
nHeights=self.dataIn.heightList.size
|
|
1634
|
nHeights=self.dataIn.heightList.size
|
|
|
1635
|
|
|
1635
|
|
|
|
1636
|
#Parameters Array
|
|
1636
|
#Parameters Array
|
|
|
1637
|
self.dataOut.data_param = None
|
|
1637
|
self.dataOut.data_param = None
|
|
|
1638
|
|
|
1638
|
|
|
|
1639
|
#Set constants
|
|
1639
|
#Set constants
|
|
|
1640
|
constants = self.dataOut.library.setConstants(self.dataIn)
|
|
1640
|
constants = self.dataOut.library.setConstants(self.dataIn)
|
|
|
1641
|
self.dataOut.constants = constants
|
|
1641
|
self.dataOut.constants = constants
|
|
|
1642
|
M = self.dataIn.normFactor
|
|
1642
|
M = self.dataIn.normFactor
|
|
|
1643
|
N = self.dataIn.nFFTPoints
|
|
1643
|
N = self.dataIn.nFFTPoints
|
|
|
1644
|
ippSeconds = self.dataIn.ippSeconds
|
|
1644
|
ippSeconds = self.dataIn.ippSeconds
|
|
|
1645
|
K = self.dataIn.nIncohInt
|
|
1645
|
K = self.dataIn.nIncohInt
|
|
|
1646
|
pairsArray = numpy.array(self.dataIn.pairsList)
|
|
1646
|
pairsArray = numpy.array(self.dataIn.pairsList)
|
|
|
1647
|
|
|
1647
|
|
|
|
1648
|
#List of possible combinations
|
|
1648
|
#List of possible combinations
|
|
|
1649
|
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
|
|
1649
|
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
|
|
|
1650
|
indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
|
|
1650
|
indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
|
|
|
1651
|
|
|
1651
|
|
|
|
1652
|
if getSNR:
|
|
1652
|
if getSNR:
|
|
|
1653
|
listChannels = groupArray.reshape((groupArray.size))
|
|
1653
|
listChannels = groupArray.reshape((groupArray.size))
|
|
|
1654
|
listChannels.sort()
|
|
1654
|
listChannels.sort()
|
|
|
1655
|
noise = self.dataIn.getNoise()
|
|
1655
|
noise = self.dataIn.getNoise()
|
|
|
1656
|
self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
|
|
1656
|
self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
|
|
|
1657
|
|
|
1657
|
|
|
|
1658
|
for i in range(nGroups):
|
|
1658
|
for i in range(nGroups):
|
|
|
1659
|
coord = groupArray[i,:]
|
|
1659
|
coord = groupArray[i,:]
|
|
|
1660
|
|
|
1660
|
|
|
|
1661
|
#Input data array
|
|
1661
|
#Input data array
|
|
|
1662
|
data = self.dataIn.data_spc[coord,:,:]/(M*N)
|
|
1662
|
data = self.dataIn.data_spc[coord,:,:]/(M*N)
|
|
|
1663
|
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
|
|
1663
|
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
|
|
|
1664
|
|
|
1664
|
|
|
|
1665
|
#Cross Spectra data array for Covariance Matrixes
|
|
1665
|
#Cross Spectra data array for Covariance Matrixes
|
|
|
1666
|
ind = 0
|
|
1666
|
ind = 0
|
|
|
1667
|
for pairs in listComb:
|
|
1667
|
for pairs in listComb:
|
|
|
1668
|
pairsSel = numpy.array([coord[x],coord[y]])
|
|
1668
|
pairsSel = numpy.array([coord[x],coord[y]])
|
|
|
1669
|
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
|
|
1669
|
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
|
|
|
1670
|
ind += 1
|
|
1670
|
ind += 1
|
|
|
1671
|
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
|
|
1671
|
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
|
|
|
1672
|
dataCross = dataCross**2/K
|
|
1672
|
dataCross = dataCross**2/K
|
|
|
1673
|
|
|
1673
|
|
|
|
1674
|
for h in range(nHeights):
|
|
1674
|
for h in range(nHeights):
|
|
|
1675
|
# print self.dataOut.heightList[h]
|
|
1675
|
# print self.dataOut.heightList[h]
|
|
|
1676
|
|
|
1676
|
|
|
|
1677
|
#Input
|
|
1677
|
#Input
|
|
|
1678
|
d = data[:,h]
|
|
1678
|
d = data[:,h]
|
|
|
1679
|
|
|
1679
|
|
|
|
1680
|
#Covariance Matrix
|
|
1680
|
#Covariance Matrix
|
|
|
1681
|
D = numpy.diag(d**2/K)
|
|
1681
|
D = numpy.diag(d**2/K)
|
|
|
1682
|
ind = 0
|
|
1682
|
ind = 0
|
|
|
1683
|
for pairs in listComb:
|
|
1683
|
for pairs in listComb:
|
|
|
1684
|
#Coordinates in Covariance Matrix
|
|
1684
|
#Coordinates in Covariance Matrix
|
|
|
1685
|
x = pairs[0]
|
|
1685
|
x = pairs[0]
|
|
|
1686
|
y = pairs[1]
|
|
1686
|
y = pairs[1]
|
|
|
1687
|
#Channel Index
|
|
1687
|
#Channel Index
|
|
|
1688
|
S12 = dataCross[ind,:,h]
|
|
1688
|
S12 = dataCross[ind,:,h]
|
|
|
1689
|
D12 = numpy.diag(S12)
|
|
1689
|
D12 = numpy.diag(S12)
|
|
|
1690
|
#Completing Covariance Matrix with Cross Spectras
|
|
1690
|
#Completing Covariance Matrix with Cross Spectras
|
|
|
1691
|
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
|
|
1691
|
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
|
|
|
1692
|
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
|
|
1692
|
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
|
|
|
1693
|
ind += 1
|
|
1693
|
ind += 1
|
|
|
1694
|
Dinv=numpy.linalg.inv(D)
|
|
1694
|
Dinv=numpy.linalg.inv(D)
|
|
|
1695
|
L=numpy.linalg.cholesky(Dinv)
|
|
1695
|
L=numpy.linalg.cholesky(Dinv)
|
|
|
1696
|
LT=L.T
|
|
1696
|
LT=L.T
|
|
|
1697
|
|
|
1697
|
|
|
|
1698
|
dp = numpy.dot(LT,d)
|
|
1698
|
dp = numpy.dot(LT,d)
|
|
|
1699
|
|
|
1699
|
|
|
|
1700
|
#Initial values
|
|
1700
|
#Initial values
|
|
|
1701
|
data_spc = self.dataIn.data_spc[coord,:,h]
|
|
1701
|
data_spc = self.dataIn.data_spc[coord,:,h]
|
|
|
1702
|
|
|
1702
|
|
|
|
1703
|
if (h>0)and(error1[3]<5):
|
|
1703
|
if (h>0)and(error1[3]<5):
|
|
|
1704
|
p0 = self.dataOut.data_param[i,:,h-1]
|
|
1704
|
p0 = self.dataOut.data_param[i,:,h-1]
|
|
|
1705
|
else:
|
|
1705
|
else:
|
|
|
1706
|
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
|
|
1706
|
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
|
|
|
1707
|
|
|
1707
|
|
|
|
1708
|
try:
|
|
1708
|
try:
|
|
|
1709
|
#Least Squares
|
|
1709
|
#Least Squares
|
|
|
1710
|
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
|
|
1710
|
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
|
|
|
1711
|
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
|
|
1711
|
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
|
|
|
1712
|
#Chi square error
|
|
1712
|
#Chi square error
|
|
|
1713
|
error0 = numpy.sum(infodict['fvec']**2)/(2*N)
|
|
1713
|
error0 = numpy.sum(infodict['fvec']**2)/(2*N)
|
|
|
1714
|
#Error with Jacobian
|
|
1714
|
#Error with Jacobian
|
|
|
1715
|
error1 = self.dataOut.library.errorFunction(minp,constants,LT)
|
|
1715
|
error1 = self.dataOut.library.errorFunction(minp,constants,LT)
|
|
|
1716
|
except:
|
|
1716
|
except:
|
|
|
1717
|
minp = p0*numpy.nan
|
|
1717
|
minp = p0*numpy.nan
|
|
|
1718
|
error0 = numpy.nan
|
|
1718
|
error0 = numpy.nan
|
|
|
1719
|
error1 = p0*numpy.nan
|
|
1719
|
error1 = p0*numpy.nan
|
|
|
1720
|
|
|
1720
|
|
|
|
1721
|
#Save
|
|
1721
|
#Save
|
|
|
1722
|
if self.dataOut.data_param == None:
|
|
1722
|
if self.dataOut.data_param == None:
|
|
|
1723
|
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
|
|
1723
|
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
|
|
|
1724
|
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
|
|
1724
|
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
|
|
|
1725
|
|
|
1725
|
|
|
|
1726
|
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
|
|
1726
|
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
|
|
|
1727
|
self.dataOut.data_param[i,:,h] = minp
|
|
1727
|
self.dataOut.data_param[i,:,h] = minp
|
|
|
1728
|
return
|
|
1728
|
return
|
|
|
1729
|
|
|
1729
|
|
|
|
1730
|
def __residFunction(self, p, dp, LT, constants):
|
|
1730
|
def __residFunction(self, p, dp, LT, constants):
|
|
|
1731
|
|
|
1731
|
|
|
|
1732
|
fm = self.dataOut.library.modelFunction(p, constants)
|
|
1732
|
fm = self.dataOut.library.modelFunction(p, constants)
|
|
|
1733
|
fmp=numpy.dot(LT,fm)
|
|
1733
|
fmp=numpy.dot(LT,fm)
|
|
|
1734
|
|
|
1734
|
|
|
|
1735
|
return dp-fmp
|
|
1735
|
return dp-fmp
|
|
|
1736
|
|
|
1736
|
|
|
|
1737
|
def __getSNR(self, z, noise):
|
|
1737
|
def __getSNR(self, z, noise):
|
|
|
1738
|
|
|
1738
|
|
|
|
1739
|
avg = numpy.average(z, axis=1)
|
|
1739
|
avg = numpy.average(z, axis=1)
|
|
|
1740
|
SNR = (avg.T-noise)/noise
|
|
1740
|
SNR = (avg.T-noise)/noise
|
|
|
1741
|
SNR = SNR.T
|
|
1741
|
SNR = SNR.T
|
|
|
1742
|
return SNR
|
|
1742
|
return SNR
|
|
|
1743
|
|
|
1743
|
|
|
|
1744
|
def __chisq(p,chindex,hindex):
|
|
1744
|
def __chisq(p,chindex,hindex):
|
|
|
1745
|
#similar to Resid but calculates CHI**2
|
|
1745
|
#similar to Resid but calculates CHI**2
|
|
|
1746
|
[LT,d,fm]=setupLTdfm(p,chindex,hindex)
|
|
1746
|
[LT,d,fm]=setupLTdfm(p,chindex,hindex)
|
|
|
1747
|
dp=numpy.dot(LT,d)
|
|
1747
|
dp=numpy.dot(LT,d)
|
|
|
1748
|
fmp=numpy.dot(LT,fm)
|
|
1748
|
fmp=numpy.dot(LT,fm)
|
|
|
1749
|
chisq=numpy.dot((dp-fmp).T,(dp-fmp))
|
|
1749
|
chisq=numpy.dot((dp-fmp).T,(dp-fmp))
|
|
|
1750
|
return chisq
|
|
1750
|
return chisq
|
|
|
1751
|
|
|
1751
|
|
|
|
1752
|
class WindProfiler(Operation):
|
|
1752
|
class WindProfiler(Operation):
|
|
|
1753
|
|
|
1753
|
|
|
|
1754
|
__isConfig = False
|
|
1754
|
__isConfig = False
|
|
|
1755
|
|
|
1755
|
|
|
|
1756
|
__initime = None
|
|
1756
|
__initime = None
|
|
|
1757
|
__lastdatatime = None
|
|
1757
|
__lastdatatime = None
|
|
|
1758
|
__integrationtime = None
|
|
1758
|
__integrationtime = None
|
|
|
1759
|
|
|
1759
|
|
|
|
1760
|
__buffer = None
|
|
1760
|
__buffer = None
|
|
|
1761
|
|
|
1761
|
|
|
|
1762
|
__dataReady = False
|
|
1762
|
__dataReady = False
|
|
|
1763
|
|
|
1763
|
|
|
|
1764
|
__firstdata = None
|
|
1764
|
__firstdata = None
|
|
|
1765
|
|
|
1765
|
|
|
|
1766
|
n = None
|
|
1766
|
n = None
|
|
|
1767
|
|
|
1767
|
|
|
|
1768
|
def __init__(self):
|
|
1768
|
def __init__(self, **kwargs):
|
|
|
1769
|
Operation.__init__(self)
|
|
1769
|
Operation.__init__(self, **kwargs)
|
|
|
1770
|
|
|
1770
|
|
|
|
1771
|
def __calculateCosDir(self, elev, azim):
|
|
1771
|
def __calculateCosDir(self, elev, azim):
|
|
|
1772
|
zen = (90 - elev)*numpy.pi/180
|
|
1772
|
zen = (90 - elev)*numpy.pi/180
|
|
|
1773
|
azim = azim*numpy.pi/180
|
|
1773
|
azim = azim*numpy.pi/180
|
|
|
1774
|
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
|
|
1774
|
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
|
|
|
1775
|
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
|
|
1775
|
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
|
|
|
1776
|
|
|
1776
|
|
|
|
1777
|
signX = numpy.sign(numpy.cos(azim))
|
|
1777
|
signX = numpy.sign(numpy.cos(azim))
|
|
|
1778
|
signY = numpy.sign(numpy.sin(azim))
|
|
1778
|
signY = numpy.sign(numpy.sin(azim))
|
|
|
1779
|
|
|
1779
|
|
|
|
1780
|
cosDirX = numpy.copysign(cosDirX, signX)
|
|
1780
|
cosDirX = numpy.copysign(cosDirX, signX)
|
|
|
1781
|
cosDirY = numpy.copysign(cosDirY, signY)
|
|
1781
|
cosDirY = numpy.copysign(cosDirY, signY)
|
|
|
1782
|
return cosDirX, cosDirY
|
|
1782
|
return cosDirX, cosDirY
|
|
|
1783
|
|
|
1783
|
|
|
|
1784
|
def __calculateAngles(self, theta_x, theta_y, azimuth):
|
|
1784
|
def __calculateAngles(self, theta_x, theta_y, azimuth):
|
|
|
1785
|
|
|
1785
|
|
|
|
1786
|
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
|
|
1786
|
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
|
|
|
1787
|
zenith_arr = numpy.arccos(dir_cosw)
|
|
1787
|
zenith_arr = numpy.arccos(dir_cosw)
|
|
|
1788
|
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
|
|
1788
|
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
|
|
|
1789
|
|
|
1789
|
|
|
|
1790
|
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
|
|
1790
|
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
|
|
|
1791
|
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
|
|
1791
|
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
|
|
|
1792
|
|
|
1792
|
|
|
|
1793
|
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
|
|
1793
|
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
|
|
|
1794
|
|
|
1794
|
|
|
|
1795
|
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
|
|
1795
|
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
|
|
|
1796
|
|
|
1796
|
|
|
|
1797
|
#
|
|
1797
|
#
|
|
|
1798
|
if horOnly:
|
|
1798
|
if horOnly:
|
|
|
1799
|
A = numpy.c_[dir_cosu,dir_cosv]
|
|
1799
|
A = numpy.c_[dir_cosu,dir_cosv]
|
|
|
1800
|
else:
|
|
1800
|
else:
|
|
|
1801
|
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
|
|
1801
|
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
|
|
|
1802
|
A = numpy.asmatrix(A)
|
|
1802
|
A = numpy.asmatrix(A)
|
|
|
1803
|
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
|
|
1803
|
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
|
|
|
1804
|
|
|
1804
|
|
|
|
1805
|
return A1
|
|
1805
|
return A1
|
|
|
1806
|
|
|
1806
|
|
|
|
1807
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
1807
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
|
1808
|
listPhi = phi.tolist()
|
|
1808
|
listPhi = phi.tolist()
|
|
|
1809
|
maxid = listPhi.index(max(listPhi))
|
|
1809
|
maxid = listPhi.index(max(listPhi))
|
|
|
1810
|
minid = listPhi.index(min(listPhi))
|
|
1810
|
minid = listPhi.index(min(listPhi))
|
|
|
1811
|
|
|
1811
|
|
|
|
1812
|
rango = range(len(phi))
|
|
1812
|
rango = range(len(phi))
|
|
|
1813
|
# rango = numpy.delete(rango,maxid)
|
|
1813
|
# rango = numpy.delete(rango,maxid)
|
|
|
1814
|
|
|
1814
|
|
|
|
1815
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
1815
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
|
1816
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
1816
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
|
1817
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
1817
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
|
1818
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
1818
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
|
1819
|
|
|
1819
|
|
|
|
1820
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
1820
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
1821
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
1821
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
1822
|
|
|
1822
|
|
|
|
1823
|
for i in rango:
|
|
1823
|
for i in rango:
|
|
|
1824
|
x = heiRang*math.cos(phi[i])
|
|
1824
|
x = heiRang*math.cos(phi[i])
|
|
|
1825
|
y1 = velRadial[i,:]
|
|
1825
|
y1 = velRadial[i,:]
|
|
|
1826
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
1826
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
|
1827
|
|
|
1827
|
|
|
|
1828
|
x1 = heiRang1
|
|
1828
|
x1 = heiRang1
|
|
|
1829
|
y11 = f1(x1)
|
|
1829
|
y11 = f1(x1)
|
|
|
1830
|
|
|
1830
|
|
|
|
1831
|
y2 = SNR[i,:]
|
|
1831
|
y2 = SNR[i,:]
|
|
|
1832
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
1832
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
|
1833
|
y21 = f2(x1)
|
|
1833
|
y21 = f2(x1)
|
|
|
1834
|
|
|
1834
|
|
|
|
1835
|
velRadial1[i,:] = y11
|
|
1835
|
velRadial1[i,:] = y11
|
|
|
1836
|
SNR1[i,:] = y21
|
|
1836
|
SNR1[i,:] = y21
|
|
|
1837
|
|
|
1837
|
|
|
|
1838
|
return heiRang1, velRadial1, SNR1
|
|
1838
|
return heiRang1, velRadial1, SNR1
|
|
|
1839
|
|
|
1839
|
|
|
|
1840
|
def __calculateVelUVW(self, A, velRadial):
|
|
1840
|
def __calculateVelUVW(self, A, velRadial):
|
|
|
1841
|
|
|
1841
|
|
|
|
1842
|
#Operacion Matricial
|
|
1842
|
#Operacion Matricial
|
|
|
1843
|
# velUVW = numpy.zeros((velRadial.shape[1],3))
|
|
1843
|
# velUVW = numpy.zeros((velRadial.shape[1],3))
|
|
|
1844
|
# for ind in range(velRadial.shape[1]):
|
|
1844
|
# for ind in range(velRadial.shape[1]):
|
|
|
1845
|
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
|
|
1845
|
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
|
|
|
1846
|
# velUVW = velUVW.transpose()
|
|
1846
|
# velUVW = velUVW.transpose()
|
|
|
1847
|
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
|
|
1847
|
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
|
|
|
1848
|
velUVW[:,:] = numpy.dot(A,velRadial)
|
|
1848
|
velUVW[:,:] = numpy.dot(A,velRadial)
|
|
|
1849
|
|
|
1849
|
|
|
|
1850
|
|
|
1850
|
|
|
|
1851
|
return velUVW
|
|
1851
|
return velUVW
|
|
|
1852
|
|
|
1852
|
|
|
|
1853
|
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
|
|
1853
|
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
|
|
|
1854
|
|
|
1854
|
|
|
|
1855
|
def techniqueDBS(self, kwargs):
|
|
1855
|
def techniqueDBS(self, kwargs):
|
|
|
1856
|
"""
|
|
1856
|
"""
|
|
|
1857
|
Function that implements Doppler Beam Swinging (DBS) technique.
|
|
1857
|
Function that implements Doppler Beam Swinging (DBS) technique.
|
|
|
1858
|
|
|
1858
|
|
|
|
1859
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
1859
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
|
1860
|
Direction correction (if necessary), Ranges and SNR
|
|
1860
|
Direction correction (if necessary), Ranges and SNR
|
|
|
1861
|
|
|
1861
|
|
|
|
1862
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
1862
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
|
1863
|
|
|
1863
|
|
|
|
1864
|
Parameters affected: Winds, height range, SNR
|
|
1864
|
Parameters affected: Winds, height range, SNR
|
|
|
1865
|
"""
|
|
1865
|
"""
|
|
|
1866
|
velRadial0 = kwargs['velRadial']
|
|
1866
|
velRadial0 = kwargs['velRadial']
|
|
|
1867
|
heiRang = kwargs['heightList']
|
|
1867
|
heiRang = kwargs['heightList']
|
|
|
1868
|
SNR0 = kwargs['SNR']
|
|
1868
|
SNR0 = kwargs['SNR']
|
|
|
1869
|
|
|
1869
|
|
|
|
1870
|
if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'):
|
|
1870
|
if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'):
|
|
|
1871
|
theta_x = numpy.array(kwargs['dirCosx'])
|
|
1871
|
theta_x = numpy.array(kwargs['dirCosx'])
|
|
|
1872
|
theta_y = numpy.array(kwargs['dirCosy'])
|
|
1872
|
theta_y = numpy.array(kwargs['dirCosy'])
|
|
|
1873
|
else:
|
|
1873
|
else:
|
|
|
1874
|
elev = numpy.array(kwargs['elevation'])
|
|
1874
|
elev = numpy.array(kwargs['elevation'])
|
|
|
1875
|
azim = numpy.array(kwargs['azimuth'])
|
|
1875
|
azim = numpy.array(kwargs['azimuth'])
|
|
|
1876
|
theta_x, theta_y = self.__calculateCosDir(elev, azim)
|
|
1876
|
theta_x, theta_y = self.__calculateCosDir(elev, azim)
|
|
|
1877
|
azimuth = kwargs['correctAzimuth']
|
|
1877
|
azimuth = kwargs['correctAzimuth']
|
|
|
1878
|
if kwargs.has_key('horizontalOnly'):
|
|
1878
|
if kwargs.has_key('horizontalOnly'):
|
|
|
1879
|
horizontalOnly = kwargs['horizontalOnly']
|
|
1879
|
horizontalOnly = kwargs['horizontalOnly']
|
|
|
1880
|
else: horizontalOnly = False
|
|
1880
|
else: horizontalOnly = False
|
|
|
1881
|
if kwargs.has_key('correctFactor'):
|
|
1881
|
if kwargs.has_key('correctFactor'):
|
|
|
1882
|
correctFactor = kwargs['correctFactor']
|
|
1882
|
correctFactor = kwargs['correctFactor']
|
|
|
1883
|
else: correctFactor = 1
|
|
1883
|
else: correctFactor = 1
|
|
|
1884
|
if kwargs.has_key('channelList'):
|
|
1884
|
if kwargs.has_key('channelList'):
|
|
|
1885
|
channelList = kwargs['channelList']
|
|
1885
|
channelList = kwargs['channelList']
|
|
|
1886
|
if len(channelList) == 2:
|
|
1886
|
if len(channelList) == 2:
|
|
|
1887
|
horizontalOnly = True
|
|
1887
|
horizontalOnly = True
|
|
|
1888
|
arrayChannel = numpy.array(channelList)
|
|
1888
|
arrayChannel = numpy.array(channelList)
|
|
|
1889
|
param = param[arrayChannel,:,:]
|
|
1889
|
param = param[arrayChannel,:,:]
|
|
|
1890
|
theta_x = theta_x[arrayChannel]
|
|
1890
|
theta_x = theta_x[arrayChannel]
|
|
|
1891
|
theta_y = theta_y[arrayChannel]
|
|
1891
|
theta_y = theta_y[arrayChannel]
|
|
|
1892
|
|
|
1892
|
|
|
|
1893
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
1893
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
|
1894
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
|
|
1894
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
|
|
|
1895
|
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
|
|
1895
|
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
|
|
|
1896
|
|
|
1896
|
|
|
|
1897
|
#Calculo de Componentes de la velocidad con DBS
|
|
1897
|
#Calculo de Componentes de la velocidad con DBS
|
|
|
1898
|
winds = self.__calculateVelUVW(A,velRadial1)
|
|
1898
|
winds = self.__calculateVelUVW(A,velRadial1)
|
|
|
1899
|
|
|
1899
|
|
|
|
1900
|
return winds, heiRang1, SNR1
|
|
1900
|
return winds, heiRang1, SNR1
|
|
|
1901
|
|
|
1901
|
|
|
|
1902
|
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
|
|
1902
|
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
|
|
|
1903
|
|
|
1903
|
|
|
|
1904
|
nPairs = len(pairs_ccf)
|
|
1904
|
nPairs = len(pairs_ccf)
|
|
|
1905
|
posx = numpy.asarray(posx)
|
|
1905
|
posx = numpy.asarray(posx)
|
|
|
1906
|
posy = numpy.asarray(posy)
|
|
1906
|
posy = numpy.asarray(posy)
|
|
|
1907
|
|
|
1907
|
|
|
|
1908
|
#Rotacion Inversa para alinear con el azimuth
|
|
1908
|
#Rotacion Inversa para alinear con el azimuth
|
|
|
1909
|
if azimuth!= None:
|
|
1909
|
if azimuth!= None:
|
|
|
1910
|
azimuth = azimuth*math.pi/180
|
|
1910
|
azimuth = azimuth*math.pi/180
|
|
|
1911
|
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
|
|
1911
|
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
|
|
|
1912
|
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
|
|
1912
|
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
|
|
|
1913
|
else:
|
|
1913
|
else:
|
|
|
1914
|
posx1 = posx
|
|
1914
|
posx1 = posx
|
|
|
1915
|
posy1 = posy
|
|
1915
|
posy1 = posy
|
|
|
1916
|
|
|
1916
|
|
|
|
1917
|
#Calculo de Distancias
|
|
1917
|
#Calculo de Distancias
|
|
|
1918
|
distx = numpy.zeros(nPairs)
|
|
1918
|
distx = numpy.zeros(nPairs)
|
|
|
1919
|
disty = numpy.zeros(nPairs)
|
|
1919
|
disty = numpy.zeros(nPairs)
|
|
|
1920
|
dist = numpy.zeros(nPairs)
|
|
1920
|
dist = numpy.zeros(nPairs)
|
|
|
1921
|
ang = numpy.zeros(nPairs)
|
|
1921
|
ang = numpy.zeros(nPairs)
|
|
|
1922
|
|
|
1922
|
|
|
|
1923
|
for i in range(nPairs):
|
|
1923
|
for i in range(nPairs):
|
|
|
1924
|
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
|
|
1924
|
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
|
|
|
1925
|
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
|
|
1925
|
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
|
|
|
1926
|
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
|
|
1926
|
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
|
|
|
1927
|
ang[i] = numpy.arctan2(disty[i],distx[i])
|
|
1927
|
ang[i] = numpy.arctan2(disty[i],distx[i])
|
|
|
1928
|
|
|
1928
|
|
|
|
1929
|
return distx, disty, dist, ang
|
|
1929
|
return distx, disty, dist, ang
|
|
|
1930
|
#Calculo de Matrices
|
|
1930
|
#Calculo de Matrices
|
|
|
1931
|
# nPairs = len(pairs)
|
|
1931
|
# nPairs = len(pairs)
|
|
|
1932
|
# ang1 = numpy.zeros((nPairs, 2, 1))
|
|
1932
|
# ang1 = numpy.zeros((nPairs, 2, 1))
|
|
|
1933
|
# dist1 = numpy.zeros((nPairs, 2, 1))
|
|
1933
|
# dist1 = numpy.zeros((nPairs, 2, 1))
|
|
|
1934
|
#
|
|
1934
|
#
|
|
|
1935
|
# for j in range(nPairs):
|
|
1935
|
# for j in range(nPairs):
|
|
|
1936
|
# dist1[j,0,0] = dist[pairs[j][0]]
|
|
1936
|
# dist1[j,0,0] = dist[pairs[j][0]]
|
|
|
1937
|
# dist1[j,1,0] = dist[pairs[j][1]]
|
|
1937
|
# dist1[j,1,0] = dist[pairs[j][1]]
|
|
|
1938
|
# ang1[j,0,0] = ang[pairs[j][0]]
|
|
1938
|
# ang1[j,0,0] = ang[pairs[j][0]]
|
|
|
1939
|
# ang1[j,1,0] = ang[pairs[j][1]]
|
|
1939
|
# ang1[j,1,0] = ang[pairs[j][1]]
|
|
|
1940
|
#
|
|
1940
|
#
|
|
|
1941
|
# return distx,disty, dist1,ang1
|
|
1941
|
# return distx,disty, dist1,ang1
|
|
|
1942
|
|
|
1942
|
|
|
|
1943
|
|
|
1943
|
|
|
|
1944
|
def __calculateVelVer(self, phase, lagTRange, _lambda):
|
|
1944
|
def __calculateVelVer(self, phase, lagTRange, _lambda):
|
|
|
1945
|
|
|
1945
|
|
|
|
1946
|
Ts = lagTRange[1] - lagTRange[0]
|
|
1946
|
Ts = lagTRange[1] - lagTRange[0]
|
|
|
1947
|
velW = -_lambda*phase/(4*math.pi*Ts)
|
|
1947
|
velW = -_lambda*phase/(4*math.pi*Ts)
|
|
|
1948
|
|
|
1948
|
|
|
|
1949
|
return velW
|
|
1949
|
return velW
|
|
|
1950
|
|
|
1950
|
|
|
|
1951
|
def __calculateVelHorDir(self, dist, tau1, tau2, ang):
|
|
1951
|
def __calculateVelHorDir(self, dist, tau1, tau2, ang):
|
|
|
1952
|
nPairs = tau1.shape[0]
|
|
1952
|
nPairs = tau1.shape[0]
|
|
|
1953
|
nHeights = tau1.shape[1]
|
|
1953
|
nHeights = tau1.shape[1]
|
|
|
1954
|
vel = numpy.zeros((nPairs,3,nHeights))
|
|
1954
|
vel = numpy.zeros((nPairs,3,nHeights))
|
|
|
1955
|
dist1 = numpy.reshape(dist, (dist.size,1))
|
|
1955
|
dist1 = numpy.reshape(dist, (dist.size,1))
|
|
|
1956
|
|
|
1956
|
|
|
|
1957
|
angCos = numpy.cos(ang)
|
|
1957
|
angCos = numpy.cos(ang)
|
|
|
1958
|
angSin = numpy.sin(ang)
|
|
1958
|
angSin = numpy.sin(ang)
|
|
|
1959
|
|
|
1959
|
|
|
|
1960
|
vel0 = dist1*tau1/(2*tau2**2)
|
|
1960
|
vel0 = dist1*tau1/(2*tau2**2)
|
|
|
1961
|
vel[:,0,:] = (vel0*angCos).sum(axis = 1)
|
|
1961
|
vel[:,0,:] = (vel0*angCos).sum(axis = 1)
|
|
|
1962
|
vel[:,1,:] = (vel0*angSin).sum(axis = 1)
|
|
1962
|
vel[:,1,:] = (vel0*angSin).sum(axis = 1)
|
|
|
1963
|
|
|
1963
|
|
|
|
1964
|
ind = numpy.where(numpy.isinf(vel))
|
|
1964
|
ind = numpy.where(numpy.isinf(vel))
|
|
|
1965
|
vel[ind] = numpy.nan
|
|
1965
|
vel[ind] = numpy.nan
|
|
|
1966
|
|
|
1966
|
|
|
|
1967
|
return vel
|
|
1967
|
return vel
|
|
|
1968
|
|
|
1968
|
|
|
|
1969
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
1969
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
|
1970
|
#
|
|
1970
|
#
|
|
|
1971
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
1971
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
|
1972
|
#
|
|
1972
|
#
|
|
|
1973
|
# for l in range(len(pairsList)):
|
|
1973
|
# for l in range(len(pairsList)):
|
|
|
1974
|
# firstChannel = pairsList[l][0]
|
|
1974
|
# firstChannel = pairsList[l][0]
|
|
|
1975
|
# secondChannel = pairsList[l][1]
|
|
1975
|
# secondChannel = pairsList[l][1]
|
|
|
1976
|
#
|
|
1976
|
#
|
|
|
1977
|
# #Obteniendo pares de Autocorrelacion
|
|
1977
|
# #Obteniendo pares de Autocorrelacion
|
|
|
1978
|
# if firstChannel == secondChannel:
|
|
1978
|
# if firstChannel == secondChannel:
|
|
|
1979
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
1979
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
|
1980
|
#
|
|
1980
|
#
|
|
|
1981
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
1981
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
|
1982
|
#
|
|
1982
|
#
|
|
|
1983
|
# pairsCrossCorr = range(len(pairsList))
|
|
1983
|
# pairsCrossCorr = range(len(pairsList))
|
|
|
1984
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
1984
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
|
1985
|
#
|
|
1985
|
#
|
|
|
1986
|
# return pairsAutoCorr, pairsCrossCorr
|
|
1986
|
# return pairsAutoCorr, pairsCrossCorr
|
|
|
1987
|
|
|
1987
|
|
|
|
1988
|
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
|
|
1988
|
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
|
|
|
1989
|
def techniqueSA(self, kwargs):
|
|
1989
|
def techniqueSA(self, kwargs):
|
|
|
1990
|
|
|
1990
|
|
|
|
1991
|
"""
|
|
1991
|
"""
|
|
|
1992
|
Function that implements Spaced Antenna (SA) technique.
|
|
1992
|
Function that implements Spaced Antenna (SA) technique.
|
|
|
1993
|
|
|
1993
|
|
|
|
1994
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
1994
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
|
1995
|
Direction correction (if necessary), Ranges and SNR
|
|
1995
|
Direction correction (if necessary), Ranges and SNR
|
|
|
1996
|
|
|
1996
|
|
|
|
1997
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
1997
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
|
1998
|
|
|
1998
|
|
|
|
1999
|
Parameters affected: Winds
|
|
1999
|
Parameters affected: Winds
|
|
|
2000
|
"""
|
|
2000
|
"""
|
|
|
2001
|
position_x = kwargs['positionX']
|
|
2001
|
position_x = kwargs['positionX']
|
|
|
2002
|
position_y = kwargs['positionY']
|
|
2002
|
position_y = kwargs['positionY']
|
|
|
2003
|
azimuth = kwargs['azimuth']
|
|
2003
|
azimuth = kwargs['azimuth']
|
|
|
2004
|
|
|
2004
|
|
|
|
2005
|
if kwargs.has_key('correctFactor'):
|
|
2005
|
if kwargs.has_key('correctFactor'):
|
|
|
2006
|
correctFactor = kwargs['correctFactor']
|
|
2006
|
correctFactor = kwargs['correctFactor']
|
|
|
2007
|
else:
|
|
2007
|
else:
|
|
|
2008
|
correctFactor = 1
|
|
2008
|
correctFactor = 1
|
|
|
2009
|
|
|
2009
|
|
|
|
2010
|
groupList = kwargs['groupList']
|
|
2010
|
groupList = kwargs['groupList']
|
|
|
2011
|
pairs_ccf = groupList[1]
|
|
2011
|
pairs_ccf = groupList[1]
|
|
|
2012
|
tau = kwargs['tau']
|
|
2012
|
tau = kwargs['tau']
|
|
|
2013
|
_lambda = kwargs['_lambda']
|
|
2013
|
_lambda = kwargs['_lambda']
|
|
|
2014
|
|
|
2014
|
|
|
|
2015
|
#Cross Correlation pairs obtained
|
|
2015
|
#Cross Correlation pairs obtained
|
|
|
2016
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
|
|
2016
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
|
|
|
2017
|
# pairsArray = numpy.array(pairsList)[pairsCrossCorr]
|
|
2017
|
# pairsArray = numpy.array(pairsList)[pairsCrossCorr]
|
|
|
2018
|
# pairsSelArray = numpy.array(pairsSelected)
|
|
2018
|
# pairsSelArray = numpy.array(pairsSelected)
|
|
|
2019
|
# pairs = []
|
|
2019
|
# pairs = []
|
|
|
2020
|
#
|
|
2020
|
#
|
|
|
2021
|
# #Wind estimation pairs obtained
|
|
2021
|
# #Wind estimation pairs obtained
|
|
|
2022
|
# for i in range(pairsSelArray.shape[0]/2):
|
|
2022
|
# for i in range(pairsSelArray.shape[0]/2):
|
|
|
2023
|
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
|
|
2023
|
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
|
|
|
2024
|
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
|
|
2024
|
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
|
|
|
2025
|
# pairs.append((ind1,ind2))
|
|
2025
|
# pairs.append((ind1,ind2))
|
|
|
2026
|
|
|
2026
|
|
|
|
2027
|
indtau = tau.shape[0]/2
|
|
2027
|
indtau = tau.shape[0]/2
|
|
|
2028
|
tau1 = tau[:indtau,:]
|
|
2028
|
tau1 = tau[:indtau,:]
|
|
|
2029
|
tau2 = tau[indtau:-1,:]
|
|
2029
|
tau2 = tau[indtau:-1,:]
|
|
|
2030
|
# tau1 = tau1[pairs,:]
|
|
2030
|
# tau1 = tau1[pairs,:]
|
|
|
2031
|
# tau2 = tau2[pairs,:]
|
|
2031
|
# tau2 = tau2[pairs,:]
|
|
|
2032
|
phase1 = tau[-1,:]
|
|
2032
|
phase1 = tau[-1,:]
|
|
|
2033
|
|
|
2033
|
|
|
|
2034
|
#---------------------------------------------------------------------
|
|
2034
|
#---------------------------------------------------------------------
|
|
|
2035
|
#Metodo Directo
|
|
2035
|
#Metodo Directo
|
|
|
2036
|
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
|
|
2036
|
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
|
|
|
2037
|
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
|
|
2037
|
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
|
|
|
2038
|
winds = stats.nanmean(winds, axis=0)
|
|
2038
|
winds = stats.nanmean(winds, axis=0)
|
|
|
2039
|
#---------------------------------------------------------------------
|
|
2039
|
#---------------------------------------------------------------------
|
|
|
2040
|
#Metodo General
|
|
2040
|
#Metodo General
|
|
|
2041
|
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
|
|
2041
|
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
|
|
|
2042
|
# #Calculo Coeficientes de Funcion de Correlacion
|
|
2042
|
# #Calculo Coeficientes de Funcion de Correlacion
|
|
|
2043
|
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
|
|
2043
|
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
|
|
|
2044
|
# #Calculo de Velocidades
|
|
2044
|
# #Calculo de Velocidades
|
|
|
2045
|
# winds = self.calculateVelUV(F,G,A,B,H)
|
|
2045
|
# winds = self.calculateVelUV(F,G,A,B,H)
|
|
|
2046
|
|
|
2046
|
|
|
|
2047
|
#---------------------------------------------------------------------
|
|
2047
|
#---------------------------------------------------------------------
|
|
|
2048
|
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
|
|
2048
|
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
|
|
|
2049
|
winds = correctFactor*winds
|
|
2049
|
winds = correctFactor*winds
|
|
|
2050
|
return winds
|
|
2050
|
return winds
|
|
|
2051
|
|
|
2051
|
|
|
|
2052
|
def __checkTime(self, currentTime, paramInterval, outputInterval):
|
|
2052
|
def __checkTime(self, currentTime, paramInterval, outputInterval):
|
|
|
2053
|
|
|
2053
|
|
|
|
2054
|
dataTime = currentTime + paramInterval
|
|
2054
|
dataTime = currentTime + paramInterval
|
|
|
2055
|
deltaTime = dataTime - self.__initime
|
|
2055
|
deltaTime = dataTime - self.__initime
|
|
|
2056
|
|
|
2056
|
|
|
|
2057
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
2057
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
|
2058
|
self.__dataReady = True
|
|
2058
|
self.__dataReady = True
|
|
|
2059
|
return
|
|
2059
|
return
|
|
|
2060
|
|
|
2060
|
|
|
|
2061
|
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
|
|
2061
|
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
|
|
|
2062
|
'''
|
|
2062
|
'''
|
|
|
2063
|
Function that implements winds estimation technique with detected meteors.
|
|
2063
|
Function that implements winds estimation technique with detected meteors.
|
|
|
2064
|
|
|
2064
|
|
|
|
2065
|
Input: Detected meteors, Minimum meteor quantity to wind estimation
|
|
2065
|
Input: Detected meteors, Minimum meteor quantity to wind estimation
|
|
|
2066
|
|
|
2066
|
|
|
|
2067
|
Output: Winds estimation (Zonal and Meridional)
|
|
2067
|
Output: Winds estimation (Zonal and Meridional)
|
|
|
2068
|
|
|
2068
|
|
|
|
2069
|
Parameters affected: Winds
|
|
2069
|
Parameters affected: Winds
|
|
|
2070
|
'''
|
|
2070
|
'''
|
|
|
2071
|
# print arrayMeteor.shape
|
|
2071
|
# print arrayMeteor.shape
|
|
|
2072
|
#Settings
|
|
2072
|
#Settings
|
|
|
2073
|
nInt = (heightMax - heightMin)/2
|
|
2073
|
nInt = (heightMax - heightMin)/2
|
|
|
2074
|
# print nInt
|
|
2074
|
# print nInt
|
|
|
2075
|
nInt = int(nInt)
|
|
2075
|
nInt = int(nInt)
|
|
|
2076
|
# print nInt
|
|
2076
|
# print nInt
|
|
|
2077
|
winds = numpy.zeros((2,nInt))*numpy.nan
|
|
2077
|
winds = numpy.zeros((2,nInt))*numpy.nan
|
|
|
2078
|
|
|
2078
|
|
|
|
2079
|
#Filter errors
|
|
2079
|
#Filter errors
|
|
|
2080
|
error = numpy.where(arrayMeteor[:,-1] == 0)[0]
|
|
2080
|
error = numpy.where(arrayMeteor[:,-1] == 0)[0]
|
|
|
2081
|
finalMeteor = arrayMeteor[error,:]
|
|
2081
|
finalMeteor = arrayMeteor[error,:]
|
|
|
2082
|
|
|
2082
|
|
|
|
2083
|
#Meteor Histogram
|
|
2083
|
#Meteor Histogram
|
|
|
2084
|
finalHeights = finalMeteor[:,2]
|
|
2084
|
finalHeights = finalMeteor[:,2]
|
|
|
2085
|
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
|
|
2085
|
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
|
|
|
2086
|
nMeteorsPerI = hist[0]
|
|
2086
|
nMeteorsPerI = hist[0]
|
|
|
2087
|
heightPerI = hist[1]
|
|
2087
|
heightPerI = hist[1]
|
|
|
2088
|
|
|
2088
|
|
|
|
2089
|
#Sort of meteors
|
|
2089
|
#Sort of meteors
|
|
|
2090
|
indSort = finalHeights.argsort()
|
|
2090
|
indSort = finalHeights.argsort()
|
|
|
2091
|
finalMeteor2 = finalMeteor[indSort,:]
|
|
2091
|
finalMeteor2 = finalMeteor[indSort,:]
|
|
|
2092
|
|
|
2092
|
|
|
|
2093
|
# Calculating winds
|
|
2093
|
# Calculating winds
|
|
|
2094
|
ind1 = 0
|
|
2094
|
ind1 = 0
|
|
|
2095
|
ind2 = 0
|
|
2095
|
ind2 = 0
|
|
|
2096
|
|
|
2096
|
|
|
|
2097
|
for i in range(nInt):
|
|
2097
|
for i in range(nInt):
|
|
|
2098
|
nMet = nMeteorsPerI[i]
|
|
2098
|
nMet = nMeteorsPerI[i]
|
|
|
2099
|
ind1 = ind2
|
|
2099
|
ind1 = ind2
|
|
|
2100
|
ind2 = ind1 + nMet
|
|
2100
|
ind2 = ind1 + nMet
|
|
|
2101
|
|
|
2101
|
|
|
|
2102
|
meteorAux = finalMeteor2[ind1:ind2,:]
|
|
2102
|
meteorAux = finalMeteor2[ind1:ind2,:]
|
|
|
2103
|
|
|
2103
|
|
|
|
2104
|
if meteorAux.shape[0] >= meteorThresh:
|
|
2104
|
if meteorAux.shape[0] >= meteorThresh:
|
|
|
2105
|
vel = meteorAux[:, 6]
|
|
2105
|
vel = meteorAux[:, 6]
|
|
|
2106
|
zen = meteorAux[:, 4]*numpy.pi/180
|
|
2106
|
zen = meteorAux[:, 4]*numpy.pi/180
|
|
|
2107
|
azim = meteorAux[:, 3]*numpy.pi/180
|
|
2107
|
azim = meteorAux[:, 3]*numpy.pi/180
|
|
|
2108
|
|
|
2108
|
|
|
|
2109
|
n = numpy.cos(zen)
|
|
2109
|
n = numpy.cos(zen)
|
|
|
2110
|
# m = (1 - n**2)/(1 - numpy.tan(azim)**2)
|
|
2110
|
# m = (1 - n**2)/(1 - numpy.tan(azim)**2)
|
|
|
2111
|
# l = m*numpy.tan(azim)
|
|
2111
|
# l = m*numpy.tan(azim)
|
|
|
2112
|
l = numpy.sin(zen)*numpy.sin(azim)
|
|
2112
|
l = numpy.sin(zen)*numpy.sin(azim)
|
|
|
2113
|
m = numpy.sin(zen)*numpy.cos(azim)
|
|
2113
|
m = numpy.sin(zen)*numpy.cos(azim)
|
|
|
2114
|
|
|
2114
|
|
|
|
2115
|
A = numpy.vstack((l, m)).transpose()
|
|
2115
|
A = numpy.vstack((l, m)).transpose()
|
|
|
2116
|
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
|
|
2116
|
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
|
|
|
2117
|
windsAux = numpy.dot(A1, vel)
|
|
2117
|
windsAux = numpy.dot(A1, vel)
|
|
|
2118
|
|
|
2118
|
|
|
|
2119
|
winds[0,i] = windsAux[0]
|
|
2119
|
winds[0,i] = windsAux[0]
|
|
|
2120
|
winds[1,i] = windsAux[1]
|
|
2120
|
winds[1,i] = windsAux[1]
|
|
|
2121
|
|
|
2121
|
|
|
|
2122
|
return winds, heightPerI[:-1]
|
|
2122
|
return winds, heightPerI[:-1]
|
|
|
2123
|
|
|
2123
|
|
|
|
2124
|
def techniqueNSM_SA(self, **kwargs):
|
|
2124
|
def techniqueNSM_SA(self, **kwargs):
|
|
|
2125
|
metArray = kwargs['metArray']
|
|
2125
|
metArray = kwargs['metArray']
|
|
|
2126
|
heightList = kwargs['heightList']
|
|
2126
|
heightList = kwargs['heightList']
|
|
|
2127
|
timeList = kwargs['timeList']
|
|
2127
|
timeList = kwargs['timeList']
|
|
|
2128
|
|
|
2128
|
|
|
|
2129
|
rx_location = kwargs['rx_location']
|
|
2129
|
rx_location = kwargs['rx_location']
|
|
|
2130
|
groupList = kwargs['groupList']
|
|
2130
|
groupList = kwargs['groupList']
|
|
|
2131
|
azimuth = kwargs['azimuth']
|
|
2131
|
azimuth = kwargs['azimuth']
|
|
|
2132
|
dfactor = kwargs['dfactor']
|
|
2132
|
dfactor = kwargs['dfactor']
|
|
|
2133
|
k = kwargs['k']
|
|
2133
|
k = kwargs['k']
|
|
|
2134
|
|
|
2134
|
|
|
|
2135
|
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
|
|
2135
|
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
|
|
|
2136
|
d = dist*dfactor
|
|
2136
|
d = dist*dfactor
|
|
|
2137
|
#Phase calculation
|
|
2137
|
#Phase calculation
|
|
|
2138
|
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
|
|
2138
|
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
|
|
|
2139
|
|
|
2139
|
|
|
|
2140
|
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
|
|
2140
|
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
|
|
|
2141
|
|
|
2141
|
|
|
|
2142
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
2142
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
|
2143
|
azimuth1 = azimuth1*numpy.pi/180
|
|
2143
|
azimuth1 = azimuth1*numpy.pi/180
|
|
|
2144
|
|
|
2144
|
|
|
|
2145
|
for i in range(heightList.size):
|
|
2145
|
for i in range(heightList.size):
|
|
|
2146
|
h = heightList[i]
|
|
2146
|
h = heightList[i]
|
|
|
2147
|
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
|
|
2147
|
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
|
|
|
2148
|
metHeight = metArray1[indH,:]
|
|
2148
|
metHeight = metArray1[indH,:]
|
|
|
2149
|
if metHeight.shape[0] >= 2:
|
|
2149
|
if metHeight.shape[0] >= 2:
|
|
|
2150
|
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities
|
|
2150
|
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities
|
|
|
2151
|
iazim = metHeight[:,1].astype(int)
|
|
2151
|
iazim = metHeight[:,1].astype(int)
|
|
|
2152
|
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths
|
|
2152
|
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths
|
|
|
2153
|
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
|
|
2153
|
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
|
|
|
2154
|
A = numpy.asmatrix(A)
|
|
2154
|
A = numpy.asmatrix(A)
|
|
|
2155
|
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
|
|
2155
|
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
|
|
|
2156
|
velHor = numpy.dot(A1,velAux)
|
|
2156
|
velHor = numpy.dot(A1,velAux)
|
|
|
2157
|
|
|
2157
|
|
|
|
2158
|
velEst[i,:] = numpy.squeeze(velHor)
|
|
2158
|
velEst[i,:] = numpy.squeeze(velHor)
|
|
|
2159
|
return velEst
|
|
2159
|
return velEst
|
|
|
2160
|
|
|
2160
|
|
|
|
2161
|
def __getPhaseSlope(self, metArray, heightList, timeList):
|
|
2161
|
def __getPhaseSlope(self, metArray, heightList, timeList):
|
|
|
2162
|
meteorList = []
|
|
2162
|
meteorList = []
|
|
|
2163
|
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
|
|
2163
|
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
|
|
|
2164
|
#Putting back together the meteor matrix
|
|
2164
|
#Putting back together the meteor matrix
|
|
|
2165
|
utctime = metArray[:,0]
|
|
2165
|
utctime = metArray[:,0]
|
|
|
2166
|
uniqueTime = numpy.unique(utctime)
|
|
2166
|
uniqueTime = numpy.unique(utctime)
|
|
|
2167
|
|
|
2167
|
|
|
|
2168
|
phaseDerThresh = 0.5
|
|
2168
|
phaseDerThresh = 0.5
|
|
|
2169
|
ippSeconds = timeList[1] - timeList[0]
|
|
2169
|
ippSeconds = timeList[1] - timeList[0]
|
|
|
2170
|
sec = numpy.where(timeList>1)[0][0]
|
|
2170
|
sec = numpy.where(timeList>1)[0][0]
|
|
|
2171
|
nPairs = metArray.shape[1] - 6
|
|
2171
|
nPairs = metArray.shape[1] - 6
|
|
|
2172
|
nHeights = len(heightList)
|
|
2172
|
nHeights = len(heightList)
|
|
|
2173
|
|
|
2173
|
|
|
|
2174
|
for t in uniqueTime:
|
|
2174
|
for t in uniqueTime:
|
|
|
2175
|
metArray1 = metArray[utctime==t,:]
|
|
2175
|
metArray1 = metArray[utctime==t,:]
|
|
|
2176
|
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh
|
|
2176
|
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh
|
|
|
2177
|
tmet = metArray1[:,1].astype(int)
|
|
2177
|
tmet = metArray1[:,1].astype(int)
|
|
|
2178
|
hmet = metArray1[:,2].astype(int)
|
|
2178
|
hmet = metArray1[:,2].astype(int)
|
|
|
2179
|
|
|
2179
|
|
|
|
2180
|
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
|
|
2180
|
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
|
|
|
2181
|
metPhase[:,:] = numpy.nan
|
|
2181
|
metPhase[:,:] = numpy.nan
|
|
|
2182
|
metPhase[:,hmet,tmet] = metArray1[:,6:].T
|
|
2182
|
metPhase[:,hmet,tmet] = metArray1[:,6:].T
|
|
|
2183
|
|
|
2183
|
|
|
|
2184
|
#Delete short trails
|
|
2184
|
#Delete short trails
|
|
|
2185
|
metBool = ~numpy.isnan(metPhase[0,:,:])
|
|
2185
|
metBool = ~numpy.isnan(metPhase[0,:,:])
|
|
|
2186
|
heightVect = numpy.sum(metBool, axis = 1)
|
|
2186
|
heightVect = numpy.sum(metBool, axis = 1)
|
|
|
2187
|
metBool[heightVect<sec,:] = False
|
|
2187
|
metBool[heightVect<sec,:] = False
|
|
|
2188
|
metPhase[:,heightVect<sec,:] = numpy.nan
|
|
2188
|
metPhase[:,heightVect<sec,:] = numpy.nan
|
|
|
2189
|
|
|
2189
|
|
|
|
2190
|
#Derivative
|
|
2190
|
#Derivative
|
|
|
2191
|
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
|
|
2191
|
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
|
|
|
2192
|
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
|
|
2192
|
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
|
|
|
2193
|
metPhase[phDerAux] = numpy.nan
|
|
2193
|
metPhase[phDerAux] = numpy.nan
|
|
|
2194
|
|
|
2194
|
|
|
|
2195
|
#--------------------------METEOR DETECTION -----------------------------------------
|
|
2195
|
#--------------------------METEOR DETECTION -----------------------------------------
|
|
|
2196
|
indMet = numpy.where(numpy.any(metBool,axis=1))[0]
|
|
2196
|
indMet = numpy.where(numpy.any(metBool,axis=1))[0]
|
|
|
2197
|
|
|
2197
|
|
|
|
2198
|
for p in numpy.arange(nPairs):
|
|
2198
|
for p in numpy.arange(nPairs):
|
|
|
2199
|
phase = metPhase[p,:,:]
|
|
2199
|
phase = metPhase[p,:,:]
|
|
|
2200
|
phDer = metDer[p,:,:]
|
|
2200
|
phDer = metDer[p,:,:]
|
|
|
2201
|
|
|
2201
|
|
|
|
2202
|
for h in indMet:
|
|
2202
|
for h in indMet:
|
|
|
2203
|
height = heightList[h]
|
|
2203
|
height = heightList[h]
|
|
|
2204
|
phase1 = phase[h,:] #82
|
|
2204
|
phase1 = phase[h,:] #82
|
|
|
2205
|
phDer1 = phDer[h,:]
|
|
2205
|
phDer1 = phDer[h,:]
|
|
|
2206
|
|
|
2206
|
|
|
|
2207
|
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap
|
|
2207
|
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap
|
|
|
2208
|
|
|
2208
|
|
|
|
2209
|
indValid = numpy.where(~numpy.isnan(phase1))[0]
|
|
2209
|
indValid = numpy.where(~numpy.isnan(phase1))[0]
|
|
|
2210
|
initMet = indValid[0]
|
|
2210
|
initMet = indValid[0]
|
|
|
2211
|
endMet = 0
|
|
2211
|
endMet = 0
|
|
|
2212
|
|
|
2212
|
|
|
|
2213
|
for i in range(len(indValid)-1):
|
|
2213
|
for i in range(len(indValid)-1):
|
|
|
2214
|
|
|
2214
|
|
|
|
2215
|
#Time difference
|
|
2215
|
#Time difference
|
|
|
2216
|
inow = indValid[i]
|
|
2216
|
inow = indValid[i]
|
|
|
2217
|
inext = indValid[i+1]
|
|
2217
|
inext = indValid[i+1]
|
|
|
2218
|
idiff = inext - inow
|
|
2218
|
idiff = inext - inow
|
|
|
2219
|
#Phase difference
|
|
2219
|
#Phase difference
|
|
|
2220
|
phDiff = numpy.abs(phase1[inext] - phase1[inow])
|
|
2220
|
phDiff = numpy.abs(phase1[inext] - phase1[inow])
|
|
|
2221
|
|
|
2221
|
|
|
|
2222
|
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor
|
|
2222
|
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor
|
|
|
2223
|
sizeTrail = inow - initMet + 1
|
|
2223
|
sizeTrail = inow - initMet + 1
|
|
|
2224
|
if sizeTrail>3*sec: #Too short meteors
|
|
2224
|
if sizeTrail>3*sec: #Too short meteors
|
|
|
2225
|
x = numpy.arange(initMet,inow+1)*ippSeconds
|
|
2225
|
x = numpy.arange(initMet,inow+1)*ippSeconds
|
|
|
2226
|
y = phase1[initMet:inow+1]
|
|
2226
|
y = phase1[initMet:inow+1]
|
|
|
2227
|
ynnan = ~numpy.isnan(y)
|
|
2227
|
ynnan = ~numpy.isnan(y)
|
|
|
2228
|
x = x[ynnan]
|
|
2228
|
x = x[ynnan]
|
|
|
2229
|
y = y[ynnan]
|
|
2229
|
y = y[ynnan]
|
|
|
2230
|
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
|
|
2230
|
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
|
|
|
2231
|
ylin = x*slope + intercept
|
|
2231
|
ylin = x*slope + intercept
|
|
|
2232
|
rsq = r_value**2
|
|
2232
|
rsq = r_value**2
|
|
|
2233
|
if rsq > 0.5:
|
|
2233
|
if rsq > 0.5:
|
|
|
2234
|
vel = slope#*height*1000/(k*d)
|
|
2234
|
vel = slope#*height*1000/(k*d)
|
|
|
2235
|
estAux = numpy.array([utctime,p,height, vel, rsq])
|
|
2235
|
estAux = numpy.array([utctime,p,height, vel, rsq])
|
|
|
2236
|
meteorList.append(estAux)
|
|
2236
|
meteorList.append(estAux)
|
|
|
2237
|
initMet = inext
|
|
2237
|
initMet = inext
|
|
|
2238
|
metArray2 = numpy.array(meteorList)
|
|
2238
|
metArray2 = numpy.array(meteorList)
|
|
|
2239
|
|
|
2239
|
|
|
|
2240
|
return metArray2
|
|
2240
|
return metArray2
|
|
|
2241
|
|
|
2241
|
|
|
|
2242
|
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
|
|
2242
|
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
|
|
|
2243
|
|
|
2243
|
|
|
|
2244
|
azimuth1 = numpy.zeros(len(pairslist))
|
|
2244
|
azimuth1 = numpy.zeros(len(pairslist))
|
|
|
2245
|
dist = numpy.zeros(len(pairslist))
|
|
2245
|
dist = numpy.zeros(len(pairslist))
|
|
|
2246
|
|
|
2246
|
|
|
|
2247
|
for i in range(len(rx_location)):
|
|
2247
|
for i in range(len(rx_location)):
|
|
|
2248
|
ch0 = pairslist[i][0]
|
|
2248
|
ch0 = pairslist[i][0]
|
|
|
2249
|
ch1 = pairslist[i][1]
|
|
2249
|
ch1 = pairslist[i][1]
|
|
|
2250
|
|
|
2250
|
|
|
|
2251
|
diffX = rx_location[ch0][0] - rx_location[ch1][0]
|
|
2251
|
diffX = rx_location[ch0][0] - rx_location[ch1][0]
|
|
|
2252
|
diffY = rx_location[ch0][1] - rx_location[ch1][1]
|
|
2252
|
diffY = rx_location[ch0][1] - rx_location[ch1][1]
|
|
|
2253
|
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
|
|
2253
|
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
|
|
|
2254
|
dist[i] = numpy.sqrt(diffX**2 + diffY**2)
|
|
2254
|
dist[i] = numpy.sqrt(diffX**2 + diffY**2)
|
|
|
2255
|
|
|
2255
|
|
|
|
2256
|
azimuth1 -= azimuth0
|
|
2256
|
azimuth1 -= azimuth0
|
|
|
2257
|
return azimuth1, dist
|
|
2257
|
return azimuth1, dist
|
|
|
2258
|
|
|
2258
|
|
|
|
2259
|
def techniqueNSM_DBS(self, **kwargs):
|
|
2259
|
def techniqueNSM_DBS(self, **kwargs):
|
|
|
2260
|
metArray = kwargs['metArray']
|
|
2260
|
metArray = kwargs['metArray']
|
|
|
2261
|
heightList = kwargs['heightList']
|
|
2261
|
heightList = kwargs['heightList']
|
|
|
2262
|
timeList = kwargs['timeList']
|
|
2262
|
timeList = kwargs['timeList']
|
|
|
2263
|
azimuth = kwargs['azimuth']
|
|
2263
|
azimuth = kwargs['azimuth']
|
|
|
2264
|
theta_x = numpy.array(kwargs['theta_x'])
|
|
2264
|
theta_x = numpy.array(kwargs['theta_x'])
|
|
|
2265
|
theta_y = numpy.array(kwargs['theta_y'])
|
|
2265
|
theta_y = numpy.array(kwargs['theta_y'])
|
|
|
2266
|
|
|
2266
|
|
|
|
2267
|
utctime = metArray[:,0]
|
|
2267
|
utctime = metArray[:,0]
|
|
|
2268
|
cmet = metArray[:,1].astype(int)
|
|
2268
|
cmet = metArray[:,1].astype(int)
|
|
|
2269
|
hmet = metArray[:,3].astype(int)
|
|
2269
|
hmet = metArray[:,3].astype(int)
|
|
|
2270
|
SNRmet = metArray[:,4]
|
|
2270
|
SNRmet = metArray[:,4]
|
|
|
2271
|
vmet = metArray[:,5]
|
|
2271
|
vmet = metArray[:,5]
|
|
|
2272
|
spcmet = metArray[:,6]
|
|
2272
|
spcmet = metArray[:,6]
|
|
|
2273
|
|
|
2273
|
|
|
|
2274
|
nChan = numpy.max(cmet) + 1
|
|
2274
|
nChan = numpy.max(cmet) + 1
|
|
|
2275
|
nHeights = len(heightList)
|
|
2275
|
nHeights = len(heightList)
|
|
|
2276
|
|
|
2276
|
|
|
|
2277
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
2277
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
|
2278
|
hmet = heightList[hmet]
|
|
2278
|
hmet = heightList[hmet]
|
|
|
2279
|
h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights
|
|
2279
|
h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights
|
|
|
2280
|
|
|
2280
|
|
|
|
2281
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
2281
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
|
2282
|
|
|
2282
|
|
|
|
2283
|
for i in range(nHeights - 1):
|
|
2283
|
for i in range(nHeights - 1):
|
|
|
2284
|
hmin = heightList[i]
|
|
2284
|
hmin = heightList[i]
|
|
|
2285
|
hmax = heightList[i + 1]
|
|
2285
|
hmax = heightList[i + 1]
|
|
|
2286
|
|
|
2286
|
|
|
|
2287
|
thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
|
|
2287
|
thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
|
|
|
2288
|
indthisH = numpy.where(thisH)
|
|
2288
|
indthisH = numpy.where(thisH)
|
|
|
2289
|
|
|
2289
|
|
|
|
2290
|
if numpy.size(indthisH) > 3:
|
|
2290
|
if numpy.size(indthisH) > 3:
|
|
|
2291
|
|
|
2291
|
|
|
|
2292
|
vel_aux = vmet[thisH]
|
|
2292
|
vel_aux = vmet[thisH]
|
|
|
2293
|
chan_aux = cmet[thisH]
|
|
2293
|
chan_aux = cmet[thisH]
|
|
|
2294
|
cosu_aux = dir_cosu[chan_aux]
|
|
2294
|
cosu_aux = dir_cosu[chan_aux]
|
|
|
2295
|
cosv_aux = dir_cosv[chan_aux]
|
|
2295
|
cosv_aux = dir_cosv[chan_aux]
|
|
|
2296
|
cosw_aux = dir_cosw[chan_aux]
|
|
2296
|
cosw_aux = dir_cosw[chan_aux]
|
|
|
2297
|
|
|
2297
|
|
|
|
2298
|
nch = numpy.size(numpy.unique(chan_aux))
|
|
2298
|
nch = numpy.size(numpy.unique(chan_aux))
|
|
|
2299
|
if nch > 1:
|
|
2299
|
if nch > 1:
|
|
|
2300
|
A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
|
|
2300
|
A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
|
|
|
2301
|
velEst[i,:] = numpy.dot(A,vel_aux)
|
|
2301
|
velEst[i,:] = numpy.dot(A,vel_aux)
|
|
|
2302
|
|
|
2302
|
|
|
|
2303
|
return velEst
|
|
2303
|
return velEst
|
|
|
2304
|
|
|
2304
|
|
|
|
2305
|
def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
|
|
2305
|
def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
|
|
|
2306
|
|
|
2306
|
|
|
|
2307
|
param = dataOut.data_param
|
|
2307
|
param = dataOut.data_param
|
|
|
2308
|
if dataOut.abscissaList != None:
|
|
2308
|
if dataOut.abscissaList != None:
|
|
|
2309
|
absc = dataOut.abscissaList[:-1]
|
|
2309
|
absc = dataOut.abscissaList[:-1]
|
|
|
2310
|
# noise = dataOut.noise
|
|
2310
|
# noise = dataOut.noise
|
|
|
2311
|
heightList = dataOut.heightList
|
|
2311
|
heightList = dataOut.heightList
|
|
|
2312
|
SNR = dataOut.data_SNR
|
|
2312
|
SNR = dataOut.data_SNR
|
|
|
2313
|
|
|
2313
|
|
|
|
2314
|
if technique == 'DBS':
|
|
2314
|
if technique == 'DBS':
|
|
|
2315
|
|
|
2315
|
|
|
|
2316
|
kwargs['velRadial'] = param[:,1,:] #Radial velocity
|
|
2316
|
kwargs['velRadial'] = param[:,1,:] #Radial velocity
|
|
|
2317
|
kwargs['heightList'] = heightList
|
|
2317
|
kwargs['heightList'] = heightList
|
|
|
2318
|
kwargs['SNR'] = SNR
|
|
2318
|
kwargs['SNR'] = SNR
|
|
|
2319
|
|
|
2319
|
|
|
|
2320
|
dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function
|
|
2320
|
dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function
|
|
|
2321
|
dataOut.utctimeInit = dataOut.utctime
|
|
2321
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2322
|
dataOut.outputInterval = dataOut.paramInterval
|
|
2322
|
dataOut.outputInterval = dataOut.paramInterval
|
|
|
2323
|
|
|
2323
|
|
|
|
2324
|
elif technique == 'SA':
|
|
2324
|
elif technique == 'SA':
|
|
|
2325
|
|
|
2325
|
|
|
|
2326
|
#Parameters
|
|
2326
|
#Parameters
|
|
|
2327
|
# position_x = kwargs['positionX']
|
|
2327
|
# position_x = kwargs['positionX']
|
|
|
2328
|
# position_y = kwargs['positionY']
|
|
2328
|
# position_y = kwargs['positionY']
|
|
|
2329
|
# azimuth = kwargs['azimuth']
|
|
2329
|
# azimuth = kwargs['azimuth']
|
|
|
2330
|
#
|
|
2330
|
#
|
|
|
2331
|
# if kwargs.has_key('crosspairsList'):
|
|
2331
|
# if kwargs.has_key('crosspairsList'):
|
|
|
2332
|
# pairs = kwargs['crosspairsList']
|
|
2332
|
# pairs = kwargs['crosspairsList']
|
|
|
2333
|
# else:
|
|
2333
|
# else:
|
|
|
2334
|
# pairs = None
|
|
2334
|
# pairs = None
|
|
|
2335
|
#
|
|
2335
|
#
|
|
|
2336
|
# if kwargs.has_key('correctFactor'):
|
|
2336
|
# if kwargs.has_key('correctFactor'):
|
|
|
2337
|
# correctFactor = kwargs['correctFactor']
|
|
2337
|
# correctFactor = kwargs['correctFactor']
|
|
|
2338
|
# else:
|
|
2338
|
# else:
|
|
|
2339
|
# correctFactor = 1
|
|
2339
|
# correctFactor = 1
|
|
|
2340
|
|
|
2340
|
|
|
|
2341
|
# tau = dataOut.data_param
|
|
2341
|
# tau = dataOut.data_param
|
|
|
2342
|
# _lambda = dataOut.C/dataOut.frequency
|
|
2342
|
# _lambda = dataOut.C/dataOut.frequency
|
|
|
2343
|
# pairsList = dataOut.groupList
|
|
2343
|
# pairsList = dataOut.groupList
|
|
|
2344
|
# nChannels = dataOut.nChannels
|
|
2344
|
# nChannels = dataOut.nChannels
|
|
|
2345
|
|
|
2345
|
|
|
|
2346
|
kwargs['groupList'] = dataOut.groupList
|
|
2346
|
kwargs['groupList'] = dataOut.groupList
|
|
|
2347
|
kwargs['tau'] = dataOut.data_param
|
|
2347
|
kwargs['tau'] = dataOut.data_param
|
|
|
2348
|
kwargs['_lambda'] = dataOut.C/dataOut.frequency
|
|
2348
|
kwargs['_lambda'] = dataOut.C/dataOut.frequency
|
|
|
2349
|
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
|
|
2349
|
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
|
|
|
2350
|
dataOut.data_output = self.techniqueSA(kwargs)
|
|
2350
|
dataOut.data_output = self.techniqueSA(kwargs)
|
|
|
2351
|
dataOut.utctimeInit = dataOut.utctime
|
|
2351
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2352
|
dataOut.outputInterval = dataOut.timeInterval
|
|
2352
|
dataOut.outputInterval = dataOut.timeInterval
|
|
|
2353
|
|
|
2353
|
|
|
|
2354
|
elif technique == 'Meteors':
|
|
2354
|
elif technique == 'Meteors':
|
|
|
2355
|
dataOut.flagNoData = True
|
|
2355
|
dataOut.flagNoData = True
|
|
|
2356
|
self.__dataReady = False
|
|
2356
|
self.__dataReady = False
|
|
|
2357
|
|
|
2357
|
|
|
|
2358
|
if kwargs.has_key('nHours'):
|
|
2358
|
if kwargs.has_key('nHours'):
|
|
|
2359
|
nHours = kwargs['nHours']
|
|
2359
|
nHours = kwargs['nHours']
|
|
|
2360
|
else:
|
|
2360
|
else:
|
|
|
2361
|
nHours = 1
|
|
2361
|
nHours = 1
|
|
|
2362
|
|
|
2362
|
|
|
|
2363
|
if kwargs.has_key('meteorsPerBin'):
|
|
2363
|
if kwargs.has_key('meteorsPerBin'):
|
|
|
2364
|
meteorThresh = kwargs['meteorsPerBin']
|
|
2364
|
meteorThresh = kwargs['meteorsPerBin']
|
|
|
2365
|
else:
|
|
2365
|
else:
|
|
|
2366
|
meteorThresh = 6
|
|
2366
|
meteorThresh = 6
|
|
|
2367
|
|
|
2367
|
|
|
|
2368
|
if kwargs.has_key('hmin'):
|
|
2368
|
if kwargs.has_key('hmin'):
|
|
|
2369
|
hmin = kwargs['hmin']
|
|
2369
|
hmin = kwargs['hmin']
|
|
|
2370
|
else: hmin = 70
|
|
2370
|
else: hmin = 70
|
|
|
2371
|
if kwargs.has_key('hmax'):
|
|
2371
|
if kwargs.has_key('hmax'):
|
|
|
2372
|
hmax = kwargs['hmax']
|
|
2372
|
hmax = kwargs['hmax']
|
|
|
2373
|
else: hmax = 110
|
|
2373
|
else: hmax = 110
|
|
|
2374
|
|
|
2374
|
|
|
|
2375
|
dataOut.outputInterval = nHours*3600
|
|
2375
|
dataOut.outputInterval = nHours*3600
|
|
|
2376
|
|
|
2376
|
|
|
|
2377
|
if self.__isConfig == False:
|
|
2377
|
if self.__isConfig == False:
|
|
|
2378
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
2378
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
2379
|
#Get Initial LTC time
|
|
2379
|
#Get Initial LTC time
|
|
|
2380
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
2380
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
2381
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
2381
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
2382
|
|
|
2382
|
|
|
|
2383
|
self.__isConfig = True
|
|
2383
|
self.__isConfig = True
|
|
|
2384
|
|
|
2384
|
|
|
|
2385
|
if self.__buffer == None:
|
|
2385
|
if self.__buffer == None:
|
|
|
2386
|
self.__buffer = dataOut.data_param
|
|
2386
|
self.__buffer = dataOut.data_param
|
|
|
2387
|
self.__firstdata = copy.copy(dataOut)
|
|
2387
|
self.__firstdata = copy.copy(dataOut)
|
|
|
2388
|
|
|
2388
|
|
|
|
2389
|
else:
|
|
2389
|
else:
|
|
|
2390
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
2390
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
2391
|
|
|
2391
|
|
|
|
2392
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
2392
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
2393
|
|
|
2393
|
|
|
|
2394
|
if self.__dataReady:
|
|
2394
|
if self.__dataReady:
|
|
|
2395
|
dataOut.utctimeInit = self.__initime
|
|
2395
|
dataOut.utctimeInit = self.__initime
|
|
|
2396
|
|
|
2396
|
|
|
|
2397
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
2397
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
2398
|
|
|
2398
|
|
|
|
2399
|
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
|
|
2399
|
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
|
|
|
2400
|
dataOut.flagNoData = False
|
|
2400
|
dataOut.flagNoData = False
|
|
|
2401
|
self.__buffer = None
|
|
2401
|
self.__buffer = None
|
|
|
2402
|
|
|
2402
|
|
|
|
2403
|
elif technique == 'Meteors1':
|
|
2403
|
elif technique == 'Meteors1':
|
|
|
2404
|
dataOut.flagNoData = True
|
|
2404
|
dataOut.flagNoData = True
|
|
|
2405
|
self.__dataReady = False
|
|
2405
|
self.__dataReady = False
|
|
|
2406
|
|
|
2406
|
|
|
|
2407
|
if kwargs.has_key('nMins'):
|
|
2407
|
if kwargs.has_key('nMins'):
|
|
|
2408
|
nMins = kwargs['nMins']
|
|
2408
|
nMins = kwargs['nMins']
|
|
|
2409
|
else: nMins = 20
|
|
2409
|
else: nMins = 20
|
|
|
2410
|
if kwargs.has_key('rx_location'):
|
|
2410
|
if kwargs.has_key('rx_location'):
|
|
|
2411
|
rx_location = kwargs['rx_location']
|
|
2411
|
rx_location = kwargs['rx_location']
|
|
|
2412
|
else: rx_location = [(0,1),(1,1),(1,0)]
|
|
2412
|
else: rx_location = [(0,1),(1,1),(1,0)]
|
|
|
2413
|
if kwargs.has_key('azimuth'):
|
|
2413
|
if kwargs.has_key('azimuth'):
|
|
|
2414
|
azimuth = kwargs['azimuth']
|
|
2414
|
azimuth = kwargs['azimuth']
|
|
|
2415
|
else: azimuth = 51.06
|
|
2415
|
else: azimuth = 51.06
|
|
|
2416
|
if kwargs.has_key('dfactor'):
|
|
2416
|
if kwargs.has_key('dfactor'):
|
|
|
2417
|
dfactor = kwargs['dfactor']
|
|
2417
|
dfactor = kwargs['dfactor']
|
|
|
2418
|
if kwargs.has_key('mode'):
|
|
2418
|
if kwargs.has_key('mode'):
|
|
|
2419
|
mode = kwargs['mode']
|
|
2419
|
mode = kwargs['mode']
|
|
|
2420
|
if kwargs.has_key('theta_x'):
|
|
2420
|
if kwargs.has_key('theta_x'):
|
|
|
2421
|
theta_x = kwargs['theta_x']
|
|
2421
|
theta_x = kwargs['theta_x']
|
|
|
2422
|
if kwargs.has_key('theta_y'):
|
|
2422
|
if kwargs.has_key('theta_y'):
|
|
|
2423
|
theta_y = kwargs['theta_y']
|
|
2423
|
theta_y = kwargs['theta_y']
|
|
|
2424
|
else: mode = 'SA'
|
|
2424
|
else: mode = 'SA'
|
|
|
2425
|
|
|
2425
|
|
|
|
2426
|
#Borrar luego esto
|
|
2426
|
#Borrar luego esto
|
|
|
2427
|
if dataOut.groupList == None:
|
|
2427
|
if dataOut.groupList == None:
|
|
|
2428
|
dataOut.groupList = [(0,1),(0,2),(1,2)]
|
|
2428
|
dataOut.groupList = [(0,1),(0,2),(1,2)]
|
|
|
2429
|
groupList = dataOut.groupList
|
|
2429
|
groupList = dataOut.groupList
|
|
|
2430
|
C = 3e8
|
|
2430
|
C = 3e8
|
|
|
2431
|
freq = 50e6
|
|
2431
|
freq = 50e6
|
|
|
2432
|
lamb = C/freq
|
|
2432
|
lamb = C/freq
|
|
|
2433
|
k = 2*numpy.pi/lamb
|
|
2433
|
k = 2*numpy.pi/lamb
|
|
|
2434
|
|
|
2434
|
|
|
|
2435
|
timeList = dataOut.abscissaList
|
|
2435
|
timeList = dataOut.abscissaList
|
|
|
2436
|
heightList = dataOut.heightList
|
|
2436
|
heightList = dataOut.heightList
|
|
|
2437
|
|
|
2437
|
|
|
|
2438
|
if self.__isConfig == False:
|
|
2438
|
if self.__isConfig == False:
|
|
|
2439
|
dataOut.outputInterval = nMins*60
|
|
2439
|
dataOut.outputInterval = nMins*60
|
|
|
2440
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
2440
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
2441
|
#Get Initial LTC time
|
|
2441
|
#Get Initial LTC time
|
|
|
2442
|
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
2442
|
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
2443
|
minuteAux = initime.minute
|
|
2443
|
minuteAux = initime.minute
|
|
|
2444
|
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
|
|
2444
|
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
|
|
|
2445
|
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
2445
|
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
2446
|
|
|
2446
|
|
|
|
2447
|
self.__isConfig = True
|
|
2447
|
self.__isConfig = True
|
|
|
2448
|
|
|
2448
|
|
|
|
2449
|
if self.__buffer == None:
|
|
2449
|
if self.__buffer == None:
|
|
|
2450
|
self.__buffer = dataOut.data_param
|
|
2450
|
self.__buffer = dataOut.data_param
|
|
|
2451
|
self.__firstdata = copy.copy(dataOut)
|
|
2451
|
self.__firstdata = copy.copy(dataOut)
|
|
|
2452
|
|
|
2452
|
|
|
|
2453
|
else:
|
|
2453
|
else:
|
|
|
2454
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
2454
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
2455
|
|
|
2455
|
|
|
|
2456
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
2456
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
2457
|
|
|
2457
|
|
|
|
2458
|
if self.__dataReady:
|
|
2458
|
if self.__dataReady:
|
|
|
2459
|
dataOut.utctimeInit = self.__initime
|
|
2459
|
dataOut.utctimeInit = self.__initime
|
|
|
2460
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
2460
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
2461
|
|
|
2461
|
|
|
|
2462
|
metArray = self.__buffer
|
|
2462
|
metArray = self.__buffer
|
|
|
2463
|
if mode == 'SA':
|
|
2463
|
if mode == 'SA':
|
|
|
2464
|
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
|
|
2464
|
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
|
|
|
2465
|
elif mode == 'DBS':
|
|
2465
|
elif mode == 'DBS':
|
|
|
2466
|
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
|
|
2466
|
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
|
|
|
2467
|
dataOut.data_output = dataOut.data_output.T
|
|
2467
|
dataOut.data_output = dataOut.data_output.T
|
|
|
2468
|
dataOut.flagNoData = False
|
|
2468
|
dataOut.flagNoData = False
|
|
|
2469
|
self.__buffer = None
|
|
2469
|
self.__buffer = None
|
|
|
2470
|
|
|
2470
|
|
|
|
2471
|
return
|
|
2471
|
return
|
|
|
2472
|
|
|
2472
|
|
|
|
2473
|
class EWDriftsEstimation(Operation):
|
|
2473
|
class EWDriftsEstimation(Operation):
|
|
|
2474
|
|
|
2474
|
|
|
|
2475
|
def __init__(self):
|
|
2475
|
def __init__(self, **kwargs):
|
|
|
2476
|
Operation.__init__(self)
|
|
2476
|
Operation.__init__(self, **kwargs)
|
|
|
2477
|
|
|
2477
|
|
|
|
2478
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
2478
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
|
2479
|
listPhi = phi.tolist()
|
|
2479
|
listPhi = phi.tolist()
|
|
|
2480
|
maxid = listPhi.index(max(listPhi))
|
|
2480
|
maxid = listPhi.index(max(listPhi))
|
|
|
2481
|
minid = listPhi.index(min(listPhi))
|
|
2481
|
minid = listPhi.index(min(listPhi))
|
|
|
2482
|
|
|
2482
|
|
|
|
2483
|
rango = range(len(phi))
|
|
2483
|
rango = range(len(phi))
|
|
|
2484
|
# rango = numpy.delete(rango,maxid)
|
|
2484
|
# rango = numpy.delete(rango,maxid)
|
|
|
2485
|
|
|
2485
|
|
|
|
2486
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
2486
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
|
2487
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
2487
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
|
2488
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
2488
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
|
2489
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
2489
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
|
2490
|
|
|
2490
|
|
|
|
2491
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
2491
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
2492
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
2492
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
2493
|
|
|
2493
|
|
|
|
2494
|
for i in rango:
|
|
2494
|
for i in rango:
|
|
|
2495
|
x = heiRang*math.cos(phi[i])
|
|
2495
|
x = heiRang*math.cos(phi[i])
|
|
|
2496
|
y1 = velRadial[i,:]
|
|
2496
|
y1 = velRadial[i,:]
|
|
|
2497
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
2497
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
|
2498
|
|
|
2498
|
|
|
|
2499
|
x1 = heiRang1
|
|
2499
|
x1 = heiRang1
|
|
|
2500
|
y11 = f1(x1)
|
|
2500
|
y11 = f1(x1)
|
|
|
2501
|
|
|
2501
|
|
|
|
2502
|
y2 = SNR[i,:]
|
|
2502
|
y2 = SNR[i,:]
|
|
|
2503
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
2503
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
|
2504
|
y21 = f2(x1)
|
|
2504
|
y21 = f2(x1)
|
|
|
2505
|
|
|
2505
|
|
|
|
2506
|
velRadial1[i,:] = y11
|
|
2506
|
velRadial1[i,:] = y11
|
|
|
2507
|
SNR1[i,:] = y21
|
|
2507
|
SNR1[i,:] = y21
|
|
|
2508
|
|
|
2508
|
|
|
|
2509
|
return heiRang1, velRadial1, SNR1
|
|
2509
|
return heiRang1, velRadial1, SNR1
|
|
|
2510
|
|
|
2510
|
|
|
|
2511
|
def run(self, dataOut, zenith, zenithCorrection):
|
|
2511
|
def run(self, dataOut, zenith, zenithCorrection):
|
|
|
2512
|
heiRang = dataOut.heightList
|
|
2512
|
heiRang = dataOut.heightList
|
|
|
2513
|
velRadial = dataOut.data_param[:,3,:]
|
|
2513
|
velRadial = dataOut.data_param[:,3,:]
|
|
|
2514
|
SNR = dataOut.data_SNR
|
|
2514
|
SNR = dataOut.data_SNR
|
|
|
2515
|
|
|
2515
|
|
|
|
2516
|
zenith = numpy.array(zenith)
|
|
2516
|
zenith = numpy.array(zenith)
|
|
|
2517
|
zenith -= zenithCorrection
|
|
2517
|
zenith -= zenithCorrection
|
|
|
2518
|
zenith *= numpy.pi/180
|
|
2518
|
zenith *= numpy.pi/180
|
|
|
2519
|
|
|
2519
|
|
|
|
2520
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
|
|
2520
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
|
|
|
2521
|
|
|
2521
|
|
|
|
2522
|
alp = zenith[0]
|
|
2522
|
alp = zenith[0]
|
|
|
2523
|
bet = zenith[1]
|
|
2523
|
bet = zenith[1]
|
|
|
2524
|
|
|
2524
|
|
|
|
2525
|
w_w = velRadial1[0,:]
|
|
2525
|
w_w = velRadial1[0,:]
|
|
|
2526
|
w_e = velRadial1[1,:]
|
|
2526
|
w_e = velRadial1[1,:]
|
|
|
2527
|
|
|
2527
|
|
|
|
2528
|
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
|
|
2528
|
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
|
|
|
2529
|
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
|
|
2529
|
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
|
|
|
2530
|
|
|
2530
|
|
|
|
2531
|
winds = numpy.vstack((u,w))
|
|
2531
|
winds = numpy.vstack((u,w))
|
|
|
2532
|
|
|
2532
|
|
|
|
2533
|
dataOut.heightList = heiRang1
|
|
2533
|
dataOut.heightList = heiRang1
|
|
|
2534
|
dataOut.data_output = winds
|
|
2534
|
dataOut.data_output = winds
|
|
|
2535
|
dataOut.data_SNR = SNR1
|
|
2535
|
dataOut.data_SNR = SNR1
|
|
|
2536
|
|
|
2536
|
|
|
|
2537
|
dataOut.utctimeInit = dataOut.utctime
|
|
2537
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2538
|
dataOut.outputInterval = dataOut.timeInterval
|
|
2538
|
dataOut.outputInterval = dataOut.timeInterval
|
|
|
2539
|
return
|
|
2539
|
return
|
|
|
2540
|
|
|
2540
|
|
|
|
2541
|
#--------------- Non Specular Meteor ----------------
|
|
2541
|
#--------------- Non Specular Meteor ----------------
|
|
|
2542
|
|
|
2542
|
|
|
|
2543
|
class NonSpecularMeteorDetection(Operation):
|
|
2543
|
class NonSpecularMeteorDetection(Operation):
|
|
|
2544
|
|
|
2544
|
|
|
|
2545
|
def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
|
|
2545
|
def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
|
|
|
2546
|
data_acf = dataOut.data_pre[0]
|
|
2546
|
data_acf = dataOut.data_pre[0]
|
|
|
2547
|
data_ccf = dataOut.data_pre[1]
|
|
2547
|
data_ccf = dataOut.data_pre[1]
|
|
|
2548
|
pairsList = dataOut.groupList[1]
|
|
2548
|
pairsList = dataOut.groupList[1]
|
|
|
2549
|
|
|
2549
|
|
|
|
2550
|
lamb = dataOut.C/dataOut.frequency
|
|
2550
|
lamb = dataOut.C/dataOut.frequency
|
|
|
2551
|
tSamp = dataOut.ippSeconds*dataOut.nCohInt
|
|
2551
|
tSamp = dataOut.ippSeconds*dataOut.nCohInt
|
|
|
2552
|
paramInterval = dataOut.paramInterval
|
|
2552
|
paramInterval = dataOut.paramInterval
|
|
|
2553
|
|
|
2553
|
|
|
|
2554
|
nChannels = data_acf.shape[0]
|
|
2554
|
nChannels = data_acf.shape[0]
|
|
|
2555
|
nLags = data_acf.shape[1]
|
|
2555
|
nLags = data_acf.shape[1]
|
|
|
2556
|
nProfiles = data_acf.shape[2]
|
|
2556
|
nProfiles = data_acf.shape[2]
|
|
|
2557
|
nHeights = dataOut.nHeights
|
|
2557
|
nHeights = dataOut.nHeights
|
|
|
2558
|
nCohInt = dataOut.nCohInt
|
|
2558
|
nCohInt = dataOut.nCohInt
|
|
|
2559
|
sec = numpy.round(nProfiles/dataOut.paramInterval)
|
|
2559
|
sec = numpy.round(nProfiles/dataOut.paramInterval)
|
|
|
2560
|
heightList = dataOut.heightList
|
|
2560
|
heightList = dataOut.heightList
|
|
|
2561
|
ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
|
|
2561
|
ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
|
|
|
2562
|
utctime = dataOut.utctime
|
|
2562
|
utctime = dataOut.utctime
|
|
|
2563
|
|
|
2563
|
|
|
|
2564
|
dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
|
|
2564
|
dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
|
|
|
2565
|
|
|
2565
|
|
|
|
2566
|
#------------------------ SNR --------------------------------------
|
|
2566
|
#------------------------ SNR --------------------------------------
|
|
|
2567
|
power = data_acf[:,0,:,:].real
|
|
2567
|
power = data_acf[:,0,:,:].real
|
|
|
2568
|
noise = numpy.zeros(nChannels)
|
|
2568
|
noise = numpy.zeros(nChannels)
|
|
|
2569
|
SNR = numpy.zeros(power.shape)
|
|
2569
|
SNR = numpy.zeros(power.shape)
|
|
|
2570
|
for i in range(nChannels):
|
|
2570
|
for i in range(nChannels):
|
|
|
2571
|
noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
|
|
2571
|
noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
|
|
|
2572
|
SNR[i] = (power[i]-noise[i])/noise[i]
|
|
2572
|
SNR[i] = (power[i]-noise[i])/noise[i]
|
|
|
2573
|
SNRm = numpy.nanmean(SNR, axis = 0)
|
|
2573
|
SNRm = numpy.nanmean(SNR, axis = 0)
|
|
|
2574
|
SNRdB = 10*numpy.log10(SNR)
|
|
2574
|
SNRdB = 10*numpy.log10(SNR)
|
|
|
2575
|
|
|
2575
|
|
|
|
2576
|
if mode == 'SA':
|
|
2576
|
if mode == 'SA':
|
|
|
2577
|
dataOut.groupList = dataOut.groupList[1]
|
|
2577
|
dataOut.groupList = dataOut.groupList[1]
|
|
|
2578
|
nPairs = data_ccf.shape[0]
|
|
2578
|
nPairs = data_ccf.shape[0]
|
|
|
2579
|
#---------------------- Coherence and Phase --------------------------
|
|
2579
|
#---------------------- Coherence and Phase --------------------------
|
|
|
2580
|
phase = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
2580
|
phase = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
|
2581
|
# phase1 = numpy.copy(phase)
|
|
2581
|
# phase1 = numpy.copy(phase)
|
|
|
2582
|
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
2582
|
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
|
2583
|
|
|
2583
|
|
|
|
2584
|
for p in range(nPairs):
|
|
2584
|
for p in range(nPairs):
|
|
|
2585
|
ch0 = pairsList[p][0]
|
|
2585
|
ch0 = pairsList[p][0]
|
|
|
2586
|
ch1 = pairsList[p][1]
|
|
2586
|
ch1 = pairsList[p][1]
|
|
|
2587
|
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
|
|
2587
|
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
|
|
|
2588
|
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
|
|
2588
|
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
|
|
|
2589
|
# phase1[p,:,:] = numpy.angle(ccf) #median filter
|
|
2589
|
# phase1[p,:,:] = numpy.angle(ccf) #median filter
|
|
|
2590
|
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
|
|
2590
|
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
|
|
|
2591
|
# coh1[p,:,:] = numpy.abs(ccf) #median filter
|
|
2591
|
# coh1[p,:,:] = numpy.abs(ccf) #median filter
|
|
|
2592
|
coh = numpy.nanmax(coh1, axis = 0)
|
|
2592
|
coh = numpy.nanmax(coh1, axis = 0)
|
|
|
2593
|
# struc = numpy.ones((5,1))
|
|
2593
|
# struc = numpy.ones((5,1))
|
|
|
2594
|
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
|
|
2594
|
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
|
|
|
2595
|
#---------------------- Radial Velocity ----------------------------
|
|
2595
|
#---------------------- Radial Velocity ----------------------------
|
|
|
2596
|
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
|
|
2596
|
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
|
|
|
2597
|
velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
|
|
2597
|
velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
|
|
|
2598
|
|
|
2598
|
|
|
|
2599
|
if allData:
|
|
2599
|
if allData:
|
|
|
2600
|
boolMetFin = ~numpy.isnan(SNRm)
|
|
2600
|
boolMetFin = ~numpy.isnan(SNRm)
|
|
|
2601
|
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
2601
|
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
|
2602
|
else:
|
|
2602
|
else:
|
|
|
2603
|
#------------------------ Meteor mask ---------------------------------
|
|
2603
|
#------------------------ Meteor mask ---------------------------------
|
|
|
2604
|
# #SNR mask
|
|
2604
|
# #SNR mask
|
|
|
2605
|
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
|
|
2605
|
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
|
|
|
2606
|
#
|
|
2606
|
#
|
|
|
2607
|
# #Erase small objects
|
|
2607
|
# #Erase small objects
|
|
|
2608
|
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
|
|
2608
|
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
|
|
|
2609
|
#
|
|
2609
|
#
|
|
|
2610
|
# auxEEJ = numpy.sum(boolMet1,axis=0)
|
|
2610
|
# auxEEJ = numpy.sum(boolMet1,axis=0)
|
|
|
2611
|
# indOver = auxEEJ>nProfiles*0.8 #Use this later
|
|
2611
|
# indOver = auxEEJ>nProfiles*0.8 #Use this later
|
|
|
2612
|
# indEEJ = numpy.where(indOver)[0]
|
|
2612
|
# indEEJ = numpy.where(indOver)[0]
|
|
|
2613
|
# indNEEJ = numpy.where(~indOver)[0]
|
|
2613
|
# indNEEJ = numpy.where(~indOver)[0]
|
|
|
2614
|
#
|
|
2614
|
#
|
|
|
2615
|
# boolMetFin = boolMet1
|
|
2615
|
# boolMetFin = boolMet1
|
|
|
2616
|
#
|
|
2616
|
#
|
|
|
2617
|
# if indEEJ.size > 0:
|
|
2617
|
# if indEEJ.size > 0:
|
|
|
2618
|
# boolMet1[:,indEEJ] = False #Erase heights with EEJ
|
|
2618
|
# boolMet1[:,indEEJ] = False #Erase heights with EEJ
|
|
|
2619
|
#
|
|
2619
|
#
|
|
|
2620
|
# boolMet2 = coh > cohThresh
|
|
2620
|
# boolMet2 = coh > cohThresh
|
|
|
2621
|
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
|
|
2621
|
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
|
|
|
2622
|
#
|
|
2622
|
#
|
|
|
2623
|
# #Final Meteor mask
|
|
2623
|
# #Final Meteor mask
|
|
|
2624
|
# boolMetFin = boolMet1|boolMet2
|
|
2624
|
# boolMetFin = boolMet1|boolMet2
|
|
|
2625
|
|
|
2625
|
|
|
|
2626
|
#Coherence mask
|
|
2626
|
#Coherence mask
|
|
|
2627
|
boolMet1 = coh > 0.75
|
|
2627
|
boolMet1 = coh > 0.75
|
|
|
2628
|
struc = numpy.ones((30,1))
|
|
2628
|
struc = numpy.ones((30,1))
|
|
|
2629
|
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
|
|
2629
|
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
|
|
|
2630
|
|
|
2630
|
|
|
|
2631
|
#Derivative mask
|
|
2631
|
#Derivative mask
|
|
|
2632
|
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
2632
|
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
|
2633
|
boolMet2 = derPhase < 0.2
|
|
2633
|
boolMet2 = derPhase < 0.2
|
|
|
2634
|
# boolMet2 = ndimage.morphology.binary_opening(boolMet2)
|
|
2634
|
# boolMet2 = ndimage.morphology.binary_opening(boolMet2)
|
|
|
2635
|
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
|
|
2635
|
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
|
|
|
2636
|
boolMet2 = ndimage.median_filter(boolMet2,size=5)
|
|
2636
|
boolMet2 = ndimage.median_filter(boolMet2,size=5)
|
|
|
2637
|
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
|
|
2637
|
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
|
|
|
2638
|
# #Final mask
|
|
2638
|
# #Final mask
|
|
|
2639
|
# boolMetFin = boolMet2
|
|
2639
|
# boolMetFin = boolMet2
|
|
|
2640
|
boolMetFin = boolMet1&boolMet2
|
|
2640
|
boolMetFin = boolMet1&boolMet2
|
|
|
2641
|
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
|
|
2641
|
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
|
|
|
2642
|
#Creating data_param
|
|
2642
|
#Creating data_param
|
|
|
2643
|
coordMet = numpy.where(boolMetFin)
|
|
2643
|
coordMet = numpy.where(boolMetFin)
|
|
|
2644
|
|
|
2644
|
|
|
|
2645
|
tmet = coordMet[0]
|
|
2645
|
tmet = coordMet[0]
|
|
|
2646
|
hmet = coordMet[1]
|
|
2646
|
hmet = coordMet[1]
|
|
|
2647
|
|
|
2647
|
|
|
|
2648
|
data_param = numpy.zeros((tmet.size, 6 + nPairs))
|
|
2648
|
data_param = numpy.zeros((tmet.size, 6 + nPairs))
|
|
|
2649
|
data_param[:,0] = utctime
|
|
2649
|
data_param[:,0] = utctime
|
|
|
2650
|
data_param[:,1] = tmet
|
|
2650
|
data_param[:,1] = tmet
|
|
|
2651
|
data_param[:,2] = hmet
|
|
2651
|
data_param[:,2] = hmet
|
|
|
2652
|
data_param[:,3] = SNRm[tmet,hmet]
|
|
2652
|
data_param[:,3] = SNRm[tmet,hmet]
|
|
|
2653
|
data_param[:,4] = velRad[tmet,hmet]
|
|
2653
|
data_param[:,4] = velRad[tmet,hmet]
|
|
|
2654
|
data_param[:,5] = coh[tmet,hmet]
|
|
2654
|
data_param[:,5] = coh[tmet,hmet]
|
|
|
2655
|
data_param[:,6:] = phase[:,tmet,hmet].T
|
|
2655
|
data_param[:,6:] = phase[:,tmet,hmet].T
|
|
|
2656
|
|
|
2656
|
|
|
|
2657
|
elif mode == 'DBS':
|
|
2657
|
elif mode == 'DBS':
|
|
|
2658
|
dataOut.groupList = numpy.arange(nChannels)
|
|
2658
|
dataOut.groupList = numpy.arange(nChannels)
|
|
|
2659
|
|
|
2659
|
|
|
|
2660
|
#Radial Velocities
|
|
2660
|
#Radial Velocities
|
|
|
2661
|
phase = numpy.angle(data_acf[:,1,:,:])
|
|
2661
|
phase = numpy.angle(data_acf[:,1,:,:])
|
|
|
2662
|
# phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
|
|
2662
|
# phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
|
|
|
2663
|
velRad = phase*lamb/(4*numpy.pi*tSamp)
|
|
2663
|
velRad = phase*lamb/(4*numpy.pi*tSamp)
|
|
|
2664
|
|
|
2664
|
|
|
|
2665
|
#Spectral width
|
|
2665
|
#Spectral width
|
|
|
2666
|
# acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
|
|
2666
|
# acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
|
|
|
2667
|
# acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
|
|
2667
|
# acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
|
|
|
2668
|
acf1 = data_acf[:,1,:,:]
|
|
2668
|
acf1 = data_acf[:,1,:,:]
|
|
|
2669
|
acf2 = data_acf[:,2,:,:]
|
|
2669
|
acf2 = data_acf[:,2,:,:]
|
|
|
2670
|
|
|
2670
|
|
|
|
2671
|
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
|
|
2671
|
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
|
|
|
2672
|
# velRad = ndimage.median_filter(velRad, size = (1,5,1))
|
|
2672
|
# velRad = ndimage.median_filter(velRad, size = (1,5,1))
|
|
|
2673
|
if allData:
|
|
2673
|
if allData:
|
|
|
2674
|
boolMetFin = ~numpy.isnan(SNRdB)
|
|
2674
|
boolMetFin = ~numpy.isnan(SNRdB)
|
|
|
2675
|
else:
|
|
2675
|
else:
|
|
|
2676
|
#SNR
|
|
2676
|
#SNR
|
|
|
2677
|
boolMet1 = (SNRdB>SNRthresh) #SNR mask
|
|
2677
|
boolMet1 = (SNRdB>SNRthresh) #SNR mask
|
|
|
2678
|
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
|
|
2678
|
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
|
|
|
2679
|
|
|
2679
|
|
|
|
2680
|
#Radial velocity
|
|
2680
|
#Radial velocity
|
|
|
2681
|
boolMet2 = numpy.abs(velRad) < 20
|
|
2681
|
boolMet2 = numpy.abs(velRad) < 20
|
|
|
2682
|
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
|
|
2682
|
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
|
|
|
2683
|
|
|
2683
|
|
|
|
2684
|
#Spectral Width
|
|
2684
|
#Spectral Width
|
|
|
2685
|
boolMet3 = spcWidth < 30
|
|
2685
|
boolMet3 = spcWidth < 30
|
|
|
2686
|
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
|
|
2686
|
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
|
|
|
2687
|
# boolMetFin = self.__erase_small(boolMet1, 10,5)
|
|
2687
|
# boolMetFin = self.__erase_small(boolMet1, 10,5)
|
|
|
2688
|
boolMetFin = boolMet1&boolMet2&boolMet3
|
|
2688
|
boolMetFin = boolMet1&boolMet2&boolMet3
|
|
|
2689
|
|
|
2689
|
|
|
|
2690
|
#Creating data_param
|
|
2690
|
#Creating data_param
|
|
|
2691
|
coordMet = numpy.where(boolMetFin)
|
|
2691
|
coordMet = numpy.where(boolMetFin)
|
|
|
2692
|
|
|
2692
|
|
|
|
2693
|
cmet = coordMet[0]
|
|
2693
|
cmet = coordMet[0]
|
|
|
2694
|
tmet = coordMet[1]
|
|
2694
|
tmet = coordMet[1]
|
|
|
2695
|
hmet = coordMet[2]
|
|
2695
|
hmet = coordMet[2]
|
|
|
2696
|
|
|
2696
|
|
|
|
2697
|
data_param = numpy.zeros((tmet.size, 7))
|
|
2697
|
data_param = numpy.zeros((tmet.size, 7))
|
|
|
2698
|
data_param[:,0] = utctime
|
|
2698
|
data_param[:,0] = utctime
|
|
|
2699
|
data_param[:,1] = cmet
|
|
2699
|
data_param[:,1] = cmet
|
|
|
2700
|
data_param[:,2] = tmet
|
|
2700
|
data_param[:,2] = tmet
|
|
|
2701
|
data_param[:,3] = hmet
|
|
2701
|
data_param[:,3] = hmet
|
|
|
2702
|
data_param[:,4] = SNR[cmet,tmet,hmet].T
|
|
2702
|
data_param[:,4] = SNR[cmet,tmet,hmet].T
|
|
|
2703
|
data_param[:,5] = velRad[cmet,tmet,hmet].T
|
|
2703
|
data_param[:,5] = velRad[cmet,tmet,hmet].T
|
|
|
2704
|
data_param[:,6] = spcWidth[cmet,tmet,hmet].T
|
|
2704
|
data_param[:,6] = spcWidth[cmet,tmet,hmet].T
|
|
|
2705
|
|
|
2705
|
|
|
|
2706
|
# self.dataOut.data_param = data_int
|
|
2706
|
# self.dataOut.data_param = data_int
|
|
|
2707
|
if len(data_param) == 0:
|
|
2707
|
if len(data_param) == 0:
|
|
|
2708
|
dataOut.flagNoData = True
|
|
2708
|
dataOut.flagNoData = True
|
|
|
2709
|
else:
|
|
2709
|
else:
|
|
|
2710
|
dataOut.data_param = data_param
|
|
2710
|
dataOut.data_param = data_param
|
|
|
2711
|
|
|
2711
|
|
|
|
2712
|
def __erase_small(self, binArray, threshX, threshY):
|
|
2712
|
def __erase_small(self, binArray, threshX, threshY):
|
|
|
2713
|
labarray, numfeat = ndimage.measurements.label(binArray)
|
|
2713
|
labarray, numfeat = ndimage.measurements.label(binArray)
|
|
|
2714
|
binArray1 = numpy.copy(binArray)
|
|
2714
|
binArray1 = numpy.copy(binArray)
|
|
|
2715
|
|
|
2715
|
|
|
|
2716
|
for i in range(1,numfeat + 1):
|
|
2716
|
for i in range(1,numfeat + 1):
|
|
|
2717
|
auxBin = (labarray==i)
|
|
2717
|
auxBin = (labarray==i)
|
|
|
2718
|
auxSize = auxBin.sum()
|
|
2718
|
auxSize = auxBin.sum()
|
|
|
2719
|
|
|
2719
|
|
|
|
2720
|
x,y = numpy.where(auxBin)
|
|
2720
|
x,y = numpy.where(auxBin)
|
|
|
2721
|
widthX = x.max() - x.min()
|
|
2721
|
widthX = x.max() - x.min()
|
|
|
2722
|
widthY = y.max() - y.min()
|
|
2722
|
widthY = y.max() - y.min()
|
|
|
2723
|
|
|
2723
|
|
|
|
2724
|
#width X: 3 seg -> 12.5*3
|
|
2724
|
#width X: 3 seg -> 12.5*3
|
|
|
2725
|
#width Y:
|
|
2725
|
#width Y:
|
|
|
2726
|
|
|
2726
|
|
|
|
2727
|
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
|
|
2727
|
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
|
|
|
2728
|
binArray1[auxBin] = False
|
|
2728
|
binArray1[auxBin] = False
|
|
|
2729
|
|
|
2729
|
|
|
|
2730
|
return binArray1
|
|
2730
|
return binArray1
|
|
|
2731
|
|
|
2731
|
|
|
|
2732
|
#--------------- Specular Meteor ----------------
|
|
2732
|
#--------------- Specular Meteor ----------------
|
|
|
2733
|
|
|
2733
|
|
|
|
2734
|
class SMDetection(Operation):
|
|
2734
|
class SMDetection(Operation):
|
|
|
2735
|
'''
|
|
2735
|
'''
|
|
|
2736
|
Function DetectMeteors()
|
|
2736
|
Function DetectMeteors()
|
|
|
2737
|
Project developed with paper:
|
|
2737
|
Project developed with paper:
|
|
|
2738
|
HOLDSWORTH ET AL. 2004
|
|
2738
|
HOLDSWORTH ET AL. 2004
|
|
|
2739
|
|
|
2739
|
|
|
|
2740
|
Input:
|
|
2740
|
Input:
|
|
|
2741
|
self.dataOut.data_pre
|
|
2741
|
self.dataOut.data_pre
|
|
|
2742
|
|
|
2742
|
|
|
|
2743
|
centerReceiverIndex: From the channels, which is the center receiver
|
|
2743
|
centerReceiverIndex: From the channels, which is the center receiver
|
|
|
2744
|
|
|
2744
|
|
|
|
2745
|
hei_ref: Height reference for the Beacon signal extraction
|
|
2745
|
hei_ref: Height reference for the Beacon signal extraction
|
|
|
2746
|
tauindex:
|
|
2746
|
tauindex:
|
|
|
2747
|
predefinedPhaseShifts: Predefined phase offset for the voltge signals
|
|
2747
|
predefinedPhaseShifts: Predefined phase offset for the voltge signals
|
|
|
2748
|
|
|
2748
|
|
|
|
2749
|
cohDetection: Whether to user Coherent detection or not
|
|
2749
|
cohDetection: Whether to user Coherent detection or not
|
|
|
2750
|
cohDet_timeStep: Coherent Detection calculation time step
|
|
2750
|
cohDet_timeStep: Coherent Detection calculation time step
|
|
|
2751
|
cohDet_thresh: Coherent Detection phase threshold to correct phases
|
|
2751
|
cohDet_thresh: Coherent Detection phase threshold to correct phases
|
|
|
2752
|
|
|
2752
|
|
|
|
2753
|
noise_timeStep: Noise calculation time step
|
|
2753
|
noise_timeStep: Noise calculation time step
|
|
|
2754
|
noise_multiple: Noise multiple to define signal threshold
|
|
2754
|
noise_multiple: Noise multiple to define signal threshold
|
|
|
2755
|
|
|
2755
|
|
|
|
2756
|
multDet_timeLimit: Multiple Detection Removal time limit in seconds
|
|
2756
|
multDet_timeLimit: Multiple Detection Removal time limit in seconds
|
|
|
2757
|
multDet_rangeLimit: Multiple Detection Removal range limit in km
|
|
2757
|
multDet_rangeLimit: Multiple Detection Removal range limit in km
|
|
|
2758
|
|
|
2758
|
|
|
|
2759
|
phaseThresh: Maximum phase difference between receiver to be consider a meteor
|
|
2759
|
phaseThresh: Maximum phase difference between receiver to be consider a meteor
|
|
|
2760
|
SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor
|
|
2760
|
SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor
|
|
|
2761
|
|
|
2761
|
|
|
|
2762
|
hmin: Minimum Height of the meteor to use it in the further wind estimations
|
|
2762
|
hmin: Minimum Height of the meteor to use it in the further wind estimations
|
|
|
2763
|
hmax: Maximum Height of the meteor to use it in the further wind estimations
|
|
2763
|
hmax: Maximum Height of the meteor to use it in the further wind estimations
|
|
|
2764
|
azimuth: Azimuth angle correction
|
|
2764
|
azimuth: Azimuth angle correction
|
|
|
2765
|
|
|
2765
|
|
|
|
2766
|
Affected:
|
|
2766
|
Affected:
|
|
|
2767
|
self.dataOut.data_param
|
|
2767
|
self.dataOut.data_param
|
|
|
2768
|
|
|
2768
|
|
|
|
2769
|
Rejection Criteria (Errors):
|
|
2769
|
Rejection Criteria (Errors):
|
|
|
2770
|
0: No error; analysis OK
|
|
2770
|
0: No error; analysis OK
|
|
|
2771
|
1: SNR < SNR threshold
|
|
2771
|
1: SNR < SNR threshold
|
|
|
2772
|
2: angle of arrival (AOA) ambiguously determined
|
|
2772
|
2: angle of arrival (AOA) ambiguously determined
|
|
|
2773
|
3: AOA estimate not feasible
|
|
2773
|
3: AOA estimate not feasible
|
|
|
2774
|
4: Large difference in AOAs obtained from different antenna baselines
|
|
2774
|
4: Large difference in AOAs obtained from different antenna baselines
|
|
|
2775
|
5: echo at start or end of time series
|
|
2775
|
5: echo at start or end of time series
|
|
|
2776
|
6: echo less than 5 examples long; too short for analysis
|
|
2776
|
6: echo less than 5 examples long; too short for analysis
|
|
|
2777
|
7: echo rise exceeds 0.3s
|
|
2777
|
7: echo rise exceeds 0.3s
|
|
|
2778
|
8: echo decay time less than twice rise time
|
|
2778
|
8: echo decay time less than twice rise time
|
|
|
2779
|
9: large power level before echo
|
|
2779
|
9: large power level before echo
|
|
|
2780
|
10: large power level after echo
|
|
2780
|
10: large power level after echo
|
|
|
2781
|
11: poor fit to amplitude for estimation of decay time
|
|
2781
|
11: poor fit to amplitude for estimation of decay time
|
|
|
2782
|
12: poor fit to CCF phase variation for estimation of radial drift velocity
|
|
2782
|
12: poor fit to CCF phase variation for estimation of radial drift velocity
|
|
|
2783
|
13: height unresolvable echo: not valid height within 70 to 110 km
|
|
2783
|
13: height unresolvable echo: not valid height within 70 to 110 km
|
|
|
2784
|
14: height ambiguous echo: more then one possible height within 70 to 110 km
|
|
2784
|
14: height ambiguous echo: more then one possible height within 70 to 110 km
|
|
|
2785
|
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
2785
|
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
|
2786
|
16: oscilatory echo, indicating event most likely not an underdense echo
|
|
2786
|
16: oscilatory echo, indicating event most likely not an underdense echo
|
|
|
2787
|
|
|
2787
|
|
|
|
2788
|
17: phase difference in meteor Reestimation
|
|
2788
|
17: phase difference in meteor Reestimation
|
|
|
2789
|
|
|
2789
|
|
|
|
2790
|
Data Storage:
|
|
2790
|
Data Storage:
|
|
|
2791
|
Meteors for Wind Estimation (8):
|
|
2791
|
Meteors for Wind Estimation (8):
|
|
|
2792
|
Utc Time | Range Height
|
|
2792
|
Utc Time | Range Height
|
|
|
2793
|
Azimuth Zenith errorCosDir
|
|
2793
|
Azimuth Zenith errorCosDir
|
|
|
2794
|
VelRad errorVelRad
|
|
2794
|
VelRad errorVelRad
|
|
|
2795
|
Phase0 Phase1 Phase2 Phase3
|
|
2795
|
Phase0 Phase1 Phase2 Phase3
|
|
|
2796
|
TypeError
|
|
2796
|
TypeError
|
|
|
2797
|
|
|
2797
|
|
|
|
2798
|
'''
|
|
2798
|
'''
|
|
|
2799
|
|
|
2799
|
|
|
|
2800
|
def run(self, dataOut, hei_ref = None, tauindex = 0,
|
|
2800
|
def run(self, dataOut, hei_ref = None, tauindex = 0,
|
|
|
2801
|
phaseOffsets = None,
|
|
2801
|
phaseOffsets = None,
|
|
|
2802
|
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
|
|
2802
|
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
|
|
|
2803
|
noise_timeStep = 4, noise_multiple = 4,
|
|
2803
|
noise_timeStep = 4, noise_multiple = 4,
|
|
|
2804
|
multDet_timeLimit = 1, multDet_rangeLimit = 3,
|
|
2804
|
multDet_timeLimit = 1, multDet_rangeLimit = 3,
|
|
|
2805
|
phaseThresh = 20, SNRThresh = 5,
|
|
2805
|
phaseThresh = 20, SNRThresh = 5,
|
|
|
2806
|
hmin = 50, hmax=150, azimuth = 0,
|
|
2806
|
hmin = 50, hmax=150, azimuth = 0,
|
|
|
2807
|
channelPositions = None) :
|
|
2807
|
channelPositions = None) :
|
|
|
2808
|
|
|
2808
|
|
|
|
2809
|
|
|
2809
|
|
|
|
2810
|
#Getting Pairslist
|
|
2810
|
#Getting Pairslist
|
|
|
2811
|
if channelPositions == None:
|
|
2811
|
if channelPositions == None:
|
|
|
2812
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
2812
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
|
2813
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
2813
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
|
2814
|
meteorOps = SMOperations()
|
|
2814
|
meteorOps = SMOperations()
|
|
|
2815
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
2815
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
|
2816
|
heiRang = dataOut.getHeiRange()
|
|
2816
|
heiRang = dataOut.getHeiRange()
|
|
|
2817
|
#Get Beacon signal - No Beacon signal anymore
|
|
2817
|
#Get Beacon signal - No Beacon signal anymore
|
|
|
2818
|
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
|
|
2818
|
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
|
|
|
2819
|
#
|
|
2819
|
#
|
|
|
2820
|
# if hei_ref != None:
|
|
2820
|
# if hei_ref != None:
|
|
|
2821
|
# newheis = numpy.where(self.dataOut.heightList>hei_ref)
|
|
2821
|
# newheis = numpy.where(self.dataOut.heightList>hei_ref)
|
|
|
2822
|
#
|
|
2822
|
#
|
|
|
2823
|
|
|
2823
|
|
|
|
2824
|
|
|
2824
|
|
|
|
2825
|
#****************REMOVING HARDWARE PHASE DIFFERENCES***************
|
|
2825
|
#****************REMOVING HARDWARE PHASE DIFFERENCES***************
|
|
|
2826
|
# see if the user put in pre defined phase shifts
|
|
2826
|
# see if the user put in pre defined phase shifts
|
|
|
2827
|
voltsPShift = dataOut.data_pre.copy()
|
|
2827
|
voltsPShift = dataOut.data_pre.copy()
|
|
|
2828
|
|
|
2828
|
|
|
|
2829
|
# if predefinedPhaseShifts != None:
|
|
2829
|
# if predefinedPhaseShifts != None:
|
|
|
2830
|
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
|
|
2830
|
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
|
|
|
2831
|
#
|
|
2831
|
#
|
|
|
2832
|
# # elif beaconPhaseShifts:
|
|
2832
|
# # elif beaconPhaseShifts:
|
|
|
2833
|
# # #get hardware phase shifts using beacon signal
|
|
2833
|
# # #get hardware phase shifts using beacon signal
|
|
|
2834
|
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
|
|
2834
|
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
|
|
|
2835
|
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
|
|
2835
|
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
|
|
|
2836
|
#
|
|
2836
|
#
|
|
|
2837
|
# else:
|
|
2837
|
# else:
|
|
|
2838
|
# hardwarePhaseShifts = numpy.zeros(5)
|
|
2838
|
# hardwarePhaseShifts = numpy.zeros(5)
|
|
|
2839
|
#
|
|
2839
|
#
|
|
|
2840
|
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
|
|
2840
|
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
|
|
|
2841
|
# for i in range(self.dataOut.data_pre.shape[0]):
|
|
2841
|
# for i in range(self.dataOut.data_pre.shape[0]):
|
|
|
2842
|
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
|
|
2842
|
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
|
|
|
2843
|
|
|
2843
|
|
|
|
2844
|
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
|
|
2844
|
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
|
|
|
2845
|
|
|
2845
|
|
|
|
2846
|
#Remove DC
|
|
2846
|
#Remove DC
|
|
|
2847
|
voltsDC = numpy.mean(voltsPShift,1)
|
|
2847
|
voltsDC = numpy.mean(voltsPShift,1)
|
|
|
2848
|
voltsDC = numpy.mean(voltsDC,1)
|
|
2848
|
voltsDC = numpy.mean(voltsDC,1)
|
|
|
2849
|
for i in range(voltsDC.shape[0]):
|
|
2849
|
for i in range(voltsDC.shape[0]):
|
|
|
2850
|
voltsPShift[i] = voltsPShift[i] - voltsDC[i]
|
|
2850
|
voltsPShift[i] = voltsPShift[i] - voltsDC[i]
|
|
|
2851
|
|
|
2851
|
|
|
|
2852
|
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift
|
|
2852
|
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift
|
|
|
2853
|
# voltsPShift = voltsPShift[:,:,:newheis[0][0]]
|
|
2853
|
# voltsPShift = voltsPShift[:,:,:newheis[0][0]]
|
|
|
2854
|
|
|
2854
|
|
|
|
2855
|
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
|
|
2855
|
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
|
|
|
2856
|
#Coherent Detection
|
|
2856
|
#Coherent Detection
|
|
|
2857
|
if cohDetection:
|
|
2857
|
if cohDetection:
|
|
|
2858
|
#use coherent detection to get the net power
|
|
2858
|
#use coherent detection to get the net power
|
|
|
2859
|
cohDet_thresh = cohDet_thresh*numpy.pi/180
|
|
2859
|
cohDet_thresh = cohDet_thresh*numpy.pi/180
|
|
|
2860
|
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
|
|
2860
|
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
|
|
|
2861
|
|
|
2861
|
|
|
|
2862
|
#Non-coherent detection!
|
|
2862
|
#Non-coherent detection!
|
|
|
2863
|
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
|
|
2863
|
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
|
|
|
2864
|
#********** END OF COH/NON-COH POWER CALCULATION**********************
|
|
2864
|
#********** END OF COH/NON-COH POWER CALCULATION**********************
|
|
|
2865
|
|
|
2865
|
|
|
|
2866
|
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
|
|
2866
|
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
|
|
|
2867
|
#Get noise
|
|
2867
|
#Get noise
|
|
|
2868
|
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
|
|
2868
|
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
|
|
|
2869
|
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
|
|
2869
|
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
|
|
|
2870
|
#Get signal threshold
|
|
2870
|
#Get signal threshold
|
|
|
2871
|
signalThresh = noise_multiple*noise
|
|
2871
|
signalThresh = noise_multiple*noise
|
|
|
2872
|
#Meteor echoes detection
|
|
2872
|
#Meteor echoes detection
|
|
|
2873
|
listMeteors = self.__findMeteors(powerNet, signalThresh)
|
|
2873
|
listMeteors = self.__findMeteors(powerNet, signalThresh)
|
|
|
2874
|
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
|
|
2874
|
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
|
|
|
2875
|
|
|
2875
|
|
|
|
2876
|
#************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
|
|
2876
|
#************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
|
|
|
2877
|
#Parameters
|
|
2877
|
#Parameters
|
|
|
2878
|
heiRange = dataOut.getHeiRange()
|
|
2878
|
heiRange = dataOut.getHeiRange()
|
|
|
2879
|
rangeInterval = heiRange[1] - heiRange[0]
|
|
2879
|
rangeInterval = heiRange[1] - heiRange[0]
|
|
|
2880
|
rangeLimit = multDet_rangeLimit/rangeInterval
|
|
2880
|
rangeLimit = multDet_rangeLimit/rangeInterval
|
|
|
2881
|
timeLimit = multDet_timeLimit/dataOut.timeInterval
|
|
2881
|
timeLimit = multDet_timeLimit/dataOut.timeInterval
|
|
|
2882
|
#Multiple detection removals
|
|
2882
|
#Multiple detection removals
|
|
|
2883
|
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
|
|
2883
|
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
|
|
|
2884
|
#************ END OF REMOVE MULTIPLE DETECTIONS **********************
|
|
2884
|
#************ END OF REMOVE MULTIPLE DETECTIONS **********************
|
|
|
2885
|
|
|
2885
|
|
|
|
2886
|
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ********************
|
|
2886
|
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ********************
|
|
|
2887
|
#Parameters
|
|
2887
|
#Parameters
|
|
|
2888
|
phaseThresh = phaseThresh*numpy.pi/180
|
|
2888
|
phaseThresh = phaseThresh*numpy.pi/180
|
|
|
2889
|
thresh = [phaseThresh, noise_multiple, SNRThresh]
|
|
2889
|
thresh = [phaseThresh, noise_multiple, SNRThresh]
|
|
|
2890
|
#Meteor reestimation (Errors N 1, 6, 12, 17)
|
|
2890
|
#Meteor reestimation (Errors N 1, 6, 12, 17)
|
|
|
2891
|
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
|
|
2891
|
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
|
|
|
2892
|
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
|
|
2892
|
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
|
|
|
2893
|
#Estimation of decay times (Errors N 7, 8, 11)
|
|
2893
|
#Estimation of decay times (Errors N 7, 8, 11)
|
|
|
2894
|
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
|
|
2894
|
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
|
|
|
2895
|
#******************* END OF METEOR REESTIMATION *******************
|
|
2895
|
#******************* END OF METEOR REESTIMATION *******************
|
|
|
2896
|
|
|
2896
|
|
|
|
2897
|
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) **************************
|
|
2897
|
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) **************************
|
|
|
2898
|
#Calculating Radial Velocity (Error N 15)
|
|
2898
|
#Calculating Radial Velocity (Error N 15)
|
|
|
2899
|
radialStdThresh = 10
|
|
2899
|
radialStdThresh = 10
|
|
|
2900
|
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
|
|
2900
|
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
|
|
|
2901
|
|
|
2901
|
|
|
|
2902
|
if len(listMeteors4) > 0:
|
|
2902
|
if len(listMeteors4) > 0:
|
|
|
2903
|
#Setting New Array
|
|
2903
|
#Setting New Array
|
|
|
2904
|
date = dataOut.utctime
|
|
2904
|
date = dataOut.utctime
|
|
|
2905
|
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
|
|
2905
|
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
|
|
|
2906
|
|
|
2906
|
|
|
|
2907
|
#Correcting phase offset
|
|
2907
|
#Correcting phase offset
|
|
|
2908
|
if phaseOffsets != None:
|
|
2908
|
if phaseOffsets != None:
|
|
|
2909
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
2909
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
|
2910
|
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
2910
|
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
|
2911
|
|
|
2911
|
|
|
|
2912
|
#Second Pairslist
|
|
2912
|
#Second Pairslist
|
|
|
2913
|
pairsList = []
|
|
2913
|
pairsList = []
|
|
|
2914
|
pairx = (0,1)
|
|
2914
|
pairx = (0,1)
|
|
|
2915
|
pairy = (2,3)
|
|
2915
|
pairy = (2,3)
|
|
|
2916
|
pairsList.append(pairx)
|
|
2916
|
pairsList.append(pairx)
|
|
|
2917
|
pairsList.append(pairy)
|
|
2917
|
pairsList.append(pairy)
|
|
|
2918
|
|
|
2918
|
|
|
|
2919
|
jph = numpy.array([0,0,0,0])
|
|
2919
|
jph = numpy.array([0,0,0,0])
|
|
|
2920
|
h = (hmin,hmax)
|
|
2920
|
h = (hmin,hmax)
|
|
|
2921
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
2921
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
|
2922
|
|
|
2922
|
|
|
|
2923
|
# #Calculate AOA (Error N 3, 4)
|
|
2923
|
# #Calculate AOA (Error N 3, 4)
|
|
|
2924
|
# #JONES ET AL. 1998
|
|
2924
|
# #JONES ET AL. 1998
|
|
|
2925
|
# error = arrayParameters[:,-1]
|
|
2925
|
# error = arrayParameters[:,-1]
|
|
|
2926
|
# AOAthresh = numpy.pi/8
|
|
2926
|
# AOAthresh = numpy.pi/8
|
|
|
2927
|
# phases = -arrayParameters[:,9:13]
|
|
2927
|
# phases = -arrayParameters[:,9:13]
|
|
|
2928
|
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
|
|
2928
|
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
|
|
|
2929
|
#
|
|
2929
|
#
|
|
|
2930
|
# #Calculate Heights (Error N 13 and 14)
|
|
2930
|
# #Calculate Heights (Error N 13 and 14)
|
|
|
2931
|
# error = arrayParameters[:,-1]
|
|
2931
|
# error = arrayParameters[:,-1]
|
|
|
2932
|
# Ranges = arrayParameters[:,2]
|
|
2932
|
# Ranges = arrayParameters[:,2]
|
|
|
2933
|
# zenith = arrayParameters[:,5]
|
|
2933
|
# zenith = arrayParameters[:,5]
|
|
|
2934
|
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
|
|
2934
|
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
|
|
|
2935
|
# error = arrayParameters[:,-1]
|
|
2935
|
# error = arrayParameters[:,-1]
|
|
|
2936
|
#********************* END OF PARAMETERS CALCULATION **************************
|
|
2936
|
#********************* END OF PARAMETERS CALCULATION **************************
|
|
|
2937
|
|
|
2937
|
|
|
|
2938
|
#***************************+ PASS DATA TO NEXT STEP **********************
|
|
2938
|
#***************************+ PASS DATA TO NEXT STEP **********************
|
|
|
2939
|
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
|
|
2939
|
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
|
|
|
2940
|
dataOut.data_param = arrayParameters
|
|
2940
|
dataOut.data_param = arrayParameters
|
|
|
2941
|
|
|
2941
|
|
|
|
2942
|
if arrayParameters == None:
|
|
2942
|
if arrayParameters == None:
|
|
|
2943
|
dataOut.flagNoData = True
|
|
2943
|
dataOut.flagNoData = True
|
|
|
2944
|
else:
|
|
2944
|
else:
|
|
|
2945
|
dataOut.flagNoData = True
|
|
2945
|
dataOut.flagNoData = True
|
|
|
2946
|
|
|
2946
|
|
|
|
2947
|
return
|
|
2947
|
return
|
|
|
2948
|
|
|
2948
|
|
|
|
2949
|
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
|
|
2949
|
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
|
|
|
2950
|
|
|
2950
|
|
|
|
2951
|
minIndex = min(newheis[0])
|
|
2951
|
minIndex = min(newheis[0])
|
|
|
2952
|
maxIndex = max(newheis[0])
|
|
2952
|
maxIndex = max(newheis[0])
|
|
|
2953
|
|
|
2953
|
|
|
|
2954
|
voltage = voltage0[:,:,minIndex:maxIndex+1]
|
|
2954
|
voltage = voltage0[:,:,minIndex:maxIndex+1]
|
|
|
2955
|
nLength = voltage.shape[1]/n
|
|
2955
|
nLength = voltage.shape[1]/n
|
|
|
2956
|
nMin = 0
|
|
2956
|
nMin = 0
|
|
|
2957
|
nMax = 0
|
|
2957
|
nMax = 0
|
|
|
2958
|
phaseOffset = numpy.zeros((len(pairslist),n))
|
|
2958
|
phaseOffset = numpy.zeros((len(pairslist),n))
|
|
|
2959
|
|
|
2959
|
|
|
|
2960
|
for i in range(n):
|
|
2960
|
for i in range(n):
|
|
|
2961
|
nMax += nLength
|
|
2961
|
nMax += nLength
|
|
|
2962
|
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
|
|
2962
|
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
|
|
|
2963
|
phaseCCF = numpy.mean(phaseCCF, axis = 2)
|
|
2963
|
phaseCCF = numpy.mean(phaseCCF, axis = 2)
|
|
|
2964
|
phaseOffset[:,i] = phaseCCF.transpose()
|
|
2964
|
phaseOffset[:,i] = phaseCCF.transpose()
|
|
|
2965
|
nMin = nMax
|
|
2965
|
nMin = nMax
|
|
|
2966
|
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
|
|
2966
|
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
|
|
|
2967
|
|
|
2967
|
|
|
|
2968
|
#Remove Outliers
|
|
2968
|
#Remove Outliers
|
|
|
2969
|
factor = 2
|
|
2969
|
factor = 2
|
|
|
2970
|
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
|
|
2970
|
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
|
|
|
2971
|
dw = numpy.std(wt,axis = 1)
|
|
2971
|
dw = numpy.std(wt,axis = 1)
|
|
|
2972
|
dw = dw.reshape((dw.size,1))
|
|
2972
|
dw = dw.reshape((dw.size,1))
|
|
|
2973
|
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
|
|
2973
|
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
|
|
|
2974
|
phaseOffset[ind] = numpy.nan
|
|
2974
|
phaseOffset[ind] = numpy.nan
|
|
|
2975
|
phaseOffset = stats.nanmean(phaseOffset, axis=1)
|
|
2975
|
phaseOffset = stats.nanmean(phaseOffset, axis=1)
|
|
|
2976
|
|
|
2976
|
|
|
|
2977
|
return phaseOffset
|
|
2977
|
return phaseOffset
|
|
|
2978
|
|
|
2978
|
|
|
|
2979
|
def __shiftPhase(self, data, phaseShift):
|
|
2979
|
def __shiftPhase(self, data, phaseShift):
|
|
|
2980
|
#this will shift the phase of a complex number
|
|
2980
|
#this will shift the phase of a complex number
|
|
|
2981
|
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
|
|
2981
|
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
|
|
|
2982
|
return dataShifted
|
|
2982
|
return dataShifted
|
|
|
2983
|
|
|
2983
|
|
|
|
2984
|
def __estimatePhaseDifference(self, array, pairslist):
|
|
2984
|
def __estimatePhaseDifference(self, array, pairslist):
|
|
|
2985
|
nChannel = array.shape[0]
|
|
2985
|
nChannel = array.shape[0]
|
|
|
2986
|
nHeights = array.shape[2]
|
|
2986
|
nHeights = array.shape[2]
|
|
|
2987
|
numPairs = len(pairslist)
|
|
2987
|
numPairs = len(pairslist)
|
|
|
2988
|
# phaseCCF = numpy.zeros((nChannel, 5, nHeights))
|
|
2988
|
# phaseCCF = numpy.zeros((nChannel, 5, nHeights))
|
|
|
2989
|
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
|
|
2989
|
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
|
|
|
2990
|
|
|
2990
|
|
|
|
2991
|
#Correct phases
|
|
2991
|
#Correct phases
|
|
|
2992
|
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
|
|
2992
|
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
|
|
|
2993
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
2993
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
|
2994
|
|
|
2994
|
|
|
|
2995
|
if indDer[0].shape[0] > 0:
|
|
2995
|
if indDer[0].shape[0] > 0:
|
|
|
2996
|
for i in range(indDer[0].shape[0]):
|
|
2996
|
for i in range(indDer[0].shape[0]):
|
|
|
2997
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
|
|
2997
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
|
|
|
2998
|
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
|
|
2998
|
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
|
|
|
2999
|
|
|
2999
|
|
|
|
3000
|
# for j in range(numSides):
|
|
3000
|
# for j in range(numSides):
|
|
|
3001
|
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
|
|
3001
|
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
|
|
|
3002
|
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
|
|
3002
|
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
|
|
|
3003
|
#
|
|
3003
|
#
|
|
|
3004
|
#Linear
|
|
3004
|
#Linear
|
|
|
3005
|
phaseInt = numpy.zeros((numPairs,1))
|
|
3005
|
phaseInt = numpy.zeros((numPairs,1))
|
|
|
3006
|
angAllCCF = phaseCCF[:,[0,1,3,4],0]
|
|
3006
|
angAllCCF = phaseCCF[:,[0,1,3,4],0]
|
|
|
3007
|
for j in range(numPairs):
|
|
3007
|
for j in range(numPairs):
|
|
|
3008
|
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
|
|
3008
|
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
|
|
|
3009
|
phaseInt[j] = fit[1]
|
|
3009
|
phaseInt[j] = fit[1]
|
|
|
3010
|
#Phase Differences
|
|
3010
|
#Phase Differences
|
|
|
3011
|
phaseDiff = phaseInt - phaseCCF[:,2,:]
|
|
3011
|
phaseDiff = phaseInt - phaseCCF[:,2,:]
|
|
|
3012
|
phaseArrival = phaseInt.reshape(phaseInt.size)
|
|
3012
|
phaseArrival = phaseInt.reshape(phaseInt.size)
|
|
|
3013
|
|
|
3013
|
|
|
|
3014
|
#Dealias
|
|
3014
|
#Dealias
|
|
|
3015
|
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
|
|
3015
|
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
|
|
|
3016
|
# indAlias = numpy.where(phaseArrival > numpy.pi)
|
|
3016
|
# indAlias = numpy.where(phaseArrival > numpy.pi)
|
|
|
3017
|
# phaseArrival[indAlias] -= 2*numpy.pi
|
|
3017
|
# phaseArrival[indAlias] -= 2*numpy.pi
|
|
|
3018
|
# indAlias = numpy.where(phaseArrival < -numpy.pi)
|
|
3018
|
# indAlias = numpy.where(phaseArrival < -numpy.pi)
|
|
|
3019
|
# phaseArrival[indAlias] += 2*numpy.pi
|
|
3019
|
# phaseArrival[indAlias] += 2*numpy.pi
|
|
|
3020
|
|
|
3020
|
|
|
|
3021
|
return phaseDiff, phaseArrival
|
|
3021
|
return phaseDiff, phaseArrival
|
|
|
3022
|
|
|
3022
|
|
|
|
3023
|
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
|
|
3023
|
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
|
|
|
3024
|
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
|
|
3024
|
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
|
|
|
3025
|
#find the phase shifts of each channel over 1 second intervals
|
|
3025
|
#find the phase shifts of each channel over 1 second intervals
|
|
|
3026
|
#only look at ranges below the beacon signal
|
|
3026
|
#only look at ranges below the beacon signal
|
|
|
3027
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
3027
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
|
3028
|
numBlocks = int(volts.shape[1]/numProfPerBlock)
|
|
3028
|
numBlocks = int(volts.shape[1]/numProfPerBlock)
|
|
|
3029
|
numHeights = volts.shape[2]
|
|
3029
|
numHeights = volts.shape[2]
|
|
|
3030
|
nChannel = volts.shape[0]
|
|
3030
|
nChannel = volts.shape[0]
|
|
|
3031
|
voltsCohDet = volts.copy()
|
|
3031
|
voltsCohDet = volts.copy()
|
|
|
3032
|
|
|
3032
|
|
|
|
3033
|
pairsarray = numpy.array(pairslist)
|
|
3033
|
pairsarray = numpy.array(pairslist)
|
|
|
3034
|
indSides = pairsarray[:,1]
|
|
3034
|
indSides = pairsarray[:,1]
|
|
|
3035
|
# indSides = numpy.array(range(nChannel))
|
|
3035
|
# indSides = numpy.array(range(nChannel))
|
|
|
3036
|
# indSides = numpy.delete(indSides, indCenter)
|
|
3036
|
# indSides = numpy.delete(indSides, indCenter)
|
|
|
3037
|
#
|
|
3037
|
#
|
|
|
3038
|
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
|
|
3038
|
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
|
|
|
3039
|
listBlocks = numpy.array_split(volts, numBlocks, 1)
|
|
3039
|
listBlocks = numpy.array_split(volts, numBlocks, 1)
|
|
|
3040
|
|
|
3040
|
|
|
|
3041
|
startInd = 0
|
|
3041
|
startInd = 0
|
|
|
3042
|
endInd = 0
|
|
3042
|
endInd = 0
|
|
|
3043
|
|
|
3043
|
|
|
|
3044
|
for i in range(numBlocks):
|
|
3044
|
for i in range(numBlocks):
|
|
|
3045
|
startInd = endInd
|
|
3045
|
startInd = endInd
|
|
|
3046
|
endInd = endInd + listBlocks[i].shape[1]
|
|
3046
|
endInd = endInd + listBlocks[i].shape[1]
|
|
|
3047
|
|
|
3047
|
|
|
|
3048
|
arrayBlock = listBlocks[i]
|
|
3048
|
arrayBlock = listBlocks[i]
|
|
|
3049
|
# arrayBlockCenter = listCenter[i]
|
|
3049
|
# arrayBlockCenter = listCenter[i]
|
|
|
3050
|
|
|
3050
|
|
|
|
3051
|
#Estimate the Phase Difference
|
|
3051
|
#Estimate the Phase Difference
|
|
|
3052
|
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
|
|
3052
|
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
|
|
|
3053
|
#Phase Difference RMS
|
|
3053
|
#Phase Difference RMS
|
|
|
3054
|
arrayPhaseRMS = numpy.abs(phaseDiff)
|
|
3054
|
arrayPhaseRMS = numpy.abs(phaseDiff)
|
|
|
3055
|
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
|
|
3055
|
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
|
|
|
3056
|
indPhase = numpy.where(phaseRMSaux==4)
|
|
3056
|
indPhase = numpy.where(phaseRMSaux==4)
|
|
|
3057
|
#Shifting
|
|
3057
|
#Shifting
|
|
|
3058
|
if indPhase[0].shape[0] > 0:
|
|
3058
|
if indPhase[0].shape[0] > 0:
|
|
|
3059
|
for j in range(indSides.size):
|
|
3059
|
for j in range(indSides.size):
|
|
|
3060
|
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
|
|
3060
|
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
|
|
|
3061
|
voltsCohDet[:,startInd:endInd,:] = arrayBlock
|
|
3061
|
voltsCohDet[:,startInd:endInd,:] = arrayBlock
|
|
|
3062
|
|
|
3062
|
|
|
|
3063
|
return voltsCohDet
|
|
3063
|
return voltsCohDet
|
|
|
3064
|
|
|
3064
|
|
|
|
3065
|
def __calculateCCF(self, volts, pairslist ,laglist):
|
|
3065
|
def __calculateCCF(self, volts, pairslist ,laglist):
|
|
|
3066
|
|
|
3066
|
|
|
|
3067
|
nHeights = volts.shape[2]
|
|
3067
|
nHeights = volts.shape[2]
|
|
|
3068
|
nPoints = volts.shape[1]
|
|
3068
|
nPoints = volts.shape[1]
|
|
|
3069
|
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
|
|
3069
|
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
|
|
|
3070
|
|
|
3070
|
|
|
|
3071
|
for i in range(len(pairslist)):
|
|
3071
|
for i in range(len(pairslist)):
|
|
|
3072
|
volts1 = volts[pairslist[i][0]]
|
|
3072
|
volts1 = volts[pairslist[i][0]]
|
|
|
3073
|
volts2 = volts[pairslist[i][1]]
|
|
3073
|
volts2 = volts[pairslist[i][1]]
|
|
|
3074
|
|
|
3074
|
|
|
|
3075
|
for t in range(len(laglist)):
|
|
3075
|
for t in range(len(laglist)):
|
|
|
3076
|
idxT = laglist[t]
|
|
3076
|
idxT = laglist[t]
|
|
|
3077
|
if idxT >= 0:
|
|
3077
|
if idxT >= 0:
|
|
|
3078
|
vStacked = numpy.vstack((volts2[idxT:,:],
|
|
3078
|
vStacked = numpy.vstack((volts2[idxT:,:],
|
|
|
3079
|
numpy.zeros((idxT, nHeights),dtype='complex')))
|
|
3079
|
numpy.zeros((idxT, nHeights),dtype='complex')))
|
|
|
3080
|
else:
|
|
3080
|
else:
|
|
|
3081
|
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
|
|
3081
|
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
|
|
|
3082
|
volts2[:(nPoints + idxT),:]))
|
|
3082
|
volts2[:(nPoints + idxT),:]))
|
|
|
3083
|
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
|
|
3083
|
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
|
|
|
3084
|
|
|
3084
|
|
|
|
3085
|
vStacked = None
|
|
3085
|
vStacked = None
|
|
|
3086
|
return voltsCCF
|
|
3086
|
return voltsCCF
|
|
|
3087
|
|
|
3087
|
|
|
|
3088
|
def __getNoise(self, power, timeSegment, timeInterval):
|
|
3088
|
def __getNoise(self, power, timeSegment, timeInterval):
|
|
|
3089
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
3089
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
|
3090
|
numBlocks = int(power.shape[0]/numProfPerBlock)
|
|
3090
|
numBlocks = int(power.shape[0]/numProfPerBlock)
|
|
|
3091
|
numHeights = power.shape[1]
|
|
3091
|
numHeights = power.shape[1]
|
|
|
3092
|
|
|
3092
|
|
|
|
3093
|
listPower = numpy.array_split(power, numBlocks, 0)
|
|
3093
|
listPower = numpy.array_split(power, numBlocks, 0)
|
|
|
3094
|
noise = numpy.zeros((power.shape[0], power.shape[1]))
|
|
3094
|
noise = numpy.zeros((power.shape[0], power.shape[1]))
|
|
|
3095
|
noise1 = numpy.zeros((power.shape[0], power.shape[1]))
|
|
3095
|
noise1 = numpy.zeros((power.shape[0], power.shape[1]))
|
|
|
3096
|
|
|
3096
|
|
|
|
3097
|
startInd = 0
|
|
3097
|
startInd = 0
|
|
|
3098
|
endInd = 0
|
|
3098
|
endInd = 0
|
|
|
3099
|
|
|
3099
|
|
|
|
3100
|
for i in range(numBlocks): #split por canal
|
|
3100
|
for i in range(numBlocks): #split por canal
|
|
|
3101
|
startInd = endInd
|
|
3101
|
startInd = endInd
|
|
|
3102
|
endInd = endInd + listPower[i].shape[0]
|
|
3102
|
endInd = endInd + listPower[i].shape[0]
|
|
|
3103
|
|
|
3103
|
|
|
|
3104
|
arrayBlock = listPower[i]
|
|
3104
|
arrayBlock = listPower[i]
|
|
|
3105
|
noiseAux = numpy.mean(arrayBlock, 0)
|
|
3105
|
noiseAux = numpy.mean(arrayBlock, 0)
|
|
|
3106
|
# noiseAux = numpy.median(noiseAux)
|
|
3106
|
# noiseAux = numpy.median(noiseAux)
|
|
|
3107
|
# noiseAux = numpy.mean(arrayBlock)
|
|
3107
|
# noiseAux = numpy.mean(arrayBlock)
|
|
|
3108
|
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
|
|
3108
|
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
|
|
|
3109
|
|
|
3109
|
|
|
|
3110
|
noiseAux1 = numpy.mean(arrayBlock)
|
|
3110
|
noiseAux1 = numpy.mean(arrayBlock)
|
|
|
3111
|
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
|
|
3111
|
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
|
|
|
3112
|
|
|
3112
|
|
|
|
3113
|
return noise, noise1
|
|
3113
|
return noise, noise1
|
|
|
3114
|
|
|
3114
|
|
|
|
3115
|
def __findMeteors(self, power, thresh):
|
|
3115
|
def __findMeteors(self, power, thresh):
|
|
|
3116
|
nProf = power.shape[0]
|
|
3116
|
nProf = power.shape[0]
|
|
|
3117
|
nHeights = power.shape[1]
|
|
3117
|
nHeights = power.shape[1]
|
|
|
3118
|
listMeteors = []
|
|
3118
|
listMeteors = []
|
|
|
3119
|
|
|
3119
|
|
|
|
3120
|
for i in range(nHeights):
|
|
3120
|
for i in range(nHeights):
|
|
|
3121
|
powerAux = power[:,i]
|
|
3121
|
powerAux = power[:,i]
|
|
|
3122
|
threshAux = thresh[:,i]
|
|
3122
|
threshAux = thresh[:,i]
|
|
|
3123
|
|
|
3123
|
|
|
|
3124
|
indUPthresh = numpy.where(powerAux > threshAux)[0]
|
|
3124
|
indUPthresh = numpy.where(powerAux > threshAux)[0]
|
|
|
3125
|
indDNthresh = numpy.where(powerAux <= threshAux)[0]
|
|
3125
|
indDNthresh = numpy.where(powerAux <= threshAux)[0]
|
|
|
3126
|
|
|
3126
|
|
|
|
3127
|
j = 0
|
|
3127
|
j = 0
|
|
|
3128
|
|
|
3128
|
|
|
|
3129
|
while (j < indUPthresh.size - 2):
|
|
3129
|
while (j < indUPthresh.size - 2):
|
|
|
3130
|
if (indUPthresh[j + 2] == indUPthresh[j] + 2):
|
|
3130
|
if (indUPthresh[j + 2] == indUPthresh[j] + 2):
|
|
|
3131
|
indDNAux = numpy.where(indDNthresh > indUPthresh[j])
|
|
3131
|
indDNAux = numpy.where(indDNthresh > indUPthresh[j])
|
|
|
3132
|
indDNthresh = indDNthresh[indDNAux]
|
|
3132
|
indDNthresh = indDNthresh[indDNAux]
|
|
|
3133
|
|
|
3133
|
|
|
|
3134
|
if (indDNthresh.size > 0):
|
|
3134
|
if (indDNthresh.size > 0):
|
|
|
3135
|
indEnd = indDNthresh[0] - 1
|
|
3135
|
indEnd = indDNthresh[0] - 1
|
|
|
3136
|
indInit = indUPthresh[j]
|
|
3136
|
indInit = indUPthresh[j]
|
|
|
3137
|
|
|
3137
|
|
|
|
3138
|
meteor = powerAux[indInit:indEnd + 1]
|
|
3138
|
meteor = powerAux[indInit:indEnd + 1]
|
|
|
3139
|
indPeak = meteor.argmax() + indInit
|
|
3139
|
indPeak = meteor.argmax() + indInit
|
|
|
3140
|
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
|
|
3140
|
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
|
|
|
3141
|
|
|
3141
|
|
|
|
3142
|
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
|
|
3142
|
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
|
|
|
3143
|
j = numpy.where(indUPthresh == indEnd)[0] + 1
|
|
3143
|
j = numpy.where(indUPthresh == indEnd)[0] + 1
|
|
|
3144
|
else: j+=1
|
|
3144
|
else: j+=1
|
|
|
3145
|
else: j+=1
|
|
3145
|
else: j+=1
|
|
|
3146
|
|
|
3146
|
|
|
|
3147
|
return listMeteors
|
|
3147
|
return listMeteors
|
|
|
3148
|
|
|
3148
|
|
|
|
3149
|
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
|
|
3149
|
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
|
|
|
3150
|
|
|
3150
|
|
|
|
3151
|
arrayMeteors = numpy.asarray(listMeteors)
|
|
3151
|
arrayMeteors = numpy.asarray(listMeteors)
|
|
|
3152
|
listMeteors1 = []
|
|
3152
|
listMeteors1 = []
|
|
|
3153
|
|
|
3153
|
|
|
|
3154
|
while arrayMeteors.shape[0] > 0:
|
|
3154
|
while arrayMeteors.shape[0] > 0:
|
|
|
3155
|
FLAs = arrayMeteors[:,4]
|
|
3155
|
FLAs = arrayMeteors[:,4]
|
|
|
3156
|
maxFLA = FLAs.argmax()
|
|
3156
|
maxFLA = FLAs.argmax()
|
|
|
3157
|
listMeteors1.append(arrayMeteors[maxFLA,:])
|
|
3157
|
listMeteors1.append(arrayMeteors[maxFLA,:])
|
|
|
3158
|
|
|
3158
|
|
|
|
3159
|
MeteorInitTime = arrayMeteors[maxFLA,1]
|
|
3159
|
MeteorInitTime = arrayMeteors[maxFLA,1]
|
|
|
3160
|
MeteorEndTime = arrayMeteors[maxFLA,3]
|
|
3160
|
MeteorEndTime = arrayMeteors[maxFLA,3]
|
|
|
3161
|
MeteorHeight = arrayMeteors[maxFLA,0]
|
|
3161
|
MeteorHeight = arrayMeteors[maxFLA,0]
|
|
|
3162
|
|
|
3162
|
|
|
|
3163
|
#Check neighborhood
|
|
3163
|
#Check neighborhood
|
|
|
3164
|
maxHeightIndex = MeteorHeight + rangeLimit
|
|
3164
|
maxHeightIndex = MeteorHeight + rangeLimit
|
|
|
3165
|
minHeightIndex = MeteorHeight - rangeLimit
|
|
3165
|
minHeightIndex = MeteorHeight - rangeLimit
|
|
|
3166
|
minTimeIndex = MeteorInitTime - timeLimit
|
|
3166
|
minTimeIndex = MeteorInitTime - timeLimit
|
|
|
3167
|
maxTimeIndex = MeteorEndTime + timeLimit
|
|
3167
|
maxTimeIndex = MeteorEndTime + timeLimit
|
|
|
3168
|
|
|
3168
|
|
|
|
3169
|
#Check Heights
|
|
3169
|
#Check Heights
|
|
|
3170
|
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
|
|
3170
|
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
|
|
|
3171
|
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
|
|
3171
|
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
|
|
|
3172
|
indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
|
|
3172
|
indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
|
|
|
3173
|
|
|
3173
|
|
|
|
3174
|
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
|
|
3174
|
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
|
|
|
3175
|
|
|
3175
|
|
|
|
3176
|
return listMeteors1
|
|
3176
|
return listMeteors1
|
|
|
3177
|
|
|
3177
|
|
|
|
3178
|
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
|
|
3178
|
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
|
|
|
3179
|
numHeights = volts.shape[2]
|
|
3179
|
numHeights = volts.shape[2]
|
|
|
3180
|
nChannel = volts.shape[0]
|
|
3180
|
nChannel = volts.shape[0]
|
|
|
3181
|
|
|
3181
|
|
|
|
3182
|
thresholdPhase = thresh[0]
|
|
3182
|
thresholdPhase = thresh[0]
|
|
|
3183
|
thresholdNoise = thresh[1]
|
|
3183
|
thresholdNoise = thresh[1]
|
|
|
3184
|
thresholdDB = float(thresh[2])
|
|
3184
|
thresholdDB = float(thresh[2])
|
|
|
3185
|
|
|
3185
|
|
|
|
3186
|
thresholdDB1 = 10**(thresholdDB/10)
|
|
3186
|
thresholdDB1 = 10**(thresholdDB/10)
|
|
|
3187
|
pairsarray = numpy.array(pairslist)
|
|
3187
|
pairsarray = numpy.array(pairslist)
|
|
|
3188
|
indSides = pairsarray[:,1]
|
|
3188
|
indSides = pairsarray[:,1]
|
|
|
3189
|
|
|
3189
|
|
|
|
3190
|
pairslist1 = list(pairslist)
|
|
3190
|
pairslist1 = list(pairslist)
|
|
|
3191
|
pairslist1.append((0,1))
|
|
3191
|
pairslist1.append((0,1))
|
|
|
3192
|
pairslist1.append((3,4))
|
|
3192
|
pairslist1.append((3,4))
|
|
|
3193
|
|
|
3193
|
|
|
|
3194
|
listMeteors1 = []
|
|
3194
|
listMeteors1 = []
|
|
|
3195
|
listPowerSeries = []
|
|
3195
|
listPowerSeries = []
|
|
|
3196
|
listVoltageSeries = []
|
|
3196
|
listVoltageSeries = []
|
|
|
3197
|
#volts has the war data
|
|
3197
|
#volts has the war data
|
|
|
3198
|
|
|
3198
|
|
|
|
3199
|
if frequency == 30e6:
|
|
3199
|
if frequency == 30e6:
|
|
|
3200
|
timeLag = 45*10**-3
|
|
3200
|
timeLag = 45*10**-3
|
|
|
3201
|
else:
|
|
3201
|
else:
|
|
|
3202
|
timeLag = 15*10**-3
|
|
3202
|
timeLag = 15*10**-3
|
|
|
3203
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3203
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3204
|
|
|
3204
|
|
|
|
3205
|
for i in range(len(listMeteors)):
|
|
3205
|
for i in range(len(listMeteors)):
|
|
|
3206
|
|
|
3206
|
|
|
|
3207
|
###################### 3.6 - 3.7 PARAMETERS REESTIMATION #########################
|
|
3207
|
###################### 3.6 - 3.7 PARAMETERS REESTIMATION #########################
|
|
|
3208
|
meteorAux = numpy.zeros(16)
|
|
3208
|
meteorAux = numpy.zeros(16)
|
|
|
3209
|
|
|
3209
|
|
|
|
3210
|
#Loading meteor Data (mHeight, mStart, mPeak, mEnd)
|
|
3210
|
#Loading meteor Data (mHeight, mStart, mPeak, mEnd)
|
|
|
3211
|
mHeight = listMeteors[i][0]
|
|
3211
|
mHeight = listMeteors[i][0]
|
|
|
3212
|
mStart = listMeteors[i][1]
|
|
3212
|
mStart = listMeteors[i][1]
|
|
|
3213
|
mPeak = listMeteors[i][2]
|
|
3213
|
mPeak = listMeteors[i][2]
|
|
|
3214
|
mEnd = listMeteors[i][3]
|
|
3214
|
mEnd = listMeteors[i][3]
|
|
|
3215
|
|
|
3215
|
|
|
|
3216
|
#get the volt data between the start and end times of the meteor
|
|
3216
|
#get the volt data between the start and end times of the meteor
|
|
|
3217
|
meteorVolts = volts[:,mStart:mEnd+1,mHeight]
|
|
3217
|
meteorVolts = volts[:,mStart:mEnd+1,mHeight]
|
|
|
3218
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
3218
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
|
3219
|
|
|
3219
|
|
|
|
3220
|
#3.6. Phase Difference estimation
|
|
3220
|
#3.6. Phase Difference estimation
|
|
|
3221
|
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
|
|
3221
|
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
|
|
|
3222
|
|
|
3222
|
|
|
|
3223
|
#3.7. Phase difference removal & meteor start, peak and end times reestimated
|
|
3223
|
#3.7. Phase difference removal & meteor start, peak and end times reestimated
|
|
|
3224
|
#meteorVolts0.- all Channels, all Profiles
|
|
3224
|
#meteorVolts0.- all Channels, all Profiles
|
|
|
3225
|
meteorVolts0 = volts[:,:,mHeight]
|
|
3225
|
meteorVolts0 = volts[:,:,mHeight]
|
|
|
3226
|
meteorThresh = noise[:,mHeight]*thresholdNoise
|
|
3226
|
meteorThresh = noise[:,mHeight]*thresholdNoise
|
|
|
3227
|
meteorNoise = noise[:,mHeight]
|
|
3227
|
meteorNoise = noise[:,mHeight]
|
|
|
3228
|
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
|
|
3228
|
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
|
|
|
3229
|
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power
|
|
3229
|
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power
|
|
|
3230
|
|
|
3230
|
|
|
|
3231
|
#Times reestimation
|
|
3231
|
#Times reestimation
|
|
|
3232
|
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
|
|
3232
|
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
|
|
|
3233
|
if mStart1.size > 0:
|
|
3233
|
if mStart1.size > 0:
|
|
|
3234
|
mStart1 = mStart1[-1] + 1
|
|
3234
|
mStart1 = mStart1[-1] + 1
|
|
|
3235
|
|
|
3235
|
|
|
|
3236
|
else:
|
|
3236
|
else:
|
|
|
3237
|
mStart1 = mPeak
|
|
3237
|
mStart1 = mPeak
|
|
|
3238
|
|
|
3238
|
|
|
|
3239
|
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
|
|
3239
|
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
|
|
|
3240
|
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
|
|
3240
|
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
|
|
|
3241
|
if mEndDecayTime1.size == 0:
|
|
3241
|
if mEndDecayTime1.size == 0:
|
|
|
3242
|
mEndDecayTime1 = powerNet0.size
|
|
3242
|
mEndDecayTime1 = powerNet0.size
|
|
|
3243
|
else:
|
|
3243
|
else:
|
|
|
3244
|
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
|
|
3244
|
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
|
|
|
3245
|
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
|
|
3245
|
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
|
|
|
3246
|
|
|
3246
|
|
|
|
3247
|
#meteorVolts1.- all Channels, from start to end
|
|
3247
|
#meteorVolts1.- all Channels, from start to end
|
|
|
3248
|
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
|
|
3248
|
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
|
|
|
3249
|
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
|
|
3249
|
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
|
|
|
3250
|
if meteorVolts2.shape[1] == 0:
|
|
3250
|
if meteorVolts2.shape[1] == 0:
|
|
|
3251
|
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
|
|
3251
|
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
|
|
|
3252
|
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
|
|
3252
|
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
|
|
|
3253
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
|
|
3253
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
|
|
|
3254
|
##################### END PARAMETERS REESTIMATION #########################
|
|
3254
|
##################### END PARAMETERS REESTIMATION #########################
|
|
|
3255
|
|
|
3255
|
|
|
|
3256
|
##################### 3.8 PHASE DIFFERENCE REESTIMATION ########################
|
|
3256
|
##################### 3.8 PHASE DIFFERENCE REESTIMATION ########################
|
|
|
3257
|
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
3257
|
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
|
3258
|
if meteorVolts2.shape[1] > 0:
|
|
3258
|
if meteorVolts2.shape[1] > 0:
|
|
|
3259
|
#Phase Difference re-estimation
|
|
3259
|
#Phase Difference re-estimation
|
|
|
3260
|
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation
|
|
3260
|
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation
|
|
|
3261
|
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
|
|
3261
|
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
|
|
|
3262
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
|
|
3262
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
|
|
|
3263
|
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
|
|
3263
|
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
|
|
|
3264
|
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting
|
|
3264
|
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting
|
|
|
3265
|
|
|
3265
|
|
|
|
3266
|
#Phase Difference RMS
|
|
3266
|
#Phase Difference RMS
|
|
|
3267
|
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
|
|
3267
|
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
|
|
|
3268
|
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
|
|
3268
|
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
|
|
|
3269
|
#Data from Meteor
|
|
3269
|
#Data from Meteor
|
|
|
3270
|
mPeak1 = powerNet1.argmax() + mStart1
|
|
3270
|
mPeak1 = powerNet1.argmax() + mStart1
|
|
|
3271
|
mPeakPower1 = powerNet1.max()
|
|
3271
|
mPeakPower1 = powerNet1.max()
|
|
|
3272
|
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
|
|
3272
|
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
|
|
|
3273
|
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
|
|
3273
|
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
|
|
|
3274
|
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
|
|
3274
|
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
|
|
|
3275
|
Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
|
|
3275
|
Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
|
|
|
3276
|
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1]
|
|
3276
|
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1]
|
|
|
3277
|
#Vectorize
|
|
3277
|
#Vectorize
|
|
|
3278
|
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
|
|
3278
|
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
|
|
|
3279
|
meteorAux[7:11] = phaseDiffint[0:4]
|
|
3279
|
meteorAux[7:11] = phaseDiffint[0:4]
|
|
|
3280
|
|
|
3280
|
|
|
|
3281
|
#Rejection Criterions
|
|
3281
|
#Rejection Criterions
|
|
|
3282
|
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation
|
|
3282
|
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation
|
|
|
3283
|
meteorAux[-1] = 17
|
|
3283
|
meteorAux[-1] = 17
|
|
|
3284
|
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB
|
|
3284
|
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB
|
|
|
3285
|
meteorAux[-1] = 1
|
|
3285
|
meteorAux[-1] = 1
|
|
|
3286
|
|
|
3286
|
|
|
|
3287
|
|
|
3287
|
|
|
|
3288
|
else:
|
|
3288
|
else:
|
|
|
3289
|
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
|
|
3289
|
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
|
|
|
3290
|
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
3290
|
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
|
3291
|
PowerSeries = 0
|
|
3291
|
PowerSeries = 0
|
|
|
3292
|
|
|
3292
|
|
|
|
3293
|
listMeteors1.append(meteorAux)
|
|
3293
|
listMeteors1.append(meteorAux)
|
|
|
3294
|
listPowerSeries.append(PowerSeries)
|
|
3294
|
listPowerSeries.append(PowerSeries)
|
|
|
3295
|
listVoltageSeries.append(meteorVolts1)
|
|
3295
|
listVoltageSeries.append(meteorVolts1)
|
|
|
3296
|
|
|
3296
|
|
|
|
3297
|
return listMeteors1, listPowerSeries, listVoltageSeries
|
|
3297
|
return listMeteors1, listPowerSeries, listVoltageSeries
|
|
|
3298
|
|
|
3298
|
|
|
|
3299
|
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
|
|
3299
|
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
|
|
|
3300
|
|
|
3300
|
|
|
|
3301
|
threshError = 10
|
|
3301
|
threshError = 10
|
|
|
3302
|
#Depending if it is 30 or 50 MHz
|
|
3302
|
#Depending if it is 30 or 50 MHz
|
|
|
3303
|
if frequency == 30e6:
|
|
3303
|
if frequency == 30e6:
|
|
|
3304
|
timeLag = 45*10**-3
|
|
3304
|
timeLag = 45*10**-3
|
|
|
3305
|
else:
|
|
3305
|
else:
|
|
|
3306
|
timeLag = 15*10**-3
|
|
3306
|
timeLag = 15*10**-3
|
|
|
3307
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3307
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3308
|
|
|
3308
|
|
|
|
3309
|
listMeteors1 = []
|
|
3309
|
listMeteors1 = []
|
|
|
3310
|
|
|
3310
|
|
|
|
3311
|
for i in range(len(listMeteors)):
|
|
3311
|
for i in range(len(listMeteors)):
|
|
|
3312
|
meteorPower = listPower[i]
|
|
3312
|
meteorPower = listPower[i]
|
|
|
3313
|
meteorAux = listMeteors[i]
|
|
3313
|
meteorAux = listMeteors[i]
|
|
|
3314
|
|
|
3314
|
|
|
|
3315
|
if meteorAux[-1] == 0:
|
|
3315
|
if meteorAux[-1] == 0:
|
|
|
3316
|
|
|
3316
|
|
|
|
3317
|
try:
|
|
3317
|
try:
|
|
|
3318
|
indmax = meteorPower.argmax()
|
|
3318
|
indmax = meteorPower.argmax()
|
|
|
3319
|
indlag = indmax + lag
|
|
3319
|
indlag = indmax + lag
|
|
|
3320
|
|
|
3320
|
|
|
|
3321
|
y = meteorPower[indlag:]
|
|
3321
|
y = meteorPower[indlag:]
|
|
|
3322
|
x = numpy.arange(0, y.size)*timeLag
|
|
3322
|
x = numpy.arange(0, y.size)*timeLag
|
|
|
3323
|
|
|
3323
|
|
|
|
3324
|
#first guess
|
|
3324
|
#first guess
|
|
|
3325
|
a = y[0]
|
|
3325
|
a = y[0]
|
|
|
3326
|
tau = timeLag
|
|
3326
|
tau = timeLag
|
|
|
3327
|
#exponential fit
|
|
3327
|
#exponential fit
|
|
|
3328
|
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
|
|
3328
|
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
|
|
|
3329
|
y1 = self.__exponential_function(x, *popt)
|
|
3329
|
y1 = self.__exponential_function(x, *popt)
|
|
|
3330
|
#error estimation
|
|
3330
|
#error estimation
|
|
|
3331
|
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
|
|
3331
|
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
|
|
|
3332
|
|
|
3332
|
|
|
|
3333
|
decayTime = popt[1]
|
|
3333
|
decayTime = popt[1]
|
|
|
3334
|
riseTime = indmax*timeInterval
|
|
3334
|
riseTime = indmax*timeInterval
|
|
|
3335
|
meteorAux[11:13] = [decayTime, error]
|
|
3335
|
meteorAux[11:13] = [decayTime, error]
|
|
|
3336
|
|
|
3336
|
|
|
|
3337
|
#Table items 7, 8 and 11
|
|
3337
|
#Table items 7, 8 and 11
|
|
|
3338
|
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s
|
|
3338
|
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s
|
|
|
3339
|
meteorAux[-1] = 7
|
|
3339
|
meteorAux[-1] = 7
|
|
|
3340
|
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
|
|
3340
|
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
|
|
|
3341
|
meteorAux[-1] = 8
|
|
3341
|
meteorAux[-1] = 8
|
|
|
3342
|
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time
|
|
3342
|
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time
|
|
|
3343
|
meteorAux[-1] = 11
|
|
3343
|
meteorAux[-1] = 11
|
|
|
3344
|
|
|
3344
|
|
|
|
3345
|
|
|
3345
|
|
|
|
3346
|
except:
|
|
3346
|
except:
|
|
|
3347
|
meteorAux[-1] = 11
|
|
3347
|
meteorAux[-1] = 11
|
|
|
3348
|
|
|
3348
|
|
|
|
3349
|
|
|
3349
|
|
|
|
3350
|
listMeteors1.append(meteorAux)
|
|
3350
|
listMeteors1.append(meteorAux)
|
|
|
3351
|
|
|
3351
|
|
|
|
3352
|
return listMeteors1
|
|
3352
|
return listMeteors1
|
|
|
3353
|
|
|
3353
|
|
|
|
3354
|
#Exponential Function
|
|
3354
|
#Exponential Function
|
|
|
3355
|
|
|
3355
|
|
|
|
3356
|
def __exponential_function(self, x, a, tau):
|
|
3356
|
def __exponential_function(self, x, a, tau):
|
|
|
3357
|
y = a*numpy.exp(-x/tau)
|
|
3357
|
y = a*numpy.exp(-x/tau)
|
|
|
3358
|
return y
|
|
3358
|
return y
|
|
|
3359
|
|
|
3359
|
|
|
|
3360
|
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval):
|
|
3360
|
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval):
|
|
|
3361
|
|
|
3361
|
|
|
|
3362
|
pairslist1 = list(pairslist)
|
|
3362
|
pairslist1 = list(pairslist)
|
|
|
3363
|
pairslist1.append((0,1))
|
|
3363
|
pairslist1.append((0,1))
|
|
|
3364
|
pairslist1.append((3,4))
|
|
3364
|
pairslist1.append((3,4))
|
|
|
3365
|
numPairs = len(pairslist1)
|
|
3365
|
numPairs = len(pairslist1)
|
|
|
3366
|
#Time Lag
|
|
3366
|
#Time Lag
|
|
|
3367
|
timeLag = 45*10**-3
|
|
3367
|
timeLag = 45*10**-3
|
|
|
3368
|
c = 3e8
|
|
3368
|
c = 3e8
|
|
|
3369
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3369
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3370
|
freq = 30e6
|
|
3370
|
freq = 30e6
|
|
|
3371
|
|
|
3371
|
|
|
|
3372
|
listMeteors1 = []
|
|
3372
|
listMeteors1 = []
|
|
|
3373
|
|
|
3373
|
|
|
|
3374
|
for i in range(len(listMeteors)):
|
|
3374
|
for i in range(len(listMeteors)):
|
|
|
3375
|
meteorAux = listMeteors[i]
|
|
3375
|
meteorAux = listMeteors[i]
|
|
|
3376
|
if meteorAux[-1] == 0:
|
|
3376
|
if meteorAux[-1] == 0:
|
|
|
3377
|
mStart = listMeteors[i][1]
|
|
3377
|
mStart = listMeteors[i][1]
|
|
|
3378
|
mPeak = listMeteors[i][2]
|
|
3378
|
mPeak = listMeteors[i][2]
|
|
|
3379
|
mLag = mPeak - mStart + lag
|
|
3379
|
mLag = mPeak - mStart + lag
|
|
|
3380
|
|
|
3380
|
|
|
|
3381
|
#get the volt data between the start and end times of the meteor
|
|
3381
|
#get the volt data between the start and end times of the meteor
|
|
|
3382
|
meteorVolts = listVolts[i]
|
|
3382
|
meteorVolts = listVolts[i]
|
|
|
3383
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
3383
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
|
3384
|
|
|
3384
|
|
|
|
3385
|
#Get CCF
|
|
3385
|
#Get CCF
|
|
|
3386
|
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
|
|
3386
|
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
|
|
|
3387
|
|
|
3387
|
|
|
|
3388
|
#Method 2
|
|
3388
|
#Method 2
|
|
|
3389
|
slopes = numpy.zeros(numPairs)
|
|
3389
|
slopes = numpy.zeros(numPairs)
|
|
|
3390
|
time = numpy.array([-2,-1,1,2])*timeInterval
|
|
3390
|
time = numpy.array([-2,-1,1,2])*timeInterval
|
|
|
3391
|
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
|
|
3391
|
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
|
|
|
3392
|
|
|
3392
|
|
|
|
3393
|
#Correct phases
|
|
3393
|
#Correct phases
|
|
|
3394
|
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
|
|
3394
|
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
|
|
|
3395
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
3395
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
|
3396
|
|
|
3396
|
|
|
|
3397
|
if indDer[0].shape[0] > 0:
|
|
3397
|
if indDer[0].shape[0] > 0:
|
|
|
3398
|
for i in range(indDer[0].shape[0]):
|
|
3398
|
for i in range(indDer[0].shape[0]):
|
|
|
3399
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
|
|
3399
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
|
|
|
3400
|
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
|
|
3400
|
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
|
|
|
3401
|
|
|
3401
|
|
|
|
3402
|
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
|
|
3402
|
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
|
|
|
3403
|
for j in range(numPairs):
|
|
3403
|
for j in range(numPairs):
|
|
|
3404
|
fit = stats.linregress(time, angAllCCF[j,:])
|
|
3404
|
fit = stats.linregress(time, angAllCCF[j,:])
|
|
|
3405
|
slopes[j] = fit[0]
|
|
3405
|
slopes[j] = fit[0]
|
|
|
3406
|
|
|
3406
|
|
|
|
3407
|
#Remove Outlier
|
|
3407
|
#Remove Outlier
|
|
|
3408
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
3408
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
|
3409
|
# slopes = numpy.delete(slopes,indOut)
|
|
3409
|
# slopes = numpy.delete(slopes,indOut)
|
|
|
3410
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
3410
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
|
3411
|
# slopes = numpy.delete(slopes,indOut)
|
|
3411
|
# slopes = numpy.delete(slopes,indOut)
|
|
|
3412
|
|
|
3412
|
|
|
|
3413
|
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
3413
|
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
|
3414
|
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
3414
|
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
|
3415
|
meteorAux[-2] = radialError
|
|
3415
|
meteorAux[-2] = radialError
|
|
|
3416
|
meteorAux[-3] = radialVelocity
|
|
3416
|
meteorAux[-3] = radialVelocity
|
|
|
3417
|
|
|
3417
|
|
|
|
3418
|
#Setting Error
|
|
3418
|
#Setting Error
|
|
|
3419
|
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
3419
|
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
|
3420
|
if numpy.abs(radialVelocity) > 200:
|
|
3420
|
if numpy.abs(radialVelocity) > 200:
|
|
|
3421
|
meteorAux[-1] = 15
|
|
3421
|
meteorAux[-1] = 15
|
|
|
3422
|
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity
|
|
3422
|
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity
|
|
|
3423
|
elif radialError > radialStdThresh:
|
|
3423
|
elif radialError > radialStdThresh:
|
|
|
3424
|
meteorAux[-1] = 12
|
|
3424
|
meteorAux[-1] = 12
|
|
|
3425
|
|
|
3425
|
|
|
|
3426
|
listMeteors1.append(meteorAux)
|
|
3426
|
listMeteors1.append(meteorAux)
|
|
|
3427
|
return listMeteors1
|
|
3427
|
return listMeteors1
|
|
|
3428
|
|
|
3428
|
|
|
|
3429
|
def __setNewArrays(self, listMeteors, date, heiRang):
|
|
3429
|
def __setNewArrays(self, listMeteors, date, heiRang):
|
|
|
3430
|
|
|
3430
|
|
|
|
3431
|
#New arrays
|
|
3431
|
#New arrays
|
|
|
3432
|
arrayMeteors = numpy.array(listMeteors)
|
|
3432
|
arrayMeteors = numpy.array(listMeteors)
|
|
|
3433
|
arrayParameters = numpy.zeros((len(listMeteors), 13))
|
|
3433
|
arrayParameters = numpy.zeros((len(listMeteors), 13))
|
|
|
3434
|
|
|
3434
|
|
|
|
3435
|
#Date inclusion
|
|
3435
|
#Date inclusion
|
|
|
3436
|
# date = re.findall(r'\((.*?)\)', date)
|
|
3436
|
# date = re.findall(r'\((.*?)\)', date)
|
|
|
3437
|
# date = date[0].split(',')
|
|
3437
|
# date = date[0].split(',')
|
|
|
3438
|
# date = map(int, date)
|
|
3438
|
# date = map(int, date)
|
|
|
3439
|
#
|
|
3439
|
#
|
|
|
3440
|
# if len(date)<6:
|
|
3440
|
# if len(date)<6:
|
|
|
3441
|
# date.append(0)
|
|
3441
|
# date.append(0)
|
|
|
3442
|
#
|
|
3442
|
#
|
|
|
3443
|
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
|
|
3443
|
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
|
|
|
3444
|
# arrayDate = numpy.tile(date, (len(listMeteors), 1))
|
|
3444
|
# arrayDate = numpy.tile(date, (len(listMeteors), 1))
|
|
|
3445
|
arrayDate = numpy.tile(date, (len(listMeteors)))
|
|
3445
|
arrayDate = numpy.tile(date, (len(listMeteors)))
|
|
|
3446
|
|
|
3446
|
|
|
|
3447
|
#Meteor array
|
|
3447
|
#Meteor array
|
|
|
3448
|
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
|
|
3448
|
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
|
|
|
3449
|
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
|
|
3449
|
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
|
|
|
3450
|
|
|
3450
|
|
|
|
3451
|
#Parameters Array
|
|
3451
|
#Parameters Array
|
|
|
3452
|
arrayParameters[:,0] = arrayDate #Date
|
|
3452
|
arrayParameters[:,0] = arrayDate #Date
|
|
|
3453
|
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
|
|
3453
|
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
|
|
|
3454
|
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
|
|
3454
|
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
|
|
|
3455
|
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases
|
|
3455
|
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases
|
|
|
3456
|
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error
|
|
3456
|
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error
|
|
|
3457
|
|
|
3457
|
|
|
|
3458
|
|
|
3458
|
|
|
|
3459
|
return arrayParameters
|
|
3459
|
return arrayParameters
|
|
|
3460
|
|
|
3460
|
|
|
|
3461
|
class CorrectSMPhases(Operation):
|
|
3461
|
class CorrectSMPhases(Operation):
|
|
|
3462
|
|
|
3462
|
|
|
|
3463
|
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
|
|
3463
|
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
|
|
|
3464
|
|
|
3464
|
|
|
|
3465
|
arrayParameters = dataOut.data_param
|
|
3465
|
arrayParameters = dataOut.data_param
|
|
|
3466
|
pairsList = []
|
|
3466
|
pairsList = []
|
|
|
3467
|
pairx = (0,1)
|
|
3467
|
pairx = (0,1)
|
|
|
3468
|
pairy = (2,3)
|
|
3468
|
pairy = (2,3)
|
|
|
3469
|
pairsList.append(pairx)
|
|
3469
|
pairsList.append(pairx)
|
|
|
3470
|
pairsList.append(pairy)
|
|
3470
|
pairsList.append(pairy)
|
|
|
3471
|
jph = numpy.zeros(4)
|
|
3471
|
jph = numpy.zeros(4)
|
|
|
3472
|
|
|
3472
|
|
|
|
3473
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
3473
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
|
3474
|
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
3474
|
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
|
3475
|
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
|
|
3475
|
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
|
|
|
3476
|
|
|
3476
|
|
|
|
3477
|
meteorOps = SMOperations()
|
|
3477
|
meteorOps = SMOperations()
|
|
|
3478
|
if channelPositions == None:
|
|
3478
|
if channelPositions == None:
|
|
|
3479
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
3479
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
|
3480
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
3480
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
|
3481
|
|
|
3481
|
|
|
|
3482
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
3482
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
|
3483
|
h = (hmin,hmax)
|
|
3483
|
h = (hmin,hmax)
|
|
|
3484
|
|
|
3484
|
|
|
|
3485
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
3485
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
|
3486
|
|
|
3486
|
|
|
|
3487
|
dataOut.data_param = arrayParameters
|
|
3487
|
dataOut.data_param = arrayParameters
|
|
|
3488
|
return
|
|
3488
|
return
|
|
|
3489
|
|
|
3489
|
|
|
|
3490
|
class SMPhaseCalibration(Operation):
|
|
3490
|
class SMPhaseCalibration(Operation):
|
|
|
3491
|
|
|
3491
|
|
|
|
3492
|
__buffer = None
|
|
3492
|
__buffer = None
|
|
|
3493
|
|
|
3493
|
|
|
|
3494
|
__initime = None
|
|
3494
|
__initime = None
|
|
|
3495
|
|
|
3495
|
|
|
|
3496
|
__dataReady = False
|
|
3496
|
__dataReady = False
|
|
|
3497
|
|
|
3497
|
|
|
|
3498
|
__isConfig = False
|
|
3498
|
__isConfig = False
|
|
|
3499
|
|
|
3499
|
|
|
|
3500
|
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
|
|
3500
|
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
|
|
|
3501
|
|
|
3501
|
|
|
|
3502
|
dataTime = currentTime + paramInterval
|
|
3502
|
dataTime = currentTime + paramInterval
|
|
|
3503
|
deltaTime = dataTime - initTime
|
|
3503
|
deltaTime = dataTime - initTime
|
|
|
3504
|
|
|
3504
|
|
|
|
3505
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
3505
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
|
3506
|
return True
|
|
3506
|
return True
|
|
|
3507
|
|
|
3507
|
|
|
|
3508
|
return False
|
|
3508
|
return False
|
|
|
3509
|
|
|
3509
|
|
|
|
3510
|
def __getGammas(self, pairs, d, phases):
|
|
3510
|
def __getGammas(self, pairs, d, phases):
|
|
|
3511
|
gammas = numpy.zeros(2)
|
|
3511
|
gammas = numpy.zeros(2)
|
|
|
3512
|
|
|
3512
|
|
|
|
3513
|
for i in range(len(pairs)):
|
|
3513
|
for i in range(len(pairs)):
|
|
|
3514
|
|
|
3514
|
|
|
|
3515
|
pairi = pairs[i]
|
|
3515
|
pairi = pairs[i]
|
|
|
3516
|
|
|
3516
|
|
|
|
3517
|
phip3 = phases[:,pairi[0]]
|
|
3517
|
phip3 = phases[:,pairi[0]]
|
|
|
3518
|
d3 = d[pairi[0]]
|
|
3518
|
d3 = d[pairi[0]]
|
|
|
3519
|
phip2 = phases[:,pairi[1]]
|
|
3519
|
phip2 = phases[:,pairi[1]]
|
|
|
3520
|
d2 = d[pairi[1]]
|
|
3520
|
d2 = d[pairi[1]]
|
|
|
3521
|
#Calculating gamma
|
|
3521
|
#Calculating gamma
|
|
|
3522
|
# jdcos = alp1/(k*d1)
|
|
3522
|
# jdcos = alp1/(k*d1)
|
|
|
3523
|
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
|
|
3523
|
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
|
|
|
3524
|
jgamma = -phip2*d3/d2 - phip3
|
|
3524
|
jgamma = -phip2*d3/d2 - phip3
|
|
|
3525
|
jgamma = numpy.angle(numpy.exp(1j*jgamma))
|
|
3525
|
jgamma = numpy.angle(numpy.exp(1j*jgamma))
|
|
|
3526
|
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi
|
|
3526
|
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi
|
|
|
3527
|
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi
|
|
3527
|
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi
|
|
|
3528
|
|
|
3528
|
|
|
|
3529
|
#Revised distribution
|
|
3529
|
#Revised distribution
|
|
|
3530
|
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
|
|
3530
|
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
|
|
|
3531
|
|
|
3531
|
|
|
|
3532
|
#Histogram
|
|
3532
|
#Histogram
|
|
|
3533
|
nBins = 64
|
|
3533
|
nBins = 64
|
|
|
3534
|
rmin = -0.5*numpy.pi
|
|
3534
|
rmin = -0.5*numpy.pi
|
|
|
3535
|
rmax = 0.5*numpy.pi
|
|
3535
|
rmax = 0.5*numpy.pi
|
|
|
3536
|
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
|
|
3536
|
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
|
|
|
3537
|
|
|
3537
|
|
|
|
3538
|
meteorsY = phaseHisto[0]
|
|
3538
|
meteorsY = phaseHisto[0]
|
|
|
3539
|
phasesX = phaseHisto[1][:-1]
|
|
3539
|
phasesX = phaseHisto[1][:-1]
|
|
|
3540
|
width = phasesX[1] - phasesX[0]
|
|
3540
|
width = phasesX[1] - phasesX[0]
|
|
|
3541
|
phasesX += width/2
|
|
3541
|
phasesX += width/2
|
|
|
3542
|
|
|
3542
|
|
|
|
3543
|
#Gaussian aproximation
|
|
3543
|
#Gaussian aproximation
|
|
|
3544
|
bpeak = meteorsY.argmax()
|
|
3544
|
bpeak = meteorsY.argmax()
|
|
|
3545
|
peak = meteorsY.max()
|
|
3545
|
peak = meteorsY.max()
|
|
|
3546
|
jmin = bpeak - 5
|
|
3546
|
jmin = bpeak - 5
|
|
|
3547
|
jmax = bpeak + 5 + 1
|
|
3547
|
jmax = bpeak + 5 + 1
|
|
|
3548
|
|
|
3548
|
|
|
|
3549
|
if jmin<0:
|
|
3549
|
if jmin<0:
|
|
|
3550
|
jmin = 0
|
|
3550
|
jmin = 0
|
|
|
3551
|
jmax = 6
|
|
3551
|
jmax = 6
|
|
|
3552
|
elif jmax > meteorsY.size:
|
|
3552
|
elif jmax > meteorsY.size:
|
|
|
3553
|
jmin = meteorsY.size - 6
|
|
3553
|
jmin = meteorsY.size - 6
|
|
|
3554
|
jmax = meteorsY.size
|
|
3554
|
jmax = meteorsY.size
|
|
|
3555
|
|
|
3555
|
|
|
|
3556
|
x0 = numpy.array([peak,bpeak,50])
|
|
3556
|
x0 = numpy.array([peak,bpeak,50])
|
|
|
3557
|
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
|
|
3557
|
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
|
|
|
3558
|
|
|
3558
|
|
|
|
3559
|
#Gammas
|
|
3559
|
#Gammas
|
|
|
3560
|
gammas[i] = coeff[0][1]
|
|
3560
|
gammas[i] = coeff[0][1]
|
|
|
3561
|
|
|
3561
|
|
|
|
3562
|
return gammas
|
|
3562
|
return gammas
|
|
|
3563
|
|
|
3563
|
|
|
|
3564
|
def __residualFunction(self, coeffs, y, t):
|
|
3564
|
def __residualFunction(self, coeffs, y, t):
|
|
|
3565
|
|
|
3565
|
|
|
|
3566
|
return y - self.__gauss_function(t, coeffs)
|
|
3566
|
return y - self.__gauss_function(t, coeffs)
|
|
|
3567
|
|
|
3567
|
|
|
|
3568
|
def __gauss_function(self, t, coeffs):
|
|
3568
|
def __gauss_function(self, t, coeffs):
|
|
|
3569
|
|
|
3569
|
|
|
|
3570
|
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
|
|
3570
|
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
|
|
|
3571
|
|
|
3571
|
|
|
|
3572
|
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
|
|
3572
|
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
|
|
|
3573
|
meteorOps = SMOperations()
|
|
3573
|
meteorOps = SMOperations()
|
|
|
3574
|
nchan = 4
|
|
3574
|
nchan = 4
|
|
|
3575
|
pairx = pairsList[0] #x es 0
|
|
3575
|
pairx = pairsList[0] #x es 0
|
|
|
3576
|
pairy = pairsList[1] #y es 1
|
|
3576
|
pairy = pairsList[1] #y es 1
|
|
|
3577
|
center_xangle = 0
|
|
3577
|
center_xangle = 0
|
|
|
3578
|
center_yangle = 0
|
|
3578
|
center_yangle = 0
|
|
|
3579
|
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
|
|
3579
|
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
|
|
|
3580
|
ntimes = len(range_angle)
|
|
3580
|
ntimes = len(range_angle)
|
|
|
3581
|
|
|
3581
|
|
|
|
3582
|
nstepsx = 20
|
|
3582
|
nstepsx = 20
|
|
|
3583
|
nstepsy = 20
|
|
3583
|
nstepsy = 20
|
|
|
3584
|
|
|
3584
|
|
|
|
3585
|
for iz in range(ntimes):
|
|
3585
|
for iz in range(ntimes):
|
|
|
3586
|
min_xangle = -range_angle[iz]/2 + center_xangle
|
|
3586
|
min_xangle = -range_angle[iz]/2 + center_xangle
|
|
|
3587
|
max_xangle = range_angle[iz]/2 + center_xangle
|
|
3587
|
max_xangle = range_angle[iz]/2 + center_xangle
|
|
|
3588
|
min_yangle = -range_angle[iz]/2 + center_yangle
|
|
3588
|
min_yangle = -range_angle[iz]/2 + center_yangle
|
|
|
3589
|
max_yangle = range_angle[iz]/2 + center_yangle
|
|
3589
|
max_yangle = range_angle[iz]/2 + center_yangle
|
|
|
3590
|
|
|
3590
|
|
|
|
3591
|
inc_x = (max_xangle-min_xangle)/nstepsx
|
|
3591
|
inc_x = (max_xangle-min_xangle)/nstepsx
|
|
|
3592
|
inc_y = (max_yangle-min_yangle)/nstepsy
|
|
3592
|
inc_y = (max_yangle-min_yangle)/nstepsy
|
|
|
3593
|
|
|
3593
|
|
|
|
3594
|
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
|
|
3594
|
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
|
|
|
3595
|
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
|
|
3595
|
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
|
|
|
3596
|
penalty = numpy.zeros((nstepsx,nstepsy))
|
|
3596
|
penalty = numpy.zeros((nstepsx,nstepsy))
|
|
|
3597
|
jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
|
|
3597
|
jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
|
|
|
3598
|
jph = numpy.zeros(nchan)
|
|
3598
|
jph = numpy.zeros(nchan)
|
|
|
3599
|
|
|
3599
|
|
|
|
3600
|
# Iterations looking for the offset
|
|
3600
|
# Iterations looking for the offset
|
|
|
3601
|
for iy in range(int(nstepsy)):
|
|
3601
|
for iy in range(int(nstepsy)):
|
|
|
3602
|
for ix in range(int(nstepsx)):
|
|
3602
|
for ix in range(int(nstepsx)):
|
|
|
3603
|
d3 = d[pairsList[1][0]]
|
|
3603
|
d3 = d[pairsList[1][0]]
|
|
|
3604
|
d2 = d[pairsList[1][1]]
|
|
3604
|
d2 = d[pairsList[1][1]]
|
|
|
3605
|
d5 = d[pairsList[0][0]]
|
|
3605
|
d5 = d[pairsList[0][0]]
|
|
|
3606
|
d4 = d[pairsList[0][1]]
|
|
3606
|
d4 = d[pairsList[0][1]]
|
|
|
3607
|
|
|
3607
|
|
|
|
3608
|
alp2 = alpha_y[iy] #gamma 1
|
|
3608
|
alp2 = alpha_y[iy] #gamma 1
|
|
|
3609
|
alp4 = alpha_x[ix] #gamma 0
|
|
3609
|
alp4 = alpha_x[ix] #gamma 0
|
|
|
3610
|
|
|
3610
|
|
|
|
3611
|
alp3 = -alp2*d3/d2 - gammas[1]
|
|
3611
|
alp3 = -alp2*d3/d2 - gammas[1]
|
|
|
3612
|
alp5 = -alp4*d5/d4 - gammas[0]
|
|
3612
|
alp5 = -alp4*d5/d4 - gammas[0]
|
|
|
3613
|
# jph[pairy[1]] = alpha_y[iy]
|
|
3613
|
# jph[pairy[1]] = alpha_y[iy]
|
|
|
3614
|
# jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
|
|
3614
|
# jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
|
|
|
3615
|
|
|
3615
|
|
|
|
3616
|
# jph[pairx[1]] = alpha_x[ix]
|
|
3616
|
# jph[pairx[1]] = alpha_x[ix]
|
|
|
3617
|
# jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
|
|
3617
|
# jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
|
|
|
3618
|
jph[pairsList[0][1]] = alp4
|
|
3618
|
jph[pairsList[0][1]] = alp4
|
|
|
3619
|
jph[pairsList[0][0]] = alp5
|
|
3619
|
jph[pairsList[0][0]] = alp5
|
|
|
3620
|
jph[pairsList[1][0]] = alp3
|
|
3620
|
jph[pairsList[1][0]] = alp3
|
|
|
3621
|
jph[pairsList[1][1]] = alp2
|
|
3621
|
jph[pairsList[1][1]] = alp2
|
|
|
3622
|
jph_array[:,ix,iy] = jph
|
|
3622
|
jph_array[:,ix,iy] = jph
|
|
|
3623
|
# d = [2.0,2.5,2.5,2.0]
|
|
3623
|
# d = [2.0,2.5,2.5,2.0]
|
|
|
3624
|
#falta chequear si va a leer bien los meteoros
|
|
3624
|
#falta chequear si va a leer bien los meteoros
|
|
|
3625
|
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
|
|
3625
|
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
|
|
|
3626
|
error = meteorsArray1[:,-1]
|
|
3626
|
error = meteorsArray1[:,-1]
|
|
|
3627
|
ind1 = numpy.where(error==0)[0]
|
|
3627
|
ind1 = numpy.where(error==0)[0]
|
|
|
3628
|
penalty[ix,iy] = ind1.size
|
|
3628
|
penalty[ix,iy] = ind1.size
|
|
|
3629
|
|
|
3629
|
|
|
|
3630
|
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
|
|
3630
|
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
|
|
|
3631
|
phOffset = jph_array[:,i,j]
|
|
3631
|
phOffset = jph_array[:,i,j]
|
|
|
3632
|
|
|
3632
|
|
|
|
3633
|
center_xangle = phOffset[pairx[1]]
|
|
3633
|
center_xangle = phOffset[pairx[1]]
|
|
|
3634
|
center_yangle = phOffset[pairy[1]]
|
|
3634
|
center_yangle = phOffset[pairy[1]]
|
|
|
3635
|
|
|
3635
|
|
|
|
3636
|
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
|
|
3636
|
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
|
|
|
3637
|
phOffset = phOffset*180/numpy.pi
|
|
3637
|
phOffset = phOffset*180/numpy.pi
|
|
|
3638
|
return phOffset
|
|
3638
|
return phOffset
|
|
|
3639
|
|
|
3639
|
|
|
|
3640
|
|
|
3640
|
|
|
|
3641
|
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
|
|
3641
|
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
|
|
|
3642
|
|
|
3642
|
|
|
|
3643
|
dataOut.flagNoData = True
|
|
3643
|
dataOut.flagNoData = True
|
|
|
3644
|
self.__dataReady = False
|
|
3644
|
self.__dataReady = False
|
|
|
3645
|
dataOut.outputInterval = nHours*3600
|
|
3645
|
dataOut.outputInterval = nHours*3600
|
|
|
3646
|
|
|
3646
|
|
|
|
3647
|
if self.__isConfig == False:
|
|
3647
|
if self.__isConfig == False:
|
|
|
3648
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
3648
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
3649
|
#Get Initial LTC time
|
|
3649
|
#Get Initial LTC time
|
|
|
3650
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
3650
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
3651
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
3651
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
3652
|
|
|
3652
|
|
|
|
3653
|
self.__isConfig = True
|
|
3653
|
self.__isConfig = True
|
|
|
3654
|
|
|
3654
|
|
|
|
3655
|
if self.__buffer == None:
|
|
3655
|
if self.__buffer == None:
|
|
|
3656
|
self.__buffer = dataOut.data_param.copy()
|
|
3656
|
self.__buffer = dataOut.data_param.copy()
|
|
|
3657
|
|
|
3657
|
|
|
|
3658
|
else:
|
|
3658
|
else:
|
|
|
3659
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
3659
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
3660
|
|
|
3660
|
|
|
|
3661
|
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
3661
|
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
3662
|
|
|
3662
|
|
|
|
3663
|
if self.__dataReady:
|
|
3663
|
if self.__dataReady:
|
|
|
3664
|
dataOut.utctimeInit = self.__initime
|
|
3664
|
dataOut.utctimeInit = self.__initime
|
|
|
3665
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
3665
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
3666
|
|
|
3666
|
|
|
|
3667
|
freq = dataOut.frequency
|
|
3667
|
freq = dataOut.frequency
|
|
|
3668
|
c = dataOut.C #m/s
|
|
3668
|
c = dataOut.C #m/s
|
|
|
3669
|
lamb = c/freq
|
|
3669
|
lamb = c/freq
|
|
|
3670
|
k = 2*numpy.pi/lamb
|
|
3670
|
k = 2*numpy.pi/lamb
|
|
|
3671
|
azimuth = 0
|
|
3671
|
azimuth = 0
|
|
|
3672
|
h = (hmin, hmax)
|
|
3672
|
h = (hmin, hmax)
|
|
|
3673
|
# pairs = ((0,1),(2,3)) #Estrella
|
|
3673
|
# pairs = ((0,1),(2,3)) #Estrella
|
|
|
3674
|
# pairs = ((1,0),(2,3)) #T
|
|
3674
|
# pairs = ((1,0),(2,3)) #T
|
|
|
3675
|
|
|
3675
|
|
|
|
3676
|
if channelPositions is None:
|
|
3676
|
if channelPositions is None:
|
|
|
3677
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
3677
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
|
3678
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
3678
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
|
3679
|
meteorOps = SMOperations()
|
|
3679
|
meteorOps = SMOperations()
|
|
|
3680
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
3680
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
|
3681
|
|
|
3681
|
|
|
|
3682
|
#Checking correct order of pairs
|
|
3682
|
#Checking correct order of pairs
|
|
|
3683
|
pairs = []
|
|
3683
|
pairs = []
|
|
|
3684
|
if distances[1] > distances[0]:
|
|
3684
|
if distances[1] > distances[0]:
|
|
|
3685
|
pairs.append((1,0))
|
|
3685
|
pairs.append((1,0))
|
|
|
3686
|
else:
|
|
3686
|
else:
|
|
|
3687
|
pairs.append((0,1))
|
|
3687
|
pairs.append((0,1))
|
|
|
3688
|
|
|
3688
|
|
|
|
3689
|
if distances[3] > distances[2]:
|
|
3689
|
if distances[3] > distances[2]:
|
|
|
3690
|
pairs.append((3,2))
|
|
3690
|
pairs.append((3,2))
|
|
|
3691
|
else:
|
|
3691
|
else:
|
|
|
3692
|
pairs.append((2,3))
|
|
3692
|
pairs.append((2,3))
|
|
|
3693
|
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
|
|
3693
|
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
|
|
|
3694
|
|
|
3694
|
|
|
|
3695
|
meteorsArray = self.__buffer
|
|
3695
|
meteorsArray = self.__buffer
|
|
|
3696
|
error = meteorsArray[:,-1]
|
|
3696
|
error = meteorsArray[:,-1]
|
|
|
3697
|
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
|
|
3697
|
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
|
|
|
3698
|
ind1 = numpy.where(boolError)[0]
|
|
3698
|
ind1 = numpy.where(boolError)[0]
|
|
|
3699
|
meteorsArray = meteorsArray[ind1,:]
|
|
3699
|
meteorsArray = meteorsArray[ind1,:]
|
|
|
3700
|
meteorsArray[:,-1] = 0
|
|
3700
|
meteorsArray[:,-1] = 0
|
|
|
3701
|
phases = meteorsArray[:,8:12]
|
|
3701
|
phases = meteorsArray[:,8:12]
|
|
|
3702
|
|
|
3702
|
|
|
|
3703
|
#Calculate Gammas
|
|
3703
|
#Calculate Gammas
|
|
|
3704
|
gammas = self.__getGammas(pairs, distances, phases)
|
|
3704
|
gammas = self.__getGammas(pairs, distances, phases)
|
|
|
3705
|
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
|
|
3705
|
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
|
|
|
3706
|
#Calculate Phases
|
|
3706
|
#Calculate Phases
|
|
|
3707
|
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
|
|
3707
|
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
|
|
|
3708
|
phasesOff = phasesOff.reshape((1,phasesOff.size))
|
|
3708
|
phasesOff = phasesOff.reshape((1,phasesOff.size))
|
|
|
3709
|
dataOut.data_output = -phasesOff
|
|
3709
|
dataOut.data_output = -phasesOff
|
|
|
3710
|
dataOut.flagNoData = False
|
|
3710
|
dataOut.flagNoData = False
|
|
|
3711
|
self.__buffer = None
|
|
3711
|
self.__buffer = None
|
|
|
3712
|
|
|
3712
|
|
|
|
3713
|
|
|
3713
|
|
|
|
3714
|
return
|
|
3714
|
return
|
|
|
3715
|
|
|
3715
|
|
|
|
3716
|
class SMOperations():
|
|
3716
|
class SMOperations():
|
|
|
3717
|
|
|
3717
|
|
|
|
3718
|
def __init__(self):
|
|
3718
|
def __init__(self):
|
|
|
3719
|
|
|
3719
|
|
|
|
3720
|
return
|
|
3720
|
return
|
|
|
3721
|
|
|
3721
|
|
|
|
3722
|
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
|
|
3722
|
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
|
|
|
3723
|
|
|
3723
|
|
|
|
3724
|
arrayParameters = arrayParameters0.copy()
|
|
3724
|
arrayParameters = arrayParameters0.copy()
|
|
|
3725
|
hmin = h[0]
|
|
3725
|
hmin = h[0]
|
|
|
3726
|
hmax = h[1]
|
|
3726
|
hmax = h[1]
|
|
|
3727
|
|
|
3727
|
|
|
|
3728
|
#Calculate AOA (Error N 3, 4)
|
|
3728
|
#Calculate AOA (Error N 3, 4)
|
|
|
3729
|
#JONES ET AL. 1998
|
|
3729
|
#JONES ET AL. 1998
|
|
|
3730
|
AOAthresh = numpy.pi/8
|
|
3730
|
AOAthresh = numpy.pi/8
|
|
|
3731
|
error = arrayParameters[:,-1]
|
|
3731
|
error = arrayParameters[:,-1]
|
|
|
3732
|
phases = -arrayParameters[:,8:12] + jph
|
|
3732
|
phases = -arrayParameters[:,8:12] + jph
|
|
|
3733
|
# phases = numpy.unwrap(phases)
|
|
3733
|
# phases = numpy.unwrap(phases)
|
|
|
3734
|
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
|
|
3734
|
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
|
|
|
3735
|
|
|
3735
|
|
|
|
3736
|
#Calculate Heights (Error N 13 and 14)
|
|
3736
|
#Calculate Heights (Error N 13 and 14)
|
|
|
3737
|
error = arrayParameters[:,-1]
|
|
3737
|
error = arrayParameters[:,-1]
|
|
|
3738
|
Ranges = arrayParameters[:,1]
|
|
3738
|
Ranges = arrayParameters[:,1]
|
|
|
3739
|
zenith = arrayParameters[:,4]
|
|
3739
|
zenith = arrayParameters[:,4]
|
|
|
3740
|
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
|
|
3740
|
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
|
|
|
3741
|
|
|
3741
|
|
|
|
3742
|
#----------------------- Get Final data ------------------------------------
|
|
3742
|
#----------------------- Get Final data ------------------------------------
|
|
|
3743
|
# error = arrayParameters[:,-1]
|
|
3743
|
# error = arrayParameters[:,-1]
|
|
|
3744
|
# ind1 = numpy.where(error==0)[0]
|
|
3744
|
# ind1 = numpy.where(error==0)[0]
|
|
|
3745
|
# arrayParameters = arrayParameters[ind1,:]
|
|
3745
|
# arrayParameters = arrayParameters[ind1,:]
|
|
|
3746
|
|
|
3746
|
|
|
|
3747
|
return arrayParameters
|
|
3747
|
return arrayParameters
|
|
|
3748
|
|
|
3748
|
|
|
|
3749
|
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
|
|
3749
|
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
|
|
|
3750
|
|
|
3750
|
|
|
|
3751
|
arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
3751
|
arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
|
3752
|
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
|
|
3752
|
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
|
|
|
3753
|
|
|
3753
|
|
|
|
3754
|
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
3754
|
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
|
3755
|
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
3755
|
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
|
3756
|
arrayAOA[:,2] = cosDirError
|
|
3756
|
arrayAOA[:,2] = cosDirError
|
|
|
3757
|
|
|
3757
|
|
|
|
3758
|
azimuthAngle = arrayAOA[:,0]
|
|
3758
|
azimuthAngle = arrayAOA[:,0]
|
|
|
3759
|
zenithAngle = arrayAOA[:,1]
|
|
3759
|
zenithAngle = arrayAOA[:,1]
|
|
|
3760
|
|
|
3760
|
|
|
|
3761
|
#Setting Error
|
|
3761
|
#Setting Error
|
|
|
3762
|
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
|
|
3762
|
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
|
|
|
3763
|
error[indError] = 0
|
|
3763
|
error[indError] = 0
|
|
|
3764
|
#Number 3: AOA not fesible
|
|
3764
|
#Number 3: AOA not fesible
|
|
|
3765
|
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
3765
|
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
|
3766
|
error[indInvalid] = 3
|
|
3766
|
error[indInvalid] = 3
|
|
|
3767
|
#Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
3767
|
#Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
|
3768
|
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
3768
|
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
|
3769
|
error[indInvalid] = 4
|
|
3769
|
error[indInvalid] = 4
|
|
|
3770
|
return arrayAOA, error
|
|
3770
|
return arrayAOA, error
|
|
|
3771
|
|
|
3771
|
|
|
|
3772
|
def __getDirectionCosines(self, arrayPhase, pairsList, distances):
|
|
3772
|
def __getDirectionCosines(self, arrayPhase, pairsList, distances):
|
|
|
3773
|
|
|
3773
|
|
|
|
3774
|
#Initializing some variables
|
|
3774
|
#Initializing some variables
|
|
|
3775
|
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
3775
|
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
|
3776
|
ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
3776
|
ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
|
3777
|
|
|
3777
|
|
|
|
3778
|
cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
3778
|
cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3779
|
cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
3779
|
cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3780
|
|
|
3780
|
|
|
|
3781
|
|
|
3781
|
|
|
|
3782
|
for i in range(2):
|
|
3782
|
for i in range(2):
|
|
|
3783
|
ph0 = arrayPhase[:,pairsList[i][0]]
|
|
3783
|
ph0 = arrayPhase[:,pairsList[i][0]]
|
|
|
3784
|
ph1 = arrayPhase[:,pairsList[i][1]]
|
|
3784
|
ph1 = arrayPhase[:,pairsList[i][1]]
|
|
|
3785
|
d0 = distances[pairsList[i][0]]
|
|
3785
|
d0 = distances[pairsList[i][0]]
|
|
|
3786
|
d1 = distances[pairsList[i][1]]
|
|
3786
|
d1 = distances[pairsList[i][1]]
|
|
|
3787
|
|
|
3787
|
|
|
|
3788
|
ph0_aux = ph0 + ph1
|
|
3788
|
ph0_aux = ph0 + ph1
|
|
|
3789
|
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
|
|
3789
|
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
|
|
|
3790
|
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
|
|
3790
|
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
|
|
|
3791
|
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
|
|
3791
|
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
|
|
|
3792
|
#First Estimation
|
|
3792
|
#First Estimation
|
|
|
3793
|
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
|
|
3793
|
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
|
|
|
3794
|
|
|
3794
|
|
|
|
3795
|
#Most-Accurate Second Estimation
|
|
3795
|
#Most-Accurate Second Estimation
|
|
|
3796
|
phi1_aux = ph0 - ph1
|
|
3796
|
phi1_aux = ph0 - ph1
|
|
|
3797
|
phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
3797
|
phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
|
3798
|
#Direction Cosine 1
|
|
3798
|
#Direction Cosine 1
|
|
|
3799
|
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
|
|
3799
|
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
|
|
|
3800
|
|
|
3800
|
|
|
|
3801
|
#Searching the correct Direction Cosine
|
|
3801
|
#Searching the correct Direction Cosine
|
|
|
3802
|
cosdir0_aux = cosdir0[:,i]
|
|
3802
|
cosdir0_aux = cosdir0[:,i]
|
|
|
3803
|
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
3803
|
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
|
3804
|
#Minimum Distance
|
|
3804
|
#Minimum Distance
|
|
|
3805
|
cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
3805
|
cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
|
3806
|
indcos = cosDiff.argmin(axis = 1)
|
|
3806
|
indcos = cosDiff.argmin(axis = 1)
|
|
|
3807
|
#Saving Value obtained
|
|
3807
|
#Saving Value obtained
|
|
|
3808
|
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
3808
|
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
|
3809
|
|
|
3809
|
|
|
|
3810
|
return cosdir0, cosdir
|
|
3810
|
return cosdir0, cosdir
|
|
|
3811
|
|
|
3811
|
|
|
|
3812
|
def __calculateAOA(self, cosdir, azimuth):
|
|
3812
|
def __calculateAOA(self, cosdir, azimuth):
|
|
|
3813
|
cosdirX = cosdir[:,0]
|
|
3813
|
cosdirX = cosdir[:,0]
|
|
|
3814
|
cosdirY = cosdir[:,1]
|
|
3814
|
cosdirY = cosdir[:,1]
|
|
|
3815
|
|
|
3815
|
|
|
|
3816
|
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
3816
|
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
|
3817
|
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
|
|
3817
|
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
|
|
|
3818
|
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
3818
|
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
|
3819
|
|
|
3819
|
|
|
|
3820
|
return angles
|
|
3820
|
return angles
|
|
|
3821
|
|
|
3821
|
|
|
|
3822
|
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
3822
|
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
|
3823
|
|
|
3823
|
|
|
|
3824
|
Ramb = 375 #Ramb = c/(2*PRF)
|
|
3824
|
Ramb = 375 #Ramb = c/(2*PRF)
|
|
|
3825
|
Re = 6371 #Earth Radius
|
|
3825
|
Re = 6371 #Earth Radius
|
|
|
3826
|
heights = numpy.zeros(Ranges.shape)
|
|
3826
|
heights = numpy.zeros(Ranges.shape)
|
|
|
3827
|
|
|
3827
|
|
|
|
3828
|
R_aux = numpy.array([0,1,2])*Ramb
|
|
3828
|
R_aux = numpy.array([0,1,2])*Ramb
|
|
|
3829
|
R_aux = R_aux.reshape(1,R_aux.size)
|
|
3829
|
R_aux = R_aux.reshape(1,R_aux.size)
|
|
|
3830
|
|
|
3830
|
|
|
|
3831
|
Ranges = Ranges.reshape(Ranges.size,1)
|
|
3831
|
Ranges = Ranges.reshape(Ranges.size,1)
|
|
|
3832
|
|
|
3832
|
|
|
|
3833
|
Ri = Ranges + R_aux
|
|
3833
|
Ri = Ranges + R_aux
|
|
|
3834
|
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
3834
|
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
|
3835
|
|
|
3835
|
|
|
|
3836
|
#Check if there is a height between 70 and 110 km
|
|
3836
|
#Check if there is a height between 70 and 110 km
|
|
|
3837
|
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
3837
|
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
|
3838
|
ind_h = numpy.where(h_bool == 1)[0]
|
|
3838
|
ind_h = numpy.where(h_bool == 1)[0]
|
|
|
3839
|
|
|
3839
|
|
|
|
3840
|
hCorr = hi[ind_h, :]
|
|
3840
|
hCorr = hi[ind_h, :]
|
|
|
3841
|
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
3841
|
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
|
3842
|
|
|
3842
|
|
|
|
3843
|
hCorr = hi[ind_hCorr][:len(ind_h)]
|
|
3843
|
hCorr = hi[ind_hCorr][:len(ind_h)]
|
|
|
3844
|
heights[ind_h] = hCorr
|
|
3844
|
heights[ind_h] = hCorr
|
|
|
3845
|
|
|
3845
|
|
|
|
3846
|
#Setting Error
|
|
3846
|
#Setting Error
|
|
|
3847
|
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
3847
|
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
|
3848
|
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
3848
|
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
|
3849
|
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
|
|
3849
|
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
|
|
|
3850
|
error[indError] = 0
|
|
3850
|
error[indError] = 0
|
|
|
3851
|
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
3851
|
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
|
3852
|
error[indInvalid2] = 14
|
|
3852
|
error[indInvalid2] = 14
|
|
|
3853
|
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
3853
|
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
|
3854
|
error[indInvalid1] = 13
|
|
3854
|
error[indInvalid1] = 13
|
|
|
3855
|
|
|
3855
|
|
|
|
3856
|
return heights, error
|
|
3856
|
return heights, error
|
|
|
3857
|
|
|
3857
|
|
|
|
3858
|
def getPhasePairs(self, channelPositions):
|
|
3858
|
def getPhasePairs(self, channelPositions):
|
|
|
3859
|
chanPos = numpy.array(channelPositions)
|
|
3859
|
chanPos = numpy.array(channelPositions)
|
|
|
3860
|
listOper = list(itertools.combinations(range(5),2))
|
|
3860
|
listOper = list(itertools.combinations(range(5),2))
|
|
|
3861
|
|
|
3861
|
|
|
|
3862
|
distances = numpy.zeros(4)
|
|
3862
|
distances = numpy.zeros(4)
|
|
|
3863
|
axisX = []
|
|
3863
|
axisX = []
|
|
|
3864
|
axisY = []
|
|
3864
|
axisY = []
|
|
|
3865
|
distX = numpy.zeros(3)
|
|
3865
|
distX = numpy.zeros(3)
|
|
|
3866
|
distY = numpy.zeros(3)
|
|
3866
|
distY = numpy.zeros(3)
|
|
|
3867
|
ix = 0
|
|
3867
|
ix = 0
|
|
|
3868
|
iy = 0
|
|
3868
|
iy = 0
|
|
|
3869
|
|
|
3869
|
|
|
|
3870
|
pairX = numpy.zeros((2,2))
|
|
3870
|
pairX = numpy.zeros((2,2))
|
|
|
3871
|
pairY = numpy.zeros((2,2))
|
|
3871
|
pairY = numpy.zeros((2,2))
|
|
|
3872
|
|
|
3872
|
|
|
|
3873
|
for i in range(len(listOper)):
|
|
3873
|
for i in range(len(listOper)):
|
|
|
3874
|
pairi = listOper[i]
|
|
3874
|
pairi = listOper[i]
|
|
|
3875
|
|
|
3875
|
|
|
|
3876
|
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
|
|
3876
|
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
|
|
|
3877
|
|
|
3877
|
|
|
|
3878
|
if posDif[0] == 0:
|
|
3878
|
if posDif[0] == 0:
|
|
|
3879
|
axisY.append(pairi)
|
|
3879
|
axisY.append(pairi)
|
|
|
3880
|
distY[iy] = posDif[1]
|
|
3880
|
distY[iy] = posDif[1]
|
|
|
3881
|
iy += 1
|
|
3881
|
iy += 1
|
|
|
3882
|
elif posDif[1] == 0:
|
|
3882
|
elif posDif[1] == 0:
|
|
|
3883
|
axisX.append(pairi)
|
|
3883
|
axisX.append(pairi)
|
|
|
3884
|
distX[ix] = posDif[0]
|
|
3884
|
distX[ix] = posDif[0]
|
|
|
3885
|
ix += 1
|
|
3885
|
ix += 1
|
|
|
3886
|
|
|
3886
|
|
|
|
3887
|
for i in range(2):
|
|
3887
|
for i in range(2):
|
|
|
3888
|
if i==0:
|
|
3888
|
if i==0:
|
|
|
3889
|
dist0 = distX
|
|
3889
|
dist0 = distX
|
|
|
3890
|
axis0 = axisX
|
|
3890
|
axis0 = axisX
|
|
|
3891
|
else:
|
|
3891
|
else:
|
|
|
3892
|
dist0 = distY
|
|
3892
|
dist0 = distY
|
|
|
3893
|
axis0 = axisY
|
|
3893
|
axis0 = axisY
|
|
|
3894
|
|
|
3894
|
|
|
|
3895
|
side = numpy.argsort(dist0)[:-1]
|
|
3895
|
side = numpy.argsort(dist0)[:-1]
|
|
|
3896
|
axis0 = numpy.array(axis0)[side,:]
|
|
3896
|
axis0 = numpy.array(axis0)[side,:]
|
|
|
3897
|
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
|
|
3897
|
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
|
|
|
3898
|
axis1 = numpy.unique(numpy.reshape(axis0,4))
|
|
3898
|
axis1 = numpy.unique(numpy.reshape(axis0,4))
|
|
|
3899
|
side = axis1[axis1 != chanC]
|
|
3899
|
side = axis1[axis1 != chanC]
|
|
|
3900
|
diff1 = chanPos[chanC,i] - chanPos[side[0],i]
|
|
3900
|
diff1 = chanPos[chanC,i] - chanPos[side[0],i]
|
|
|
3901
|
diff2 = chanPos[chanC,i] - chanPos[side[1],i]
|
|
3901
|
diff2 = chanPos[chanC,i] - chanPos[side[1],i]
|
|
|
3902
|
if diff1<0:
|
|
3902
|
if diff1<0:
|
|
|
3903
|
chan2 = side[0]
|
|
3903
|
chan2 = side[0]
|
|
|
3904
|
d2 = numpy.abs(diff1)
|
|
3904
|
d2 = numpy.abs(diff1)
|
|
|
3905
|
chan1 = side[1]
|
|
3905
|
chan1 = side[1]
|
|
|
3906
|
d1 = numpy.abs(diff2)
|
|
3906
|
d1 = numpy.abs(diff2)
|
|
|
3907
|
else:
|
|
3907
|
else:
|
|
|
3908
|
chan2 = side[1]
|
|
3908
|
chan2 = side[1]
|
|
|
3909
|
d2 = numpy.abs(diff2)
|
|
3909
|
d2 = numpy.abs(diff2)
|
|
|
3910
|
chan1 = side[0]
|
|
3910
|
chan1 = side[0]
|
|
|
3911
|
d1 = numpy.abs(diff1)
|
|
3911
|
d1 = numpy.abs(diff1)
|
|
|
3912
|
|
|
3912
|
|
|
|
3913
|
if i==0:
|
|
3913
|
if i==0:
|
|
|
3914
|
chanCX = chanC
|
|
3914
|
chanCX = chanC
|
|
|
3915
|
chan1X = chan1
|
|
3915
|
chan1X = chan1
|
|
|
3916
|
chan2X = chan2
|
|
3916
|
chan2X = chan2
|
|
|
3917
|
distances[0:2] = numpy.array([d1,d2])
|
|
3917
|
distances[0:2] = numpy.array([d1,d2])
|
|
|
3918
|
else:
|
|
3918
|
else:
|
|
|
3919
|
chanCY = chanC
|
|
3919
|
chanCY = chanC
|
|
|
3920
|
chan1Y = chan1
|
|
3920
|
chan1Y = chan1
|
|
|
3921
|
chan2Y = chan2
|
|
3921
|
chan2Y = chan2
|
|
|
3922
|
distances[2:4] = numpy.array([d1,d2])
|
|
3922
|
distances[2:4] = numpy.array([d1,d2])
|
|
|
3923
|
# axisXsides = numpy.reshape(axisX[ix,:],4)
|
|
3923
|
# axisXsides = numpy.reshape(axisX[ix,:],4)
|
|
|
3924
|
#
|
|
3924
|
#
|
|
|
3925
|
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
|
|
3925
|
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
|
|
|
3926
|
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
|
|
3926
|
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
|
|
|
3927
|
#
|
|
3927
|
#
|
|
|
3928
|
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
|
|
3928
|
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
|
|
|
3929
|
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
|
|
3929
|
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
|
|
|
3930
|
# channel25X = int(pairX[0,ind25X])
|
|
3930
|
# channel25X = int(pairX[0,ind25X])
|
|
|
3931
|
# channel20X = int(pairX[1,ind20X])
|
|
3931
|
# channel20X = int(pairX[1,ind20X])
|
|
|
3932
|
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
|
|
3932
|
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
|
|
|
3933
|
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
|
|
3933
|
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
|
|
|
3934
|
# channel25Y = int(pairY[0,ind25Y])
|
|
3934
|
# channel25Y = int(pairY[0,ind25Y])
|
|
|
3935
|
# channel20Y = int(pairY[1,ind20Y])
|
|
3935
|
# channel20Y = int(pairY[1,ind20Y])
|
|
|
3936
|
|
|
3936
|
|
|
|
3937
|
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
|
|
3937
|
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
|
|
|
3938
|
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
|
|
3938
|
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
|
|
|
3939
|
|
|
3939
|
|
|
|
3940
|
return pairslist, distances
|
|
3940
|
return pairslist, distances
|
|
|
3941
|
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
|
|
3941
|
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
|
|
|
3942
|
#
|
|
3942
|
#
|
|
|
3943
|
# arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
3943
|
# arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
|
3944
|
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
|
|
3944
|
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
|
|
|
3945
|
#
|
|
3945
|
#
|
|
|
3946
|
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
3946
|
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
|
3947
|
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
3947
|
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
|
3948
|
# arrayAOA[:,2] = cosDirError
|
|
3948
|
# arrayAOA[:,2] = cosDirError
|
|
|
3949
|
#
|
|
3949
|
#
|
|
|
3950
|
# azimuthAngle = arrayAOA[:,0]
|
|
3950
|
# azimuthAngle = arrayAOA[:,0]
|
|
|
3951
|
# zenithAngle = arrayAOA[:,1]
|
|
3951
|
# zenithAngle = arrayAOA[:,1]
|
|
|
3952
|
#
|
|
3952
|
#
|
|
|
3953
|
# #Setting Error
|
|
3953
|
# #Setting Error
|
|
|
3954
|
# #Number 3: AOA not fesible
|
|
3954
|
# #Number 3: AOA not fesible
|
|
|
3955
|
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
3955
|
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
|
3956
|
# error[indInvalid] = 3
|
|
3956
|
# error[indInvalid] = 3
|
|
|
3957
|
# #Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
3957
|
# #Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
|
3958
|
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
3958
|
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
|
3959
|
# error[indInvalid] = 4
|
|
3959
|
# error[indInvalid] = 4
|
|
|
3960
|
# return arrayAOA, error
|
|
3960
|
# return arrayAOA, error
|
|
|
3961
|
#
|
|
3961
|
#
|
|
|
3962
|
# def __getDirectionCosines(self, arrayPhase, pairsList):
|
|
3962
|
# def __getDirectionCosines(self, arrayPhase, pairsList):
|
|
|
3963
|
#
|
|
3963
|
#
|
|
|
3964
|
# #Initializing some variables
|
|
3964
|
# #Initializing some variables
|
|
|
3965
|
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
3965
|
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
|
3966
|
# ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
3966
|
# ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
|
3967
|
#
|
|
3967
|
#
|
|
|
3968
|
# cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
3968
|
# cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3969
|
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
3969
|
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3970
|
#
|
|
3970
|
#
|
|
|
3971
|
#
|
|
3971
|
#
|
|
|
3972
|
# for i in range(2):
|
|
3972
|
# for i in range(2):
|
|
|
3973
|
# #First Estimation
|
|
3973
|
# #First Estimation
|
|
|
3974
|
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
|
|
3974
|
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
|
|
|
3975
|
# #Dealias
|
|
3975
|
# #Dealias
|
|
|
3976
|
# indcsi = numpy.where(phi0_aux > numpy.pi)
|
|
3976
|
# indcsi = numpy.where(phi0_aux > numpy.pi)
|
|
|
3977
|
# phi0_aux[indcsi] -= 2*numpy.pi
|
|
3977
|
# phi0_aux[indcsi] -= 2*numpy.pi
|
|
|
3978
|
# indcsi = numpy.where(phi0_aux < -numpy.pi)
|
|
3978
|
# indcsi = numpy.where(phi0_aux < -numpy.pi)
|
|
|
3979
|
# phi0_aux[indcsi] += 2*numpy.pi
|
|
3979
|
# phi0_aux[indcsi] += 2*numpy.pi
|
|
|
3980
|
# #Direction Cosine 0
|
|
3980
|
# #Direction Cosine 0
|
|
|
3981
|
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
|
|
3981
|
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
|
|
|
3982
|
#
|
|
3982
|
#
|
|
|
3983
|
# #Most-Accurate Second Estimation
|
|
3983
|
# #Most-Accurate Second Estimation
|
|
|
3984
|
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
|
|
3984
|
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
|
|
|
3985
|
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
3985
|
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
|
3986
|
# #Direction Cosine 1
|
|
3986
|
# #Direction Cosine 1
|
|
|
3987
|
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
|
|
3987
|
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
|
|
|
3988
|
#
|
|
3988
|
#
|
|
|
3989
|
# #Searching the correct Direction Cosine
|
|
3989
|
# #Searching the correct Direction Cosine
|
|
|
3990
|
# cosdir0_aux = cosdir0[:,i]
|
|
3990
|
# cosdir0_aux = cosdir0[:,i]
|
|
|
3991
|
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
3991
|
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
|
3992
|
# #Minimum Distance
|
|
3992
|
# #Minimum Distance
|
|
|
3993
|
# cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
3993
|
# cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
|
3994
|
# indcos = cosDiff.argmin(axis = 1)
|
|
3994
|
# indcos = cosDiff.argmin(axis = 1)
|
|
|
3995
|
# #Saving Value obtained
|
|
3995
|
# #Saving Value obtained
|
|
|
3996
|
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
3996
|
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
|
3997
|
#
|
|
3997
|
#
|
|
|
3998
|
# return cosdir0, cosdir
|
|
3998
|
# return cosdir0, cosdir
|
|
|
3999
|
#
|
|
3999
|
#
|
|
|
4000
|
# def __calculateAOA(self, cosdir, azimuth):
|
|
4000
|
# def __calculateAOA(self, cosdir, azimuth):
|
|
|
4001
|
# cosdirX = cosdir[:,0]
|
|
4001
|
# cosdirX = cosdir[:,0]
|
|
|
4002
|
# cosdirY = cosdir[:,1]
|
|
4002
|
# cosdirY = cosdir[:,1]
|
|
|
4003
|
#
|
|
4003
|
#
|
|
|
4004
|
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
4004
|
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
|
4005
|
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
|
|
4005
|
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
|
|
|
4006
|
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
4006
|
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
|
4007
|
#
|
|
4007
|
#
|
|
|
4008
|
# return angles
|
|
4008
|
# return angles
|
|
|
4009
|
#
|
|
4009
|
#
|
|
|
4010
|
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
4010
|
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
|
4011
|
#
|
|
4011
|
#
|
|
|
4012
|
# Ramb = 375 #Ramb = c/(2*PRF)
|
|
4012
|
# Ramb = 375 #Ramb = c/(2*PRF)
|
|
|
4013
|
# Re = 6371 #Earth Radius
|
|
4013
|
# Re = 6371 #Earth Radius
|
|
|
4014
|
# heights = numpy.zeros(Ranges.shape)
|
|
4014
|
# heights = numpy.zeros(Ranges.shape)
|
|
|
4015
|
#
|
|
4015
|
#
|
|
|
4016
|
# R_aux = numpy.array([0,1,2])*Ramb
|
|
4016
|
# R_aux = numpy.array([0,1,2])*Ramb
|
|
|
4017
|
# R_aux = R_aux.reshape(1,R_aux.size)
|
|
4017
|
# R_aux = R_aux.reshape(1,R_aux.size)
|
|
|
4018
|
#
|
|
4018
|
#
|
|
|
4019
|
# Ranges = Ranges.reshape(Ranges.size,1)
|
|
4019
|
# Ranges = Ranges.reshape(Ranges.size,1)
|
|
|
4020
|
#
|
|
4020
|
#
|
|
|
4021
|
# Ri = Ranges + R_aux
|
|
4021
|
# Ri = Ranges + R_aux
|
|
|
4022
|
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
4022
|
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
|
4023
|
#
|
|
4023
|
#
|
|
|
4024
|
# #Check if there is a height between 70 and 110 km
|
|
4024
|
# #Check if there is a height between 70 and 110 km
|
|
|
4025
|
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
4025
|
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
|
4026
|
# ind_h = numpy.where(h_bool == 1)[0]
|
|
4026
|
# ind_h = numpy.where(h_bool == 1)[0]
|
|
|
4027
|
#
|
|
4027
|
#
|
|
|
4028
|
# hCorr = hi[ind_h, :]
|
|
4028
|
# hCorr = hi[ind_h, :]
|
|
|
4029
|
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
4029
|
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
|
4030
|
#
|
|
4030
|
#
|
|
|
4031
|
# hCorr = hi[ind_hCorr]
|
|
4031
|
# hCorr = hi[ind_hCorr]
|
|
|
4032
|
# heights[ind_h] = hCorr
|
|
4032
|
# heights[ind_h] = hCorr
|
|
|
4033
|
#
|
|
4033
|
#
|
|
|
4034
|
# #Setting Error
|
|
4034
|
# #Setting Error
|
|
|
4035
|
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
4035
|
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
|
4036
|
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
4036
|
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
|
4037
|
#
|
|
4037
|
#
|
|
|
4038
|
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
4038
|
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
|
4039
|
# error[indInvalid2] = 14
|
|
4039
|
# error[indInvalid2] = 14
|
|
|
4040
|
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
4040
|
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
|
4041
|
# error[indInvalid1] = 13
|
|
4041
|
# error[indInvalid1] = 13
|
|
|
4042
|
#
|
|
4042
|
#
|
|
|
4043
|
# return heights, error
|
|
4043
|
# return heights, error
|
|
|
4044
|
No newline at end of file
|
|
4044
|
|
