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import numpy,os,h5py
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import numpy,os,h5py
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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 time
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import time
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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, MPDecorator
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from .jroproc_base import ProcessingUnit, Operation, MPDecorator
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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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from schainpy.utils import log
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from schainpy.utils import log
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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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from time import sleep
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from time import sleep
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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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.__func__.__name__
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func_name = method.__func__.__name__
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obj = method.__self__
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obj = method.__self__
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cls = method.__self__.__class__
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cls = method.__self__.__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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def isNumber(str):
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def isNumber(str):
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try:
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try:
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float(str)
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float(str)
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return True
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return True
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except:
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except:
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return False
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return False
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class ParametersProc(ProcessingUnit):
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class ParametersProc(ProcessingUnit):
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METHODS = {}
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METHODS = {}
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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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self.setupReq = False #Agregar a todas las unidades de proc
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self.setupReq = False #Agregar a todas las unidades de proc
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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.heightList
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self.dataOut.heightList = self.dataIn.heightList
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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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#print("HOLA MUNDO SOY YO")
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#print("HOLA MUNDO SOY YO")
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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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if hasattr(self.dataIn, 'flagDataAsBlock'):
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if hasattr(self.dataIn, 'flagDataAsBlock'):
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self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
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self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
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if hasattr(self.dataIn, 'profileIndex'):
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if hasattr(self.dataIn, 'profileIndex'):
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self.dataOut.profileIndex = self.dataIn.profileIndex
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self.dataOut.profileIndex = self.dataIn.profileIndex
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if hasattr(self.dataIn, 'dataPP_POW'):
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if hasattr(self.dataIn, 'dataPP_POW'):
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self.dataOut.dataPP_POW = self.dataIn.dataPP_POW
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self.dataOut.dataPP_POW = self.dataIn.dataPP_POW
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if hasattr(self.dataIn, 'dataPP_POWER'):
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if hasattr(self.dataIn, 'dataPP_POWER'):
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self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER
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self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER
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if hasattr(self.dataIn, 'dataPP_DOP'):
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if hasattr(self.dataIn, 'dataPP_DOP'):
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self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP
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self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP
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if hasattr(self.dataIn, 'dataPP_SNR'):
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if hasattr(self.dataIn, 'dataPP_SNR'):
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self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR
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self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR
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if hasattr(self.dataIn, 'dataPP_WIDTH'):
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if hasattr(self.dataIn, 'dataPP_WIDTH'):
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self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH
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self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH
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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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#print("que paso en spectra")
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#print("que paso en 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) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
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self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
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# self.dataOut.normFactor = self.dataIn.normFactor
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# self.dataOut.normFactor = self.dataIn.normFactor
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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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160
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160
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if hasattr(self.dataIn, 'flagDataAsBlock'):
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161
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if hasattr(self.dataIn, 'flagDataAsBlock'):
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162
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self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
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162
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self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
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163
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163
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164
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if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
|
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164
|
if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
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165
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self.dataOut.ChanDist = self.dataIn.ChanDist
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165
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self.dataOut.ChanDist = self.dataIn.ChanDist
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166
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else: self.dataOut.ChanDist = None
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166
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else: self.dataOut.ChanDist = None
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167
|
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167
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168
|
#if hasattr(self.dataIn, 'VelRange'): #Velocities range
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168
|
#if hasattr(self.dataIn, 'VelRange'): #Velocities range
|
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169
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# self.dataOut.VelRange = self.dataIn.VelRange
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169
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# self.dataOut.VelRange = self.dataIn.VelRange
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170
|
#else: self.dataOut.VelRange = None
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170
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#else: self.dataOut.VelRange = None
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171
|
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171
|
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172
|
if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
|
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172
|
if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
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173
|
self.dataOut.RadarConst = self.dataIn.RadarConst
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173
|
self.dataOut.RadarConst = self.dataIn.RadarConst
|
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174
|
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174
|
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|
175
|
if hasattr(self.dataIn, 'NPW'): #NPW
|
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175
|
if hasattr(self.dataIn, 'NPW'): #NPW
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176
|
self.dataOut.NPW = self.dataIn.NPW
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176
|
self.dataOut.NPW = self.dataIn.NPW
|
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177
|
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177
|
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178
|
if hasattr(self.dataIn, 'COFA'): #COFA
|
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178
|
if hasattr(self.dataIn, 'COFA'): #COFA
|
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179
|
self.dataOut.COFA = self.dataIn.COFA
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179
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self.dataOut.COFA = self.dataIn.COFA
|
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180
|
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180
|
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|
181
|
|
|
181
|
|
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|
182
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182
|
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|
183
|
#---------------------- Correlation Data ---------------------------
|
|
183
|
#---------------------- Correlation Data ---------------------------
|
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|
184
|
|
|
184
|
|
|
|
185
|
if self.dataIn.type == "Correlation":
|
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185
|
if self.dataIn.type == "Correlation":
|
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|
186
|
acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
|
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186
|
acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
|
|
|
187
|
|
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187
|
|
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|
188
|
self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
|
|
188
|
self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
|
|
|
189
|
self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
|
|
189
|
self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
|
|
|
190
|
self.dataOut.groupList = (acf_pairs, ccf_pairs)
|
|
190
|
self.dataOut.groupList = (acf_pairs, ccf_pairs)
|
|
|
191
|
|
|
191
|
|
|
|
192
|
self.dataOut.abscissaList = self.dataIn.lagRange
|
|
192
|
self.dataOut.abscissaList = self.dataIn.lagRange
|
|
|
193
|
self.dataOut.noise = self.dataIn.noise
|
|
193
|
self.dataOut.noise = self.dataIn.noise
|
|
|
194
|
self.dataOut.data_snr = self.dataIn.SNR
|
|
194
|
self.dataOut.data_snr = self.dataIn.SNR
|
|
|
195
|
self.dataOut.flagNoData = False
|
|
195
|
self.dataOut.flagNoData = False
|
|
|
196
|
self.dataOut.nAvg = self.dataIn.nAvg
|
|
196
|
self.dataOut.nAvg = self.dataIn.nAvg
|
|
|
197
|
|
|
197
|
|
|
|
198
|
#---------------------- Parameters Data ---------------------------
|
|
198
|
#---------------------- Parameters Data ---------------------------
|
|
|
199
|
|
|
199
|
|
|
|
200
|
if self.dataIn.type == "Parameters":
|
|
200
|
if self.dataIn.type == "Parameters":
|
|
|
201
|
self.dataOut.copy(self.dataIn)
|
|
201
|
self.dataOut.copy(self.dataIn)
|
|
|
202
|
self.dataOut.flagNoData = False
|
|
202
|
self.dataOut.flagNoData = False
|
|
|
203
|
#print("yo si entre")
|
|
203
|
#print("yo si entre")
|
|
|
204
|
|
|
204
|
|
|
|
205
|
return True
|
|
205
|
return True
|
|
|
206
|
|
|
206
|
|
|
|
207
|
self.__updateObjFromInput()
|
|
207
|
self.__updateObjFromInput()
|
|
|
208
|
#print("yo si entre2")
|
|
208
|
#print("yo si entre2")
|
|
|
209
|
|
|
209
|
|
|
|
210
|
self.dataOut.utctimeInit = self.dataIn.utctime
|
|
210
|
self.dataOut.utctimeInit = self.dataIn.utctime
|
|
|
211
|
self.dataOut.paramInterval = self.dataIn.timeInterval
|
|
211
|
self.dataOut.paramInterval = self.dataIn.timeInterval
|
|
|
212
|
#print("soy spectra ",self.dataOut.utctimeInit)
|
|
212
|
#print("soy spectra ",self.dataOut.utctimeInit)
|
|
|
213
|
return
|
|
213
|
return
|
|
|
214
|
|
|
214
|
|
|
|
215
|
|
|
215
|
|
|
|
216
|
def target(tups):
|
|
216
|
def target(tups):
|
|
|
217
|
|
|
217
|
|
|
|
218
|
obj, args = tups
|
|
218
|
obj, args = tups
|
|
|
219
|
|
|
219
|
|
|
|
220
|
return obj.FitGau(args)
|
|
220
|
return obj.FitGau(args)
|
|
|
221
|
|
|
221
|
|
|
|
222
|
class RemoveWideGC(Operation):
|
|
222
|
class RemoveWideGC(Operation):
|
|
|
223
|
''' This class remove the wide clutter and replace it with a simple interpolation points
|
|
223
|
''' This class remove the wide clutter and replace it with a simple interpolation points
|
|
|
224
|
This mainly applies to CLAIRE radar
|
|
224
|
This mainly applies to CLAIRE radar
|
|
|
225
|
|
|
225
|
|
|
|
226
|
ClutterWidth : Width to look for the clutter peak
|
|
226
|
ClutterWidth : Width to look for the clutter peak
|
|
|
227
|
|
|
227
|
|
|
|
228
|
Input:
|
|
228
|
Input:
|
|
|
229
|
|
|
229
|
|
|
|
230
|
self.dataOut.data_pre : SPC and CSPC
|
|
230
|
self.dataOut.data_pre : SPC and CSPC
|
|
|
231
|
self.dataOut.spc_range : To select wind and rainfall velocities
|
|
231
|
self.dataOut.spc_range : To select wind and rainfall velocities
|
|
|
232
|
|
|
232
|
|
|
|
233
|
Affected:
|
|
233
|
Affected:
|
|
|
234
|
|
|
234
|
|
|
|
235
|
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind
|
|
235
|
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind
|
|
|
236
|
|
|
236
|
|
|
|
237
|
Written by D. ScipiΓ³n 25.02.2021
|
|
237
|
Written by D. ScipiΓ³n 25.02.2021
|
|
|
238
|
'''
|
|
238
|
'''
|
|
|
239
|
def __init__(self):
|
|
239
|
def __init__(self):
|
|
|
240
|
Operation.__init__(self)
|
|
240
|
Operation.__init__(self)
|
|
|
241
|
self.i = 0
|
|
241
|
self.i = 0
|
|
|
242
|
self.ich = 0
|
|
242
|
self.ich = 0
|
|
|
243
|
self.ir = 0
|
|
243
|
self.ir = 0
|
|
|
244
|
|
|
244
|
|
|
|
245
|
def run(self, dataOut, ClutterWidth=2.5):
|
|
245
|
def run(self, dataOut, ClutterWidth=2.5):
|
|
|
246
|
# print ('Entering RemoveWideGC ... ')
|
|
246
|
# print ('Entering RemoveWideGC ... ')
|
|
|
247
|
|
|
247
|
|
|
|
248
|
self.spc = dataOut.data_pre[0].copy()
|
|
248
|
self.spc = dataOut.data_pre[0].copy()
|
|
|
249
|
self.spc_out = dataOut.data_pre[0].copy()
|
|
249
|
self.spc_out = dataOut.data_pre[0].copy()
|
|
|
250
|
self.Num_Chn = self.spc.shape[0]
|
|
250
|
self.Num_Chn = self.spc.shape[0]
|
|
|
251
|
self.Num_Hei = self.spc.shape[2]
|
|
251
|
self.Num_Hei = self.spc.shape[2]
|
|
|
252
|
VelRange = dataOut.spc_range[2][:-1]
|
|
252
|
VelRange = dataOut.spc_range[2][:-1]
|
|
|
253
|
dv = VelRange[1]-VelRange[0]
|
|
253
|
dv = VelRange[1]-VelRange[0]
|
|
|
254
|
|
|
254
|
|
|
|
255
|
# Find the velocities that corresponds to zero
|
|
255
|
# Find the velocities that corresponds to zero
|
|
|
256
|
gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth))
|
|
256
|
gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth))
|
|
|
257
|
|
|
257
|
|
|
|
258
|
# Removing novalid data from the spectra
|
|
258
|
# Removing novalid data from the spectra
|
|
|
259
|
for ich in range(self.Num_Chn) :
|
|
259
|
for ich in range(self.Num_Chn) :
|
|
|
260
|
for ir in range(self.Num_Hei) :
|
|
260
|
for ir in range(self.Num_Hei) :
|
|
|
261
|
# Estimate the noise at each range
|
|
261
|
# Estimate the noise at each range
|
|
|
262
|
HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt)
|
|
262
|
HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt)
|
|
|
263
|
|
|
263
|
|
|
|
264
|
# Removing the noise floor at each range
|
|
264
|
# Removing the noise floor at each range
|
|
|
265
|
novalid = numpy.where(self.spc[ich,:,ir] < HSn)
|
|
265
|
novalid = numpy.where(self.spc[ich,:,ir] < HSn)
|
|
|
266
|
self.spc[ich,novalid,ir] = HSn
|
|
266
|
self.spc[ich,novalid,ir] = HSn
|
|
|
267
|
|
|
267
|
|
|
|
268
|
junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn)
|
|
268
|
junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn)
|
|
|
269
|
j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0))
|
|
269
|
j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0))
|
|
|
270
|
j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0))
|
|
270
|
j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0))
|
|
|
271
|
if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) :
|
|
271
|
if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) :
|
|
|
272
|
continue
|
|
272
|
continue
|
|
|
273
|
junk3 = numpy.squeeze(numpy.diff(j1index))
|
|
273
|
junk3 = numpy.squeeze(numpy.diff(j1index))
|
|
|
274
|
junk4 = numpy.squeeze(numpy.diff(j2index))
|
|
274
|
junk4 = numpy.squeeze(numpy.diff(j2index))
|
|
|
275
|
|
|
275
|
|
|
|
276
|
valleyindex = j2index[numpy.where(junk4>1)]
|
|
276
|
valleyindex = j2index[numpy.where(junk4>1)]
|
|
|
277
|
peakindex = j1index[numpy.where(junk3>1)]
|
|
277
|
peakindex = j1index[numpy.where(junk3>1)]
|
|
|
278
|
|
|
278
|
|
|
|
279
|
isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv))
|
|
279
|
isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv))
|
|
|
280
|
if numpy.size(isvalid) == 0 :
|
|
280
|
if numpy.size(isvalid) == 0 :
|
|
|
281
|
continue
|
|
281
|
continue
|
|
|
282
|
if numpy.size(isvalid) >1 :
|
|
282
|
if numpy.size(isvalid) >1 :
|
|
|
283
|
vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir])
|
|
283
|
vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir])
|
|
|
284
|
isvalid = isvalid[vindex]
|
|
284
|
isvalid = isvalid[vindex]
|
|
|
285
|
|
|
285
|
|
|
|
286
|
# clutter peak
|
|
286
|
# clutter peak
|
|
|
287
|
gcpeak = peakindex[isvalid]
|
|
287
|
gcpeak = peakindex[isvalid]
|
|
|
288
|
vl = numpy.where(valleyindex < gcpeak)
|
|
288
|
vl = numpy.where(valleyindex < gcpeak)
|
|
|
289
|
if numpy.size(vl) == 0:
|
|
289
|
if numpy.size(vl) == 0:
|
|
|
290
|
continue
|
|
290
|
continue
|
|
|
291
|
gcvl = valleyindex[vl[0][-1]]
|
|
291
|
gcvl = valleyindex[vl[0][-1]]
|
|
|
292
|
vr = numpy.where(valleyindex > gcpeak)
|
|
292
|
vr = numpy.where(valleyindex > gcpeak)
|
|
|
293
|
if numpy.size(vr) == 0:
|
|
293
|
if numpy.size(vr) == 0:
|
|
|
294
|
continue
|
|
294
|
continue
|
|
|
295
|
gcvr = valleyindex[vr[0][0]]
|
|
295
|
gcvr = valleyindex[vr[0][0]]
|
|
|
296
|
|
|
296
|
|
|
|
297
|
# Removing the clutter
|
|
297
|
# Removing the clutter
|
|
|
298
|
interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]])
|
|
298
|
interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]])
|
|
|
299
|
gcindex = gc_values[gcvl+1:gcvr-1]
|
|
299
|
gcindex = gc_values[gcvl+1:gcvr-1]
|
|
|
300
|
self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir])
|
|
300
|
self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir])
|
|
|
301
|
|
|
301
|
|
|
|
302
|
dataOut.data_pre[0] = self.spc_out
|
|
302
|
dataOut.data_pre[0] = self.spc_out
|
|
|
303
|
#print ('Leaving RemoveWideGC ... ')
|
|
303
|
#print ('Leaving RemoveWideGC ... ')
|
|
|
304
|
return dataOut
|
|
304
|
return dataOut
|
|
|
305
|
|
|
305
|
|
|
|
306
|
class SpectralFilters(Operation):
|
|
306
|
class SpectralFilters(Operation):
|
|
|
307
|
''' This class allows to replace the novalid values with noise for each channel
|
|
307
|
''' This class allows to replace the novalid values with noise for each channel
|
|
|
308
|
This applies to CLAIRE RADAR
|
|
308
|
This applies to CLAIRE RADAR
|
|
|
309
|
|
|
309
|
|
|
|
310
|
PositiveLimit : RightLimit of novalid data
|
|
310
|
PositiveLimit : RightLimit of novalid data
|
|
|
311
|
NegativeLimit : LeftLimit of novalid data
|
|
311
|
NegativeLimit : LeftLimit of novalid data
|
|
|
312
|
|
|
312
|
|
|
|
313
|
Input:
|
|
313
|
Input:
|
|
|
314
|
|
|
314
|
|
|
|
315
|
self.dataOut.data_pre : SPC and CSPC
|
|
315
|
self.dataOut.data_pre : SPC and CSPC
|
|
|
316
|
self.dataOut.spc_range : To select wind and rainfall velocities
|
|
316
|
self.dataOut.spc_range : To select wind and rainfall velocities
|
|
|
317
|
|
|
317
|
|
|
|
318
|
Affected:
|
|
318
|
Affected:
|
|
|
319
|
|
|
319
|
|
|
|
320
|
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind
|
|
320
|
self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind
|
|
|
321
|
|
|
321
|
|
|
|
322
|
Written by D. ScipiΓ³n 29.01.2021
|
|
322
|
Written by D. ScipiΓ³n 29.01.2021
|
|
|
323
|
'''
|
|
323
|
'''
|
|
|
324
|
def __init__(self):
|
|
324
|
def __init__(self):
|
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325
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Operation.__init__(self)
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325
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Operation.__init__(self)
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326
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self.i = 0
|
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326
|
self.i = 0
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327
|
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327
|
|
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328
|
def run(self, dataOut, ):
|
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328
|
def run(self, dataOut, ):
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329
|
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329
|
|
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|
330
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self.spc = dataOut.data_pre[0].copy()
|
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330
|
self.spc = dataOut.data_pre[0].copy()
|
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331
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self.Num_Chn = self.spc.shape[0]
|
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331
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self.Num_Chn = self.spc.shape[0]
|
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|
332
|
VelRange = dataOut.spc_range[2]
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332
|
VelRange = dataOut.spc_range[2]
|
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|
333
|
|
|
333
|
|
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334
|
# novalid corresponds to data within the Negative and PositiveLimit
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334
|
# novalid corresponds to data within the Negative and PositiveLimit
|
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|
335
|
|
|
335
|
|
|
|
336
|
|
|
336
|
|
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337
|
# Removing novalid data from the spectra
|
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337
|
# Removing novalid data from the spectra
|
|
|
338
|
for i in range(self.Num_Chn):
|
|
338
|
for i in range(self.Num_Chn):
|
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|
339
|
self.spc[i,novalid,:] = dataOut.noise[i]
|
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339
|
self.spc[i,novalid,:] = dataOut.noise[i]
|
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|
340
|
dataOut.data_pre[0] = self.spc
|
|
340
|
dataOut.data_pre[0] = self.spc
|
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341
|
return dataOut
|
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341
|
return dataOut
|
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|
342
|
|
|
342
|
|
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|
343
|
class GaussianFit(Operation):
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343
|
class GaussianFit(Operation):
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|
344
|
|
|
344
|
|
|
|
345
|
'''
|
|
345
|
'''
|
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|
346
|
Function that fit of one and two generalized gaussians (gg) based
|
|
346
|
Function that fit of one and two generalized gaussians (gg) based
|
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|
347
|
on the PSD shape across an "power band" identified from a cumsum of
|
|
347
|
on the PSD shape across an "power band" identified from a cumsum of
|
|
|
348
|
the measured spectrum - noise.
|
|
348
|
the measured spectrum - noise.
|
|
|
349
|
|
|
349
|
|
|
|
350
|
Input:
|
|
350
|
Input:
|
|
|
351
|
self.dataOut.data_pre : SelfSpectra
|
|
351
|
self.dataOut.data_pre : SelfSpectra
|
|
|
352
|
|
|
352
|
|
|
|
353
|
Output:
|
|
353
|
Output:
|
|
|
354
|
self.dataOut.SPCparam : SPC_ch1, SPC_ch2
|
|
354
|
self.dataOut.SPCparam : SPC_ch1, SPC_ch2
|
|
|
355
|
|
|
355
|
|
|
|
356
|
'''
|
|
356
|
'''
|
|
|
357
|
def __init__(self):
|
|
357
|
def __init__(self):
|
|
|
358
|
Operation.__init__(self)
|
|
358
|
Operation.__init__(self)
|
|
|
359
|
self.i=0
|
|
359
|
self.i=0
|
|
|
360
|
|
|
360
|
|
|
|
361
|
|
|
361
|
|
|
|
362
|
# def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
|
|
362
|
# def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
|
|
|
363
|
def run(self, dataOut, SNRdBlimit=-9, method='generalized'):
|
|
363
|
def run(self, dataOut, SNRdBlimit=-9, method='generalized'):
|
|
|
364
|
"""This routine will find a couple of generalized Gaussians to a power spectrum
|
|
364
|
"""This routine will find a couple of generalized Gaussians to a power spectrum
|
|
|
365
|
methods: generalized, squared
|
|
365
|
methods: generalized, squared
|
|
|
366
|
input: spc
|
|
366
|
input: spc
|
|
|
367
|
output:
|
|
367
|
output:
|
|
|
368
|
noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1
|
|
368
|
noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1
|
|
|
369
|
"""
|
|
369
|
"""
|
|
|
370
|
print ('Entering ',method,' double Gaussian fit')
|
|
370
|
print ('Entering ',method,' double Gaussian fit')
|
|
|
371
|
self.spc = dataOut.data_pre[0].copy()
|
|
371
|
self.spc = dataOut.data_pre[0].copy()
|
|
|
372
|
self.Num_Hei = self.spc.shape[2]
|
|
372
|
self.Num_Hei = self.spc.shape[2]
|
|
|
373
|
self.Num_Bin = self.spc.shape[1]
|
|
373
|
self.Num_Bin = self.spc.shape[1]
|
|
|
374
|
self.Num_Chn = self.spc.shape[0]
|
|
374
|
self.Num_Chn = self.spc.shape[0]
|
|
|
375
|
|
|
375
|
|
|
|
376
|
start_time = time.time()
|
|
376
|
start_time = time.time()
|
|
|
377
|
|
|
377
|
|
|
|
378
|
pool = Pool(processes=self.Num_Chn)
|
|
378
|
pool = Pool(processes=self.Num_Chn)
|
|
|
379
|
args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)]
|
|
379
|
args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)]
|
|
|
380
|
objs = [self for __ in range(self.Num_Chn)]
|
|
380
|
objs = [self for __ in range(self.Num_Chn)]
|
|
|
381
|
attrs = list(zip(objs, args))
|
|
381
|
attrs = list(zip(objs, args))
|
|
|
382
|
DGauFitParam = pool.map(target, attrs)
|
|
382
|
DGauFitParam = pool.map(target, attrs)
|
|
|
383
|
# Parameters:
|
|
383
|
# Parameters:
|
|
|
384
|
# 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power
|
|
384
|
# 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power
|
|
|
385
|
dataOut.DGauFitParams = numpy.asarray(DGauFitParam)
|
|
385
|
dataOut.DGauFitParams = numpy.asarray(DGauFitParam)
|
|
|
386
|
|
|
386
|
|
|
|
387
|
# Double Gaussian Curves
|
|
387
|
# Double Gaussian Curves
|
|
|
388
|
gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
388
|
gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
|
389
|
gau0[:] = numpy.NaN
|
|
389
|
gau0[:] = numpy.NaN
|
|
|
390
|
gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
390
|
gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
|
391
|
gau1[:] = numpy.NaN
|
|
391
|
gau1[:] = numpy.NaN
|
|
|
392
|
x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1)))
|
|
392
|
x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1)))
|
|
|
393
|
for iCh in range(self.Num_Chn):
|
|
393
|
for iCh in range(self.Num_Chn):
|
|
|
394
|
N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin))
|
|
394
|
N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin))
|
|
|
395
|
N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin))
|
|
395
|
N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin))
|
|
|
396
|
A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin))
|
|
396
|
A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin))
|
|
|
397
|
A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin))
|
|
397
|
A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin))
|
|
|
398
|
v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin))
|
|
398
|
v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin))
|
|
|
399
|
v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin))
|
|
399
|
v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin))
|
|
|
400
|
s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin))
|
|
400
|
s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin))
|
|
|
401
|
s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin))
|
|
401
|
s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin))
|
|
|
402
|
if method == 'genealized':
|
|
402
|
if method == 'genealized':
|
|
|
403
|
p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin))
|
|
403
|
p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin))
|
|
|
404
|
p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin))
|
|
404
|
p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin))
|
|
|
405
|
elif method == 'squared':
|
|
405
|
elif method == 'squared':
|
|
|
406
|
p0 = 2.
|
|
406
|
p0 = 2.
|
|
|
407
|
p1 = 2.
|
|
407
|
p1 = 2.
|
|
|
408
|
gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0
|
|
408
|
gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0
|
|
|
409
|
gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1
|
|
409
|
gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1
|
|
|
410
|
dataOut.GaussFit0 = gau0
|
|
410
|
dataOut.GaussFit0 = gau0
|
|
|
411
|
dataOut.GaussFit1 = gau1
|
|
411
|
dataOut.GaussFit1 = gau1
|
|
|
412
|
|
|
412
|
|
|
|
413
|
print('Leaving ',method ,' double Gaussian fit')
|
|
413
|
print('Leaving ',method ,' double Gaussian fit')
|
|
|
414
|
return dataOut
|
|
414
|
return dataOut
|
|
|
415
|
|
|
415
|
|
|
|
416
|
def FitGau(self, X):
|
|
416
|
def FitGau(self, X):
|
|
|
417
|
# print('Entering FitGau')
|
|
417
|
# print('Entering FitGau')
|
|
|
418
|
# Assigning the variables
|
|
418
|
# Assigning the variables
|
|
|
419
|
Vrange, ch, wnoise, num_intg, SNRlimit = X
|
|
419
|
Vrange, ch, wnoise, num_intg, SNRlimit = X
|
|
|
420
|
# Noise Limits
|
|
420
|
# Noise Limits
|
|
|
421
|
noisebl = wnoise * 0.9
|
|
421
|
noisebl = wnoise * 0.9
|
|
|
422
|
noisebh = wnoise * 1.1
|
|
422
|
noisebh = wnoise * 1.1
|
|
|
423
|
# Radar Velocity
|
|
423
|
# Radar Velocity
|
|
|
424
|
Va = max(Vrange)
|
|
424
|
Va = max(Vrange)
|
|
|
425
|
deltav = Vrange[1] - Vrange[0]
|
|
425
|
deltav = Vrange[1] - Vrange[0]
|
|
|
426
|
x = numpy.arange(self.Num_Bin)
|
|
426
|
x = numpy.arange(self.Num_Bin)
|
|
|
427
|
|
|
427
|
|
|
|
428
|
# print ('stop 0')
|
|
428
|
# print ('stop 0')
|
|
|
429
|
|
|
429
|
|
|
|
430
|
# 5 parameters, 2 Gaussians
|
|
430
|
# 5 parameters, 2 Gaussians
|
|
|
431
|
DGauFitParam = numpy.zeros([5, self.Num_Hei,2])
|
|
431
|
DGauFitParam = numpy.zeros([5, self.Num_Hei,2])
|
|
|
432
|
DGauFitParam[:] = numpy.NaN
|
|
432
|
DGauFitParam[:] = numpy.NaN
|
|
|
433
|
|
|
433
|
|
|
|
434
|
# SPCparam = []
|
|
434
|
# SPCparam = []
|
|
|
435
|
# SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei])
|
|
435
|
# SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei])
|
|
|
436
|
# SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei])
|
|
436
|
# SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei])
|
|
|
437
|
# SPC_ch1[:] = 0 #numpy.NaN
|
|
437
|
# SPC_ch1[:] = 0 #numpy.NaN
|
|
|
438
|
# SPC_ch2[:] = 0 #numpy.NaN
|
|
438
|
# SPC_ch2[:] = 0 #numpy.NaN
|
|
|
439
|
# print ('stop 1')
|
|
439
|
# print ('stop 1')
|
|
|
440
|
for ht in range(self.Num_Hei):
|
|
440
|
for ht in range(self.Num_Hei):
|
|
|
441
|
# print (ht)
|
|
441
|
# print (ht)
|
|
|
442
|
# print ('stop 2')
|
|
442
|
# print ('stop 2')
|
|
|
443
|
# Spectra at each range
|
|
443
|
# Spectra at each range
|
|
|
444
|
spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
444
|
spc = numpy.asarray(self.spc)[ch,:,ht]
|
|
|
445
|
snr = ( spc.mean() - wnoise ) / wnoise
|
|
445
|
snr = ( spc.mean() - wnoise ) / wnoise
|
|
|
446
|
snrdB = 10.*numpy.log10(snr)
|
|
446
|
snrdB = 10.*numpy.log10(snr)
|
|
|
447
|
|
|
447
|
|
|
|
448
|
#print ('stop 3')
|
|
448
|
#print ('stop 3')
|
|
|
449
|
if snrdB < SNRlimit :
|
|
449
|
if snrdB < SNRlimit :
|
|
|
450
|
# snr = numpy.NaN
|
|
450
|
# snr = numpy.NaN
|
|
|
451
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
451
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
452
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
452
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
453
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
453
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
|
454
|
# print ('SNR less than SNRth')
|
|
454
|
# print ('SNR less than SNRth')
|
|
|
455
|
continue
|
|
455
|
continue
|
|
|
456
|
# wnoise = hildebrand_sekhon(spc,num_intg)
|
|
456
|
# wnoise = hildebrand_sekhon(spc,num_intg)
|
|
|
457
|
# print ('stop 2.01')
|
|
457
|
# print ('stop 2.01')
|
|
|
458
|
#############################################
|
|
458
|
#############################################
|
|
|
459
|
# normalizing spc and noise
|
|
459
|
# normalizing spc and noise
|
|
|
460
|
# This part differs from gg1
|
|
460
|
# This part differs from gg1
|
|
|
461
|
# spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021
|
|
461
|
# spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021
|
|
|
462
|
#spc = spc / spc_norm_max
|
|
462
|
#spc = spc / spc_norm_max
|
|
|
463
|
# pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021
|
|
463
|
# pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021
|
|
|
464
|
#############################################
|
|
464
|
#############################################
|
|
|
465
|
|
|
465
|
|
|
|
466
|
# print ('stop 2.1')
|
|
466
|
# print ('stop 2.1')
|
|
|
467
|
fatspectra=1.0
|
|
467
|
fatspectra=1.0
|
|
|
468
|
# noise per channel.... we might want to use the noise at each range
|
|
468
|
# noise per channel.... we might want to use the noise at each range
|
|
|
469
|
|
|
469
|
|
|
|
470
|
# wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021
|
|
470
|
# wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021
|
|
|
471
|
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
471
|
#wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
|
|
|
472
|
#if wnoise>1.1*pnoise: # to be tested later
|
|
472
|
#if wnoise>1.1*pnoise: # to be tested later
|
|
|
473
|
# wnoise=pnoise
|
|
473
|
# wnoise=pnoise
|
|
|
474
|
# noisebl = wnoise*0.9
|
|
474
|
# noisebl = wnoise*0.9
|
|
|
475
|
# noisebh = wnoise*1.1
|
|
475
|
# noisebh = wnoise*1.1
|
|
|
476
|
spc = spc - wnoise # signal
|
|
476
|
spc = spc - wnoise # signal
|
|
|
477
|
|
|
477
|
|
|
|
478
|
# print ('stop 2.2')
|
|
478
|
# print ('stop 2.2')
|
|
|
479
|
minx = numpy.argmin(spc)
|
|
479
|
minx = numpy.argmin(spc)
|
|
|
480
|
#spcs=spc.copy()
|
|
480
|
#spcs=spc.copy()
|
|
|
481
|
spcs = numpy.roll(spc,-minx)
|
|
481
|
spcs = numpy.roll(spc,-minx)
|
|
|
482
|
cum = numpy.cumsum(spcs)
|
|
482
|
cum = numpy.cumsum(spcs)
|
|
|
483
|
# tot_noise = wnoise * self.Num_Bin #64;
|
|
483
|
# tot_noise = wnoise * self.Num_Bin #64;
|
|
|
484
|
|
|
484
|
|
|
|
485
|
# print ('stop 2.3')
|
|
485
|
# print ('stop 2.3')
|
|
|
486
|
# snr = sum(spcs) / tot_noise
|
|
486
|
# snr = sum(spcs) / tot_noise
|
|
|
487
|
# snrdB = 10.*numpy.log10(snr)
|
|
487
|
# snrdB = 10.*numpy.log10(snr)
|
|
|
488
|
#print ('stop 3')
|
|
488
|
#print ('stop 3')
|
|
|
489
|
# if snrdB < SNRlimit :
|
|
489
|
# if snrdB < SNRlimit :
|
|
|
490
|
# snr = numpy.NaN
|
|
490
|
# snr = numpy.NaN
|
|
|
491
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
491
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
492
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
492
|
# SPC_ch1[:,ht] = 0#numpy.NaN
|
|
|
493
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
493
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
|
494
|
# print ('SNR less than SNRth')
|
|
494
|
# print ('SNR less than SNRth')
|
|
|
495
|
# continue
|
|
495
|
# continue
|
|
|
496
|
|
|
496
|
|
|
|
497
|
|
|
497
|
|
|
|
498
|
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
498
|
#if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
|
|
|
499
|
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
499
|
# return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
|
|
|
500
|
# print ('stop 4')
|
|
500
|
# print ('stop 4')
|
|
|
501
|
cummax = max(cum)
|
|
501
|
cummax = max(cum)
|
|
|
502
|
epsi = 0.08 * fatspectra # cumsum to narrow down the energy region
|
|
502
|
epsi = 0.08 * fatspectra # cumsum to narrow down the energy region
|
|
|
503
|
cumlo = cummax * epsi
|
|
503
|
cumlo = cummax * epsi
|
|
|
504
|
cumhi = cummax * (1-epsi)
|
|
504
|
cumhi = cummax * (1-epsi)
|
|
|
505
|
powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
505
|
powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
|
|
|
506
|
|
|
506
|
|
|
|
507
|
# print ('stop 5')
|
|
507
|
# print ('stop 5')
|
|
|
508
|
if len(powerindex) < 1:# case for powerindex 0
|
|
508
|
if len(powerindex) < 1:# case for powerindex 0
|
|
|
509
|
# print ('powerindex < 1')
|
|
509
|
# print ('powerindex < 1')
|
|
|
510
|
continue
|
|
510
|
continue
|
|
|
511
|
powerlo = powerindex[0]
|
|
511
|
powerlo = powerindex[0]
|
|
|
512
|
powerhi = powerindex[-1]
|
|
512
|
powerhi = powerindex[-1]
|
|
|
513
|
powerwidth = powerhi-powerlo
|
|
513
|
powerwidth = powerhi-powerlo
|
|
|
514
|
if powerwidth <= 1:
|
|
514
|
if powerwidth <= 1:
|
|
|
515
|
# print('powerwidth <= 1')
|
|
515
|
# print('powerwidth <= 1')
|
|
|
516
|
continue
|
|
516
|
continue
|
|
|
517
|
|
|
517
|
|
|
|
518
|
# print ('stop 6')
|
|
518
|
# print ('stop 6')
|
|
|
519
|
firstpeak = powerlo + powerwidth/10.# first gaussian energy location
|
|
519
|
firstpeak = powerlo + powerwidth/10.# first gaussian energy location
|
|
|
520
|
secondpeak = powerhi - powerwidth/10. #second gaussian energy location
|
|
520
|
secondpeak = powerhi - powerwidth/10. #second gaussian energy location
|
|
|
521
|
midpeak = (firstpeak + secondpeak)/2.
|
|
521
|
midpeak = (firstpeak + secondpeak)/2.
|
|
|
522
|
firstamp = spcs[int(firstpeak)]
|
|
522
|
firstamp = spcs[int(firstpeak)]
|
|
|
523
|
secondamp = spcs[int(secondpeak)]
|
|
523
|
secondamp = spcs[int(secondpeak)]
|
|
|
524
|
midamp = spcs[int(midpeak)]
|
|
524
|
midamp = spcs[int(midpeak)]
|
|
|
525
|
|
|
525
|
|
|
|
526
|
y_data = spc + wnoise
|
|
526
|
y_data = spc + wnoise
|
|
|
527
|
|
|
527
|
|
|
|
528
|
''' single Gaussian '''
|
|
528
|
''' single Gaussian '''
|
|
|
529
|
shift0 = numpy.mod(midpeak+minx, self.Num_Bin )
|
|
529
|
shift0 = numpy.mod(midpeak+minx, self.Num_Bin )
|
|
|
530
|
width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
530
|
width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4
|
|
|
531
|
power0 = 2.
|
|
531
|
power0 = 2.
|
|
|
532
|
amplitude0 = midamp
|
|
532
|
amplitude0 = midamp
|
|
|
533
|
state0 = [shift0,width0,amplitude0,power0,wnoise]
|
|
533
|
state0 = [shift0,width0,amplitude0,power0,wnoise]
|
|
|
534
|
bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
534
|
bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
535
|
lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True)
|
|
535
|
lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True)
|
|
|
536
|
# print ('stop 7.1')
|
|
536
|
# print ('stop 7.1')
|
|
|
537
|
# print (bnds)
|
|
537
|
# print (bnds)
|
|
|
538
|
|
|
538
|
|
|
|
539
|
chiSq1=lsq1[1]
|
|
539
|
chiSq1=lsq1[1]
|
|
|
540
|
|
|
540
|
|
|
|
541
|
# print ('stop 8')
|
|
541
|
# print ('stop 8')
|
|
|
542
|
if fatspectra<1.0 and powerwidth<4:
|
|
542
|
if fatspectra<1.0 and powerwidth<4:
|
|
|
543
|
choice=0
|
|
543
|
choice=0
|
|
|
544
|
Amplitude0=lsq1[0][2]
|
|
544
|
Amplitude0=lsq1[0][2]
|
|
|
545
|
shift0=lsq1[0][0]
|
|
545
|
shift0=lsq1[0][0]
|
|
|
546
|
width0=lsq1[0][1]
|
|
546
|
width0=lsq1[0][1]
|
|
|
547
|
p0=lsq1[0][3]
|
|
547
|
p0=lsq1[0][3]
|
|
|
548
|
Amplitude1=0.
|
|
548
|
Amplitude1=0.
|
|
|
549
|
shift1=0.
|
|
549
|
shift1=0.
|
|
|
550
|
width1=0.
|
|
550
|
width1=0.
|
|
|
551
|
p1=0.
|
|
551
|
p1=0.
|
|
|
552
|
noise=lsq1[0][4]
|
|
552
|
noise=lsq1[0][4]
|
|
|
553
|
#return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
553
|
#return (numpy.array([shift0,width0,Amplitude0,p0]),
|
|
|
554
|
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
554
|
# numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
|
|
|
555
|
|
|
555
|
|
|
|
556
|
# print ('stop 9')
|
|
556
|
# print ('stop 9')
|
|
|
557
|
''' two Gaussians '''
|
|
557
|
''' two Gaussians '''
|
|
|
558
|
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
558
|
#shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
|
|
|
559
|
shift0 = numpy.mod(firstpeak+minx, self.Num_Bin )
|
|
559
|
shift0 = numpy.mod(firstpeak+minx, self.Num_Bin )
|
|
|
560
|
shift1 = numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
560
|
shift1 = numpy.mod(secondpeak+minx, self.Num_Bin )
|
|
|
561
|
width0 = powerwidth/6.
|
|
561
|
width0 = powerwidth/6.
|
|
|
562
|
width1 = width0
|
|
562
|
width1 = width0
|
|
|
563
|
power0 = 2.
|
|
563
|
power0 = 2.
|
|
|
564
|
power1 = power0
|
|
564
|
power1 = power0
|
|
|
565
|
amplitude0 = firstamp
|
|
565
|
amplitude0 = firstamp
|
|
|
566
|
amplitude1 = secondamp
|
|
566
|
amplitude1 = secondamp
|
|
|
567
|
state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
567
|
state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
|
|
|
568
|
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
568
|
#bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
|
|
|
569
|
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))
|
|
569
|
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))
|
|
|
570
|
#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))
|
|
570
|
#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))
|
|
|
571
|
|
|
571
|
|
|
|
572
|
# print ('stop 10')
|
|
572
|
# print ('stop 10')
|
|
|
573
|
lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True )
|
|
573
|
lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True )
|
|
|
574
|
|
|
574
|
|
|
|
575
|
# print ('stop 11')
|
|
575
|
# print ('stop 11')
|
|
|
576
|
chiSq2 = lsq2[1]
|
|
576
|
chiSq2 = lsq2[1]
|
|
|
577
|
|
|
577
|
|
|
|
578
|
# print ('stop 12')
|
|
578
|
# print ('stop 12')
|
|
|
579
|
|
|
579
|
|
|
|
580
|
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)
|
|
580
|
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)
|
|
|
581
|
|
|
581
|
|
|
|
582
|
# print ('stop 13')
|
|
582
|
# print ('stop 13')
|
|
|
583
|
if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
583
|
if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error
|
|
|
584
|
if oneG:
|
|
584
|
if oneG:
|
|
|
585
|
choice = 0
|
|
585
|
choice = 0
|
|
|
586
|
else:
|
|
586
|
else:
|
|
|
587
|
w1 = lsq2[0][1]; w2 = lsq2[0][5]
|
|
587
|
w1 = lsq2[0][1]; w2 = lsq2[0][5]
|
|
|
588
|
a1 = lsq2[0][2]; a2 = lsq2[0][6]
|
|
588
|
a1 = lsq2[0][2]; a2 = lsq2[0][6]
|
|
|
589
|
p1 = lsq2[0][3]; p2 = lsq2[0][7]
|
|
589
|
p1 = lsq2[0][3]; p2 = lsq2[0][7]
|
|
|
590
|
s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1
|
|
590
|
s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1
|
|
|
591
|
s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2
|
|
591
|
s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2
|
|
|
592
|
gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
592
|
gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling
|
|
|
593
|
|
|
593
|
|
|
|
594
|
if gp1>gp2:
|
|
594
|
if gp1>gp2:
|
|
|
595
|
if a1>0.7*a2:
|
|
595
|
if a1>0.7*a2:
|
|
|
596
|
choice = 1
|
|
596
|
choice = 1
|
|
|
597
|
else:
|
|
597
|
else:
|
|
|
598
|
choice = 2
|
|
598
|
choice = 2
|
|
|
599
|
elif gp2>gp1:
|
|
599
|
elif gp2>gp1:
|
|
|
600
|
if a2>0.7*a1:
|
|
600
|
if a2>0.7*a1:
|
|
|
601
|
choice = 2
|
|
601
|
choice = 2
|
|
|
602
|
else:
|
|
602
|
else:
|
|
|
603
|
choice = 1
|
|
603
|
choice = 1
|
|
|
604
|
else:
|
|
604
|
else:
|
|
|
605
|
choice = numpy.argmax([a1,a2])+1
|
|
605
|
choice = numpy.argmax([a1,a2])+1
|
|
|
606
|
#else:
|
|
606
|
#else:
|
|
|
607
|
#choice=argmin([std2a,std2b])+1
|
|
607
|
#choice=argmin([std2a,std2b])+1
|
|
|
608
|
|
|
608
|
|
|
|
609
|
else: # with low SNR go to the most energetic peak
|
|
609
|
else: # with low SNR go to the most energetic peak
|
|
|
610
|
choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
610
|
choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
|
|
|
611
|
|
|
611
|
|
|
|
612
|
# print ('stop 14')
|
|
612
|
# print ('stop 14')
|
|
|
613
|
shift0 = lsq2[0][0]
|
|
613
|
shift0 = lsq2[0][0]
|
|
|
614
|
vel0 = Vrange[0] + shift0 * deltav
|
|
614
|
vel0 = Vrange[0] + shift0 * deltav
|
|
|
615
|
shift1 = lsq2[0][4]
|
|
615
|
shift1 = lsq2[0][4]
|
|
|
616
|
# vel1=Vrange[0] + shift1 * deltav
|
|
616
|
# vel1=Vrange[0] + shift1 * deltav
|
|
|
617
|
|
|
617
|
|
|
|
618
|
# max_vel = 1.0
|
|
618
|
# max_vel = 1.0
|
|
|
619
|
# Va = max(Vrange)
|
|
619
|
# Va = max(Vrange)
|
|
|
620
|
# deltav = Vrange[1]-Vrange[0]
|
|
620
|
# deltav = Vrange[1]-Vrange[0]
|
|
|
621
|
# print ('stop 15')
|
|
621
|
# print ('stop 15')
|
|
|
622
|
#first peak will be 0, second peak will be 1
|
|
622
|
#first peak will be 0, second peak will be 1
|
|
|
623
|
# if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021
|
|
623
|
# if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021
|
|
|
624
|
if vel0 > -Va and vel0 < Va : #first peak is in the correct range
|
|
624
|
if vel0 > -Va and vel0 < Va : #first peak is in the correct range
|
|
|
625
|
shift0 = lsq2[0][0]
|
|
625
|
shift0 = lsq2[0][0]
|
|
|
626
|
width0 = lsq2[0][1]
|
|
626
|
width0 = lsq2[0][1]
|
|
|
627
|
Amplitude0 = lsq2[0][2]
|
|
627
|
Amplitude0 = lsq2[0][2]
|
|
|
628
|
p0 = lsq2[0][3]
|
|
628
|
p0 = lsq2[0][3]
|
|
|
629
|
|
|
629
|
|
|
|
630
|
shift1 = lsq2[0][4]
|
|
630
|
shift1 = lsq2[0][4]
|
|
|
631
|
width1 = lsq2[0][5]
|
|
631
|
width1 = lsq2[0][5]
|
|
|
632
|
Amplitude1 = lsq2[0][6]
|
|
632
|
Amplitude1 = lsq2[0][6]
|
|
|
633
|
p1 = lsq2[0][7]
|
|
633
|
p1 = lsq2[0][7]
|
|
|
634
|
noise = lsq2[0][8]
|
|
634
|
noise = lsq2[0][8]
|
|
|
635
|
else:
|
|
635
|
else:
|
|
|
636
|
shift1 = lsq2[0][0]
|
|
636
|
shift1 = lsq2[0][0]
|
|
|
637
|
width1 = lsq2[0][1]
|
|
637
|
width1 = lsq2[0][1]
|
|
|
638
|
Amplitude1 = lsq2[0][2]
|
|
638
|
Amplitude1 = lsq2[0][2]
|
|
|
639
|
p1 = lsq2[0][3]
|
|
639
|
p1 = lsq2[0][3]
|
|
|
640
|
|
|
640
|
|
|
|
641
|
shift0 = lsq2[0][4]
|
|
641
|
shift0 = lsq2[0][4]
|
|
|
642
|
width0 = lsq2[0][5]
|
|
642
|
width0 = lsq2[0][5]
|
|
|
643
|
Amplitude0 = lsq2[0][6]
|
|
643
|
Amplitude0 = lsq2[0][6]
|
|
|
644
|
p0 = lsq2[0][7]
|
|
644
|
p0 = lsq2[0][7]
|
|
|
645
|
noise = lsq2[0][8]
|
|
645
|
noise = lsq2[0][8]
|
|
|
646
|
|
|
646
|
|
|
|
647
|
if Amplitude0<0.05: # in case the peak is noise
|
|
647
|
if Amplitude0<0.05: # in case the peak is noise
|
|
|
648
|
shift0,width0,Amplitude0,p0 = 4*[numpy.NaN]
|
|
648
|
shift0,width0,Amplitude0,p0 = 4*[numpy.NaN]
|
|
|
649
|
if Amplitude1<0.05:
|
|
649
|
if Amplitude1<0.05:
|
|
|
650
|
shift1,width1,Amplitude1,p1 = 4*[numpy.NaN]
|
|
650
|
shift1,width1,Amplitude1,p1 = 4*[numpy.NaN]
|
|
|
651
|
|
|
651
|
|
|
|
652
|
# print ('stop 16 ')
|
|
652
|
# print ('stop 16 ')
|
|
|
653
|
# SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0)
|
|
653
|
# SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0)
|
|
|
654
|
# SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1)
|
|
654
|
# SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1)
|
|
|
655
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
655
|
# SPCparam = (SPC_ch1,SPC_ch2)
|
|
|
656
|
|
|
656
|
|
|
|
657
|
DGauFitParam[0,ht,0] = noise
|
|
657
|
DGauFitParam[0,ht,0] = noise
|
|
|
658
|
DGauFitParam[0,ht,1] = noise
|
|
658
|
DGauFitParam[0,ht,1] = noise
|
|
|
659
|
DGauFitParam[1,ht,0] = Amplitude0
|
|
659
|
DGauFitParam[1,ht,0] = Amplitude0
|
|
|
660
|
DGauFitParam[1,ht,1] = Amplitude1
|
|
660
|
DGauFitParam[1,ht,1] = Amplitude1
|
|
|
661
|
DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav
|
|
661
|
DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav
|
|
|
662
|
DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav
|
|
662
|
DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav
|
|
|
663
|
DGauFitParam[3,ht,0] = width0 * deltav
|
|
663
|
DGauFitParam[3,ht,0] = width0 * deltav
|
|
|
664
|
DGauFitParam[3,ht,1] = width1 * deltav
|
|
664
|
DGauFitParam[3,ht,1] = width1 * deltav
|
|
|
665
|
DGauFitParam[4,ht,0] = p0
|
|
665
|
DGauFitParam[4,ht,0] = p0
|
|
|
666
|
DGauFitParam[4,ht,1] = p1
|
|
666
|
DGauFitParam[4,ht,1] = p1
|
|
|
667
|
|
|
667
|
|
|
|
668
|
# print (DGauFitParam.shape)
|
|
668
|
# print (DGauFitParam.shape)
|
|
|
669
|
# print ('Leaving FitGau')
|
|
669
|
# print ('Leaving FitGau')
|
|
|
670
|
return DGauFitParam
|
|
670
|
return DGauFitParam
|
|
|
671
|
# return SPCparam
|
|
671
|
# return SPCparam
|
|
|
672
|
# return GauSPC
|
|
672
|
# return GauSPC
|
|
|
673
|
|
|
673
|
|
|
|
674
|
def y_model1(self,x,state):
|
|
674
|
def y_model1(self,x,state):
|
|
|
675
|
shift0, width0, amplitude0, power0, noise = state
|
|
675
|
shift0, width0, amplitude0, power0, noise = state
|
|
|
676
|
model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0)
|
|
676
|
model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0)
|
|
|
677
|
model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
|
|
677
|
model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
|
|
|
678
|
model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
|
|
678
|
model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
|
|
|
679
|
return model0 + model0u + model0d + noise
|
|
679
|
return model0 + model0u + model0d + noise
|
|
|
680
|
|
|
680
|
|
|
|
681
|
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
|
|
681
|
def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
|
|
|
682
|
shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state
|
|
682
|
shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state
|
|
|
683
|
model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
683
|
model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
|
|
|
684
|
model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
|
|
684
|
model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
|
|
|
685
|
model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
|
|
685
|
model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
|
|
|
686
|
|
|
686
|
|
|
|
687
|
model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1)
|
|
687
|
model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1)
|
|
|
688
|
model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1)
|
|
688
|
model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1)
|
|
|
689
|
model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1)
|
|
689
|
model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1)
|
|
|
690
|
return model0 + model0u + model0d + model1 + model1u + model1d + noise
|
|
690
|
return model0 + model0u + model0d + model1 + model1u + model1d + noise
|
|
|
691
|
|
|
691
|
|
|
|
692
|
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.
|
|
692
|
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.
|
|
|
693
|
|
|
693
|
|
|
|
694
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
|
|
694
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
|
|
|
695
|
|
|
695
|
|
|
|
696
|
def misfit2(self,state,y_data,x,num_intg):
|
|
696
|
def misfit2(self,state,y_data,x,num_intg):
|
|
|
697
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
|
|
697
|
return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
|
|
|
698
|
|
|
698
|
|
|
|
699
|
|
|
699
|
|
|
|
700
|
|
|
700
|
|
|
|
701
|
class PrecipitationProc(Operation):
|
|
701
|
class PrecipitationProc(Operation):
|
|
|
702
|
|
|
702
|
|
|
|
703
|
'''
|
|
703
|
'''
|
|
|
704
|
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
|
|
704
|
Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
|
|
|
705
|
|
|
705
|
|
|
|
706
|
Input:
|
|
706
|
Input:
|
|
|
707
|
self.dataOut.data_pre : SelfSpectra
|
|
707
|
self.dataOut.data_pre : SelfSpectra
|
|
|
708
|
|
|
708
|
|
|
|
709
|
Output:
|
|
709
|
Output:
|
|
|
710
|
|
|
710
|
|
|
|
711
|
self.dataOut.data_output : Reflectivity factor, rainfall Rate
|
|
711
|
self.dataOut.data_output : Reflectivity factor, rainfall Rate
|
|
|
712
|
|
|
712
|
|
|
|
713
|
|
|
713
|
|
|
|
714
|
Parameters affected:
|
|
714
|
Parameters affected:
|
|
|
715
|
'''
|
|
715
|
'''
|
|
|
716
|
|
|
716
|
|
|
|
717
|
def __init__(self):
|
|
717
|
def __init__(self):
|
|
|
718
|
Operation.__init__(self)
|
|
718
|
Operation.__init__(self)
|
|
|
719
|
self.i=0
|
|
719
|
self.i=0
|
|
|
720
|
|
|
720
|
|
|
|
721
|
def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118,
|
|
721
|
def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118,
|
|
|
722
|
tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30):
|
|
722
|
tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30):
|
|
|
723
|
|
|
723
|
|
|
|
724
|
# print ('Entering PrecepitationProc ... ')
|
|
724
|
# print ('Entering PrecepitationProc ... ')
|
|
|
725
|
|
|
725
|
|
|
|
726
|
if radar == "MIRA35C" :
|
|
726
|
if radar == "MIRA35C" :
|
|
|
727
|
|
|
727
|
|
|
|
728
|
self.spc = dataOut.data_pre[0].copy()
|
|
728
|
self.spc = dataOut.data_pre[0].copy()
|
|
|
729
|
self.Num_Hei = self.spc.shape[2]
|
|
729
|
self.Num_Hei = self.spc.shape[2]
|
|
|
730
|
self.Num_Bin = self.spc.shape[1]
|
|
730
|
self.Num_Bin = self.spc.shape[1]
|
|
|
731
|
self.Num_Chn = self.spc.shape[0]
|
|
731
|
self.Num_Chn = self.spc.shape[0]
|
|
|
732
|
Ze = self.dBZeMODE2(dataOut)
|
|
732
|
Ze = self.dBZeMODE2(dataOut)
|
|
|
733
|
|
|
733
|
|
|
|
734
|
else:
|
|
734
|
else:
|
|
|
735
|
|
|
735
|
|
|
|
736
|
self.spc = dataOut.data_pre[0].copy()
|
|
736
|
self.spc = dataOut.data_pre[0].copy()
|
|
|
737
|
|
|
737
|
|
|
|
738
|
#NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX
|
|
738
|
#NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX
|
|
|
739
|
self.spc[:,:,0:7]= numpy.NaN
|
|
739
|
self.spc[:,:,0:7]= numpy.NaN
|
|
|
740
|
|
|
740
|
|
|
|
741
|
self.Num_Hei = self.spc.shape[2]
|
|
741
|
self.Num_Hei = self.spc.shape[2]
|
|
|
742
|
self.Num_Bin = self.spc.shape[1]
|
|
742
|
self.Num_Bin = self.spc.shape[1]
|
|
|
743
|
self.Num_Chn = self.spc.shape[0]
|
|
743
|
self.Num_Chn = self.spc.shape[0]
|
|
|
744
|
|
|
744
|
|
|
|
745
|
VelRange = dataOut.spc_range[2]
|
|
745
|
VelRange = dataOut.spc_range[2]
|
|
|
746
|
|
|
746
|
|
|
|
747
|
''' Se obtiene la constante del RADAR '''
|
|
747
|
''' Se obtiene la constante del RADAR '''
|
|
|
748
|
|
|
748
|
|
|
|
749
|
self.Pt = Pt
|
|
749
|
self.Pt = Pt
|
|
|
750
|
self.Gt = Gt
|
|
750
|
self.Gt = Gt
|
|
|
751
|
self.Gr = Gr
|
|
751
|
self.Gr = Gr
|
|
|
752
|
self.Lambda = Lambda
|
|
752
|
self.Lambda = Lambda
|
|
|
753
|
self.aL = aL
|
|
753
|
self.aL = aL
|
|
|
754
|
self.tauW = tauW
|
|
754
|
self.tauW = tauW
|
|
|
755
|
self.ThetaT = ThetaT
|
|
755
|
self.ThetaT = ThetaT
|
|
|
756
|
self.ThetaR = ThetaR
|
|
756
|
self.ThetaR = ThetaR
|
|
|
757
|
self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB
|
|
757
|
self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB
|
|
|
758
|
self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB
|
|
758
|
self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB
|
|
|
759
|
self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB
|
|
759
|
self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB
|
|
|
760
|
|
|
760
|
|
|
|
761
|
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
761
|
Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
|
762
|
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
|
|
762
|
Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
|
|
|
763
|
RadarConstant = 10e-26 * Numerator / Denominator #
|
|
763
|
RadarConstant = 10e-26 * Numerator / Denominator #
|
|
|
764
|
ExpConstant = 10**(40/10) #Constante Experimental
|
|
764
|
ExpConstant = 10**(40/10) #Constante Experimental
|
|
|
765
|
|
|
765
|
|
|
|
766
|
SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
766
|
SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
|
|
|
767
|
for i in range(self.Num_Chn):
|
|
767
|
for i in range(self.Num_Chn):
|
|
|
768
|
SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i]
|
|
768
|
SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i]
|
|
|
769
|
SignalPower[numpy.where(SignalPower < 0)] = 1e-20
|
|
769
|
SignalPower[numpy.where(SignalPower < 0)] = 1e-20
|
|
|
770
|
|
|
770
|
|
|
|
771
|
SPCmean = numpy.mean(SignalPower, 0)
|
|
771
|
SPCmean = numpy.mean(SignalPower, 0)
|
|
|
772
|
Pr = SPCmean[:,:]/dataOut.normFactor
|
|
772
|
Pr = SPCmean[:,:]/dataOut.normFactor
|
|
|
773
|
|
|
773
|
|
|
|
774
|
# Declaring auxiliary variables
|
|
774
|
# Declaring auxiliary variables
|
|
|
775
|
Range = dataOut.heightList*1000. #Range in m
|
|
775
|
Range = dataOut.heightList*1000. #Range in m
|
|
|
776
|
# replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei]
|
|
776
|
# replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei]
|
|
|
777
|
rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin))
|
|
777
|
rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin))
|
|
|
778
|
zMtrx = rMtrx+Altitude
|
|
778
|
zMtrx = rMtrx+Altitude
|
|
|
779
|
# replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei]
|
|
779
|
# replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei]
|
|
|
780
|
VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1)))
|
|
780
|
VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1)))
|
|
|
781
|
|
|
781
|
|
|
|
782
|
# height dependence to air density Foote and Du Toit (1969)
|
|
782
|
# height dependence to air density Foote and Du Toit (1969)
|
|
|
783
|
delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2
|
|
783
|
delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2
|
|
|
784
|
VMtrx = VelMtrx / delv_z #Normalized velocity
|
|
784
|
VMtrx = VelMtrx / delv_z #Normalized velocity
|
|
|
785
|
VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN
|
|
785
|
VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN
|
|
|
786
|
# Diameter is related to the fall speed of falling drops
|
|
786
|
# Diameter is related to the fall speed of falling drops
|
|
|
787
|
D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm]
|
|
787
|
D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm]
|
|
|
788
|
# Only valid for D>= 0.16 mm
|
|
788
|
# Only valid for D>= 0.16 mm
|
|
|
789
|
D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN
|
|
789
|
D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN
|
|
|
790
|
|
|
790
|
|
|
|
791
|
#Calculate Radar Reflectivity ETAn
|
|
791
|
#Calculate Radar Reflectivity ETAn
|
|
|
792
|
ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA)
|
|
792
|
ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA)
|
|
|
793
|
ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z
|
|
793
|
ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z
|
|
|
794
|
# Radar Cross Section
|
|
794
|
# Radar Cross Section
|
|
|
795
|
sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4
|
|
795
|
sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4
|
|
|
796
|
# Drop Size Distribution
|
|
796
|
# Drop Size Distribution
|
|
|
797
|
DSD = ETAn / sigmaD
|
|
797
|
DSD = ETAn / sigmaD
|
|
|
798
|
# Equivalente Reflectivy
|
|
798
|
# Equivalente Reflectivy
|
|
|
799
|
Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0)
|
|
799
|
Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0)
|
|
|
800
|
Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3]
|
|
800
|
Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3]
|
|
|
801
|
# RainFall Rate
|
|
801
|
# RainFall Rate
|
|
|
802
|
RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr
|
|
802
|
RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr
|
|
|
803
|
|
|
803
|
|
|
|
804
|
# Censoring the data
|
|
804
|
# Censoring the data
|
|
|
805
|
# Removing data with SNRth < 0dB se debe considerar el SNR por canal
|
|
805
|
# Removing data with SNRth < 0dB se debe considerar el SNR por canal
|
|
|
806
|
SNRth = 10**(SNRdBlimit/10) #-30dB
|
|
806
|
SNRth = 10**(SNRdBlimit/10) #-30dB
|
|
|
807
|
novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better
|
|
807
|
novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better
|
|
|
808
|
W = numpy.nanmean(dataOut.data_dop,0)
|
|
808
|
W = numpy.nanmean(dataOut.data_dop,0)
|
|
|
809
|
W[novalid] = numpy.NaN
|
|
809
|
W[novalid] = numpy.NaN
|
|
|
810
|
Ze_org[novalid] = numpy.NaN
|
|
810
|
Ze_org[novalid] = numpy.NaN
|
|
|
811
|
RR[novalid] = numpy.NaN
|
|
811
|
RR[novalid] = numpy.NaN
|
|
|
812
|
|
|
812
|
|
|
|
813
|
dataOut.data_output = RR[8]
|
|
813
|
dataOut.data_output = RR[8]
|
|
|
814
|
dataOut.data_param = numpy.ones([3,self.Num_Hei])
|
|
814
|
dataOut.data_param = numpy.ones([3,self.Num_Hei])
|
|
|
815
|
dataOut.channelList = [0,1,2]
|
|
815
|
dataOut.channelList = [0,1,2]
|
|
|
816
|
|
|
816
|
|
|
|
817
|
dataOut.data_param[0]=10*numpy.log10(Ze_org)
|
|
817
|
dataOut.data_param[0]=10*numpy.log10(Ze_org)
|
|
|
818
|
dataOut.data_param[1]=-W
|
|
818
|
dataOut.data_param[1]=-W
|
|
|
819
|
dataOut.data_param[2]=RR
|
|
819
|
dataOut.data_param[2]=RR
|
|
|
820
|
|
|
820
|
|
|
|
821
|
# print ('Leaving PrecepitationProc ... ')
|
|
821
|
# print ('Leaving PrecepitationProc ... ')
|
|
|
822
|
return dataOut
|
|
822
|
return dataOut
|
|
|
823
|
|
|
823
|
|
|
|
824
|
def dBZeMODE2(self, dataOut): # Processing for MIRA35C
|
|
824
|
def dBZeMODE2(self, dataOut): # Processing for MIRA35C
|
|
|
825
|
|
|
825
|
|
|
|
826
|
NPW = dataOut.NPW
|
|
826
|
NPW = dataOut.NPW
|
|
|
827
|
COFA = dataOut.COFA
|
|
827
|
COFA = dataOut.COFA
|
|
|
828
|
|
|
828
|
|
|
|
829
|
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
|
|
829
|
SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
|
|
|
830
|
RadarConst = dataOut.RadarConst
|
|
830
|
RadarConst = dataOut.RadarConst
|
|
|
831
|
#frequency = 34.85*10**9
|
|
831
|
#frequency = 34.85*10**9
|
|
|
832
|
|
|
832
|
|
|
|
833
|
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
|
|
833
|
ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
|
|
|
834
|
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
|
|
834
|
data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
|
|
|
835
|
|
|
835
|
|
|
|
836
|
ETA = numpy.sum(SNR,1)
|
|
836
|
ETA = numpy.sum(SNR,1)
|
|
|
837
|
|
|
837
|
|
|
|
838
|
ETA = numpy.where(ETA != 0. , ETA, numpy.NaN)
|
|
838
|
ETA = numpy.where(ETA != 0. , ETA, numpy.NaN)
|
|
|
839
|
|
|
839
|
|
|
|
840
|
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
|
|
840
|
Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
|
|
|
841
|
|
|
841
|
|
|
|
842
|
for r in range(self.Num_Hei):
|
|
842
|
for r in range(self.Num_Hei):
|
|
|
843
|
|
|
843
|
|
|
|
844
|
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
|
|
844
|
Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
|
|
|
845
|
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
|
|
845
|
#Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
|
|
|
846
|
|
|
846
|
|
|
|
847
|
return Ze
|
|
847
|
return Ze
|
|
|
848
|
|
|
848
|
|
|
|
849
|
# def GetRadarConstant(self):
|
|
849
|
# def GetRadarConstant(self):
|
|
|
850
|
#
|
|
850
|
#
|
|
|
851
|
# """
|
|
851
|
# """
|
|
|
852
|
# Constants:
|
|
852
|
# Constants:
|
|
|
853
|
#
|
|
853
|
#
|
|
|
854
|
# Pt: Transmission Power dB 5kW 5000
|
|
854
|
# Pt: Transmission Power dB 5kW 5000
|
|
|
855
|
# Gt: Transmission Gain dB 24.7 dB 295.1209
|
|
855
|
# Gt: Transmission Gain dB 24.7 dB 295.1209
|
|
|
856
|
# Gr: Reception Gain dB 18.5 dB 70.7945
|
|
856
|
# Gr: Reception Gain dB 18.5 dB 70.7945
|
|
|
857
|
# Lambda: Wavelenght m 0.6741 m 0.6741
|
|
857
|
# Lambda: Wavelenght m 0.6741 m 0.6741
|
|
|
858
|
# aL: Attenuation loses dB 4dB 2.5118
|
|
858
|
# aL: Attenuation loses dB 4dB 2.5118
|
|
|
859
|
# tauW: Width of transmission pulse s 4us 4e-6
|
|
859
|
# tauW: Width of transmission pulse s 4us 4e-6
|
|
|
860
|
# ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317
|
|
860
|
# ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317
|
|
|
861
|
# ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087
|
|
861
|
# ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087
|
|
|
862
|
#
|
|
862
|
#
|
|
|
863
|
# """
|
|
863
|
# """
|
|
|
864
|
#
|
|
864
|
#
|
|
|
865
|
# Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
865
|
# Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
|
|
|
866
|
# Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
|
|
866
|
# Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
|
|
|
867
|
# RadarConstant = Numerator / Denominator
|
|
867
|
# RadarConstant = Numerator / Denominator
|
|
|
868
|
#
|
|
868
|
#
|
|
|
869
|
# return RadarConstant
|
|
869
|
# return RadarConstant
|
|
|
870
|
|
|
870
|
|
|
|
871
|
|
|
871
|
|
|
|
872
|
|
|
872
|
|
|
|
873
|
class FullSpectralAnalysis(Operation):
|
|
873
|
class FullSpectralAnalysis(Operation):
|
|
|
874
|
|
|
874
|
|
|
|
875
|
"""
|
|
875
|
"""
|
|
|
876
|
Function that implements Full Spectral Analysis technique.
|
|
876
|
Function that implements Full Spectral Analysis technique.
|
|
|
877
|
|
|
877
|
|
|
|
878
|
Input:
|
|
878
|
Input:
|
|
|
879
|
self.dataOut.data_pre : SelfSpectra and CrossSpectra data
|
|
879
|
self.dataOut.data_pre : SelfSpectra and CrossSpectra data
|
|
|
880
|
self.dataOut.groupList : Pairlist of channels
|
|
880
|
self.dataOut.groupList : Pairlist of channels
|
|
|
881
|
self.dataOut.ChanDist : Physical distance between receivers
|
|
881
|
self.dataOut.ChanDist : Physical distance between receivers
|
|
|
882
|
|
|
882
|
|
|
|
883
|
|
|
883
|
|
|
|
884
|
Output:
|
|
884
|
Output:
|
|
|
885
|
|
|
885
|
|
|
|
886
|
self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind
|
|
886
|
self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind
|
|
|
887
|
|
|
887
|
|
|
|
888
|
|
|
888
|
|
|
|
889
|
Parameters affected: Winds, height range, SNR
|
|
889
|
Parameters affected: Winds, height range, SNR
|
|
|
890
|
|
|
890
|
|
|
|
891
|
"""
|
|
891
|
"""
|
|
|
892
|
def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30,
|
|
892
|
def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30,
|
|
|
893
|
minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None):
|
|
893
|
minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None):
|
|
|
894
|
|
|
894
|
|
|
|
895
|
spc = dataOut.data_pre[0].copy()
|
|
895
|
spc = dataOut.data_pre[0].copy()
|
|
|
896
|
cspc = dataOut.data_pre[1]
|
|
896
|
cspc = dataOut.data_pre[1]
|
|
|
897
|
nHeights = spc.shape[2]
|
|
897
|
nHeights = spc.shape[2]
|
|
|
898
|
|
|
898
|
|
|
|
899
|
# first_height = 0.75 #km (ref: data header 20170822)
|
|
899
|
# first_height = 0.75 #km (ref: data header 20170822)
|
|
|
900
|
# resolution_height = 0.075 #km
|
|
900
|
# resolution_height = 0.075 #km
|
|
|
901
|
'''
|
|
901
|
'''
|
|
|
902
|
finding height range. check this when radar parameters are changed!
|
|
902
|
finding height range. check this when radar parameters are changed!
|
|
|
903
|
'''
|
|
903
|
'''
|
|
|
904
|
if maxheight is not None:
|
|
904
|
if maxheight is not None:
|
|
|
905
|
# range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical
|
|
905
|
# range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical
|
|
|
906
|
range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better
|
|
906
|
range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better
|
|
|
907
|
else:
|
|
907
|
else:
|
|
|
908
|
range_max = nHeights
|
|
908
|
range_max = nHeights
|
|
|
909
|
if minheight is not None:
|
|
909
|
if minheight is not None:
|
|
|
910
|
# range_min = int((minheight - first_height) / resolution_height) # theoretical
|
|
910
|
# range_min = int((minheight - first_height) / resolution_height) # theoretical
|
|
|
911
|
range_min = int(13.26 * minheight - 5) # empirical, works better
|
|
911
|
range_min = int(13.26 * minheight - 5) # empirical, works better
|
|
|
912
|
if range_min < 0:
|
|
912
|
if range_min < 0:
|
|
|
913
|
range_min = 0
|
|
913
|
range_min = 0
|
|
|
914
|
else:
|
|
914
|
else:
|
|
|
915
|
range_min = 0
|
|
915
|
range_min = 0
|
|
|
916
|
|
|
916
|
|
|
|
917
|
pairsList = dataOut.groupList
|
|
917
|
pairsList = dataOut.groupList
|
|
|
918
|
if dataOut.ChanDist is not None :
|
|
918
|
if dataOut.ChanDist is not None :
|
|
|
919
|
ChanDist = dataOut.ChanDist
|
|
919
|
ChanDist = dataOut.ChanDist
|
|
|
920
|
else:
|
|
920
|
else:
|
|
|
921
|
ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]])
|
|
921
|
ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]])
|
|
|
922
|
|
|
922
|
|
|
|
923
|
# 4 variables: zonal, meridional, vertical, and average SNR
|
|
923
|
# 4 variables: zonal, meridional, vertical, and average SNR
|
|
|
924
|
data_param = numpy.zeros([4,nHeights]) * numpy.NaN
|
|
924
|
data_param = numpy.zeros([4,nHeights]) * numpy.NaN
|
|
|
925
|
velocityX = numpy.zeros([nHeights]) * numpy.NaN
|
|
925
|
velocityX = numpy.zeros([nHeights]) * numpy.NaN
|
|
|
926
|
velocityY = numpy.zeros([nHeights]) * numpy.NaN
|
|
926
|
velocityY = numpy.zeros([nHeights]) * numpy.NaN
|
|
|
927
|
velocityZ = numpy.zeros([nHeights]) * numpy.NaN
|
|
927
|
velocityZ = numpy.zeros([nHeights]) * numpy.NaN
|
|
|
928
|
|
|
928
|
|
|
|
929
|
dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0))
|
|
929
|
dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0))
|
|
|
930
|
|
|
930
|
|
|
|
931
|
'''***********************************************WIND ESTIMATION**************************************'''
|
|
931
|
'''***********************************************WIND ESTIMATION**************************************'''
|
|
|
932
|
for Height in range(nHeights):
|
|
932
|
for Height in range(nHeights):
|
|
|
933
|
|
|
933
|
|
|
|
934
|
if Height >= range_min and Height < range_max:
|
|
934
|
if Height >= range_min and Height < range_max:
|
|
|
935
|
# error_code will be useful in future analysis
|
|
935
|
# error_code will be useful in future analysis
|
|
|
936
|
[Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList,
|
|
936
|
[Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList,
|
|
|
937
|
ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency)
|
|
937
|
ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency)
|
|
|
938
|
|
|
938
|
|
|
|
939
|
if abs(Vzon) < 100. and abs(Vmer) < 100.:
|
|
939
|
if abs(Vzon) < 100. and abs(Vmer) < 100.:
|
|
|
940
|
velocityX[Height] = Vzon
|
|
940
|
velocityX[Height] = Vzon
|
|
|
941
|
velocityY[Height] = -Vmer
|
|
941
|
velocityY[Height] = -Vmer
|
|
|
942
|
velocityZ[Height] = Vver
|
|
942
|
velocityZ[Height] = Vver
|
|
|
943
|
|
|
943
|
|
|
|
944
|
# Censoring data with SNR threshold
|
|
944
|
# Censoring data with SNR threshold
|
|
|
945
|
dbSNR [dbSNR < SNRdBlimit] = numpy.NaN
|
|
945
|
dbSNR [dbSNR < SNRdBlimit] = numpy.NaN
|
|
|
946
|
|
|
946
|
|
|
|
947
|
data_param[0] = velocityX
|
|
947
|
data_param[0] = velocityX
|
|
|
948
|
data_param[1] = velocityY
|
|
948
|
data_param[1] = velocityY
|
|
|
949
|
data_param[2] = velocityZ
|
|
949
|
data_param[2] = velocityZ
|
|
|
950
|
data_param[3] = dbSNR
|
|
950
|
data_param[3] = dbSNR
|
|
|
951
|
dataOut.data_param = data_param
|
|
951
|
dataOut.data_param = data_param
|
|
|
952
|
return dataOut
|
|
952
|
return dataOut
|
|
|
953
|
|
|
953
|
|
|
|
954
|
def moving_average(self,x, N=2):
|
|
954
|
def moving_average(self,x, N=2):
|
|
|
955
|
""" convolution for smoothenig data. note that last N-1 values are convolution with zeroes """
|
|
955
|
""" convolution for smoothenig data. note that last N-1 values are convolution with zeroes """
|
|
|
956
|
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
|
|
956
|
return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
|
|
|
957
|
|
|
957
|
|
|
|
958
|
def gaus(self,xSamples,Amp,Mu,Sigma):
|
|
958
|
def gaus(self,xSamples,Amp,Mu,Sigma):
|
|
|
959
|
return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2)
|
|
959
|
return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2)
|
|
|
960
|
|
|
960
|
|
|
|
961
|
def Moments(self, ySamples, xSamples):
|
|
961
|
def Moments(self, ySamples, xSamples):
|
|
|
962
|
Power = numpy.nanmean(ySamples) # Power, 0th Moment
|
|
962
|
Power = numpy.nanmean(ySamples) # Power, 0th Moment
|
|
|
963
|
yNorm = ySamples / numpy.nansum(ySamples)
|
|
963
|
yNorm = ySamples / numpy.nansum(ySamples)
|
|
|
964
|
RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment
|
|
964
|
RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment
|
|
|
965
|
Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment
|
|
965
|
Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment
|
|
|
966
|
StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral
|
|
966
|
StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral
|
|
|
967
|
return numpy.array([Power,RadVel,StdDev])
|
|
967
|
return numpy.array([Power,RadVel,StdDev])
|
|
|
968
|
|
|
968
|
|
|
|
969
|
def StopWindEstimation(self, error_code):
|
|
969
|
def StopWindEstimation(self, error_code):
|
|
|
970
|
Vzon = numpy.NaN
|
|
970
|
Vzon = numpy.NaN
|
|
|
971
|
Vmer = numpy.NaN
|
|
971
|
Vmer = numpy.NaN
|
|
|
972
|
Vver = numpy.NaN
|
|
972
|
Vver = numpy.NaN
|
|
|
973
|
return Vzon, Vmer, Vver, error_code
|
|
973
|
return Vzon, Vmer, Vver, error_code
|
|
|
974
|
|
|
974
|
|
|
|
975
|
def AntiAliasing(self, interval, maxstep):
|
|
975
|
def AntiAliasing(self, interval, maxstep):
|
|
|
976
|
"""
|
|
976
|
"""
|
|
|
977
|
function to prevent errors from aliased values when computing phaseslope
|
|
977
|
function to prevent errors from aliased values when computing phaseslope
|
|
|
978
|
"""
|
|
978
|
"""
|
|
|
979
|
antialiased = numpy.zeros(len(interval))
|
|
979
|
antialiased = numpy.zeros(len(interval))
|
|
|
980
|
copyinterval = interval.copy()
|
|
980
|
copyinterval = interval.copy()
|
|
|
981
|
|
|
981
|
|
|
|
982
|
antialiased[0] = copyinterval[0]
|
|
982
|
antialiased[0] = copyinterval[0]
|
|
|
983
|
|
|
983
|
|
|
|
984
|
for i in range(1,len(antialiased)):
|
|
984
|
for i in range(1,len(antialiased)):
|
|
|
985
|
step = interval[i] - interval[i-1]
|
|
985
|
step = interval[i] - interval[i-1]
|
|
|
986
|
if step > maxstep:
|
|
986
|
if step > maxstep:
|
|
|
987
|
copyinterval -= 2*numpy.pi
|
|
987
|
copyinterval -= 2*numpy.pi
|
|
|
988
|
antialiased[i] = copyinterval[i]
|
|
988
|
antialiased[i] = copyinterval[i]
|
|
|
989
|
elif step < maxstep*(-1):
|
|
989
|
elif step < maxstep*(-1):
|
|
|
990
|
copyinterval += 2*numpy.pi
|
|
990
|
copyinterval += 2*numpy.pi
|
|
|
991
|
antialiased[i] = copyinterval[i]
|
|
991
|
antialiased[i] = copyinterval[i]
|
|
|
992
|
else:
|
|
992
|
else:
|
|
|
993
|
antialiased[i] = copyinterval[i].copy()
|
|
993
|
antialiased[i] = copyinterval[i].copy()
|
|
|
994
|
|
|
994
|
|
|
|
995
|
return antialiased
|
|
995
|
return antialiased
|
|
|
996
|
|
|
996
|
|
|
|
997
|
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq):
|
|
997
|
def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq):
|
|
|
998
|
"""
|
|
998
|
"""
|
|
|
999
|
Function that Calculates Zonal, Meridional and Vertical wind velocities.
|
|
999
|
Function that Calculates Zonal, Meridional and Vertical wind velocities.
|
|
|
1000
|
Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019.
|
|
1000
|
Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019.
|
|
|
1001
|
|
|
1001
|
|
|
|
1002
|
Input:
|
|
1002
|
Input:
|
|
|
1003
|
spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively.
|
|
1003
|
spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively.
|
|
|
1004
|
pairsList : Pairlist of channels
|
|
1004
|
pairsList : Pairlist of channels
|
|
|
1005
|
ChanDist : array of xi_ij and eta_ij
|
|
1005
|
ChanDist : array of xi_ij and eta_ij
|
|
|
1006
|
Height : height at which data is processed
|
|
1006
|
Height : height at which data is processed
|
|
|
1007
|
noise : noise in [channels] format for specific height
|
|
1007
|
noise : noise in [channels] format for specific height
|
|
|
1008
|
Abbsisarange : range of the frequencies or velocities
|
|
1008
|
Abbsisarange : range of the frequencies or velocities
|
|
|
1009
|
dbSNR, SNRlimit : signal to noise ratio in db, lower limit
|
|
1009
|
dbSNR, SNRlimit : signal to noise ratio in db, lower limit
|
|
|
1010
|
|
|
1010
|
|
|
|
1011
|
Output:
|
|
1011
|
Output:
|
|
|
1012
|
Vzon, Vmer, Vver : wind velocities
|
|
1012
|
Vzon, Vmer, Vver : wind velocities
|
|
|
1013
|
error_code : int that states where code is terminated
|
|
1013
|
error_code : int that states where code is terminated
|
|
|
1014
|
|
|
1014
|
|
|
|
1015
|
0 : no error detected
|
|
1015
|
0 : no error detected
|
|
|
1016
|
1 : Gaussian of mean spc exceeds widthlimit
|
|
1016
|
1 : Gaussian of mean spc exceeds widthlimit
|
|
|
1017
|
2 : no Gaussian of mean spc found
|
|
1017
|
2 : no Gaussian of mean spc found
|
|
|
1018
|
3 : SNR to low or velocity to high -> prec. e.g.
|
|
1018
|
3 : SNR to low or velocity to high -> prec. e.g.
|
|
|
1019
|
4 : at least one Gaussian of cspc exceeds widthlimit
|
|
1019
|
4 : at least one Gaussian of cspc exceeds widthlimit
|
|
|
1020
|
5 : zero out of three cspc Gaussian fits converged
|
|
1020
|
5 : zero out of three cspc Gaussian fits converged
|
|
|
1021
|
6 : phase slope fit could not be found
|
|
1021
|
6 : phase slope fit could not be found
|
|
|
1022
|
7 : arrays used to fit phase have different length
|
|
1022
|
7 : arrays used to fit phase have different length
|
|
|
1023
|
8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc)
|
|
1023
|
8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc)
|
|
|
1024
|
|
|
1024
|
|
|
|
1025
|
"""
|
|
1025
|
"""
|
|
|
1026
|
|
|
1026
|
|
|
|
1027
|
error_code = 0
|
|
1027
|
error_code = 0
|
|
|
1028
|
|
|
1028
|
|
|
|
1029
|
nChan = spc.shape[0]
|
|
1029
|
nChan = spc.shape[0]
|
|
|
1030
|
nProf = spc.shape[1]
|
|
1030
|
nProf = spc.shape[1]
|
|
|
1031
|
nPair = cspc.shape[0]
|
|
1031
|
nPair = cspc.shape[0]
|
|
|
1032
|
|
|
1032
|
|
|
|
1033
|
SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height
|
|
1033
|
SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height
|
|
|
1034
|
CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values
|
|
1034
|
CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values
|
|
|
1035
|
phase = numpy.zeros([nPair, nProf]) # phase between channels
|
|
1035
|
phase = numpy.zeros([nPair, nProf]) # phase between channels
|
|
|
1036
|
PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise
|
|
1036
|
PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise
|
|
|
1037
|
PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise
|
|
1037
|
PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise
|
|
|
1038
|
xFrec = AbbsisaRange[0][:-1] # frequency range
|
|
1038
|
xFrec = AbbsisaRange[0][:-1] # frequency range
|
|
|
1039
|
xVel = AbbsisaRange[2][:-1] # velocity range
|
|
1039
|
xVel = AbbsisaRange[2][:-1] # velocity range
|
|
|
1040
|
xSamples = xFrec # the frequency range is taken
|
|
1040
|
xSamples = xFrec # the frequency range is taken
|
|
|
1041
|
delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x
|
|
1041
|
delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x
|
|
|
1042
|
|
|
1042
|
|
|
|
1043
|
# only consider velocities with in NegativeLimit and PositiveLimit
|
|
1043
|
# only consider velocities with in NegativeLimit and PositiveLimit
|
|
|
1044
|
if (NegativeLimit is None):
|
|
1044
|
if (NegativeLimit is None):
|
|
|
1045
|
NegativeLimit = numpy.min(xVel)
|
|
1045
|
NegativeLimit = numpy.min(xVel)
|
|
|
1046
|
if (PositiveLimit is None):
|
|
1046
|
if (PositiveLimit is None):
|
|
|
1047
|
PositiveLimit = numpy.max(xVel)
|
|
1047
|
PositiveLimit = numpy.max(xVel)
|
|
|
1048
|
xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit))
|
|
1048
|
xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit))
|
|
|
1049
|
xSamples_zoom = xSamples[xvalid]
|
|
1049
|
xSamples_zoom = xSamples[xvalid]
|
|
|
1050
|
|
|
1050
|
|
|
|
1051
|
'''Getting Eij and Nij'''
|
|
1051
|
'''Getting Eij and Nij'''
|
|
|
1052
|
Xi01, Xi02, Xi12 = ChanDist[:,0]
|
|
1052
|
Xi01, Xi02, Xi12 = ChanDist[:,0]
|
|
|
1053
|
Eta01, Eta02, Eta12 = ChanDist[:,1]
|
|
1053
|
Eta01, Eta02, Eta12 = ChanDist[:,1]
|
|
|
1054
|
|
|
1054
|
|
|
|
1055
|
# spwd limit - updated by D. ScipiΓ³n 30.03.2021
|
|
1055
|
# spwd limit - updated by D. ScipiΓ³n 30.03.2021
|
|
|
1056
|
widthlimit = 10
|
|
1056
|
widthlimit = 10
|
|
|
1057
|
'''************************* SPC is normalized ********************************'''
|
|
1057
|
'''************************* SPC is normalized ********************************'''
|
|
|
1058
|
spc_norm = spc.copy()
|
|
1058
|
spc_norm = spc.copy()
|
|
|
1059
|
# For each channel
|
|
1059
|
# For each channel
|
|
|
1060
|
for i in range(nChan):
|
|
1060
|
for i in range(nChan):
|
|
|
1061
|
spc_sub = spc_norm[i,:] - noise[i] # only the signal power
|
|
1061
|
spc_sub = spc_norm[i,:] - noise[i] # only the signal power
|
|
|
1062
|
SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x)
|
|
1062
|
SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x)
|
|
|
1063
|
|
|
1063
|
|
|
|
1064
|
'''********************** FITTING MEAN SPC GAUSSIAN **********************'''
|
|
1064
|
'''********************** FITTING MEAN SPC GAUSSIAN **********************'''
|
|
|
1065
|
|
|
1065
|
|
|
|
1066
|
""" the gaussian of the mean: first subtract noise, then normalize. this is legal because
|
|
1066
|
""" the gaussian of the mean: first subtract noise, then normalize. this is legal because
|
|
|
1067
|
you only fit the curve and don't need the absolute value of height for calculation,
|
|
1067
|
you only fit the curve and don't need the absolute value of height for calculation,
|
|
|
1068
|
only for estimation of width. for normalization of cross spectra, you need initial,
|
|
1068
|
only for estimation of width. for normalization of cross spectra, you need initial,
|
|
|
1069
|
unnormalized self-spectra With noise.
|
|
1069
|
unnormalized self-spectra With noise.
|
|
|
1070
|
|
|
1070
|
|
|
|
1071
|
Technically, you don't even need to normalize the self-spectra, as you only need the
|
|
1071
|
Technically, you don't even need to normalize the self-spectra, as you only need the
|
|
|
1072
|
width of the peak. However, it was left this way. Note that the normalization has a flaw:
|
|
1072
|
width of the peak. However, it was left this way. Note that the normalization has a flaw:
|
|
|
1073
|
due to subtraction of the noise, some values are below zero. Raw "spc" values should be
|
|
1073
|
due to subtraction of the noise, some values are below zero. Raw "spc" values should be
|
|
|
1074
|
>= 0, as it is the modulus squared of the signals (complex * it's conjugate)
|
|
1074
|
>= 0, as it is the modulus squared of the signals (complex * it's conjugate)
|
|
|
1075
|
"""
|
|
1075
|
"""
|
|
|
1076
|
# initial conditions
|
|
1076
|
# initial conditions
|
|
|
1077
|
popt = [1e-10,0,1e-10]
|
|
1077
|
popt = [1e-10,0,1e-10]
|
|
|
1078
|
# Spectra average
|
|
1078
|
# Spectra average
|
|
|
1079
|
SPCMean = numpy.average(SPC_Samples,0)
|
|
1079
|
SPCMean = numpy.average(SPC_Samples,0)
|
|
|
1080
|
# Moments in frequency
|
|
1080
|
# Moments in frequency
|
|
|
1081
|
SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom)
|
|
1081
|
SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom)
|
|
|
1082
|
|
|
1082
|
|
|
|
1083
|
# Gauss Fit SPC in frequency domain
|
|
1083
|
# Gauss Fit SPC in frequency domain
|
|
|
1084
|
if dbSNR > SNRlimit: # only if SNR > SNRth
|
|
1084
|
if dbSNR > SNRlimit: # only if SNR > SNRth
|
|
|
1085
|
try:
|
|
1085
|
try:
|
|
|
1086
|
popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments)
|
|
1086
|
popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments)
|
|
|
1087
|
if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION
|
|
1087
|
if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION
|
|
|
1088
|
return self.StopWindEstimation(error_code = 1)
|
|
1088
|
return self.StopWindEstimation(error_code = 1)
|
|
|
1089
|
FitGauss = self.gaus(xSamples_zoom,*popt)
|
|
1089
|
FitGauss = self.gaus(xSamples_zoom,*popt)
|
|
|
1090
|
except :#RuntimeError:
|
|
1090
|
except :#RuntimeError:
|
|
|
1091
|
return self.StopWindEstimation(error_code = 2)
|
|
1091
|
return self.StopWindEstimation(error_code = 2)
|
|
|
1092
|
else:
|
|
1092
|
else:
|
|
|
1093
|
return self.StopWindEstimation(error_code = 3)
|
|
1093
|
return self.StopWindEstimation(error_code = 3)
|
|
|
1094
|
|
|
1094
|
|
|
|
1095
|
'''***************************** CSPC Normalization *************************
|
|
1095
|
'''***************************** CSPC Normalization *************************
|
|
|
1096
|
The Spc spectra are used to normalize the crossspectra. Peaks from precipitation
|
|
1096
|
The Spc spectra are used to normalize the crossspectra. Peaks from precipitation
|
|
|
1097
|
influence the norm which is not desired. First, a range is identified where the
|
|
1097
|
influence the norm which is not desired. First, a range is identified where the
|
|
|
1098
|
wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area
|
|
1098
|
wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area
|
|
|
1099
|
around it gets cut off and values replaced by mean determined by the boundary
|
|
1099
|
around it gets cut off and values replaced by mean determined by the boundary
|
|
|
1100
|
data -> sum_noise (spc is not normalized here, thats why the noise is important)
|
|
1100
|
data -> sum_noise (spc is not normalized here, thats why the noise is important)
|
|
|
1101
|
|
|
1101
|
|
|
|
1102
|
The sums are then added and multiplied by range/datapoints, because you need
|
|
1102
|
The sums are then added and multiplied by range/datapoints, because you need
|
|
|
1103
|
an integral and not a sum for normalization.
|
|
1103
|
an integral and not a sum for normalization.
|
|
|
1104
|
|
|
1104
|
|
|
|
1105
|
A norm is found according to Briggs 92.
|
|
1105
|
A norm is found according to Briggs 92.
|
|
|
1106
|
'''
|
|
1106
|
'''
|
|
|
1107
|
# for each pair
|
|
1107
|
# for each pair
|
|
|
1108
|
for i in range(nPair):
|
|
1108
|
for i in range(nPair):
|
|
|
1109
|
cspc_norm = cspc[i,:].copy()
|
|
1109
|
cspc_norm = cspc[i,:].copy()
|
|
|
1110
|
chan_index0 = pairsList[i][0]
|
|
1110
|
chan_index0 = pairsList[i][0]
|
|
|
1111
|
chan_index1 = pairsList[i][1]
|
|
1111
|
chan_index1 = pairsList[i][1]
|
|
|
1112
|
CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x)
|
|
1112
|
CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x)
|
|
|
1113
|
phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real)
|
|
1113
|
phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real)
|
|
|
1114
|
|
|
1114
|
|
|
|
1115
|
CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom),
|
|
1115
|
CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom),
|
|
|
1116
|
self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom),
|
|
1116
|
self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom),
|
|
|
1117
|
self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)])
|
|
1117
|
self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)])
|
|
|
1118
|
|
|
1118
|
|
|
|
1119
|
popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10]
|
|
1119
|
popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10]
|
|
|
1120
|
FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples))
|
|
1120
|
FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples))
|
|
|
1121
|
|
|
1121
|
|
|
|
1122
|
'''*******************************FIT GAUSS CSPC************************************'''
|
|
1122
|
'''*******************************FIT GAUSS CSPC************************************'''
|
|
|
1123
|
try:
|
|
1123
|
try:
|
|
|
1124
|
popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0])
|
|
1124
|
popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0])
|
|
|
1125
|
if popt01[2] > widthlimit: # CONDITION
|
|
1125
|
if popt01[2] > widthlimit: # CONDITION
|
|
|
1126
|
return self.StopWindEstimation(error_code = 4)
|
|
1126
|
return self.StopWindEstimation(error_code = 4)
|
|
|
1127
|
popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1])
|
|
1127
|
popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1])
|
|
|
1128
|
if popt02[2] > widthlimit: # CONDITION
|
|
1128
|
if popt02[2] > widthlimit: # CONDITION
|
|
|
1129
|
return self.StopWindEstimation(error_code = 4)
|
|
1129
|
return self.StopWindEstimation(error_code = 4)
|
|
|
1130
|
popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2])
|
|
1130
|
popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2])
|
|
|
1131
|
if popt12[2] > widthlimit: # CONDITION
|
|
1131
|
if popt12[2] > widthlimit: # CONDITION
|
|
|
1132
|
return self.StopWindEstimation(error_code = 4)
|
|
1132
|
return self.StopWindEstimation(error_code = 4)
|
|
|
1133
|
|
|
1133
|
|
|
|
1134
|
FitGauss01 = self.gaus(xSamples_zoom, *popt01)
|
|
1134
|
FitGauss01 = self.gaus(xSamples_zoom, *popt01)
|
|
|
1135
|
FitGauss02 = self.gaus(xSamples_zoom, *popt02)
|
|
1135
|
FitGauss02 = self.gaus(xSamples_zoom, *popt02)
|
|
|
1136
|
FitGauss12 = self.gaus(xSamples_zoom, *popt12)
|
|
1136
|
FitGauss12 = self.gaus(xSamples_zoom, *popt12)
|
|
|
1137
|
except:
|
|
1137
|
except:
|
|
|
1138
|
return self.StopWindEstimation(error_code = 5)
|
|
1138
|
return self.StopWindEstimation(error_code = 5)
|
|
|
1139
|
|
|
1139
|
|
|
|
1140
|
|
|
1140
|
|
|
|
1141
|
'''************* Getting Fij ***************'''
|
|
1141
|
'''************* Getting Fij ***************'''
|
|
|
1142
|
# x-axis point of the gaussian where the center is located from GaussFit of spectra
|
|
1142
|
# x-axis point of the gaussian where the center is located from GaussFit of spectra
|
|
|
1143
|
GaussCenter = popt[1]
|
|
1143
|
GaussCenter = popt[1]
|
|
|
1144
|
ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()]
|
|
1144
|
ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()]
|
|
|
1145
|
PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0]
|
|
1145
|
PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0]
|
|
|
1146
|
|
|
1146
|
|
|
|
1147
|
# Point where e^-1 is located in the gaussian
|
|
1147
|
# Point where e^-1 is located in the gaussian
|
|
|
1148
|
PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1)
|
|
1148
|
PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1)
|
|
|
1149
|
FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss"
|
|
1149
|
FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss"
|
|
|
1150
|
PointFij = numpy.where(FitGauss==FijClosest)[0][0]
|
|
1150
|
PointFij = numpy.where(FitGauss==FijClosest)[0][0]
|
|
|
1151
|
Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter])
|
|
1151
|
Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter])
|
|
|
1152
|
|
|
1152
|
|
|
|
1153
|
'''********** Taking frequency ranges from mean SPCs **********'''
|
|
1153
|
'''********** Taking frequency ranges from mean SPCs **********'''
|
|
|
1154
|
GauWidth = popt[2] * 3/2 # Bandwidth of Gau01
|
|
1154
|
GauWidth = popt[2] * 3/2 # Bandwidth of Gau01
|
|
|
1155
|
Range = numpy.empty(2)
|
|
1155
|
Range = numpy.empty(2)
|
|
|
1156
|
Range[0] = GaussCenter - GauWidth
|
|
1156
|
Range[0] = GaussCenter - GauWidth
|
|
|
1157
|
Range[1] = GaussCenter + GauWidth
|
|
1157
|
Range[1] = GaussCenter + GauWidth
|
|
|
1158
|
# Point in x-axis where the bandwidth is located (min:max)
|
|
1158
|
# Point in x-axis where the bandwidth is located (min:max)
|
|
|
1159
|
ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()]
|
|
1159
|
ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()]
|
|
|
1160
|
ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()]
|
|
1160
|
ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()]
|
|
|
1161
|
PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0]
|
|
1161
|
PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0]
|
|
|
1162
|
PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0]
|
|
1162
|
PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0]
|
|
|
1163
|
Range = numpy.array([ PointRangeMin, PointRangeMax ])
|
|
1163
|
Range = numpy.array([ PointRangeMin, PointRangeMax ])
|
|
|
1164
|
FrecRange = xSamples_zoom[ Range[0] : Range[1] ]
|
|
1164
|
FrecRange = xSamples_zoom[ Range[0] : Range[1] ]
|
|
|
1165
|
|
|
1165
|
|
|
|
1166
|
'''************************** Getting Phase Slope ***************************'''
|
|
1166
|
'''************************** Getting Phase Slope ***************************'''
|
|
|
1167
|
for i in range(nPair):
|
|
1167
|
for i in range(nPair):
|
|
|
1168
|
if len(FrecRange) > 5:
|
|
1168
|
if len(FrecRange) > 5:
|
|
|
1169
|
PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy()
|
|
1169
|
PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy()
|
|
|
1170
|
mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange)
|
|
1170
|
mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange)
|
|
|
1171
|
if len(FrecRange) == len(PhaseRange):
|
|
1171
|
if len(FrecRange) == len(PhaseRange):
|
|
|
1172
|
try:
|
|
1172
|
try:
|
|
|
1173
|
slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5))
|
|
1173
|
slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5))
|
|
|
1174
|
PhaseSlope[i] = slope
|
|
1174
|
PhaseSlope[i] = slope
|
|
|
1175
|
PhaseInter[i] = intercept
|
|
1175
|
PhaseInter[i] = intercept
|
|
|
1176
|
except:
|
|
1176
|
except:
|
|
|
1177
|
return self.StopWindEstimation(error_code = 6)
|
|
1177
|
return self.StopWindEstimation(error_code = 6)
|
|
|
1178
|
else:
|
|
1178
|
else:
|
|
|
1179
|
return self.StopWindEstimation(error_code = 7)
|
|
1179
|
return self.StopWindEstimation(error_code = 7)
|
|
|
1180
|
else:
|
|
1180
|
else:
|
|
|
1181
|
return self.StopWindEstimation(error_code = 8)
|
|
1181
|
return self.StopWindEstimation(error_code = 8)
|
|
|
1182
|
|
|
1182
|
|
|
|
1183
|
'''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***'''
|
|
1183
|
'''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***'''
|
|
|
1184
|
|
|
1184
|
|
|
|
1185
|
'''Getting constant C'''
|
|
1185
|
'''Getting constant C'''
|
|
|
1186
|
cC=(Fij*numpy.pi)**2
|
|
1186
|
cC=(Fij*numpy.pi)**2
|
|
|
1187
|
|
|
1187
|
|
|
|
1188
|
'''****** Getting constants F and G ******'''
|
|
1188
|
'''****** Getting constants F and G ******'''
|
|
|
1189
|
MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]])
|
|
1189
|
MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]])
|
|
|
1190
|
# MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]])
|
|
1190
|
# MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]])
|
|
|
1191
|
# MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi)
|
|
1191
|
# MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi)
|
|
|
1192
|
MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi)
|
|
1192
|
MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi)
|
|
|
1193
|
MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi)
|
|
1193
|
MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi)
|
|
|
1194
|
# MijResults = numpy.array([MijResult0, MijResult1, MijResult2])
|
|
1194
|
# MijResults = numpy.array([MijResult0, MijResult1, MijResult2])
|
|
|
1195
|
MijResults = numpy.array([MijResult1, MijResult2])
|
|
1195
|
MijResults = numpy.array([MijResult1, MijResult2])
|
|
|
1196
|
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
|
|
1196
|
(cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
|
|
|
1197
|
|
|
1197
|
|
|
|
1198
|
'''****** Getting constants A, B and H ******'''
|
|
1198
|
'''****** Getting constants A, B and H ******'''
|
|
|
1199
|
W01 = numpy.nanmax( FitGauss01 )
|
|
1199
|
W01 = numpy.nanmax( FitGauss01 )
|
|
|
1200
|
W02 = numpy.nanmax( FitGauss02 )
|
|
1200
|
W02 = numpy.nanmax( FitGauss02 )
|
|
|
1201
|
W12 = numpy.nanmax( FitGauss12 )
|
|
1201
|
W12 = numpy.nanmax( FitGauss12 )
|
|
|
1202
|
|
|
1202
|
|
|
|
1203
|
WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC))
|
|
1203
|
WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC))
|
|
|
1204
|
WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC))
|
|
1204
|
WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC))
|
|
|
1205
|
WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC))
|
|
1205
|
WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC))
|
|
|
1206
|
WijResults = numpy.array([WijResult01, WijResult02, WijResult12])
|
|
1206
|
WijResults = numpy.array([WijResult01, WijResult02, WijResult12])
|
|
|
1207
|
|
|
1207
|
|
|
|
1208
|
WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ])
|
|
1208
|
WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ])
|
|
|
1209
|
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
|
|
1209
|
(cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
|
|
|
1210
|
|
|
1210
|
|
|
|
1211
|
VxVy = numpy.array([[cA,cH],[cH,cB]])
|
|
1211
|
VxVy = numpy.array([[cA,cH],[cH,cB]])
|
|
|
1212
|
VxVyResults = numpy.array([-cF,-cG])
|
|
1212
|
VxVyResults = numpy.array([-cF,-cG])
|
|
|
1213
|
(Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults)
|
|
1213
|
(Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults)
|
|
|
1214
|
Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq)
|
|
1214
|
Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq)
|
|
|
1215
|
error_code = 0
|
|
1215
|
error_code = 0
|
|
|
1216
|
|
|
1216
|
|
|
|
1217
|
return Vzon, Vmer, Vver, error_code
|
|
1217
|
return Vzon, Vmer, Vver, error_code
|
|
|
1218
|
|
|
1218
|
|
|
|
1219
|
class SpectralMoments(Operation):
|
|
1219
|
class SpectralMoments(Operation):
|
|
|
1220
|
|
|
1220
|
|
|
|
1221
|
'''
|
|
1221
|
'''
|
|
|
1222
|
Function SpectralMoments()
|
|
1222
|
Function SpectralMoments()
|
|
|
1223
|
|
|
1223
|
|
|
|
1224
|
Calculates moments (power, mean, standard deviation) and SNR of the signal
|
|
1224
|
Calculates moments (power, mean, standard deviation) and SNR of the signal
|
|
|
1225
|
|
|
1225
|
|
|
|
1226
|
Type of dataIn: Spectra
|
|
1226
|
Type of dataIn: Spectra
|
|
|
1227
|
|
|
1227
|
|
|
|
1228
|
Configuration Parameters:
|
|
1228
|
Configuration Parameters:
|
|
|
1229
|
|
|
1229
|
|
|
|
1230
|
dirCosx : Cosine director in X axis
|
|
1230
|
dirCosx : Cosine director in X axis
|
|
|
1231
|
dirCosy : Cosine director in Y axis
|
|
1231
|
dirCosy : Cosine director in Y axis
|
|
|
1232
|
|
|
1232
|
|
|
|
1233
|
elevation :
|
|
1233
|
elevation :
|
|
|
1234
|
azimuth :
|
|
1234
|
azimuth :
|
|
|
1235
|
|
|
1235
|
|
|
|
1236
|
Input:
|
|
1236
|
Input:
|
|
|
1237
|
channelList : simple channel list to select e.g. [2,3,7]
|
|
1237
|
channelList : simple channel list to select e.g. [2,3,7]
|
|
|
1238
|
self.dataOut.data_pre : Spectral data
|
|
1238
|
self.dataOut.data_pre : Spectral data
|
|
|
1239
|
self.dataOut.abscissaList : List of frequencies
|
|
1239
|
self.dataOut.abscissaList : List of frequencies
|
|
|
1240
|
self.dataOut.noise : Noise level per channel
|
|
1240
|
self.dataOut.noise : Noise level per channel
|
|
|
1241
|
|
|
1241
|
|
|
|
1242
|
Affected:
|
|
1242
|
Affected:
|
|
|
1243
|
self.dataOut.moments : Parameters per channel
|
|
1243
|
self.dataOut.moments : Parameters per channel
|
|
|
1244
|
self.dataOut.data_snr : SNR per channel
|
|
1244
|
self.dataOut.data_snr : SNR per channel
|
|
|
1245
|
|
|
1245
|
|
|
|
1246
|
'''
|
|
1246
|
'''
|
|
|
1247
|
|
|
1247
|
|
|
|
1248
|
def run(self, dataOut):
|
|
1248
|
def run(self, dataOut):
|
|
|
1249
|
|
|
1249
|
|
|
|
1250
|
data = dataOut.data_pre[0]
|
|
1250
|
data = dataOut.data_pre[0]
|
|
|
1251
|
absc = dataOut.abscissaList[:-1]
|
|
1251
|
absc = dataOut.abscissaList[:-1]
|
|
|
1252
|
noise = dataOut.noise
|
|
1252
|
noise = dataOut.noise
|
|
|
1253
|
nChannel = data.shape[0]
|
|
1253
|
nChannel = data.shape[0]
|
|
|
1254
|
data_param = numpy.zeros((nChannel, 4, data.shape[2]))
|
|
1254
|
data_param = numpy.zeros((nChannel, 4, data.shape[2]))
|
|
|
1255
|
|
|
1255
|
|
|
|
1256
|
for ind in range(nChannel):
|
|
1256
|
for ind in range(nChannel):
|
|
|
1257
|
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
|
|
1257
|
data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
|
|
|
1258
|
|
|
1258
|
|
|
|
1259
|
dataOut.moments = data_param[:,1:,:]
|
|
1259
|
dataOut.moments = data_param[:,1:,:]
|
|
|
1260
|
dataOut.data_snr = data_param[:,0]
|
|
1260
|
dataOut.data_snr = data_param[:,0]
|
|
|
1261
|
dataOut.data_pow = data_param[:,1]
|
|
1261
|
dataOut.data_pow = data_param[:,1]
|
|
|
1262
|
dataOut.data_dop = data_param[:,2]
|
|
1262
|
dataOut.data_dop = data_param[:,2]
|
|
|
1263
|
dataOut.data_width = data_param[:,3]
|
|
1263
|
dataOut.data_width = data_param[:,3]
|
|
|
1264
|
return dataOut
|
|
1264
|
return dataOut
|
|
|
1265
|
|
|
1265
|
|
|
|
1266
|
def __calculateMoments(self, oldspec, oldfreq, n0,
|
|
1266
|
def __calculateMoments(self, oldspec, oldfreq, n0,
|
|
|
1267
|
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
|
|
1267
|
nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
|
|
|
1268
|
|
|
1268
|
|
|
|
1269
|
if (nicoh is None): nicoh = 1
|
|
1269
|
if (nicoh is None): nicoh = 1
|
|
|
1270
|
if (graph is None): graph = 0
|
|
1270
|
if (graph is None): graph = 0
|
|
|
1271
|
if (smooth is None): smooth = 0
|
|
1271
|
if (smooth is None): smooth = 0
|
|
|
1272
|
elif (self.smooth < 3): smooth = 0
|
|
1272
|
elif (self.smooth < 3): smooth = 0
|
|
|
1273
|
|
|
1273
|
|
|
|
1274
|
if (type1 is None): type1 = 0
|
|
1274
|
if (type1 is None): type1 = 0
|
|
|
1275
|
if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1
|
|
1275
|
if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1
|
|
|
1276
|
if (snrth is None): snrth = -3
|
|
1276
|
if (snrth is None): snrth = -3
|
|
|
1277
|
if (dc is None): dc = 0
|
|
1277
|
if (dc is None): dc = 0
|
|
|
1278
|
if (aliasing is None): aliasing = 0
|
|
1278
|
if (aliasing is None): aliasing = 0
|
|
|
1279
|
if (oldfd is None): oldfd = 0
|
|
1279
|
if (oldfd is None): oldfd = 0
|
|
|
1280
|
if (wwauto is None): wwauto = 0
|
|
1280
|
if (wwauto is None): wwauto = 0
|
|
|
1281
|
|
|
1281
|
|
|
|
1282
|
if (n0 < 1.e-20): n0 = 1.e-20
|
|
1282
|
if (n0 < 1.e-20): n0 = 1.e-20
|
|
|
1283
|
|
|
1283
|
|
|
|
1284
|
freq = oldfreq
|
|
1284
|
freq = oldfreq
|
|
|
1285
|
vec_power = numpy.zeros(oldspec.shape[1])
|
|
1285
|
vec_power = numpy.zeros(oldspec.shape[1])
|
|
|
1286
|
vec_fd = numpy.zeros(oldspec.shape[1])
|
|
1286
|
vec_fd = numpy.zeros(oldspec.shape[1])
|
|
|
1287
|
vec_w = numpy.zeros(oldspec.shape[1])
|
|
1287
|
vec_w = numpy.zeros(oldspec.shape[1])
|
|
|
1288
|
vec_snr = numpy.zeros(oldspec.shape[1])
|
|
1288
|
vec_snr = numpy.zeros(oldspec.shape[1])
|
|
|
1289
|
|
|
1289
|
|
|
|
1290
|
# oldspec = numpy.ma.masked_invalid(oldspec)
|
|
1290
|
# oldspec = numpy.ma.masked_invalid(oldspec)
|
|
|
1291
|
for ind in range(oldspec.shape[1]):
|
|
1291
|
for ind in range(oldspec.shape[1]):
|
|
|
1292
|
|
|
1292
|
|
|
|
1293
|
spec = oldspec[:,ind]
|
|
1293
|
spec = oldspec[:,ind]
|
|
|
1294
|
aux = spec*fwindow
|
|
1294
|
aux = spec*fwindow
|
|
|
1295
|
max_spec = aux.max()
|
|
1295
|
max_spec = aux.max()
|
|
|
1296
|
m = aux.tolist().index(max_spec)
|
|
1296
|
m = aux.tolist().index(max_spec)
|
|
|
1297
|
|
|
1297
|
|
|
|
1298
|
# Smooth
|
|
1298
|
# Smooth
|
|
|
1299
|
if (smooth == 0):
|
|
1299
|
if (smooth == 0):
|
|
|
1300
|
spec2 = spec
|
|
1300
|
spec2 = spec
|
|
|
1301
|
else:
|
|
1301
|
else:
|
|
|
1302
|
spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
|
|
1302
|
spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
|
|
|
1303
|
|
|
1303
|
|
|
|
1304
|
# Moments Estimation
|
|
1304
|
# Moments Estimation
|
|
|
1305
|
bb = spec2[numpy.arange(m,spec2.size)]
|
|
1305
|
bb = spec2[numpy.arange(m,spec2.size)]
|
|
|
1306
|
bb = (bb<n0).nonzero()
|
|
1306
|
bb = (bb<n0).nonzero()
|
|
|
1307
|
bb = bb[0]
|
|
1307
|
bb = bb[0]
|
|
|
1308
|
|
|
1308
|
|
|
|
1309
|
ss = spec2[numpy.arange(0,m + 1)]
|
|
1309
|
ss = spec2[numpy.arange(0,m + 1)]
|
|
|
1310
|
ss = (ss<n0).nonzero()
|
|
1310
|
ss = (ss<n0).nonzero()
|
|
|
1311
|
ss = ss[0]
|
|
1311
|
ss = ss[0]
|
|
|
1312
|
|
|
1312
|
|
|
|
1313
|
if (bb.size == 0):
|
|
1313
|
if (bb.size == 0):
|
|
|
1314
|
bb0 = spec.size - 1 - m
|
|
1314
|
bb0 = spec.size - 1 - m
|
|
|
1315
|
else:
|
|
1315
|
else:
|
|
|
1316
|
bb0 = bb[0] - 1
|
|
1316
|
bb0 = bb[0] - 1
|
|
|
1317
|
if (bb0 < 0):
|
|
1317
|
if (bb0 < 0):
|
|
|
1318
|
bb0 = 0
|
|
1318
|
bb0 = 0
|
|
|
1319
|
|
|
1319
|
|
|
|
1320
|
if (ss.size == 0):
|
|
1320
|
if (ss.size == 0):
|
|
|
1321
|
ss1 = 1
|
|
1321
|
ss1 = 1
|
|
|
1322
|
else:
|
|
1322
|
else:
|
|
|
1323
|
ss1 = max(ss) + 1
|
|
1323
|
ss1 = max(ss) + 1
|
|
|
1324
|
|
|
1324
|
|
|
|
1325
|
if (ss1 > m):
|
|
1325
|
if (ss1 > m):
|
|
|
1326
|
ss1 = m
|
|
1326
|
ss1 = m
|
|
|
1327
|
|
|
1327
|
|
|
|
1328
|
#valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1
|
|
1328
|
#valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1
|
|
|
1329
|
valid = numpy.arange(1,oldspec.shape[0])# valid perfil completo igual pulsepair
|
|
1329
|
valid = numpy.arange(1,oldspec.shape[0])# valid perfil completo igual pulsepair
|
|
|
1330
|
signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition
|
|
1330
|
signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition
|
|
|
1331
|
total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition
|
|
1331
|
total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition
|
|
|
1332
|
power = ((spec2[valid] - n0) * fwindow[valid]).sum()
|
|
1332
|
power = ((spec2[valid] - n0) * fwindow[valid]).sum()
|
|
|
1333
|
fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
|
|
1333
|
fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
|
|
|
1334
|
w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
|
|
1334
|
w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
|
|
|
1335
|
snr = (spec2.mean()-n0)/n0
|
|
1335
|
snr = (spec2.mean()-n0)/n0
|
|
|
1336
|
if (snr < 1.e-20) :
|
|
1336
|
if (snr < 1.e-20) :
|
|
|
1337
|
snr = 1.e-20
|
|
1337
|
snr = 1.e-20
|
|
|
1338
|
|
|
1338
|
|
|
|
1339
|
# vec_power[ind] = power #D. ScipiΓ³n replaced with the line below
|
|
1339
|
# vec_power[ind] = power #D. ScipiΓ³n replaced with the line below
|
|
|
1340
|
vec_power[ind] = total_power
|
|
1340
|
vec_power[ind] = total_power
|
|
|
1341
|
vec_fd[ind] = fd
|
|
1341
|
vec_fd[ind] = fd
|
|
|
1342
|
vec_w[ind] = w
|
|
1342
|
vec_w[ind] = w
|
|
|
1343
|
vec_snr[ind] = snr
|
|
1343
|
vec_snr[ind] = snr
|
|
|
1344
|
|
|
1344
|
|
|
|
1345
|
return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
|
|
1345
|
return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
|
|
|
1346
|
|
|
1346
|
|
|
|
1347
|
#------------------ Get SA Parameters --------------------------
|
|
1347
|
#------------------ Get SA Parameters --------------------------
|
|
|
1348
|
|
|
1348
|
|
|
|
1349
|
def GetSAParameters(self):
|
|
1349
|
def GetSAParameters(self):
|
|
|
1350
|
#SA en frecuencia
|
|
1350
|
#SA en frecuencia
|
|
|
1351
|
pairslist = self.dataOut.groupList
|
|
1351
|
pairslist = self.dataOut.groupList
|
|
|
1352
|
num_pairs = len(pairslist)
|
|
1352
|
num_pairs = len(pairslist)
|
|
|
1353
|
|
|
1353
|
|
|
|
1354
|
vel = self.dataOut.abscissaList
|
|
1354
|
vel = self.dataOut.abscissaList
|
|
|
1355
|
spectra = self.dataOut.data_pre
|
|
1355
|
spectra = self.dataOut.data_pre
|
|
|
1356
|
cspectra = self.dataIn.data_cspc
|
|
1356
|
cspectra = self.dataIn.data_cspc
|
|
|
1357
|
delta_v = vel[1] - vel[0]
|
|
1357
|
delta_v = vel[1] - vel[0]
|
|
|
1358
|
|
|
1358
|
|
|
|
1359
|
#Calculating the power spectrum
|
|
1359
|
#Calculating the power spectrum
|
|
|
1360
|
spc_pow = numpy.sum(spectra, 3)*delta_v
|
|
1360
|
spc_pow = numpy.sum(spectra, 3)*delta_v
|
|
|
1361
|
#Normalizing Spectra
|
|
1361
|
#Normalizing Spectra
|
|
|
1362
|
norm_spectra = spectra/spc_pow
|
|
1362
|
norm_spectra = spectra/spc_pow
|
|
|
1363
|
#Calculating the norm_spectra at peak
|
|
1363
|
#Calculating the norm_spectra at peak
|
|
|
1364
|
max_spectra = numpy.max(norm_spectra, 3)
|
|
1364
|
max_spectra = numpy.max(norm_spectra, 3)
|
|
|
1365
|
|
|
1365
|
|
|
|
1366
|
#Normalizing Cross Spectra
|
|
1366
|
#Normalizing Cross Spectra
|
|
|
1367
|
norm_cspectra = numpy.zeros(cspectra.shape)
|
|
1367
|
norm_cspectra = numpy.zeros(cspectra.shape)
|
|
|
1368
|
|
|
1368
|
|
|
|
1369
|
for i in range(num_chan):
|
|
1369
|
for i in range(num_chan):
|
|
|
1370
|
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
|
|
1370
|
norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
|
|
|
1371
|
|
|
1371
|
|
|
|
1372
|
max_cspectra = numpy.max(norm_cspectra,2)
|
|
1372
|
max_cspectra = numpy.max(norm_cspectra,2)
|
|
|
1373
|
max_cspectra_index = numpy.argmax(norm_cspectra, 2)
|
|
1373
|
max_cspectra_index = numpy.argmax(norm_cspectra, 2)
|
|
|
1374
|
|
|
1374
|
|
|
|
1375
|
for i in range(num_pairs):
|
|
1375
|
for i in range(num_pairs):
|
|
|
1376
|
cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
|
|
1376
|
cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
|
|
|
1377
|
#------------------- Get Lags ----------------------------------
|
|
1377
|
#------------------- Get Lags ----------------------------------
|
|
|
1378
|
|
|
1378
|
|
|
|
1379
|
class SALags(Operation):
|
|
1379
|
class SALags(Operation):
|
|
|
1380
|
'''
|
|
1380
|
'''
|
|
|
1381
|
Function GetMoments()
|
|
1381
|
Function GetMoments()
|
|
|
1382
|
|
|
1382
|
|
|
|
1383
|
Input:
|
|
1383
|
Input:
|
|
|
1384
|
self.dataOut.data_pre
|
|
1384
|
self.dataOut.data_pre
|
|
|
1385
|
self.dataOut.abscissaList
|
|
1385
|
self.dataOut.abscissaList
|
|
|
1386
|
self.dataOut.noise
|
|
1386
|
self.dataOut.noise
|
|
|
1387
|
self.dataOut.normFactor
|
|
1387
|
self.dataOut.normFactor
|
|
|
1388
|
self.dataOut.data_snr
|
|
1388
|
self.dataOut.data_snr
|
|
|
1389
|
self.dataOut.groupList
|
|
1389
|
self.dataOut.groupList
|
|
|
1390
|
self.dataOut.nChannels
|
|
1390
|
self.dataOut.nChannels
|
|
|
1391
|
|
|
1391
|
|
|
|
1392
|
Affected:
|
|
1392
|
Affected:
|
|
|
1393
|
self.dataOut.data_param
|
|
1393
|
self.dataOut.data_param
|
|
|
1394
|
|
|
1394
|
|
|
|
1395
|
'''
|
|
1395
|
'''
|
|
|
1396
|
def run(self, dataOut):
|
|
1396
|
def run(self, dataOut):
|
|
|
1397
|
data_acf = dataOut.data_pre[0]
|
|
1397
|
data_acf = dataOut.data_pre[0]
|
|
|
1398
|
data_ccf = dataOut.data_pre[1]
|
|
1398
|
data_ccf = dataOut.data_pre[1]
|
|
|
1399
|
normFactor_acf = dataOut.normFactor[0]
|
|
1399
|
normFactor_acf = dataOut.normFactor[0]
|
|
|
1400
|
normFactor_ccf = dataOut.normFactor[1]
|
|
1400
|
normFactor_ccf = dataOut.normFactor[1]
|
|
|
1401
|
pairs_acf = dataOut.groupList[0]
|
|
1401
|
pairs_acf = dataOut.groupList[0]
|
|
|
1402
|
pairs_ccf = dataOut.groupList[1]
|
|
1402
|
pairs_ccf = dataOut.groupList[1]
|
|
|
1403
|
|
|
1403
|
|
|
|
1404
|
nHeights = dataOut.nHeights
|
|
1404
|
nHeights = dataOut.nHeights
|
|
|
1405
|
absc = dataOut.abscissaList
|
|
1405
|
absc = dataOut.abscissaList
|
|
|
1406
|
noise = dataOut.noise
|
|
1406
|
noise = dataOut.noise
|
|
|
1407
|
SNR = dataOut.data_snr
|
|
1407
|
SNR = dataOut.data_snr
|
|
|
1408
|
nChannels = dataOut.nChannels
|
|
1408
|
nChannels = dataOut.nChannels
|
|
|
1409
|
# pairsList = dataOut.groupList
|
|
1409
|
# pairsList = dataOut.groupList
|
|
|
1410
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
|
|
1410
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
|
|
|
1411
|
|
|
1411
|
|
|
|
1412
|
for l in range(len(pairs_acf)):
|
|
1412
|
for l in range(len(pairs_acf)):
|
|
|
1413
|
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
|
|
1413
|
data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
|
|
|
1414
|
|
|
1414
|
|
|
|
1415
|
for l in range(len(pairs_ccf)):
|
|
1415
|
for l in range(len(pairs_ccf)):
|
|
|
1416
|
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
|
|
1416
|
data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
|
|
|
1417
|
|
|
1417
|
|
|
|
1418
|
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
|
|
1418
|
dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
|
|
|
1419
|
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
|
|
1419
|
dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
|
|
|
1420
|
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
|
|
1420
|
dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
|
|
|
1421
|
return
|
|
1421
|
return
|
|
|
1422
|
|
|
1422
|
|
|
|
1423
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
1423
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
|
1424
|
#
|
|
1424
|
#
|
|
|
1425
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
1425
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
|
1426
|
#
|
|
1426
|
#
|
|
|
1427
|
# for l in range(len(pairsList)):
|
|
1427
|
# for l in range(len(pairsList)):
|
|
|
1428
|
# firstChannel = pairsList[l][0]
|
|
1428
|
# firstChannel = pairsList[l][0]
|
|
|
1429
|
# secondChannel = pairsList[l][1]
|
|
1429
|
# secondChannel = pairsList[l][1]
|
|
|
1430
|
#
|
|
1430
|
#
|
|
|
1431
|
# #Obteniendo pares de Autocorrelacion
|
|
1431
|
# #Obteniendo pares de Autocorrelacion
|
|
|
1432
|
# if firstChannel == secondChannel:
|
|
1432
|
# if firstChannel == secondChannel:
|
|
|
1433
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
1433
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
|
1434
|
#
|
|
1434
|
#
|
|
|
1435
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
1435
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
|
1436
|
#
|
|
1436
|
#
|
|
|
1437
|
# pairsCrossCorr = range(len(pairsList))
|
|
1437
|
# pairsCrossCorr = range(len(pairsList))
|
|
|
1438
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
1438
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
|
1439
|
#
|
|
1439
|
#
|
|
|
1440
|
# return pairsAutoCorr, pairsCrossCorr
|
|
1440
|
# return pairsAutoCorr, pairsCrossCorr
|
|
|
1441
|
|
|
1441
|
|
|
|
1442
|
def __calculateTaus(self, data_acf, data_ccf, lagRange):
|
|
1442
|
def __calculateTaus(self, data_acf, data_ccf, lagRange):
|
|
|
1443
|
|
|
1443
|
|
|
|
1444
|
lag0 = data_acf.shape[1]/2
|
|
1444
|
lag0 = data_acf.shape[1]/2
|
|
|
1445
|
#Funcion de Autocorrelacion
|
|
1445
|
#Funcion de Autocorrelacion
|
|
|
1446
|
mean_acf = stats.nanmean(data_acf, axis = 0)
|
|
1446
|
mean_acf = stats.nanmean(data_acf, axis = 0)
|
|
|
1447
|
|
|
1447
|
|
|
|
1448
|
#Obtencion Indice de TauCross
|
|
1448
|
#Obtencion Indice de TauCross
|
|
|
1449
|
ind_ccf = data_ccf.argmax(axis = 1)
|
|
1449
|
ind_ccf = data_ccf.argmax(axis = 1)
|
|
|
1450
|
#Obtencion Indice de TauAuto
|
|
1450
|
#Obtencion Indice de TauAuto
|
|
|
1451
|
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
|
|
1451
|
ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
|
|
|
1452
|
ccf_lag0 = data_ccf[:,lag0,:]
|
|
1452
|
ccf_lag0 = data_ccf[:,lag0,:]
|
|
|
1453
|
|
|
1453
|
|
|
|
1454
|
for i in range(ccf_lag0.shape[0]):
|
|
1454
|
for i in range(ccf_lag0.shape[0]):
|
|
|
1455
|
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
|
|
1455
|
ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
|
|
|
1456
|
|
|
1456
|
|
|
|
1457
|
#Obtencion de TauCross y TauAuto
|
|
1457
|
#Obtencion de TauCross y TauAuto
|
|
|
1458
|
tau_ccf = lagRange[ind_ccf]
|
|
1458
|
tau_ccf = lagRange[ind_ccf]
|
|
|
1459
|
tau_acf = lagRange[ind_acf]
|
|
1459
|
tau_acf = lagRange[ind_acf]
|
|
|
1460
|
|
|
1460
|
|
|
|
1461
|
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
|
|
1461
|
Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
|
|
|
1462
|
|
|
1462
|
|
|
|
1463
|
tau_ccf[Nan1,Nan2] = numpy.nan
|
|
1463
|
tau_ccf[Nan1,Nan2] = numpy.nan
|
|
|
1464
|
tau_acf[Nan1,Nan2] = numpy.nan
|
|
1464
|
tau_acf[Nan1,Nan2] = numpy.nan
|
|
|
1465
|
tau = numpy.vstack((tau_ccf,tau_acf))
|
|
1465
|
tau = numpy.vstack((tau_ccf,tau_acf))
|
|
|
1466
|
|
|
1466
|
|
|
|
1467
|
return tau
|
|
1467
|
return tau
|
|
|
1468
|
|
|
1468
|
|
|
|
1469
|
def __calculateLag1Phase(self, data, lagTRange):
|
|
1469
|
def __calculateLag1Phase(self, data, lagTRange):
|
|
|
1470
|
data1 = stats.nanmean(data, axis = 0)
|
|
1470
|
data1 = stats.nanmean(data, axis = 0)
|
|
|
1471
|
lag1 = numpy.where(lagTRange == 0)[0][0] + 1
|
|
1471
|
lag1 = numpy.where(lagTRange == 0)[0][0] + 1
|
|
|
1472
|
|
|
1472
|
|
|
|
1473
|
phase = numpy.angle(data1[lag1,:])
|
|
1473
|
phase = numpy.angle(data1[lag1,:])
|
|
|
1474
|
|
|
1474
|
|
|
|
1475
|
return phase
|
|
1475
|
return phase
|
|
|
1476
|
|
|
1476
|
|
|
|
1477
|
class SpectralFitting(Operation):
|
|
1477
|
class SpectralFitting(Operation):
|
|
|
1478
|
'''
|
|
1478
|
'''
|
|
|
1479
|
Function GetMoments()
|
|
1479
|
Function GetMoments()
|
|
|
1480
|
|
|
1480
|
|
|
|
1481
|
Input:
|
|
1481
|
Input:
|
|
|
1482
|
Output:
|
|
1482
|
Output:
|
|
|
1483
|
Variables modified:
|
|
1483
|
Variables modified:
|
|
|
1484
|
'''
|
|
1484
|
'''
|
|
|
1485
|
|
|
1485
|
|
|
|
1486
|
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
|
|
1486
|
def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
|
|
|
1487
|
|
|
1487
|
|
|
|
1488
|
|
|
1488
|
|
|
|
1489
|
if path != None:
|
|
1489
|
if path != None:
|
|
|
1490
|
sys.path.append(path)
|
|
1490
|
sys.path.append(path)
|
|
|
1491
|
self.dataOut.library = importlib.import_module(file)
|
|
1491
|
self.dataOut.library = importlib.import_module(file)
|
|
|
1492
|
|
|
1492
|
|
|
|
1493
|
#To be inserted as a parameter
|
|
1493
|
#To be inserted as a parameter
|
|
|
1494
|
groupArray = numpy.array(groupList)
|
|
1494
|
groupArray = numpy.array(groupList)
|
|
|
1495
|
# groupArray = numpy.array([[0,1],[2,3]])
|
|
1495
|
# groupArray = numpy.array([[0,1],[2,3]])
|
|
|
1496
|
self.dataOut.groupList = groupArray
|
|
1496
|
self.dataOut.groupList = groupArray
|
|
|
1497
|
|
|
1497
|
|
|
|
1498
|
nGroups = groupArray.shape[0]
|
|
1498
|
nGroups = groupArray.shape[0]
|
|
|
1499
|
nChannels = self.dataIn.nChannels
|
|
1499
|
nChannels = self.dataIn.nChannels
|
|
|
1500
|
nHeights=self.dataIn.heightList.size
|
|
1500
|
nHeights=self.dataIn.heightList.size
|
|
|
1501
|
|
|
1501
|
|
|
|
1502
|
#Parameters Array
|
|
1502
|
#Parameters Array
|
|
|
1503
|
self.dataOut.data_param = None
|
|
1503
|
self.dataOut.data_param = None
|
|
|
1504
|
|
|
1504
|
|
|
|
1505
|
#Set constants
|
|
1505
|
#Set constants
|
|
|
1506
|
constants = self.dataOut.library.setConstants(self.dataIn)
|
|
1506
|
constants = self.dataOut.library.setConstants(self.dataIn)
|
|
|
1507
|
self.dataOut.constants = constants
|
|
1507
|
self.dataOut.constants = constants
|
|
|
1508
|
M = self.dataIn.normFactor
|
|
1508
|
M = self.dataIn.normFactor
|
|
|
1509
|
N = self.dataIn.nFFTPoints
|
|
1509
|
N = self.dataIn.nFFTPoints
|
|
|
1510
|
ippSeconds = self.dataIn.ippSeconds
|
|
1510
|
ippSeconds = self.dataIn.ippSeconds
|
|
|
1511
|
K = self.dataIn.nIncohInt
|
|
1511
|
K = self.dataIn.nIncohInt
|
|
|
1512
|
pairsArray = numpy.array(self.dataIn.pairsList)
|
|
1512
|
pairsArray = numpy.array(self.dataIn.pairsList)
|
|
|
1513
|
|
|
1513
|
|
|
|
1514
|
#List of possible combinations
|
|
1514
|
#List of possible combinations
|
|
|
1515
|
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
|
|
1515
|
listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
|
|
|
1516
|
indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
|
|
1516
|
indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
|
|
|
1517
|
|
|
1517
|
|
|
|
1518
|
if getSNR:
|
|
1518
|
if getSNR:
|
|
|
1519
|
listChannels = groupArray.reshape((groupArray.size))
|
|
1519
|
listChannels = groupArray.reshape((groupArray.size))
|
|
|
1520
|
listChannels.sort()
|
|
1520
|
listChannels.sort()
|
|
|
1521
|
noise = self.dataIn.getNoise()
|
|
1521
|
noise = self.dataIn.getNoise()
|
|
|
1522
|
self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
|
|
1522
|
self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
|
|
|
1523
|
|
|
1523
|
|
|
|
1524
|
for i in range(nGroups):
|
|
1524
|
for i in range(nGroups):
|
|
|
1525
|
coord = groupArray[i,:]
|
|
1525
|
coord = groupArray[i,:]
|
|
|
1526
|
|
|
1526
|
|
|
|
1527
|
#Input data array
|
|
1527
|
#Input data array
|
|
|
1528
|
data = self.dataIn.data_spc[coord,:,:]/(M*N)
|
|
1528
|
data = self.dataIn.data_spc[coord,:,:]/(M*N)
|
|
|
1529
|
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
|
|
1529
|
data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
|
|
|
1530
|
|
|
1530
|
|
|
|
1531
|
#Cross Spectra data array for Covariance Matrixes
|
|
1531
|
#Cross Spectra data array for Covariance Matrixes
|
|
|
1532
|
ind = 0
|
|
1532
|
ind = 0
|
|
|
1533
|
for pairs in listComb:
|
|
1533
|
for pairs in listComb:
|
|
|
1534
|
pairsSel = numpy.array([coord[x],coord[y]])
|
|
1534
|
pairsSel = numpy.array([coord[x],coord[y]])
|
|
|
1535
|
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
|
|
1535
|
indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
|
|
|
1536
|
ind += 1
|
|
1536
|
ind += 1
|
|
|
1537
|
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
|
|
1537
|
dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
|
|
|
1538
|
dataCross = dataCross**2/K
|
|
1538
|
dataCross = dataCross**2/K
|
|
|
1539
|
|
|
1539
|
|
|
|
1540
|
for h in range(nHeights):
|
|
1540
|
for h in range(nHeights):
|
|
|
1541
|
|
|
1541
|
|
|
|
1542
|
#Input
|
|
1542
|
#Input
|
|
|
1543
|
d = data[:,h]
|
|
1543
|
d = data[:,h]
|
|
|
1544
|
|
|
1544
|
|
|
|
1545
|
#Covariance Matrix
|
|
1545
|
#Covariance Matrix
|
|
|
1546
|
D = numpy.diag(d**2/K)
|
|
1546
|
D = numpy.diag(d**2/K)
|
|
|
1547
|
ind = 0
|
|
1547
|
ind = 0
|
|
|
1548
|
for pairs in listComb:
|
|
1548
|
for pairs in listComb:
|
|
|
1549
|
#Coordinates in Covariance Matrix
|
|
1549
|
#Coordinates in Covariance Matrix
|
|
|
1550
|
x = pairs[0]
|
|
1550
|
x = pairs[0]
|
|
|
1551
|
y = pairs[1]
|
|
1551
|
y = pairs[1]
|
|
|
1552
|
#Channel Index
|
|
1552
|
#Channel Index
|
|
|
1553
|
S12 = dataCross[ind,:,h]
|
|
1553
|
S12 = dataCross[ind,:,h]
|
|
|
1554
|
D12 = numpy.diag(S12)
|
|
1554
|
D12 = numpy.diag(S12)
|
|
|
1555
|
#Completing Covariance Matrix with Cross Spectras
|
|
1555
|
#Completing Covariance Matrix with Cross Spectras
|
|
|
1556
|
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
|
|
1556
|
D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
|
|
|
1557
|
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
|
|
1557
|
D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
|
|
|
1558
|
ind += 1
|
|
1558
|
ind += 1
|
|
|
1559
|
Dinv=numpy.linalg.inv(D)
|
|
1559
|
Dinv=numpy.linalg.inv(D)
|
|
|
1560
|
L=numpy.linalg.cholesky(Dinv)
|
|
1560
|
L=numpy.linalg.cholesky(Dinv)
|
|
|
1561
|
LT=L.T
|
|
1561
|
LT=L.T
|
|
|
1562
|
|
|
1562
|
|
|
|
1563
|
dp = numpy.dot(LT,d)
|
|
1563
|
dp = numpy.dot(LT,d)
|
|
|
1564
|
|
|
1564
|
|
|
|
1565
|
#Initial values
|
|
1565
|
#Initial values
|
|
|
1566
|
data_spc = self.dataIn.data_spc[coord,:,h]
|
|
1566
|
data_spc = self.dataIn.data_spc[coord,:,h]
|
|
|
1567
|
|
|
1567
|
|
|
|
1568
|
if (h>0)and(error1[3]<5):
|
|
1568
|
if (h>0)and(error1[3]<5):
|
|
|
1569
|
p0 = self.dataOut.data_param[i,:,h-1]
|
|
1569
|
p0 = self.dataOut.data_param[i,:,h-1]
|
|
|
1570
|
else:
|
|
1570
|
else:
|
|
|
1571
|
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
|
|
1571
|
p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
|
|
|
1572
|
|
|
1572
|
|
|
|
1573
|
try:
|
|
1573
|
try:
|
|
|
1574
|
#Least Squares
|
|
1574
|
#Least Squares
|
|
|
1575
|
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
|
|
1575
|
minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
|
|
|
1576
|
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
|
|
1576
|
# minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
|
|
|
1577
|
#Chi square error
|
|
1577
|
#Chi square error
|
|
|
1578
|
error0 = numpy.sum(infodict['fvec']**2)/(2*N)
|
|
1578
|
error0 = numpy.sum(infodict['fvec']**2)/(2*N)
|
|
|
1579
|
#Error with Jacobian
|
|
1579
|
#Error with Jacobian
|
|
|
1580
|
error1 = self.dataOut.library.errorFunction(minp,constants,LT)
|
|
1580
|
error1 = self.dataOut.library.errorFunction(minp,constants,LT)
|
|
|
1581
|
except:
|
|
1581
|
except:
|
|
|
1582
|
minp = p0*numpy.nan
|
|
1582
|
minp = p0*numpy.nan
|
|
|
1583
|
error0 = numpy.nan
|
|
1583
|
error0 = numpy.nan
|
|
|
1584
|
error1 = p0*numpy.nan
|
|
1584
|
error1 = p0*numpy.nan
|
|
|
1585
|
|
|
1585
|
|
|
|
1586
|
#Save
|
|
1586
|
#Save
|
|
|
1587
|
if self.dataOut.data_param is None:
|
|
1587
|
if self.dataOut.data_param is None:
|
|
|
1588
|
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
|
|
1588
|
self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
|
|
|
1589
|
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
|
|
1589
|
self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
|
|
|
1590
|
|
|
1590
|
|
|
|
1591
|
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
|
|
1591
|
self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
|
|
|
1592
|
self.dataOut.data_param[i,:,h] = minp
|
|
1592
|
self.dataOut.data_param[i,:,h] = minp
|
|
|
1593
|
return
|
|
1593
|
return
|
|
|
1594
|
|
|
1594
|
|
|
|
1595
|
def __residFunction(self, p, dp, LT, constants):
|
|
1595
|
def __residFunction(self, p, dp, LT, constants):
|
|
|
1596
|
|
|
1596
|
|
|
|
1597
|
fm = self.dataOut.library.modelFunction(p, constants)
|
|
1597
|
fm = self.dataOut.library.modelFunction(p, constants)
|
|
|
1598
|
fmp=numpy.dot(LT,fm)
|
|
1598
|
fmp=numpy.dot(LT,fm)
|
|
|
1599
|
|
|
1599
|
|
|
|
1600
|
return dp-fmp
|
|
1600
|
return dp-fmp
|
|
|
1601
|
|
|
1601
|
|
|
|
1602
|
def __getSNR(self, z, noise):
|
|
1602
|
def __getSNR(self, z, noise):
|
|
|
1603
|
|
|
1603
|
|
|
|
1604
|
avg = numpy.average(z, axis=1)
|
|
1604
|
avg = numpy.average(z, axis=1)
|
|
|
1605
|
SNR = (avg.T-noise)/noise
|
|
1605
|
SNR = (avg.T-noise)/noise
|
|
|
1606
|
SNR = SNR.T
|
|
1606
|
SNR = SNR.T
|
|
|
1607
|
return SNR
|
|
1607
|
return SNR
|
|
|
1608
|
|
|
1608
|
|
|
|
1609
|
def __chisq(p,chindex,hindex):
|
|
1609
|
def __chisq(p,chindex,hindex):
|
|
|
1610
|
#similar to Resid but calculates CHI**2
|
|
1610
|
#similar to Resid but calculates CHI**2
|
|
|
1611
|
[LT,d,fm]=setupLTdfm(p,chindex,hindex)
|
|
1611
|
[LT,d,fm]=setupLTdfm(p,chindex,hindex)
|
|
|
1612
|
dp=numpy.dot(LT,d)
|
|
1612
|
dp=numpy.dot(LT,d)
|
|
|
1613
|
fmp=numpy.dot(LT,fm)
|
|
1613
|
fmp=numpy.dot(LT,fm)
|
|
|
1614
|
chisq=numpy.dot((dp-fmp).T,(dp-fmp))
|
|
1614
|
chisq=numpy.dot((dp-fmp).T,(dp-fmp))
|
|
|
1615
|
return chisq
|
|
1615
|
return chisq
|
|
|
1616
|
|
|
1616
|
|
|
|
1617
|
class WindProfiler(Operation):
|
|
1617
|
class WindProfiler(Operation):
|
|
|
1618
|
|
|
1618
|
|
|
|
1619
|
__isConfig = False
|
|
1619
|
__isConfig = False
|
|
|
1620
|
|
|
1620
|
|
|
|
1621
|
__initime = None
|
|
1621
|
__initime = None
|
|
|
1622
|
__lastdatatime = None
|
|
1622
|
__lastdatatime = None
|
|
|
1623
|
__integrationtime = None
|
|
1623
|
__integrationtime = None
|
|
|
1624
|
|
|
1624
|
|
|
|
1625
|
__buffer = None
|
|
1625
|
__buffer = None
|
|
|
1626
|
|
|
1626
|
|
|
|
1627
|
__dataReady = False
|
|
1627
|
__dataReady = False
|
|
|
1628
|
|
|
1628
|
|
|
|
1629
|
__firstdata = None
|
|
1629
|
__firstdata = None
|
|
|
1630
|
|
|
1630
|
|
|
|
1631
|
n = None
|
|
1631
|
n = None
|
|
|
1632
|
|
|
1632
|
|
|
|
1633
|
def __init__(self):
|
|
1633
|
def __init__(self):
|
|
|
1634
|
Operation.__init__(self)
|
|
1634
|
Operation.__init__(self)
|
|
|
1635
|
|
|
1635
|
|
|
|
1636
|
def __calculateCosDir(self, elev, azim):
|
|
1636
|
def __calculateCosDir(self, elev, azim):
|
|
|
1637
|
zen = (90 - elev)*numpy.pi/180
|
|
1637
|
zen = (90 - elev)*numpy.pi/180
|
|
|
1638
|
azim = azim*numpy.pi/180
|
|
1638
|
azim = azim*numpy.pi/180
|
|
|
1639
|
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
|
|
1639
|
cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
|
|
|
1640
|
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
|
|
1640
|
cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
|
|
|
1641
|
|
|
1641
|
|
|
|
1642
|
signX = numpy.sign(numpy.cos(azim))
|
|
1642
|
signX = numpy.sign(numpy.cos(azim))
|
|
|
1643
|
signY = numpy.sign(numpy.sin(azim))
|
|
1643
|
signY = numpy.sign(numpy.sin(azim))
|
|
|
1644
|
|
|
1644
|
|
|
|
1645
|
cosDirX = numpy.copysign(cosDirX, signX)
|
|
1645
|
cosDirX = numpy.copysign(cosDirX, signX)
|
|
|
1646
|
cosDirY = numpy.copysign(cosDirY, signY)
|
|
1646
|
cosDirY = numpy.copysign(cosDirY, signY)
|
|
|
1647
|
return cosDirX, cosDirY
|
|
1647
|
return cosDirX, cosDirY
|
|
|
1648
|
|
|
1648
|
|
|
|
1649
|
def __calculateAngles(self, theta_x, theta_y, azimuth):
|
|
1649
|
def __calculateAngles(self, theta_x, theta_y, azimuth):
|
|
|
1650
|
|
|
1650
|
|
|
|
1651
|
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
|
|
1651
|
dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
|
|
|
1652
|
zenith_arr = numpy.arccos(dir_cosw)
|
|
1652
|
zenith_arr = numpy.arccos(dir_cosw)
|
|
|
1653
|
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
|
|
1653
|
azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
|
|
|
1654
|
|
|
1654
|
|
|
|
1655
|
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
|
|
1655
|
dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
|
|
|
1656
|
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
|
|
1656
|
dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
|
|
|
1657
|
|
|
1657
|
|
|
|
1658
|
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
|
|
1658
|
return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
|
|
|
1659
|
|
|
1659
|
|
|
|
1660
|
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
|
|
1660
|
def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
|
|
|
1661
|
|
|
1661
|
|
|
|
1662
|
#
|
|
1662
|
#
|
|
|
1663
|
if horOnly:
|
|
1663
|
if horOnly:
|
|
|
1664
|
A = numpy.c_[dir_cosu,dir_cosv]
|
|
1664
|
A = numpy.c_[dir_cosu,dir_cosv]
|
|
|
1665
|
else:
|
|
1665
|
else:
|
|
|
1666
|
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
|
|
1666
|
A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
|
|
|
1667
|
A = numpy.asmatrix(A)
|
|
1667
|
A = numpy.asmatrix(A)
|
|
|
1668
|
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
|
|
1668
|
A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
|
|
|
1669
|
|
|
1669
|
|
|
|
1670
|
return A1
|
|
1670
|
return A1
|
|
|
1671
|
|
|
1671
|
|
|
|
1672
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
1672
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
|
1673
|
listPhi = phi.tolist()
|
|
1673
|
listPhi = phi.tolist()
|
|
|
1674
|
maxid = listPhi.index(max(listPhi))
|
|
1674
|
maxid = listPhi.index(max(listPhi))
|
|
|
1675
|
minid = listPhi.index(min(listPhi))
|
|
1675
|
minid = listPhi.index(min(listPhi))
|
|
|
1676
|
|
|
1676
|
|
|
|
1677
|
rango = list(range(len(phi)))
|
|
1677
|
rango = list(range(len(phi)))
|
|
|
1678
|
# rango = numpy.delete(rango,maxid)
|
|
1678
|
# rango = numpy.delete(rango,maxid)
|
|
|
1679
|
|
|
1679
|
|
|
|
1680
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
1680
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
|
1681
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
1681
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
|
1682
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
1682
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
|
1683
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
1683
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
|
1684
|
|
|
1684
|
|
|
|
1685
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
1685
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
1686
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
1686
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
1687
|
|
|
1687
|
|
|
|
1688
|
for i in rango:
|
|
1688
|
for i in rango:
|
|
|
1689
|
x = heiRang*math.cos(phi[i])
|
|
1689
|
x = heiRang*math.cos(phi[i])
|
|
|
1690
|
y1 = velRadial[i,:]
|
|
1690
|
y1 = velRadial[i,:]
|
|
|
1691
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
1691
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
|
1692
|
|
|
1692
|
|
|
|
1693
|
x1 = heiRang1
|
|
1693
|
x1 = heiRang1
|
|
|
1694
|
y11 = f1(x1)
|
|
1694
|
y11 = f1(x1)
|
|
|
1695
|
|
|
1695
|
|
|
|
1696
|
y2 = SNR[i,:]
|
|
1696
|
y2 = SNR[i,:]
|
|
|
1697
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
1697
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
|
1698
|
y21 = f2(x1)
|
|
1698
|
y21 = f2(x1)
|
|
|
1699
|
|
|
1699
|
|
|
|
1700
|
velRadial1[i,:] = y11
|
|
1700
|
velRadial1[i,:] = y11
|
|
|
1701
|
SNR1[i,:] = y21
|
|
1701
|
SNR1[i,:] = y21
|
|
|
1702
|
|
|
1702
|
|
|
|
1703
|
return heiRang1, velRadial1, SNR1
|
|
1703
|
return heiRang1, velRadial1, SNR1
|
|
|
1704
|
|
|
1704
|
|
|
|
1705
|
def __calculateVelUVW(self, A, velRadial):
|
|
1705
|
def __calculateVelUVW(self, A, velRadial):
|
|
|
1706
|
|
|
1706
|
|
|
|
1707
|
#Operacion Matricial
|
|
1707
|
#Operacion Matricial
|
|
|
1708
|
# velUVW = numpy.zeros((velRadial.shape[1],3))
|
|
1708
|
# velUVW = numpy.zeros((velRadial.shape[1],3))
|
|
|
1709
|
# for ind in range(velRadial.shape[1]):
|
|
1709
|
# for ind in range(velRadial.shape[1]):
|
|
|
1710
|
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
|
|
1710
|
# velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
|
|
|
1711
|
# velUVW = velUVW.transpose()
|
|
1711
|
# velUVW = velUVW.transpose()
|
|
|
1712
|
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
|
|
1712
|
velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
|
|
|
1713
|
velUVW[:,:] = numpy.dot(A,velRadial)
|
|
1713
|
velUVW[:,:] = numpy.dot(A,velRadial)
|
|
|
1714
|
|
|
1714
|
|
|
|
1715
|
|
|
1715
|
|
|
|
1716
|
return velUVW
|
|
1716
|
return velUVW
|
|
|
1717
|
|
|
1717
|
|
|
|
1718
|
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
|
|
1718
|
# def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
|
|
|
1719
|
|
|
1719
|
|
|
|
1720
|
def techniqueDBS(self, kwargs):
|
|
1720
|
def techniqueDBS(self, kwargs):
|
|
|
1721
|
"""
|
|
1721
|
"""
|
|
|
1722
|
Function that implements Doppler Beam Swinging (DBS) technique.
|
|
1722
|
Function that implements Doppler Beam Swinging (DBS) technique.
|
|
|
1723
|
|
|
1723
|
|
|
|
1724
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
1724
|
Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
|
|
|
1725
|
Direction correction (if necessary), Ranges and SNR
|
|
1725
|
Direction correction (if necessary), Ranges and SNR
|
|
|
1726
|
|
|
1726
|
|
|
|
1727
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
1727
|
Output: Winds estimation (Zonal, Meridional and Vertical)
|
|
|
1728
|
|
|
1728
|
|
|
|
1729
|
Parameters affected: Winds, height range, SNR
|
|
1729
|
Parameters affected: Winds, height range, SNR
|
|
|
1730
|
"""
|
|
1730
|
"""
|
|
|
1731
|
velRadial0 = kwargs['velRadial']
|
|
1731
|
velRadial0 = kwargs['velRadial']
|
|
|
1732
|
heiRang = kwargs['heightList']
|
|
1732
|
heiRang = kwargs['heightList']
|
|
|
1733
|
SNR0 = kwargs['SNR']
|
|
1733
|
SNR0 = kwargs['SNR']
|
|
|
1734
|
|
|
1734
|
|
|
|
1735
|
if 'dirCosx' in kwargs and 'dirCosy' in kwargs:
|
|
1735
|
if 'dirCosx' in kwargs and 'dirCosy' in kwargs:
|
|
|
1736
|
theta_x = numpy.array(kwargs['dirCosx'])
|
|
1736
|
theta_x = numpy.array(kwargs['dirCosx'])
|
|
|
1737
|
theta_y = numpy.array(kwargs['dirCosy'])
|
|
1737
|
theta_y = numpy.array(kwargs['dirCosy'])
|
|
|
1738
|
else:
|
|
1738
|
else:
|
|
|
1739
|
elev = numpy.array(kwargs['elevation'])
|
|
1739
|
elev = numpy.array(kwargs['elevation'])
|
|
|
1740
|
azim = numpy.array(kwargs['azimuth'])
|
|
1740
|
azim = numpy.array(kwargs['azimuth'])
|
|
|
1741
|
theta_x, theta_y = self.__calculateCosDir(elev, azim)
|
|
1741
|
theta_x, theta_y = self.__calculateCosDir(elev, azim)
|
|
|
1742
|
azimuth = kwargs['correctAzimuth']
|
|
1742
|
azimuth = kwargs['correctAzimuth']
|
|
|
1743
|
if 'horizontalOnly' in kwargs:
|
|
1743
|
if 'horizontalOnly' in kwargs:
|
|
|
1744
|
horizontalOnly = kwargs['horizontalOnly']
|
|
1744
|
horizontalOnly = kwargs['horizontalOnly']
|
|
|
1745
|
else: horizontalOnly = False
|
|
1745
|
else: horizontalOnly = False
|
|
|
1746
|
if 'correctFactor' in kwargs:
|
|
1746
|
if 'correctFactor' in kwargs:
|
|
|
1747
|
correctFactor = kwargs['correctFactor']
|
|
1747
|
correctFactor = kwargs['correctFactor']
|
|
|
1748
|
else: correctFactor = 1
|
|
1748
|
else: correctFactor = 1
|
|
|
1749
|
if 'channelList' in kwargs:
|
|
1749
|
if 'channelList' in kwargs:
|
|
|
1750
|
channelList = kwargs['channelList']
|
|
1750
|
channelList = kwargs['channelList']
|
|
|
1751
|
if len(channelList) == 2:
|
|
1751
|
if len(channelList) == 2:
|
|
|
1752
|
horizontalOnly = True
|
|
1752
|
horizontalOnly = True
|
|
|
1753
|
arrayChannel = numpy.array(channelList)
|
|
1753
|
arrayChannel = numpy.array(channelList)
|
|
|
1754
|
param = param[arrayChannel,:,:]
|
|
1754
|
param = param[arrayChannel,:,:]
|
|
|
1755
|
theta_x = theta_x[arrayChannel]
|
|
1755
|
theta_x = theta_x[arrayChannel]
|
|
|
1756
|
theta_y = theta_y[arrayChannel]
|
|
1756
|
theta_y = theta_y[arrayChannel]
|
|
|
1757
|
|
|
1757
|
|
|
|
1758
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
1758
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
|
1759
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
|
|
1759
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
|
|
|
1760
|
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
|
|
1760
|
A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
|
|
|
1761
|
|
|
1761
|
|
|
|
1762
|
#Calculo de Componentes de la velocidad con DBS
|
|
1762
|
#Calculo de Componentes de la velocidad con DBS
|
|
|
1763
|
winds = self.__calculateVelUVW(A,velRadial1)
|
|
1763
|
winds = self.__calculateVelUVW(A,velRadial1)
|
|
|
1764
|
|
|
1764
|
|
|
|
1765
|
return winds, heiRang1, SNR1
|
|
1765
|
return winds, heiRang1, SNR1
|
|
|
1766
|
|
|
1766
|
|
|
|
1767
|
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
|
|
1767
|
def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
|
|
|
1768
|
|
|
1768
|
|
|
|
1769
|
nPairs = len(pairs_ccf)
|
|
1769
|
nPairs = len(pairs_ccf)
|
|
|
1770
|
posx = numpy.asarray(posx)
|
|
1770
|
posx = numpy.asarray(posx)
|
|
|
1771
|
posy = numpy.asarray(posy)
|
|
1771
|
posy = numpy.asarray(posy)
|
|
|
1772
|
|
|
1772
|
|
|
|
1773
|
#Rotacion Inversa para alinear con el azimuth
|
|
1773
|
#Rotacion Inversa para alinear con el azimuth
|
|
|
1774
|
if azimuth!= None:
|
|
1774
|
if azimuth!= None:
|
|
|
1775
|
azimuth = azimuth*math.pi/180
|
|
1775
|
azimuth = azimuth*math.pi/180
|
|
|
1776
|
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
|
|
1776
|
posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
|
|
|
1777
|
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
|
|
1777
|
posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
|
|
|
1778
|
else:
|
|
1778
|
else:
|
|
|
1779
|
posx1 = posx
|
|
1779
|
posx1 = posx
|
|
|
1780
|
posy1 = posy
|
|
1780
|
posy1 = posy
|
|
|
1781
|
|
|
1781
|
|
|
|
1782
|
#Calculo de Distancias
|
|
1782
|
#Calculo de Distancias
|
|
|
1783
|
distx = numpy.zeros(nPairs)
|
|
1783
|
distx = numpy.zeros(nPairs)
|
|
|
1784
|
disty = numpy.zeros(nPairs)
|
|
1784
|
disty = numpy.zeros(nPairs)
|
|
|
1785
|
dist = numpy.zeros(nPairs)
|
|
1785
|
dist = numpy.zeros(nPairs)
|
|
|
1786
|
ang = numpy.zeros(nPairs)
|
|
1786
|
ang = numpy.zeros(nPairs)
|
|
|
1787
|
|
|
1787
|
|
|
|
1788
|
for i in range(nPairs):
|
|
1788
|
for i in range(nPairs):
|
|
|
1789
|
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
|
|
1789
|
distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
|
|
|
1790
|
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
|
|
1790
|
disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
|
|
|
1791
|
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
|
|
1791
|
dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
|
|
|
1792
|
ang[i] = numpy.arctan2(disty[i],distx[i])
|
|
1792
|
ang[i] = numpy.arctan2(disty[i],distx[i])
|
|
|
1793
|
|
|
1793
|
|
|
|
1794
|
return distx, disty, dist, ang
|
|
1794
|
return distx, disty, dist, ang
|
|
|
1795
|
#Calculo de Matrices
|
|
1795
|
#Calculo de Matrices
|
|
|
1796
|
# nPairs = len(pairs)
|
|
1796
|
# nPairs = len(pairs)
|
|
|
1797
|
# ang1 = numpy.zeros((nPairs, 2, 1))
|
|
1797
|
# ang1 = numpy.zeros((nPairs, 2, 1))
|
|
|
1798
|
# dist1 = numpy.zeros((nPairs, 2, 1))
|
|
1798
|
# dist1 = numpy.zeros((nPairs, 2, 1))
|
|
|
1799
|
#
|
|
1799
|
#
|
|
|
1800
|
# for j in range(nPairs):
|
|
1800
|
# for j in range(nPairs):
|
|
|
1801
|
# dist1[j,0,0] = dist[pairs[j][0]]
|
|
1801
|
# dist1[j,0,0] = dist[pairs[j][0]]
|
|
|
1802
|
# dist1[j,1,0] = dist[pairs[j][1]]
|
|
1802
|
# dist1[j,1,0] = dist[pairs[j][1]]
|
|
|
1803
|
# ang1[j,0,0] = ang[pairs[j][0]]
|
|
1803
|
# ang1[j,0,0] = ang[pairs[j][0]]
|
|
|
1804
|
# ang1[j,1,0] = ang[pairs[j][1]]
|
|
1804
|
# ang1[j,1,0] = ang[pairs[j][1]]
|
|
|
1805
|
#
|
|
1805
|
#
|
|
|
1806
|
# return distx,disty, dist1,ang1
|
|
1806
|
# return distx,disty, dist1,ang1
|
|
|
1807
|
|
|
1807
|
|
|
|
1808
|
|
|
1808
|
|
|
|
1809
|
def __calculateVelVer(self, phase, lagTRange, _lambda):
|
|
1809
|
def __calculateVelVer(self, phase, lagTRange, _lambda):
|
|
|
1810
|
|
|
1810
|
|
|
|
1811
|
Ts = lagTRange[1] - lagTRange[0]
|
|
1811
|
Ts = lagTRange[1] - lagTRange[0]
|
|
|
1812
|
velW = -_lambda*phase/(4*math.pi*Ts)
|
|
1812
|
velW = -_lambda*phase/(4*math.pi*Ts)
|
|
|
1813
|
|
|
1813
|
|
|
|
1814
|
return velW
|
|
1814
|
return velW
|
|
|
1815
|
|
|
1815
|
|
|
|
1816
|
def __calculateVelHorDir(self, dist, tau1, tau2, ang):
|
|
1816
|
def __calculateVelHorDir(self, dist, tau1, tau2, ang):
|
|
|
1817
|
nPairs = tau1.shape[0]
|
|
1817
|
nPairs = tau1.shape[0]
|
|
|
1818
|
nHeights = tau1.shape[1]
|
|
1818
|
nHeights = tau1.shape[1]
|
|
|
1819
|
vel = numpy.zeros((nPairs,3,nHeights))
|
|
1819
|
vel = numpy.zeros((nPairs,3,nHeights))
|
|
|
1820
|
dist1 = numpy.reshape(dist, (dist.size,1))
|
|
1820
|
dist1 = numpy.reshape(dist, (dist.size,1))
|
|
|
1821
|
|
|
1821
|
|
|
|
1822
|
angCos = numpy.cos(ang)
|
|
1822
|
angCos = numpy.cos(ang)
|
|
|
1823
|
angSin = numpy.sin(ang)
|
|
1823
|
angSin = numpy.sin(ang)
|
|
|
1824
|
|
|
1824
|
|
|
|
1825
|
vel0 = dist1*tau1/(2*tau2**2)
|
|
1825
|
vel0 = dist1*tau1/(2*tau2**2)
|
|
|
1826
|
vel[:,0,:] = (vel0*angCos).sum(axis = 1)
|
|
1826
|
vel[:,0,:] = (vel0*angCos).sum(axis = 1)
|
|
|
1827
|
vel[:,1,:] = (vel0*angSin).sum(axis = 1)
|
|
1827
|
vel[:,1,:] = (vel0*angSin).sum(axis = 1)
|
|
|
1828
|
|
|
1828
|
|
|
|
1829
|
ind = numpy.where(numpy.isinf(vel))
|
|
1829
|
ind = numpy.where(numpy.isinf(vel))
|
|
|
1830
|
vel[ind] = numpy.nan
|
|
1830
|
vel[ind] = numpy.nan
|
|
|
1831
|
|
|
1831
|
|
|
|
1832
|
return vel
|
|
1832
|
return vel
|
|
|
1833
|
|
|
1833
|
|
|
|
1834
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
1834
|
# def __getPairsAutoCorr(self, pairsList, nChannels):
|
|
|
1835
|
#
|
|
1835
|
#
|
|
|
1836
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
1836
|
# pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
|
|
|
1837
|
#
|
|
1837
|
#
|
|
|
1838
|
# for l in range(len(pairsList)):
|
|
1838
|
# for l in range(len(pairsList)):
|
|
|
1839
|
# firstChannel = pairsList[l][0]
|
|
1839
|
# firstChannel = pairsList[l][0]
|
|
|
1840
|
# secondChannel = pairsList[l][1]
|
|
1840
|
# secondChannel = pairsList[l][1]
|
|
|
1841
|
#
|
|
1841
|
#
|
|
|
1842
|
# #Obteniendo pares de Autocorrelacion
|
|
1842
|
# #Obteniendo pares de Autocorrelacion
|
|
|
1843
|
# if firstChannel == secondChannel:
|
|
1843
|
# if firstChannel == secondChannel:
|
|
|
1844
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
1844
|
# pairsAutoCorr[firstChannel] = int(l)
|
|
|
1845
|
#
|
|
1845
|
#
|
|
|
1846
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
1846
|
# pairsAutoCorr = pairsAutoCorr.astype(int)
|
|
|
1847
|
#
|
|
1847
|
#
|
|
|
1848
|
# pairsCrossCorr = range(len(pairsList))
|
|
1848
|
# pairsCrossCorr = range(len(pairsList))
|
|
|
1849
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
1849
|
# pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
|
|
|
1850
|
#
|
|
1850
|
#
|
|
|
1851
|
# return pairsAutoCorr, pairsCrossCorr
|
|
1851
|
# return pairsAutoCorr, pairsCrossCorr
|
|
|
1852
|
|
|
1852
|
|
|
|
1853
|
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
|
|
1853
|
# def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
|
|
|
1854
|
def techniqueSA(self, kwargs):
|
|
1854
|
def techniqueSA(self, kwargs):
|
|
|
1855
|
|
|
1855
|
|
|
|
1856
|
"""
|
|
1856
|
"""
|
|
|
1857
|
Function that implements Spaced Antenna (SA) technique.
|
|
1857
|
Function that implements Spaced Antenna (SA) 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
|
|
1864
|
Parameters affected: Winds
|
|
|
1865
|
"""
|
|
1865
|
"""
|
|
|
1866
|
position_x = kwargs['positionX']
|
|
1866
|
position_x = kwargs['positionX']
|
|
|
1867
|
position_y = kwargs['positionY']
|
|
1867
|
position_y = kwargs['positionY']
|
|
|
1868
|
azimuth = kwargs['azimuth']
|
|
1868
|
azimuth = kwargs['azimuth']
|
|
|
1869
|
|
|
1869
|
|
|
|
1870
|
if 'correctFactor' in kwargs:
|
|
1870
|
if 'correctFactor' in kwargs:
|
|
|
1871
|
correctFactor = kwargs['correctFactor']
|
|
1871
|
correctFactor = kwargs['correctFactor']
|
|
|
1872
|
else:
|
|
1872
|
else:
|
|
|
1873
|
correctFactor = 1
|
|
1873
|
correctFactor = 1
|
|
|
1874
|
|
|
1874
|
|
|
|
1875
|
groupList = kwargs['groupList']
|
|
1875
|
groupList = kwargs['groupList']
|
|
|
1876
|
pairs_ccf = groupList[1]
|
|
1876
|
pairs_ccf = groupList[1]
|
|
|
1877
|
tau = kwargs['tau']
|
|
1877
|
tau = kwargs['tau']
|
|
|
1878
|
_lambda = kwargs['_lambda']
|
|
1878
|
_lambda = kwargs['_lambda']
|
|
|
1879
|
|
|
1879
|
|
|
|
1880
|
#Cross Correlation pairs obtained
|
|
1880
|
#Cross Correlation pairs obtained
|
|
|
1881
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
|
|
1881
|
# pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
|
|
|
1882
|
# pairsArray = numpy.array(pairsList)[pairsCrossCorr]
|
|
1882
|
# pairsArray = numpy.array(pairsList)[pairsCrossCorr]
|
|
|
1883
|
# pairsSelArray = numpy.array(pairsSelected)
|
|
1883
|
# pairsSelArray = numpy.array(pairsSelected)
|
|
|
1884
|
# pairs = []
|
|
1884
|
# pairs = []
|
|
|
1885
|
#
|
|
1885
|
#
|
|
|
1886
|
# #Wind estimation pairs obtained
|
|
1886
|
# #Wind estimation pairs obtained
|
|
|
1887
|
# for i in range(pairsSelArray.shape[0]/2):
|
|
1887
|
# for i in range(pairsSelArray.shape[0]/2):
|
|
|
1888
|
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
|
|
1888
|
# ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
|
|
|
1889
|
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
|
|
1889
|
# ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
|
|
|
1890
|
# pairs.append((ind1,ind2))
|
|
1890
|
# pairs.append((ind1,ind2))
|
|
|
1891
|
|
|
1891
|
|
|
|
1892
|
indtau = tau.shape[0]/2
|
|
1892
|
indtau = tau.shape[0]/2
|
|
|
1893
|
tau1 = tau[:indtau,:]
|
|
1893
|
tau1 = tau[:indtau,:]
|
|
|
1894
|
tau2 = tau[indtau:-1,:]
|
|
1894
|
tau2 = tau[indtau:-1,:]
|
|
|
1895
|
# tau1 = tau1[pairs,:]
|
|
1895
|
# tau1 = tau1[pairs,:]
|
|
|
1896
|
# tau2 = tau2[pairs,:]
|
|
1896
|
# tau2 = tau2[pairs,:]
|
|
|
1897
|
phase1 = tau[-1,:]
|
|
1897
|
phase1 = tau[-1,:]
|
|
|
1898
|
|
|
1898
|
|
|
|
1899
|
#---------------------------------------------------------------------
|
|
1899
|
#---------------------------------------------------------------------
|
|
|
1900
|
#Metodo Directo
|
|
1900
|
#Metodo Directo
|
|
|
1901
|
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
|
|
1901
|
distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
|
|
|
1902
|
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
|
|
1902
|
winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
|
|
|
1903
|
winds = stats.nanmean(winds, axis=0)
|
|
1903
|
winds = stats.nanmean(winds, axis=0)
|
|
|
1904
|
#---------------------------------------------------------------------
|
|
1904
|
#---------------------------------------------------------------------
|
|
|
1905
|
#Metodo General
|
|
1905
|
#Metodo General
|
|
|
1906
|
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
|
|
1906
|
# distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
|
|
|
1907
|
# #Calculo Coeficientes de Funcion de Correlacion
|
|
1907
|
# #Calculo Coeficientes de Funcion de Correlacion
|
|
|
1908
|
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
|
|
1908
|
# F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
|
|
|
1909
|
# #Calculo de Velocidades
|
|
1909
|
# #Calculo de Velocidades
|
|
|
1910
|
# winds = self.calculateVelUV(F,G,A,B,H)
|
|
1910
|
# winds = self.calculateVelUV(F,G,A,B,H)
|
|
|
1911
|
|
|
1911
|
|
|
|
1912
|
#---------------------------------------------------------------------
|
|
1912
|
#---------------------------------------------------------------------
|
|
|
1913
|
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
|
|
1913
|
winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
|
|
|
1914
|
winds = correctFactor*winds
|
|
1914
|
winds = correctFactor*winds
|
|
|
1915
|
return winds
|
|
1915
|
return winds
|
|
|
1916
|
|
|
1916
|
|
|
|
1917
|
def __checkTime(self, currentTime, paramInterval, outputInterval):
|
|
1917
|
def __checkTime(self, currentTime, paramInterval, outputInterval):
|
|
|
1918
|
|
|
1918
|
|
|
|
1919
|
dataTime = currentTime + paramInterval
|
|
1919
|
dataTime = currentTime + paramInterval
|
|
|
1920
|
deltaTime = dataTime - self.__initime
|
|
1920
|
deltaTime = dataTime - self.__initime
|
|
|
1921
|
|
|
1921
|
|
|
|
1922
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
1922
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
|
1923
|
self.__dataReady = True
|
|
1923
|
self.__dataReady = True
|
|
|
1924
|
return
|
|
1924
|
return
|
|
|
1925
|
|
|
1925
|
|
|
|
1926
|
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
|
|
1926
|
def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
|
|
|
1927
|
'''
|
|
1927
|
'''
|
|
|
1928
|
Function that implements winds estimation technique with detected meteors.
|
|
1928
|
Function that implements winds estimation technique with detected meteors.
|
|
|
1929
|
|
|
1929
|
|
|
|
1930
|
Input: Detected meteors, Minimum meteor quantity to wind estimation
|
|
1930
|
Input: Detected meteors, Minimum meteor quantity to wind estimation
|
|
|
1931
|
|
|
1931
|
|
|
|
1932
|
Output: Winds estimation (Zonal and Meridional)
|
|
1932
|
Output: Winds estimation (Zonal and Meridional)
|
|
|
1933
|
|
|
1933
|
|
|
|
1934
|
Parameters affected: Winds
|
|
1934
|
Parameters affected: Winds
|
|
|
1935
|
'''
|
|
1935
|
'''
|
|
|
1936
|
#Settings
|
|
1936
|
#Settings
|
|
|
1937
|
nInt = (heightMax - heightMin)/2
|
|
1937
|
nInt = (heightMax - heightMin)/2
|
|
|
1938
|
nInt = int(nInt)
|
|
1938
|
nInt = int(nInt)
|
|
|
1939
|
winds = numpy.zeros((2,nInt))*numpy.nan
|
|
1939
|
winds = numpy.zeros((2,nInt))*numpy.nan
|
|
|
1940
|
|
|
1940
|
|
|
|
1941
|
#Filter errors
|
|
1941
|
#Filter errors
|
|
|
1942
|
error = numpy.where(arrayMeteor[:,-1] == 0)[0]
|
|
1942
|
error = numpy.where(arrayMeteor[:,-1] == 0)[0]
|
|
|
1943
|
finalMeteor = arrayMeteor[error,:]
|
|
1943
|
finalMeteor = arrayMeteor[error,:]
|
|
|
1944
|
|
|
1944
|
|
|
|
1945
|
#Meteor Histogram
|
|
1945
|
#Meteor Histogram
|
|
|
1946
|
finalHeights = finalMeteor[:,2]
|
|
1946
|
finalHeights = finalMeteor[:,2]
|
|
|
1947
|
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
|
|
1947
|
hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
|
|
|
1948
|
nMeteorsPerI = hist[0]
|
|
1948
|
nMeteorsPerI = hist[0]
|
|
|
1949
|
heightPerI = hist[1]
|
|
1949
|
heightPerI = hist[1]
|
|
|
1950
|
|
|
1950
|
|
|
|
1951
|
#Sort of meteors
|
|
1951
|
#Sort of meteors
|
|
|
1952
|
indSort = finalHeights.argsort()
|
|
1952
|
indSort = finalHeights.argsort()
|
|
|
1953
|
finalMeteor2 = finalMeteor[indSort,:]
|
|
1953
|
finalMeteor2 = finalMeteor[indSort,:]
|
|
|
1954
|
|
|
1954
|
|
|
|
1955
|
# Calculating winds
|
|
1955
|
# Calculating winds
|
|
|
1956
|
ind1 = 0
|
|
1956
|
ind1 = 0
|
|
|
1957
|
ind2 = 0
|
|
1957
|
ind2 = 0
|
|
|
1958
|
|
|
1958
|
|
|
|
1959
|
for i in range(nInt):
|
|
1959
|
for i in range(nInt):
|
|
|
1960
|
nMet = nMeteorsPerI[i]
|
|
1960
|
nMet = nMeteorsPerI[i]
|
|
|
1961
|
ind1 = ind2
|
|
1961
|
ind1 = ind2
|
|
|
1962
|
ind2 = ind1 + nMet
|
|
1962
|
ind2 = ind1 + nMet
|
|
|
1963
|
|
|
1963
|
|
|
|
1964
|
meteorAux = finalMeteor2[ind1:ind2,:]
|
|
1964
|
meteorAux = finalMeteor2[ind1:ind2,:]
|
|
|
1965
|
|
|
1965
|
|
|
|
1966
|
if meteorAux.shape[0] >= meteorThresh:
|
|
1966
|
if meteorAux.shape[0] >= meteorThresh:
|
|
|
1967
|
vel = meteorAux[:, 6]
|
|
1967
|
vel = meteorAux[:, 6]
|
|
|
1968
|
zen = meteorAux[:, 4]*numpy.pi/180
|
|
1968
|
zen = meteorAux[:, 4]*numpy.pi/180
|
|
|
1969
|
azim = meteorAux[:, 3]*numpy.pi/180
|
|
1969
|
azim = meteorAux[:, 3]*numpy.pi/180
|
|
|
1970
|
|
|
1970
|
|
|
|
1971
|
n = numpy.cos(zen)
|
|
1971
|
n = numpy.cos(zen)
|
|
|
1972
|
# m = (1 - n**2)/(1 - numpy.tan(azim)**2)
|
|
1972
|
# m = (1 - n**2)/(1 - numpy.tan(azim)**2)
|
|
|
1973
|
# l = m*numpy.tan(azim)
|
|
1973
|
# l = m*numpy.tan(azim)
|
|
|
1974
|
l = numpy.sin(zen)*numpy.sin(azim)
|
|
1974
|
l = numpy.sin(zen)*numpy.sin(azim)
|
|
|
1975
|
m = numpy.sin(zen)*numpy.cos(azim)
|
|
1975
|
m = numpy.sin(zen)*numpy.cos(azim)
|
|
|
1976
|
|
|
1976
|
|
|
|
1977
|
A = numpy.vstack((l, m)).transpose()
|
|
1977
|
A = numpy.vstack((l, m)).transpose()
|
|
|
1978
|
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
|
|
1978
|
A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
|
|
|
1979
|
windsAux = numpy.dot(A1, vel)
|
|
1979
|
windsAux = numpy.dot(A1, vel)
|
|
|
1980
|
|
|
1980
|
|
|
|
1981
|
winds[0,i] = windsAux[0]
|
|
1981
|
winds[0,i] = windsAux[0]
|
|
|
1982
|
winds[1,i] = windsAux[1]
|
|
1982
|
winds[1,i] = windsAux[1]
|
|
|
1983
|
|
|
1983
|
|
|
|
1984
|
return winds, heightPerI[:-1]
|
|
1984
|
return winds, heightPerI[:-1]
|
|
|
1985
|
|
|
1985
|
|
|
|
1986
|
def techniqueNSM_SA(self, **kwargs):
|
|
1986
|
def techniqueNSM_SA(self, **kwargs):
|
|
|
1987
|
metArray = kwargs['metArray']
|
|
1987
|
metArray = kwargs['metArray']
|
|
|
1988
|
heightList = kwargs['heightList']
|
|
1988
|
heightList = kwargs['heightList']
|
|
|
1989
|
timeList = kwargs['timeList']
|
|
1989
|
timeList = kwargs['timeList']
|
|
|
1990
|
|
|
1990
|
|
|
|
1991
|
rx_location = kwargs['rx_location']
|
|
1991
|
rx_location = kwargs['rx_location']
|
|
|
1992
|
groupList = kwargs['groupList']
|
|
1992
|
groupList = kwargs['groupList']
|
|
|
1993
|
azimuth = kwargs['azimuth']
|
|
1993
|
azimuth = kwargs['azimuth']
|
|
|
1994
|
dfactor = kwargs['dfactor']
|
|
1994
|
dfactor = kwargs['dfactor']
|
|
|
1995
|
k = kwargs['k']
|
|
1995
|
k = kwargs['k']
|
|
|
1996
|
|
|
1996
|
|
|
|
1997
|
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
|
|
1997
|
azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
|
|
|
1998
|
d = dist*dfactor
|
|
1998
|
d = dist*dfactor
|
|
|
1999
|
#Phase calculation
|
|
1999
|
#Phase calculation
|
|
|
2000
|
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
|
|
2000
|
metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
|
|
|
2001
|
|
|
2001
|
|
|
|
2002
|
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
|
|
2002
|
metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
|
|
|
2003
|
|
|
2003
|
|
|
|
2004
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
2004
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
|
2005
|
azimuth1 = azimuth1*numpy.pi/180
|
|
2005
|
azimuth1 = azimuth1*numpy.pi/180
|
|
|
2006
|
|
|
2006
|
|
|
|
2007
|
for i in range(heightList.size):
|
|
2007
|
for i in range(heightList.size):
|
|
|
2008
|
h = heightList[i]
|
|
2008
|
h = heightList[i]
|
|
|
2009
|
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
|
|
2009
|
indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
|
|
|
2010
|
metHeight = metArray1[indH,:]
|
|
2010
|
metHeight = metArray1[indH,:]
|
|
|
2011
|
if metHeight.shape[0] >= 2:
|
|
2011
|
if metHeight.shape[0] >= 2:
|
|
|
2012
|
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities
|
|
2012
|
velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities
|
|
|
2013
|
iazim = metHeight[:,1].astype(int)
|
|
2013
|
iazim = metHeight[:,1].astype(int)
|
|
|
2014
|
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths
|
|
2014
|
azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths
|
|
|
2015
|
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
|
|
2015
|
A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
|
|
|
2016
|
A = numpy.asmatrix(A)
|
|
2016
|
A = numpy.asmatrix(A)
|
|
|
2017
|
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
|
|
2017
|
A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
|
|
|
2018
|
velHor = numpy.dot(A1,velAux)
|
|
2018
|
velHor = numpy.dot(A1,velAux)
|
|
|
2019
|
|
|
2019
|
|
|
|
2020
|
velEst[i,:] = numpy.squeeze(velHor)
|
|
2020
|
velEst[i,:] = numpy.squeeze(velHor)
|
|
|
2021
|
return velEst
|
|
2021
|
return velEst
|
|
|
2022
|
|
|
2022
|
|
|
|
2023
|
def __getPhaseSlope(self, metArray, heightList, timeList):
|
|
2023
|
def __getPhaseSlope(self, metArray, heightList, timeList):
|
|
|
2024
|
meteorList = []
|
|
2024
|
meteorList = []
|
|
|
2025
|
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
|
|
2025
|
#utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
|
|
|
2026
|
#Putting back together the meteor matrix
|
|
2026
|
#Putting back together the meteor matrix
|
|
|
2027
|
utctime = metArray[:,0]
|
|
2027
|
utctime = metArray[:,0]
|
|
|
2028
|
uniqueTime = numpy.unique(utctime)
|
|
2028
|
uniqueTime = numpy.unique(utctime)
|
|
|
2029
|
|
|
2029
|
|
|
|
2030
|
phaseDerThresh = 0.5
|
|
2030
|
phaseDerThresh = 0.5
|
|
|
2031
|
ippSeconds = timeList[1] - timeList[0]
|
|
2031
|
ippSeconds = timeList[1] - timeList[0]
|
|
|
2032
|
sec = numpy.where(timeList>1)[0][0]
|
|
2032
|
sec = numpy.where(timeList>1)[0][0]
|
|
|
2033
|
nPairs = metArray.shape[1] - 6
|
|
2033
|
nPairs = metArray.shape[1] - 6
|
|
|
2034
|
nHeights = len(heightList)
|
|
2034
|
nHeights = len(heightList)
|
|
|
2035
|
|
|
2035
|
|
|
|
2036
|
for t in uniqueTime:
|
|
2036
|
for t in uniqueTime:
|
|
|
2037
|
metArray1 = metArray[utctime==t,:]
|
|
2037
|
metArray1 = metArray[utctime==t,:]
|
|
|
2038
|
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh
|
|
2038
|
# phaseDerThresh = numpy.pi/4 #reducir Phase thresh
|
|
|
2039
|
tmet = metArray1[:,1].astype(int)
|
|
2039
|
tmet = metArray1[:,1].astype(int)
|
|
|
2040
|
hmet = metArray1[:,2].astype(int)
|
|
2040
|
hmet = metArray1[:,2].astype(int)
|
|
|
2041
|
|
|
2041
|
|
|
|
2042
|
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
|
|
2042
|
metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
|
|
|
2043
|
metPhase[:,:] = numpy.nan
|
|
2043
|
metPhase[:,:] = numpy.nan
|
|
|
2044
|
metPhase[:,hmet,tmet] = metArray1[:,6:].T
|
|
2044
|
metPhase[:,hmet,tmet] = metArray1[:,6:].T
|
|
|
2045
|
|
|
2045
|
|
|
|
2046
|
#Delete short trails
|
|
2046
|
#Delete short trails
|
|
|
2047
|
metBool = ~numpy.isnan(metPhase[0,:,:])
|
|
2047
|
metBool = ~numpy.isnan(metPhase[0,:,:])
|
|
|
2048
|
heightVect = numpy.sum(metBool, axis = 1)
|
|
2048
|
heightVect = numpy.sum(metBool, axis = 1)
|
|
|
2049
|
metBool[heightVect<sec,:] = False
|
|
2049
|
metBool[heightVect<sec,:] = False
|
|
|
2050
|
metPhase[:,heightVect<sec,:] = numpy.nan
|
|
2050
|
metPhase[:,heightVect<sec,:] = numpy.nan
|
|
|
2051
|
|
|
2051
|
|
|
|
2052
|
#Derivative
|
|
2052
|
#Derivative
|
|
|
2053
|
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
|
|
2053
|
metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
|
|
|
2054
|
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
|
|
2054
|
phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
|
|
|
2055
|
metPhase[phDerAux] = numpy.nan
|
|
2055
|
metPhase[phDerAux] = numpy.nan
|
|
|
2056
|
|
|
2056
|
|
|
|
2057
|
#--------------------------METEOR DETECTION -----------------------------------------
|
|
2057
|
#--------------------------METEOR DETECTION -----------------------------------------
|
|
|
2058
|
indMet = numpy.where(numpy.any(metBool,axis=1))[0]
|
|
2058
|
indMet = numpy.where(numpy.any(metBool,axis=1))[0]
|
|
|
2059
|
|
|
2059
|
|
|
|
2060
|
for p in numpy.arange(nPairs):
|
|
2060
|
for p in numpy.arange(nPairs):
|
|
|
2061
|
phase = metPhase[p,:,:]
|
|
2061
|
phase = metPhase[p,:,:]
|
|
|
2062
|
phDer = metDer[p,:,:]
|
|
2062
|
phDer = metDer[p,:,:]
|
|
|
2063
|
|
|
2063
|
|
|
|
2064
|
for h in indMet:
|
|
2064
|
for h in indMet:
|
|
|
2065
|
height = heightList[h]
|
|
2065
|
height = heightList[h]
|
|
|
2066
|
phase1 = phase[h,:] #82
|
|
2066
|
phase1 = phase[h,:] #82
|
|
|
2067
|
phDer1 = phDer[h,:]
|
|
2067
|
phDer1 = phDer[h,:]
|
|
|
2068
|
|
|
2068
|
|
|
|
2069
|
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap
|
|
2069
|
phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap
|
|
|
2070
|
|
|
2070
|
|
|
|
2071
|
indValid = numpy.where(~numpy.isnan(phase1))[0]
|
|
2071
|
indValid = numpy.where(~numpy.isnan(phase1))[0]
|
|
|
2072
|
initMet = indValid[0]
|
|
2072
|
initMet = indValid[0]
|
|
|
2073
|
endMet = 0
|
|
2073
|
endMet = 0
|
|
|
2074
|
|
|
2074
|
|
|
|
2075
|
for i in range(len(indValid)-1):
|
|
2075
|
for i in range(len(indValid)-1):
|
|
|
2076
|
|
|
2076
|
|
|
|
2077
|
#Time difference
|
|
2077
|
#Time difference
|
|
|
2078
|
inow = indValid[i]
|
|
2078
|
inow = indValid[i]
|
|
|
2079
|
inext = indValid[i+1]
|
|
2079
|
inext = indValid[i+1]
|
|
|
2080
|
idiff = inext - inow
|
|
2080
|
idiff = inext - inow
|
|
|
2081
|
#Phase difference
|
|
2081
|
#Phase difference
|
|
|
2082
|
phDiff = numpy.abs(phase1[inext] - phase1[inow])
|
|
2082
|
phDiff = numpy.abs(phase1[inext] - phase1[inow])
|
|
|
2083
|
|
|
2083
|
|
|
|
2084
|
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor
|
|
2084
|
if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor
|
|
|
2085
|
sizeTrail = inow - initMet + 1
|
|
2085
|
sizeTrail = inow - initMet + 1
|
|
|
2086
|
if sizeTrail>3*sec: #Too short meteors
|
|
2086
|
if sizeTrail>3*sec: #Too short meteors
|
|
|
2087
|
x = numpy.arange(initMet,inow+1)*ippSeconds
|
|
2087
|
x = numpy.arange(initMet,inow+1)*ippSeconds
|
|
|
2088
|
y = phase1[initMet:inow+1]
|
|
2088
|
y = phase1[initMet:inow+1]
|
|
|
2089
|
ynnan = ~numpy.isnan(y)
|
|
2089
|
ynnan = ~numpy.isnan(y)
|
|
|
2090
|
x = x[ynnan]
|
|
2090
|
x = x[ynnan]
|
|
|
2091
|
y = y[ynnan]
|
|
2091
|
y = y[ynnan]
|
|
|
2092
|
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
|
|
2092
|
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
|
|
|
2093
|
ylin = x*slope + intercept
|
|
2093
|
ylin = x*slope + intercept
|
|
|
2094
|
rsq = r_value**2
|
|
2094
|
rsq = r_value**2
|
|
|
2095
|
if rsq > 0.5:
|
|
2095
|
if rsq > 0.5:
|
|
|
2096
|
vel = slope#*height*1000/(k*d)
|
|
2096
|
vel = slope#*height*1000/(k*d)
|
|
|
2097
|
estAux = numpy.array([utctime,p,height, vel, rsq])
|
|
2097
|
estAux = numpy.array([utctime,p,height, vel, rsq])
|
|
|
2098
|
meteorList.append(estAux)
|
|
2098
|
meteorList.append(estAux)
|
|
|
2099
|
initMet = inext
|
|
2099
|
initMet = inext
|
|
|
2100
|
metArray2 = numpy.array(meteorList)
|
|
2100
|
metArray2 = numpy.array(meteorList)
|
|
|
2101
|
|
|
2101
|
|
|
|
2102
|
return metArray2
|
|
2102
|
return metArray2
|
|
|
2103
|
|
|
2103
|
|
|
|
2104
|
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
|
|
2104
|
def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
|
|
|
2105
|
|
|
2105
|
|
|
|
2106
|
azimuth1 = numpy.zeros(len(pairslist))
|
|
2106
|
azimuth1 = numpy.zeros(len(pairslist))
|
|
|
2107
|
dist = numpy.zeros(len(pairslist))
|
|
2107
|
dist = numpy.zeros(len(pairslist))
|
|
|
2108
|
|
|
2108
|
|
|
|
2109
|
for i in range(len(rx_location)):
|
|
2109
|
for i in range(len(rx_location)):
|
|
|
2110
|
ch0 = pairslist[i][0]
|
|
2110
|
ch0 = pairslist[i][0]
|
|
|
2111
|
ch1 = pairslist[i][1]
|
|
2111
|
ch1 = pairslist[i][1]
|
|
|
2112
|
|
|
2112
|
|
|
|
2113
|
diffX = rx_location[ch0][0] - rx_location[ch1][0]
|
|
2113
|
diffX = rx_location[ch0][0] - rx_location[ch1][0]
|
|
|
2114
|
diffY = rx_location[ch0][1] - rx_location[ch1][1]
|
|
2114
|
diffY = rx_location[ch0][1] - rx_location[ch1][1]
|
|
|
2115
|
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
|
|
2115
|
azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
|
|
|
2116
|
dist[i] = numpy.sqrt(diffX**2 + diffY**2)
|
|
2116
|
dist[i] = numpy.sqrt(diffX**2 + diffY**2)
|
|
|
2117
|
|
|
2117
|
|
|
|
2118
|
azimuth1 -= azimuth0
|
|
2118
|
azimuth1 -= azimuth0
|
|
|
2119
|
return azimuth1, dist
|
|
2119
|
return azimuth1, dist
|
|
|
2120
|
|
|
2120
|
|
|
|
2121
|
def techniqueNSM_DBS(self, **kwargs):
|
|
2121
|
def techniqueNSM_DBS(self, **kwargs):
|
|
|
2122
|
metArray = kwargs['metArray']
|
|
2122
|
metArray = kwargs['metArray']
|
|
|
2123
|
heightList = kwargs['heightList']
|
|
2123
|
heightList = kwargs['heightList']
|
|
|
2124
|
timeList = kwargs['timeList']
|
|
2124
|
timeList = kwargs['timeList']
|
|
|
2125
|
azimuth = kwargs['azimuth']
|
|
2125
|
azimuth = kwargs['azimuth']
|
|
|
2126
|
theta_x = numpy.array(kwargs['theta_x'])
|
|
2126
|
theta_x = numpy.array(kwargs['theta_x'])
|
|
|
2127
|
theta_y = numpy.array(kwargs['theta_y'])
|
|
2127
|
theta_y = numpy.array(kwargs['theta_y'])
|
|
|
2128
|
|
|
2128
|
|
|
|
2129
|
utctime = metArray[:,0]
|
|
2129
|
utctime = metArray[:,0]
|
|
|
2130
|
cmet = metArray[:,1].astype(int)
|
|
2130
|
cmet = metArray[:,1].astype(int)
|
|
|
2131
|
hmet = metArray[:,3].astype(int)
|
|
2131
|
hmet = metArray[:,3].astype(int)
|
|
|
2132
|
SNRmet = metArray[:,4]
|
|
2132
|
SNRmet = metArray[:,4]
|
|
|
2133
|
vmet = metArray[:,5]
|
|
2133
|
vmet = metArray[:,5]
|
|
|
2134
|
spcmet = metArray[:,6]
|
|
2134
|
spcmet = metArray[:,6]
|
|
|
2135
|
|
|
2135
|
|
|
|
2136
|
nChan = numpy.max(cmet) + 1
|
|
2136
|
nChan = numpy.max(cmet) + 1
|
|
|
2137
|
nHeights = len(heightList)
|
|
2137
|
nHeights = len(heightList)
|
|
|
2138
|
|
|
2138
|
|
|
|
2139
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
2139
|
azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
|
|
|
2140
|
hmet = heightList[hmet]
|
|
2140
|
hmet = heightList[hmet]
|
|
|
2141
|
h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights
|
|
2141
|
h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights
|
|
|
2142
|
|
|
2142
|
|
|
|
2143
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
2143
|
velEst = numpy.zeros((heightList.size,2))*numpy.nan
|
|
|
2144
|
|
|
2144
|
|
|
|
2145
|
for i in range(nHeights - 1):
|
|
2145
|
for i in range(nHeights - 1):
|
|
|
2146
|
hmin = heightList[i]
|
|
2146
|
hmin = heightList[i]
|
|
|
2147
|
hmax = heightList[i + 1]
|
|
2147
|
hmax = heightList[i + 1]
|
|
|
2148
|
|
|
2148
|
|
|
|
2149
|
thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
|
|
2149
|
thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
|
|
|
2150
|
indthisH = numpy.where(thisH)
|
|
2150
|
indthisH = numpy.where(thisH)
|
|
|
2151
|
|
|
2151
|
|
|
|
2152
|
if numpy.size(indthisH) > 3:
|
|
2152
|
if numpy.size(indthisH) > 3:
|
|
|
2153
|
|
|
2153
|
|
|
|
2154
|
vel_aux = vmet[thisH]
|
|
2154
|
vel_aux = vmet[thisH]
|
|
|
2155
|
chan_aux = cmet[thisH]
|
|
2155
|
chan_aux = cmet[thisH]
|
|
|
2156
|
cosu_aux = dir_cosu[chan_aux]
|
|
2156
|
cosu_aux = dir_cosu[chan_aux]
|
|
|
2157
|
cosv_aux = dir_cosv[chan_aux]
|
|
2157
|
cosv_aux = dir_cosv[chan_aux]
|
|
|
2158
|
cosw_aux = dir_cosw[chan_aux]
|
|
2158
|
cosw_aux = dir_cosw[chan_aux]
|
|
|
2159
|
|
|
2159
|
|
|
|
2160
|
nch = numpy.size(numpy.unique(chan_aux))
|
|
2160
|
nch = numpy.size(numpy.unique(chan_aux))
|
|
|
2161
|
if nch > 1:
|
|
2161
|
if nch > 1:
|
|
|
2162
|
A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
|
|
2162
|
A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
|
|
|
2163
|
velEst[i,:] = numpy.dot(A,vel_aux)
|
|
2163
|
velEst[i,:] = numpy.dot(A,vel_aux)
|
|
|
2164
|
|
|
2164
|
|
|
|
2165
|
return velEst
|
|
2165
|
return velEst
|
|
|
2166
|
|
|
2166
|
|
|
|
2167
|
def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
|
|
2167
|
def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
|
|
|
2168
|
|
|
2168
|
|
|
|
2169
|
param = dataOut.data_param
|
|
2169
|
param = dataOut.data_param
|
|
|
2170
|
if dataOut.abscissaList != None:
|
|
2170
|
if dataOut.abscissaList != None:
|
|
|
2171
|
absc = dataOut.abscissaList[:-1]
|
|
2171
|
absc = dataOut.abscissaList[:-1]
|
|
|
2172
|
# noise = dataOut.noise
|
|
2172
|
# noise = dataOut.noise
|
|
|
2173
|
heightList = dataOut.heightList
|
|
2173
|
heightList = dataOut.heightList
|
|
|
2174
|
SNR = dataOut.data_snr
|
|
2174
|
SNR = dataOut.data_snr
|
|
|
2175
|
|
|
2175
|
|
|
|
2176
|
if technique == 'DBS':
|
|
2176
|
if technique == 'DBS':
|
|
|
2177
|
|
|
2177
|
|
|
|
2178
|
kwargs['velRadial'] = param[:,1,:] #Radial velocity
|
|
2178
|
kwargs['velRadial'] = param[:,1,:] #Radial velocity
|
|
|
2179
|
kwargs['heightList'] = heightList
|
|
2179
|
kwargs['heightList'] = heightList
|
|
|
2180
|
kwargs['SNR'] = SNR
|
|
2180
|
kwargs['SNR'] = SNR
|
|
|
2181
|
|
|
2181
|
|
|
|
2182
|
dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function
|
|
2182
|
dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function
|
|
|
2183
|
dataOut.utctimeInit = dataOut.utctime
|
|
2183
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2184
|
dataOut.outputInterval = dataOut.paramInterval
|
|
2184
|
dataOut.outputInterval = dataOut.paramInterval
|
|
|
2185
|
|
|
2185
|
|
|
|
2186
|
elif technique == 'SA':
|
|
2186
|
elif technique == 'SA':
|
|
|
2187
|
|
|
2187
|
|
|
|
2188
|
#Parameters
|
|
2188
|
#Parameters
|
|
|
2189
|
# position_x = kwargs['positionX']
|
|
2189
|
# position_x = kwargs['positionX']
|
|
|
2190
|
# position_y = kwargs['positionY']
|
|
2190
|
# position_y = kwargs['positionY']
|
|
|
2191
|
# azimuth = kwargs['azimuth']
|
|
2191
|
# azimuth = kwargs['azimuth']
|
|
|
2192
|
#
|
|
2192
|
#
|
|
|
2193
|
# if kwargs.has_key('crosspairsList'):
|
|
2193
|
# if kwargs.has_key('crosspairsList'):
|
|
|
2194
|
# pairs = kwargs['crosspairsList']
|
|
2194
|
# pairs = kwargs['crosspairsList']
|
|
|
2195
|
# else:
|
|
2195
|
# else:
|
|
|
2196
|
# pairs = None
|
|
2196
|
# pairs = None
|
|
|
2197
|
#
|
|
2197
|
#
|
|
|
2198
|
# if kwargs.has_key('correctFactor'):
|
|
2198
|
# if kwargs.has_key('correctFactor'):
|
|
|
2199
|
# correctFactor = kwargs['correctFactor']
|
|
2199
|
# correctFactor = kwargs['correctFactor']
|
|
|
2200
|
# else:
|
|
2200
|
# else:
|
|
|
2201
|
# correctFactor = 1
|
|
2201
|
# correctFactor = 1
|
|
|
2202
|
|
|
2202
|
|
|
|
2203
|
# tau = dataOut.data_param
|
|
2203
|
# tau = dataOut.data_param
|
|
|
2204
|
# _lambda = dataOut.C/dataOut.frequency
|
|
2204
|
# _lambda = dataOut.C/dataOut.frequency
|
|
|
2205
|
# pairsList = dataOut.groupList
|
|
2205
|
# pairsList = dataOut.groupList
|
|
|
2206
|
# nChannels = dataOut.nChannels
|
|
2206
|
# nChannels = dataOut.nChannels
|
|
|
2207
|
|
|
2207
|
|
|
|
2208
|
kwargs['groupList'] = dataOut.groupList
|
|
2208
|
kwargs['groupList'] = dataOut.groupList
|
|
|
2209
|
kwargs['tau'] = dataOut.data_param
|
|
2209
|
kwargs['tau'] = dataOut.data_param
|
|
|
2210
|
kwargs['_lambda'] = dataOut.C/dataOut.frequency
|
|
2210
|
kwargs['_lambda'] = dataOut.C/dataOut.frequency
|
|
|
2211
|
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
|
|
2211
|
# dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
|
|
|
2212
|
dataOut.data_output = self.techniqueSA(kwargs)
|
|
2212
|
dataOut.data_output = self.techniqueSA(kwargs)
|
|
|
2213
|
dataOut.utctimeInit = dataOut.utctime
|
|
2213
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2214
|
dataOut.outputInterval = dataOut.timeInterval
|
|
2214
|
dataOut.outputInterval = dataOut.timeInterval
|
|
|
2215
|
|
|
2215
|
|
|
|
2216
|
elif technique == 'Meteors':
|
|
2216
|
elif technique == 'Meteors':
|
|
|
2217
|
dataOut.flagNoData = True
|
|
2217
|
dataOut.flagNoData = True
|
|
|
2218
|
self.__dataReady = False
|
|
2218
|
self.__dataReady = False
|
|
|
2219
|
|
|
2219
|
|
|
|
2220
|
if 'nHours' in kwargs:
|
|
2220
|
if 'nHours' in kwargs:
|
|
|
2221
|
nHours = kwargs['nHours']
|
|
2221
|
nHours = kwargs['nHours']
|
|
|
2222
|
else:
|
|
2222
|
else:
|
|
|
2223
|
nHours = 1
|
|
2223
|
nHours = 1
|
|
|
2224
|
|
|
2224
|
|
|
|
2225
|
if 'meteorsPerBin' in kwargs:
|
|
2225
|
if 'meteorsPerBin' in kwargs:
|
|
|
2226
|
meteorThresh = kwargs['meteorsPerBin']
|
|
2226
|
meteorThresh = kwargs['meteorsPerBin']
|
|
|
2227
|
else:
|
|
2227
|
else:
|
|
|
2228
|
meteorThresh = 6
|
|
2228
|
meteorThresh = 6
|
|
|
2229
|
|
|
2229
|
|
|
|
2230
|
if 'hmin' in kwargs:
|
|
2230
|
if 'hmin' in kwargs:
|
|
|
2231
|
hmin = kwargs['hmin']
|
|
2231
|
hmin = kwargs['hmin']
|
|
|
2232
|
else: hmin = 70
|
|
2232
|
else: hmin = 70
|
|
|
2233
|
if 'hmax' in kwargs:
|
|
2233
|
if 'hmax' in kwargs:
|
|
|
2234
|
hmax = kwargs['hmax']
|
|
2234
|
hmax = kwargs['hmax']
|
|
|
2235
|
else: hmax = 110
|
|
2235
|
else: hmax = 110
|
|
|
2236
|
|
|
2236
|
|
|
|
2237
|
dataOut.outputInterval = nHours*3600
|
|
2237
|
dataOut.outputInterval = nHours*3600
|
|
|
2238
|
|
|
2238
|
|
|
|
2239
|
if self.__isConfig == False:
|
|
2239
|
if self.__isConfig == False:
|
|
|
2240
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
2240
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
2241
|
#Get Initial LTC time
|
|
2241
|
#Get Initial LTC time
|
|
|
2242
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
2242
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
2243
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
2243
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
2244
|
|
|
2244
|
|
|
|
2245
|
self.__isConfig = True
|
|
2245
|
self.__isConfig = True
|
|
|
2246
|
|
|
2246
|
|
|
|
2247
|
if self.__buffer is None:
|
|
2247
|
if self.__buffer is None:
|
|
|
2248
|
self.__buffer = dataOut.data_param
|
|
2248
|
self.__buffer = dataOut.data_param
|
|
|
2249
|
self.__firstdata = copy.copy(dataOut)
|
|
2249
|
self.__firstdata = copy.copy(dataOut)
|
|
|
2250
|
|
|
2250
|
|
|
|
2251
|
else:
|
|
2251
|
else:
|
|
|
2252
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
2252
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
2253
|
|
|
2253
|
|
|
|
2254
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
2254
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
2255
|
|
|
2255
|
|
|
|
2256
|
if self.__dataReady:
|
|
2256
|
if self.__dataReady:
|
|
|
2257
|
dataOut.utctimeInit = self.__initime
|
|
2257
|
dataOut.utctimeInit = self.__initime
|
|
|
2258
|
|
|
2258
|
|
|
|
2259
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
2259
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
2260
|
|
|
2260
|
|
|
|
2261
|
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
|
|
2261
|
dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
|
|
|
2262
|
dataOut.flagNoData = False
|
|
2262
|
dataOut.flagNoData = False
|
|
|
2263
|
self.__buffer = None
|
|
2263
|
self.__buffer = None
|
|
|
2264
|
|
|
2264
|
|
|
|
2265
|
elif technique == 'Meteors1':
|
|
2265
|
elif technique == 'Meteors1':
|
|
|
2266
|
dataOut.flagNoData = True
|
|
2266
|
dataOut.flagNoData = True
|
|
|
2267
|
self.__dataReady = False
|
|
2267
|
self.__dataReady = False
|
|
|
2268
|
|
|
2268
|
|
|
|
2269
|
if 'nMins' in kwargs:
|
|
2269
|
if 'nMins' in kwargs:
|
|
|
2270
|
nMins = kwargs['nMins']
|
|
2270
|
nMins = kwargs['nMins']
|
|
|
2271
|
else: nMins = 20
|
|
2271
|
else: nMins = 20
|
|
|
2272
|
if 'rx_location' in kwargs:
|
|
2272
|
if 'rx_location' in kwargs:
|
|
|
2273
|
rx_location = kwargs['rx_location']
|
|
2273
|
rx_location = kwargs['rx_location']
|
|
|
2274
|
else: rx_location = [(0,1),(1,1),(1,0)]
|
|
2274
|
else: rx_location = [(0,1),(1,1),(1,0)]
|
|
|
2275
|
if 'azimuth' in kwargs:
|
|
2275
|
if 'azimuth' in kwargs:
|
|
|
2276
|
azimuth = kwargs['azimuth']
|
|
2276
|
azimuth = kwargs['azimuth']
|
|
|
2277
|
else: azimuth = 51.06
|
|
2277
|
else: azimuth = 51.06
|
|
|
2278
|
if 'dfactor' in kwargs:
|
|
2278
|
if 'dfactor' in kwargs:
|
|
|
2279
|
dfactor = kwargs['dfactor']
|
|
2279
|
dfactor = kwargs['dfactor']
|
|
|
2280
|
if 'mode' in kwargs:
|
|
2280
|
if 'mode' in kwargs:
|
|
|
2281
|
mode = kwargs['mode']
|
|
2281
|
mode = kwargs['mode']
|
|
|
2282
|
if 'theta_x' in kwargs:
|
|
2282
|
if 'theta_x' in kwargs:
|
|
|
2283
|
theta_x = kwargs['theta_x']
|
|
2283
|
theta_x = kwargs['theta_x']
|
|
|
2284
|
if 'theta_y' in kwargs:
|
|
2284
|
if 'theta_y' in kwargs:
|
|
|
2285
|
theta_y = kwargs['theta_y']
|
|
2285
|
theta_y = kwargs['theta_y']
|
|
|
2286
|
else: mode = 'SA'
|
|
2286
|
else: mode = 'SA'
|
|
|
2287
|
|
|
2287
|
|
|
|
2288
|
#Borrar luego esto
|
|
2288
|
#Borrar luego esto
|
|
|
2289
|
if dataOut.groupList is None:
|
|
2289
|
if dataOut.groupList is None:
|
|
|
2290
|
dataOut.groupList = [(0,1),(0,2),(1,2)]
|
|
2290
|
dataOut.groupList = [(0,1),(0,2),(1,2)]
|
|
|
2291
|
groupList = dataOut.groupList
|
|
2291
|
groupList = dataOut.groupList
|
|
|
2292
|
C = 3e8
|
|
2292
|
C = 3e8
|
|
|
2293
|
freq = 50e6
|
|
2293
|
freq = 50e6
|
|
|
2294
|
lamb = C/freq
|
|
2294
|
lamb = C/freq
|
|
|
2295
|
k = 2*numpy.pi/lamb
|
|
2295
|
k = 2*numpy.pi/lamb
|
|
|
2296
|
|
|
2296
|
|
|
|
2297
|
timeList = dataOut.abscissaList
|
|
2297
|
timeList = dataOut.abscissaList
|
|
|
2298
|
heightList = dataOut.heightList
|
|
2298
|
heightList = dataOut.heightList
|
|
|
2299
|
|
|
2299
|
|
|
|
2300
|
if self.__isConfig == False:
|
|
2300
|
if self.__isConfig == False:
|
|
|
2301
|
dataOut.outputInterval = nMins*60
|
|
2301
|
dataOut.outputInterval = nMins*60
|
|
|
2302
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
2302
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
2303
|
#Get Initial LTC time
|
|
2303
|
#Get Initial LTC time
|
|
|
2304
|
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
2304
|
initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
2305
|
minuteAux = initime.minute
|
|
2305
|
minuteAux = initime.minute
|
|
|
2306
|
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
|
|
2306
|
minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
|
|
|
2307
|
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
2307
|
self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
2308
|
|
|
2308
|
|
|
|
2309
|
self.__isConfig = True
|
|
2309
|
self.__isConfig = True
|
|
|
2310
|
|
|
2310
|
|
|
|
2311
|
if self.__buffer is None:
|
|
2311
|
if self.__buffer is None:
|
|
|
2312
|
self.__buffer = dataOut.data_param
|
|
2312
|
self.__buffer = dataOut.data_param
|
|
|
2313
|
self.__firstdata = copy.copy(dataOut)
|
|
2313
|
self.__firstdata = copy.copy(dataOut)
|
|
|
2314
|
|
|
2314
|
|
|
|
2315
|
else:
|
|
2315
|
else:
|
|
|
2316
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
2316
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
2317
|
|
|
2317
|
|
|
|
2318
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
2318
|
self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
2319
|
|
|
2319
|
|
|
|
2320
|
if self.__dataReady:
|
|
2320
|
if self.__dataReady:
|
|
|
2321
|
dataOut.utctimeInit = self.__initime
|
|
2321
|
dataOut.utctimeInit = self.__initime
|
|
|
2322
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
2322
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
2323
|
|
|
2323
|
|
|
|
2324
|
metArray = self.__buffer
|
|
2324
|
metArray = self.__buffer
|
|
|
2325
|
if mode == 'SA':
|
|
2325
|
if mode == 'SA':
|
|
|
2326
|
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
|
|
2326
|
dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
|
|
|
2327
|
elif mode == 'DBS':
|
|
2327
|
elif mode == 'DBS':
|
|
|
2328
|
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
|
|
2328
|
dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
|
|
|
2329
|
dataOut.data_output = dataOut.data_output.T
|
|
2329
|
dataOut.data_output = dataOut.data_output.T
|
|
|
2330
|
dataOut.flagNoData = False
|
|
2330
|
dataOut.flagNoData = False
|
|
|
2331
|
self.__buffer = None
|
|
2331
|
self.__buffer = None
|
|
|
2332
|
|
|
2332
|
|
|
|
2333
|
return
|
|
2333
|
return
|
|
|
2334
|
|
|
2334
|
|
|
|
2335
|
class EWDriftsEstimation(Operation):
|
|
2335
|
class EWDriftsEstimation(Operation):
|
|
|
2336
|
|
|
2336
|
|
|
|
2337
|
def __init__(self):
|
|
2337
|
def __init__(self):
|
|
|
2338
|
Operation.__init__(self)
|
|
2338
|
Operation.__init__(self)
|
|
|
2339
|
|
|
2339
|
|
|
|
2340
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
2340
|
def __correctValues(self, heiRang, phi, velRadial, SNR):
|
|
|
2341
|
listPhi = phi.tolist()
|
|
2341
|
listPhi = phi.tolist()
|
|
|
2342
|
maxid = listPhi.index(max(listPhi))
|
|
2342
|
maxid = listPhi.index(max(listPhi))
|
|
|
2343
|
minid = listPhi.index(min(listPhi))
|
|
2343
|
minid = listPhi.index(min(listPhi))
|
|
|
2344
|
|
|
2344
|
|
|
|
2345
|
rango = list(range(len(phi)))
|
|
2345
|
rango = list(range(len(phi)))
|
|
|
2346
|
# rango = numpy.delete(rango,maxid)
|
|
2346
|
# rango = numpy.delete(rango,maxid)
|
|
|
2347
|
|
|
2347
|
|
|
|
2348
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
2348
|
heiRang1 = heiRang*math.cos(phi[maxid])
|
|
|
2349
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
2349
|
heiRangAux = heiRang*math.cos(phi[minid])
|
|
|
2350
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
2350
|
indOut = (heiRang1 < heiRangAux[0]).nonzero()
|
|
|
2351
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
2351
|
heiRang1 = numpy.delete(heiRang1,indOut)
|
|
|
2352
|
|
|
2352
|
|
|
|
2353
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
2353
|
velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
2354
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
2354
|
SNR1 = numpy.zeros([len(phi),len(heiRang1)])
|
|
|
2355
|
|
|
2355
|
|
|
|
2356
|
for i in rango:
|
|
2356
|
for i in rango:
|
|
|
2357
|
x = heiRang*math.cos(phi[i])
|
|
2357
|
x = heiRang*math.cos(phi[i])
|
|
|
2358
|
y1 = velRadial[i,:]
|
|
2358
|
y1 = velRadial[i,:]
|
|
|
2359
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
2359
|
f1 = interpolate.interp1d(x,y1,kind = 'cubic')
|
|
|
2360
|
|
|
2360
|
|
|
|
2361
|
x1 = heiRang1
|
|
2361
|
x1 = heiRang1
|
|
|
2362
|
y11 = f1(x1)
|
|
2362
|
y11 = f1(x1)
|
|
|
2363
|
|
|
2363
|
|
|
|
2364
|
y2 = SNR[i,:]
|
|
2364
|
y2 = SNR[i,:]
|
|
|
2365
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
2365
|
f2 = interpolate.interp1d(x,y2,kind = 'cubic')
|
|
|
2366
|
y21 = f2(x1)
|
|
2366
|
y21 = f2(x1)
|
|
|
2367
|
|
|
2367
|
|
|
|
2368
|
velRadial1[i,:] = y11
|
|
2368
|
velRadial1[i,:] = y11
|
|
|
2369
|
SNR1[i,:] = y21
|
|
2369
|
SNR1[i,:] = y21
|
|
|
2370
|
|
|
2370
|
|
|
|
2371
|
return heiRang1, velRadial1, SNR1
|
|
2371
|
return heiRang1, velRadial1, SNR1
|
|
|
2372
|
|
|
2372
|
|
|
|
2373
|
def run(self, dataOut, zenith, zenithCorrection):
|
|
2373
|
def run(self, dataOut, zenith, zenithCorrection):
|
|
|
2374
|
heiRang = dataOut.heightList
|
|
2374
|
heiRang = dataOut.heightList
|
|
|
2375
|
velRadial = dataOut.data_param[:,3,:]
|
|
2375
|
velRadial = dataOut.data_param[:,3,:]
|
|
|
2376
|
SNR = dataOut.data_snr
|
|
2376
|
SNR = dataOut.data_snr
|
|
|
2377
|
|
|
2377
|
|
|
|
2378
|
zenith = numpy.array(zenith)
|
|
2378
|
zenith = numpy.array(zenith)
|
|
|
2379
|
zenith -= zenithCorrection
|
|
2379
|
zenith -= zenithCorrection
|
|
|
2380
|
zenith *= numpy.pi/180
|
|
2380
|
zenith *= numpy.pi/180
|
|
|
2381
|
|
|
2381
|
|
|
|
2382
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
|
|
2382
|
heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
|
|
|
2383
|
|
|
2383
|
|
|
|
2384
|
alp = zenith[0]
|
|
2384
|
alp = zenith[0]
|
|
|
2385
|
bet = zenith[1]
|
|
2385
|
bet = zenith[1]
|
|
|
2386
|
|
|
2386
|
|
|
|
2387
|
w_w = velRadial1[0,:]
|
|
2387
|
w_w = velRadial1[0,:]
|
|
|
2388
|
w_e = velRadial1[1,:]
|
|
2388
|
w_e = velRadial1[1,:]
|
|
|
2389
|
|
|
2389
|
|
|
|
2390
|
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
|
|
2390
|
w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
|
|
|
2391
|
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
|
|
2391
|
u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
|
|
|
2392
|
|
|
2392
|
|
|
|
2393
|
winds = numpy.vstack((u,w))
|
|
2393
|
winds = numpy.vstack((u,w))
|
|
|
2394
|
|
|
2394
|
|
|
|
2395
|
dataOut.heightList = heiRang1
|
|
2395
|
dataOut.heightList = heiRang1
|
|
|
2396
|
dataOut.data_output = winds
|
|
2396
|
dataOut.data_output = winds
|
|
|
2397
|
dataOut.data_snr = SNR1
|
|
2397
|
dataOut.data_snr = SNR1
|
|
|
2398
|
|
|
2398
|
|
|
|
2399
|
dataOut.utctimeInit = dataOut.utctime
|
|
2399
|
dataOut.utctimeInit = dataOut.utctime
|
|
|
2400
|
dataOut.outputInterval = dataOut.timeInterval
|
|
2400
|
dataOut.outputInterval = dataOut.timeInterval
|
|
|
2401
|
return
|
|
2401
|
return
|
|
|
2402
|
|
|
2402
|
|
|
|
2403
|
#--------------- Non Specular Meteor ----------------
|
|
2403
|
#--------------- Non Specular Meteor ----------------
|
|
|
2404
|
|
|
2404
|
|
|
|
2405
|
class NonSpecularMeteorDetection(Operation):
|
|
2405
|
class NonSpecularMeteorDetection(Operation):
|
|
|
2406
|
|
|
2406
|
|
|
|
2407
|
def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
|
|
2407
|
def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
|
|
|
2408
|
data_acf = dataOut.data_pre[0]
|
|
2408
|
data_acf = dataOut.data_pre[0]
|
|
|
2409
|
data_ccf = dataOut.data_pre[1]
|
|
2409
|
data_ccf = dataOut.data_pre[1]
|
|
|
2410
|
pairsList = dataOut.groupList[1]
|
|
2410
|
pairsList = dataOut.groupList[1]
|
|
|
2411
|
|
|
2411
|
|
|
|
2412
|
lamb = dataOut.C/dataOut.frequency
|
|
2412
|
lamb = dataOut.C/dataOut.frequency
|
|
|
2413
|
tSamp = dataOut.ippSeconds*dataOut.nCohInt
|
|
2413
|
tSamp = dataOut.ippSeconds*dataOut.nCohInt
|
|
|
2414
|
paramInterval = dataOut.paramInterval
|
|
2414
|
paramInterval = dataOut.paramInterval
|
|
|
2415
|
|
|
2415
|
|
|
|
2416
|
nChannels = data_acf.shape[0]
|
|
2416
|
nChannels = data_acf.shape[0]
|
|
|
2417
|
nLags = data_acf.shape[1]
|
|
2417
|
nLags = data_acf.shape[1]
|
|
|
2418
|
nProfiles = data_acf.shape[2]
|
|
2418
|
nProfiles = data_acf.shape[2]
|
|
|
2419
|
nHeights = dataOut.nHeights
|
|
2419
|
nHeights = dataOut.nHeights
|
|
|
2420
|
nCohInt = dataOut.nCohInt
|
|
2420
|
nCohInt = dataOut.nCohInt
|
|
|
2421
|
sec = numpy.round(nProfiles/dataOut.paramInterval)
|
|
2421
|
sec = numpy.round(nProfiles/dataOut.paramInterval)
|
|
|
2422
|
heightList = dataOut.heightList
|
|
2422
|
heightList = dataOut.heightList
|
|
|
2423
|
ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
|
|
2423
|
ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
|
|
|
2424
|
utctime = dataOut.utctime
|
|
2424
|
utctime = dataOut.utctime
|
|
|
2425
|
|
|
2425
|
|
|
|
2426
|
dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
|
|
2426
|
dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
|
|
|
2427
|
|
|
2427
|
|
|
|
2428
|
#------------------------ SNR --------------------------------------
|
|
2428
|
#------------------------ SNR --------------------------------------
|
|
|
2429
|
power = data_acf[:,0,:,:].real
|
|
2429
|
power = data_acf[:,0,:,:].real
|
|
|
2430
|
noise = numpy.zeros(nChannels)
|
|
2430
|
noise = numpy.zeros(nChannels)
|
|
|
2431
|
SNR = numpy.zeros(power.shape)
|
|
2431
|
SNR = numpy.zeros(power.shape)
|
|
|
2432
|
for i in range(nChannels):
|
|
2432
|
for i in range(nChannels):
|
|
|
2433
|
noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
|
|
2433
|
noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
|
|
|
2434
|
SNR[i] = (power[i]-noise[i])/noise[i]
|
|
2434
|
SNR[i] = (power[i]-noise[i])/noise[i]
|
|
|
2435
|
SNRm = numpy.nanmean(SNR, axis = 0)
|
|
2435
|
SNRm = numpy.nanmean(SNR, axis = 0)
|
|
|
2436
|
SNRdB = 10*numpy.log10(SNR)
|
|
2436
|
SNRdB = 10*numpy.log10(SNR)
|
|
|
2437
|
|
|
2437
|
|
|
|
2438
|
if mode == 'SA':
|
|
2438
|
if mode == 'SA':
|
|
|
2439
|
dataOut.groupList = dataOut.groupList[1]
|
|
2439
|
dataOut.groupList = dataOut.groupList[1]
|
|
|
2440
|
nPairs = data_ccf.shape[0]
|
|
2440
|
nPairs = data_ccf.shape[0]
|
|
|
2441
|
#---------------------- Coherence and Phase --------------------------
|
|
2441
|
#---------------------- Coherence and Phase --------------------------
|
|
|
2442
|
phase = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
2442
|
phase = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
|
2443
|
# phase1 = numpy.copy(phase)
|
|
2443
|
# phase1 = numpy.copy(phase)
|
|
|
2444
|
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
2444
|
coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
|
|
|
2445
|
|
|
2445
|
|
|
|
2446
|
for p in range(nPairs):
|
|
2446
|
for p in range(nPairs):
|
|
|
2447
|
ch0 = pairsList[p][0]
|
|
2447
|
ch0 = pairsList[p][0]
|
|
|
2448
|
ch1 = pairsList[p][1]
|
|
2448
|
ch1 = pairsList[p][1]
|
|
|
2449
|
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
|
|
2449
|
ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
|
|
|
2450
|
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
|
|
2450
|
phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
|
|
|
2451
|
# phase1[p,:,:] = numpy.angle(ccf) #median filter
|
|
2451
|
# phase1[p,:,:] = numpy.angle(ccf) #median filter
|
|
|
2452
|
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
|
|
2452
|
coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
|
|
|
2453
|
# coh1[p,:,:] = numpy.abs(ccf) #median filter
|
|
2453
|
# coh1[p,:,:] = numpy.abs(ccf) #median filter
|
|
|
2454
|
coh = numpy.nanmax(coh1, axis = 0)
|
|
2454
|
coh = numpy.nanmax(coh1, axis = 0)
|
|
|
2455
|
# struc = numpy.ones((5,1))
|
|
2455
|
# struc = numpy.ones((5,1))
|
|
|
2456
|
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
|
|
2456
|
# coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
|
|
|
2457
|
#---------------------- Radial Velocity ----------------------------
|
|
2457
|
#---------------------- Radial Velocity ----------------------------
|
|
|
2458
|
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
|
|
2458
|
phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
|
|
|
2459
|
velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
|
|
2459
|
velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
|
|
|
2460
|
|
|
2460
|
|
|
|
2461
|
if allData:
|
|
2461
|
if allData:
|
|
|
2462
|
boolMetFin = ~numpy.isnan(SNRm)
|
|
2462
|
boolMetFin = ~numpy.isnan(SNRm)
|
|
|
2463
|
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
2463
|
# coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
|
2464
|
else:
|
|
2464
|
else:
|
|
|
2465
|
#------------------------ Meteor mask ---------------------------------
|
|
2465
|
#------------------------ Meteor mask ---------------------------------
|
|
|
2466
|
# #SNR mask
|
|
2466
|
# #SNR mask
|
|
|
2467
|
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
|
|
2467
|
# boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
|
|
|
2468
|
#
|
|
2468
|
#
|
|
|
2469
|
# #Erase small objects
|
|
2469
|
# #Erase small objects
|
|
|
2470
|
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
|
|
2470
|
# boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
|
|
|
2471
|
#
|
|
2471
|
#
|
|
|
2472
|
# auxEEJ = numpy.sum(boolMet1,axis=0)
|
|
2472
|
# auxEEJ = numpy.sum(boolMet1,axis=0)
|
|
|
2473
|
# indOver = auxEEJ>nProfiles*0.8 #Use this later
|
|
2473
|
# indOver = auxEEJ>nProfiles*0.8 #Use this later
|
|
|
2474
|
# indEEJ = numpy.where(indOver)[0]
|
|
2474
|
# indEEJ = numpy.where(indOver)[0]
|
|
|
2475
|
# indNEEJ = numpy.where(~indOver)[0]
|
|
2475
|
# indNEEJ = numpy.where(~indOver)[0]
|
|
|
2476
|
#
|
|
2476
|
#
|
|
|
2477
|
# boolMetFin = boolMet1
|
|
2477
|
# boolMetFin = boolMet1
|
|
|
2478
|
#
|
|
2478
|
#
|
|
|
2479
|
# if indEEJ.size > 0:
|
|
2479
|
# if indEEJ.size > 0:
|
|
|
2480
|
# boolMet1[:,indEEJ] = False #Erase heights with EEJ
|
|
2480
|
# boolMet1[:,indEEJ] = False #Erase heights with EEJ
|
|
|
2481
|
#
|
|
2481
|
#
|
|
|
2482
|
# boolMet2 = coh > cohThresh
|
|
2482
|
# boolMet2 = coh > cohThresh
|
|
|
2483
|
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
|
|
2483
|
# boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
|
|
|
2484
|
#
|
|
2484
|
#
|
|
|
2485
|
# #Final Meteor mask
|
|
2485
|
# #Final Meteor mask
|
|
|
2486
|
# boolMetFin = boolMet1|boolMet2
|
|
2486
|
# boolMetFin = boolMet1|boolMet2
|
|
|
2487
|
|
|
2487
|
|
|
|
2488
|
#Coherence mask
|
|
2488
|
#Coherence mask
|
|
|
2489
|
boolMet1 = coh > 0.75
|
|
2489
|
boolMet1 = coh > 0.75
|
|
|
2490
|
struc = numpy.ones((30,1))
|
|
2490
|
struc = numpy.ones((30,1))
|
|
|
2491
|
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
|
|
2491
|
boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
|
|
|
2492
|
|
|
2492
|
|
|
|
2493
|
#Derivative mask
|
|
2493
|
#Derivative mask
|
|
|
2494
|
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
2494
|
derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
|
|
|
2495
|
boolMet2 = derPhase < 0.2
|
|
2495
|
boolMet2 = derPhase < 0.2
|
|
|
2496
|
# boolMet2 = ndimage.morphology.binary_opening(boolMet2)
|
|
2496
|
# boolMet2 = ndimage.morphology.binary_opening(boolMet2)
|
|
|
2497
|
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
|
|
2497
|
# boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
|
|
|
2498
|
boolMet2 = ndimage.median_filter(boolMet2,size=5)
|
|
2498
|
boolMet2 = ndimage.median_filter(boolMet2,size=5)
|
|
|
2499
|
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
|
|
2499
|
boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
|
|
|
2500
|
# #Final mask
|
|
2500
|
# #Final mask
|
|
|
2501
|
# boolMetFin = boolMet2
|
|
2501
|
# boolMetFin = boolMet2
|
|
|
2502
|
boolMetFin = boolMet1&boolMet2
|
|
2502
|
boolMetFin = boolMet1&boolMet2
|
|
|
2503
|
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
|
|
2503
|
# boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
|
|
|
2504
|
#Creating data_param
|
|
2504
|
#Creating data_param
|
|
|
2505
|
coordMet = numpy.where(boolMetFin)
|
|
2505
|
coordMet = numpy.where(boolMetFin)
|
|
|
2506
|
|
|
2506
|
|
|
|
2507
|
tmet = coordMet[0]
|
|
2507
|
tmet = coordMet[0]
|
|
|
2508
|
hmet = coordMet[1]
|
|
2508
|
hmet = coordMet[1]
|
|
|
2509
|
|
|
2509
|
|
|
|
2510
|
data_param = numpy.zeros((tmet.size, 6 + nPairs))
|
|
2510
|
data_param = numpy.zeros((tmet.size, 6 + nPairs))
|
|
|
2511
|
data_param[:,0] = utctime
|
|
2511
|
data_param[:,0] = utctime
|
|
|
2512
|
data_param[:,1] = tmet
|
|
2512
|
data_param[:,1] = tmet
|
|
|
2513
|
data_param[:,2] = hmet
|
|
2513
|
data_param[:,2] = hmet
|
|
|
2514
|
data_param[:,3] = SNRm[tmet,hmet]
|
|
2514
|
data_param[:,3] = SNRm[tmet,hmet]
|
|
|
2515
|
data_param[:,4] = velRad[tmet,hmet]
|
|
2515
|
data_param[:,4] = velRad[tmet,hmet]
|
|
|
2516
|
data_param[:,5] = coh[tmet,hmet]
|
|
2516
|
data_param[:,5] = coh[tmet,hmet]
|
|
|
2517
|
data_param[:,6:] = phase[:,tmet,hmet].T
|
|
2517
|
data_param[:,6:] = phase[:,tmet,hmet].T
|
|
|
2518
|
|
|
2518
|
|
|
|
2519
|
elif mode == 'DBS':
|
|
2519
|
elif mode == 'DBS':
|
|
|
2520
|
dataOut.groupList = numpy.arange(nChannels)
|
|
2520
|
dataOut.groupList = numpy.arange(nChannels)
|
|
|
2521
|
|
|
2521
|
|
|
|
2522
|
#Radial Velocities
|
|
2522
|
#Radial Velocities
|
|
|
2523
|
phase = numpy.angle(data_acf[:,1,:,:])
|
|
2523
|
phase = numpy.angle(data_acf[:,1,:,:])
|
|
|
2524
|
# phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
|
|
2524
|
# phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
|
|
|
2525
|
velRad = phase*lamb/(4*numpy.pi*tSamp)
|
|
2525
|
velRad = phase*lamb/(4*numpy.pi*tSamp)
|
|
|
2526
|
|
|
2526
|
|
|
|
2527
|
#Spectral width
|
|
2527
|
#Spectral width
|
|
|
2528
|
# acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
|
|
2528
|
# acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
|
|
|
2529
|
# acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
|
|
2529
|
# acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
|
|
|
2530
|
acf1 = data_acf[:,1,:,:]
|
|
2530
|
acf1 = data_acf[:,1,:,:]
|
|
|
2531
|
acf2 = data_acf[:,2,:,:]
|
|
2531
|
acf2 = data_acf[:,2,:,:]
|
|
|
2532
|
|
|
2532
|
|
|
|
2533
|
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
|
|
2533
|
spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
|
|
|
2534
|
# velRad = ndimage.median_filter(velRad, size = (1,5,1))
|
|
2534
|
# velRad = ndimage.median_filter(velRad, size = (1,5,1))
|
|
|
2535
|
if allData:
|
|
2535
|
if allData:
|
|
|
2536
|
boolMetFin = ~numpy.isnan(SNRdB)
|
|
2536
|
boolMetFin = ~numpy.isnan(SNRdB)
|
|
|
2537
|
else:
|
|
2537
|
else:
|
|
|
2538
|
#SNR
|
|
2538
|
#SNR
|
|
|
2539
|
boolMet1 = (SNRdB>SNRthresh) #SNR mask
|
|
2539
|
boolMet1 = (SNRdB>SNRthresh) #SNR mask
|
|
|
2540
|
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
|
|
2540
|
boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
|
|
|
2541
|
|
|
2541
|
|
|
|
2542
|
#Radial velocity
|
|
2542
|
#Radial velocity
|
|
|
2543
|
boolMet2 = numpy.abs(velRad) < 20
|
|
2543
|
boolMet2 = numpy.abs(velRad) < 20
|
|
|
2544
|
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
|
|
2544
|
boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
|
|
|
2545
|
|
|
2545
|
|
|
|
2546
|
#Spectral Width
|
|
2546
|
#Spectral Width
|
|
|
2547
|
boolMet3 = spcWidth < 30
|
|
2547
|
boolMet3 = spcWidth < 30
|
|
|
2548
|
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
|
|
2548
|
boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
|
|
|
2549
|
# boolMetFin = self.__erase_small(boolMet1, 10,5)
|
|
2549
|
# boolMetFin = self.__erase_small(boolMet1, 10,5)
|
|
|
2550
|
boolMetFin = boolMet1&boolMet2&boolMet3
|
|
2550
|
boolMetFin = boolMet1&boolMet2&boolMet3
|
|
|
2551
|
|
|
2551
|
|
|
|
2552
|
#Creating data_param
|
|
2552
|
#Creating data_param
|
|
|
2553
|
coordMet = numpy.where(boolMetFin)
|
|
2553
|
coordMet = numpy.where(boolMetFin)
|
|
|
2554
|
|
|
2554
|
|
|
|
2555
|
cmet = coordMet[0]
|
|
2555
|
cmet = coordMet[0]
|
|
|
2556
|
tmet = coordMet[1]
|
|
2556
|
tmet = coordMet[1]
|
|
|
2557
|
hmet = coordMet[2]
|
|
2557
|
hmet = coordMet[2]
|
|
|
2558
|
|
|
2558
|
|
|
|
2559
|
data_param = numpy.zeros((tmet.size, 7))
|
|
2559
|
data_param = numpy.zeros((tmet.size, 7))
|
|
|
2560
|
data_param[:,0] = utctime
|
|
2560
|
data_param[:,0] = utctime
|
|
|
2561
|
data_param[:,1] = cmet
|
|
2561
|
data_param[:,1] = cmet
|
|
|
2562
|
data_param[:,2] = tmet
|
|
2562
|
data_param[:,2] = tmet
|
|
|
2563
|
data_param[:,3] = hmet
|
|
2563
|
data_param[:,3] = hmet
|
|
|
2564
|
data_param[:,4] = SNR[cmet,tmet,hmet].T
|
|
2564
|
data_param[:,4] = SNR[cmet,tmet,hmet].T
|
|
|
2565
|
data_param[:,5] = velRad[cmet,tmet,hmet].T
|
|
2565
|
data_param[:,5] = velRad[cmet,tmet,hmet].T
|
|
|
2566
|
data_param[:,6] = spcWidth[cmet,tmet,hmet].T
|
|
2566
|
data_param[:,6] = spcWidth[cmet,tmet,hmet].T
|
|
|
2567
|
|
|
2567
|
|
|
|
2568
|
# self.dataOut.data_param = data_int
|
|
2568
|
# self.dataOut.data_param = data_int
|
|
|
2569
|
if len(data_param) == 0:
|
|
2569
|
if len(data_param) == 0:
|
|
|
2570
|
dataOut.flagNoData = True
|
|
2570
|
dataOut.flagNoData = True
|
|
|
2571
|
else:
|
|
2571
|
else:
|
|
|
2572
|
dataOut.data_param = data_param
|
|
2572
|
dataOut.data_param = data_param
|
|
|
2573
|
|
|
2573
|
|
|
|
2574
|
def __erase_small(self, binArray, threshX, threshY):
|
|
2574
|
def __erase_small(self, binArray, threshX, threshY):
|
|
|
2575
|
labarray, numfeat = ndimage.measurements.label(binArray)
|
|
2575
|
labarray, numfeat = ndimage.measurements.label(binArray)
|
|
|
2576
|
binArray1 = numpy.copy(binArray)
|
|
2576
|
binArray1 = numpy.copy(binArray)
|
|
|
2577
|
|
|
2577
|
|
|
|
2578
|
for i in range(1,numfeat + 1):
|
|
2578
|
for i in range(1,numfeat + 1):
|
|
|
2579
|
auxBin = (labarray==i)
|
|
2579
|
auxBin = (labarray==i)
|
|
|
2580
|
auxSize = auxBin.sum()
|
|
2580
|
auxSize = auxBin.sum()
|
|
|
2581
|
|
|
2581
|
|
|
|
2582
|
x,y = numpy.where(auxBin)
|
|
2582
|
x,y = numpy.where(auxBin)
|
|
|
2583
|
widthX = x.max() - x.min()
|
|
2583
|
widthX = x.max() - x.min()
|
|
|
2584
|
widthY = y.max() - y.min()
|
|
2584
|
widthY = y.max() - y.min()
|
|
|
2585
|
|
|
2585
|
|
|
|
2586
|
#width X: 3 seg -> 12.5*3
|
|
2586
|
#width X: 3 seg -> 12.5*3
|
|
|
2587
|
#width Y:
|
|
2587
|
#width Y:
|
|
|
2588
|
|
|
2588
|
|
|
|
2589
|
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
|
|
2589
|
if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
|
|
|
2590
|
binArray1[auxBin] = False
|
|
2590
|
binArray1[auxBin] = False
|
|
|
2591
|
|
|
2591
|
|
|
|
2592
|
return binArray1
|
|
2592
|
return binArray1
|
|
|
2593
|
|
|
2593
|
|
|
|
2594
|
#--------------- Specular Meteor ----------------
|
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2594
|
#--------------- Specular Meteor ----------------
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|
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2595
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2595
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2596
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class SMDetection(Operation):
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2596
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class SMDetection(Operation):
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2597
|
'''
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2597
|
'''
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2598
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Function DetectMeteors()
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2598
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Function DetectMeteors()
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2599
|
Project developed with paper:
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2599
|
Project developed with paper:
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2600
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HOLDSWORTH ET AL. 2004
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2600
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HOLDSWORTH ET AL. 2004
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2601
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2601
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2602
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Input:
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2602
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Input:
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2603
|
self.dataOut.data_pre
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2603
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self.dataOut.data_pre
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2604
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2604
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2605
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centerReceiverIndex: From the channels, which is the center receiver
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2605
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centerReceiverIndex: From the channels, which is the center receiver
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2606
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2606
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2607
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hei_ref: Height reference for the Beacon signal extraction
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2607
|
hei_ref: Height reference for the Beacon signal extraction
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2608
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tauindex:
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2608
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tauindex:
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2609
|
predefinedPhaseShifts: Predefined phase offset for the voltge signals
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2609
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predefinedPhaseShifts: Predefined phase offset for the voltge signals
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2610
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2610
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2611
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cohDetection: Whether to user Coherent detection or not
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2611
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cohDetection: Whether to user Coherent detection or not
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2612
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cohDet_timeStep: Coherent Detection calculation time step
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2612
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cohDet_timeStep: Coherent Detection calculation time step
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2613
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cohDet_thresh: Coherent Detection phase threshold to correct phases
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2613
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cohDet_thresh: Coherent Detection phase threshold to correct phases
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2614
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2614
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2615
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noise_timeStep: Noise calculation time step
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2615
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noise_timeStep: Noise calculation time step
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2616
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noise_multiple: Noise multiple to define signal threshold
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2616
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noise_multiple: Noise multiple to define signal threshold
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2617
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2617
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2618
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multDet_timeLimit: Multiple Detection Removal time limit in seconds
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2618
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multDet_timeLimit: Multiple Detection Removal time limit in seconds
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2619
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multDet_rangeLimit: Multiple Detection Removal range limit in km
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2619
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multDet_rangeLimit: Multiple Detection Removal range limit in km
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2620
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2620
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2621
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phaseThresh: Maximum phase difference between receiver to be consider a meteor
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2621
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phaseThresh: Maximum phase difference between receiver to be consider a meteor
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2622
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SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor
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2622
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SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor
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2623
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2623
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2624
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hmin: Minimum Height of the meteor to use it in the further wind estimations
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2624
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hmin: Minimum Height of the meteor to use it in the further wind estimations
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2625
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hmax: Maximum Height of the meteor to use it in the further wind estimations
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2625
|
hmax: Maximum Height of the meteor to use it in the further wind estimations
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2626
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azimuth: Azimuth angle correction
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|
2626
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azimuth: Azimuth angle correction
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2627
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|
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2627
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2628
|
Affected:
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2628
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Affected:
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2629
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self.dataOut.data_param
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2629
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self.dataOut.data_param
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|
2630
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2630
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2631
|
Rejection Criteria (Errors):
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|
2631
|
Rejection Criteria (Errors):
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|
2632
|
0: No error; analysis OK
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2632
|
0: No error; analysis OK
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|
2633
|
1: SNR < SNR threshold
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2633
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1: SNR < SNR threshold
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2634
|
2: angle of arrival (AOA) ambiguously determined
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2634
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2: angle of arrival (AOA) ambiguously determined
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2635
|
3: AOA estimate not feasible
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2635
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3: AOA estimate not feasible
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2636
|
4: Large difference in AOAs obtained from different antenna baselines
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2636
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4: Large difference in AOAs obtained from different antenna baselines
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2637
|
5: echo at start or end of time series
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2637
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5: echo at start or end of time series
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2638
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6: echo less than 5 examples long; too short for analysis
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|
2638
|
6: echo less than 5 examples long; too short for analysis
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2639
|
7: echo rise exceeds 0.3s
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2639
|
7: echo rise exceeds 0.3s
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2640
|
8: echo decay time less than twice rise time
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2640
|
8: echo decay time less than twice rise time
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2641
|
9: large power level before echo
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2641
|
9: large power level before echo
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|
2642
|
10: large power level after echo
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|
2642
|
10: large power level after echo
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2643
|
11: poor fit to amplitude for estimation of decay time
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2643
|
11: poor fit to amplitude for estimation of decay time
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2644
|
12: poor fit to CCF phase variation for estimation of radial drift velocity
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2644
|
12: poor fit to CCF phase variation for estimation of radial drift velocity
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2645
|
13: height unresolvable echo: not valid height within 70 to 110 km
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|
2645
|
13: height unresolvable echo: not valid height within 70 to 110 km
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2646
|
14: height ambiguous echo: more then one possible height within 70 to 110 km
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|
2646
|
14: height ambiguous echo: more then one possible height within 70 to 110 km
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2647
|
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s
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|
2647
|
15: radial drift velocity or projected horizontal velocity exceeds 200 m/s
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2648
|
16: oscilatory echo, indicating event most likely not an underdense echo
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2648
|
16: oscilatory echo, indicating event most likely not an underdense echo
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2649
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|
2649
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|
2650
|
17: phase difference in meteor Reestimation
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|
2650
|
17: phase difference in meteor Reestimation
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|
|
2651
|
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|
2651
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|
2652
|
Data Storage:
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|
2652
|
Data Storage:
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|
2653
|
Meteors for Wind Estimation (8):
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|
2653
|
Meteors for Wind Estimation (8):
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|
2654
|
Utc Time | Range Height
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|
2654
|
Utc Time | Range Height
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|
2655
|
Azimuth Zenith errorCosDir
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|
2655
|
Azimuth Zenith errorCosDir
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|
2656
|
VelRad errorVelRad
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|
2656
|
VelRad errorVelRad
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|
2657
|
Phase0 Phase1 Phase2 Phase3
|
|
2657
|
Phase0 Phase1 Phase2 Phase3
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|
2658
|
TypeError
|
|
2658
|
TypeError
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|
|
2659
|
|
|
2659
|
|
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|
2660
|
'''
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|
2660
|
'''
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|
|
2661
|
|
|
2661
|
|
|
|
2662
|
def run(self, dataOut, hei_ref = None, tauindex = 0,
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|
2662
|
def run(self, dataOut, hei_ref = None, tauindex = 0,
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|
|
2663
|
phaseOffsets = None,
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|
2663
|
phaseOffsets = None,
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|
|
2664
|
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
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|
2664
|
cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
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|
|
2665
|
noise_timeStep = 4, noise_multiple = 4,
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|
2665
|
noise_timeStep = 4, noise_multiple = 4,
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|
|
2666
|
multDet_timeLimit = 1, multDet_rangeLimit = 3,
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|
2666
|
multDet_timeLimit = 1, multDet_rangeLimit = 3,
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|
|
2667
|
phaseThresh = 20, SNRThresh = 5,
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|
2667
|
phaseThresh = 20, SNRThresh = 5,
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|
2668
|
hmin = 50, hmax=150, azimuth = 0,
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|
2668
|
hmin = 50, hmax=150, azimuth = 0,
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|
2669
|
channelPositions = None) :
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|
2669
|
channelPositions = None) :
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|
|
2670
|
|
|
2670
|
|
|
|
2671
|
|
|
2671
|
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|
2672
|
#Getting Pairslist
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|
2672
|
#Getting Pairslist
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|
2673
|
if channelPositions is None:
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|
2673
|
if channelPositions is None:
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|
|
2674
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
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|
2674
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
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|
2675
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
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|
2675
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
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|
|
2676
|
meteorOps = SMOperations()
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|
2676
|
meteorOps = SMOperations()
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|
|
2677
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
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|
2677
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
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|
2678
|
heiRang = dataOut.heightList
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|
2678
|
heiRang = dataOut.heightList
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|
|
2679
|
#Get Beacon signal - No Beacon signal anymore
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|
2679
|
#Get Beacon signal - No Beacon signal anymore
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|
|
2680
|
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
|
|
2680
|
# newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
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|
|
2681
|
#
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|
2681
|
#
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|
|
2682
|
# if hei_ref != None:
|
|
2682
|
# if hei_ref != None:
|
|
|
2683
|
# newheis = numpy.where(self.dataOut.heightList>hei_ref)
|
|
2683
|
# newheis = numpy.where(self.dataOut.heightList>hei_ref)
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|
|
2684
|
#
|
|
2684
|
#
|
|
|
2685
|
|
|
2685
|
|
|
|
2686
|
|
|
2686
|
|
|
|
2687
|
#****************REMOVING HARDWARE PHASE DIFFERENCES***************
|
|
2687
|
#****************REMOVING HARDWARE PHASE DIFFERENCES***************
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|
|
2688
|
# see if the user put in pre defined phase shifts
|
|
2688
|
# see if the user put in pre defined phase shifts
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|
|
2689
|
voltsPShift = dataOut.data_pre.copy()
|
|
2689
|
voltsPShift = dataOut.data_pre.copy()
|
|
|
2690
|
|
|
2690
|
|
|
|
2691
|
# if predefinedPhaseShifts != None:
|
|
2691
|
# if predefinedPhaseShifts != None:
|
|
|
2692
|
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
|
|
2692
|
# hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
|
|
|
2693
|
#
|
|
2693
|
#
|
|
|
2694
|
# # elif beaconPhaseShifts:
|
|
2694
|
# # elif beaconPhaseShifts:
|
|
|
2695
|
# # #get hardware phase shifts using beacon signal
|
|
2695
|
# # #get hardware phase shifts using beacon signal
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|
|
2696
|
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
|
|
2696
|
# # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
|
|
|
2697
|
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
|
|
2697
|
# # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
|
|
|
2698
|
#
|
|
2698
|
#
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|
|
2699
|
# else:
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|
2699
|
# else:
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|
|
2700
|
# hardwarePhaseShifts = numpy.zeros(5)
|
|
2700
|
# hardwarePhaseShifts = numpy.zeros(5)
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|
|
2701
|
#
|
|
2701
|
#
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|
2702
|
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
|
|
2702
|
# voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
|
|
|
2703
|
# for i in range(self.dataOut.data_pre.shape[0]):
|
|
2703
|
# for i in range(self.dataOut.data_pre.shape[0]):
|
|
|
2704
|
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
|
|
2704
|
# voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
|
|
|
2705
|
|
|
2705
|
|
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|
2706
|
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
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|
2706
|
#******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
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|
|
2707
|
|
|
2707
|
|
|
|
2708
|
#Remove DC
|
|
2708
|
#Remove DC
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|
|
2709
|
voltsDC = numpy.mean(voltsPShift,1)
|
|
2709
|
voltsDC = numpy.mean(voltsPShift,1)
|
|
|
2710
|
voltsDC = numpy.mean(voltsDC,1)
|
|
2710
|
voltsDC = numpy.mean(voltsDC,1)
|
|
|
2711
|
for i in range(voltsDC.shape[0]):
|
|
2711
|
for i in range(voltsDC.shape[0]):
|
|
|
2712
|
voltsPShift[i] = voltsPShift[i] - voltsDC[i]
|
|
2712
|
voltsPShift[i] = voltsPShift[i] - voltsDC[i]
|
|
|
2713
|
|
|
2713
|
|
|
|
2714
|
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift
|
|
2714
|
#Don't considerate last heights, theyre used to calculate Hardware Phase Shift
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|
|
2715
|
# voltsPShift = voltsPShift[:,:,:newheis[0][0]]
|
|
2715
|
# voltsPShift = voltsPShift[:,:,:newheis[0][0]]
|
|
|
2716
|
|
|
2716
|
|
|
|
2717
|
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
|
|
2717
|
#************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
|
|
|
2718
|
#Coherent Detection
|
|
2718
|
#Coherent Detection
|
|
|
2719
|
if cohDetection:
|
|
2719
|
if cohDetection:
|
|
|
2720
|
#use coherent detection to get the net power
|
|
2720
|
#use coherent detection to get the net power
|
|
|
2721
|
cohDet_thresh = cohDet_thresh*numpy.pi/180
|
|
2721
|
cohDet_thresh = cohDet_thresh*numpy.pi/180
|
|
|
2722
|
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
|
|
2722
|
voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
|
|
|
2723
|
|
|
2723
|
|
|
|
2724
|
#Non-coherent detection!
|
|
2724
|
#Non-coherent detection!
|
|
|
2725
|
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
|
|
2725
|
powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
|
|
|
2726
|
#********** END OF COH/NON-COH POWER CALCULATION**********************
|
|
2726
|
#********** END OF COH/NON-COH POWER CALCULATION**********************
|
|
|
2727
|
|
|
2727
|
|
|
|
2728
|
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
|
|
2728
|
#********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
|
|
|
2729
|
#Get noise
|
|
2729
|
#Get noise
|
|
|
2730
|
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
|
|
2730
|
noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
|
|
|
2731
|
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
|
|
2731
|
# noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
|
|
|
2732
|
#Get signal threshold
|
|
2732
|
#Get signal threshold
|
|
|
2733
|
signalThresh = noise_multiple*noise
|
|
2733
|
signalThresh = noise_multiple*noise
|
|
|
2734
|
#Meteor echoes detection
|
|
2734
|
#Meteor echoes detection
|
|
|
2735
|
listMeteors = self.__findMeteors(powerNet, signalThresh)
|
|
2735
|
listMeteors = self.__findMeteors(powerNet, signalThresh)
|
|
|
2736
|
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
|
|
2736
|
#******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
|
|
|
2737
|
|
|
2737
|
|
|
|
2738
|
#************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
|
|
2738
|
#************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
|
|
|
2739
|
#Parameters
|
|
2739
|
#Parameters
|
|
|
2740
|
heiRange = dataOut.heightList
|
|
2740
|
heiRange = dataOut.heightList
|
|
|
2741
|
rangeInterval = heiRange[1] - heiRange[0]
|
|
2741
|
rangeInterval = heiRange[1] - heiRange[0]
|
|
|
2742
|
rangeLimit = multDet_rangeLimit/rangeInterval
|
|
2742
|
rangeLimit = multDet_rangeLimit/rangeInterval
|
|
|
2743
|
timeLimit = multDet_timeLimit/dataOut.timeInterval
|
|
2743
|
timeLimit = multDet_timeLimit/dataOut.timeInterval
|
|
|
2744
|
#Multiple detection removals
|
|
2744
|
#Multiple detection removals
|
|
|
2745
|
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
|
|
2745
|
listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
|
|
|
2746
|
#************ END OF REMOVE MULTIPLE DETECTIONS **********************
|
|
2746
|
#************ END OF REMOVE MULTIPLE DETECTIONS **********************
|
|
|
2747
|
|
|
2747
|
|
|
|
2748
|
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ********************
|
|
2748
|
#********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ********************
|
|
|
2749
|
#Parameters
|
|
2749
|
#Parameters
|
|
|
2750
|
phaseThresh = phaseThresh*numpy.pi/180
|
|
2750
|
phaseThresh = phaseThresh*numpy.pi/180
|
|
|
2751
|
thresh = [phaseThresh, noise_multiple, SNRThresh]
|
|
2751
|
thresh = [phaseThresh, noise_multiple, SNRThresh]
|
|
|
2752
|
#Meteor reestimation (Errors N 1, 6, 12, 17)
|
|
2752
|
#Meteor reestimation (Errors N 1, 6, 12, 17)
|
|
|
2753
|
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
|
|
2753
|
listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
|
|
|
2754
|
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
|
|
2754
|
# listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
|
|
|
2755
|
#Estimation of decay times (Errors N 7, 8, 11)
|
|
2755
|
#Estimation of decay times (Errors N 7, 8, 11)
|
|
|
2756
|
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
|
|
2756
|
listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
|
|
|
2757
|
#******************* END OF METEOR REESTIMATION *******************
|
|
2757
|
#******************* END OF METEOR REESTIMATION *******************
|
|
|
2758
|
|
|
2758
|
|
|
|
2759
|
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) **************************
|
|
2759
|
#********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) **************************
|
|
|
2760
|
#Calculating Radial Velocity (Error N 15)
|
|
2760
|
#Calculating Radial Velocity (Error N 15)
|
|
|
2761
|
radialStdThresh = 10
|
|
2761
|
radialStdThresh = 10
|
|
|
2762
|
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
|
|
2762
|
listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
|
|
|
2763
|
|
|
2763
|
|
|
|
2764
|
if len(listMeteors4) > 0:
|
|
2764
|
if len(listMeteors4) > 0:
|
|
|
2765
|
#Setting New Array
|
|
2765
|
#Setting New Array
|
|
|
2766
|
date = dataOut.utctime
|
|
2766
|
date = dataOut.utctime
|
|
|
2767
|
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
|
|
2767
|
arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
|
|
|
2768
|
|
|
2768
|
|
|
|
2769
|
#Correcting phase offset
|
|
2769
|
#Correcting phase offset
|
|
|
2770
|
if phaseOffsets != None:
|
|
2770
|
if phaseOffsets != None:
|
|
|
2771
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
2771
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
|
2772
|
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
2772
|
arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
|
2773
|
|
|
2773
|
|
|
|
2774
|
#Second Pairslist
|
|
2774
|
#Second Pairslist
|
|
|
2775
|
pairsList = []
|
|
2775
|
pairsList = []
|
|
|
2776
|
pairx = (0,1)
|
|
2776
|
pairx = (0,1)
|
|
|
2777
|
pairy = (2,3)
|
|
2777
|
pairy = (2,3)
|
|
|
2778
|
pairsList.append(pairx)
|
|
2778
|
pairsList.append(pairx)
|
|
|
2779
|
pairsList.append(pairy)
|
|
2779
|
pairsList.append(pairy)
|
|
|
2780
|
|
|
2780
|
|
|
|
2781
|
jph = numpy.array([0,0,0,0])
|
|
2781
|
jph = numpy.array([0,0,0,0])
|
|
|
2782
|
h = (hmin,hmax)
|
|
2782
|
h = (hmin,hmax)
|
|
|
2783
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
2783
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
|
2784
|
|
|
2784
|
|
|
|
2785
|
# #Calculate AOA (Error N 3, 4)
|
|
2785
|
# #Calculate AOA (Error N 3, 4)
|
|
|
2786
|
# #JONES ET AL. 1998
|
|
2786
|
# #JONES ET AL. 1998
|
|
|
2787
|
# error = arrayParameters[:,-1]
|
|
2787
|
# error = arrayParameters[:,-1]
|
|
|
2788
|
# AOAthresh = numpy.pi/8
|
|
2788
|
# AOAthresh = numpy.pi/8
|
|
|
2789
|
# phases = -arrayParameters[:,9:13]
|
|
2789
|
# phases = -arrayParameters[:,9:13]
|
|
|
2790
|
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
|
|
2790
|
# arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
|
|
|
2791
|
#
|
|
2791
|
#
|
|
|
2792
|
# #Calculate Heights (Error N 13 and 14)
|
|
2792
|
# #Calculate Heights (Error N 13 and 14)
|
|
|
2793
|
# error = arrayParameters[:,-1]
|
|
2793
|
# error = arrayParameters[:,-1]
|
|
|
2794
|
# Ranges = arrayParameters[:,2]
|
|
2794
|
# Ranges = arrayParameters[:,2]
|
|
|
2795
|
# zenith = arrayParameters[:,5]
|
|
2795
|
# zenith = arrayParameters[:,5]
|
|
|
2796
|
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
|
|
2796
|
# arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
|
|
|
2797
|
# error = arrayParameters[:,-1]
|
|
2797
|
# error = arrayParameters[:,-1]
|
|
|
2798
|
#********************* END OF PARAMETERS CALCULATION **************************
|
|
2798
|
#********************* END OF PARAMETERS CALCULATION **************************
|
|
|
2799
|
|
|
2799
|
|
|
|
2800
|
#***************************+ PASS DATA TO NEXT STEP **********************
|
|
2800
|
#***************************+ PASS DATA TO NEXT STEP **********************
|
|
|
2801
|
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
|
|
2801
|
# arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
|
|
|
2802
|
dataOut.data_param = arrayParameters
|
|
2802
|
dataOut.data_param = arrayParameters
|
|
|
2803
|
|
|
2803
|
|
|
|
2804
|
if arrayParameters is None:
|
|
2804
|
if arrayParameters is None:
|
|
|
2805
|
dataOut.flagNoData = True
|
|
2805
|
dataOut.flagNoData = True
|
|
|
2806
|
else:
|
|
2806
|
else:
|
|
|
2807
|
dataOut.flagNoData = True
|
|
2807
|
dataOut.flagNoData = True
|
|
|
2808
|
|
|
2808
|
|
|
|
2809
|
return
|
|
2809
|
return
|
|
|
2810
|
|
|
2810
|
|
|
|
2811
|
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
|
|
2811
|
def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
|
|
|
2812
|
|
|
2812
|
|
|
|
2813
|
minIndex = min(newheis[0])
|
|
2813
|
minIndex = min(newheis[0])
|
|
|
2814
|
maxIndex = max(newheis[0])
|
|
2814
|
maxIndex = max(newheis[0])
|
|
|
2815
|
|
|
2815
|
|
|
|
2816
|
voltage = voltage0[:,:,minIndex:maxIndex+1]
|
|
2816
|
voltage = voltage0[:,:,minIndex:maxIndex+1]
|
|
|
2817
|
nLength = voltage.shape[1]/n
|
|
2817
|
nLength = voltage.shape[1]/n
|
|
|
2818
|
nMin = 0
|
|
2818
|
nMin = 0
|
|
|
2819
|
nMax = 0
|
|
2819
|
nMax = 0
|
|
|
2820
|
phaseOffset = numpy.zeros((len(pairslist),n))
|
|
2820
|
phaseOffset = numpy.zeros((len(pairslist),n))
|
|
|
2821
|
|
|
2821
|
|
|
|
2822
|
for i in range(n):
|
|
2822
|
for i in range(n):
|
|
|
2823
|
nMax += nLength
|
|
2823
|
nMax += nLength
|
|
|
2824
|
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
|
|
2824
|
phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
|
|
|
2825
|
phaseCCF = numpy.mean(phaseCCF, axis = 2)
|
|
2825
|
phaseCCF = numpy.mean(phaseCCF, axis = 2)
|
|
|
2826
|
phaseOffset[:,i] = phaseCCF.transpose()
|
|
2826
|
phaseOffset[:,i] = phaseCCF.transpose()
|
|
|
2827
|
nMin = nMax
|
|
2827
|
nMin = nMax
|
|
|
2828
|
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
|
|
2828
|
# phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
|
|
|
2829
|
|
|
2829
|
|
|
|
2830
|
#Remove Outliers
|
|
2830
|
#Remove Outliers
|
|
|
2831
|
factor = 2
|
|
2831
|
factor = 2
|
|
|
2832
|
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
|
|
2832
|
wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
|
|
|
2833
|
dw = numpy.std(wt,axis = 1)
|
|
2833
|
dw = numpy.std(wt,axis = 1)
|
|
|
2834
|
dw = dw.reshape((dw.size,1))
|
|
2834
|
dw = dw.reshape((dw.size,1))
|
|
|
2835
|
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
|
|
2835
|
ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
|
|
|
2836
|
phaseOffset[ind] = numpy.nan
|
|
2836
|
phaseOffset[ind] = numpy.nan
|
|
|
2837
|
phaseOffset = stats.nanmean(phaseOffset, axis=1)
|
|
2837
|
phaseOffset = stats.nanmean(phaseOffset, axis=1)
|
|
|
2838
|
|
|
2838
|
|
|
|
2839
|
return phaseOffset
|
|
2839
|
return phaseOffset
|
|
|
2840
|
|
|
2840
|
|
|
|
2841
|
def __shiftPhase(self, data, phaseShift):
|
|
2841
|
def __shiftPhase(self, data, phaseShift):
|
|
|
2842
|
#this will shift the phase of a complex number
|
|
2842
|
#this will shift the phase of a complex number
|
|
|
2843
|
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
|
|
2843
|
dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
|
|
|
2844
|
return dataShifted
|
|
2844
|
return dataShifted
|
|
|
2845
|
|
|
2845
|
|
|
|
2846
|
def __estimatePhaseDifference(self, array, pairslist):
|
|
2846
|
def __estimatePhaseDifference(self, array, pairslist):
|
|
|
2847
|
nChannel = array.shape[0]
|
|
2847
|
nChannel = array.shape[0]
|
|
|
2848
|
nHeights = array.shape[2]
|
|
2848
|
nHeights = array.shape[2]
|
|
|
2849
|
numPairs = len(pairslist)
|
|
2849
|
numPairs = len(pairslist)
|
|
|
2850
|
# phaseCCF = numpy.zeros((nChannel, 5, nHeights))
|
|
2850
|
# phaseCCF = numpy.zeros((nChannel, 5, nHeights))
|
|
|
2851
|
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
|
|
2851
|
phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
|
|
|
2852
|
|
|
2852
|
|
|
|
2853
|
#Correct phases
|
|
2853
|
#Correct phases
|
|
|
2854
|
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
|
|
2854
|
derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
|
|
|
2855
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
2855
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
|
2856
|
|
|
2856
|
|
|
|
2857
|
if indDer[0].shape[0] > 0:
|
|
2857
|
if indDer[0].shape[0] > 0:
|
|
|
2858
|
for i in range(indDer[0].shape[0]):
|
|
2858
|
for i in range(indDer[0].shape[0]):
|
|
|
2859
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
|
|
2859
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
|
|
|
2860
|
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
|
|
2860
|
phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
|
|
|
2861
|
|
|
2861
|
|
|
|
2862
|
# for j in range(numSides):
|
|
2862
|
# for j in range(numSides):
|
|
|
2863
|
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
|
|
2863
|
# phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
|
|
|
2864
|
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
|
|
2864
|
# phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
|
|
|
2865
|
#
|
|
2865
|
#
|
|
|
2866
|
#Linear
|
|
2866
|
#Linear
|
|
|
2867
|
phaseInt = numpy.zeros((numPairs,1))
|
|
2867
|
phaseInt = numpy.zeros((numPairs,1))
|
|
|
2868
|
angAllCCF = phaseCCF[:,[0,1,3,4],0]
|
|
2868
|
angAllCCF = phaseCCF[:,[0,1,3,4],0]
|
|
|
2869
|
for j in range(numPairs):
|
|
2869
|
for j in range(numPairs):
|
|
|
2870
|
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
|
|
2870
|
fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
|
|
|
2871
|
phaseInt[j] = fit[1]
|
|
2871
|
phaseInt[j] = fit[1]
|
|
|
2872
|
#Phase Differences
|
|
2872
|
#Phase Differences
|
|
|
2873
|
phaseDiff = phaseInt - phaseCCF[:,2,:]
|
|
2873
|
phaseDiff = phaseInt - phaseCCF[:,2,:]
|
|
|
2874
|
phaseArrival = phaseInt.reshape(phaseInt.size)
|
|
2874
|
phaseArrival = phaseInt.reshape(phaseInt.size)
|
|
|
2875
|
|
|
2875
|
|
|
|
2876
|
#Dealias
|
|
2876
|
#Dealias
|
|
|
2877
|
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
|
|
2877
|
phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
|
|
|
2878
|
# indAlias = numpy.where(phaseArrival > numpy.pi)
|
|
2878
|
# indAlias = numpy.where(phaseArrival > numpy.pi)
|
|
|
2879
|
# phaseArrival[indAlias] -= 2*numpy.pi
|
|
2879
|
# phaseArrival[indAlias] -= 2*numpy.pi
|
|
|
2880
|
# indAlias = numpy.where(phaseArrival < -numpy.pi)
|
|
2880
|
# indAlias = numpy.where(phaseArrival < -numpy.pi)
|
|
|
2881
|
# phaseArrival[indAlias] += 2*numpy.pi
|
|
2881
|
# phaseArrival[indAlias] += 2*numpy.pi
|
|
|
2882
|
|
|
2882
|
|
|
|
2883
|
return phaseDiff, phaseArrival
|
|
2883
|
return phaseDiff, phaseArrival
|
|
|
2884
|
|
|
2884
|
|
|
|
2885
|
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
|
|
2885
|
def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
|
|
|
2886
|
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
|
|
2886
|
#this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
|
|
|
2887
|
#find the phase shifts of each channel over 1 second intervals
|
|
2887
|
#find the phase shifts of each channel over 1 second intervals
|
|
|
2888
|
#only look at ranges below the beacon signal
|
|
2888
|
#only look at ranges below the beacon signal
|
|
|
2889
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
2889
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
|
2890
|
numBlocks = int(volts.shape[1]/numProfPerBlock)
|
|
2890
|
numBlocks = int(volts.shape[1]/numProfPerBlock)
|
|
|
2891
|
numHeights = volts.shape[2]
|
|
2891
|
numHeights = volts.shape[2]
|
|
|
2892
|
nChannel = volts.shape[0]
|
|
2892
|
nChannel = volts.shape[0]
|
|
|
2893
|
voltsCohDet = volts.copy()
|
|
2893
|
voltsCohDet = volts.copy()
|
|
|
2894
|
|
|
2894
|
|
|
|
2895
|
pairsarray = numpy.array(pairslist)
|
|
2895
|
pairsarray = numpy.array(pairslist)
|
|
|
2896
|
indSides = pairsarray[:,1]
|
|
2896
|
indSides = pairsarray[:,1]
|
|
|
2897
|
# indSides = numpy.array(range(nChannel))
|
|
2897
|
# indSides = numpy.array(range(nChannel))
|
|
|
2898
|
# indSides = numpy.delete(indSides, indCenter)
|
|
2898
|
# indSides = numpy.delete(indSides, indCenter)
|
|
|
2899
|
#
|
|
2899
|
#
|
|
|
2900
|
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
|
|
2900
|
# listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
|
|
|
2901
|
listBlocks = numpy.array_split(volts, numBlocks, 1)
|
|
2901
|
listBlocks = numpy.array_split(volts, numBlocks, 1)
|
|
|
2902
|
|
|
2902
|
|
|
|
2903
|
startInd = 0
|
|
2903
|
startInd = 0
|
|
|
2904
|
endInd = 0
|
|
2904
|
endInd = 0
|
|
|
2905
|
|
|
2905
|
|
|
|
2906
|
for i in range(numBlocks):
|
|
2906
|
for i in range(numBlocks):
|
|
|
2907
|
startInd = endInd
|
|
2907
|
startInd = endInd
|
|
|
2908
|
endInd = endInd + listBlocks[i].shape[1]
|
|
2908
|
endInd = endInd + listBlocks[i].shape[1]
|
|
|
2909
|
|
|
2909
|
|
|
|
2910
|
arrayBlock = listBlocks[i]
|
|
2910
|
arrayBlock = listBlocks[i]
|
|
|
2911
|
# arrayBlockCenter = listCenter[i]
|
|
2911
|
# arrayBlockCenter = listCenter[i]
|
|
|
2912
|
|
|
2912
|
|
|
|
2913
|
#Estimate the Phase Difference
|
|
2913
|
#Estimate the Phase Difference
|
|
|
2914
|
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
|
|
2914
|
phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
|
|
|
2915
|
#Phase Difference RMS
|
|
2915
|
#Phase Difference RMS
|
|
|
2916
|
arrayPhaseRMS = numpy.abs(phaseDiff)
|
|
2916
|
arrayPhaseRMS = numpy.abs(phaseDiff)
|
|
|
2917
|
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
|
|
2917
|
phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
|
|
|
2918
|
indPhase = numpy.where(phaseRMSaux==4)
|
|
2918
|
indPhase = numpy.where(phaseRMSaux==4)
|
|
|
2919
|
#Shifting
|
|
2919
|
#Shifting
|
|
|
2920
|
if indPhase[0].shape[0] > 0:
|
|
2920
|
if indPhase[0].shape[0] > 0:
|
|
|
2921
|
for j in range(indSides.size):
|
|
2921
|
for j in range(indSides.size):
|
|
|
2922
|
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
|
|
2922
|
arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
|
|
|
2923
|
voltsCohDet[:,startInd:endInd,:] = arrayBlock
|
|
2923
|
voltsCohDet[:,startInd:endInd,:] = arrayBlock
|
|
|
2924
|
|
|
2924
|
|
|
|
2925
|
return voltsCohDet
|
|
2925
|
return voltsCohDet
|
|
|
2926
|
|
|
2926
|
|
|
|
2927
|
def __calculateCCF(self, volts, pairslist ,laglist):
|
|
2927
|
def __calculateCCF(self, volts, pairslist ,laglist):
|
|
|
2928
|
|
|
2928
|
|
|
|
2929
|
nHeights = volts.shape[2]
|
|
2929
|
nHeights = volts.shape[2]
|
|
|
2930
|
nPoints = volts.shape[1]
|
|
2930
|
nPoints = volts.shape[1]
|
|
|
2931
|
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
|
|
2931
|
voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
|
|
|
2932
|
|
|
2932
|
|
|
|
2933
|
for i in range(len(pairslist)):
|
|
2933
|
for i in range(len(pairslist)):
|
|
|
2934
|
volts1 = volts[pairslist[i][0]]
|
|
2934
|
volts1 = volts[pairslist[i][0]]
|
|
|
2935
|
volts2 = volts[pairslist[i][1]]
|
|
2935
|
volts2 = volts[pairslist[i][1]]
|
|
|
2936
|
|
|
2936
|
|
|
|
2937
|
for t in range(len(laglist)):
|
|
2937
|
for t in range(len(laglist)):
|
|
|
2938
|
idxT = laglist[t]
|
|
2938
|
idxT = laglist[t]
|
|
|
2939
|
if idxT >= 0:
|
|
2939
|
if idxT >= 0:
|
|
|
2940
|
vStacked = numpy.vstack((volts2[idxT:,:],
|
|
2940
|
vStacked = numpy.vstack((volts2[idxT:,:],
|
|
|
2941
|
numpy.zeros((idxT, nHeights),dtype='complex')))
|
|
2941
|
numpy.zeros((idxT, nHeights),dtype='complex')))
|
|
|
2942
|
else:
|
|
2942
|
else:
|
|
|
2943
|
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
|
|
2943
|
vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
|
|
|
2944
|
volts2[:(nPoints + idxT),:]))
|
|
2944
|
volts2[:(nPoints + idxT),:]))
|
|
|
2945
|
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
|
|
2945
|
voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
|
|
|
2946
|
|
|
2946
|
|
|
|
2947
|
vStacked = None
|
|
2947
|
vStacked = None
|
|
|
2948
|
return voltsCCF
|
|
2948
|
return voltsCCF
|
|
|
2949
|
|
|
2949
|
|
|
|
2950
|
def __getNoise(self, power, timeSegment, timeInterval):
|
|
2950
|
def __getNoise(self, power, timeSegment, timeInterval):
|
|
|
2951
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
2951
|
numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
|
|
|
2952
|
numBlocks = int(power.shape[0]/numProfPerBlock)
|
|
2952
|
numBlocks = int(power.shape[0]/numProfPerBlock)
|
|
|
2953
|
numHeights = power.shape[1]
|
|
2953
|
numHeights = power.shape[1]
|
|
|
2954
|
|
|
2954
|
|
|
|
2955
|
listPower = numpy.array_split(power, numBlocks, 0)
|
|
2955
|
listPower = numpy.array_split(power, numBlocks, 0)
|
|
|
2956
|
noise = numpy.zeros((power.shape[0], power.shape[1]))
|
|
2956
|
noise = numpy.zeros((power.shape[0], power.shape[1]))
|
|
|
2957
|
noise1 = numpy.zeros((power.shape[0], power.shape[1]))
|
|
2957
|
noise1 = numpy.zeros((power.shape[0], power.shape[1]))
|
|
|
2958
|
|
|
2958
|
|
|
|
2959
|
startInd = 0
|
|
2959
|
startInd = 0
|
|
|
2960
|
endInd = 0
|
|
2960
|
endInd = 0
|
|
|
2961
|
|
|
2961
|
|
|
|
2962
|
for i in range(numBlocks): #split por canal
|
|
2962
|
for i in range(numBlocks): #split por canal
|
|
|
2963
|
startInd = endInd
|
|
2963
|
startInd = endInd
|
|
|
2964
|
endInd = endInd + listPower[i].shape[0]
|
|
2964
|
endInd = endInd + listPower[i].shape[0]
|
|
|
2965
|
|
|
2965
|
|
|
|
2966
|
arrayBlock = listPower[i]
|
|
2966
|
arrayBlock = listPower[i]
|
|
|
2967
|
noiseAux = numpy.mean(arrayBlock, 0)
|
|
2967
|
noiseAux = numpy.mean(arrayBlock, 0)
|
|
|
2968
|
# noiseAux = numpy.median(noiseAux)
|
|
2968
|
# noiseAux = numpy.median(noiseAux)
|
|
|
2969
|
# noiseAux = numpy.mean(arrayBlock)
|
|
2969
|
# noiseAux = numpy.mean(arrayBlock)
|
|
|
2970
|
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
|
|
2970
|
noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
|
|
|
2971
|
|
|
2971
|
|
|
|
2972
|
noiseAux1 = numpy.mean(arrayBlock)
|
|
2972
|
noiseAux1 = numpy.mean(arrayBlock)
|
|
|
2973
|
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
|
|
2973
|
noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
|
|
|
2974
|
|
|
2974
|
|
|
|
2975
|
return noise, noise1
|
|
2975
|
return noise, noise1
|
|
|
2976
|
|
|
2976
|
|
|
|
2977
|
def __findMeteors(self, power, thresh):
|
|
2977
|
def __findMeteors(self, power, thresh):
|
|
|
2978
|
nProf = power.shape[0]
|
|
2978
|
nProf = power.shape[0]
|
|
|
2979
|
nHeights = power.shape[1]
|
|
2979
|
nHeights = power.shape[1]
|
|
|
2980
|
listMeteors = []
|
|
2980
|
listMeteors = []
|
|
|
2981
|
|
|
2981
|
|
|
|
2982
|
for i in range(nHeights):
|
|
2982
|
for i in range(nHeights):
|
|
|
2983
|
powerAux = power[:,i]
|
|
2983
|
powerAux = power[:,i]
|
|
|
2984
|
threshAux = thresh[:,i]
|
|
2984
|
threshAux = thresh[:,i]
|
|
|
2985
|
|
|
2985
|
|
|
|
2986
|
indUPthresh = numpy.where(powerAux > threshAux)[0]
|
|
2986
|
indUPthresh = numpy.where(powerAux > threshAux)[0]
|
|
|
2987
|
indDNthresh = numpy.where(powerAux <= threshAux)[0]
|
|
2987
|
indDNthresh = numpy.where(powerAux <= threshAux)[0]
|
|
|
2988
|
|
|
2988
|
|
|
|
2989
|
j = 0
|
|
2989
|
j = 0
|
|
|
2990
|
|
|
2990
|
|
|
|
2991
|
while (j < indUPthresh.size - 2):
|
|
2991
|
while (j < indUPthresh.size - 2):
|
|
|
2992
|
if (indUPthresh[j + 2] == indUPthresh[j] + 2):
|
|
2992
|
if (indUPthresh[j + 2] == indUPthresh[j] + 2):
|
|
|
2993
|
indDNAux = numpy.where(indDNthresh > indUPthresh[j])
|
|
2993
|
indDNAux = numpy.where(indDNthresh > indUPthresh[j])
|
|
|
2994
|
indDNthresh = indDNthresh[indDNAux]
|
|
2994
|
indDNthresh = indDNthresh[indDNAux]
|
|
|
2995
|
|
|
2995
|
|
|
|
2996
|
if (indDNthresh.size > 0):
|
|
2996
|
if (indDNthresh.size > 0):
|
|
|
2997
|
indEnd = indDNthresh[0] - 1
|
|
2997
|
indEnd = indDNthresh[0] - 1
|
|
|
2998
|
indInit = indUPthresh[j]
|
|
2998
|
indInit = indUPthresh[j]
|
|
|
2999
|
|
|
2999
|
|
|
|
3000
|
meteor = powerAux[indInit:indEnd + 1]
|
|
3000
|
meteor = powerAux[indInit:indEnd + 1]
|
|
|
3001
|
indPeak = meteor.argmax() + indInit
|
|
3001
|
indPeak = meteor.argmax() + indInit
|
|
|
3002
|
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
|
|
3002
|
FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
|
|
|
3003
|
|
|
3003
|
|
|
|
3004
|
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
|
|
3004
|
listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
|
|
|
3005
|
j = numpy.where(indUPthresh == indEnd)[0] + 1
|
|
3005
|
j = numpy.where(indUPthresh == indEnd)[0] + 1
|
|
|
3006
|
else: j+=1
|
|
3006
|
else: j+=1
|
|
|
3007
|
else: j+=1
|
|
3007
|
else: j+=1
|
|
|
3008
|
|
|
3008
|
|
|
|
3009
|
return listMeteors
|
|
3009
|
return listMeteors
|
|
|
3010
|
|
|
3010
|
|
|
|
3011
|
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
|
|
3011
|
def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
|
|
|
3012
|
|
|
3012
|
|
|
|
3013
|
arrayMeteors = numpy.asarray(listMeteors)
|
|
3013
|
arrayMeteors = numpy.asarray(listMeteors)
|
|
|
3014
|
listMeteors1 = []
|
|
3014
|
listMeteors1 = []
|
|
|
3015
|
|
|
3015
|
|
|
|
3016
|
while arrayMeteors.shape[0] > 0:
|
|
3016
|
while arrayMeteors.shape[0] > 0:
|
|
|
3017
|
FLAs = arrayMeteors[:,4]
|
|
3017
|
FLAs = arrayMeteors[:,4]
|
|
|
3018
|
maxFLA = FLAs.argmax()
|
|
3018
|
maxFLA = FLAs.argmax()
|
|
|
3019
|
listMeteors1.append(arrayMeteors[maxFLA,:])
|
|
3019
|
listMeteors1.append(arrayMeteors[maxFLA,:])
|
|
|
3020
|
|
|
3020
|
|
|
|
3021
|
MeteorInitTime = arrayMeteors[maxFLA,1]
|
|
3021
|
MeteorInitTime = arrayMeteors[maxFLA,1]
|
|
|
3022
|
MeteorEndTime = arrayMeteors[maxFLA,3]
|
|
3022
|
MeteorEndTime = arrayMeteors[maxFLA,3]
|
|
|
3023
|
MeteorHeight = arrayMeteors[maxFLA,0]
|
|
3023
|
MeteorHeight = arrayMeteors[maxFLA,0]
|
|
|
3024
|
|
|
3024
|
|
|
|
3025
|
#Check neighborhood
|
|
3025
|
#Check neighborhood
|
|
|
3026
|
maxHeightIndex = MeteorHeight + rangeLimit
|
|
3026
|
maxHeightIndex = MeteorHeight + rangeLimit
|
|
|
3027
|
minHeightIndex = MeteorHeight - rangeLimit
|
|
3027
|
minHeightIndex = MeteorHeight - rangeLimit
|
|
|
3028
|
minTimeIndex = MeteorInitTime - timeLimit
|
|
3028
|
minTimeIndex = MeteorInitTime - timeLimit
|
|
|
3029
|
maxTimeIndex = MeteorEndTime + timeLimit
|
|
3029
|
maxTimeIndex = MeteorEndTime + timeLimit
|
|
|
3030
|
|
|
3030
|
|
|
|
3031
|
#Check Heights
|
|
3031
|
#Check Heights
|
|
|
3032
|
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
|
|
3032
|
indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
|
|
|
3033
|
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
|
|
3033
|
indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
|
|
|
3034
|
indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
|
|
3034
|
indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
|
|
|
3035
|
|
|
3035
|
|
|
|
3036
|
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
|
|
3036
|
arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
|
|
|
3037
|
|
|
3037
|
|
|
|
3038
|
return listMeteors1
|
|
3038
|
return listMeteors1
|
|
|
3039
|
|
|
3039
|
|
|
|
3040
|
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
|
|
3040
|
def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
|
|
|
3041
|
numHeights = volts.shape[2]
|
|
3041
|
numHeights = volts.shape[2]
|
|
|
3042
|
nChannel = volts.shape[0]
|
|
3042
|
nChannel = volts.shape[0]
|
|
|
3043
|
|
|
3043
|
|
|
|
3044
|
thresholdPhase = thresh[0]
|
|
3044
|
thresholdPhase = thresh[0]
|
|
|
3045
|
thresholdNoise = thresh[1]
|
|
3045
|
thresholdNoise = thresh[1]
|
|
|
3046
|
thresholdDB = float(thresh[2])
|
|
3046
|
thresholdDB = float(thresh[2])
|
|
|
3047
|
|
|
3047
|
|
|
|
3048
|
thresholdDB1 = 10**(thresholdDB/10)
|
|
3048
|
thresholdDB1 = 10**(thresholdDB/10)
|
|
|
3049
|
pairsarray = numpy.array(pairslist)
|
|
3049
|
pairsarray = numpy.array(pairslist)
|
|
|
3050
|
indSides = pairsarray[:,1]
|
|
3050
|
indSides = pairsarray[:,1]
|
|
|
3051
|
|
|
3051
|
|
|
|
3052
|
pairslist1 = list(pairslist)
|
|
3052
|
pairslist1 = list(pairslist)
|
|
|
3053
|
pairslist1.append((0,1))
|
|
3053
|
pairslist1.append((0,1))
|
|
|
3054
|
pairslist1.append((3,4))
|
|
3054
|
pairslist1.append((3,4))
|
|
|
3055
|
|
|
3055
|
|
|
|
3056
|
listMeteors1 = []
|
|
3056
|
listMeteors1 = []
|
|
|
3057
|
listPowerSeries = []
|
|
3057
|
listPowerSeries = []
|
|
|
3058
|
listVoltageSeries = []
|
|
3058
|
listVoltageSeries = []
|
|
|
3059
|
#volts has the war data
|
|
3059
|
#volts has the war data
|
|
|
3060
|
|
|
3060
|
|
|
|
3061
|
if frequency == 30e6:
|
|
3061
|
if frequency == 30e6:
|
|
|
3062
|
timeLag = 45*10**-3
|
|
3062
|
timeLag = 45*10**-3
|
|
|
3063
|
else:
|
|
3063
|
else:
|
|
|
3064
|
timeLag = 15*10**-3
|
|
3064
|
timeLag = 15*10**-3
|
|
|
3065
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3065
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3066
|
|
|
3066
|
|
|
|
3067
|
for i in range(len(listMeteors)):
|
|
3067
|
for i in range(len(listMeteors)):
|
|
|
3068
|
|
|
3068
|
|
|
|
3069
|
###################### 3.6 - 3.7 PARAMETERS REESTIMATION #########################
|
|
3069
|
###################### 3.6 - 3.7 PARAMETERS REESTIMATION #########################
|
|
|
3070
|
meteorAux = numpy.zeros(16)
|
|
3070
|
meteorAux = numpy.zeros(16)
|
|
|
3071
|
|
|
3071
|
|
|
|
3072
|
#Loading meteor Data (mHeight, mStart, mPeak, mEnd)
|
|
3072
|
#Loading meteor Data (mHeight, mStart, mPeak, mEnd)
|
|
|
3073
|
mHeight = listMeteors[i][0]
|
|
3073
|
mHeight = listMeteors[i][0]
|
|
|
3074
|
mStart = listMeteors[i][1]
|
|
3074
|
mStart = listMeteors[i][1]
|
|
|
3075
|
mPeak = listMeteors[i][2]
|
|
3075
|
mPeak = listMeteors[i][2]
|
|
|
3076
|
mEnd = listMeteors[i][3]
|
|
3076
|
mEnd = listMeteors[i][3]
|
|
|
3077
|
|
|
3077
|
|
|
|
3078
|
#get the volt data between the start and end times of the meteor
|
|
3078
|
#get the volt data between the start and end times of the meteor
|
|
|
3079
|
meteorVolts = volts[:,mStart:mEnd+1,mHeight]
|
|
3079
|
meteorVolts = volts[:,mStart:mEnd+1,mHeight]
|
|
|
3080
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
3080
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
|
3081
|
|
|
3081
|
|
|
|
3082
|
#3.6. Phase Difference estimation
|
|
3082
|
#3.6. Phase Difference estimation
|
|
|
3083
|
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
|
|
3083
|
phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
|
|
|
3084
|
|
|
3084
|
|
|
|
3085
|
#3.7. Phase difference removal & meteor start, peak and end times reestimated
|
|
3085
|
#3.7. Phase difference removal & meteor start, peak and end times reestimated
|
|
|
3086
|
#meteorVolts0.- all Channels, all Profiles
|
|
3086
|
#meteorVolts0.- all Channels, all Profiles
|
|
|
3087
|
meteorVolts0 = volts[:,:,mHeight]
|
|
3087
|
meteorVolts0 = volts[:,:,mHeight]
|
|
|
3088
|
meteorThresh = noise[:,mHeight]*thresholdNoise
|
|
3088
|
meteorThresh = noise[:,mHeight]*thresholdNoise
|
|
|
3089
|
meteorNoise = noise[:,mHeight]
|
|
3089
|
meteorNoise = noise[:,mHeight]
|
|
|
3090
|
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
|
|
3090
|
meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
|
|
|
3091
|
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power
|
|
3091
|
powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power
|
|
|
3092
|
|
|
3092
|
|
|
|
3093
|
#Times reestimation
|
|
3093
|
#Times reestimation
|
|
|
3094
|
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
|
|
3094
|
mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
|
|
|
3095
|
if mStart1.size > 0:
|
|
3095
|
if mStart1.size > 0:
|
|
|
3096
|
mStart1 = mStart1[-1] + 1
|
|
3096
|
mStart1 = mStart1[-1] + 1
|
|
|
3097
|
|
|
3097
|
|
|
|
3098
|
else:
|
|
3098
|
else:
|
|
|
3099
|
mStart1 = mPeak
|
|
3099
|
mStart1 = mPeak
|
|
|
3100
|
|
|
3100
|
|
|
|
3101
|
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
|
|
3101
|
mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
|
|
|
3102
|
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
|
|
3102
|
mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
|
|
|
3103
|
if mEndDecayTime1.size == 0:
|
|
3103
|
if mEndDecayTime1.size == 0:
|
|
|
3104
|
mEndDecayTime1 = powerNet0.size
|
|
3104
|
mEndDecayTime1 = powerNet0.size
|
|
|
3105
|
else:
|
|
3105
|
else:
|
|
|
3106
|
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
|
|
3106
|
mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
|
|
|
3107
|
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
|
|
3107
|
# mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
|
|
|
3108
|
|
|
3108
|
|
|
|
3109
|
#meteorVolts1.- all Channels, from start to end
|
|
3109
|
#meteorVolts1.- all Channels, from start to end
|
|
|
3110
|
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
|
|
3110
|
meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
|
|
|
3111
|
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
|
|
3111
|
meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
|
|
|
3112
|
if meteorVolts2.shape[1] == 0:
|
|
3112
|
if meteorVolts2.shape[1] == 0:
|
|
|
3113
|
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
|
|
3113
|
meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
|
|
|
3114
|
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
|
|
3114
|
meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
|
|
|
3115
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
|
|
3115
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
|
|
|
3116
|
##################### END PARAMETERS REESTIMATION #########################
|
|
3116
|
##################### END PARAMETERS REESTIMATION #########################
|
|
|
3117
|
|
|
3117
|
|
|
|
3118
|
##################### 3.8 PHASE DIFFERENCE REESTIMATION ########################
|
|
3118
|
##################### 3.8 PHASE DIFFERENCE REESTIMATION ########################
|
|
|
3119
|
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
3119
|
# if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
|
3120
|
if meteorVolts2.shape[1] > 0:
|
|
3120
|
if meteorVolts2.shape[1] > 0:
|
|
|
3121
|
#Phase Difference re-estimation
|
|
3121
|
#Phase Difference re-estimation
|
|
|
3122
|
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation
|
|
3122
|
phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation
|
|
|
3123
|
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
|
|
3123
|
# phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
|
|
|
3124
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
|
|
3124
|
meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
|
|
|
3125
|
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
|
|
3125
|
phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
|
|
|
3126
|
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting
|
|
3126
|
meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting
|
|
|
3127
|
|
|
3127
|
|
|
|
3128
|
#Phase Difference RMS
|
|
3128
|
#Phase Difference RMS
|
|
|
3129
|
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
|
|
3129
|
phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
|
|
|
3130
|
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
|
|
3130
|
powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
|
|
|
3131
|
#Data from Meteor
|
|
3131
|
#Data from Meteor
|
|
|
3132
|
mPeak1 = powerNet1.argmax() + mStart1
|
|
3132
|
mPeak1 = powerNet1.argmax() + mStart1
|
|
|
3133
|
mPeakPower1 = powerNet1.max()
|
|
3133
|
mPeakPower1 = powerNet1.max()
|
|
|
3134
|
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
|
|
3134
|
noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
|
|
|
3135
|
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
|
|
3135
|
mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
|
|
|
3136
|
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
|
|
3136
|
Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
|
|
|
3137
|
Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
|
|
3137
|
Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
|
|
|
3138
|
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1]
|
|
3138
|
PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1]
|
|
|
3139
|
#Vectorize
|
|
3139
|
#Vectorize
|
|
|
3140
|
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
|
|
3140
|
meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
|
|
|
3141
|
meteorAux[7:11] = phaseDiffint[0:4]
|
|
3141
|
meteorAux[7:11] = phaseDiffint[0:4]
|
|
|
3142
|
|
|
3142
|
|
|
|
3143
|
#Rejection Criterions
|
|
3143
|
#Rejection Criterions
|
|
|
3144
|
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation
|
|
3144
|
if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation
|
|
|
3145
|
meteorAux[-1] = 17
|
|
3145
|
meteorAux[-1] = 17
|
|
|
3146
|
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB
|
|
3146
|
elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB
|
|
|
3147
|
meteorAux[-1] = 1
|
|
3147
|
meteorAux[-1] = 1
|
|
|
3148
|
|
|
3148
|
|
|
|
3149
|
|
|
3149
|
|
|
|
3150
|
else:
|
|
3150
|
else:
|
|
|
3151
|
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
|
|
3151
|
meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
|
|
|
3152
|
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
3152
|
meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
|
|
|
3153
|
PowerSeries = 0
|
|
3153
|
PowerSeries = 0
|
|
|
3154
|
|
|
3154
|
|
|
|
3155
|
listMeteors1.append(meteorAux)
|
|
3155
|
listMeteors1.append(meteorAux)
|
|
|
3156
|
listPowerSeries.append(PowerSeries)
|
|
3156
|
listPowerSeries.append(PowerSeries)
|
|
|
3157
|
listVoltageSeries.append(meteorVolts1)
|
|
3157
|
listVoltageSeries.append(meteorVolts1)
|
|
|
3158
|
|
|
3158
|
|
|
|
3159
|
return listMeteors1, listPowerSeries, listVoltageSeries
|
|
3159
|
return listMeteors1, listPowerSeries, listVoltageSeries
|
|
|
3160
|
|
|
3160
|
|
|
|
3161
|
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
|
|
3161
|
def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
|
|
|
3162
|
|
|
3162
|
|
|
|
3163
|
threshError = 10
|
|
3163
|
threshError = 10
|
|
|
3164
|
#Depending if it is 30 or 50 MHz
|
|
3164
|
#Depending if it is 30 or 50 MHz
|
|
|
3165
|
if frequency == 30e6:
|
|
3165
|
if frequency == 30e6:
|
|
|
3166
|
timeLag = 45*10**-3
|
|
3166
|
timeLag = 45*10**-3
|
|
|
3167
|
else:
|
|
3167
|
else:
|
|
|
3168
|
timeLag = 15*10**-3
|
|
3168
|
timeLag = 15*10**-3
|
|
|
3169
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3169
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3170
|
|
|
3170
|
|
|
|
3171
|
listMeteors1 = []
|
|
3171
|
listMeteors1 = []
|
|
|
3172
|
|
|
3172
|
|
|
|
3173
|
for i in range(len(listMeteors)):
|
|
3173
|
for i in range(len(listMeteors)):
|
|
|
3174
|
meteorPower = listPower[i]
|
|
3174
|
meteorPower = listPower[i]
|
|
|
3175
|
meteorAux = listMeteors[i]
|
|
3175
|
meteorAux = listMeteors[i]
|
|
|
3176
|
|
|
3176
|
|
|
|
3177
|
if meteorAux[-1] == 0:
|
|
3177
|
if meteorAux[-1] == 0:
|
|
|
3178
|
|
|
3178
|
|
|
|
3179
|
try:
|
|
3179
|
try:
|
|
|
3180
|
indmax = meteorPower.argmax()
|
|
3180
|
indmax = meteorPower.argmax()
|
|
|
3181
|
indlag = indmax + lag
|
|
3181
|
indlag = indmax + lag
|
|
|
3182
|
|
|
3182
|
|
|
|
3183
|
y = meteorPower[indlag:]
|
|
3183
|
y = meteorPower[indlag:]
|
|
|
3184
|
x = numpy.arange(0, y.size)*timeLag
|
|
3184
|
x = numpy.arange(0, y.size)*timeLag
|
|
|
3185
|
|
|
3185
|
|
|
|
3186
|
#first guess
|
|
3186
|
#first guess
|
|
|
3187
|
a = y[0]
|
|
3187
|
a = y[0]
|
|
|
3188
|
tau = timeLag
|
|
3188
|
tau = timeLag
|
|
|
3189
|
#exponential fit
|
|
3189
|
#exponential fit
|
|
|
3190
|
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
|
|
3190
|
popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
|
|
|
3191
|
y1 = self.__exponential_function(x, *popt)
|
|
3191
|
y1 = self.__exponential_function(x, *popt)
|
|
|
3192
|
#error estimation
|
|
3192
|
#error estimation
|
|
|
3193
|
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
|
|
3193
|
error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
|
|
|
3194
|
|
|
3194
|
|
|
|
3195
|
decayTime = popt[1]
|
|
3195
|
decayTime = popt[1]
|
|
|
3196
|
riseTime = indmax*timeInterval
|
|
3196
|
riseTime = indmax*timeInterval
|
|
|
3197
|
meteorAux[11:13] = [decayTime, error]
|
|
3197
|
meteorAux[11:13] = [decayTime, error]
|
|
|
3198
|
|
|
3198
|
|
|
|
3199
|
#Table items 7, 8 and 11
|
|
3199
|
#Table items 7, 8 and 11
|
|
|
3200
|
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s
|
|
3200
|
if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s
|
|
|
3201
|
meteorAux[-1] = 7
|
|
3201
|
meteorAux[-1] = 7
|
|
|
3202
|
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
|
|
3202
|
elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
|
|
|
3203
|
meteorAux[-1] = 8
|
|
3203
|
meteorAux[-1] = 8
|
|
|
3204
|
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time
|
|
3204
|
if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time
|
|
|
3205
|
meteorAux[-1] = 11
|
|
3205
|
meteorAux[-1] = 11
|
|
|
3206
|
|
|
3206
|
|
|
|
3207
|
|
|
3207
|
|
|
|
3208
|
except:
|
|
3208
|
except:
|
|
|
3209
|
meteorAux[-1] = 11
|
|
3209
|
meteorAux[-1] = 11
|
|
|
3210
|
|
|
3210
|
|
|
|
3211
|
|
|
3211
|
|
|
|
3212
|
listMeteors1.append(meteorAux)
|
|
3212
|
listMeteors1.append(meteorAux)
|
|
|
3213
|
|
|
3213
|
|
|
|
3214
|
return listMeteors1
|
|
3214
|
return listMeteors1
|
|
|
3215
|
|
|
3215
|
|
|
|
3216
|
#Exponential Function
|
|
3216
|
#Exponential Function
|
|
|
3217
|
|
|
3217
|
|
|
|
3218
|
def __exponential_function(self, x, a, tau):
|
|
3218
|
def __exponential_function(self, x, a, tau):
|
|
|
3219
|
y = a*numpy.exp(-x/tau)
|
|
3219
|
y = a*numpy.exp(-x/tau)
|
|
|
3220
|
return y
|
|
3220
|
return y
|
|
|
3221
|
|
|
3221
|
|
|
|
3222
|
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval):
|
|
3222
|
def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval):
|
|
|
3223
|
|
|
3223
|
|
|
|
3224
|
pairslist1 = list(pairslist)
|
|
3224
|
pairslist1 = list(pairslist)
|
|
|
3225
|
pairslist1.append((0,1))
|
|
3225
|
pairslist1.append((0,1))
|
|
|
3226
|
pairslist1.append((3,4))
|
|
3226
|
pairslist1.append((3,4))
|
|
|
3227
|
numPairs = len(pairslist1)
|
|
3227
|
numPairs = len(pairslist1)
|
|
|
3228
|
#Time Lag
|
|
3228
|
#Time Lag
|
|
|
3229
|
timeLag = 45*10**-3
|
|
3229
|
timeLag = 45*10**-3
|
|
|
3230
|
c = 3e8
|
|
3230
|
c = 3e8
|
|
|
3231
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
3231
|
lag = numpy.ceil(timeLag/timeInterval)
|
|
|
3232
|
freq = 30e6
|
|
3232
|
freq = 30e6
|
|
|
3233
|
|
|
3233
|
|
|
|
3234
|
listMeteors1 = []
|
|
3234
|
listMeteors1 = []
|
|
|
3235
|
|
|
3235
|
|
|
|
3236
|
for i in range(len(listMeteors)):
|
|
3236
|
for i in range(len(listMeteors)):
|
|
|
3237
|
meteorAux = listMeteors[i]
|
|
3237
|
meteorAux = listMeteors[i]
|
|
|
3238
|
if meteorAux[-1] == 0:
|
|
3238
|
if meteorAux[-1] == 0:
|
|
|
3239
|
mStart = listMeteors[i][1]
|
|
3239
|
mStart = listMeteors[i][1]
|
|
|
3240
|
mPeak = listMeteors[i][2]
|
|
3240
|
mPeak = listMeteors[i][2]
|
|
|
3241
|
mLag = mPeak - mStart + lag
|
|
3241
|
mLag = mPeak - mStart + lag
|
|
|
3242
|
|
|
3242
|
|
|
|
3243
|
#get the volt data between the start and end times of the meteor
|
|
3243
|
#get the volt data between the start and end times of the meteor
|
|
|
3244
|
meteorVolts = listVolts[i]
|
|
3244
|
meteorVolts = listVolts[i]
|
|
|
3245
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
3245
|
meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
|
|
|
3246
|
|
|
3246
|
|
|
|
3247
|
#Get CCF
|
|
3247
|
#Get CCF
|
|
|
3248
|
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
|
|
3248
|
allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
|
|
|
3249
|
|
|
3249
|
|
|
|
3250
|
#Method 2
|
|
3250
|
#Method 2
|
|
|
3251
|
slopes = numpy.zeros(numPairs)
|
|
3251
|
slopes = numpy.zeros(numPairs)
|
|
|
3252
|
time = numpy.array([-2,-1,1,2])*timeInterval
|
|
3252
|
time = numpy.array([-2,-1,1,2])*timeInterval
|
|
|
3253
|
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
|
|
3253
|
angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
|
|
|
3254
|
|
|
3254
|
|
|
|
3255
|
#Correct phases
|
|
3255
|
#Correct phases
|
|
|
3256
|
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
|
|
3256
|
derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
|
|
|
3257
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
3257
|
indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
|
|
|
3258
|
|
|
3258
|
|
|
|
3259
|
if indDer[0].shape[0] > 0:
|
|
3259
|
if indDer[0].shape[0] > 0:
|
|
|
3260
|
for i in range(indDer[0].shape[0]):
|
|
3260
|
for i in range(indDer[0].shape[0]):
|
|
|
3261
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
|
|
3261
|
signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
|
|
|
3262
|
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
|
|
3262
|
angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
|
|
|
3263
|
|
|
3263
|
|
|
|
3264
|
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
|
|
3264
|
# fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
|
|
|
3265
|
for j in range(numPairs):
|
|
3265
|
for j in range(numPairs):
|
|
|
3266
|
fit = stats.linregress(time, angAllCCF[j,:])
|
|
3266
|
fit = stats.linregress(time, angAllCCF[j,:])
|
|
|
3267
|
slopes[j] = fit[0]
|
|
3267
|
slopes[j] = fit[0]
|
|
|
3268
|
|
|
3268
|
|
|
|
3269
|
#Remove Outlier
|
|
3269
|
#Remove Outlier
|
|
|
3270
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
3270
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
|
3271
|
# slopes = numpy.delete(slopes,indOut)
|
|
3271
|
# slopes = numpy.delete(slopes,indOut)
|
|
|
3272
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
3272
|
# indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
|
|
|
3273
|
# slopes = numpy.delete(slopes,indOut)
|
|
3273
|
# slopes = numpy.delete(slopes,indOut)
|
|
|
3274
|
|
|
3274
|
|
|
|
3275
|
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
3275
|
radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
|
3276
|
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
3276
|
radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
|
|
|
3277
|
meteorAux[-2] = radialError
|
|
3277
|
meteorAux[-2] = radialError
|
|
|
3278
|
meteorAux[-3] = radialVelocity
|
|
3278
|
meteorAux[-3] = radialVelocity
|
|
|
3279
|
|
|
3279
|
|
|
|
3280
|
#Setting Error
|
|
3280
|
#Setting Error
|
|
|
3281
|
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
3281
|
#Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
|
|
|
3282
|
if numpy.abs(radialVelocity) > 200:
|
|
3282
|
if numpy.abs(radialVelocity) > 200:
|
|
|
3283
|
meteorAux[-1] = 15
|
|
3283
|
meteorAux[-1] = 15
|
|
|
3284
|
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity
|
|
3284
|
#Number 12: Poor fit to CCF variation for estimation of radial drift velocity
|
|
|
3285
|
elif radialError > radialStdThresh:
|
|
3285
|
elif radialError > radialStdThresh:
|
|
|
3286
|
meteorAux[-1] = 12
|
|
3286
|
meteorAux[-1] = 12
|
|
|
3287
|
|
|
3287
|
|
|
|
3288
|
listMeteors1.append(meteorAux)
|
|
3288
|
listMeteors1.append(meteorAux)
|
|
|
3289
|
return listMeteors1
|
|
3289
|
return listMeteors1
|
|
|
3290
|
|
|
3290
|
|
|
|
3291
|
def __setNewArrays(self, listMeteors, date, heiRang):
|
|
3291
|
def __setNewArrays(self, listMeteors, date, heiRang):
|
|
|
3292
|
|
|
3292
|
|
|
|
3293
|
#New arrays
|
|
3293
|
#New arrays
|
|
|
3294
|
arrayMeteors = numpy.array(listMeteors)
|
|
3294
|
arrayMeteors = numpy.array(listMeteors)
|
|
|
3295
|
arrayParameters = numpy.zeros((len(listMeteors), 13))
|
|
3295
|
arrayParameters = numpy.zeros((len(listMeteors), 13))
|
|
|
3296
|
|
|
3296
|
|
|
|
3297
|
#Date inclusion
|
|
3297
|
#Date inclusion
|
|
|
3298
|
# date = re.findall(r'\((.*?)\)', date)
|
|
3298
|
# date = re.findall(r'\((.*?)\)', date)
|
|
|
3299
|
# date = date[0].split(',')
|
|
3299
|
# date = date[0].split(',')
|
|
|
3300
|
# date = map(int, date)
|
|
3300
|
# date = map(int, date)
|
|
|
3301
|
#
|
|
3301
|
#
|
|
|
3302
|
# if len(date)<6:
|
|
3302
|
# if len(date)<6:
|
|
|
3303
|
# date.append(0)
|
|
3303
|
# date.append(0)
|
|
|
3304
|
#
|
|
3304
|
#
|
|
|
3305
|
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
|
|
3305
|
# date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
|
|
|
3306
|
# arrayDate = numpy.tile(date, (len(listMeteors), 1))
|
|
3306
|
# arrayDate = numpy.tile(date, (len(listMeteors), 1))
|
|
|
3307
|
arrayDate = numpy.tile(date, (len(listMeteors)))
|
|
3307
|
arrayDate = numpy.tile(date, (len(listMeteors)))
|
|
|
3308
|
|
|
3308
|
|
|
|
3309
|
#Meteor array
|
|
3309
|
#Meteor array
|
|
|
3310
|
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
|
|
3310
|
# arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
|
|
|
3311
|
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
|
|
3311
|
# arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
|
|
|
3312
|
|
|
3312
|
|
|
|
3313
|
#Parameters Array
|
|
3313
|
#Parameters Array
|
|
|
3314
|
arrayParameters[:,0] = arrayDate #Date
|
|
3314
|
arrayParameters[:,0] = arrayDate #Date
|
|
|
3315
|
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
|
|
3315
|
arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
|
|
|
3316
|
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
|
|
3316
|
arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
|
|
|
3317
|
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases
|
|
3317
|
arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases
|
|
|
3318
|
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error
|
|
3318
|
arrayParameters[:,-1] = arrayMeteors[:,-1] #Error
|
|
|
3319
|
|
|
3319
|
|
|
|
3320
|
|
|
3320
|
|
|
|
3321
|
return arrayParameters
|
|
3321
|
return arrayParameters
|
|
|
3322
|
|
|
3322
|
|
|
|
3323
|
class CorrectSMPhases(Operation):
|
|
3323
|
class CorrectSMPhases(Operation):
|
|
|
3324
|
|
|
3324
|
|
|
|
3325
|
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
|
|
3325
|
def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
|
|
|
3326
|
|
|
3326
|
|
|
|
3327
|
arrayParameters = dataOut.data_param
|
|
3327
|
arrayParameters = dataOut.data_param
|
|
|
3328
|
pairsList = []
|
|
3328
|
pairsList = []
|
|
|
3329
|
pairx = (0,1)
|
|
3329
|
pairx = (0,1)
|
|
|
3330
|
pairy = (2,3)
|
|
3330
|
pairy = (2,3)
|
|
|
3331
|
pairsList.append(pairx)
|
|
3331
|
pairsList.append(pairx)
|
|
|
3332
|
pairsList.append(pairy)
|
|
3332
|
pairsList.append(pairy)
|
|
|
3333
|
jph = numpy.zeros(4)
|
|
3333
|
jph = numpy.zeros(4)
|
|
|
3334
|
|
|
3334
|
|
|
|
3335
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
3335
|
phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
|
|
|
3336
|
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
3336
|
# arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
|
|
|
3337
|
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
|
|
3337
|
arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
|
|
|
3338
|
|
|
3338
|
|
|
|
3339
|
meteorOps = SMOperations()
|
|
3339
|
meteorOps = SMOperations()
|
|
|
3340
|
if channelPositions is None:
|
|
3340
|
if channelPositions is None:
|
|
|
3341
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
3341
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
|
3342
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
3342
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
|
3343
|
|
|
3343
|
|
|
|
3344
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
3344
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
|
3345
|
h = (hmin,hmax)
|
|
3345
|
h = (hmin,hmax)
|
|
|
3346
|
|
|
3346
|
|
|
|
3347
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
3347
|
arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
|
|
|
3348
|
|
|
3348
|
|
|
|
3349
|
dataOut.data_param = arrayParameters
|
|
3349
|
dataOut.data_param = arrayParameters
|
|
|
3350
|
return
|
|
3350
|
return
|
|
|
3351
|
|
|
3351
|
|
|
|
3352
|
class SMPhaseCalibration(Operation):
|
|
3352
|
class SMPhaseCalibration(Operation):
|
|
|
3353
|
|
|
3353
|
|
|
|
3354
|
__buffer = None
|
|
3354
|
__buffer = None
|
|
|
3355
|
|
|
3355
|
|
|
|
3356
|
__initime = None
|
|
3356
|
__initime = None
|
|
|
3357
|
|
|
3357
|
|
|
|
3358
|
__dataReady = False
|
|
3358
|
__dataReady = False
|
|
|
3359
|
|
|
3359
|
|
|
|
3360
|
__isConfig = False
|
|
3360
|
__isConfig = False
|
|
|
3361
|
|
|
3361
|
|
|
|
3362
|
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
|
|
3362
|
def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
|
|
|
3363
|
|
|
3363
|
|
|
|
3364
|
dataTime = currentTime + paramInterval
|
|
3364
|
dataTime = currentTime + paramInterval
|
|
|
3365
|
deltaTime = dataTime - initTime
|
|
3365
|
deltaTime = dataTime - initTime
|
|
|
3366
|
|
|
3366
|
|
|
|
3367
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
3367
|
if deltaTime >= outputInterval or deltaTime < 0:
|
|
|
3368
|
return True
|
|
3368
|
return True
|
|
|
3369
|
|
|
3369
|
|
|
|
3370
|
return False
|
|
3370
|
return False
|
|
|
3371
|
|
|
3371
|
|
|
|
3372
|
def __getGammas(self, pairs, d, phases):
|
|
3372
|
def __getGammas(self, pairs, d, phases):
|
|
|
3373
|
gammas = numpy.zeros(2)
|
|
3373
|
gammas = numpy.zeros(2)
|
|
|
3374
|
|
|
3374
|
|
|
|
3375
|
for i in range(len(pairs)):
|
|
3375
|
for i in range(len(pairs)):
|
|
|
3376
|
|
|
3376
|
|
|
|
3377
|
pairi = pairs[i]
|
|
3377
|
pairi = pairs[i]
|
|
|
3378
|
|
|
3378
|
|
|
|
3379
|
phip3 = phases[:,pairi[0]]
|
|
3379
|
phip3 = phases[:,pairi[0]]
|
|
|
3380
|
d3 = d[pairi[0]]
|
|
3380
|
d3 = d[pairi[0]]
|
|
|
3381
|
phip2 = phases[:,pairi[1]]
|
|
3381
|
phip2 = phases[:,pairi[1]]
|
|
|
3382
|
d2 = d[pairi[1]]
|
|
3382
|
d2 = d[pairi[1]]
|
|
|
3383
|
#Calculating gamma
|
|
3383
|
#Calculating gamma
|
|
|
3384
|
# jdcos = alp1/(k*d1)
|
|
3384
|
# jdcos = alp1/(k*d1)
|
|
|
3385
|
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
|
|
3385
|
# jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
|
|
|
3386
|
jgamma = -phip2*d3/d2 - phip3
|
|
3386
|
jgamma = -phip2*d3/d2 - phip3
|
|
|
3387
|
jgamma = numpy.angle(numpy.exp(1j*jgamma))
|
|
3387
|
jgamma = numpy.angle(numpy.exp(1j*jgamma))
|
|
|
3388
|
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi
|
|
3388
|
# jgamma[jgamma>numpy.pi] -= 2*numpy.pi
|
|
|
3389
|
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi
|
|
3389
|
# jgamma[jgamma<-numpy.pi] += 2*numpy.pi
|
|
|
3390
|
|
|
3390
|
|
|
|
3391
|
#Revised distribution
|
|
3391
|
#Revised distribution
|
|
|
3392
|
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
|
|
3392
|
jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
|
|
|
3393
|
|
|
3393
|
|
|
|
3394
|
#Histogram
|
|
3394
|
#Histogram
|
|
|
3395
|
nBins = 64
|
|
3395
|
nBins = 64
|
|
|
3396
|
rmin = -0.5*numpy.pi
|
|
3396
|
rmin = -0.5*numpy.pi
|
|
|
3397
|
rmax = 0.5*numpy.pi
|
|
3397
|
rmax = 0.5*numpy.pi
|
|
|
3398
|
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
|
|
3398
|
phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
|
|
|
3399
|
|
|
3399
|
|
|
|
3400
|
meteorsY = phaseHisto[0]
|
|
3400
|
meteorsY = phaseHisto[0]
|
|
|
3401
|
phasesX = phaseHisto[1][:-1]
|
|
3401
|
phasesX = phaseHisto[1][:-1]
|
|
|
3402
|
width = phasesX[1] - phasesX[0]
|
|
3402
|
width = phasesX[1] - phasesX[0]
|
|
|
3403
|
phasesX += width/2
|
|
3403
|
phasesX += width/2
|
|
|
3404
|
|
|
3404
|
|
|
|
3405
|
#Gaussian aproximation
|
|
3405
|
#Gaussian aproximation
|
|
|
3406
|
bpeak = meteorsY.argmax()
|
|
3406
|
bpeak = meteorsY.argmax()
|
|
|
3407
|
peak = meteorsY.max()
|
|
3407
|
peak = meteorsY.max()
|
|
|
3408
|
jmin = bpeak - 5
|
|
3408
|
jmin = bpeak - 5
|
|
|
3409
|
jmax = bpeak + 5 + 1
|
|
3409
|
jmax = bpeak + 5 + 1
|
|
|
3410
|
|
|
3410
|
|
|
|
3411
|
if jmin<0:
|
|
3411
|
if jmin<0:
|
|
|
3412
|
jmin = 0
|
|
3412
|
jmin = 0
|
|
|
3413
|
jmax = 6
|
|
3413
|
jmax = 6
|
|
|
3414
|
elif jmax > meteorsY.size:
|
|
3414
|
elif jmax > meteorsY.size:
|
|
|
3415
|
jmin = meteorsY.size - 6
|
|
3415
|
jmin = meteorsY.size - 6
|
|
|
3416
|
jmax = meteorsY.size
|
|
3416
|
jmax = meteorsY.size
|
|
|
3417
|
|
|
3417
|
|
|
|
3418
|
x0 = numpy.array([peak,bpeak,50])
|
|
3418
|
x0 = numpy.array([peak,bpeak,50])
|
|
|
3419
|
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
|
|
3419
|
coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
|
|
|
3420
|
|
|
3420
|
|
|
|
3421
|
#Gammas
|
|
3421
|
#Gammas
|
|
|
3422
|
gammas[i] = coeff[0][1]
|
|
3422
|
gammas[i] = coeff[0][1]
|
|
|
3423
|
|
|
3423
|
|
|
|
3424
|
return gammas
|
|
3424
|
return gammas
|
|
|
3425
|
|
|
3425
|
|
|
|
3426
|
def __residualFunction(self, coeffs, y, t):
|
|
3426
|
def __residualFunction(self, coeffs, y, t):
|
|
|
3427
|
|
|
3427
|
|
|
|
3428
|
return y - self.__gauss_function(t, coeffs)
|
|
3428
|
return y - self.__gauss_function(t, coeffs)
|
|
|
3429
|
|
|
3429
|
|
|
|
3430
|
def __gauss_function(self, t, coeffs):
|
|
3430
|
def __gauss_function(self, t, coeffs):
|
|
|
3431
|
|
|
3431
|
|
|
|
3432
|
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
|
|
3432
|
return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
|
|
|
3433
|
|
|
3433
|
|
|
|
3434
|
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
|
|
3434
|
def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
|
|
|
3435
|
meteorOps = SMOperations()
|
|
3435
|
meteorOps = SMOperations()
|
|
|
3436
|
nchan = 4
|
|
3436
|
nchan = 4
|
|
|
3437
|
pairx = pairsList[0] #x es 0
|
|
3437
|
pairx = pairsList[0] #x es 0
|
|
|
3438
|
pairy = pairsList[1] #y es 1
|
|
3438
|
pairy = pairsList[1] #y es 1
|
|
|
3439
|
center_xangle = 0
|
|
3439
|
center_xangle = 0
|
|
|
3440
|
center_yangle = 0
|
|
3440
|
center_yangle = 0
|
|
|
3441
|
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
|
|
3441
|
range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
|
|
|
3442
|
ntimes = len(range_angle)
|
|
3442
|
ntimes = len(range_angle)
|
|
|
3443
|
|
|
3443
|
|
|
|
3444
|
nstepsx = 20
|
|
3444
|
nstepsx = 20
|
|
|
3445
|
nstepsy = 20
|
|
3445
|
nstepsy = 20
|
|
|
3446
|
|
|
3446
|
|
|
|
3447
|
for iz in range(ntimes):
|
|
3447
|
for iz in range(ntimes):
|
|
|
3448
|
min_xangle = -range_angle[iz]/2 + center_xangle
|
|
3448
|
min_xangle = -range_angle[iz]/2 + center_xangle
|
|
|
3449
|
max_xangle = range_angle[iz]/2 + center_xangle
|
|
3449
|
max_xangle = range_angle[iz]/2 + center_xangle
|
|
|
3450
|
min_yangle = -range_angle[iz]/2 + center_yangle
|
|
3450
|
min_yangle = -range_angle[iz]/2 + center_yangle
|
|
|
3451
|
max_yangle = range_angle[iz]/2 + center_yangle
|
|
3451
|
max_yangle = range_angle[iz]/2 + center_yangle
|
|
|
3452
|
|
|
3452
|
|
|
|
3453
|
inc_x = (max_xangle-min_xangle)/nstepsx
|
|
3453
|
inc_x = (max_xangle-min_xangle)/nstepsx
|
|
|
3454
|
inc_y = (max_yangle-min_yangle)/nstepsy
|
|
3454
|
inc_y = (max_yangle-min_yangle)/nstepsy
|
|
|
3455
|
|
|
3455
|
|
|
|
3456
|
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
|
|
3456
|
alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
|
|
|
3457
|
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
|
|
3457
|
alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
|
|
|
3458
|
penalty = numpy.zeros((nstepsx,nstepsy))
|
|
3458
|
penalty = numpy.zeros((nstepsx,nstepsy))
|
|
|
3459
|
jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
|
|
3459
|
jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
|
|
|
3460
|
jph = numpy.zeros(nchan)
|
|
3460
|
jph = numpy.zeros(nchan)
|
|
|
3461
|
|
|
3461
|
|
|
|
3462
|
# Iterations looking for the offset
|
|
3462
|
# Iterations looking for the offset
|
|
|
3463
|
for iy in range(int(nstepsy)):
|
|
3463
|
for iy in range(int(nstepsy)):
|
|
|
3464
|
for ix in range(int(nstepsx)):
|
|
3464
|
for ix in range(int(nstepsx)):
|
|
|
3465
|
d3 = d[pairsList[1][0]]
|
|
3465
|
d3 = d[pairsList[1][0]]
|
|
|
3466
|
d2 = d[pairsList[1][1]]
|
|
3466
|
d2 = d[pairsList[1][1]]
|
|
|
3467
|
d5 = d[pairsList[0][0]]
|
|
3467
|
d5 = d[pairsList[0][0]]
|
|
|
3468
|
d4 = d[pairsList[0][1]]
|
|
3468
|
d4 = d[pairsList[0][1]]
|
|
|
3469
|
|
|
3469
|
|
|
|
3470
|
alp2 = alpha_y[iy] #gamma 1
|
|
3470
|
alp2 = alpha_y[iy] #gamma 1
|
|
|
3471
|
alp4 = alpha_x[ix] #gamma 0
|
|
3471
|
alp4 = alpha_x[ix] #gamma 0
|
|
|
3472
|
|
|
3472
|
|
|
|
3473
|
alp3 = -alp2*d3/d2 - gammas[1]
|
|
3473
|
alp3 = -alp2*d3/d2 - gammas[1]
|
|
|
3474
|
alp5 = -alp4*d5/d4 - gammas[0]
|
|
3474
|
alp5 = -alp4*d5/d4 - gammas[0]
|
|
|
3475
|
# jph[pairy[1]] = alpha_y[iy]
|
|
3475
|
# jph[pairy[1]] = alpha_y[iy]
|
|
|
3476
|
# jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
|
|
3476
|
# jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
|
|
|
3477
|
|
|
3477
|
|
|
|
3478
|
# jph[pairx[1]] = alpha_x[ix]
|
|
3478
|
# jph[pairx[1]] = alpha_x[ix]
|
|
|
3479
|
# jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
|
|
3479
|
# jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
|
|
|
3480
|
jph[pairsList[0][1]] = alp4
|
|
3480
|
jph[pairsList[0][1]] = alp4
|
|
|
3481
|
jph[pairsList[0][0]] = alp5
|
|
3481
|
jph[pairsList[0][0]] = alp5
|
|
|
3482
|
jph[pairsList[1][0]] = alp3
|
|
3482
|
jph[pairsList[1][0]] = alp3
|
|
|
3483
|
jph[pairsList[1][1]] = alp2
|
|
3483
|
jph[pairsList[1][1]] = alp2
|
|
|
3484
|
jph_array[:,ix,iy] = jph
|
|
3484
|
jph_array[:,ix,iy] = jph
|
|
|
3485
|
# d = [2.0,2.5,2.5,2.0]
|
|
3485
|
# d = [2.0,2.5,2.5,2.0]
|
|
|
3486
|
#falta chequear si va a leer bien los meteoros
|
|
3486
|
#falta chequear si va a leer bien los meteoros
|
|
|
3487
|
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
|
|
3487
|
meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
|
|
|
3488
|
error = meteorsArray1[:,-1]
|
|
3488
|
error = meteorsArray1[:,-1]
|
|
|
3489
|
ind1 = numpy.where(error==0)[0]
|
|
3489
|
ind1 = numpy.where(error==0)[0]
|
|
|
3490
|
penalty[ix,iy] = ind1.size
|
|
3490
|
penalty[ix,iy] = ind1.size
|
|
|
3491
|
|
|
3491
|
|
|
|
3492
|
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
|
|
3492
|
i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
|
|
|
3493
|
phOffset = jph_array[:,i,j]
|
|
3493
|
phOffset = jph_array[:,i,j]
|
|
|
3494
|
|
|
3494
|
|
|
|
3495
|
center_xangle = phOffset[pairx[1]]
|
|
3495
|
center_xangle = phOffset[pairx[1]]
|
|
|
3496
|
center_yangle = phOffset[pairy[1]]
|
|
3496
|
center_yangle = phOffset[pairy[1]]
|
|
|
3497
|
|
|
3497
|
|
|
|
3498
|
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
|
|
3498
|
phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
|
|
|
3499
|
phOffset = phOffset*180/numpy.pi
|
|
3499
|
phOffset = phOffset*180/numpy.pi
|
|
|
3500
|
return phOffset
|
|
3500
|
return phOffset
|
|
|
3501
|
|
|
3501
|
|
|
|
3502
|
|
|
3502
|
|
|
|
3503
|
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
|
|
3503
|
def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
|
|
|
3504
|
|
|
3504
|
|
|
|
3505
|
dataOut.flagNoData = True
|
|
3505
|
dataOut.flagNoData = True
|
|
|
3506
|
self.__dataReady = False
|
|
3506
|
self.__dataReady = False
|
|
|
3507
|
dataOut.outputInterval = nHours*3600
|
|
3507
|
dataOut.outputInterval = nHours*3600
|
|
|
3508
|
|
|
3508
|
|
|
|
3509
|
if self.__isConfig == False:
|
|
3509
|
if self.__isConfig == False:
|
|
|
3510
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
3510
|
# self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
|
|
|
3511
|
#Get Initial LTC time
|
|
3511
|
#Get Initial LTC time
|
|
|
3512
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
3512
|
self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
|
|
|
3513
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
3513
|
self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
|
|
|
3514
|
|
|
3514
|
|
|
|
3515
|
self.__isConfig = True
|
|
3515
|
self.__isConfig = True
|
|
|
3516
|
|
|
3516
|
|
|
|
3517
|
if self.__buffer is None:
|
|
3517
|
if self.__buffer is None:
|
|
|
3518
|
self.__buffer = dataOut.data_param.copy()
|
|
3518
|
self.__buffer = dataOut.data_param.copy()
|
|
|
3519
|
|
|
3519
|
|
|
|
3520
|
else:
|
|
3520
|
else:
|
|
|
3521
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
3521
|
self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
|
|
|
3522
|
|
|
3522
|
|
|
|
3523
|
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
3523
|
self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
|
|
|
3524
|
|
|
3524
|
|
|
|
3525
|
if self.__dataReady:
|
|
3525
|
if self.__dataReady:
|
|
|
3526
|
dataOut.utctimeInit = self.__initime
|
|
3526
|
dataOut.utctimeInit = self.__initime
|
|
|
3527
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
3527
|
self.__initime += dataOut.outputInterval #to erase time offset
|
|
|
3528
|
|
|
3528
|
|
|
|
3529
|
freq = dataOut.frequency
|
|
3529
|
freq = dataOut.frequency
|
|
|
3530
|
c = dataOut.C #m/s
|
|
3530
|
c = dataOut.C #m/s
|
|
|
3531
|
lamb = c/freq
|
|
3531
|
lamb = c/freq
|
|
|
3532
|
k = 2*numpy.pi/lamb
|
|
3532
|
k = 2*numpy.pi/lamb
|
|
|
3533
|
azimuth = 0
|
|
3533
|
azimuth = 0
|
|
|
3534
|
h = (hmin, hmax)
|
|
3534
|
h = (hmin, hmax)
|
|
|
3535
|
# pairs = ((0,1),(2,3)) #Estrella
|
|
3535
|
# pairs = ((0,1),(2,3)) #Estrella
|
|
|
3536
|
# pairs = ((1,0),(2,3)) #T
|
|
3536
|
# pairs = ((1,0),(2,3)) #T
|
|
|
3537
|
|
|
3537
|
|
|
|
3538
|
if channelPositions is None:
|
|
3538
|
if channelPositions is None:
|
|
|
3539
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
3539
|
# channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T
|
|
|
3540
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
3540
|
channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella
|
|
|
3541
|
meteorOps = SMOperations()
|
|
3541
|
meteorOps = SMOperations()
|
|
|
3542
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
3542
|
pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
|
|
|
3543
|
|
|
3543
|
|
|
|
3544
|
#Checking correct order of pairs
|
|
3544
|
#Checking correct order of pairs
|
|
|
3545
|
pairs = []
|
|
3545
|
pairs = []
|
|
|
3546
|
if distances[1] > distances[0]:
|
|
3546
|
if distances[1] > distances[0]:
|
|
|
3547
|
pairs.append((1,0))
|
|
3547
|
pairs.append((1,0))
|
|
|
3548
|
else:
|
|
3548
|
else:
|
|
|
3549
|
pairs.append((0,1))
|
|
3549
|
pairs.append((0,1))
|
|
|
3550
|
|
|
3550
|
|
|
|
3551
|
if distances[3] > distances[2]:
|
|
3551
|
if distances[3] > distances[2]:
|
|
|
3552
|
pairs.append((3,2))
|
|
3552
|
pairs.append((3,2))
|
|
|
3553
|
else:
|
|
3553
|
else:
|
|
|
3554
|
pairs.append((2,3))
|
|
3554
|
pairs.append((2,3))
|
|
|
3555
|
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
|
|
3555
|
# distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
|
|
|
3556
|
|
|
3556
|
|
|
|
3557
|
meteorsArray = self.__buffer
|
|
3557
|
meteorsArray = self.__buffer
|
|
|
3558
|
error = meteorsArray[:,-1]
|
|
3558
|
error = meteorsArray[:,-1]
|
|
|
3559
|
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
|
|
3559
|
boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
|
|
|
3560
|
ind1 = numpy.where(boolError)[0]
|
|
3560
|
ind1 = numpy.where(boolError)[0]
|
|
|
3561
|
meteorsArray = meteorsArray[ind1,:]
|
|
3561
|
meteorsArray = meteorsArray[ind1,:]
|
|
|
3562
|
meteorsArray[:,-1] = 0
|
|
3562
|
meteorsArray[:,-1] = 0
|
|
|
3563
|
phases = meteorsArray[:,8:12]
|
|
3563
|
phases = meteorsArray[:,8:12]
|
|
|
3564
|
|
|
3564
|
|
|
|
3565
|
#Calculate Gammas
|
|
3565
|
#Calculate Gammas
|
|
|
3566
|
gammas = self.__getGammas(pairs, distances, phases)
|
|
3566
|
gammas = self.__getGammas(pairs, distances, phases)
|
|
|
3567
|
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
|
|
3567
|
# gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
|
|
|
3568
|
#Calculate Phases
|
|
3568
|
#Calculate Phases
|
|
|
3569
|
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
|
|
3569
|
phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
|
|
|
3570
|
phasesOff = phasesOff.reshape((1,phasesOff.size))
|
|
3570
|
phasesOff = phasesOff.reshape((1,phasesOff.size))
|
|
|
3571
|
dataOut.data_output = -phasesOff
|
|
3571
|
dataOut.data_output = -phasesOff
|
|
|
3572
|
dataOut.flagNoData = False
|
|
3572
|
dataOut.flagNoData = False
|
|
|
3573
|
self.__buffer = None
|
|
3573
|
self.__buffer = None
|
|
|
3574
|
|
|
3574
|
|
|
|
3575
|
|
|
3575
|
|
|
|
3576
|
return
|
|
3576
|
return
|
|
|
3577
|
|
|
3577
|
|
|
|
3578
|
class SMOperations():
|
|
3578
|
class SMOperations():
|
|
|
3579
|
|
|
3579
|
|
|
|
3580
|
def __init__(self):
|
|
3580
|
def __init__(self):
|
|
|
3581
|
|
|
3581
|
|
|
|
3582
|
return
|
|
3582
|
return
|
|
|
3583
|
|
|
3583
|
|
|
|
3584
|
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
|
|
3584
|
def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
|
|
|
3585
|
|
|
3585
|
|
|
|
3586
|
arrayParameters = arrayParameters0.copy()
|
|
3586
|
arrayParameters = arrayParameters0.copy()
|
|
|
3587
|
hmin = h[0]
|
|
3587
|
hmin = h[0]
|
|
|
3588
|
hmax = h[1]
|
|
3588
|
hmax = h[1]
|
|
|
3589
|
|
|
3589
|
|
|
|
3590
|
#Calculate AOA (Error N 3, 4)
|
|
3590
|
#Calculate AOA (Error N 3, 4)
|
|
|
3591
|
#JONES ET AL. 1998
|
|
3591
|
#JONES ET AL. 1998
|
|
|
3592
|
AOAthresh = numpy.pi/8
|
|
3592
|
AOAthresh = numpy.pi/8
|
|
|
3593
|
error = arrayParameters[:,-1]
|
|
3593
|
error = arrayParameters[:,-1]
|
|
|
3594
|
phases = -arrayParameters[:,8:12] + jph
|
|
3594
|
phases = -arrayParameters[:,8:12] + jph
|
|
|
3595
|
# phases = numpy.unwrap(phases)
|
|
3595
|
# phases = numpy.unwrap(phases)
|
|
|
3596
|
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
|
|
3596
|
arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
|
|
|
3597
|
|
|
3597
|
|
|
|
3598
|
#Calculate Heights (Error N 13 and 14)
|
|
3598
|
#Calculate Heights (Error N 13 and 14)
|
|
|
3599
|
error = arrayParameters[:,-1]
|
|
3599
|
error = arrayParameters[:,-1]
|
|
|
3600
|
Ranges = arrayParameters[:,1]
|
|
3600
|
Ranges = arrayParameters[:,1]
|
|
|
3601
|
zenith = arrayParameters[:,4]
|
|
3601
|
zenith = arrayParameters[:,4]
|
|
|
3602
|
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
|
|
3602
|
arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
|
|
|
3603
|
|
|
3603
|
|
|
|
3604
|
#----------------------- Get Final data ------------------------------------
|
|
3604
|
#----------------------- Get Final data ------------------------------------
|
|
|
3605
|
# error = arrayParameters[:,-1]
|
|
3605
|
# error = arrayParameters[:,-1]
|
|
|
3606
|
# ind1 = numpy.where(error==0)[0]
|
|
3606
|
# ind1 = numpy.where(error==0)[0]
|
|
|
3607
|
# arrayParameters = arrayParameters[ind1,:]
|
|
3607
|
# arrayParameters = arrayParameters[ind1,:]
|
|
|
3608
|
|
|
3608
|
|
|
|
3609
|
return arrayParameters
|
|
3609
|
return arrayParameters
|
|
|
3610
|
|
|
3610
|
|
|
|
3611
|
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
|
|
3611
|
def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
|
|
|
3612
|
|
|
3612
|
|
|
|
3613
|
arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
3613
|
arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
|
3614
|
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
|
|
3614
|
cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
|
|
|
3615
|
|
|
3615
|
|
|
|
3616
|
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
3616
|
arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
|
3617
|
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
3617
|
cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
|
3618
|
arrayAOA[:,2] = cosDirError
|
|
3618
|
arrayAOA[:,2] = cosDirError
|
|
|
3619
|
|
|
3619
|
|
|
|
3620
|
azimuthAngle = arrayAOA[:,0]
|
|
3620
|
azimuthAngle = arrayAOA[:,0]
|
|
|
3621
|
zenithAngle = arrayAOA[:,1]
|
|
3621
|
zenithAngle = arrayAOA[:,1]
|
|
|
3622
|
|
|
3622
|
|
|
|
3623
|
#Setting Error
|
|
3623
|
#Setting Error
|
|
|
3624
|
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
|
|
3624
|
indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
|
|
|
3625
|
error[indError] = 0
|
|
3625
|
error[indError] = 0
|
|
|
3626
|
#Number 3: AOA not fesible
|
|
3626
|
#Number 3: AOA not fesible
|
|
|
3627
|
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
3627
|
indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
|
3628
|
error[indInvalid] = 3
|
|
3628
|
error[indInvalid] = 3
|
|
|
3629
|
#Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
3629
|
#Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
|
3630
|
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
3630
|
indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
|
3631
|
error[indInvalid] = 4
|
|
3631
|
error[indInvalid] = 4
|
|
|
3632
|
return arrayAOA, error
|
|
3632
|
return arrayAOA, error
|
|
|
3633
|
|
|
3633
|
|
|
|
3634
|
def __getDirectionCosines(self, arrayPhase, pairsList, distances):
|
|
3634
|
def __getDirectionCosines(self, arrayPhase, pairsList, distances):
|
|
|
3635
|
|
|
3635
|
|
|
|
3636
|
#Initializing some variables
|
|
3636
|
#Initializing some variables
|
|
|
3637
|
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
3637
|
ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
|
3638
|
ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
3638
|
ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
|
3639
|
|
|
3639
|
|
|
|
3640
|
cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
3640
|
cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3641
|
cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
3641
|
cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3642
|
|
|
3642
|
|
|
|
3643
|
|
|
3643
|
|
|
|
3644
|
for i in range(2):
|
|
3644
|
for i in range(2):
|
|
|
3645
|
ph0 = arrayPhase[:,pairsList[i][0]]
|
|
3645
|
ph0 = arrayPhase[:,pairsList[i][0]]
|
|
|
3646
|
ph1 = arrayPhase[:,pairsList[i][1]]
|
|
3646
|
ph1 = arrayPhase[:,pairsList[i][1]]
|
|
|
3647
|
d0 = distances[pairsList[i][0]]
|
|
3647
|
d0 = distances[pairsList[i][0]]
|
|
|
3648
|
d1 = distances[pairsList[i][1]]
|
|
3648
|
d1 = distances[pairsList[i][1]]
|
|
|
3649
|
|
|
3649
|
|
|
|
3650
|
ph0_aux = ph0 + ph1
|
|
3650
|
ph0_aux = ph0 + ph1
|
|
|
3651
|
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
|
|
3651
|
ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
|
|
|
3652
|
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
|
|
3652
|
# ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
|
|
|
3653
|
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
|
|
3653
|
# ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
|
|
|
3654
|
#First Estimation
|
|
3654
|
#First Estimation
|
|
|
3655
|
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
|
|
3655
|
cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
|
|
|
3656
|
|
|
3656
|
|
|
|
3657
|
#Most-Accurate Second Estimation
|
|
3657
|
#Most-Accurate Second Estimation
|
|
|
3658
|
phi1_aux = ph0 - ph1
|
|
3658
|
phi1_aux = ph0 - ph1
|
|
|
3659
|
phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
3659
|
phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
|
3660
|
#Direction Cosine 1
|
|
3660
|
#Direction Cosine 1
|
|
|
3661
|
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
|
|
3661
|
cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
|
|
|
3662
|
|
|
3662
|
|
|
|
3663
|
#Searching the correct Direction Cosine
|
|
3663
|
#Searching the correct Direction Cosine
|
|
|
3664
|
cosdir0_aux = cosdir0[:,i]
|
|
3664
|
cosdir0_aux = cosdir0[:,i]
|
|
|
3665
|
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
3665
|
cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
|
3666
|
#Minimum Distance
|
|
3666
|
#Minimum Distance
|
|
|
3667
|
cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
3667
|
cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
|
3668
|
indcos = cosDiff.argmin(axis = 1)
|
|
3668
|
indcos = cosDiff.argmin(axis = 1)
|
|
|
3669
|
#Saving Value obtained
|
|
3669
|
#Saving Value obtained
|
|
|
3670
|
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
3670
|
cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
|
3671
|
|
|
3671
|
|
|
|
3672
|
return cosdir0, cosdir
|
|
3672
|
return cosdir0, cosdir
|
|
|
3673
|
|
|
3673
|
|
|
|
3674
|
def __calculateAOA(self, cosdir, azimuth):
|
|
3674
|
def __calculateAOA(self, cosdir, azimuth):
|
|
|
3675
|
cosdirX = cosdir[:,0]
|
|
3675
|
cosdirX = cosdir[:,0]
|
|
|
3676
|
cosdirY = cosdir[:,1]
|
|
3676
|
cosdirY = cosdir[:,1]
|
|
|
3677
|
|
|
3677
|
|
|
|
3678
|
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
3678
|
zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
|
3679
|
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
|
|
3679
|
azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
|
|
|
3680
|
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
3680
|
angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
|
3681
|
|
|
3681
|
|
|
|
3682
|
return angles
|
|
3682
|
return angles
|
|
|
3683
|
|
|
3683
|
|
|
|
3684
|
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
3684
|
def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
|
3685
|
|
|
3685
|
|
|
|
3686
|
Ramb = 375 #Ramb = c/(2*PRF)
|
|
3686
|
Ramb = 375 #Ramb = c/(2*PRF)
|
|
|
3687
|
Re = 6371 #Earth Radius
|
|
3687
|
Re = 6371 #Earth Radius
|
|
|
3688
|
heights = numpy.zeros(Ranges.shape)
|
|
3688
|
heights = numpy.zeros(Ranges.shape)
|
|
|
3689
|
|
|
3689
|
|
|
|
3690
|
R_aux = numpy.array([0,1,2])*Ramb
|
|
3690
|
R_aux = numpy.array([0,1,2])*Ramb
|
|
|
3691
|
R_aux = R_aux.reshape(1,R_aux.size)
|
|
3691
|
R_aux = R_aux.reshape(1,R_aux.size)
|
|
|
3692
|
|
|
3692
|
|
|
|
3693
|
Ranges = Ranges.reshape(Ranges.size,1)
|
|
3693
|
Ranges = Ranges.reshape(Ranges.size,1)
|
|
|
3694
|
|
|
3694
|
|
|
|
3695
|
Ri = Ranges + R_aux
|
|
3695
|
Ri = Ranges + R_aux
|
|
|
3696
|
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
3696
|
hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
|
3697
|
|
|
3697
|
|
|
|
3698
|
#Check if there is a height between 70 and 110 km
|
|
3698
|
#Check if there is a height between 70 and 110 km
|
|
|
3699
|
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
3699
|
h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
|
3700
|
ind_h = numpy.where(h_bool == 1)[0]
|
|
3700
|
ind_h = numpy.where(h_bool == 1)[0]
|
|
|
3701
|
|
|
3701
|
|
|
|
3702
|
hCorr = hi[ind_h, :]
|
|
3702
|
hCorr = hi[ind_h, :]
|
|
|
3703
|
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
3703
|
ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
|
3704
|
|
|
3704
|
|
|
|
3705
|
hCorr = hi[ind_hCorr][:len(ind_h)]
|
|
3705
|
hCorr = hi[ind_hCorr][:len(ind_h)]
|
|
|
3706
|
heights[ind_h] = hCorr
|
|
3706
|
heights[ind_h] = hCorr
|
|
|
3707
|
|
|
3707
|
|
|
|
3708
|
#Setting Error
|
|
3708
|
#Setting Error
|
|
|
3709
|
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
3709
|
#Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
|
3710
|
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
3710
|
#Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
|
3711
|
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
|
|
3711
|
indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
|
|
|
3712
|
error[indError] = 0
|
|
3712
|
error[indError] = 0
|
|
|
3713
|
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
3713
|
indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
|
3714
|
error[indInvalid2] = 14
|
|
3714
|
error[indInvalid2] = 14
|
|
|
3715
|
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
3715
|
indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
|
3716
|
error[indInvalid1] = 13
|
|
3716
|
error[indInvalid1] = 13
|
|
|
3717
|
|
|
3717
|
|
|
|
3718
|
return heights, error
|
|
3718
|
return heights, error
|
|
|
3719
|
|
|
3719
|
|
|
|
3720
|
def getPhasePairs(self, channelPositions):
|
|
3720
|
def getPhasePairs(self, channelPositions):
|
|
|
3721
|
chanPos = numpy.array(channelPositions)
|
|
3721
|
chanPos = numpy.array(channelPositions)
|
|
|
3722
|
listOper = list(itertools.combinations(list(range(5)),2))
|
|
3722
|
listOper = list(itertools.combinations(list(range(5)),2))
|
|
|
3723
|
|
|
3723
|
|
|
|
3724
|
distances = numpy.zeros(4)
|
|
3724
|
distances = numpy.zeros(4)
|
|
|
3725
|
axisX = []
|
|
3725
|
axisX = []
|
|
|
3726
|
axisY = []
|
|
3726
|
axisY = []
|
|
|
3727
|
distX = numpy.zeros(3)
|
|
3727
|
distX = numpy.zeros(3)
|
|
|
3728
|
distY = numpy.zeros(3)
|
|
3728
|
distY = numpy.zeros(3)
|
|
|
3729
|
ix = 0
|
|
3729
|
ix = 0
|
|
|
3730
|
iy = 0
|
|
3730
|
iy = 0
|
|
|
3731
|
|
|
3731
|
|
|
|
3732
|
pairX = numpy.zeros((2,2))
|
|
3732
|
pairX = numpy.zeros((2,2))
|
|
|
3733
|
pairY = numpy.zeros((2,2))
|
|
3733
|
pairY = numpy.zeros((2,2))
|
|
|
3734
|
|
|
3734
|
|
|
|
3735
|
for i in range(len(listOper)):
|
|
3735
|
for i in range(len(listOper)):
|
|
|
3736
|
pairi = listOper[i]
|
|
3736
|
pairi = listOper[i]
|
|
|
3737
|
|
|
3737
|
|
|
|
3738
|
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
|
|
3738
|
posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
|
|
|
3739
|
|
|
3739
|
|
|
|
3740
|
if posDif[0] == 0:
|
|
3740
|
if posDif[0] == 0:
|
|
|
3741
|
axisY.append(pairi)
|
|
3741
|
axisY.append(pairi)
|
|
|
3742
|
distY[iy] = posDif[1]
|
|
3742
|
distY[iy] = posDif[1]
|
|
|
3743
|
iy += 1
|
|
3743
|
iy += 1
|
|
|
3744
|
elif posDif[1] == 0:
|
|
3744
|
elif posDif[1] == 0:
|
|
|
3745
|
axisX.append(pairi)
|
|
3745
|
axisX.append(pairi)
|
|
|
3746
|
distX[ix] = posDif[0]
|
|
3746
|
distX[ix] = posDif[0]
|
|
|
3747
|
ix += 1
|
|
3747
|
ix += 1
|
|
|
3748
|
|
|
3748
|
|
|
|
3749
|
for i in range(2):
|
|
3749
|
for i in range(2):
|
|
|
3750
|
if i==0:
|
|
3750
|
if i==0:
|
|
|
3751
|
dist0 = distX
|
|
3751
|
dist0 = distX
|
|
|
3752
|
axis0 = axisX
|
|
3752
|
axis0 = axisX
|
|
|
3753
|
else:
|
|
3753
|
else:
|
|
|
3754
|
dist0 = distY
|
|
3754
|
dist0 = distY
|
|
|
3755
|
axis0 = axisY
|
|
3755
|
axis0 = axisY
|
|
|
3756
|
|
|
3756
|
|
|
|
3757
|
side = numpy.argsort(dist0)[:-1]
|
|
3757
|
side = numpy.argsort(dist0)[:-1]
|
|
|
3758
|
axis0 = numpy.array(axis0)[side,:]
|
|
3758
|
axis0 = numpy.array(axis0)[side,:]
|
|
|
3759
|
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
|
|
3759
|
chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
|
|
|
3760
|
axis1 = numpy.unique(numpy.reshape(axis0,4))
|
|
3760
|
axis1 = numpy.unique(numpy.reshape(axis0,4))
|
|
|
3761
|
side = axis1[axis1 != chanC]
|
|
3761
|
side = axis1[axis1 != chanC]
|
|
|
3762
|
diff1 = chanPos[chanC,i] - chanPos[side[0],i]
|
|
3762
|
diff1 = chanPos[chanC,i] - chanPos[side[0],i]
|
|
|
3763
|
diff2 = chanPos[chanC,i] - chanPos[side[1],i]
|
|
3763
|
diff2 = chanPos[chanC,i] - chanPos[side[1],i]
|
|
|
3764
|
if diff1<0:
|
|
3764
|
if diff1<0:
|
|
|
3765
|
chan2 = side[0]
|
|
3765
|
chan2 = side[0]
|
|
|
3766
|
d2 = numpy.abs(diff1)
|
|
3766
|
d2 = numpy.abs(diff1)
|
|
|
3767
|
chan1 = side[1]
|
|
3767
|
chan1 = side[1]
|
|
|
3768
|
d1 = numpy.abs(diff2)
|
|
3768
|
d1 = numpy.abs(diff2)
|
|
|
3769
|
else:
|
|
3769
|
else:
|
|
|
3770
|
chan2 = side[1]
|
|
3770
|
chan2 = side[1]
|
|
|
3771
|
d2 = numpy.abs(diff2)
|
|
3771
|
d2 = numpy.abs(diff2)
|
|
|
3772
|
chan1 = side[0]
|
|
3772
|
chan1 = side[0]
|
|
|
3773
|
d1 = numpy.abs(diff1)
|
|
3773
|
d1 = numpy.abs(diff1)
|
|
|
3774
|
|
|
3774
|
|
|
|
3775
|
if i==0:
|
|
3775
|
if i==0:
|
|
|
3776
|
chanCX = chanC
|
|
3776
|
chanCX = chanC
|
|
|
3777
|
chan1X = chan1
|
|
3777
|
chan1X = chan1
|
|
|
3778
|
chan2X = chan2
|
|
3778
|
chan2X = chan2
|
|
|
3779
|
distances[0:2] = numpy.array([d1,d2])
|
|
3779
|
distances[0:2] = numpy.array([d1,d2])
|
|
|
3780
|
else:
|
|
3780
|
else:
|
|
|
3781
|
chanCY = chanC
|
|
3781
|
chanCY = chanC
|
|
|
3782
|
chan1Y = chan1
|
|
3782
|
chan1Y = chan1
|
|
|
3783
|
chan2Y = chan2
|
|
3783
|
chan2Y = chan2
|
|
|
3784
|
distances[2:4] = numpy.array([d1,d2])
|
|
3784
|
distances[2:4] = numpy.array([d1,d2])
|
|
|
3785
|
# axisXsides = numpy.reshape(axisX[ix,:],4)
|
|
3785
|
# axisXsides = numpy.reshape(axisX[ix,:],4)
|
|
|
3786
|
#
|
|
3786
|
#
|
|
|
3787
|
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
|
|
3787
|
# channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
|
|
|
3788
|
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
|
|
3788
|
# channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
|
|
|
3789
|
#
|
|
3789
|
#
|
|
|
3790
|
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
|
|
3790
|
# ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
|
|
|
3791
|
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
|
|
3791
|
# ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
|
|
|
3792
|
# channel25X = int(pairX[0,ind25X])
|
|
3792
|
# channel25X = int(pairX[0,ind25X])
|
|
|
3793
|
# channel20X = int(pairX[1,ind20X])
|
|
3793
|
# channel20X = int(pairX[1,ind20X])
|
|
|
3794
|
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
|
|
3794
|
# ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
|
|
|
3795
|
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
|
|
3795
|
# ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
|
|
|
3796
|
# channel25Y = int(pairY[0,ind25Y])
|
|
3796
|
# channel25Y = int(pairY[0,ind25Y])
|
|
|
3797
|
# channel20Y = int(pairY[1,ind20Y])
|
|
3797
|
# channel20Y = int(pairY[1,ind20Y])
|
|
|
3798
|
|
|
3798
|
|
|
|
3799
|
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
|
|
3799
|
# pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
|
|
|
3800
|
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
|
|
3800
|
pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
|
|
|
3801
|
|
|
3801
|
|
|
|
3802
|
return pairslist, distances
|
|
3802
|
return pairslist, distances
|
|
|
3803
|
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
|
|
3803
|
# def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
|
|
|
3804
|
#
|
|
3804
|
#
|
|
|
3805
|
# arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
3805
|
# arrayAOA = numpy.zeros((phases.shape[0],3))
|
|
|
3806
|
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
|
|
3806
|
# cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
|
|
|
3807
|
#
|
|
3807
|
#
|
|
|
3808
|
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
3808
|
# arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
|
|
|
3809
|
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
3809
|
# cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
|
|
|
3810
|
# arrayAOA[:,2] = cosDirError
|
|
3810
|
# arrayAOA[:,2] = cosDirError
|
|
|
3811
|
#
|
|
3811
|
#
|
|
|
3812
|
# azimuthAngle = arrayAOA[:,0]
|
|
3812
|
# azimuthAngle = arrayAOA[:,0]
|
|
|
3813
|
# zenithAngle = arrayAOA[:,1]
|
|
3813
|
# zenithAngle = arrayAOA[:,1]
|
|
|
3814
|
#
|
|
3814
|
#
|
|
|
3815
|
# #Setting Error
|
|
3815
|
# #Setting Error
|
|
|
3816
|
# #Number 3: AOA not fesible
|
|
3816
|
# #Number 3: AOA not fesible
|
|
|
3817
|
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
3817
|
# indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
|
|
|
3818
|
# error[indInvalid] = 3
|
|
3818
|
# error[indInvalid] = 3
|
|
|
3819
|
# #Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
3819
|
# #Number 4: Large difference in AOAs obtained from different antenna baselines
|
|
|
3820
|
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
3820
|
# indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
|
|
|
3821
|
# error[indInvalid] = 4
|
|
3821
|
# error[indInvalid] = 4
|
|
|
3822
|
# return arrayAOA, error
|
|
3822
|
# return arrayAOA, error
|
|
|
3823
|
#
|
|
3823
|
#
|
|
|
3824
|
# def __getDirectionCosines(self, arrayPhase, pairsList):
|
|
3824
|
# def __getDirectionCosines(self, arrayPhase, pairsList):
|
|
|
3825
|
#
|
|
3825
|
#
|
|
|
3826
|
# #Initializing some variables
|
|
3826
|
# #Initializing some variables
|
|
|
3827
|
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
3827
|
# ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
|
|
|
3828
|
# ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
3828
|
# ang_aux = ang_aux.reshape(1,ang_aux.size)
|
|
|
3829
|
#
|
|
3829
|
#
|
|
|
3830
|
# cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
3830
|
# cosdir = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3831
|
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
3831
|
# cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
|
|
|
3832
|
#
|
|
3832
|
#
|
|
|
3833
|
#
|
|
3833
|
#
|
|
|
3834
|
# for i in range(2):
|
|
3834
|
# for i in range(2):
|
|
|
3835
|
# #First Estimation
|
|
3835
|
# #First Estimation
|
|
|
3836
|
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
|
|
3836
|
# phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
|
|
|
3837
|
# #Dealias
|
|
3837
|
# #Dealias
|
|
|
3838
|
# indcsi = numpy.where(phi0_aux > numpy.pi)
|
|
3838
|
# indcsi = numpy.where(phi0_aux > numpy.pi)
|
|
|
3839
|
# phi0_aux[indcsi] -= 2*numpy.pi
|
|
3839
|
# phi0_aux[indcsi] -= 2*numpy.pi
|
|
|
3840
|
# indcsi = numpy.where(phi0_aux < -numpy.pi)
|
|
3840
|
# indcsi = numpy.where(phi0_aux < -numpy.pi)
|
|
|
3841
|
# phi0_aux[indcsi] += 2*numpy.pi
|
|
3841
|
# phi0_aux[indcsi] += 2*numpy.pi
|
|
|
3842
|
# #Direction Cosine 0
|
|
3842
|
# #Direction Cosine 0
|
|
|
3843
|
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
|
|
3843
|
# cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
|
|
|
3844
|
#
|
|
3844
|
#
|
|
|
3845
|
# #Most-Accurate Second Estimation
|
|
3845
|
# #Most-Accurate Second Estimation
|
|
|
3846
|
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
|
|
3846
|
# phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
|
|
|
3847
|
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
3847
|
# phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
|
|
|
3848
|
# #Direction Cosine 1
|
|
3848
|
# #Direction Cosine 1
|
|
|
3849
|
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
|
|
3849
|
# cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
|
|
|
3850
|
#
|
|
3850
|
#
|
|
|
3851
|
# #Searching the correct Direction Cosine
|
|
3851
|
# #Searching the correct Direction Cosine
|
|
|
3852
|
# cosdir0_aux = cosdir0[:,i]
|
|
3852
|
# cosdir0_aux = cosdir0[:,i]
|
|
|
3853
|
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
3853
|
# cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
|
|
|
3854
|
# #Minimum Distance
|
|
3854
|
# #Minimum Distance
|
|
|
3855
|
# cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
3855
|
# cosDiff = (cosdir1 - cosdir0_aux)**2
|
|
|
3856
|
# indcos = cosDiff.argmin(axis = 1)
|
|
3856
|
# indcos = cosDiff.argmin(axis = 1)
|
|
|
3857
|
# #Saving Value obtained
|
|
3857
|
# #Saving Value obtained
|
|
|
3858
|
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
3858
|
# cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
|
|
|
3859
|
#
|
|
3859
|
#
|
|
|
3860
|
# return cosdir0, cosdir
|
|
3860
|
# return cosdir0, cosdir
|
|
|
3861
|
#
|
|
3861
|
#
|
|
|
3862
|
# def __calculateAOA(self, cosdir, azimuth):
|
|
3862
|
# def __calculateAOA(self, cosdir, azimuth):
|
|
|
3863
|
# cosdirX = cosdir[:,0]
|
|
3863
|
# cosdirX = cosdir[:,0]
|
|
|
3864
|
# cosdirY = cosdir[:,1]
|
|
3864
|
# cosdirY = cosdir[:,1]
|
|
|
3865
|
#
|
|
3865
|
#
|
|
|
3866
|
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
3866
|
# zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
|
|
|
3867
|
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
|
|
3867
|
# azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
|
|
|
3868
|
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
3868
|
# angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
|
|
|
3869
|
#
|
|
3869
|
#
|
|
|
3870
|
# return angles
|
|
3870
|
# return angles
|
|
|
3871
|
#
|
|
3871
|
#
|
|
|
3872
|
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
3872
|
# def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
|
|
|
3873
|
#
|
|
3873
|
#
|
|
|
3874
|
# Ramb = 375 #Ramb = c/(2*PRF)
|
|
3874
|
# Ramb = 375 #Ramb = c/(2*PRF)
|
|
|
3875
|
# Re = 6371 #Earth Radius
|
|
3875
|
# Re = 6371 #Earth Radius
|
|
|
3876
|
# heights = numpy.zeros(Ranges.shape)
|
|
3876
|
# heights = numpy.zeros(Ranges.shape)
|
|
|
3877
|
#
|
|
3877
|
#
|
|
|
3878
|
# R_aux = numpy.array([0,1,2])*Ramb
|
|
3878
|
# R_aux = numpy.array([0,1,2])*Ramb
|
|
|
3879
|
# R_aux = R_aux.reshape(1,R_aux.size)
|
|
3879
|
# R_aux = R_aux.reshape(1,R_aux.size)
|
|
|
3880
|
#
|
|
3880
|
#
|
|
|
3881
|
# Ranges = Ranges.reshape(Ranges.size,1)
|
|
3881
|
# Ranges = Ranges.reshape(Ranges.size,1)
|
|
|
3882
|
#
|
|
3882
|
#
|
|
|
3883
|
# Ri = Ranges + R_aux
|
|
3883
|
# Ri = Ranges + R_aux
|
|
|
3884
|
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
3884
|
# hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
|
|
|
3885
|
#
|
|
3885
|
#
|
|
|
3886
|
# #Check if there is a height between 70 and 110 km
|
|
3886
|
# #Check if there is a height between 70 and 110 km
|
|
|
3887
|
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
3887
|
# h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
|
|
|
3888
|
# ind_h = numpy.where(h_bool == 1)[0]
|
|
3888
|
# ind_h = numpy.where(h_bool == 1)[0]
|
|
|
3889
|
#
|
|
3889
|
#
|
|
|
3890
|
# hCorr = hi[ind_h, :]
|
|
3890
|
# hCorr = hi[ind_h, :]
|
|
|
3891
|
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
3891
|
# ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
|
|
|
3892
|
#
|
|
3892
|
#
|
|
|
3893
|
# hCorr = hi[ind_hCorr]
|
|
3893
|
# hCorr = hi[ind_hCorr]
|
|
|
3894
|
# heights[ind_h] = hCorr
|
|
3894
|
# heights[ind_h] = hCorr
|
|
|
3895
|
#
|
|
3895
|
#
|
|
|
3896
|
# #Setting Error
|
|
3896
|
# #Setting Error
|
|
|
3897
|
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
3897
|
# #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
|
|
|
3898
|
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
3898
|
# #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
|
|
|
3899
|
#
|
|
3899
|
#
|
|
|
3900
|
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
3900
|
# indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
|
|
|
3901
|
# error[indInvalid2] = 14
|
|
3901
|
# error[indInvalid2] = 14
|
|
|
3902
|
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
3902
|
# indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
|
|
|
3903
|
# error[indInvalid1] = 13
|
|
3903
|
# error[indInvalid1] = 13
|
|
|
3904
|
#
|
|
3904
|
#
|
|
|
3905
|
# return heights, error
|
|
3905
|
# return heights, error
|
|
|
3906
|
|
|
3906
|
|
|
|
3907
|
|
|
3907
|
|
|
|
3908
|
class WeatherRadar(Operation):
|
|
3908
|
class WeatherRadar(Operation):
|
|
|
3909
|
'''
|
|
3909
|
'''
|
|
|
3910
|
Function tat implements Weather Radar operations-
|
|
3910
|
Function tat implements Weather Radar operations-
|
|
|
3911
|
Input:
|
|
3911
|
Input:
|
|
|
3912
|
Output:
|
|
3912
|
Output:
|
|
|
3913
|
Parameters affected:
|
|
3913
|
Parameters affected:
|
|
|
3914
|
'''
|
|
3914
|
'''
|
|
|
3915
|
isConfig = False
|
|
3915
|
isConfig = False
|
|
|
3916
|
|
|
3916
|
|
|
|
3917
|
def __init__(self):
|
|
3917
|
def __init__(self):
|
|
|
3918
|
Operation.__init__(self)
|
|
3918
|
Operation.__init__(self)
|
|
|
3919
|
|
|
3919
|
|
|
|
3920
|
def setup(self,dataOut,Pt=0,Gt=0,Gr=0,lambda_=0, aL=0,
|
|
3920
|
def setup(self,dataOut,Pt=0,Gt=0,Gr=0,lambda_=0, aL=0,
|
|
|
3921
|
tauW= 0,thetaT=0,thetaR=0,Km =0):
|
|
3921
|
tauW= 0,thetaT=0,thetaR=0,Km =0):
|
|
|
3922
|
self.nCh = dataOut.nChannels
|
|
3922
|
self.nCh = dataOut.nChannels
|
|
|
3923
|
self.nHeis = dataOut.nHeights
|
|
3923
|
self.nHeis = dataOut.nHeights
|
|
|
3924
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
3924
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
3925
|
self.Range = numpy.arange(dataOut.nHeights)*deltaHeight + dataOut.heightList[0]
|
|
3925
|
self.Range = numpy.arange(dataOut.nHeights)*deltaHeight + dataOut.heightList[0]
|
|
|
3926
|
self.Range = self.Range.reshape(1,self.nHeis)
|
|
3926
|
self.Range = self.Range.reshape(1,self.nHeis)
|
|
|
3927
|
self.Range = numpy.tile(self.Range,[self.nCh,1])
|
|
3927
|
self.Range = numpy.tile(self.Range,[self.nCh,1])
|
|
|
3928
|
'''-----------1 Constante del Radar----------'''
|
|
3928
|
'''-----------1 Constante del Radar----------'''
|
|
|
3929
|
self.Pt = Pt
|
|
3929
|
self.Pt = Pt
|
|
|
3930
|
self.Gt = Gt
|
|
3930
|
self.Gt = Gt
|
|
|
3931
|
self.Gr = Gr
|
|
3931
|
self.Gr = Gr
|
|
|
3932
|
self.lambda_ = lambda_
|
|
3932
|
self.lambda_ = lambda_
|
|
|
3933
|
self.aL = aL
|
|
3933
|
self.aL = aL
|
|
|
3934
|
self.tauW = tauW
|
|
3934
|
self.tauW = tauW
|
|
|
3935
|
self.thetaT = thetaT
|
|
3935
|
self.thetaT = thetaT
|
|
|
3936
|
self.thetaR = thetaR
|
|
3936
|
self.thetaR = thetaR
|
|
|
3937
|
self.Km = Km
|
|
3937
|
self.Km = Km
|
|
|
3938
|
Numerator = ((4*numpy.pi)**3 * aL**2 * 16 *numpy.log(2))
|
|
3938
|
Numerator = ((4*numpy.pi)**3 * aL**2 * 16 *numpy.log(2))
|
|
|
3939
|
Denominator = (Pt * Gt * Gr * lambda_**2 * SPEED_OF_LIGHT * tauW * numpy.pi*thetaT*thetaR)
|
|
3939
|
Denominator = (Pt * Gt * Gr * lambda_**2 * SPEED_OF_LIGHT * tauW * numpy.pi*thetaT*thetaR)
|
|
|
3940
|
self.RadarConstant = Numerator/Denominator
|
|
3940
|
self.RadarConstant = Numerator/Denominator
|
|
|
3941
|
'''-----------2 Reflectividad del Radar y Factor de Reflectividad------'''
|
|
3941
|
'''-----------2 Reflectividad del Radar y Factor de Reflectividad------'''
|
|
|
3942
|
self.n_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
3942
|
self.n_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
|
3943
|
self.Z_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
3943
|
self.Z_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
|
3944
|
|
|
3944
|
|
|
|
3945
|
def setMoments(self,dataOut,i):
|
|
3945
|
def setMoments(self,dataOut,i):
|
|
|
3946
|
|
|
3946
|
|
|
|
3947
|
type = dataOut.inputUnit
|
|
3947
|
type = dataOut.inputUnit
|
|
|
3948
|
nCh = dataOut.nChannels
|
|
3948
|
nCh = dataOut.nChannels
|
|
|
3949
|
nHeis = dataOut.nHeights
|
|
3949
|
nHeis = dataOut.nHeights
|
|
|
3950
|
data_param = numpy.zeros((nCh,4,nHeis))
|
|
3950
|
data_param = numpy.zeros((nCh,4,nHeis))
|
|
|
3951
|
if type == "Voltage":
|
|
3951
|
if type == "Voltage":
|
|
|
3952
|
factor = dataOut.normFactor
|
|
3952
|
factor = dataOut.normFactor
|
|
|
3953
|
data_param[:,0,:] = dataOut.dataPP_POW/(factor)
|
|
3953
|
data_param[:,0,:] = dataOut.dataPP_POW/(factor)
|
|
|
3954
|
data_param[:,1,:] = dataOut.dataPP_DOP
|
|
3954
|
data_param[:,1,:] = dataOut.dataPP_DOP
|
|
|
3955
|
data_param[:,2,:] = dataOut.dataPP_WIDTH
|
|
3955
|
data_param[:,2,:] = dataOut.dataPP_WIDTH
|
|
|
3956
|
data_param[:,3,:] = dataOut.dataPP_SNR
|
|
3956
|
data_param[:,3,:] = dataOut.dataPP_SNR
|
|
|
3957
|
if type == "Spectra":
|
|
3957
|
if type == "Spectra":
|
|
|
3958
|
data_param[:,0,:] = dataOut.data_POW
|
|
3958
|
data_param[:,0,:] = dataOut.data_POW
|
|
|
3959
|
data_param[:,1,:] = dataOut.data_DOP
|
|
3959
|
data_param[:,1,:] = dataOut.data_DOP
|
|
|
3960
|
data_param[:,2,:] = dataOut.data_WIDTH
|
|
3960
|
data_param[:,2,:] = dataOut.data_WIDTH
|
|
|
3961
|
data_param[:,3,:] = dataOut.data_SNR
|
|
3961
|
data_param[:,3,:] = dataOut.data_SNR
|
|
|
3962
|
|
|
3962
|
|
|
|
3963
|
return data_param[:,i,:]
|
|
3963
|
return data_param[:,i,:]
|
|
|
3964
|
|
|
3964
|
|
|
|
3965
|
|
|
3965
|
|
|
|
3966
|
def run(self,dataOut,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
|
|
3966
|
def run(self,dataOut,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
|
|
|
3967
|
tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93):
|
|
3967
|
tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93):
|
|
|
3968
|
|
|
3968
|
|
|
|
3969
|
if not self.isConfig:
|
|
3969
|
if not self.isConfig:
|
|
|
3970
|
self.setup(dataOut= dataOut,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
|
|
3970
|
self.setup(dataOut= dataOut,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
|
|
|
3971
|
tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93)
|
|
3971
|
tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93)
|
|
|
3972
|
self.isConfig = True
|
|
3972
|
self.isConfig = True
|
|
|
3973
|
'''-----------------------------Potencia de Radar -Signal S-----------------------------'''
|
|
3973
|
'''-----------------------------Potencia de Radar -Signal S-----------------------------'''
|
|
|
3974
|
Pr = self.setMoments(dataOut,0)
|
|
3974
|
Pr = self.setMoments(dataOut,0)
|
|
|
3975
|
|
|
3975
|
|
|
|
3976
|
for R in range(self.nHeis):
|
|
3976
|
for R in range(self.nHeis):
|
|
|
3977
|
self.n_radar[:,R] = self.RadarConstant*Pr[:,R]* (self.Range[:,R])**2
|
|
3977
|
self.n_radar[:,R] = self.RadarConstant*Pr[:,R]* (self.Range[:,R])**2
|
|
|
3978
|
|
|
3978
|
|
|
|
3979
|
self.Z_radar[:,R] = self.n_radar[:,R]* self.lambda_**4/( numpy.pi**5 * self.Km**2)
|
|
3979
|
self.Z_radar[:,R] = self.n_radar[:,R]* self.lambda_**4/( numpy.pi**5 * self.Km**2)
|
|
|
3980
|
|
|
3980
|
|
|
|
3981
|
'''----------- Factor de Reflectividad Equivalente lamda_ < 10 cm , lamda_= 3.2cm-------'''
|
|
3981
|
'''----------- Factor de Reflectividad Equivalente lamda_ < 10 cm , lamda_= 3.2cm-------'''
|
|
|
3982
|
Zeh = self.Z_radar
|
|
3982
|
Zeh = self.Z_radar
|
|
|
3983
|
dBZeh = 10*numpy.log10(Zeh)
|
|
3983
|
dBZeh = 10*numpy.log10(Zeh)
|
|
|
3984
|
dataOut.factor_Zeh= dBZeh
|
|
3984
|
dataOut.factor_Zeh= dBZeh
|
|
|
3985
|
self.n_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
3985
|
self.n_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
|
3986
|
self.Z_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
3986
|
self.Z_radar = numpy.zeros((self.nCh,self.nHeis))
|
|
|
3987
|
|
|
3987
|
|
|
|
3988
|
return dataOut
|
|
3988
|
return dataOut
|
|
|
3989
|
|
|
3989
|
|
|
|
3990
|
class PedestalInformation(Operation):
|
|
3990
|
class PedestalInformation(Operation):
|
|
|
3991
|
path_ped = None
|
|
3991
|
path_ped = None
|
|
|
3992
|
path_adq = None
|
|
3992
|
path_adq = None
|
|
|
3993
|
t_Interval_p = None
|
|
3993
|
t_Interval_p = None
|
|
|
3994
|
n_Muestras_p = None
|
|
3994
|
n_Muestras_p = None
|
|
|
3995
|
isConfig = False
|
|
3995
|
isConfig = False
|
|
|
3996
|
blocksPerfile= None
|
|
3996
|
blocksPerfile= None
|
|
|
3997
|
f_a_p = None
|
|
3997
|
f_a_p = None
|
|
|
3998
|
online = None
|
|
3998
|
online = None
|
|
|
3999
|
angulo_adq = None
|
|
3999
|
angulo_adq = None
|
|
|
4000
|
nro_file = None
|
|
4000
|
nro_file = None
|
|
|
4001
|
nro_key_p = None
|
|
4001
|
nro_key_p = None
|
|
|
4002
|
tmp = None
|
|
4002
|
tmp = None
|
|
|
4003
|
|
|
4003
|
|
|
|
4004
|
|
|
4004
|
|
|
|
4005
|
def __init__(self):
|
|
4005
|
def __init__(self):
|
|
|
4006
|
Operation.__init__(self)
|
|
4006
|
Operation.__init__(self)
|
|
|
4007
|
|
|
4007
|
|
|
|
4008
|
|
|
4008
|
|
|
|
4009
|
def getAnguloProfile(self,utc_adq,list_pedestal):
|
|
4009
|
def getAnguloProfile(self,utc_adq,list_pedestal):
|
|
|
4010
|
utc_adq = utc_adq
|
|
4010
|
utc_adq = utc_adq
|
|
|
4011
|
list_pedestal = list_pedestal
|
|
4011
|
list_pedestal = list_pedestal
|
|
|
4012
|
utc_ped_list = []
|
|
4012
|
utc_ped_list = []
|
|
|
4013
|
|
|
|
|
|
|
4014
|
for i in range(len(list_pedestal)):
|
|
4013
|
for i in range(len(list_pedestal)):
|
|
|
4015
|
#print(i)# OJO IDENTIFICADOR DE SINCRONISMO
|
|
4014
|
#print(i)# OJO IDENTIFICADOR DE SINCRONISMO
|
|
|
4016
|
utc_ped_list.append(self.gettimeutcfromDirFilename(path=self.path_ped,file=list_pedestal[i]))
|
|
4015
|
utc_ped_list.append(self.gettimeutcfromDirFilename(path=self.path_ped,file=list_pedestal[i]))
|
|
|
4017
|
|
|
4016
|
|
|
|
4018
|
nro_file,utc_ped,utc_ped_1 =self.getNROFile(utc_adq,utc_ped_list)
|
|
4017
|
nro_file,utc_ped,utc_ped_1 =self.getNROFile(utc_adq,utc_ped_list)
|
|
|
4019
|
|
|
4018
|
|
|
|
4020
|
if nro_file < 0:
|
|
4019
|
if nro_file < 0:
|
|
|
4021
|
return numpy.NaN
|
|
4020
|
return numpy.NaN,numpy.NaN
|
|
|
4022
|
else:
|
|
4021
|
else:
|
|
|
4023
|
nro_key_p = int((utc_adq-utc_ped)/self.t_Interval_p)-1 # ojito al -1 estimado alex
|
|
4022
|
nro_key_p = int((utc_adq-utc_ped)/self.t_Interval_p)-1 # ojito al -1 estimado alex
|
|
|
4024
|
ff_pedestal = list_pedestal[nro_file]
|
|
4023
|
ff_pedestal = list_pedestal[nro_file]
|
|
|
4025
|
#angulo = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azimuth")
|
|
4024
|
#angulo = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azimuth")
|
|
|
4026
|
angulo = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azi_pos")
|
|
4025
|
angulo = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azi_pos")
|
|
|
4027
|
angulo_ele = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="ele_pos")
|
|
4026
|
angulo_ele = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="ele_pos")
|
|
|
4028
|
|
|
4027
|
|
|
|
4029
|
if 99>=nro_key_p>0:
|
|
4028
|
if 99>=nro_key_p>0:
|
|
|
4030
|
##print("angulo_array :",angulo[nro_key_p])
|
|
4029
|
##print("angulo_array :",angulo[nro_key_p])
|
|
|
4031
|
return angulo[nro_key_p],angulo_ele[nro_key_p]
|
|
4030
|
return angulo[nro_key_p],angulo_ele[nro_key_p]
|
|
|
4032
|
else:
|
|
4031
|
else:
|
|
|
4033
|
#print("-----------------------------------------------------------------")
|
|
4032
|
#print("-----------------------------------------------------------------")
|
|
|
4034
|
return numpy.NaN,numpy.NaN
|
|
4033
|
return numpy.NaN,numpy.NaN
|
|
|
4035
|
|
|
4034
|
|
|
|
4036
|
|
|
4035
|
|
|
|
4037
|
def getfirstFilefromPath(self,path,meta,ext):
|
|
4036
|
def getfirstFilefromPath(self,path,meta,ext):
|
|
|
4038
|
validFilelist = []
|
|
4037
|
validFilelist = []
|
|
|
4039
|
#print("SEARH",path)
|
|
4038
|
#print("SEARH",path)
|
|
|
4040
|
try:
|
|
4039
|
try:
|
|
|
4041
|
fileList = os.listdir(path)
|
|
4040
|
fileList = os.listdir(path)
|
|
|
4042
|
except:
|
|
4041
|
except:
|
|
|
4043
|
print("check path - fileList")
|
|
4042
|
print("check path - fileList")
|
|
|
4044
|
if len(fileList)<1:
|
|
4043
|
if len(fileList)<1:
|
|
|
4045
|
return None
|
|
4044
|
return None
|
|
|
4046
|
# meta 1234 567 8-18 BCDE
|
|
4045
|
# meta 1234 567 8-18 BCDE
|
|
|
4047
|
# H,D,PE YYYY DDD EPOC .ext
|
|
4046
|
# H,D,PE YYYY DDD EPOC .ext
|
|
|
4048
|
|
|
4047
|
|
|
|
4049
|
for thisFile in fileList:
|
|
4048
|
for thisFile in fileList:
|
|
|
4050
|
#print("HI",thisFile)
|
|
4049
|
#print("HI",thisFile)
|
|
|
4051
|
if meta =="PE":
|
|
4050
|
if meta =="PE":
|
|
|
4052
|
try:
|
|
4051
|
try:
|
|
|
4053
|
number= int(thisFile[len(meta)+7:len(meta)+17])
|
|
4052
|
number= int(thisFile[len(meta)+7:len(meta)+17])
|
|
|
4054
|
except:
|
|
4053
|
except:
|
|
|
4055
|
print("There is a file or folder with different format")
|
|
4054
|
print("There is a file or folder with different format")
|
|
|
4056
|
if meta == "D":
|
|
4055
|
if meta == "D":
|
|
|
4057
|
try:
|
|
4056
|
try:
|
|
|
4058
|
number= int(thisFile[8:11])
|
|
4057
|
number= int(thisFile[8:11])
|
|
|
4059
|
except:
|
|
4058
|
except:
|
|
|
4060
|
print("There is a file or folder with different format")
|
|
4059
|
print("There is a file or folder with different format")
|
|
|
4061
|
|
|
4060
|
|
|
|
4062
|
if not isNumber(str=number):
|
|
4061
|
if not isNumber(str=number):
|
|
|
4063
|
continue
|
|
4062
|
continue
|
|
|
4064
|
if (os.path.splitext(thisFile)[-1].lower() != ext.lower()):
|
|
4063
|
if (os.path.splitext(thisFile)[-1].lower() != ext.lower()):
|
|
|
4065
|
continue
|
|
4064
|
continue
|
|
|
4066
|
validFilelist.sort()
|
|
4065
|
validFilelist.sort()
|
|
|
4067
|
validFilelist.append(thisFile)
|
|
4066
|
validFilelist.append(thisFile)
|
|
|
4068
|
if len(validFilelist)>0:
|
|
4067
|
if len(validFilelist)>0:
|
|
|
4069
|
validFilelist = sorted(validFilelist,key=str.lower)
|
|
4068
|
validFilelist = sorted(validFilelist,key=str.lower)
|
|
|
4070
|
return validFilelist
|
|
4069
|
return validFilelist
|
|
|
4071
|
return None
|
|
4070
|
return None
|
|
|
4072
|
|
|
4071
|
|
|
|
4073
|
def gettimeutcfromDirFilename(self,path,file):
|
|
4072
|
def gettimeutcfromDirFilename(self,path,file):
|
|
|
4074
|
dir_file= path+"/"+file
|
|
4073
|
dir_file= path+"/"+file
|
|
|
4075
|
fp = h5py.File(dir_file,'r')
|
|
4074
|
fp = h5py.File(dir_file,'r')
|
|
|
4076
|
#epoc = fp['Metadata'].get('utctimeInit')[()]
|
|
4075
|
#epoc = fp['Metadata'].get('utctimeInit')[()]
|
|
|
4077
|
epoc = fp['Data'].get('utc')[()]
|
|
4076
|
epoc = fp['Data'].get('utc')[()]
|
|
|
4078
|
fp.close()
|
|
4077
|
fp.close()
|
|
|
4079
|
return epoc
|
|
4078
|
return epoc
|
|
|
4080
|
|
|
4079
|
|
|
|
4081
|
def gettimeutcadqfromDirFilename(self,path,file):
|
|
4080
|
def gettimeutcadqfromDirFilename(self,path,file):
|
|
|
4082
|
pass
|
|
4081
|
pass
|
|
|
4083
|
|
|
4082
|
|
|
|
4084
|
def getDatavaluefromDirFilename(self,path,file,value):
|
|
4083
|
def getDatavaluefromDirFilename(self,path,file,value):
|
|
|
4085
|
dir_file= path+"/"+file
|
|
4084
|
dir_file= path+"/"+file
|
|
|
4086
|
fp = h5py.File(dir_file,'r')
|
|
4085
|
fp = h5py.File(dir_file,'r')
|
|
|
4087
|
array = fp['Data'].get(value)[()]
|
|
4086
|
array = fp['Data'].get(value)[()]
|
|
|
4088
|
fp.close()
|
|
4087
|
fp.close()
|
|
|
4089
|
return array
|
|
4088
|
return array
|
|
|
4090
|
|
|
4089
|
|
|
|
4091
|
|
|
4090
|
|
|
|
4092
|
def getNROFile(self,utc_adq,utc_ped_list):
|
|
4091
|
def getNROFile(self,utc_adq,utc_ped_list):
|
|
|
4093
|
c=0
|
|
4092
|
c=0
|
|
|
4094
|
#print(utc_adq)
|
|
4093
|
#print(utc_adq)
|
|
|
4095
|
#print(len(utc_ped_list))
|
|
4094
|
#print(len(utc_ped_list))
|
|
|
|
|
|
4095
|
#print(utc_ped_list)
|
|
|
4096
|
for i in range(len(utc_ped_list)):
|
|
4096
|
for i in range(len(utc_ped_list)):
|
|
|
4097
|
if utc_adq>utc_ped_list[i]:
|
|
4097
|
if utc_adq>utc_ped_list[i]:
|
|
|
4098
|
#print("mayor")
|
|
4098
|
#print("mayor")
|
|
|
4099
|
#print("utc_ped_list",utc_ped_list[i])
|
|
4099
|
#print("utc_ped_list",utc_ped_list[i])
|
|
|
4100
|
c +=1
|
|
4100
|
c +=1
|
|
|
4101
|
|
|
4101
|
|
|
|
4102
|
return c-1,utc_ped_list[c-1],utc_ped_list[c]
|
|
4102
|
return c-1,utc_ped_list[c-1],utc_ped_list[c]
|
|
|
4103
|
|
|
4103
|
|
|
|
4104
|
def verificarNROFILE(self,dataOut,utc_ped,f_a_p,n_Muestras_p):
|
|
4104
|
def verificarNROFILE(self,dataOut,utc_ped,f_a_p,n_Muestras_p):
|
|
|
4105
|
pass
|
|
4105
|
pass
|
|
|
4106
|
|
|
4106
|
|
|
|
4107
|
def setup_offline(self,dataOut,list_pedestal):
|
|
4107
|
def setup_offline(self,dataOut,list_pedestal):
|
|
|
4108
|
pass
|
|
4108
|
pass
|
|
|
4109
|
|
|
4109
|
|
|
|
4110
|
def setup_online(self,dataOut):
|
|
4110
|
def setup_online(self,dataOut):
|
|
|
4111
|
pass
|
|
4111
|
pass
|
|
|
4112
|
|
|
4112
|
|
|
|
4113
|
#def setup(self,dataOut,path_ped,path_adq,t_Interval_p,n_Muestras_p,blocksPerfile,f_a_p,online):
|
|
4113
|
#def setup(self,dataOut,path_ped,path_adq,t_Interval_p,n_Muestras_p,blocksPerfile,f_a_p,online):
|
|
|
4114
|
def setup(self,dataOut,path_ped,t_Interval_p):
|
|
4114
|
def setup(self,dataOut,path_ped,t_Interval_p):
|
|
|
4115
|
self.__dataReady = False
|
|
4115
|
self.__dataReady = False
|
|
|
4116
|
self.path_ped = path_ped
|
|
4116
|
self.path_ped = path_ped
|
|
|
4117
|
self.t_Interval_p = t_Interval_p
|
|
4117
|
self.t_Interval_p = t_Interval_p
|
|
|
4118
|
self.list_pedestal = self.getfirstFilefromPath(path=self.path_ped,meta="PE",ext=".hdf5")
|
|
4118
|
self.list_pedestal = self.getfirstFilefromPath(path=self.path_ped,meta="PE",ext=".hdf5")
|
|
|
4119
|
|
|
4119
|
|
|
|
4120
|
def setNextFileP(self,dataOut):
|
|
4120
|
def setNextFileP(self,dataOut):
|
|
|
4121
|
pass
|
|
4121
|
pass
|
|
|
4122
|
|
|
4122
|
|
|
|
4123
|
def checkPedFile(self,path,nro_file):
|
|
4123
|
def checkPedFile(self,path,nro_file):
|
|
|
4124
|
pass
|
|
4124
|
pass
|
|
|
4125
|
|
|
4125
|
|
|
|
4126
|
def setNextFileoffline(self,dataOut):
|
|
4126
|
def setNextFileoffline(self,dataOut):
|
|
|
4127
|
pass
|
|
4127
|
pass
|
|
|
4128
|
|
|
4128
|
|
|
|
4129
|
def setNextFileonline(self):
|
|
4129
|
def setNextFileonline(self):
|
|
|
4130
|
pass
|
|
4130
|
pass
|
|
|
4131
|
|
|
4131
|
|
|
|
4132
|
def run(self, dataOut,path_ped,t_Interval_p):
|
|
4132
|
def run(self, dataOut,path_ped,t_Interval_p):
|
|
|
4133
|
if not self.isConfig:
|
|
4133
|
if not self.isConfig:
|
|
|
4134
|
self.setup(dataOut, path_ped,t_Interval_p)
|
|
4134
|
self.setup(dataOut, path_ped,t_Interval_p)
|
|
|
4135
|
self.__dataReady = True
|
|
4135
|
self.__dataReady = True
|
|
|
4136
|
self.isConfig = True
|
|
4136
|
self.isConfig = True
|
|
|
4137
|
#print("config TRUE")
|
|
4137
|
#print("config TRUE")
|
|
|
4138
|
utc_adq = dataOut.utctime
|
|
4138
|
utc_adq = dataOut.utctime
|
|
|
4139
|
####print("utc_adq---------------",utc_adq)
|
|
4139
|
####print("utc_adq---------------",utc_adq)
|
|
|
4140
|
list_pedestal = self.list_pedestal
|
|
4140
|
list_pedestal = self.list_pedestal
|
|
|
4141
|
angulo,angulo_ele = self.getAnguloProfile(utc_adq=utc_adq,list_pedestal=list_pedestal)
|
|
4141
|
angulo,angulo_ele = self.getAnguloProfile(utc_adq=utc_adq,list_pedestal=list_pedestal)
|
|
|
4142
|
####print("angulo**********",angulo)
|
|
4142
|
####print("angulo**********",angulo)
|
|
|
4143
|
dataOut.flagNoData = False
|
|
4143
|
dataOut.flagNoData = False
|
|
|
4144
|
if numpy.isnan(angulo) or numpy.isnan(angulo_ele) :
|
|
4144
|
if numpy.isnan(angulo) or numpy.isnan(angulo_ele) :
|
|
|
4145
|
dataOut.flagNoData = True
|
|
4145
|
dataOut.flagNoData = True
|
|
|
4146
|
return dataOut
|
|
4146
|
return dataOut
|
|
|
4147
|
dataOut.azimuth = angulo
|
|
4147
|
dataOut.azimuth = angulo
|
|
|
4148
|
dataOut.elevation = angulo_ele
|
|
4148
|
dataOut.elevation = angulo_ele
|
|
|
4149
|
return dataOut
|
|
4149
|
return dataOut
|
|
|
4150
|
|
|
4150
|
|
|
|
4151
|
class Block360(Operation):
|
|
4151
|
class Block360(Operation):
|
|
|
4152
|
'''
|
|
4152
|
'''
|
|
|
4153
|
'''
|
|
4153
|
'''
|
|
|
4154
|
isConfig = False
|
|
4154
|
isConfig = False
|
|
|
4155
|
__profIndex = 0
|
|
4155
|
__profIndex = 0
|
|
|
4156
|
__initime = None
|
|
4156
|
__initime = None
|
|
|
4157
|
__lastdatatime = None
|
|
4157
|
__lastdatatime = None
|
|
|
4158
|
__buffer = None
|
|
4158
|
__buffer = None
|
|
|
4159
|
__dataReady = False
|
|
4159
|
__dataReady = False
|
|
|
4160
|
n = None
|
|
4160
|
n = None
|
|
|
4161
|
__nch = 0
|
|
4161
|
__nch = 0
|
|
|
4162
|
__nHeis = 0
|
|
4162
|
__nHeis = 0
|
|
|
4163
|
index = 0
|
|
4163
|
index = 0
|
|
|
4164
|
mode = 0
|
|
4164
|
mode = 0
|
|
|
4165
|
|
|
4165
|
|
|
|
4166
|
def __init__(self,**kwargs):
|
|
4166
|
def __init__(self,**kwargs):
|
|
|
4167
|
Operation.__init__(self,**kwargs)
|
|
4167
|
Operation.__init__(self,**kwargs)
|
|
|
4168
|
|
|
4168
|
|
|
|
4169
|
def setup(self, dataOut, n = None, mode = None):
|
|
4169
|
def setup(self, dataOut, n = None, mode = None):
|
|
|
4170
|
'''
|
|
4170
|
'''
|
|
|
4171
|
n= Numero de PRF's de entrada
|
|
4171
|
n= Numero de PRF's de entrada
|
|
|
4172
|
'''
|
|
4172
|
'''
|
|
|
4173
|
self.__initime = None
|
|
4173
|
self.__initime = None
|
|
|
4174
|
self.__lastdatatime = 0
|
|
4174
|
self.__lastdatatime = 0
|
|
|
4175
|
self.__dataReady = False
|
|
4175
|
self.__dataReady = False
|
|
|
4176
|
self.__buffer = 0
|
|
4176
|
self.__buffer = 0
|
|
|
4177
|
self.__buffer_1D = 0
|
|
4177
|
self.__buffer_1D = 0
|
|
|
4178
|
self.__profIndex = 0
|
|
4178
|
self.__profIndex = 0
|
|
|
4179
|
self.index = 0
|
|
4179
|
self.index = 0
|
|
|
4180
|
self.__nch = dataOut.nChannels
|
|
4180
|
self.__nch = dataOut.nChannels
|
|
|
4181
|
self.__nHeis = dataOut.nHeights
|
|
4181
|
self.__nHeis = dataOut.nHeights
|
|
|
4182
|
##print("ELVALOR DE n es:", n)
|
|
4182
|
##print("ELVALOR DE n es:", n)
|
|
|
4183
|
if n == None:
|
|
4183
|
if n == None:
|
|
|
4184
|
raise ValueError("n should be specified.")
|
|
4184
|
raise ValueError("n should be specified.")
|
|
|
4185
|
|
|
4185
|
|
|
|
4186
|
if mode == None:
|
|
4186
|
if mode == None:
|
|
|
4187
|
raise ValueError("mode should be specified.")
|
|
4187
|
raise ValueError("mode should be specified.")
|
|
|
4188
|
|
|
4188
|
|
|
|
4189
|
if n != None:
|
|
4189
|
if n != None:
|
|
|
4190
|
if n<1:
|
|
4190
|
if n<1:
|
|
|
4191
|
print("n should be greater than 2")
|
|
4191
|
print("n should be greater than 2")
|
|
|
4192
|
raise ValueError("n should be greater than 2")
|
|
4192
|
raise ValueError("n should be greater than 2")
|
|
|
4193
|
|
|
4193
|
|
|
|
4194
|
self.n = n
|
|
4194
|
self.n = n
|
|
|
4195
|
self.mode = mode
|
|
4195
|
self.mode = mode
|
|
|
4196
|
print("self.mode",self.mode)
|
|
4196
|
#print("self.mode",self.mode)
|
|
|
4197
|
#print("nHeights")
|
|
4197
|
#print("nHeights")
|
|
|
4198
|
self.__buffer = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights))
|
|
4198
|
self.__buffer = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights))
|
|
|
4199
|
self.__buffer2 = numpy.zeros(n)
|
|
4199
|
self.__buffer2 = numpy.zeros(n)
|
|
|
4200
|
self.__buffer3 = numpy.zeros(n)
|
|
4200
|
self.__buffer3 = numpy.zeros(n)
|
|
|
4201
|
|
|
4201
|
|
|
|
4202
|
|
|
4202
|
|
|
|
4203
|
def putData(self,data,mode):
|
|
4203
|
def putData(self,data,mode):
|
|
|
4204
|
'''
|
|
4204
|
'''
|
|
|
4205
|
Add a profile to he __buffer and increase in one the __profiel Index
|
|
4205
|
Add a profile to he __buffer and increase in one the __profiel Index
|
|
|
4206
|
'''
|
|
4206
|
'''
|
|
|
4207
|
#print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10])
|
|
4207
|
#print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10])
|
|
|
4208
|
#print("line 4049",data.azimuth.shape,data.azimuth)
|
|
4208
|
#print("line 4049",data.azimuth.shape,data.azimuth)
|
|
|
4209
|
if self.mode==0:
|
|
4209
|
if self.mode==0:
|
|
|
4210
|
self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO
|
|
4210
|
self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO
|
|
|
4211
|
if self.mode==1:
|
|
4211
|
if self.mode==1:
|
|
|
4212
|
self.__buffer[:,self.__profIndex,:]= data.data_pow
|
|
4212
|
self.__buffer[:,self.__profIndex,:]= data.data_pow
|
|
|
4213
|
#print("me casi",self.index,data.azimuth[self.index])
|
|
4213
|
#print("me casi",self.index,data.azimuth[self.index])
|
|
|
4214
|
#print(self.__profIndex, self.index , data.azimuth[self.index] )
|
|
4214
|
#print(self.__profIndex, self.index , data.azimuth[self.index] )
|
|
|
4215
|
#print("magic",data.profileIndex)
|
|
4215
|
#print("magic",data.profileIndex)
|
|
|
4216
|
#print(data.azimuth[self.index])
|
|
4216
|
#print(data.azimuth[self.index])
|
|
|
4217
|
#print("index",self.index)
|
|
4217
|
#print("index",self.index)
|
|
|
4218
|
|
|
4218
|
|
|
|
4219
|
#####self.__buffer2[self.__profIndex] = data.azimuth[self.index]
|
|
4219
|
#####self.__buffer2[self.__profIndex] = data.azimuth[self.index]
|
|
|
4220
|
self.__buffer2[self.__profIndex] = data.azimuth
|
|
4220
|
self.__buffer2[self.__profIndex] = data.azimuth
|
|
|
4221
|
self.__buffer3[self.__profIndex] = data.elevation
|
|
4221
|
self.__buffer3[self.__profIndex] = data.elevation
|
|
|
4222
|
#print("q pasa")
|
|
4222
|
#print("q pasa")
|
|
|
4223
|
#####self.index+=1
|
|
4223
|
#####self.index+=1
|
|
|
4224
|
#print("index",self.index,data.azimuth[:10])
|
|
4224
|
#print("index",self.index,data.azimuth[:10])
|
|
|
4225
|
self.__profIndex += 1
|
|
4225
|
self.__profIndex += 1
|
|
|
4226
|
return #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
|
|
4226
|
return #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
|
|
|
4227
|
|
|
4227
|
|
|
|
4228
|
def pushData(self,data):
|
|
4228
|
def pushData(self,data):
|
|
|
4229
|
'''
|
|
4229
|
'''
|
|
|
4230
|
Return the PULSEPAIR and the profiles used in the operation
|
|
4230
|
Return the PULSEPAIR and the profiles used in the operation
|
|
|
4231
|
Affected : self.__profileIndex
|
|
4231
|
Affected : self.__profileIndex
|
|
|
4232
|
'''
|
|
4232
|
'''
|
|
|
4233
|
#print("pushData")
|
|
4233
|
#print("pushData")
|
|
|
4234
|
|
|
4234
|
|
|
|
4235
|
data_360 = self.__buffer
|
|
4235
|
data_360 = self.__buffer
|
|
|
4236
|
data_p = self.__buffer2
|
|
4236
|
data_p = self.__buffer2
|
|
|
4237
|
data_e = self.__buffer3
|
|
4237
|
data_e = self.__buffer3
|
|
|
4238
|
n = self.__profIndex
|
|
4238
|
n = self.__profIndex
|
|
|
4239
|
|
|
4239
|
|
|
|
4240
|
self.__buffer = numpy.zeros((self.__nch, self.n,self.__nHeis))
|
|
4240
|
self.__buffer = numpy.zeros((self.__nch, self.n,self.__nHeis))
|
|
|
4241
|
self.__buffer2 = numpy.zeros(self.n)
|
|
4241
|
self.__buffer2 = numpy.zeros(self.n)
|
|
|
4242
|
self.__buffer3 = numpy.zeros(self.n)
|
|
4242
|
self.__buffer3 = numpy.zeros(self.n)
|
|
|
4243
|
self.__profIndex = 0
|
|
4243
|
self.__profIndex = 0
|
|
|
4244
|
#print("pushData")
|
|
4244
|
#print("pushData")
|
|
|
4245
|
return data_360,n,data_p,data_e
|
|
4245
|
return data_360,n,data_p,data_e
|
|
|
4246
|
|
|
4246
|
|
|
|
4247
|
|
|
4247
|
|
|
|
4248
|
def byProfiles(self,dataOut):
|
|
4248
|
def byProfiles(self,dataOut):
|
|
|
4249
|
|
|
4249
|
|
|
|
4250
|
self.__dataReady = False
|
|
4250
|
self.__dataReady = False
|
|
|
4251
|
data_360 = None
|
|
4251
|
data_360 = None
|
|
|
4252
|
data_p = None
|
|
4252
|
data_p = None
|
|
|
4253
|
data_e = None
|
|
4253
|
data_e = None
|
|
|
4254
|
#print("dataOu",dataOut.dataPP_POW)
|
|
4254
|
#print("dataOu",dataOut.dataPP_POW)
|
|
|
4255
|
self.putData(data=dataOut,mode = self.mode)
|
|
4255
|
self.putData(data=dataOut,mode = self.mode)
|
|
|
4256
|
##### print("profIndex",self.__profIndex)
|
|
4256
|
##### print("profIndex",self.__profIndex)
|
|
|
4257
|
if self.__profIndex == self.n:
|
|
4257
|
if self.__profIndex == self.n:
|
|
|
4258
|
data_360,n,data_p,data_e = self.pushData(data=dataOut)
|
|
4258
|
data_360,n,data_p,data_e = self.pushData(data=dataOut)
|
|
|
4259
|
self.__dataReady = True
|
|
4259
|
self.__dataReady = True
|
|
|
4260
|
|
|
4260
|
|
|
|
4261
|
return data_360,data_p,data_e
|
|
4261
|
return data_360,data_p,data_e
|
|
|
4262
|
|
|
4262
|
|
|
|
4263
|
|
|
4263
|
|
|
|
4264
|
def blockOp(self, dataOut, datatime= None):
|
|
4264
|
def blockOp(self, dataOut, datatime= None):
|
|
|
4265
|
if self.__initime == None:
|
|
4265
|
if self.__initime == None:
|
|
|
4266
|
self.__initime = datatime
|
|
4266
|
self.__initime = datatime
|
|
|
4267
|
data_360,data_p,data_e = self.byProfiles(dataOut)
|
|
4267
|
data_360,data_p,data_e = self.byProfiles(dataOut)
|
|
|
4268
|
self.__lastdatatime = datatime
|
|
4268
|
self.__lastdatatime = datatime
|
|
|
4269
|
|
|
4269
|
|
|
|
4270
|
if data_360 is None:
|
|
4270
|
if data_360 is None:
|
|
|
4271
|
return None, None,None,None
|
|
4271
|
return None, None,None,None
|
|
|
4272
|
|
|
4272
|
|
|
|
4273
|
|
|
4273
|
|
|
|
4274
|
avgdatatime = self.__initime
|
|
4274
|
avgdatatime = self.__initime
|
|
|
4275
|
if self.n==1:
|
|
4275
|
if self.n==1:
|
|
|
4276
|
avgdatatime = datatime
|
|
4276
|
avgdatatime = datatime
|
|
|
4277
|
deltatime = datatime - self.__lastdatatime
|
|
4277
|
deltatime = datatime - self.__lastdatatime
|
|
|
4278
|
self.__initime = datatime
|
|
4278
|
self.__initime = datatime
|
|
|
4279
|
#print(data_360.shape,avgdatatime,data_p.shape)
|
|
4279
|
#print(data_360.shape,avgdatatime,data_p.shape)
|
|
|
4280
|
return data_360,avgdatatime,data_p,data_e
|
|
4280
|
return data_360,avgdatatime,data_p,data_e
|
|
|
4281
|
|
|
4281
|
|
|
|
4282
|
def run(self, dataOut,n = None,mode=None,**kwargs):
|
|
4282
|
def run(self, dataOut,n = None,mode=None,**kwargs):
|
|
|
4283
|
#print("BLOCK 360 HERE WE GO MOMENTOS")
|
|
4283
|
#print("BLOCK 360 HERE WE GO MOMENTOS")
|
|
|
4284
|
if not self.isConfig:
|
|
4284
|
if not self.isConfig:
|
|
|
4285
|
self.setup(dataOut = dataOut, n = n ,mode= mode ,**kwargs)
|
|
4285
|
self.setup(dataOut = dataOut, n = n ,mode= mode ,**kwargs)
|
|
|
4286
|
####self.index = 0
|
|
4286
|
####self.index = 0
|
|
|
4287
|
#print("comova",self.isConfig)
|
|
4287
|
#print("comova",self.isConfig)
|
|
|
4288
|
self.isConfig = True
|
|
4288
|
self.isConfig = True
|
|
|
4289
|
####if self.index==dataOut.azimuth.shape[0]:
|
|
4289
|
####if self.index==dataOut.azimuth.shape[0]:
|
|
|
4290
|
#### self.index=0
|
|
4290
|
#### self.index=0
|
|
|
4291
|
data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime)
|
|
4291
|
data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime)
|
|
|
4292
|
dataOut.flagNoData = True
|
|
4292
|
dataOut.flagNoData = True
|
|
|
4293
|
|
|
4293
|
|
|
|
4294
|
if self.__dataReady:
|
|
4294
|
if self.__dataReady:
|
|
|
4295
|
dataOut.data_360 = data_360 # S
|
|
4295
|
dataOut.data_360 = data_360 # S
|
|
|
4296
|
##print("---------------------------------------------------------------------------------")
|
|
4296
|
##print("---------------------------------------------------------------------------------")
|
|
|
4297
|
#####print("---------------------------DATAREADY---------------------------------------------")
|
|
4297
|
#####print("---------------------------DATAREADY---------------------------------------------")
|
|
|
4298
|
##print("---------------------------------------------------------------------------------")
|
|
4298
|
##print("---------------------------------------------------------------------------------")
|
|
|
4299
|
##print("data_360",dataOut.data_360.shape)
|
|
4299
|
##print("data_360",dataOut.data_360.shape)
|
|
|
4300
|
dataOut.data_azi = data_p
|
|
4300
|
dataOut.data_azi = data_p
|
|
|
4301
|
dataOut.data_ele = data_e
|
|
4301
|
dataOut.data_ele = data_e
|
|
|
4302
|
#####print("azi: ",dataOut.data_azi)
|
|
4302
|
#####print("azi: ",dataOut.data_azi)
|
|
|
4303
|
#print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime)
|
|
4303
|
#print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime)
|
|
|
4304
|
dataOut.utctime = avgdatatime
|
|
4304
|
dataOut.utctime = avgdatatime
|
|
|
4305
|
dataOut.flagNoData = False
|
|
4305
|
dataOut.flagNoData = False
|
|
|
4306
|
return dataOut
|
|
4306
|
return dataOut
|
