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-1,1686
+1,1719
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import sys
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import sys
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import numpy
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import numpy
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from scipy import interpolate
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from scipy import interpolate
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from schainpy import cSchain
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from schainpy import cSchain
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from jroproc_base import ProcessingUnit, Operation
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from jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Voltage
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from schainpy.model.data.jrodata import Voltage
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from time import time
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from time import time
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import math
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import math
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def rep_seq(x, rep=10):
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def rep_seq(x, rep=10):
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L = len(x) * rep
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L = len(x) * rep
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res = numpy.zeros(L, dtype=x.dtype)
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res = numpy.zeros(L, dtype=x.dtype)
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idx = numpy.arange(len(x)) * rep
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idx = numpy.arange(len(x)) * rep
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for i in numpy.arange(rep):
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for i in numpy.arange(rep):
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res[idx + i] = x
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res[idx + i] = x
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return(res)
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return(res)
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def create_pseudo_random_code(clen=10000, seed=0):
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def create_pseudo_random_code(clen=10000, seed=0):
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"""
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"""
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seed is a way of reproducing the random code without
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seed is a way of reproducing the random code without
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having to store all actual codes. the seed can then
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having to store all actual codes. the seed can then
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act as a sort of station_id.
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act as a sort of station_id.
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"""
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"""
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numpy.random.seed(seed)
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numpy.random.seed(seed)
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phases = numpy.array(
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phases = numpy.array(
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numpy.exp(1.0j * 2.0 * math.pi * numpy.random.random(clen)),
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numpy.exp(1.0j * 2.0 * math.pi * numpy.random.random(clen)),
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dtype=numpy.complex64,
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dtype=numpy.complex64,
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)
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)
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return(phases)
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return(phases)
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def periodic_convolution_matrix(envelope, rmin=0, rmax=100):
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def periodic_convolution_matrix(envelope, rmin=0, rmax=100):
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"""
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"""
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we imply that the number of measurements is equal to the number of elements
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we imply that the number of measurements is equal to the number of elements
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in code
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in code
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"""
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"""
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L = len(envelope)
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L = len(envelope)
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ridx = numpy.arange(rmin, rmax)
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ridx = numpy.arange(rmin, rmax)
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A = numpy.zeros([L, rmax-rmin], dtype=numpy.complex64)
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A = numpy.zeros([L, rmax-rmin], dtype=numpy.complex64)
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for i in numpy.arange(L):
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for i in numpy.arange(L):
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A[i, :] = envelope[(i-ridx) % L]
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A[i, :] = envelope[(i-ridx) % L]
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result = {}
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result = {}
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result['A'] = A
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result['A'] = A
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result['ridx'] = ridx
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result['ridx'] = ridx
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return(result)
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return(result)
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B_cache = 0
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B_cache = 0
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r_cache = 0
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r_cache = 0
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B_cached = False
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B_cached = False
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def create_estimation_matrix(code, rmin=0, rmax=1000, cache=True):
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def create_estimation_matrix(code, rmin=0, rmax=1000, cache=True):
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global B_cache
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global B_cache
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global r_cache
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global r_cache
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global B_cached
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global B_cached
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if not cache or not B_cached:
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if not cache or not B_cached:
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r_cache = periodic_convolution_matrix(
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r_cache = periodic_convolution_matrix(
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envelope=code, rmin=rmin, rmax=rmax,
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envelope=code, rmin=rmin, rmax=rmax,
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)
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)
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A = r_cache['A']
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A = r_cache['A']
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Ah = numpy.transpose(numpy.conjugate(A))
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Ah = numpy.transpose(numpy.conjugate(A))
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B_cache = numpy.dot(numpy.linalg.inv(numpy.dot(Ah, A)), Ah)
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B_cache = numpy.dot(numpy.linalg.inv(numpy.dot(Ah, A)), Ah)
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r_cache['B'] = B_cache
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r_cache['B'] = B_cache
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B_cached = True
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B_cached = True
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return(r_cache)
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return(r_cache)
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else:
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else:
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return(r_cache)
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return(r_cache)
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class VoltageProc(ProcessingUnit):
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class VoltageProc(ProcessingUnit):
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def __init__(self, **kwargs):
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def __init__(self, **kwargs):
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ProcessingUnit.__init__(self, **kwargs)
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ProcessingUnit.__init__(self, **kwargs)
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# self.objectDict = {}
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# self.objectDict = {}
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self.dataOut = Voltage()
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self.dataOut = Voltage()
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self.flip = 1
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self.flip = 1
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def run(self):
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def run(self):
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if self.dataIn.type == 'AMISR':
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if self.dataIn.type == 'AMISR':
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self.__updateObjFromAmisrInput()
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self.__updateObjFromAmisrInput()
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if self.dataIn.type == 'Voltage':
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if self.dataIn.type == 'Voltage':
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self.dataOut.copy(self.dataIn)
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self.dataOut.copy(self.dataIn)
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# self.dataOut.copy(self.dataIn)
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# self.dataOut.copy(self.dataIn)
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def __updateObjFromAmisrInput(self):
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def __updateObjFromAmisrInput(self):
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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.flagNoData = self.dataIn.flagNoData
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self.dataOut.flagNoData = self.dataIn.flagNoData
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self.dataOut.data = self.dataIn.data
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self.dataOut.data = self.dataIn.data
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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.channelList = self.dataIn.channelList
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self.dataOut.channelList = self.dataIn.channelList
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# self.dataOut.timeInterval = self.dataIn.timeInterval
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# self.dataOut.timeInterval = 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.nProfiles = self.dataIn.nProfiles
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self.dataOut.nProfiles = self.dataIn.nProfiles
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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.ippSeconds = self.dataIn.ippSeconds
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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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.azimuth = self.dataIn.azimuth
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self.dataOut.azimuth = self.dataIn.azimuth
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self.dataOut.zenith = self.dataIn.zenith
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self.dataOut.zenith = self.dataIn.zenith
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self.dataOut.beam.codeList = self.dataIn.beam.codeList
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self.dataOut.beam.codeList = self.dataIn.beam.codeList
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self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
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self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
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self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
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self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
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#
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#
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# pass#
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# pass#
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#
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#
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# def init(self):
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# def init(self):
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#
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#
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#
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#
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# if self.dataIn.type == 'AMISR':
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# if self.dataIn.type == 'AMISR':
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# self.__updateObjFromAmisrInput()
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# self.__updateObjFromAmisrInput()
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#
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#
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# if self.dataIn.type == 'Voltage':
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# if self.dataIn.type == 'Voltage':
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# self.dataOut.copy(self.dataIn)
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# self.dataOut.copy(self.dataIn)
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# # No necesita copiar en cada init() los atributos de dataIn
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# # No necesita copiar en cada init() los atributos de dataIn
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# # la copia deberia hacerse por cada nuevo bloque de datos
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# # la copia deberia hacerse por cada nuevo bloque de datos
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def selectChannels(self, channelList):
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def selectChannels(self, channelList):
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channelIndexList = []
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channelIndexList = []
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for channel in channelList:
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for channel in channelList:
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if channel not in self.dataOut.channelList:
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if channel not in self.dataOut.channelList:
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raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
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raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
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index = self.dataOut.channelList.index(channel)
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index = self.dataOut.channelList.index(channel)
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channelIndexList.append(index)
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channelIndexList.append(index)
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self.selectChannelsByIndex(channelIndexList)
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self.selectChannelsByIndex(channelIndexList)
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def selectChannelsByIndex(self, channelIndexList):
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def selectChannelsByIndex(self, channelIndexList):
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"""
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"""
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Selecciona un bloque de datos en base a canales segun el channelIndexList
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Selecciona un bloque de datos en base a canales segun el channelIndexList
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Input:
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Input:
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channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
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channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
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Affected:
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Affected:
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self.dataOut.data
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self.dataOut.data
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self.dataOut.channelIndexList
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self.dataOut.channelIndexList
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self.dataOut.nChannels
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self.dataOut.nChannels
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self.dataOut.m_ProcessingHeader.totalSpectra
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self.dataOut.m_ProcessingHeader.totalSpectra
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self.dataOut.systemHeaderObj.numChannels
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self.dataOut.systemHeaderObj.numChannels
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self.dataOut.m_ProcessingHeader.blockSize
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self.dataOut.m_ProcessingHeader.blockSize
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Return:
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Return:
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None
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None
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"""
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"""
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for channelIndex in channelIndexList:
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for channelIndex in channelIndexList:
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if channelIndex not in self.dataOut.channelIndexList:
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if channelIndex not in self.dataOut.channelIndexList:
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print channelIndexList
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print channelIndexList
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raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
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raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
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if self.dataOut.flagDataAsBlock:
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if self.dataOut.flagDataAsBlock:
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"""
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"""
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Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
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Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
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"""
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"""
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data = self.dataOut.data[channelIndexList,:,:]
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data = self.dataOut.data[channelIndexList,:,:]
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else:
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else:
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data = self.dataOut.data[channelIndexList,:]
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data = self.dataOut.data[channelIndexList,:]
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self.dataOut.data = data
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177
|
self.dataOut.data = data
|
|
|
178
|
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
|
|
178
|
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
|
|
|
179
|
# self.dataOut.nChannels = nChannels
|
|
179
|
# self.dataOut.nChannels = nChannels
|
|
|
180
|
|
|
180
|
|
|
|
181
|
return 1
|
|
181
|
return 1
|
|
|
182
|
|
|
182
|
|
|
|
183
|
def selectHeights(self, minHei=None, maxHei=None):
|
|
183
|
def selectHeights(self, minHei=None, maxHei=None):
|
|
|
184
|
"""
|
|
184
|
"""
|
|
|
185
|
Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
|
|
185
|
Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
|
|
|
186
|
minHei <= height <= maxHei
|
|
186
|
minHei <= height <= maxHei
|
|
|
187
|
|
|
187
|
|
|
|
188
|
Input:
|
|
188
|
Input:
|
|
|
189
|
minHei : valor minimo de altura a considerar
|
|
189
|
minHei : valor minimo de altura a considerar
|
|
|
190
|
maxHei : valor maximo de altura a considerar
|
|
190
|
maxHei : valor maximo de altura a considerar
|
|
|
191
|
|
|
191
|
|
|
|
192
|
Affected:
|
|
192
|
Affected:
|
|
|
193
|
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
|
|
193
|
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
|
|
|
194
|
|
|
194
|
|
|
|
195
|
Return:
|
|
195
|
Return:
|
|
|
196
|
1 si el metodo se ejecuto con exito caso contrario devuelve 0
|
|
196
|
1 si el metodo se ejecuto con exito caso contrario devuelve 0
|
|
|
197
|
"""
|
|
197
|
"""
|
|
|
198
|
|
|
198
|
|
|
|
199
|
if minHei == None:
|
|
199
|
if minHei == None:
|
|
|
200
|
minHei = self.dataOut.heightList[0]
|
|
200
|
minHei = self.dataOut.heightList[0]
|
|
|
201
|
|
|
201
|
|
|
|
202
|
if maxHei == None:
|
|
202
|
if maxHei == None:
|
|
|
203
|
maxHei = self.dataOut.heightList[-1]
|
|
203
|
maxHei = self.dataOut.heightList[-1]
|
|
|
204
|
|
|
204
|
|
|
|
205
|
if (minHei < self.dataOut.heightList[0]):
|
|
205
|
if (minHei < self.dataOut.heightList[0]):
|
|
|
206
|
minHei = self.dataOut.heightList[0]
|
|
206
|
minHei = self.dataOut.heightList[0]
|
|
|
207
|
|
|
207
|
|
|
|
208
|
if (maxHei > self.dataOut.heightList[-1]):
|
|
208
|
if (maxHei > self.dataOut.heightList[-1]):
|
|
|
209
|
maxHei = self.dataOut.heightList[-1]
|
|
209
|
maxHei = self.dataOut.heightList[-1]
|
|
|
210
|
|
|
210
|
|
|
|
211
|
minIndex = 0
|
|
211
|
minIndex = 0
|
|
|
212
|
maxIndex = 0
|
|
212
|
maxIndex = 0
|
|
|
213
|
heights = self.dataOut.heightList
|
|
213
|
heights = self.dataOut.heightList
|
|
|
214
|
|
|
214
|
|
|
|
215
|
inda = numpy.where(heights >= minHei)
|
|
215
|
inda = numpy.where(heights >= minHei)
|
|
|
216
|
indb = numpy.where(heights <= maxHei)
|
|
216
|
indb = numpy.where(heights <= maxHei)
|
|
|
217
|
|
|
217
|
|
|
|
218
|
try:
|
|
218
|
try:
|
|
|
219
|
minIndex = inda[0][0]
|
|
219
|
minIndex = inda[0][0]
|
|
|
220
|
except:
|
|
220
|
except:
|
|
|
221
|
minIndex = 0
|
|
221
|
minIndex = 0
|
|
|
222
|
|
|
222
|
|
|
|
223
|
try:
|
|
223
|
try:
|
|
|
224
|
maxIndex = indb[0][-1]
|
|
224
|
maxIndex = indb[0][-1]
|
|
|
225
|
except:
|
|
225
|
except:
|
|
|
226
|
maxIndex = len(heights)
|
|
226
|
maxIndex = len(heights)
|
|
|
227
|
|
|
227
|
|
|
|
228
|
self.selectHeightsByIndex(minIndex, maxIndex)
|
|
228
|
self.selectHeightsByIndex(minIndex, maxIndex)
|
|
|
229
|
|
|
229
|
|
|
|
230
|
return 1
|
|
230
|
return 1
|
|
|
231
|
|
|
231
|
|
|
|
232
|
|
|
232
|
|
|
|
233
|
def selectHeightsByIndex(self, minIndex, maxIndex):
|
|
233
|
def selectHeightsByIndex(self, minIndex, maxIndex):
|
|
|
234
|
"""
|
|
234
|
"""
|
|
|
235
|
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
|
|
235
|
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
|
|
|
236
|
minIndex <= index <= maxIndex
|
|
236
|
minIndex <= index <= maxIndex
|
|
|
237
|
|
|
237
|
|
|
|
238
|
Input:
|
|
238
|
Input:
|
|
|
239
|
minIndex : valor de indice minimo de altura a considerar
|
|
239
|
minIndex : valor de indice minimo de altura a considerar
|
|
|
240
|
maxIndex : valor de indice maximo de altura a considerar
|
|
240
|
maxIndex : valor de indice maximo de altura a considerar
|
|
|
241
|
|
|
241
|
|
|
|
242
|
Affected:
|
|
242
|
Affected:
|
|
|
243
|
self.dataOut.data
|
|
243
|
self.dataOut.data
|
|
|
244
|
self.dataOut.heightList
|
|
244
|
self.dataOut.heightList
|
|
|
245
|
|
|
245
|
|
|
|
246
|
Return:
|
|
246
|
Return:
|
|
|
247
|
1 si el metodo se ejecuto con exito caso contrario devuelve 0
|
|
247
|
1 si el metodo se ejecuto con exito caso contrario devuelve 0
|
|
|
248
|
"""
|
|
248
|
"""
|
|
|
249
|
|
|
249
|
|
|
|
250
|
if (minIndex < 0) or (minIndex > maxIndex):
|
|
250
|
if (minIndex < 0) or (minIndex > maxIndex):
|
|
|
251
|
raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
|
|
251
|
raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
|
|
|
252
|
|
|
252
|
|
|
|
253
|
if (maxIndex >= self.dataOut.nHeights):
|
|
253
|
if (maxIndex >= self.dataOut.nHeights):
|
|
|
254
|
maxIndex = self.dataOut.nHeights
|
|
254
|
maxIndex = self.dataOut.nHeights
|
|
|
255
|
|
|
255
|
|
|
|
256
|
#voltage
|
|
256
|
#voltage
|
|
|
257
|
if self.dataOut.flagDataAsBlock:
|
|
257
|
if self.dataOut.flagDataAsBlock:
|
|
|
258
|
"""
|
|
258
|
"""
|
|
|
259
|
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
259
|
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
|
260
|
"""
|
|
260
|
"""
|
|
|
261
|
data = self.dataOut.data[:,:, minIndex:maxIndex]
|
|
261
|
data = self.dataOut.data[:,:, minIndex:maxIndex]
|
|
|
262
|
else:
|
|
262
|
else:
|
|
|
263
|
data = self.dataOut.data[:, minIndex:maxIndex]
|
|
263
|
data = self.dataOut.data[:, minIndex:maxIndex]
|
|
|
264
|
|
|
264
|
|
|
|
265
|
# firstHeight = self.dataOut.heightList[minIndex]
|
|
265
|
# firstHeight = self.dataOut.heightList[minIndex]
|
|
|
266
|
|
|
266
|
|
|
|
267
|
self.dataOut.data = data
|
|
267
|
self.dataOut.data = data
|
|
|
268
|
self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
|
|
268
|
self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
|
|
|
269
|
|
|
269
|
|
|
|
270
|
if self.dataOut.nHeights <= 1:
|
|
270
|
if self.dataOut.nHeights <= 1:
|
|
|
271
|
raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
|
|
271
|
raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
|
|
|
272
|
|
|
272
|
|
|
|
273
|
return 1
|
|
273
|
return 1
|
|
|
274
|
|
|
274
|
|
|
|
275
|
|
|
275
|
|
|
|
276
|
def filterByHeights(self, window):
|
|
276
|
def filterByHeights(self, window):
|
|
|
277
|
|
|
277
|
|
|
|
278
|
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
|
|
278
|
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
|
|
|
279
|
|
|
279
|
|
|
|
280
|
if window == None:
|
|
280
|
if window == None:
|
|
|
281
|
window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
|
|
281
|
window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
|
|
|
282
|
|
|
282
|
|
|
|
283
|
newdelta = deltaHeight * window
|
|
283
|
newdelta = deltaHeight * window
|
|
|
284
|
r = self.dataOut.nHeights % window
|
|
284
|
r = self.dataOut.nHeights % window
|
|
|
285
|
newheights = (self.dataOut.nHeights-r)/window
|
|
285
|
newheights = (self.dataOut.nHeights-r)/window
|
|
|
286
|
|
|
286
|
|
|
|
287
|
if newheights <= 1:
|
|
287
|
if newheights <= 1:
|
|
|
288
|
raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
|
|
288
|
raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
|
|
|
289
|
|
|
289
|
|
|
|
290
|
if self.dataOut.flagDataAsBlock:
|
|
290
|
if self.dataOut.flagDataAsBlock:
|
|
|
291
|
"""
|
|
291
|
"""
|
|
|
292
|
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
292
|
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
|
293
|
"""
|
|
293
|
"""
|
|
|
294
|
buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
|
|
294
|
buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
|
|
|
295
|
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
|
|
295
|
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
|
|
|
296
|
buffer = numpy.sum(buffer,3)
|
|
296
|
buffer = numpy.sum(buffer,3)
|
|
|
297
|
|
|
297
|
|
|
|
298
|
else:
|
|
298
|
else:
|
|
|
299
|
buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
|
|
299
|
buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
|
|
|
300
|
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
|
|
300
|
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
|
|
|
301
|
buffer = numpy.sum(buffer,2)
|
|
301
|
buffer = numpy.sum(buffer,2)
|
|
|
302
|
|
|
302
|
|
|
|
303
|
self.dataOut.data = buffer
|
|
303
|
self.dataOut.data = buffer
|
|
|
304
|
self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
|
|
304
|
self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
|
|
|
305
|
self.dataOut.windowOfFilter = window
|
|
305
|
self.dataOut.windowOfFilter = window
|
|
|
306
|
|
|
306
|
|
|
|
307
|
def setH0(self, h0, deltaHeight = None):
|
|
307
|
def setH0(self, h0, deltaHeight = None):
|
|
|
308
|
|
|
308
|
|
|
|
309
|
if not deltaHeight:
|
|
309
|
if not deltaHeight:
|
|
|
310
|
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
|
|
310
|
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
|
|
|
311
|
|
|
311
|
|
|
|
312
|
nHeights = self.dataOut.nHeights
|
|
312
|
nHeights = self.dataOut.nHeights
|
|
|
313
|
|
|
313
|
|
|
|
314
|
newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
|
|
314
|
newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
|
|
|
315
|
|
|
315
|
|
|
|
316
|
self.dataOut.heightList = newHeiRange
|
|
316
|
self.dataOut.heightList = newHeiRange
|
|
|
317
|
|
|
317
|
|
|
|
318
|
def deFlip(self, channelList = []):
|
|
318
|
def deFlip(self, channelList = []):
|
|
|
319
|
|
|
319
|
|
|
|
320
|
data = self.dataOut.data.copy()
|
|
320
|
data = self.dataOut.data.copy()
|
|
|
321
|
|
|
321
|
|
|
|
322
|
if self.dataOut.flagDataAsBlock:
|
|
322
|
if self.dataOut.flagDataAsBlock:
|
|
|
323
|
flip = self.flip
|
|
323
|
flip = self.flip
|
|
|
324
|
profileList = range(self.dataOut.nProfiles)
|
|
324
|
profileList = range(self.dataOut.nProfiles)
|
|
|
325
|
|
|
325
|
|
|
|
326
|
if not channelList:
|
|
326
|
if not channelList:
|
|
|
327
|
for thisProfile in profileList:
|
|
327
|
for thisProfile in profileList:
|
|
|
328
|
data[:,thisProfile,:] = data[:,thisProfile,:]*flip
|
|
328
|
data[:,thisProfile,:] = data[:,thisProfile,:]*flip
|
|
|
329
|
flip *= -1.0
|
|
329
|
flip *= -1.0
|
|
|
330
|
else:
|
|
330
|
else:
|
|
|
331
|
for thisChannel in channelList:
|
|
331
|
for thisChannel in channelList:
|
|
|
332
|
if thisChannel not in self.dataOut.channelList:
|
|
332
|
if thisChannel not in self.dataOut.channelList:
|
|
|
333
|
continue
|
|
333
|
continue
|
|
|
334
|
|
|
334
|
|
|
|
335
|
for thisProfile in profileList:
|
|
335
|
for thisProfile in profileList:
|
|
|
336
|
data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
|
|
336
|
data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
|
|
|
337
|
flip *= -1.0
|
|
337
|
flip *= -1.0
|
|
|
338
|
|
|
338
|
|
|
|
339
|
self.flip = flip
|
|
339
|
self.flip = flip
|
|
|
340
|
|
|
340
|
|
|
|
341
|
else:
|
|
341
|
else:
|
|
|
342
|
if not channelList:
|
|
342
|
if not channelList:
|
|
|
343
|
data[:,:] = data[:,:]*self.flip
|
|
343
|
data[:,:] = data[:,:]*self.flip
|
|
|
344
|
else:
|
|
344
|
else:
|
|
|
345
|
for thisChannel in channelList:
|
|
345
|
for thisChannel in channelList:
|
|
|
346
|
if thisChannel not in self.dataOut.channelList:
|
|
346
|
if thisChannel not in self.dataOut.channelList:
|
|
|
347
|
continue
|
|
347
|
continue
|
|
|
348
|
|
|
348
|
|
|
|
349
|
data[thisChannel,:] = data[thisChannel,:]*self.flip
|
|
349
|
data[thisChannel,:] = data[thisChannel,:]*self.flip
|
|
|
350
|
|
|
350
|
|
|
|
351
|
self.flip *= -1.
|
|
351
|
self.flip *= -1.
|
|
|
352
|
|
|
352
|
|
|
|
353
|
self.dataOut.data = data
|
|
353
|
self.dataOut.data = data
|
|
|
354
|
|
|
354
|
|
|
|
355
|
def setRadarFrequency(self, frequency=None):
|
|
355
|
def setRadarFrequency(self, frequency=None):
|
|
|
356
|
|
|
356
|
|
|
|
357
|
if frequency != None:
|
|
357
|
if frequency != None:
|
|
|
358
|
self.dataOut.frequency = frequency
|
|
358
|
self.dataOut.frequency = frequency
|
|
|
359
|
|
|
359
|
|
|
|
360
|
return 1
|
|
360
|
return 1
|
|
|
361
|
|
|
361
|
|
|
|
362
|
def interpolateHeights(self, topLim, botLim):
|
|
362
|
def interpolateHeights(self, topLim, botLim):
|
|
|
363
|
#69 al 72 para julia
|
|
363
|
#69 al 72 para julia
|
|
|
364
|
#82-84 para meteoros
|
|
364
|
#82-84 para meteoros
|
|
|
365
|
if len(numpy.shape(self.dataOut.data))==2:
|
|
365
|
if len(numpy.shape(self.dataOut.data))==2:
|
|
|
366
|
sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
|
|
366
|
sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
|
|
|
367
|
sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
|
|
367
|
sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
|
|
|
368
|
#self.dataOut.data[:,botLim:limSup+1] = sampInterp
|
|
368
|
#self.dataOut.data[:,botLim:limSup+1] = sampInterp
|
|
|
369
|
self.dataOut.data[:,botLim:topLim+1] = sampInterp
|
|
369
|
self.dataOut.data[:,botLim:topLim+1] = sampInterp
|
|
|
370
|
else:
|
|
370
|
else:
|
|
|
371
|
nHeights = self.dataOut.data.shape[2]
|
|
371
|
nHeights = self.dataOut.data.shape[2]
|
|
|
372
|
x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
|
|
372
|
x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
|
|
|
373
|
y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)]
|
|
373
|
y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)]
|
|
|
374
|
f = interpolate.interp1d(x, y, axis = 2)
|
|
374
|
f = interpolate.interp1d(x, y, axis = 2)
|
|
|
375
|
xnew = numpy.arange(botLim,topLim+1)
|
|
375
|
xnew = numpy.arange(botLim,topLim+1)
|
|
|
376
|
ynew = f(xnew)
|
|
376
|
ynew = f(xnew)
|
|
|
377
|
|
|
377
|
|
|
|
378
|
self.dataOut.data[:,:,botLim:topLim+1] = ynew
|
|
378
|
self.dataOut.data[:,:,botLim:topLim+1] = ynew
|
|
|
379
|
|
|
379
|
|
|
|
380
|
# import collections
|
|
380
|
# import collections
|
|
|
381
|
|
|
381
|
|
|
|
382
|
class CohInt(Operation):
|
|
382
|
class CohInt(Operation):
|
|
|
383
|
|
|
383
|
|
|
|
384
|
isConfig = False
|
|
384
|
isConfig = False
|
|
|
385
|
__profIndex = 0
|
|
385
|
__profIndex = 0
|
|
|
386
|
__byTime = False
|
|
386
|
__byTime = False
|
|
|
387
|
__initime = None
|
|
387
|
__initime = None
|
|
|
388
|
__lastdatatime = None
|
|
388
|
__lastdatatime = None
|
|
|
389
|
__integrationtime = None
|
|
389
|
__integrationtime = None
|
|
|
390
|
__buffer = None
|
|
390
|
__buffer = None
|
|
|
391
|
__bufferStride = []
|
|
391
|
__bufferStride = []
|
|
|
392
|
__dataReady = False
|
|
392
|
__dataReady = False
|
|
|
393
|
__profIndexStride = 0
|
|
393
|
__profIndexStride = 0
|
|
|
394
|
__dataToPutStride = False
|
|
394
|
__dataToPutStride = False
|
|
|
395
|
n = None
|
|
395
|
n = None
|
|
|
396
|
|
|
396
|
|
|
|
397
|
def __init__(self, **kwargs):
|
|
397
|
def __init__(self, **kwargs):
|
|
|
398
|
|
|
398
|
|
|
|
399
|
Operation.__init__(self, **kwargs)
|
|
399
|
Operation.__init__(self, **kwargs)
|
|
|
400
|
|
|
400
|
|
|
|
401
|
# self.isConfig = False
|
|
401
|
# self.isConfig = False
|
|
|
402
|
|
|
402
|
|
|
|
403
|
def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
|
|
403
|
def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
|
|
|
404
|
"""
|
|
404
|
"""
|
|
|
405
|
Set the parameters of the integration class.
|
|
405
|
Set the parameters of the integration class.
|
|
|
406
|
|
|
406
|
|
|
|
407
|
Inputs:
|
|
407
|
Inputs:
|
|
|
408
|
|
|
408
|
|
|
|
409
|
n : Number of coherent integrations
|
|
409
|
n : Number of coherent integrations
|
|
|
410
|
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
|
|
410
|
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
|
|
|
411
|
overlapping :
|
|
411
|
overlapping :
|
|
|
412
|
"""
|
|
412
|
"""
|
|
|
413
|
|
|
413
|
|
|
|
414
|
self.__initime = None
|
|
414
|
self.__initime = None
|
|
|
415
|
self.__lastdatatime = 0
|
|
415
|
self.__lastdatatime = 0
|
|
|
416
|
self.__buffer = None
|
|
416
|
self.__buffer = None
|
|
|
417
|
self.__dataReady = False
|
|
417
|
self.__dataReady = False
|
|
|
418
|
self.byblock = byblock
|
|
418
|
self.byblock = byblock
|
|
|
419
|
self.stride = stride
|
|
419
|
self.stride = stride
|
|
|
420
|
|
|
420
|
|
|
|
421
|
if n == None and timeInterval == None:
|
|
421
|
if n == None and timeInterval == None:
|
|
|
422
|
raise ValueError, "n or timeInterval should be specified ..."
|
|
422
|
raise ValueError, "n or timeInterval should be specified ..."
|
|
|
423
|
|
|
423
|
|
|
|
424
|
if n != None:
|
|
424
|
if n != None:
|
|
|
425
|
self.n = n
|
|
425
|
self.n = n
|
|
|
426
|
self.__byTime = False
|
|
426
|
self.__byTime = False
|
|
|
427
|
else:
|
|
427
|
else:
|
|
|
428
|
self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
|
|
428
|
self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
|
|
|
429
|
self.n = 9999
|
|
429
|
self.n = 9999
|
|
|
430
|
self.__byTime = True
|
|
430
|
self.__byTime = True
|
|
|
431
|
|
|
431
|
|
|
|
432
|
if overlapping:
|
|
432
|
if overlapping:
|
|
|
433
|
self.__withOverlapping = True
|
|
433
|
self.__withOverlapping = True
|
|
|
434
|
self.__buffer = None
|
|
434
|
self.__buffer = None
|
|
|
435
|
else:
|
|
435
|
else:
|
|
|
436
|
self.__withOverlapping = False
|
|
436
|
self.__withOverlapping = False
|
|
|
437
|
self.__buffer = 0
|
|
437
|
self.__buffer = 0
|
|
|
438
|
|
|
438
|
|
|
|
439
|
self.__profIndex = 0
|
|
439
|
self.__profIndex = 0
|
|
|
440
|
|
|
440
|
|
|
|
441
|
def putData(self, data):
|
|
441
|
def putData(self, data):
|
|
|
442
|
|
|
442
|
|
|
|
443
|
"""
|
|
443
|
"""
|
|
|
444
|
Add a profile to the __buffer and increase in one the __profileIndex
|
|
444
|
Add a profile to the __buffer and increase in one the __profileIndex
|
|
|
445
|
|
|
445
|
|
|
|
446
|
"""
|
|
446
|
"""
|
|
|
447
|
|
|
447
|
|
|
|
448
|
if not self.__withOverlapping:
|
|
448
|
if not self.__withOverlapping:
|
|
|
449
|
self.__buffer += data.copy()
|
|
449
|
self.__buffer += data.copy()
|
|
|
450
|
self.__profIndex += 1
|
|
450
|
self.__profIndex += 1
|
|
|
451
|
return
|
|
451
|
return
|
|
|
452
|
|
|
452
|
|
|
|
453
|
#Overlapping data
|
|
453
|
#Overlapping data
|
|
|
454
|
nChannels, nHeis = data.shape
|
|
454
|
nChannels, nHeis = data.shape
|
|
|
455
|
data = numpy.reshape(data, (1, nChannels, nHeis))
|
|
455
|
data = numpy.reshape(data, (1, nChannels, nHeis))
|
|
|
456
|
|
|
456
|
|
|
|
457
|
#If the buffer is empty then it takes the data value
|
|
457
|
#If the buffer is empty then it takes the data value
|
|
|
458
|
if self.__buffer is None:
|
|
458
|
if self.__buffer is None:
|
|
|
459
|
self.__buffer = data
|
|
459
|
self.__buffer = data
|
|
|
460
|
self.__profIndex += 1
|
|
460
|
self.__profIndex += 1
|
|
|
461
|
return
|
|
461
|
return
|
|
|
462
|
|
|
462
|
|
|
|
463
|
#If the buffer length is lower than n then stakcing the data value
|
|
463
|
#If the buffer length is lower than n then stakcing the data value
|
|
|
464
|
if self.__profIndex < self.n:
|
|
464
|
if self.__profIndex < self.n:
|
|
|
465
|
self.__buffer = numpy.vstack((self.__buffer, data))
|
|
465
|
self.__buffer = numpy.vstack((self.__buffer, data))
|
|
|
466
|
self.__profIndex += 1
|
|
466
|
self.__profIndex += 1
|
|
|
467
|
return
|
|
467
|
return
|
|
|
468
|
|
|
468
|
|
|
|
469
|
#If the buffer length is equal to n then replacing the last buffer value with the data value
|
|
469
|
#If the buffer length is equal to n then replacing the last buffer value with the data value
|
|
|
470
|
self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
|
|
470
|
self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
|
|
|
471
|
self.__buffer[self.n-1] = data
|
|
471
|
self.__buffer[self.n-1] = data
|
|
|
472
|
self.__profIndex = self.n
|
|
472
|
self.__profIndex = self.n
|
|
|
473
|
return
|
|
473
|
return
|
|
|
474
|
|
|
474
|
|
|
|
475
|
|
|
475
|
|
|
|
476
|
def pushData(self):
|
|
476
|
def pushData(self):
|
|
|
477
|
"""
|
|
477
|
"""
|
|
|
478
|
Return the sum of the last profiles and the profiles used in the sum.
|
|
478
|
Return the sum of the last profiles and the profiles used in the sum.
|
|
|
479
|
|
|
479
|
|
|
|
480
|
Affected:
|
|
480
|
Affected:
|
|
|
481
|
|
|
481
|
|
|
|
482
|
self.__profileIndex
|
|
482
|
self.__profileIndex
|
|
|
483
|
|
|
483
|
|
|
|
484
|
"""
|
|
484
|
"""
|
|
|
485
|
|
|
485
|
|
|
|
486
|
if not self.__withOverlapping:
|
|
486
|
if not self.__withOverlapping:
|
|
|
487
|
data = self.__buffer
|
|
487
|
data = self.__buffer
|
|
|
488
|
n = self.__profIndex
|
|
488
|
n = self.__profIndex
|
|
|
489
|
|
|
489
|
|
|
|
490
|
self.__buffer = 0
|
|
490
|
self.__buffer = 0
|
|
|
491
|
self.__profIndex = 0
|
|
491
|
self.__profIndex = 0
|
|
|
492
|
|
|
492
|
|
|
|
493
|
return data, n
|
|
493
|
return data, n
|
|
|
494
|
|
|
494
|
|
|
|
495
|
#Integration with Overlapping
|
|
495
|
#Integration with Overlapping
|
|
|
496
|
data = numpy.sum(self.__buffer, axis=0)
|
|
496
|
data = numpy.sum(self.__buffer, axis=0)
|
|
|
497
|
# print data
|
|
497
|
# print data
|
|
|
498
|
# raise
|
|
498
|
# raise
|
|
|
499
|
n = self.__profIndex
|
|
499
|
n = self.__profIndex
|
|
|
500
|
|
|
500
|
|
|
|
501
|
return data, n
|
|
501
|
return data, n
|
|
|
502
|
|
|
502
|
|
|
|
503
|
def byProfiles(self, data):
|
|
503
|
def byProfiles(self, data):
|
|
|
504
|
|
|
504
|
|
|
|
505
|
self.__dataReady = False
|
|
505
|
self.__dataReady = False
|
|
|
506
|
avgdata = None
|
|
506
|
avgdata = None
|
|
|
507
|
# n = None
|
|
507
|
# n = None
|
|
|
508
|
# print data
|
|
508
|
# print data
|
|
|
509
|
# raise
|
|
509
|
# raise
|
|
|
510
|
self.putData(data)
|
|
510
|
self.putData(data)
|
|
|
511
|
|
|
511
|
|
|
|
512
|
if self.__profIndex == self.n:
|
|
512
|
if self.__profIndex == self.n:
|
|
|
513
|
avgdata, n = self.pushData()
|
|
513
|
avgdata, n = self.pushData()
|
|
|
514
|
self.__dataReady = True
|
|
514
|
self.__dataReady = True
|
|
|
515
|
|
|
515
|
|
|
|
516
|
return avgdata
|
|
516
|
return avgdata
|
|
|
517
|
|
|
517
|
|
|
|
518
|
def byTime(self, data, datatime):
|
|
518
|
def byTime(self, data, datatime):
|
|
|
519
|
|
|
519
|
|
|
|
520
|
self.__dataReady = False
|
|
520
|
self.__dataReady = False
|
|
|
521
|
avgdata = None
|
|
521
|
avgdata = None
|
|
|
522
|
n = None
|
|
522
|
n = None
|
|
|
523
|
|
|
523
|
|
|
|
524
|
self.putData(data)
|
|
524
|
self.putData(data)
|
|
|
525
|
|
|
525
|
|
|
|
526
|
if (datatime - self.__initime) >= self.__integrationtime:
|
|
526
|
if (datatime - self.__initime) >= self.__integrationtime:
|
|
|
527
|
avgdata, n = self.pushData()
|
|
527
|
avgdata, n = self.pushData()
|
|
|
528
|
self.n = n
|
|
528
|
self.n = n
|
|
|
529
|
self.__dataReady = True
|
|
529
|
self.__dataReady = True
|
|
|
530
|
|
|
530
|
|
|
|
531
|
return avgdata
|
|
531
|
return avgdata
|
|
|
532
|
|
|
532
|
|
|
|
533
|
def integrateByStride(self, data, datatime):
|
|
533
|
def integrateByStride(self, data, datatime):
|
|
|
534
|
# print data
|
|
534
|
# print data
|
|
|
535
|
if self.__profIndex == 0:
|
|
535
|
if self.__profIndex == 0:
|
|
|
536
|
self.__buffer = [[data.copy(), datatime]]
|
|
536
|
self.__buffer = [[data.copy(), datatime]]
|
|
|
537
|
else:
|
|
537
|
else:
|
|
|
538
|
self.__buffer.append([data.copy(),datatime])
|
|
538
|
self.__buffer.append([data.copy(),datatime])
|
|
|
539
|
self.__profIndex += 1
|
|
539
|
self.__profIndex += 1
|
|
|
540
|
self.__dataReady = False
|
|
540
|
self.__dataReady = False
|
|
|
541
|
|
|
541
|
|
|
|
542
|
if self.__profIndex == self.n * self.stride :
|
|
542
|
if self.__profIndex == self.n * self.stride :
|
|
|
543
|
self.__dataToPutStride = True
|
|
543
|
self.__dataToPutStride = True
|
|
|
544
|
self.__profIndexStride = 0
|
|
544
|
self.__profIndexStride = 0
|
|
|
545
|
self.__profIndex = 0
|
|
545
|
self.__profIndex = 0
|
|
|
546
|
self.__bufferStride = []
|
|
546
|
self.__bufferStride = []
|
|
|
547
|
for i in range(self.stride):
|
|
547
|
for i in range(self.stride):
|
|
|
548
|
current = self.__buffer[i::self.stride]
|
|
548
|
current = self.__buffer[i::self.stride]
|
|
|
549
|
data = numpy.sum([t[0] for t in current], axis=0)
|
|
549
|
data = numpy.sum([t[0] for t in current], axis=0)
|
|
|
550
|
avgdatatime = numpy.average([t[1] for t in current])
|
|
550
|
avgdatatime = numpy.average([t[1] for t in current])
|
|
|
551
|
# print data
|
|
551
|
# print data
|
|
|
552
|
self.__bufferStride.append((data, avgdatatime))
|
|
552
|
self.__bufferStride.append((data, avgdatatime))
|
|
|
553
|
|
|
553
|
|
|
|
554
|
if self.__dataToPutStride:
|
|
554
|
if self.__dataToPutStride:
|
|
|
555
|
self.__dataReady = True
|
|
555
|
self.__dataReady = True
|
|
|
556
|
self.__profIndexStride += 1
|
|
556
|
self.__profIndexStride += 1
|
|
|
557
|
if self.__profIndexStride == self.stride:
|
|
557
|
if self.__profIndexStride == self.stride:
|
|
|
558
|
self.__dataToPutStride = False
|
|
558
|
self.__dataToPutStride = False
|
|
|
559
|
# print self.__bufferStride[self.__profIndexStride - 1]
|
|
559
|
# print self.__bufferStride[self.__profIndexStride - 1]
|
|
|
560
|
# raise
|
|
560
|
# raise
|
|
|
561
|
return self.__bufferStride[self.__profIndexStride - 1]
|
|
561
|
return self.__bufferStride[self.__profIndexStride - 1]
|
|
|
562
|
|
|
562
|
|
|
|
563
|
|
|
563
|
|
|
|
564
|
return None, None
|
|
564
|
return None, None
|
|
|
565
|
|
|
565
|
|
|
|
566
|
def integrate(self, data, datatime=None):
|
|
566
|
def integrate(self, data, datatime=None):
|
|
|
567
|
|
|
567
|
|
|
|
568
|
if self.__initime == None:
|
|
568
|
if self.__initime == None:
|
|
|
569
|
self.__initime = datatime
|
|
569
|
self.__initime = datatime
|
|
|
570
|
|
|
570
|
|
|
|
571
|
if self.__byTime:
|
|
571
|
if self.__byTime:
|
|
|
572
|
avgdata = self.byTime(data, datatime)
|
|
572
|
avgdata = self.byTime(data, datatime)
|
|
|
573
|
else:
|
|
573
|
else:
|
|
|
574
|
avgdata = self.byProfiles(data)
|
|
574
|
avgdata = self.byProfiles(data)
|
|
|
575
|
|
|
575
|
|
|
|
576
|
|
|
576
|
|
|
|
577
|
self.__lastdatatime = datatime
|
|
577
|
self.__lastdatatime = datatime
|
|
|
578
|
|
|
578
|
|
|
|
579
|
if avgdata is None:
|
|
579
|
if avgdata is None:
|
|
|
580
|
return None, None
|
|
580
|
return None, None
|
|
|
581
|
|
|
581
|
|
|
|
582
|
avgdatatime = self.__initime
|
|
582
|
avgdatatime = self.__initime
|
|
|
583
|
|
|
583
|
|
|
|
584
|
deltatime = datatime - self.__lastdatatime
|
|
584
|
deltatime = datatime - self.__lastdatatime
|
|
|
585
|
|
|
585
|
|
|
|
586
|
if not self.__withOverlapping:
|
|
586
|
if not self.__withOverlapping:
|
|
|
587
|
self.__initime = datatime
|
|
587
|
self.__initime = datatime
|
|
|
588
|
else:
|
|
588
|
else:
|
|
|
589
|
self.__initime += deltatime
|
|
589
|
self.__initime += deltatime
|
|
|
590
|
|
|
590
|
|
|
|
591
|
return avgdata, avgdatatime
|
|
591
|
return avgdata, avgdatatime
|
|
|
592
|
|
|
592
|
|
|
|
593
|
def integrateByBlock(self, dataOut):
|
|
593
|
def integrateByBlock(self, dataOut):
|
|
|
594
|
|
|
594
|
|
|
|
595
|
times = int(dataOut.data.shape[1]/self.n)
|
|
595
|
times = int(dataOut.data.shape[1]/self.n)
|
|
|
596
|
avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
|
|
596
|
avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
|
|
|
597
|
|
|
597
|
|
|
|
598
|
id_min = 0
|
|
598
|
id_min = 0
|
|
|
599
|
id_max = self.n
|
|
599
|
id_max = self.n
|
|
|
600
|
|
|
600
|
|
|
|
601
|
for i in range(times):
|
|
601
|
for i in range(times):
|
|
|
602
|
junk = dataOut.data[:,id_min:id_max,:]
|
|
602
|
junk = dataOut.data[:,id_min:id_max,:]
|
|
|
603
|
avgdata[:,i,:] = junk.sum(axis=1)
|
|
603
|
avgdata[:,i,:] = junk.sum(axis=1)
|
|
|
604
|
id_min += self.n
|
|
604
|
id_min += self.n
|
|
|
605
|
id_max += self.n
|
|
605
|
id_max += self.n
|
|
|
606
|
|
|
606
|
|
|
|
607
|
timeInterval = dataOut.ippSeconds*self.n
|
|
607
|
timeInterval = dataOut.ippSeconds*self.n
|
|
|
608
|
avgdatatime = (times - 1) * timeInterval + dataOut.utctime
|
|
608
|
avgdatatime = (times - 1) * timeInterval + dataOut.utctime
|
|
|
609
|
self.__dataReady = True
|
|
609
|
self.__dataReady = True
|
|
|
610
|
return avgdata, avgdatatime
|
|
610
|
return avgdata, avgdatatime
|
|
|
611
|
|
|
611
|
|
|
|
612
|
def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
|
|
612
|
def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
|
|
|
613
|
if not self.isConfig:
|
|
613
|
if not self.isConfig:
|
|
|
614
|
self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
|
|
614
|
self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
|
|
|
615
|
self.isConfig = True
|
|
615
|
self.isConfig = True
|
|
|
616
|
|
|
616
|
|
|
|
617
|
if dataOut.flagDataAsBlock:
|
|
617
|
if dataOut.flagDataAsBlock:
|
|
|
618
|
"""
|
|
618
|
"""
|
|
|
619
|
Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
619
|
Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
|
|
|
620
|
"""
|
|
620
|
"""
|
|
|
621
|
avgdata, avgdatatime = self.integrateByBlock(dataOut)
|
|
621
|
avgdata, avgdatatime = self.integrateByBlock(dataOut)
|
|
|
622
|
dataOut.nProfiles /= self.n
|
|
622
|
dataOut.nProfiles /= self.n
|
|
|
623
|
else:
|
|
623
|
else:
|
|
|
624
|
if stride is None:
|
|
624
|
if stride is None:
|
|
|
625
|
avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
|
|
625
|
avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
|
|
|
626
|
else:
|
|
626
|
else:
|
|
|
627
|
avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
|
|
627
|
avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
|
|
|
628
|
|
|
628
|
|
|
|
629
|
|
|
629
|
|
|
|
630
|
# dataOut.timeInterval *= n
|
|
630
|
# dataOut.timeInterval *= n
|
|
|
631
|
dataOut.flagNoData = True
|
|
631
|
dataOut.flagNoData = True
|
|
|
632
|
|
|
632
|
|
|
|
633
|
if self.__dataReady:
|
|
633
|
if self.__dataReady:
|
|
|
634
|
dataOut.data = avgdata
|
|
634
|
dataOut.data = avgdata
|
|
|
635
|
dataOut.nCohInt *= self.n
|
|
635
|
dataOut.nCohInt *= self.n
|
|
|
636
|
dataOut.utctime = avgdatatime
|
|
636
|
dataOut.utctime = avgdatatime
|
|
|
637
|
# print avgdata, avgdatatime
|
|
637
|
# print avgdata, avgdatatime
|
|
|
638
|
# raise
|
|
638
|
# raise
|
|
|
639
|
# dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
|
|
639
|
# dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
|
|
|
640
|
dataOut.flagNoData = False
|
|
640
|
dataOut.flagNoData = False
|
|
|
641
|
|
|
641
|
|
|
|
642
|
class Decoder(Operation):
|
|
642
|
class Decoder(Operation):
|
|
|
643
|
|
|
643
|
|
|
|
644
|
isConfig = False
|
|
644
|
isConfig = False
|
|
|
645
|
__profIndex = 0
|
|
645
|
__profIndex = 0
|
|
|
646
|
|
|
646
|
|
|
|
647
|
code = None
|
|
647
|
code = None
|
|
|
648
|
|
|
648
|
|
|
|
649
|
nCode = None
|
|
649
|
nCode = None
|
|
|
650
|
nBaud = None
|
|
650
|
nBaud = None
|
|
|
651
|
|
|
651
|
|
|
|
652
|
def __init__(self, **kwargs):
|
|
652
|
def __init__(self, **kwargs):
|
|
|
653
|
|
|
653
|
|
|
|
654
|
Operation.__init__(self, **kwargs)
|
|
654
|
Operation.__init__(self, **kwargs)
|
|
|
655
|
|
|
655
|
|
|
|
656
|
self.times = None
|
|
656
|
self.times = None
|
|
|
657
|
self.osamp = None
|
|
657
|
self.osamp = None
|
|
|
658
|
# self.__setValues = False
|
|
658
|
# self.__setValues = False
|
|
|
659
|
self.isConfig = False
|
|
659
|
self.isConfig = False
|
|
|
660
|
|
|
660
|
|
|
|
661
|
def setup(self, code, osamp, dataOut):
|
|
661
|
def setup(self, code, osamp, dataOut):
|
|
|
662
|
|
|
662
|
|
|
|
663
|
self.__profIndex = 0
|
|
663
|
self.__profIndex = 0
|
|
|
664
|
|
|
664
|
|
|
|
665
|
self.code = code
|
|
665
|
self.code = code
|
|
|
666
|
|
|
666
|
|
|
|
667
|
self.nCode = len(code)
|
|
667
|
self.nCode = len(code)
|
|
|
668
|
self.nBaud = len(code[0])
|
|
668
|
self.nBaud = len(code[0])
|
|
|
669
|
|
|
669
|
|
|
|
670
|
if (osamp != None) and (osamp >1):
|
|
670
|
if (osamp != None) and (osamp >1):
|
|
|
671
|
self.osamp = osamp
|
|
671
|
self.osamp = osamp
|
|
|
672
|
self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
|
|
672
|
self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
|
|
|
673
|
self.nBaud = self.nBaud*self.osamp
|
|
673
|
self.nBaud = self.nBaud*self.osamp
|
|
|
674
|
|
|
674
|
|
|
|
675
|
self.__nChannels = dataOut.nChannels
|
|
675
|
self.__nChannels = dataOut.nChannels
|
|
|
676
|
self.__nProfiles = dataOut.nProfiles
|
|
676
|
self.__nProfiles = dataOut.nProfiles
|
|
|
677
|
self.__nHeis = dataOut.nHeights
|
|
677
|
self.__nHeis = dataOut.nHeights
|
|
|
678
|
|
|
678
|
|
|
|
679
|
if self.__nHeis < self.nBaud:
|
|
679
|
if self.__nHeis < self.nBaud:
|
|
|
680
|
raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
|
|
680
|
raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
|
|
|
681
|
|
|
681
|
|
|
|
682
|
#Frequency
|
|
682
|
#Frequency
|
|
|
683
|
__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
|
|
683
|
__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
|
|
|
684
|
|
|
684
|
|
|
|
685
|
__codeBuffer[:,0:self.nBaud] = self.code
|
|
685
|
__codeBuffer[:,0:self.nBaud] = self.code
|
|
|
686
|
|
|
686
|
|
|
|
687
|
self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
|
|
687
|
self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
|
|
|
688
|
|
|
688
|
|
|
|
689
|
if dataOut.flagDataAsBlock:
|
|
689
|
if dataOut.flagDataAsBlock:
|
|
|
690
|
|
|
690
|
|
|
|
691
|
self.ndatadec = self.__nHeis #- self.nBaud + 1
|
|
691
|
self.ndatadec = self.__nHeis #- self.nBaud + 1
|
|
|
692
|
|
|
692
|
|
|
|
693
|
self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
|
|
693
|
self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
|
|
|
694
|
|
|
694
|
|
|
|
695
|
else:
|
|
695
|
else:
|
|
|
696
|
|
|
696
|
|
|
|
697
|
#Time
|
|
697
|
#Time
|
|
|
698
|
self.ndatadec = self.__nHeis #- self.nBaud + 1
|
|
698
|
self.ndatadec = self.__nHeis #- self.nBaud + 1
|
|
|
699
|
|
|
699
|
|
|
|
700
|
self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
|
|
700
|
self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
|
|
|
701
|
|
|
701
|
|
|
|
702
|
def __convolutionInFreq(self, data):
|
|
702
|
def __convolutionInFreq(self, data):
|
|
|
703
|
|
|
703
|
|
|
|
704
|
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
|
|
704
|
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
|
|
|
705
|
|
|
705
|
|
|
|
706
|
fft_data = numpy.fft.fft(data, axis=1)
|
|
706
|
fft_data = numpy.fft.fft(data, axis=1)
|
|
|
707
|
|
|
707
|
|
|
|
708
|
conv = fft_data*fft_code
|
|
708
|
conv = fft_data*fft_code
|
|
|
709
|
|
|
709
|
|
|
|
710
|
data = numpy.fft.ifft(conv,axis=1)
|
|
710
|
data = numpy.fft.ifft(conv,axis=1)
|
|
|
711
|
|
|
711
|
|
|
|
712
|
return data
|
|
712
|
return data
|
|
|
713
|
|
|
713
|
|
|
|
714
|
def __convolutionInFreqOpt(self, data):
|
|
714
|
def __convolutionInFreqOpt(self, data):
|
|
|
715
|
|
|
715
|
|
|
|
716
|
raise NotImplementedError
|
|
716
|
raise NotImplementedError
|
|
|
717
|
|
|
717
|
|
|
|
718
|
def __convolutionInTime(self, data):
|
|
718
|
def __convolutionInTime(self, data):
|
|
|
719
|
|
|
719
|
|
|
|
720
|
code = self.code[self.__profIndex]
|
|
720
|
code = self.code[self.__profIndex]
|
|
|
721
|
for i in range(self.__nChannels):
|
|
721
|
for i in range(self.__nChannels):
|
|
|
722
|
self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
|
|
722
|
self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
|
|
|
723
|
|
|
723
|
|
|
|
724
|
return self.datadecTime
|
|
724
|
return self.datadecTime
|
|
|
725
|
|
|
725
|
|
|
|
726
|
def __convolutionByBlockInTime(self, data):
|
|
726
|
def __convolutionByBlockInTime(self, data):
|
|
|
727
|
|
|
727
|
|
|
|
728
|
repetitions = self.__nProfiles / self.nCode
|
|
728
|
repetitions = self.__nProfiles / self.nCode
|
|
|
729
|
|
|
729
|
|
|
|
730
|
junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
|
|
730
|
junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
|
|
|
731
|
junk = junk.flatten()
|
|
731
|
junk = junk.flatten()
|
|
|
732
|
code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
|
|
732
|
code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
|
|
|
733
|
profilesList = xrange(self.__nProfiles)
|
|
733
|
profilesList = xrange(self.__nProfiles)
|
|
|
734
|
|
|
734
|
|
|
|
735
|
for i in range(self.__nChannels):
|
|
735
|
for i in range(self.__nChannels):
|
|
|
736
|
for j in profilesList:
|
|
736
|
for j in profilesList:
|
|
|
737
|
self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
|
|
737
|
self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
|
|
|
738
|
return self.datadecTime
|
|
738
|
return self.datadecTime
|
|
|
739
|
|
|
739
|
|
|
|
740
|
def __convolutionByBlockInFreq(self, data):
|
|
740
|
def __convolutionByBlockInFreq(self, data):
|
|
|
741
|
|
|
741
|
|
|
|
742
|
raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
|
|
742
|
raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
|
|
|
743
|
|
|
743
|
|
|
|
744
|
|
|
744
|
|
|
|
745
|
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
|
|
745
|
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
|
|
|
746
|
|
|
746
|
|
|
|
747
|
fft_data = numpy.fft.fft(data, axis=2)
|
|
747
|
fft_data = numpy.fft.fft(data, axis=2)
|
|
|
748
|
|
|
748
|
|
|
|
749
|
conv = fft_data*fft_code
|
|
749
|
conv = fft_data*fft_code
|
|
|
750
|
|
|
750
|
|
|
|
751
|
data = numpy.fft.ifft(conv,axis=2)
|
|
751
|
data = numpy.fft.ifft(conv,axis=2)
|
|
|
752
|
|
|
752
|
|
|
|
753
|
return data
|
|
753
|
return data
|
|
|
754
|
|
|
754
|
|
|
|
755
|
|
|
755
|
|
|
|
756
|
def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
|
|
756
|
def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
|
|
|
757
|
|
|
757
|
|
|
|
758
|
if dataOut.flagDecodeData:
|
|
758
|
if dataOut.flagDecodeData:
|
|
|
759
|
print "This data is already decoded, recoding again ..."
|
|
759
|
print "This data is already decoded, recoding again ..."
|
|
|
760
|
|
|
760
|
|
|
|
761
|
if not self.isConfig:
|
|
761
|
if not self.isConfig:
|
|
|
762
|
|
|
762
|
|
|
|
763
|
if code is None:
|
|
763
|
if code is None:
|
|
|
764
|
if dataOut.code is None:
|
|
764
|
if dataOut.code is None:
|
|
|
765
|
raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
|
|
765
|
raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
|
|
|
766
|
|
|
766
|
|
|
|
767
|
code = dataOut.code
|
|
767
|
code = dataOut.code
|
|
|
768
|
else:
|
|
768
|
else:
|
|
|
769
|
code = numpy.array(code).reshape(nCode,nBaud)
|
|
769
|
code = numpy.array(code).reshape(nCode,nBaud)
|
|
|
770
|
self.setup(code, osamp, dataOut)
|
|
770
|
self.setup(code, osamp, dataOut)
|
|
|
771
|
|
|
771
|
|
|
|
772
|
self.isConfig = True
|
|
772
|
self.isConfig = True
|
|
|
773
|
|
|
773
|
|
|
|
774
|
if mode == 3:
|
|
774
|
if mode == 3:
|
|
|
775
|
sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
|
|
775
|
sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
|
|
|
776
|
|
|
776
|
|
|
|
777
|
if times != None:
|
|
777
|
if times != None:
|
|
|
778
|
sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
|
|
778
|
sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
|
|
|
779
|
|
|
779
|
|
|
|
780
|
if self.code is None:
|
|
780
|
if self.code is None:
|
|
|
781
|
print "Fail decoding: Code is not defined."
|
|
781
|
print "Fail decoding: Code is not defined."
|
|
|
782
|
return
|
|
782
|
return
|
|
|
783
|
|
|
783
|
|
|
|
784
|
self.__nProfiles = dataOut.nProfiles
|
|
784
|
self.__nProfiles = dataOut.nProfiles
|
|
|
785
|
datadec = None
|
|
785
|
datadec = None
|
|
|
786
|
|
|
786
|
|
|
|
787
|
if mode == 3:
|
|
787
|
if mode == 3:
|
|
|
788
|
mode = 0
|
|
788
|
mode = 0
|
|
|
789
|
|
|
789
|
|
|
|
790
|
if dataOut.flagDataAsBlock:
|
|
790
|
if dataOut.flagDataAsBlock:
|
|
|
791
|
"""
|
|
791
|
"""
|
|
|
792
|
Decoding when data have been read as block,
|
|
792
|
Decoding when data have been read as block,
|
|
|
793
|
"""
|
|
793
|
"""
|
|
|
794
|
|
|
794
|
|
|
|
795
|
if mode == 0:
|
|
795
|
if mode == 0:
|
|
|
796
|
datadec = self.__convolutionByBlockInTime(dataOut.data)
|
|
796
|
datadec = self.__convolutionByBlockInTime(dataOut.data)
|
|
|
797
|
if mode == 1:
|
|
797
|
if mode == 1:
|
|
|
798
|
datadec = self.__convolutionByBlockInFreq(dataOut.data)
|
|
798
|
datadec = self.__convolutionByBlockInFreq(dataOut.data)
|
|
|
799
|
else:
|
|
799
|
else:
|
|
|
800
|
"""
|
|
800
|
"""
|
|
|
801
|
Decoding when data have been read profile by profile
|
|
801
|
Decoding when data have been read profile by profile
|
|
|
802
|
"""
|
|
802
|
"""
|
|
|
803
|
if mode == 0:
|
|
803
|
if mode == 0:
|
|
|
804
|
datadec = self.__convolutionInTime(dataOut.data)
|
|
804
|
datadec = self.__convolutionInTime(dataOut.data)
|
|
|
805
|
|
|
805
|
|
|
|
806
|
if mode == 1:
|
|
806
|
if mode == 1:
|
|
|
807
|
datadec = self.__convolutionInFreq(dataOut.data)
|
|
807
|
datadec = self.__convolutionInFreq(dataOut.data)
|
|
|
808
|
|
|
808
|
|
|
|
809
|
if mode == 2:
|
|
809
|
if mode == 2:
|
|
|
810
|
datadec = self.__convolutionInFreqOpt(dataOut.data)
|
|
810
|
datadec = self.__convolutionInFreqOpt(dataOut.data)
|
|
|
811
|
|
|
811
|
|
|
|
812
|
if datadec is None:
|
|
812
|
if datadec is None:
|
|
|
813
|
raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
|
|
813
|
raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
|
|
|
814
|
|
|
814
|
|
|
|
815
|
dataOut.code = self.code
|
|
815
|
dataOut.code = self.code
|
|
|
816
|
dataOut.nCode = self.nCode
|
|
816
|
dataOut.nCode = self.nCode
|
|
|
817
|
dataOut.nBaud = self.nBaud
|
|
817
|
dataOut.nBaud = self.nBaud
|
|
|
818
|
|
|
818
|
|
|
|
819
|
dataOut.data = datadec
|
|
819
|
dataOut.data = datadec
|
|
|
820
|
|
|
820
|
|
|
|
821
|
dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
|
|
821
|
dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
|
|
|
822
|
|
|
822
|
|
|
|
823
|
dataOut.flagDecodeData = True #asumo q la data esta decodificada
|
|
823
|
dataOut.flagDecodeData = True #asumo q la data esta decodificada
|
|
|
824
|
|
|
824
|
|
|
|
825
|
if self.__profIndex == self.nCode-1:
|
|
825
|
if self.__profIndex == self.nCode-1:
|
|
|
826
|
self.__profIndex = 0
|
|
826
|
self.__profIndex = 0
|
|
|
827
|
return 1
|
|
827
|
return 1
|
|
|
828
|
|
|
828
|
|
|
|
829
|
self.__profIndex += 1
|
|
829
|
self.__profIndex += 1
|
|
|
830
|
|
|
830
|
|
|
|
831
|
return 1
|
|
831
|
return 1
|
|
|
832
|
# dataOut.flagDeflipData = True #asumo q la data no esta sin flip
|
|
832
|
# dataOut.flagDeflipData = True #asumo q la data no esta sin flip
|
|
|
833
|
|
|
833
|
|
|
|
834
|
|
|
834
|
|
|
|
835
|
class ProfileConcat(Operation):
|
|
835
|
class ProfileConcat(Operation):
|
|
|
836
|
|
|
836
|
|
|
|
837
|
isConfig = False
|
|
837
|
isConfig = False
|
|
|
838
|
buffer = None
|
|
838
|
buffer = None
|
|
|
839
|
concat_m =None
|
|
839
|
concat_m =None
|
|
|
840
|
|
|
840
|
|
|
|
841
|
def __init__(self, **kwargs):
|
|
841
|
def __init__(self, **kwargs):
|
|
|
842
|
|
|
842
|
|
|
|
843
|
Operation.__init__(self, **kwargs)
|
|
843
|
Operation.__init__(self, **kwargs)
|
|
|
844
|
self.profileIndex = 0
|
|
844
|
self.profileIndex = 0
|
|
|
845
|
|
|
845
|
|
|
|
846
|
def reset(self):
|
|
846
|
def reset(self):
|
|
|
847
|
self.buffer = numpy.zeros_like(self.buffer)
|
|
847
|
self.buffer = numpy.zeros_like(self.buffer)
|
|
|
848
|
self.start_index = 0
|
|
848
|
self.start_index = 0
|
|
|
849
|
self.times = 1
|
|
849
|
self.times = 1
|
|
|
850
|
|
|
850
|
|
|
|
851
|
def setup(self, data, m, n=1):
|
|
851
|
def setup(self, data, m, n=1):
|
|
|
852
|
self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
|
|
852
|
self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
|
|
|
853
|
self.nHeights = data.shape[1]#.nHeights
|
|
853
|
self.nHeights = data.shape[1]#.nHeights
|
|
|
854
|
self.start_index = 0
|
|
854
|
self.start_index = 0
|
|
|
855
|
self.times = 1
|
|
855
|
self.times = 1
|
|
|
856
|
|
|
856
|
|
|
|
857
|
def concat(self, data):
|
|
857
|
def concat(self, data):
|
|
|
858
|
|
|
858
|
|
|
|
859
|
self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
|
|
859
|
self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
|
|
|
860
|
self.start_index = self.start_index + self.nHeights
|
|
860
|
self.start_index = self.start_index + self.nHeights
|
|
|
861
|
|
|
861
|
|
|
|
862
|
def run(self, dataOut, m):
|
|
862
|
def run(self, dataOut, m):
|
|
|
863
|
|
|
863
|
|
|
|
864
|
self.concat_m= m
|
|
864
|
self.concat_m= m
|
|
|
865
|
dataOut.flagNoData = True
|
|
865
|
dataOut.flagNoData = True
|
|
|
866
|
|
|
866
|
|
|
|
867
|
if not self.isConfig:
|
|
867
|
if not self.isConfig:
|
|
|
868
|
self.setup(dataOut.data, m, 1)
|
|
868
|
self.setup(dataOut.data, m, 1)
|
|
|
869
|
self.isConfig = True
|
|
869
|
self.isConfig = True
|
|
|
870
|
|
|
870
|
|
|
|
871
|
if dataOut.flagDataAsBlock:
|
|
871
|
if dataOut.flagDataAsBlock:
|
|
|
872
|
raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
|
|
872
|
raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
|
|
|
873
|
|
|
873
|
|
|
|
874
|
else:
|
|
874
|
else:
|
|
|
875
|
self.concat(dataOut.data)
|
|
875
|
self.concat(dataOut.data)
|
|
|
876
|
self.times += 1
|
|
876
|
self.times += 1
|
|
|
877
|
if self.times > m:
|
|
877
|
if self.times > m:
|
|
|
878
|
dataOut.data = self.buffer
|
|
878
|
dataOut.data = self.buffer
|
|
|
879
|
self.reset()
|
|
879
|
self.reset()
|
|
|
880
|
dataOut.flagNoData = False
|
|
880
|
dataOut.flagNoData = False
|
|
|
881
|
# se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
|
|
881
|
# se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
|
|
|
882
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
882
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
883
|
xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
|
|
883
|
xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
|
|
|
884
|
dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
|
|
884
|
dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
|
|
|
885
|
dataOut.ippSeconds *= m
|
|
885
|
dataOut.ippSeconds *= m
|
|
|
886
|
dataOut.concat_m = int(m)
|
|
886
|
dataOut.concat_m = int(m)
|
|
|
887
|
|
|
887
|
|
|
|
888
|
class ProfileSelector(Operation):
|
|
888
|
class ProfileSelector(Operation):
|
|
|
889
|
|
|
889
|
|
|
|
890
|
profileIndex = None
|
|
890
|
profileIndex = None
|
|
|
891
|
# Tamanho total de los perfiles
|
|
891
|
# Tamanho total de los perfiles
|
|
|
892
|
nProfiles = None
|
|
892
|
nProfiles = None
|
|
|
893
|
|
|
893
|
|
|
|
894
|
def __init__(self, **kwargs):
|
|
894
|
def __init__(self, **kwargs):
|
|
|
895
|
|
|
895
|
|
|
|
896
|
Operation.__init__(self, **kwargs)
|
|
896
|
Operation.__init__(self, **kwargs)
|
|
|
897
|
self.profileIndex = 0
|
|
897
|
self.profileIndex = 0
|
|
|
898
|
|
|
898
|
|
|
|
899
|
def incProfileIndex(self):
|
|
899
|
def incProfileIndex(self):
|
|
|
900
|
|
|
900
|
|
|
|
901
|
self.profileIndex += 1
|
|
901
|
self.profileIndex += 1
|
|
|
902
|
|
|
902
|
|
|
|
903
|
if self.profileIndex >= self.nProfiles:
|
|
903
|
if self.profileIndex >= self.nProfiles:
|
|
|
904
|
self.profileIndex = 0
|
|
904
|
self.profileIndex = 0
|
|
|
905
|
|
|
905
|
|
|
|
906
|
def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
|
|
906
|
def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
|
|
|
907
|
|
|
907
|
|
|
|
908
|
if profileIndex < minIndex:
|
|
908
|
if profileIndex < minIndex:
|
|
|
909
|
return False
|
|
909
|
return False
|
|
|
910
|
|
|
910
|
|
|
|
911
|
if profileIndex > maxIndex:
|
|
911
|
if profileIndex > maxIndex:
|
|
|
912
|
return False
|
|
912
|
return False
|
|
|
913
|
|
|
913
|
|
|
|
914
|
return True
|
|
914
|
return True
|
|
|
915
|
|
|
915
|
|
|
|
916
|
def isThisProfileInList(self, profileIndex, profileList):
|
|
916
|
def isThisProfileInList(self, profileIndex, profileList):
|
|
|
917
|
|
|
917
|
|
|
|
918
|
if profileIndex not in profileList:
|
|
918
|
if profileIndex not in profileList:
|
|
|
919
|
return False
|
|
919
|
return False
|
|
|
920
|
|
|
920
|
|
|
|
921
|
return True
|
|
921
|
return True
|
|
|
922
|
|
|
922
|
|
|
|
923
|
def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
|
|
923
|
def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
|
|
|
924
|
|
|
924
|
|
|
|
925
|
"""
|
|
925
|
"""
|
|
|
926
|
ProfileSelector:
|
|
926
|
ProfileSelector:
|
|
|
927
|
|
|
927
|
|
|
|
928
|
Inputs:
|
|
928
|
Inputs:
|
|
|
929
|
profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
|
|
929
|
profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
|
|
|
930
|
|
|
930
|
|
|
|
931
|
profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
|
|
931
|
profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
|
|
|
932
|
|
|
932
|
|
|
|
933
|
rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
|
|
933
|
rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
|
|
|
934
|
|
|
934
|
|
|
|
935
|
"""
|
|
935
|
"""
|
|
|
936
|
#print "HOLA MUNDO CRUEL"
|
|
936
|
#print "HOLA MUNDO CRUEL"
|
|
|
937
|
if rangeList is not None:
|
|
937
|
if rangeList is not None:
|
|
|
938
|
if type(rangeList[0]) not in (tuple, list):
|
|
938
|
if type(rangeList[0]) not in (tuple, list):
|
|
|
939
|
rangeList = [rangeList]
|
|
939
|
rangeList = [rangeList]
|
|
|
940
|
|
|
940
|
|
|
|
941
|
dataOut.flagNoData = True
|
|
941
|
dataOut.flagNoData = True
|
|
|
942
|
|
|
942
|
|
|
|
943
|
if dataOut.flagDataAsBlock:
|
|
943
|
if dataOut.flagDataAsBlock:
|
|
|
944
|
"""
|
|
944
|
"""
|
|
|
945
|
data dimension = [nChannels, nProfiles, nHeis]
|
|
945
|
data dimension = [nChannels, nProfiles, nHeis]
|
|
|
946
|
"""
|
|
946
|
"""
|
|
|
947
|
if profileList != None:
|
|
947
|
if profileList != None:
|
|
|
948
|
dataOut.data = dataOut.data[:,profileList,:]
|
|
948
|
dataOut.data = dataOut.data[:,profileList,:]
|
|
|
949
|
|
|
949
|
|
|
|
950
|
if profileRangeList != None:
|
|
950
|
if profileRangeList != None:
|
|
|
951
|
minIndex = profileRangeList[0]
|
|
951
|
minIndex = profileRangeList[0]
|
|
|
952
|
maxIndex = profileRangeList[1]
|
|
952
|
maxIndex = profileRangeList[1]
|
|
|
953
|
profileList = range(minIndex, maxIndex+1)
|
|
953
|
profileList = range(minIndex, maxIndex+1)
|
|
|
954
|
|
|
954
|
|
|
|
955
|
dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
|
|
955
|
dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
|
|
|
956
|
|
|
956
|
|
|
|
957
|
if rangeList != None:
|
|
957
|
if rangeList != None:
|
|
|
958
|
|
|
958
|
|
|
|
959
|
profileList = []
|
|
959
|
profileList = []
|
|
|
960
|
|
|
960
|
|
|
|
961
|
for thisRange in rangeList:
|
|
961
|
for thisRange in rangeList:
|
|
|
962
|
minIndex = thisRange[0]
|
|
962
|
minIndex = thisRange[0]
|
|
|
963
|
maxIndex = thisRange[1]
|
|
963
|
maxIndex = thisRange[1]
|
|
|
964
|
|
|
964
|
|
|
|
965
|
profileList.extend(range(minIndex, maxIndex+1))
|
|
965
|
profileList.extend(range(minIndex, maxIndex+1))
|
|
|
966
|
|
|
966
|
|
|
|
967
|
dataOut.data = dataOut.data[:,profileList,:]
|
|
967
|
dataOut.data = dataOut.data[:,profileList,:]
|
|
|
968
|
|
|
968
|
|
|
|
969
|
dataOut.nProfiles = len(profileList)
|
|
969
|
dataOut.nProfiles = len(profileList)
|
|
|
970
|
dataOut.profileIndex = dataOut.nProfiles - 1
|
|
970
|
dataOut.profileIndex = dataOut.nProfiles - 1
|
|
|
971
|
dataOut.flagNoData = False
|
|
971
|
dataOut.flagNoData = False
|
|
|
972
|
|
|
972
|
|
|
|
973
|
return True
|
|
973
|
return True
|
|
|
974
|
|
|
974
|
|
|
|
975
|
"""
|
|
975
|
"""
|
|
|
976
|
data dimension = [nChannels, nHeis]
|
|
976
|
data dimension = [nChannels, nHeis]
|
|
|
977
|
"""
|
|
977
|
"""
|
|
|
978
|
|
|
978
|
|
|
|
979
|
if profileList != None:
|
|
979
|
if profileList != None:
|
|
|
980
|
|
|
980
|
|
|
|
981
|
if self.isThisProfileInList(dataOut.profileIndex, profileList):
|
|
981
|
if self.isThisProfileInList(dataOut.profileIndex, profileList):
|
|
|
982
|
|
|
982
|
|
|
|
983
|
self.nProfiles = len(profileList)
|
|
983
|
self.nProfiles = len(profileList)
|
|
|
984
|
dataOut.nProfiles = self.nProfiles
|
|
984
|
dataOut.nProfiles = self.nProfiles
|
|
|
985
|
dataOut.profileIndex = self.profileIndex
|
|
985
|
dataOut.profileIndex = self.profileIndex
|
|
|
986
|
dataOut.flagNoData = False
|
|
986
|
dataOut.flagNoData = False
|
|
|
987
|
|
|
987
|
|
|
|
988
|
self.incProfileIndex()
|
|
988
|
self.incProfileIndex()
|
|
|
989
|
return True
|
|
989
|
return True
|
|
|
990
|
|
|
990
|
|
|
|
991
|
if profileRangeList != None:
|
|
991
|
if profileRangeList != None:
|
|
|
992
|
|
|
992
|
|
|
|
993
|
minIndex = profileRangeList[0]
|
|
993
|
minIndex = profileRangeList[0]
|
|
|
994
|
maxIndex = profileRangeList[1]
|
|
994
|
maxIndex = profileRangeList[1]
|
|
|
995
|
|
|
995
|
|
|
|
996
|
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
|
|
996
|
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
|
|
|
997
|
|
|
997
|
|
|
|
998
|
self.nProfiles = maxIndex - minIndex + 1
|
|
998
|
self.nProfiles = maxIndex - minIndex + 1
|
|
|
999
|
dataOut.nProfiles = self.nProfiles
|
|
999
|
dataOut.nProfiles = self.nProfiles
|
|
|
1000
|
dataOut.profileIndex = self.profileIndex
|
|
1000
|
dataOut.profileIndex = self.profileIndex
|
|
|
1001
|
dataOut.flagNoData = False
|
|
1001
|
dataOut.flagNoData = False
|
|
|
1002
|
|
|
1002
|
|
|
|
1003
|
self.incProfileIndex()
|
|
1003
|
self.incProfileIndex()
|
|
|
1004
|
return True
|
|
1004
|
return True
|
|
|
1005
|
|
|
1005
|
|
|
|
1006
|
if rangeList != None:
|
|
1006
|
if rangeList != None:
|
|
|
1007
|
|
|
1007
|
|
|
|
1008
|
nProfiles = 0
|
|
1008
|
nProfiles = 0
|
|
|
1009
|
|
|
1009
|
|
|
|
1010
|
for thisRange in rangeList:
|
|
1010
|
for thisRange in rangeList:
|
|
|
1011
|
minIndex = thisRange[0]
|
|
1011
|
minIndex = thisRange[0]
|
|
|
1012
|
maxIndex = thisRange[1]
|
|
1012
|
maxIndex = thisRange[1]
|
|
|
1013
|
|
|
1013
|
|
|
|
1014
|
nProfiles += maxIndex - minIndex + 1
|
|
1014
|
nProfiles += maxIndex - minIndex + 1
|
|
|
1015
|
|
|
1015
|
|
|
|
1016
|
for thisRange in rangeList:
|
|
1016
|
for thisRange in rangeList:
|
|
|
1017
|
|
|
1017
|
|
|
|
1018
|
minIndex = thisRange[0]
|
|
1018
|
minIndex = thisRange[0]
|
|
|
1019
|
maxIndex = thisRange[1]
|
|
1019
|
maxIndex = thisRange[1]
|
|
|
1020
|
|
|
1020
|
|
|
|
1021
|
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
|
|
1021
|
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
|
|
|
1022
|
|
|
1022
|
|
|
|
1023
|
self.nProfiles = nProfiles
|
|
1023
|
self.nProfiles = nProfiles
|
|
|
1024
|
dataOut.nProfiles = self.nProfiles
|
|
1024
|
dataOut.nProfiles = self.nProfiles
|
|
|
1025
|
dataOut.profileIndex = self.profileIndex
|
|
1025
|
dataOut.profileIndex = self.profileIndex
|
|
|
1026
|
dataOut.flagNoData = False
|
|
1026
|
dataOut.flagNoData = False
|
|
|
1027
|
|
|
1027
|
|
|
|
1028
|
self.incProfileIndex()
|
|
1028
|
self.incProfileIndex()
|
|
|
1029
|
|
|
1029
|
|
|
|
1030
|
break
|
|
1030
|
break
|
|
|
1031
|
|
|
1031
|
|
|
|
1032
|
return True
|
|
1032
|
return True
|
|
|
1033
|
|
|
1033
|
|
|
|
1034
|
|
|
1034
|
|
|
|
1035
|
if beam != None: #beam is only for AMISR data
|
|
1035
|
if beam != None: #beam is only for AMISR data
|
|
|
1036
|
if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
|
|
1036
|
if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
|
|
|
1037
|
dataOut.flagNoData = False
|
|
1037
|
dataOut.flagNoData = False
|
|
|
1038
|
dataOut.profileIndex = self.profileIndex
|
|
1038
|
dataOut.profileIndex = self.profileIndex
|
|
|
1039
|
|
|
1039
|
|
|
|
1040
|
self.incProfileIndex()
|
|
1040
|
self.incProfileIndex()
|
|
|
1041
|
|
|
1041
|
|
|
|
1042
|
return True
|
|
1042
|
return True
|
|
|
1043
|
|
|
1043
|
|
|
|
1044
|
raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
|
|
1044
|
raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
|
|
|
1045
|
|
|
1045
|
|
|
|
1046
|
return False
|
|
1046
|
return False
|
|
|
1047
|
|
|
1047
|
|
|
|
1048
|
class Reshaper(Operation):
|
|
1048
|
class Reshaper(Operation):
|
|
|
1049
|
|
|
1049
|
|
|
|
1050
|
def __init__(self, **kwargs):
|
|
1050
|
def __init__(self, **kwargs):
|
|
|
1051
|
|
|
1051
|
|
|
|
1052
|
Operation.__init__(self, **kwargs)
|
|
1052
|
Operation.__init__(self, **kwargs)
|
|
|
1053
|
|
|
1053
|
|
|
|
1054
|
self.__buffer = None
|
|
1054
|
self.__buffer = None
|
|
|
1055
|
self.__nitems = 0
|
|
1055
|
self.__nitems = 0
|
|
|
1056
|
|
|
1056
|
|
|
|
1057
|
def __appendProfile(self, dataOut, nTxs):
|
|
1057
|
def __appendProfile(self, dataOut, nTxs):
|
|
|
1058
|
|
|
1058
|
|
|
|
1059
|
if self.__buffer is None:
|
|
1059
|
if self.__buffer is None:
|
|
|
1060
|
shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
|
|
1060
|
shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
|
|
|
1061
|
self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
|
|
1061
|
self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
|
|
|
1062
|
|
|
1062
|
|
|
|
1063
|
ini = dataOut.nHeights * self.__nitems
|
|
1063
|
ini = dataOut.nHeights * self.__nitems
|
|
|
1064
|
end = ini + dataOut.nHeights
|
|
1064
|
end = ini + dataOut.nHeights
|
|
|
1065
|
|
|
1065
|
|
|
|
1066
|
self.__buffer[:, ini:end] = dataOut.data
|
|
1066
|
self.__buffer[:, ini:end] = dataOut.data
|
|
|
1067
|
|
|
1067
|
|
|
|
1068
|
self.__nitems += 1
|
|
1068
|
self.__nitems += 1
|
|
|
1069
|
|
|
1069
|
|
|
|
1070
|
return int(self.__nitems*nTxs)
|
|
1070
|
return int(self.__nitems*nTxs)
|
|
|
1071
|
|
|
1071
|
|
|
|
1072
|
def __getBuffer(self):
|
|
1072
|
def __getBuffer(self):
|
|
|
1073
|
|
|
1073
|
|
|
|
1074
|
if self.__nitems == int(1./self.__nTxs):
|
|
1074
|
if self.__nitems == int(1./self.__nTxs):
|
|
|
1075
|
|
|
1075
|
|
|
|
1076
|
self.__nitems = 0
|
|
1076
|
self.__nitems = 0
|
|
|
1077
|
|
|
1077
|
|
|
|
1078
|
return self.__buffer.copy()
|
|
1078
|
return self.__buffer.copy()
|
|
|
1079
|
|
|
1079
|
|
|
|
1080
|
return None
|
|
1080
|
return None
|
|
|
1081
|
|
|
1081
|
|
|
|
1082
|
def __checkInputs(self, dataOut, shape, nTxs):
|
|
1082
|
def __checkInputs(self, dataOut, shape, nTxs):
|
|
|
1083
|
|
|
1083
|
|
|
|
1084
|
if shape is None and nTxs is None:
|
|
1084
|
if shape is None and nTxs is None:
|
|
|
1085
|
raise ValueError, "Reshaper: shape of factor should be defined"
|
|
1085
|
raise ValueError, "Reshaper: shape of factor should be defined"
|
|
|
1086
|
|
|
1086
|
|
|
|
1087
|
if nTxs:
|
|
1087
|
if nTxs:
|
|
|
1088
|
if nTxs < 0:
|
|
1088
|
if nTxs < 0:
|
|
|
1089
|
raise ValueError, "nTxs should be greater than 0"
|
|
1089
|
raise ValueError, "nTxs should be greater than 0"
|
|
|
1090
|
|
|
1090
|
|
|
|
1091
|
if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
|
|
1091
|
if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
|
|
|
1092
|
raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
|
|
1092
|
raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
|
|
|
1093
|
|
|
1093
|
|
|
|
1094
|
shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
|
|
1094
|
shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
|
|
|
1095
|
|
|
1095
|
|
|
|
1096
|
return shape, nTxs
|
|
1096
|
return shape, nTxs
|
|
|
1097
|
|
|
1097
|
|
|
|
1098
|
if len(shape) != 2 and len(shape) != 3:
|
|
1098
|
if len(shape) != 2 and len(shape) != 3:
|
|
|
1099
|
raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
|
|
1099
|
raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
|
|
|
1100
|
|
|
1100
|
|
|
|
1101
|
if len(shape) == 2:
|
|
1101
|
if len(shape) == 2:
|
|
|
1102
|
shape_tuple = [dataOut.nChannels]
|
|
1102
|
shape_tuple = [dataOut.nChannels]
|
|
|
1103
|
shape_tuple.extend(shape)
|
|
1103
|
shape_tuple.extend(shape)
|
|
|
1104
|
else:
|
|
1104
|
else:
|
|
|
1105
|
shape_tuple = list(shape)
|
|
1105
|
shape_tuple = list(shape)
|
|
|
1106
|
|
|
1106
|
|
|
|
1107
|
nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
|
|
1107
|
nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
|
|
|
1108
|
|
|
1108
|
|
|
|
1109
|
return shape_tuple, nTxs
|
|
1109
|
return shape_tuple, nTxs
|
|
|
1110
|
|
|
1110
|
|
|
|
1111
|
def run(self, dataOut, shape=None, nTxs=None):
|
|
1111
|
def run(self, dataOut, shape=None, nTxs=None):
|
|
|
1112
|
|
|
1112
|
|
|
|
1113
|
shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
|
|
1113
|
shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
|
|
|
1114
|
|
|
1114
|
|
|
|
1115
|
dataOut.flagNoData = True
|
|
1115
|
dataOut.flagNoData = True
|
|
|
1116
|
profileIndex = None
|
|
1116
|
profileIndex = None
|
|
|
1117
|
|
|
1117
|
|
|
|
1118
|
if dataOut.flagDataAsBlock:
|
|
1118
|
if dataOut.flagDataAsBlock:
|
|
|
1119
|
|
|
1119
|
|
|
|
1120
|
dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
|
|
1120
|
dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
|
|
|
1121
|
dataOut.flagNoData = False
|
|
1121
|
dataOut.flagNoData = False
|
|
|
1122
|
|
|
1122
|
|
|
|
1123
|
profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
|
|
1123
|
profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
|
|
|
1124
|
|
|
1124
|
|
|
|
1125
|
else:
|
|
1125
|
else:
|
|
|
1126
|
|
|
1126
|
|
|
|
1127
|
if self.__nTxs < 1:
|
|
1127
|
if self.__nTxs < 1:
|
|
|
1128
|
|
|
1128
|
|
|
|
1129
|
self.__appendProfile(dataOut, self.__nTxs)
|
|
1129
|
self.__appendProfile(dataOut, self.__nTxs)
|
|
|
1130
|
new_data = self.__getBuffer()
|
|
1130
|
new_data = self.__getBuffer()
|
|
|
1131
|
|
|
1131
|
|
|
|
1132
|
if new_data is not None:
|
|
1132
|
if new_data is not None:
|
|
|
1133
|
dataOut.data = new_data
|
|
1133
|
dataOut.data = new_data
|
|
|
1134
|
dataOut.flagNoData = False
|
|
1134
|
dataOut.flagNoData = False
|
|
|
1135
|
|
|
1135
|
|
|
|
1136
|
profileIndex = dataOut.profileIndex*nTxs
|
|
1136
|
profileIndex = dataOut.profileIndex*nTxs
|
|
|
1137
|
|
|
1137
|
|
|
|
1138
|
else:
|
|
1138
|
else:
|
|
|
1139
|
raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
|
|
1139
|
raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
|
|
|
1140
|
|
|
1140
|
|
|
|
1141
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1141
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1142
|
|
|
1142
|
|
|
|
1143
|
dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
|
|
1143
|
dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
|
|
|
1144
|
|
|
1144
|
|
|
|
1145
|
dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
|
|
1145
|
dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
|
|
|
1146
|
|
|
1146
|
|
|
|
1147
|
dataOut.profileIndex = profileIndex
|
|
1147
|
dataOut.profileIndex = profileIndex
|
|
|
1148
|
|
|
1148
|
|
|
|
1149
|
dataOut.ippSeconds /= self.__nTxs
|
|
1149
|
dataOut.ippSeconds /= self.__nTxs
|
|
|
1150
|
|
|
1150
|
|
|
|
1151
|
class SplitProfiles(Operation):
|
|
1151
|
class SplitProfiles(Operation):
|
|
|
1152
|
|
|
1152
|
|
|
|
1153
|
def __init__(self, **kwargs):
|
|
1153
|
def __init__(self, **kwargs):
|
|
|
1154
|
|
|
1154
|
|
|
|
1155
|
Operation.__init__(self, **kwargs)
|
|
1155
|
Operation.__init__(self, **kwargs)
|
|
|
1156
|
|
|
1156
|
|
|
|
1157
|
def run(self, dataOut, n):
|
|
1157
|
def run(self, dataOut, n):
|
|
|
1158
|
|
|
1158
|
|
|
|
1159
|
dataOut.flagNoData = True
|
|
1159
|
dataOut.flagNoData = True
|
|
|
1160
|
profileIndex = None
|
|
1160
|
profileIndex = None
|
|
|
1161
|
|
|
1161
|
|
|
|
1162
|
if dataOut.flagDataAsBlock:
|
|
1162
|
if dataOut.flagDataAsBlock:
|
|
|
1163
|
|
|
1163
|
|
|
|
1164
|
#nchannels, nprofiles, nsamples
|
|
1164
|
#nchannels, nprofiles, nsamples
|
|
|
1165
|
shape = dataOut.data.shape
|
|
1165
|
shape = dataOut.data.shape
|
|
|
1166
|
|
|
1166
|
|
|
|
1167
|
if shape[2] % n != 0:
|
|
1167
|
if shape[2] % n != 0:
|
|
|
1168
|
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
|
|
1168
|
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
|
|
|
1169
|
|
|
1169
|
|
|
|
1170
|
new_shape = shape[0], shape[1]*n, shape[2]/n
|
|
1170
|
new_shape = shape[0], shape[1]*n, shape[2]/n
|
|
|
1171
|
|
|
1171
|
|
|
|
1172
|
dataOut.data = numpy.reshape(dataOut.data, new_shape)
|
|
1172
|
dataOut.data = numpy.reshape(dataOut.data, new_shape)
|
|
|
1173
|
dataOut.flagNoData = False
|
|
1173
|
dataOut.flagNoData = False
|
|
|
1174
|
|
|
1174
|
|
|
|
1175
|
profileIndex = int(dataOut.nProfiles/n) - 1
|
|
1175
|
profileIndex = int(dataOut.nProfiles/n) - 1
|
|
|
1176
|
|
|
1176
|
|
|
|
1177
|
else:
|
|
1177
|
else:
|
|
|
1178
|
|
|
1178
|
|
|
|
1179
|
raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
|
|
1179
|
raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
|
|
|
1180
|
|
|
1180
|
|
|
|
1181
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1181
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1182
|
|
|
1182
|
|
|
|
1183
|
dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
|
|
1183
|
dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
|
|
|
1184
|
|
|
1184
|
|
|
|
1185
|
dataOut.nProfiles = int(dataOut.nProfiles*n)
|
|
1185
|
dataOut.nProfiles = int(dataOut.nProfiles*n)
|
|
|
1186
|
|
|
1186
|
|
|
|
1187
|
dataOut.profileIndex = profileIndex
|
|
1187
|
dataOut.profileIndex = profileIndex
|
|
|
1188
|
|
|
1188
|
|
|
|
1189
|
dataOut.ippSeconds /= n
|
|
1189
|
dataOut.ippSeconds /= n
|
|
|
1190
|
|
|
1190
|
|
|
|
1191
|
class CombineProfiles(Operation):
|
|
1191
|
class CombineProfiles(Operation):
|
|
|
1192
|
|
|
1192
|
|
|
|
1193
|
def __init__(self, **kwargs):
|
|
1193
|
def __init__(self, **kwargs):
|
|
|
1194
|
|
|
1194
|
|
|
|
1195
|
Operation.__init__(self, **kwargs)
|
|
1195
|
Operation.__init__(self, **kwargs)
|
|
|
1196
|
|
|
1196
|
|
|
|
1197
|
self.__remData = None
|
|
1197
|
self.__remData = None
|
|
|
1198
|
self.__profileIndex = 0
|
|
1198
|
self.__profileIndex = 0
|
|
|
1199
|
|
|
1199
|
|
|
|
1200
|
def run(self, dataOut, n):
|
|
1200
|
def run(self, dataOut, n):
|
|
|
1201
|
|
|
1201
|
|
|
|
1202
|
dataOut.flagNoData = True
|
|
1202
|
dataOut.flagNoData = True
|
|
|
1203
|
profileIndex = None
|
|
1203
|
profileIndex = None
|
|
|
1204
|
|
|
1204
|
|
|
|
1205
|
if dataOut.flagDataAsBlock:
|
|
1205
|
if dataOut.flagDataAsBlock:
|
|
|
1206
|
|
|
1206
|
|
|
|
1207
|
#nchannels, nprofiles, nsamples
|
|
1207
|
#nchannels, nprofiles, nsamples
|
|
|
1208
|
shape = dataOut.data.shape
|
|
1208
|
shape = dataOut.data.shape
|
|
|
1209
|
new_shape = shape[0], shape[1]/n, shape[2]*n
|
|
1209
|
new_shape = shape[0], shape[1]/n, shape[2]*n
|
|
|
1210
|
|
|
1210
|
|
|
|
1211
|
if shape[1] % n != 0:
|
|
1211
|
if shape[1] % n != 0:
|
|
|
1212
|
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
|
|
1212
|
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
|
|
|
1213
|
|
|
1213
|
|
|
|
1214
|
dataOut.data = numpy.reshape(dataOut.data, new_shape)
|
|
1214
|
dataOut.data = numpy.reshape(dataOut.data, new_shape)
|
|
|
1215
|
dataOut.flagNoData = False
|
|
1215
|
dataOut.flagNoData = False
|
|
|
1216
|
|
|
1216
|
|
|
|
1217
|
profileIndex = int(dataOut.nProfiles*n) - 1
|
|
1217
|
profileIndex = int(dataOut.nProfiles*n) - 1
|
|
|
1218
|
|
|
1218
|
|
|
|
1219
|
else:
|
|
1219
|
else:
|
|
|
1220
|
|
|
1220
|
|
|
|
1221
|
#nchannels, nsamples
|
|
1221
|
#nchannels, nsamples
|
|
|
1222
|
if self.__remData is None:
|
|
1222
|
if self.__remData is None:
|
|
|
1223
|
newData = dataOut.data
|
|
1223
|
newData = dataOut.data
|
|
|
1224
|
else:
|
|
1224
|
else:
|
|
|
1225
|
newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
|
|
1225
|
newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
|
|
|
1226
|
|
|
1226
|
|
|
|
1227
|
self.__profileIndex += 1
|
|
1227
|
self.__profileIndex += 1
|
|
|
1228
|
|
|
1228
|
|
|
|
1229
|
if self.__profileIndex < n:
|
|
1229
|
if self.__profileIndex < n:
|
|
|
1230
|
self.__remData = newData
|
|
1230
|
self.__remData = newData
|
|
|
1231
|
#continue
|
|
1231
|
#continue
|
|
|
1232
|
return
|
|
1232
|
return
|
|
|
1233
|
|
|
1233
|
|
|
|
1234
|
self.__profileIndex = 0
|
|
1234
|
self.__profileIndex = 0
|
|
|
1235
|
self.__remData = None
|
|
1235
|
self.__remData = None
|
|
|
1236
|
|
|
1236
|
|
|
|
1237
|
dataOut.data = newData
|
|
1237
|
dataOut.data = newData
|
|
|
1238
|
dataOut.flagNoData = False
|
|
1238
|
dataOut.flagNoData = False
|
|
|
1239
|
|
|
1239
|
|
|
|
1240
|
profileIndex = dataOut.profileIndex/n
|
|
1240
|
profileIndex = dataOut.profileIndex/n
|
|
|
1241
|
|
|
1241
|
|
|
|
1242
|
|
|
1242
|
|
|
|
1243
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1243
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1244
|
|
|
1244
|
|
|
|
1245
|
dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
|
|
1245
|
dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
|
|
|
1246
|
|
|
1246
|
|
|
|
1247
|
dataOut.nProfiles = int(dataOut.nProfiles/n)
|
|
1247
|
dataOut.nProfiles = int(dataOut.nProfiles/n)
|
|
|
1248
|
|
|
1248
|
|
|
|
1249
|
dataOut.profileIndex = profileIndex
|
|
1249
|
dataOut.profileIndex = profileIndex
|
|
|
1250
|
|
|
1250
|
|
|
|
1251
|
dataOut.ippSeconds *= n
|
|
1251
|
dataOut.ippSeconds *= n
|
|
|
1252
|
|
|
1252
|
|
|
|
1253
|
|
|
1253
|
|
|
|
1254
|
class SSheightProfiles(Operation):
|
|
1254
|
class SSheightProfiles(Operation):
|
|
|
1255
|
|
|
1255
|
|
|
|
1256
|
step = None
|
|
1256
|
step = None
|
|
|
1257
|
nsamples = None
|
|
1257
|
nsamples = None
|
|
|
1258
|
bufferShape = None
|
|
1258
|
bufferShape = None
|
|
|
1259
|
profileShape = None
|
|
1259
|
profileShape = None
|
|
|
1260
|
sshProfiles = None
|
|
1260
|
sshProfiles = None
|
|
|
1261
|
profileIndex = None
|
|
1261
|
profileIndex = None
|
|
|
1262
|
|
|
1262
|
|
|
|
1263
|
def __init__(self, **kwargs):
|
|
1263
|
def __init__(self, **kwargs):
|
|
|
1264
|
|
|
1264
|
|
|
|
1265
|
Operation.__init__(self, **kwargs)
|
|
1265
|
Operation.__init__(self, **kwargs)
|
|
|
1266
|
self.isConfig = False
|
|
1266
|
self.isConfig = False
|
|
|
1267
|
|
|
1267
|
|
|
|
1268
|
def setup(self,dataOut ,step = None , nsamples = None):
|
|
1268
|
def setup(self,dataOut ,step = None , nsamples = None):
|
|
|
1269
|
|
|
1269
|
|
|
|
1270
|
if step == None and nsamples == None:
|
|
1270
|
if step == None and nsamples == None:
|
|
|
1271
|
raise ValueError, "step or nheights should be specified ..."
|
|
1271
|
raise ValueError, "step or nheights should be specified ..."
|
|
|
1272
|
|
|
1272
|
|
|
|
1273
|
self.step = step
|
|
1273
|
self.step = step
|
|
|
1274
|
self.nsamples = nsamples
|
|
1274
|
self.nsamples = nsamples
|
|
|
1275
|
self.__nChannels = dataOut.nChannels
|
|
1275
|
self.__nChannels = dataOut.nChannels
|
|
|
1276
|
self.__nProfiles = dataOut.nProfiles
|
|
1276
|
self.__nProfiles = dataOut.nProfiles
|
|
|
1277
|
self.__nHeis = dataOut.nHeights
|
|
1277
|
self.__nHeis = dataOut.nHeights
|
|
|
1278
|
shape = dataOut.data.shape #nchannels, nprofiles, nsamples
|
|
1278
|
shape = dataOut.data.shape #nchannels, nprofiles, nsamples
|
|
|
1279
|
print "input nChannels",self.__nChannels
|
|
1279
|
print "input nChannels",self.__nChannels
|
|
|
1280
|
print "input nProfiles",self.__nProfiles
|
|
1280
|
print "input nProfiles",self.__nProfiles
|
|
|
1281
|
print "input nHeis",self.__nHeis
|
|
1281
|
print "input nHeis",self.__nHeis
|
|
|
1282
|
print "input Shape",shape
|
|
1282
|
print "input Shape",shape
|
|
|
1283
|
|
|
1283
|
|
|
|
1284
|
|
|
1284
|
|
|
|
1285
|
|
|
1285
|
|
|
|
1286
|
residue = (shape[1] - self.nsamples) % self.step
|
|
1286
|
residue = (shape[1] - self.nsamples) % self.step
|
|
|
1287
|
if residue != 0:
|
|
1287
|
if residue != 0:
|
|
|
1288
|
print "The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)
|
|
1288
|
print "The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)
|
|
|
1289
|
|
|
1289
|
|
|
|
1290
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1290
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1291
|
numberProfile = self.nsamples
|
|
1291
|
numberProfile = self.nsamples
|
|
|
1292
|
numberSamples = (shape[1] - self.nsamples)/self.step
|
|
1292
|
numberSamples = (shape[1] - self.nsamples)/self.step
|
|
|
1293
|
|
|
1293
|
|
|
|
1294
|
print "new numberProfile",numberProfile
|
|
1294
|
print "new numberProfile",numberProfile
|
|
|
1295
|
print "new numberSamples",numberSamples
|
|
1295
|
print "new numberSamples",numberSamples
|
|
|
1296
|
|
|
1296
|
|
|
|
1297
|
print "New number of profile: %d, number of height: %d, Resolution %f Km"%(numberProfile,numberSamples,deltaHeight*self.step)
|
|
1297
|
print "New number of profile: %d, number of height: %d, Resolution %f Km"%(numberProfile,numberSamples,deltaHeight*self.step)
|
|
|
1298
|
|
|
1298
|
|
|
|
1299
|
self.bufferShape = shape[0], numberSamples, numberProfile # nchannels, nsamples , nprofiles
|
|
1299
|
self.bufferShape = shape[0], numberSamples, numberProfile # nchannels, nsamples , nprofiles
|
|
|
1300
|
self.profileShape = shape[0], numberProfile, numberSamples # nchannels, nprofiles, nsamples
|
|
1300
|
self.profileShape = shape[0], numberProfile, numberSamples # nchannels, nprofiles, nsamples
|
|
|
1301
|
|
|
1301
|
|
|
|
1302
|
self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex)
|
|
1302
|
self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex)
|
|
|
1303
|
self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex)
|
|
1303
|
self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex)
|
|
|
1304
|
|
|
1304
|
|
|
|
1305
|
def run(self, dataOut, step, nsamples):
|
|
1305
|
def run(self, dataOut, step, nsamples):
|
|
|
1306
|
|
|
1306
|
|
|
|
1307
|
dataOut.flagNoData = True
|
|
1307
|
dataOut.flagNoData = True
|
|
|
1308
|
dataOut.flagDataAsBlock = False
|
|
1308
|
dataOut.flagDataAsBlock = False
|
|
|
1309
|
profileIndex = None
|
|
1309
|
profileIndex = None
|
|
|
1310
|
|
|
1310
|
|
|
|
1311
|
|
|
1311
|
|
|
|
1312
|
if not self.isConfig:
|
|
1312
|
if not self.isConfig:
|
|
|
1313
|
self.setup(dataOut, step=step , nsamples=nsamples)
|
|
1313
|
self.setup(dataOut, step=step , nsamples=nsamples)
|
|
|
1314
|
self.isConfig = True
|
|
1314
|
self.isConfig = True
|
|
|
1315
|
|
|
1315
|
|
|
|
1316
|
for i in range(self.buffer.shape[1]):
|
|
1316
|
for i in range(self.buffer.shape[1]):
|
|
|
1317
|
#self.buffer[:,i] = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples])
|
|
1317
|
#self.buffer[:,i] = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples])
|
|
|
1318
|
self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]
|
|
1318
|
self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]
|
|
|
1319
|
#self.buffer[:,j,self.__nHeis-j*self.step - self.nheights:self.__nHeis-j*self.step] = numpy.flip(dataOut.data[:,j*self.step:j*self.step + self.nheights])
|
|
1319
|
#self.buffer[:,j,self.__nHeis-j*self.step - self.nheights:self.__nHeis-j*self.step] = numpy.flip(dataOut.data[:,j*self.step:j*self.step + self.nheights])
|
|
|
1320
|
|
|
1320
|
|
|
|
1321
|
for j in range(self.buffer.shape[0]):
|
|
1321
|
for j in range(self.buffer.shape[0]):
|
|
|
1322
|
self.sshProfiles[j] = numpy.transpose(self.buffer[j])
|
|
1322
|
self.sshProfiles[j] = numpy.transpose(self.buffer[j])
|
|
|
1323
|
|
|
1323
|
|
|
|
1324
|
profileIndex = self.nsamples
|
|
1324
|
profileIndex = self.nsamples
|
|
|
1325
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1325
|
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1326
|
ippSeconds = (deltaHeight*1.0e-6)/(0.15)
|
|
1326
|
ippSeconds = (deltaHeight*1.0e-6)/(0.15)
|
|
|
1327
|
#print "ippSeconds",ippSeconds
|
|
1327
|
#print "ippSeconds",ippSeconds
|
|
|
1328
|
try:
|
|
1328
|
try:
|
|
|
1329
|
if dataOut.concat_m is not None:
|
|
1329
|
if dataOut.concat_m is not None:
|
|
|
1330
|
ippSeconds= ippSeconds/float(dataOut.concat_m)
|
|
1330
|
ippSeconds= ippSeconds/float(dataOut.concat_m)
|
|
|
1331
|
#print "Profile concat %d"%dataOut.concat_m
|
|
1331
|
#print "Profile concat %d"%dataOut.concat_m
|
|
|
1332
|
except:
|
|
1332
|
except:
|
|
|
1333
|
pass
|
|
1333
|
pass
|
|
|
1334
|
|
|
1334
|
|
|
|
1335
|
dataOut.data = self.sshProfiles
|
|
1335
|
dataOut.data = self.sshProfiles
|
|
|
1336
|
dataOut.flagNoData = False
|
|
1336
|
dataOut.flagNoData = False
|
|
|
1337
|
dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0]
|
|
1337
|
dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0]
|
|
|
1338
|
dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples)
|
|
1338
|
dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples)
|
|
|
1339
|
dataOut.profileIndex = profileIndex
|
|
1339
|
dataOut.profileIndex = profileIndex
|
|
|
1340
|
dataOut.flagDataAsBlock = True
|
|
1340
|
dataOut.flagDataAsBlock = True
|
|
|
1341
|
dataOut.ippSeconds = ippSeconds
|
|
1341
|
dataOut.ippSeconds = ippSeconds
|
|
|
1342
|
dataOut.step = self.step
|
|
1342
|
dataOut.step = self.step
|
|
|
1343
|
|
|
1343
|
|
|
|
1344
|
class voltACFLags(Operation):
|
|
1344
|
class voltACFLags(Operation):
|
|
|
1345
|
|
|
1345
|
|
|
|
1346
|
data_acf = None
|
|
1346
|
data_acf = None
|
|
|
1347
|
lags = None
|
|
1347
|
lags = None
|
|
|
1348
|
mode = None
|
|
1348
|
mode = None
|
|
|
1349
|
fullBuffer = None
|
|
1349
|
fullBuffer = None
|
|
|
1350
|
pairsList = None
|
|
1350
|
pairsList = None
|
|
|
1351
|
tmp = None
|
|
1351
|
tmp = None
|
|
|
1352
|
|
|
1352
|
|
|
|
1353
|
def __init__(self, **kwargs):
|
|
1353
|
def __init__(self, **kwargs):
|
|
|
1354
|
Operation.__init__(self, **kwargs)
|
|
1354
|
Operation.__init__(self, **kwargs)
|
|
|
1355
|
self.isConfig = False
|
|
1355
|
self.isConfig = False
|
|
|
1356
|
self.profIndex = 0
|
|
1356
|
self.profIndex = 0
|
|
|
1357
|
self.buffer = None
|
|
1357
|
self.buffer = None
|
|
|
1358
|
self.channelList = []
|
|
1358
|
self.channelList = []
|
|
|
1359
|
|
|
1359
|
|
|
|
1360
|
|
|
1360
|
|
|
|
1361
|
def setup(self,dataOut ,lags = None,mode =None, fullBuffer= None ,pairsList = None,nAvg = 1):
|
|
1361
|
def setup(self,dataOut ,lags = None,mode =None, fullBuffer= None ,pairsList = None,nAvg = 1):
|
|
|
1362
|
|
|
1362
|
|
|
|
1363
|
self.lags = lags
|
|
1363
|
self.lags = lags
|
|
|
1364
|
print self.lags
|
|
1364
|
print self.lags
|
|
|
1365
|
self.mode = mode
|
|
1365
|
self.mode = mode
|
|
|
|
|
|
1366
|
self.buffer2 = []
|
|
|
1366
|
self.fullBuffer= fullBuffer
|
|
1367
|
self.fullBuffer= fullBuffer
|
|
|
1367
|
self.nAvg = nAvg
|
|
1368
|
self.nAvg = nAvg
|
|
|
1368
|
self.pairsList = [pairsList]
|
|
1369
|
self.pairsList = [pairsList]
|
|
|
1369
|
nChannels = dataOut.nChannels
|
|
1370
|
nChannels = dataOut.nChannels
|
|
|
1370
|
nProfiles = dataOut.nProfiles
|
|
1371
|
nProfiles = dataOut.nProfiles
|
|
|
1371
|
nHeights = dataOut.nHeights
|
|
1372
|
nHeights = dataOut.nHeights
|
|
|
1372
|
self.__nProfiles = dataOut.nProfiles
|
|
1373
|
self.__nProfiles = dataOut.nProfiles
|
|
|
1373
|
self.__nHeis = dataOut.nHeights
|
|
1374
|
self.__nHeis = dataOut.nHeights
|
|
|
1374
|
|
|
1375
|
|
|
|
1375
|
if mode == 'time':
|
|
1376
|
if mode == 'time':
|
|
|
1376
|
print "Mode lags equal time for default."
|
|
1377
|
print "Mode lags equal time for default."
|
|
|
1377
|
else:
|
|
1378
|
else:
|
|
|
1378
|
print "Mode lags equal height."
|
|
1379
|
print "Mode lags equal height."
|
|
|
1379
|
|
|
1380
|
|
|
|
1380
|
if pairsList == None:
|
|
1381
|
if pairsList == None:
|
|
|
1381
|
print "Pairs list selected by default (1,0)"
|
|
1382
|
print "Pairs list selected by default (1,0)"
|
|
|
1382
|
pairsList = [(0,1)]
|
|
1383
|
pairsList = [(0,1)]
|
|
|
1383
|
self.pairsList= pairsList
|
|
1384
|
self.pairsList= pairsList
|
|
|
1384
|
|
|
1385
|
|
|
|
1385
|
|
|
1386
|
|
|
|
1386
|
for i in range(len(self.pairsList)):
|
|
1387
|
for i in range(len(self.pairsList)):
|
|
|
1387
|
self.channelList.append(self.pairsList[i][0])
|
|
1388
|
self.channelList.append(self.pairsList[i][0])
|
|
|
1388
|
self.channelList.append(self.pairsList[i][1])
|
|
1389
|
self.channelList.append(self.pairsList[i][1])
|
|
|
1389
|
|
|
1390
|
|
|
|
1390
|
if lags == None:
|
|
1391
|
if lags == None:
|
|
|
1391
|
if mode=='time':
|
|
1392
|
if mode=='time':
|
|
|
1392
|
self.lags = numpy.arange(0,nProfiles)# -nProfiles+1, nProfiles
|
|
1393
|
self.lags = numpy.arange(0,nProfiles)# -nProfiles+1, nProfiles
|
|
|
1393
|
print "self.lags", len(self.lags)
|
|
1394
|
print "self.lags", len(self.lags)
|
|
|
1394
|
if mode=='height':
|
|
1395
|
if mode=='height':
|
|
|
1395
|
self.lags = numpy.arange(0,nHeights)# -nHeights+1, nHeights
|
|
1396
|
self.lags = numpy.arange(0,nHeights)# -nHeights+1, nHeights
|
|
|
1396
|
|
|
1397
|
|
|
|
1397
|
# buffer de canalaes, perfiles, alturas
|
|
1398
|
# buffer de canalaes, perfiles, alturas
|
|
|
1398
|
if self.buffer is None:
|
|
1399
|
if self.buffer is None:
|
|
|
1399
|
self.buffer = numpy.zeros((nChannels,nProfiles,nHeights),dtype='complex')
|
|
1400
|
self.buffer = numpy.zeros((nChannels,nProfiles,nHeights),dtype='complex')
|
|
|
1400
|
|
|
1401
|
|
|
|
1401
|
if fullBuffer:
|
|
1402
|
if fullBuffer:
|
|
|
1402
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nProfiles, nHeights), dtype = 'complex')*numpy.nan
|
|
1403
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nProfiles, nHeights), dtype = 'complex')*numpy.nan
|
|
|
1403
|
elif mode =='time':
|
|
1404
|
elif mode =='time':
|
|
|
1404
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nHeights),dtype='complex')
|
|
1405
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nHeights),dtype='complex')
|
|
|
1405
|
elif mode =='height':
|
|
1406
|
elif mode =='height':
|
|
|
1406
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nProfiles),dtype='complex')
|
|
1407
|
self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nHeights),dtype='complex')
|
|
|
|
|
|
1408
|
#self.tmp = numpy.zeros((len(self.pairsList), len(self.lags), nProfiles),dtype='complex')
|
|
|
1407
|
|
|
1409
|
|
|
|
1408
|
print "lags", len(self.lags),self.lags
|
|
1410
|
print "lags", len(self.lags),self.lags
|
|
|
1409
|
print "mode",self.mode
|
|
1411
|
print "mode",self.mode
|
|
|
1410
|
print "nChannels", nChannels
|
|
1412
|
print "nChannels", nChannels
|
|
|
1411
|
print "nProfiles", nProfiles
|
|
1413
|
print "nProfiles", nProfiles
|
|
|
1412
|
print "nHeights" , nHeights
|
|
1414
|
print "nHeights" , nHeights
|
|
|
1413
|
print "acf_channels",len(self.pairsList)
|
|
1415
|
print "acf_channels",len(self.pairsList)
|
|
|
1414
|
print "channelList",self.channelList
|
|
1416
|
print "channelList",self.channelList
|
|
|
1415
|
print "pairsList", pairsList,len(self.pairsList)
|
|
1417
|
print "pairsList", pairsList,len(self.pairsList)
|
|
|
1416
|
print "fullBuffer", fullBuffer
|
|
1418
|
print "fullBuffer", fullBuffer
|
|
|
1417
|
#print "type(pairsList)",type(pairsList)
|
|
1419
|
#print "type(pairsList)",type(pairsList)
|
|
|
1418
|
print "tmp.shape",self.tmp.shape
|
|
1420
|
print "tmp.shape",self.tmp.shape
|
|
|
1419
|
|
|
1421
|
|
|
|
1420
|
|
|
1422
|
|
|
|
1421
|
def run(self, dataOut, lags =None,mode ='time',fullBuffer= False ,pairsList = None,nAvg = 1):
|
|
1423
|
def run(self, dataOut, lags =None,mode ='time',fullBuffer= False ,pairsList = None,nAvg = 1):
|
|
|
1422
|
|
|
1424
|
|
|
|
1423
|
dataOut.flagNoData = True
|
|
1425
|
dataOut.flagNoData = True
|
|
|
1424
|
|
|
1426
|
|
|
|
1425
|
if not self.isConfig:
|
|
1427
|
if not self.isConfig:
|
|
|
1426
|
self.setup(dataOut, lags = lags,mode = mode, fullBuffer= fullBuffer ,pairsList = pairsList,nAvg=nAvg)
|
|
1428
|
self.setup(dataOut, lags = lags,mode = mode, fullBuffer= fullBuffer ,pairsList = pairsList,nAvg=nAvg)
|
|
|
1427
|
self.isConfig = True
|
|
1429
|
self.isConfig = True
|
|
|
1428
|
|
|
1430
|
|
|
|
1429
|
if dataOut.type == "Voltage":
|
|
1431
|
if dataOut.type == "Voltage":
|
|
|
1430
|
if dataOut.flagDataAsBlock:
|
|
1432
|
if dataOut.flagDataAsBlock:
|
|
|
1431
|
print "Not implemented yet"
|
|
1433
|
print "Not implemented yet"
|
|
|
1432
|
return 0
|
|
1434
|
return 0
|
|
|
1433
|
else:
|
|
1435
|
else:
|
|
|
1434
|
self.buffer[:, self.profIndex, :] = dataOut.data
|
|
1436
|
self.buffer[:, self.profIndex, :] = dataOut.data
|
|
|
1435
|
self.profIndex += 1
|
|
1437
|
self.profIndex += 1
|
|
|
1436
|
|
|
1438
|
|
|
|
1437
|
if self.profIndex == self.__nProfiles :
|
|
1439
|
if self.profIndex == self.__nProfiles :
|
|
|
1438
|
|
|
1440
|
|
|
|
1439
|
data_pre = self.buffer #data
|
|
1441
|
data_pre = self.buffer #data
|
|
|
1440
|
for l in range(len(self.pairsList)):
|
|
1442
|
for l in range(len(self.pairsList)):
|
|
|
|
|
|
1443
|
#print "l",l
|
|
|
1441
|
ch0 = self.pairsList[l][0]
|
|
1444
|
ch0 = self.pairsList[l][0]
|
|
|
1442
|
ch1 = self.pairsList[l][1]
|
|
1445
|
ch1 = self.pairsList[l][1]
|
|
|
1443
|
for i in range(len(self.lags)):
|
|
1446
|
#for i in range(len(self.lags)):
|
|
|
1444
|
idx = self.lags[i]
|
|
1447
|
for i in range(self.__nProfiles):
|
|
|
|
|
|
1448
|
k=i%len(self.lags)
|
|
|
|
|
|
1449
|
idx = self.lags[k]
|
|
|
|
|
|
1450
|
|
|
|
1445
|
if self.mode == 'time':
|
|
1451
|
if self.mode == 'time':
|
|
|
|
|
|
1452
|
|
|
|
1446
|
acf0 = data_pre[ch0,:self.__nProfiles-idx,:]*numpy.conj(data_pre[ch1,idx:,:]) # pair,lag,height
|
|
1453
|
acf0 = data_pre[ch0,:self.__nProfiles-idx,:]*numpy.conj(data_pre[ch1,idx:,:]) # pair,lag,height
|
|
|
1447
|
else:
|
|
1454
|
else:
|
|
|
1448
|
acf0 = data_pre[ch0,:,:self.__nHeights-idx]*numpy.conj(data_pre[ch1,:,idx:]) # pair,lag,profile
|
|
1455
|
#print "ESTE ES :D"
|
|
|
1449
|
|
|
1456
|
if idx==0:
|
|
|
1450
|
if self.fullBuffer:
|
|
1457
|
acf0 = data_pre[ch0,k,:self.__nHeis]*numpy.conj(data_pre[ch1,k,idx:])
|
|
|
1451
|
self.tmp[l,i,:acf0.shape[0],:]= acf0
|
|
1458
|
#acf0 = data_pre[ch0,:,:self.__nHeis-idx]*numpy.conj(data_pre[ch1,:,idx:]) # pair,lag,profile
|
|
|
1452
|
else:
|
|
1459
|
else:
|
|
|
1453
|
self.tmp[l,i,:]= numpy.sum(acf0,axis=0)
|
|
1460
|
#print "primera parte del array",(data_pre[ch1,k,idx:].shape)
|
|
|
|
|
|
1461
|
#print (data_pre[ch1,i+1,:])
|
|
|
|
|
|
1462
|
#print "segunda parte del array 6 primeros",(data_pre[ch1,i+1,:idx])
|
|
|
|
|
|
1463
|
acu=int(i/(len(self.lags)))*len(self.lags)
|
|
|
|
|
|
1464
|
#print ("acu",acu)
|
|
|
|
|
|
1465
|
if k+acu+1==self.__nProfiles:
|
|
|
|
|
|
1466
|
acu=acu-1
|
|
|
|
|
|
1467
|
acf0 = data_pre[ch0,k+acu,:self.__nHeis]*numpy.conj(numpy.concatenate((data_pre[ch1,k+acu,idx:],data_pre[ch1,k+acu+1,:idx]), axis=0)) # pair,lag,profile
|
|
|
|
|
|
1468
|
|
|
|
|
|
|
1469
|
#if k== len(self.lags)-1:
|
|
|
|
|
|
1470
|
self.tmp[l,k,:]= acf0
|
|
|
|
|
|
1471
|
if k == len(self.lags)-1:
|
|
|
|
|
|
1472
|
self.buffer2.append(self.tmp)
|
|
|
|
|
|
1473
|
|
|
|
|
|
|
1474
|
if i==self.__nProfiles-1:
|
|
|
|
|
|
1475
|
self.tmp = numpy.sum(self.buffer2,axis = 0)
|
|
|
|
|
|
1476
|
print self.tmp.shape
|
|
|
|
|
|
1477
|
self.buffer2=[]
|
|
|
|
|
|
1478
|
|
|
|
|
|
|
1479
|
|
|
|
|
|
|
1480
|
|
|
|
|
|
|
1481
|
|
|
|
|
|
|
1482
|
#if self.fullBuffer:
|
|
|
|
|
|
1483
|
# self.tmp[l,i,:acf0.shape[0],:]= acf0
|
|
|
|
|
|
1484
|
#else:
|
|
|
|
|
|
1485
|
# #print "l",l ,"i",i ,acf0.shape
|
|
|
|
|
|
1486
|
# self.tmp[l,i,:]= numpy.sum(acf0,axis=0)
|
|
|
1454
|
if self.fullBuffer:
|
|
1487
|
if self.fullBuffer:
|
|
|
1455
|
self.tmp = numpy.sum(numpy.reshape(self.tmp,(self.tmp.shape[0],self.tmp.shape[1],self.tmp.shape[2]/self.nAvg,self.nAvg,self.tmp.shape[3])),axis=3)
|
|
1488
|
self.tmp = numpy.sum(numpy.reshape(self.tmp,(self.tmp.shape[0],self.tmp.shape[1],self.tmp.shape[2]/self.nAvg,self.nAvg,self.tmp.shape[3])),axis=3)
|
|
|
1456
|
dataOut.nAvg = self.nAvg
|
|
1489
|
dataOut.nAvg = self.nAvg
|
|
|
1457
|
if self.mode == 'time':
|
|
1490
|
if self.mode == 'time':
|
|
|
1458
|
#print "entre"
|
|
1491
|
#print "entre"
|
|
|
1459
|
delta = dataOut.ippSeconds*dataOut.nCohInt
|
|
1492
|
delta = dataOut.ippSeconds*dataOut.nCohInt
|
|
|
1460
|
else:
|
|
1493
|
else:
|
|
|
1461
|
delta = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1494
|
delta = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1462
|
|
|
1495
|
|
|
|
1463
|
shape= self.tmp.shape
|
|
1496
|
shape= self.tmp.shape
|
|
|
1464
|
for i in range(len(self.pairsList)):
|
|
1497
|
for i in range(len(self.pairsList)):
|
|
|
1465
|
for j in range(shape[2]):
|
|
1498
|
for j in range(shape[2]):
|
|
|
1466
|
self.tmp[i,:,j]= self.tmp[i,:,j].real/numpy.max(numpy.abs(self.tmp[i,:,j]))
|
|
1499
|
self.tmp[i,:,j]= self.tmp[i,:,j].real/numpy.max(numpy.abs(self.tmp[i,:,j]))
|
|
|
1467
|
|
|
1500
|
|
|
|
1468
|
#import matplotlib.pyplot as plt
|
|
1501
|
#import matplotlib.pyplot as plt
|
|
|
1469
|
#print "test",self.tmp.shape
|
|
1502
|
#print "test",self.tmp.shape
|
|
|
1470
|
#print self.tmp[0,0,0]
|
|
1503
|
#print self.tmp[0,0,0]
|
|
|
1471
|
#print numpy.max(numpy.abs(self.tmp[0,:,0]))
|
|
1504
|
#print numpy.max(numpy.abs(self.tmp[0,:,0]))
|
|
|
1472
|
#acf_tmp=self.tmp[0,:,100].real/numpy.max(numpy.abs(self.tmp[0,:,100]))
|
|
1505
|
#acf_tmp=self.tmp[0,:,100].real/numpy.max(numpy.abs(self.tmp[0,:,100]))
|
|
|
1473
|
#print acf_tmp
|
|
1506
|
#print acf_tmp
|
|
|
1474
|
#plt.plot(acf_tmp)
|
|
1507
|
#plt.plot(acf_tmp)
|
|
|
1475
|
#plt.show()
|
|
1508
|
#plt.show()
|
|
|
1476
|
#import time
|
|
1509
|
#import time
|
|
|
1477
|
#time.sleep(20)
|
|
1510
|
#time.sleep(20)
|
|
|
1478
|
dataOut.data = self.buffer
|
|
1511
|
dataOut.data = self.buffer
|
|
|
1479
|
dataOut.data_acfLag = self.tmp
|
|
1512
|
dataOut.data_acfLag = self.tmp
|
|
|
1480
|
dataOut.mode = self.mode
|
|
1513
|
dataOut.mode = self.mode
|
|
|
1481
|
dataOut.nLags = len(self.lags)
|
|
1514
|
dataOut.nLags = len(self.lags)
|
|
|
1482
|
dataOut.nProfiles = len(self.lags)
|
|
1515
|
dataOut.nProfiles = len(self.lags)
|
|
|
1483
|
dataOut.pairsList = pairsList
|
|
1516
|
dataOut.pairsList = pairsList
|
|
|
1484
|
dataOut.nPairs = len(pairsList)
|
|
1517
|
dataOut.nPairs = len(pairsList)
|
|
|
1485
|
dataOut.lagRange = numpy.array(self.lags)*delta
|
|
1518
|
dataOut.lagRange = numpy.array(self.lags)*delta
|
|
|
1486
|
dataOut.flagDataAsBlock = True
|
|
1519
|
dataOut.flagDataAsBlock = True
|
|
|
1487
|
dataOut.flagNoData = False
|
|
1520
|
dataOut.flagNoData = False
|
|
|
1488
|
self.profIndex = 0
|
|
1521
|
self.profIndex = 0
|
|
|
1489
|
|
|
1522
|
|
|
|
1490
|
|
|
1523
|
|
|
|
1491
|
import time
|
|
1524
|
import time
|
|
|
1492
|
#################################################
|
|
1525
|
#################################################
|
|
|
1493
|
|
|
1526
|
|
|
|
1494
|
class decoPseudorandom(Operation):
|
|
1527
|
class decoPseudorandom(Operation):
|
|
|
1495
|
|
|
1528
|
|
|
|
1496
|
nProfiles= 0
|
|
1529
|
nProfiles= 0
|
|
|
1497
|
buffer= None
|
|
1530
|
buffer= None
|
|
|
1498
|
isConfig = False
|
|
1531
|
isConfig = False
|
|
|
1499
|
|
|
1532
|
|
|
|
1500
|
def setup(self, clen= 10000,seed= 0,Nranges= 1000,oversample=1):
|
|
1533
|
def setup(self, clen= 10000,seed= 0,Nranges= 1000,oversample=1):
|
|
|
1501
|
#code = create_pseudo_random_code(clen=clen, seed=seed)
|
|
1534
|
#code = create_pseudo_random_code(clen=clen, seed=seed)
|
|
|
1502
|
code= rep_seq(create_pseudo_random_code(clen=clen, seed=seed),rep=oversample)
|
|
1535
|
code= rep_seq(create_pseudo_random_code(clen=clen, seed=seed),rep=oversample)
|
|
|
1503
|
#print ("code_rx", code.shape)
|
|
1536
|
#print ("code_rx", code.shape)
|
|
|
1504
|
#N = int(an_len/clen) # 100
|
|
1537
|
#N = int(an_len/clen) # 100
|
|
|
1505
|
B_cache = 0
|
|
1538
|
B_cache = 0
|
|
|
1506
|
r_cache = 0
|
|
1539
|
r_cache = 0
|
|
|
1507
|
B_cached = False
|
|
1540
|
B_cached = False
|
|
|
1508
|
r = create_estimation_matrix(code=code, cache=True, rmax=Nranges)
|
|
1541
|
r = create_estimation_matrix(code=code, cache=True, rmax=Nranges)
|
|
|
1509
|
#print ("code shape", code.shape)
|
|
1542
|
#print ("code shape", code.shape)
|
|
|
1510
|
#print ("seed",seed)
|
|
1543
|
#print ("seed",seed)
|
|
|
1511
|
#print ("Code", code[0:10])
|
|
1544
|
#print ("Code", code[0:10])
|
|
|
1512
|
self.B = r['B']
|
|
1545
|
self.B = r['B']
|
|
|
1513
|
|
|
1546
|
|
|
|
1514
|
|
|
1547
|
|
|
|
1515
|
def run (self,dataOut,length_code= 10000,seed= 0,Nranges= 1000,oversample=1):
|
|
1548
|
def run (self,dataOut,length_code= 10000,seed= 0,Nranges= 1000,oversample=1):
|
|
|
1516
|
#print((dataOut.data.shape))
|
|
1549
|
#print((dataOut.data.shape))
|
|
|
1517
|
if not self.isConfig:
|
|
1550
|
if not self.isConfig:
|
|
|
1518
|
self.setup(clen= length_code,seed= seed,Nranges= Nranges,oversample=oversample)
|
|
1551
|
self.setup(clen= length_code,seed= seed,Nranges= Nranges,oversample=oversample)
|
|
|
1519
|
self.isConfig = True
|
|
1552
|
self.isConfig = True
|
|
|
1520
|
|
|
1553
|
|
|
|
1521
|
dataOut.flagNoData = True
|
|
1554
|
dataOut.flagNoData = True
|
|
|
1522
|
data =dataOut.data
|
|
1555
|
data =dataOut.data
|
|
|
1523
|
#print "length_CODE",length_code
|
|
1556
|
#print "length_CODE",length_code
|
|
|
1524
|
data_shape = (data.shape[1])
|
|
1557
|
data_shape = (data.shape[1])
|
|
|
1525
|
#print "data_shape",data_shape
|
|
1558
|
#print "data_shape",data_shape
|
|
|
1526
|
n = (length_code /data_shape)
|
|
1559
|
n = (length_code /data_shape)
|
|
|
1527
|
#print "we need this number of sample",n
|
|
1560
|
#print "we need this number of sample",n
|
|
|
1528
|
|
|
1561
|
|
|
|
1529
|
if n>0 and self.buffer is None:
|
|
1562
|
if n>0 and self.buffer is None:
|
|
|
1530
|
self.buffer = numpy.zeros([1, length_code], dtype=numpy.complex64)
|
|
1563
|
self.buffer = numpy.zeros([1, length_code], dtype=numpy.complex64)
|
|
|
1531
|
self.buffer[0][0:data_shape] = data[0]
|
|
1564
|
self.buffer[0][0:data_shape] = data[0]
|
|
|
1532
|
#print "FIRST CREATION",self.buffer.shape
|
|
1565
|
#print "FIRST CREATION",self.buffer.shape
|
|
|
1533
|
|
|
1566
|
|
|
|
1534
|
else:
|
|
1567
|
else:
|
|
|
1535
|
self.buffer[0][self.nProfiles*data_shape:(self.nProfiles+1)*data_shape]=data[0]
|
|
1568
|
self.buffer[0][self.nProfiles*data_shape:(self.nProfiles+1)*data_shape]=data[0]
|
|
|
1536
|
|
|
1569
|
|
|
|
1537
|
#print "buffer_shape",(self.buffer.shape)
|
|
1570
|
#print "buffer_shape",(self.buffer.shape)
|
|
|
1538
|
self.nProfiles += 1
|
|
1571
|
self.nProfiles += 1
|
|
|
1539
|
#print "count",self.nProfiles
|
|
1572
|
#print "count",self.nProfiles
|
|
|
1540
|
|
|
1573
|
|
|
|
1541
|
if self.nProfiles== n:
|
|
1574
|
if self.nProfiles== n:
|
|
|
1542
|
temporal = numpy.dot(self.B, numpy.transpose(self.buffer))
|
|
1575
|
temporal = numpy.dot(self.B, numpy.transpose(self.buffer))
|
|
|
1543
|
#print temporal.shape
|
|
1576
|
#print temporal.shape
|
|
|
1544
|
#import time
|
|
1577
|
#import time
|
|
|
1545
|
#time.sleep(40)
|
|
1578
|
#time.sleep(40)
|
|
|
1546
|
dataOut.data=numpy.transpose(temporal)
|
|
1579
|
dataOut.data=numpy.transpose(temporal)
|
|
|
1547
|
|
|
1580
|
|
|
|
1548
|
dataOut.flagNoData = False
|
|
1581
|
dataOut.flagNoData = False
|
|
|
1549
|
self.buffer= None
|
|
1582
|
self.buffer= None
|
|
|
1550
|
self.nProfiles = 0
|
|
1583
|
self.nProfiles = 0
|
|
|
1551
|
|
|
1584
|
|
|
|
1552
|
# import collections
|
|
1585
|
# import collections
|
|
|
1553
|
# from scipy.stats import mode
|
|
1586
|
# from scipy.stats import mode
|
|
|
1554
|
#
|
|
1587
|
#
|
|
|
1555
|
# class Synchronize(Operation):
|
|
1588
|
# class Synchronize(Operation):
|
|
|
1556
|
#
|
|
1589
|
#
|
|
|
1557
|
# isConfig = False
|
|
1590
|
# isConfig = False
|
|
|
1558
|
# __profIndex = 0
|
|
1591
|
# __profIndex = 0
|
|
|
1559
|
#
|
|
1592
|
#
|
|
|
1560
|
# def __init__(self, **kwargs):
|
|
1593
|
# def __init__(self, **kwargs):
|
|
|
1561
|
#
|
|
1594
|
#
|
|
|
1562
|
# Operation.__init__(self, **kwargs)
|
|
1595
|
# Operation.__init__(self, **kwargs)
|
|
|
1563
|
# # self.isConfig = False
|
|
1596
|
# # self.isConfig = False
|
|
|
1564
|
# self.__powBuffer = None
|
|
1597
|
# self.__powBuffer = None
|
|
|
1565
|
# self.__startIndex = 0
|
|
1598
|
# self.__startIndex = 0
|
|
|
1566
|
# self.__pulseFound = False
|
|
1599
|
# self.__pulseFound = False
|
|
|
1567
|
#
|
|
1600
|
#
|
|
|
1568
|
# def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
|
|
1601
|
# def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
|
|
|
1569
|
#
|
|
1602
|
#
|
|
|
1570
|
# #Read data
|
|
1603
|
# #Read data
|
|
|
1571
|
#
|
|
1604
|
#
|
|
|
1572
|
# powerdB = dataOut.getPower(channel = channel)
|
|
1605
|
# powerdB = dataOut.getPower(channel = channel)
|
|
|
1573
|
# noisedB = dataOut.getNoise(channel = channel)[0]
|
|
1606
|
# noisedB = dataOut.getNoise(channel = channel)[0]
|
|
|
1574
|
#
|
|
1607
|
#
|
|
|
1575
|
# self.__powBuffer.extend(powerdB.flatten())
|
|
1608
|
# self.__powBuffer.extend(powerdB.flatten())
|
|
|
1576
|
#
|
|
1609
|
#
|
|
|
1577
|
# dataArray = numpy.array(self.__powBuffer)
|
|
1610
|
# dataArray = numpy.array(self.__powBuffer)
|
|
|
1578
|
#
|
|
1611
|
#
|
|
|
1579
|
# filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
|
|
1612
|
# filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
|
|
|
1580
|
#
|
|
1613
|
#
|
|
|
1581
|
# maxValue = numpy.nanmax(filteredPower)
|
|
1614
|
# maxValue = numpy.nanmax(filteredPower)
|
|
|
1582
|
#
|
|
1615
|
#
|
|
|
1583
|
# if maxValue < noisedB + 10:
|
|
1616
|
# if maxValue < noisedB + 10:
|
|
|
1584
|
# #No se encuentra ningun pulso de transmision
|
|
1617
|
# #No se encuentra ningun pulso de transmision
|
|
|
1585
|
# return None
|
|
1618
|
# return None
|
|
|
1586
|
#
|
|
1619
|
#
|
|
|
1587
|
# maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
|
|
1620
|
# maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
|
|
|
1588
|
#
|
|
1621
|
#
|
|
|
1589
|
# if len(maxValuesIndex) < 2:
|
|
1622
|
# if len(maxValuesIndex) < 2:
|
|
|
1590
|
# #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
|
|
1623
|
# #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
|
|
|
1591
|
# return None
|
|
1624
|
# return None
|
|
|
1592
|
#
|
|
1625
|
#
|
|
|
1593
|
# phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
|
|
1626
|
# phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
|
|
|
1594
|
#
|
|
1627
|
#
|
|
|
1595
|
# #Seleccionar solo valores con un espaciamiento de nSamples
|
|
1628
|
# #Seleccionar solo valores con un espaciamiento de nSamples
|
|
|
1596
|
# pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
|
|
1629
|
# pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
|
|
|
1597
|
#
|
|
1630
|
#
|
|
|
1598
|
# if len(pulseIndex) < 2:
|
|
1631
|
# if len(pulseIndex) < 2:
|
|
|
1599
|
# #Solo se encontro un pulso de transmision con ancho mayor a 1
|
|
1632
|
# #Solo se encontro un pulso de transmision con ancho mayor a 1
|
|
|
1600
|
# return None
|
|
1633
|
# return None
|
|
|
1601
|
#
|
|
1634
|
#
|
|
|
1602
|
# spacing = pulseIndex[1:] - pulseIndex[:-1]
|
|
1635
|
# spacing = pulseIndex[1:] - pulseIndex[:-1]
|
|
|
1603
|
#
|
|
1636
|
#
|
|
|
1604
|
# #remover senales que se distancien menos de 10 unidades o muestras
|
|
1637
|
# #remover senales que se distancien menos de 10 unidades o muestras
|
|
|
1605
|
# #(No deberian existir IPP menor a 10 unidades)
|
|
1638
|
# #(No deberian existir IPP menor a 10 unidades)
|
|
|
1606
|
#
|
|
1639
|
#
|
|
|
1607
|
# realIndex = numpy.where(spacing > 10 )[0]
|
|
1640
|
# realIndex = numpy.where(spacing > 10 )[0]
|
|
|
1608
|
#
|
|
1641
|
#
|
|
|
1609
|
# if len(realIndex) < 2:
|
|
1642
|
# if len(realIndex) < 2:
|
|
|
1610
|
# #Solo se encontro un pulso de transmision con ancho mayor a 1
|
|
1643
|
# #Solo se encontro un pulso de transmision con ancho mayor a 1
|
|
|
1611
|
# return None
|
|
1644
|
# return None
|
|
|
1612
|
#
|
|
1645
|
#
|
|
|
1613
|
# #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
|
|
1646
|
# #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
|
|
|
1614
|
# realPulseIndex = pulseIndex[realIndex]
|
|
1647
|
# realPulseIndex = pulseIndex[realIndex]
|
|
|
1615
|
#
|
|
1648
|
#
|
|
|
1616
|
# period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
|
|
1649
|
# period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
|
|
|
1617
|
#
|
|
1650
|
#
|
|
|
1618
|
# print "IPP = %d samples" %period
|
|
1651
|
# print "IPP = %d samples" %period
|
|
|
1619
|
#
|
|
1652
|
#
|
|
|
1620
|
# self.__newNSamples = dataOut.nHeights #int(period)
|
|
1653
|
# self.__newNSamples = dataOut.nHeights #int(period)
|
|
|
1621
|
# self.__startIndex = int(realPulseIndex[0])
|
|
1654
|
# self.__startIndex = int(realPulseIndex[0])
|
|
|
1622
|
#
|
|
1655
|
#
|
|
|
1623
|
# return 1
|
|
1656
|
# return 1
|
|
|
1624
|
#
|
|
1657
|
#
|
|
|
1625
|
#
|
|
1658
|
#
|
|
|
1626
|
# def setup(self, nSamples, nChannels, buffer_size = 4):
|
|
1659
|
# def setup(self, nSamples, nChannels, buffer_size = 4):
|
|
|
1627
|
#
|
|
1660
|
#
|
|
|
1628
|
# self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
|
|
1661
|
# self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
|
|
|
1629
|
# maxlen = buffer_size*nSamples)
|
|
1662
|
# maxlen = buffer_size*nSamples)
|
|
|
1630
|
#
|
|
1663
|
#
|
|
|
1631
|
# bufferList = []
|
|
1664
|
# bufferList = []
|
|
|
1632
|
#
|
|
1665
|
#
|
|
|
1633
|
# for i in range(nChannels):
|
|
1666
|
# for i in range(nChannels):
|
|
|
1634
|
# bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
|
|
1667
|
# bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
|
|
|
1635
|
# maxlen = buffer_size*nSamples)
|
|
1668
|
# maxlen = buffer_size*nSamples)
|
|
|
1636
|
#
|
|
1669
|
#
|
|
|
1637
|
# bufferList.append(bufferByChannel)
|
|
1670
|
# bufferList.append(bufferByChannel)
|
|
|
1638
|
#
|
|
1671
|
#
|
|
|
1639
|
# self.__nSamples = nSamples
|
|
1672
|
# self.__nSamples = nSamples
|
|
|
1640
|
# self.__nChannels = nChannels
|
|
1673
|
# self.__nChannels = nChannels
|
|
|
1641
|
# self.__bufferList = bufferList
|
|
1674
|
# self.__bufferList = bufferList
|
|
|
1642
|
#
|
|
1675
|
#
|
|
|
1643
|
# def run(self, dataOut, channel = 0):
|
|
1676
|
# def run(self, dataOut, channel = 0):
|
|
|
1644
|
#
|
|
1677
|
#
|
|
|
1645
|
# if not self.isConfig:
|
|
1678
|
# if not self.isConfig:
|
|
|
1646
|
# nSamples = dataOut.nHeights
|
|
1679
|
# nSamples = dataOut.nHeights
|
|
|
1647
|
# nChannels = dataOut.nChannels
|
|
1680
|
# nChannels = dataOut.nChannels
|
|
|
1648
|
# self.setup(nSamples, nChannels)
|
|
1681
|
# self.setup(nSamples, nChannels)
|
|
|
1649
|
# self.isConfig = True
|
|
1682
|
# self.isConfig = True
|
|
|
1650
|
#
|
|
1683
|
#
|
|
|
1651
|
# #Append new data to internal buffer
|
|
1684
|
# #Append new data to internal buffer
|
|
|
1652
|
# for thisChannel in range(self.__nChannels):
|
|
1685
|
# for thisChannel in range(self.__nChannels):
|
|
|
1653
|
# bufferByChannel = self.__bufferList[thisChannel]
|
|
1686
|
# bufferByChannel = self.__bufferList[thisChannel]
|
|
|
1654
|
# bufferByChannel.extend(dataOut.data[thisChannel])
|
|
1687
|
# bufferByChannel.extend(dataOut.data[thisChannel])
|
|
|
1655
|
#
|
|
1688
|
#
|
|
|
1656
|
# if self.__pulseFound:
|
|
1689
|
# if self.__pulseFound:
|
|
|
1657
|
# self.__startIndex -= self.__nSamples
|
|
1690
|
# self.__startIndex -= self.__nSamples
|
|
|
1658
|
#
|
|
1691
|
#
|
|
|
1659
|
# #Finding Tx Pulse
|
|
1692
|
# #Finding Tx Pulse
|
|
|
1660
|
# if not self.__pulseFound:
|
|
1693
|
# if not self.__pulseFound:
|
|
|
1661
|
# indexFound = self.__findTxPulse(dataOut, channel)
|
|
1694
|
# indexFound = self.__findTxPulse(dataOut, channel)
|
|
|
1662
|
#
|
|
1695
|
#
|
|
|
1663
|
# if indexFound == None:
|
|
1696
|
# if indexFound == None:
|
|
|
1664
|
# dataOut.flagNoData = True
|
|
1697
|
# dataOut.flagNoData = True
|
|
|
1665
|
# return
|
|
1698
|
# return
|
|
|
1666
|
#
|
|
1699
|
#
|
|
|
1667
|
# self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
|
|
1700
|
# self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
|
|
|
1668
|
# self.__pulseFound = True
|
|
1701
|
# self.__pulseFound = True
|
|
|
1669
|
# self.__startIndex = indexFound
|
|
1702
|
# self.__startIndex = indexFound
|
|
|
1670
|
#
|
|
1703
|
#
|
|
|
1671
|
# #If pulse was found ...
|
|
1704
|
# #If pulse was found ...
|
|
|
1672
|
# for thisChannel in range(self.__nChannels):
|
|
1705
|
# for thisChannel in range(self.__nChannels):
|
|
|
1673
|
# bufferByChannel = self.__bufferList[thisChannel]
|
|
1706
|
# bufferByChannel = self.__bufferList[thisChannel]
|
|
|
1674
|
# #print self.__startIndex
|
|
1707
|
# #print self.__startIndex
|
|
|
1675
|
# x = numpy.array(bufferByChannel)
|
|
1708
|
# x = numpy.array(bufferByChannel)
|
|
|
1676
|
# self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
|
|
1709
|
# self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
|
|
|
1677
|
#
|
|
1710
|
#
|
|
|
1678
|
# deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
1711
|
# deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
|
|
|
1679
|
# dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
|
|
1712
|
# dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
|
|
|
1680
|
# # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
|
|
1713
|
# # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
|
|
|
1681
|
#
|
|
1714
|
#
|
|
|
1682
|
# dataOut.data = self.__arrayBuffer
|
|
1715
|
# dataOut.data = self.__arrayBuffer
|
|
|
1683
|
#
|
|
1716
|
#
|
|
|
1684
|
# self.__startIndex += self.__newNSamples
|
|
1717
|
# self.__startIndex += self.__newNSamples
|
|
|
1685
|
#
|
|
1718
|
#
|
|
|
1686
|
# return
|
|
1719
|
# return
|
