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sumado de stride en stride. ej 96 -> 24x4 -> 1
sumado de stride en stride. ej 96 -> 24x4 -> 1
José Chávez - - Load All Authors

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jroproc_voltage.py
1321 lines | 39.9 KiB | text/x-python | PythonLexer
/ schainpy / model / proc / jroproc_voltage.py
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import sys
import numpy
from scipy import interpolate
from schainpy import cSchain
from jroproc_base import ProcessingUnit, Operation
from schainpy.model.data.jrodata import Voltage
from time import time
class VoltageProc(ProcessingUnit):
def __init__(self, **kwargs):
ProcessingUnit.__init__(self, **kwargs)
# self.objectDict = {}
self.dataOut = Voltage()
self.flip = 1
def run(self):
if self.dataIn.type == 'AMISR':
self.__updateObjFromAmisrInput()
if self.dataIn.type == 'Voltage':
self.dataOut.copy(self.dataIn)
# self.dataOut.copy(self.dataIn)
def __updateObjFromAmisrInput(self):
self.dataOut.timeZone = self.dataIn.timeZone
self.dataOut.dstFlag = self.dataIn.dstFlag
self.dataOut.errorCount = self.dataIn.errorCount
self.dataOut.useLocalTime = self.dataIn.useLocalTime
self.dataOut.flagNoData = self.dataIn.flagNoData
self.dataOut.data = self.dataIn.data
self.dataOut.utctime = self.dataIn.utctime
self.dataOut.channelList = self.dataIn.channelList
# self.dataOut.timeInterval = self.dataIn.timeInterval
self.dataOut.heightList = self.dataIn.heightList
self.dataOut.nProfiles = self.dataIn.nProfiles
self.dataOut.nCohInt = self.dataIn.nCohInt
self.dataOut.ippSeconds = self.dataIn.ippSeconds
self.dataOut.frequency = self.dataIn.frequency
self.dataOut.azimuth = self.dataIn.azimuth
self.dataOut.zenith = self.dataIn.zenith
self.dataOut.beam.codeList = self.dataIn.beam.codeList
self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
#
# pass#
#
# def init(self):
#
#
# if self.dataIn.type == 'AMISR':
# self.__updateObjFromAmisrInput()
#
# if self.dataIn.type == 'Voltage':
# self.dataOut.copy(self.dataIn)
# # No necesita copiar en cada init() los atributos de dataIn
# # la copia deberia hacerse por cada nuevo bloque de datos
def selectChannels(self, channelList):
channelIndexList = []
for channel in channelList:
if channel not in self.dataOut.channelList:
raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
index = self.dataOut.channelList.index(channel)
channelIndexList.append(index)
self.selectChannelsByIndex(channelIndexList)
def selectChannelsByIndex(self, channelIndexList):
"""
Selecciona un bloque de datos en base a canales segun el channelIndexList
Input:
channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
Affected:
self.dataOut.data
self.dataOut.channelIndexList
self.dataOut.nChannels
self.dataOut.m_ProcessingHeader.totalSpectra
self.dataOut.systemHeaderObj.numChannels
self.dataOut.m_ProcessingHeader.blockSize
Return:
None
"""
for channelIndex in channelIndexList:
if channelIndex not in self.dataOut.channelIndexList:
print channelIndexList
raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
data = self.dataOut.data[channelIndexList,:,:]
else:
data = self.dataOut.data[channelIndexList,:]
self.dataOut.data = data
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
# self.dataOut.nChannels = nChannels
return 1
def selectHeights(self, minHei=None, maxHei=None):
"""
Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
minHei <= height <= maxHei
Input:
minHei : valor minimo de altura a considerar
maxHei : valor maximo de altura a considerar
Affected:
Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
Return:
1 si el metodo se ejecuto con exito caso contrario devuelve 0
"""
if minHei == None:
minHei = self.dataOut.heightList[0]
if maxHei == None:
maxHei = self.dataOut.heightList[-1]
if (minHei < self.dataOut.heightList[0]):
minHei = self.dataOut.heightList[0]
if (maxHei > self.dataOut.heightList[-1]):
maxHei = self.dataOut.heightList[-1]
minIndex = 0
maxIndex = 0
heights = self.dataOut.heightList
inda = numpy.where(heights >= minHei)
indb = numpy.where(heights <= maxHei)
try:
minIndex = inda[0][0]
except:
minIndex = 0
try:
maxIndex = indb[0][-1]
except:
maxIndex = len(heights)
self.selectHeightsByIndex(minIndex, maxIndex)
return 1
def selectHeightsByIndex(self, minIndex, maxIndex):
"""
Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
minIndex <= index <= maxIndex
Input:
minIndex : valor de indice minimo de altura a considerar
maxIndex : valor de indice maximo de altura a considerar
Affected:
self.dataOut.data
self.dataOut.heightList
Return:
1 si el metodo se ejecuto con exito caso contrario devuelve 0
"""
if (minIndex < 0) or (minIndex > maxIndex):
raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
if (maxIndex >= self.dataOut.nHeights):
maxIndex = self.dataOut.nHeights
#voltage
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
data = self.dataOut.data[:,:, minIndex:maxIndex]
else:
data = self.dataOut.data[:, minIndex:maxIndex]
# firstHeight = self.dataOut.heightList[minIndex]
self.dataOut.data = data
self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
if self.dataOut.nHeights <= 1:
raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
return 1
def filterByHeights(self, window):
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
if window == None:
window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
newdelta = deltaHeight * window
r = self.dataOut.nHeights % window
newheights = (self.dataOut.nHeights-r)/window
if newheights <= 1:
raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
if self.dataOut.flagDataAsBlock:
"""
Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
buffer = numpy.sum(buffer,3)
else:
buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
buffer = numpy.sum(buffer,2)
self.dataOut.data = buffer
self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
self.dataOut.windowOfFilter = window
def setH0(self, h0, deltaHeight = None):
if not deltaHeight:
deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
nHeights = self.dataOut.nHeights
newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
self.dataOut.heightList = newHeiRange
def deFlip(self, channelList = []):
data = self.dataOut.data.copy()
if self.dataOut.flagDataAsBlock:
flip = self.flip
profileList = range(self.dataOut.nProfiles)
if not channelList:
for thisProfile in profileList:
data[:,thisProfile,:] = data[:,thisProfile,:]*flip
flip *= -1.0
else:
for thisChannel in channelList:
if thisChannel not in self.dataOut.channelList:
continue
for thisProfile in profileList:
data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
flip *= -1.0
self.flip = flip
else:
if not channelList:
data[:,:] = data[:,:]*self.flip
else:
for thisChannel in channelList:
if thisChannel not in self.dataOut.channelList:
continue
data[thisChannel,:] = data[thisChannel,:]*self.flip
self.flip *= -1.
self.dataOut.data = data
def setRadarFrequency(self, frequency=None):
if frequency != None:
self.dataOut.frequency = frequency
return 1
def interpolateHeights(self, topLim, botLim):
#69 al 72 para julia
#82-84 para meteoros
if len(numpy.shape(self.dataOut.data))==2:
sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
#self.dataOut.data[:,botLim:limSup+1] = sampInterp
self.dataOut.data[:,botLim:topLim+1] = sampInterp
else:
nHeights = self.dataOut.data.shape[2]
x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)]
f = interpolate.interp1d(x, y, axis = 2)
xnew = numpy.arange(botLim,topLim+1)
ynew = f(xnew)
self.dataOut.data[:,:,botLim:topLim+1] = ynew
# import collections
class CohInt(Operation):
isConfig = False
__profIndex = 0
__byTime = False
__initime = None
__lastdatatime = None
__integrationtime = None
__buffer = None
__bufferStride = []
__dataReady = False
__profIndexStride = 0
__dataToPutStride = False
n = None
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
# self.isConfig = False
def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
"""
Set the parameters of the integration class.
Inputs:
n : Number of coherent integrations
timeInterval : Time of integration. If the parameter "n" is selected this one does not work
overlapping :
"""
self.__initime = None
self.__lastdatatime = 0
self.__buffer = None
self.__dataReady = False
self.byblock = byblock
self.stride = stride
if n == None and timeInterval == None:
raise ValueError, "n or timeInterval should be specified ..."
if n != None:
self.n = n
self.__byTime = False
else:
self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
self.n = 9999
self.__byTime = True
if overlapping:
self.__withOverlapping = True
self.__buffer = None
else:
self.__withOverlapping = False
self.__buffer = 0
self.__profIndex = 0
def putData(self, data):
"""
Add a profile to the __buffer and increase in one the __profileIndex
"""
if not self.__withOverlapping:
self.__buffer += data.copy()
self.__profIndex += 1
return
#Overlapping data
nChannels, nHeis = data.shape
data = numpy.reshape(data, (1, nChannels, nHeis))
#If the buffer is empty then it takes the data value
if self.__buffer is None:
self.__buffer = data
self.__profIndex += 1
return
#If the buffer length is lower than n then stakcing the data value
if self.__profIndex < self.n:
self.__buffer = numpy.vstack((self.__buffer, data))
self.__profIndex += 1
return
#If the buffer length is equal to n then replacing the last buffer value with the data value
self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
self.__buffer[self.n-1] = data
self.__profIndex = self.n
return
def pushData(self):
"""
Return the sum of the last profiles and the profiles used in the sum.
Affected:
self.__profileIndex
"""
if not self.__withOverlapping:
data = self.__buffer
n = self.__profIndex
self.__buffer = 0
self.__profIndex = 0
return data, n
#Integration with Overlapping
data = numpy.sum(self.__buffer, axis=0)
# print data
# raise
n = self.__profIndex
return data, n
def byProfiles(self, data):
self.__dataReady = False
avgdata = None
# n = None
# print data
# raise
self.putData(data)
if self.__profIndex == self.n:
avgdata, n = self.pushData()
self.__dataReady = True
return avgdata
def byTime(self, data, datatime):
self.__dataReady = False
avgdata = None
n = None
self.putData(data)
if (datatime - self.__initime) >= self.__integrationtime:
avgdata, n = self.pushData()
self.n = n
self.__dataReady = True
return avgdata
def integrateByStride(self, data, datatime):
# print data
if self.__profIndex == 0:
self.__buffer = [[data.copy(), datatime]]
else:
self.__buffer.append([data.copy(), datatime])
self.__profIndex += 1
self.__dataReady = False
if self.__profIndex == self.n * self.stride :
self.__dataToPutStride = True
self.__profIndexStride = 0
self.__profIndex = 0
self.__bufferStride = []
for i in range(self.stride):
current = self.__buffer[i::self.stride]
data = numpy.sum([t[0] for t in current], axis=0)
avgdatatime = numpy.average([t[1] for t in current])
# print data
self.__bufferStride.append((data, avgdatatime))
if self.__dataToPutStride:
self.__dataReady = False
self.__profIndexStride += 1
if self.__profIndexStride == self.stride:
self.__dataReady = True
self.__dataToPutStride = False
self.__profIndexStride = 0
# print self.__bufferStride[self.__profIndexStride - 1]
# raise
return (numpy.sum([t[0] for t in self.__bufferStride], axis=0), numpy.average([t[1] for t in self.__bufferStride]))
return None, None
def integrate(self, data, datatime=None):
if self.__initime == None:
self.__initime = datatime
if self.__byTime:
avgdata = self.byTime(data, datatime)
else:
avgdata = self.byProfiles(data)
self.__lastdatatime = datatime
if avgdata is None:
return None, None
avgdatatime = self.__initime
deltatime = datatime - self.__lastdatatime
if not self.__withOverlapping:
self.__initime = datatime
else:
self.__initime += deltatime
return avgdata, avgdatatime
def integrateByBlock(self, dataOut):
times = int(dataOut.data.shape[1]/self.n)
avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
id_min = 0
id_max = self.n
for i in range(times):
junk = dataOut.data[:,id_min:id_max,:]
avgdata[:,i,:] = junk.sum(axis=1)
id_min += self.n
id_max += self.n
timeInterval = dataOut.ippSeconds*self.n
avgdatatime = (times - 1) * timeInterval + dataOut.utctime
self.__dataReady = True
return avgdata, avgdatatime
def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
if not self.isConfig:
self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
self.isConfig = True
if dataOut.flagDataAsBlock:
"""
Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
"""
avgdata, avgdatatime = self.integrateByBlock(dataOut)
dataOut.nProfiles /= self.n
else:
if stride is None:
avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
else:
avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
# dataOut.timeInterval *= n
dataOut.flagNoData = True
if self.__dataReady:
dataOut.data = avgdata
dataOut.nCohInt *= self.n
dataOut.utctime = avgdatatime
# print avgdata, avgdatatime
# raise
# dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
dataOut.flagNoData = False
class Decoder(Operation):
isConfig = False
__profIndex = 0
code = None
nCode = None
nBaud = None
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
self.times = None
self.osamp = None
# self.__setValues = False
self.isConfig = False
def setup(self, code, osamp, dataOut):
self.__profIndex = 0
self.code = code
self.nCode = len(code)
self.nBaud = len(code[0])
if (osamp != None) and (osamp >1):
self.osamp = osamp
self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
self.nBaud = self.nBaud*self.osamp
self.__nChannels = dataOut.nChannels
self.__nProfiles = dataOut.nProfiles
self.__nHeis = dataOut.nHeights
if self.__nHeis < self.nBaud:
raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
#Frequency
__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
__codeBuffer[:,0:self.nBaud] = self.code
self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
if dataOut.flagDataAsBlock:
self.ndatadec = self.__nHeis #- self.nBaud + 1
self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
else:
#Time
self.ndatadec = self.__nHeis #- self.nBaud + 1
self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
def __convolutionInFreq(self, data):
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
fft_data = numpy.fft.fft(data, axis=1)
conv = fft_data*fft_code
data = numpy.fft.ifft(conv,axis=1)
return data
def __convolutionInFreqOpt(self, data):
raise NotImplementedError
def __convolutionInTime(self, data):
code = self.code[self.__profIndex]
for i in range(self.__nChannels):
self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
return self.datadecTime
def __convolutionByBlockInTime(self, data):
repetitions = self.__nProfiles / self.nCode
junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
junk = junk.flatten()
code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
profilesList = xrange(self.__nProfiles)
for i in range(self.__nChannels):
for j in profilesList:
self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
return self.datadecTime
def __convolutionByBlockInFreq(self, data):
raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
fft_data = numpy.fft.fft(data, axis=2)
conv = fft_data*fft_code
data = numpy.fft.ifft(conv,axis=2)
return data
def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
if dataOut.flagDecodeData:
print "This data is already decoded, recoding again ..."
if not self.isConfig:
if code is None:
if dataOut.code is None:
raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
code = dataOut.code
else:
code = numpy.array(code).reshape(nCode,nBaud)
self.setup(code, osamp, dataOut)
self.isConfig = True
if mode == 3:
sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
if times != None:
sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
if self.code is None:
print "Fail decoding: Code is not defined."
return
self.__nProfiles = dataOut.nProfiles
datadec = None
if mode == 3:
mode = 0
if dataOut.flagDataAsBlock:
"""
Decoding when data have been read as block,
"""
if mode == 0:
datadec = self.__convolutionByBlockInTime(dataOut.data)
if mode == 1:
datadec = self.__convolutionByBlockInFreq(dataOut.data)
else:
"""
Decoding when data have been read profile by profile
"""
if mode == 0:
datadec = self.__convolutionInTime(dataOut.data)
if mode == 1:
datadec = self.__convolutionInFreq(dataOut.data)
if mode == 2:
datadec = self.__convolutionInFreqOpt(dataOut.data)
if datadec is None:
raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
dataOut.code = self.code
dataOut.nCode = self.nCode
dataOut.nBaud = self.nBaud
dataOut.data = datadec
dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
dataOut.flagDecodeData = True #asumo q la data esta decodificada
if self.__profIndex == self.nCode-1:
self.__profIndex = 0
return 1
self.__profIndex += 1
return 1
# dataOut.flagDeflipData = True #asumo q la data no esta sin flip
class ProfileConcat(Operation):
isConfig = False
buffer = None
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
self.profileIndex = 0
def reset(self):
self.buffer = numpy.zeros_like(self.buffer)
self.start_index = 0
self.times = 1
def setup(self, data, m, n=1):
self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
self.nHeights = data.shape[1]#.nHeights
self.start_index = 0
self.times = 1
def concat(self, data):
self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
self.start_index = self.start_index + self.nHeights
def run(self, dataOut, m):
dataOut.flagNoData = True
if not self.isConfig:
self.setup(dataOut.data, m, 1)
self.isConfig = True
if dataOut.flagDataAsBlock:
raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
else:
self.concat(dataOut.data)
self.times += 1
if self.times > m:
dataOut.data = self.buffer
self.reset()
dataOut.flagNoData = False
# se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
dataOut.ippSeconds *= m
class ProfileSelector(Operation):
profileIndex = None
# Tamanho total de los perfiles
nProfiles = None
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
self.profileIndex = 0
def incProfileIndex(self):
self.profileIndex += 1
if self.profileIndex >= self.nProfiles:
self.profileIndex = 0
def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
if profileIndex < minIndex:
return False
if profileIndex > maxIndex:
return False
return True
def isThisProfileInList(self, profileIndex, profileList):
if profileIndex not in profileList:
return False
return True
def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
"""
ProfileSelector:
Inputs:
profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
"""
if rangeList is not None:
if type(rangeList[0]) not in (tuple, list):
rangeList = [rangeList]
dataOut.flagNoData = True
if dataOut.flagDataAsBlock:
"""
data dimension = [nChannels, nProfiles, nHeis]
"""
if profileList != None:
dataOut.data = dataOut.data[:,profileList,:]
if profileRangeList != None:
minIndex = profileRangeList[0]
maxIndex = profileRangeList[1]
profileList = range(minIndex, maxIndex+1)
dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
if rangeList != None:
profileList = []
for thisRange in rangeList:
minIndex = thisRange[0]
maxIndex = thisRange[1]
profileList.extend(range(minIndex, maxIndex+1))
dataOut.data = dataOut.data[:,profileList,:]
dataOut.nProfiles = len(profileList)
dataOut.profileIndex = dataOut.nProfiles - 1
dataOut.flagNoData = False
return True
"""
data dimension = [nChannels, nHeis]
"""
if profileList != None:
if self.isThisProfileInList(dataOut.profileIndex, profileList):
self.nProfiles = len(profileList)
dataOut.nProfiles = self.nProfiles
dataOut.profileIndex = self.profileIndex
dataOut.flagNoData = False
self.incProfileIndex()
return True
if profileRangeList != None:
minIndex = profileRangeList[0]
maxIndex = profileRangeList[1]
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
self.nProfiles = maxIndex - minIndex + 1
dataOut.nProfiles = self.nProfiles
dataOut.profileIndex = self.profileIndex
dataOut.flagNoData = False
self.incProfileIndex()
return True
if rangeList != None:
nProfiles = 0
for thisRange in rangeList:
minIndex = thisRange[0]
maxIndex = thisRange[1]
nProfiles += maxIndex - minIndex + 1
for thisRange in rangeList:
minIndex = thisRange[0]
maxIndex = thisRange[1]
if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
self.nProfiles = nProfiles
dataOut.nProfiles = self.nProfiles
dataOut.profileIndex = self.profileIndex
dataOut.flagNoData = False
self.incProfileIndex()
break
return True
if beam != None: #beam is only for AMISR data
if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
dataOut.flagNoData = False
dataOut.profileIndex = self.profileIndex
self.incProfileIndex()
return True
raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
return False
class Reshaper(Operation):
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
self.__buffer = None
self.__nitems = 0
def __appendProfile(self, dataOut, nTxs):
if self.__buffer is None:
shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
ini = dataOut.nHeights * self.__nitems
end = ini + dataOut.nHeights
self.__buffer[:, ini:end] = dataOut.data
self.__nitems += 1
return int(self.__nitems*nTxs)
def __getBuffer(self):
if self.__nitems == int(1./self.__nTxs):
self.__nitems = 0
return self.__buffer.copy()
return None
def __checkInputs(self, dataOut, shape, nTxs):
if shape is None and nTxs is None:
raise ValueError, "Reshaper: shape of factor should be defined"
if nTxs:
if nTxs < 0:
raise ValueError, "nTxs should be greater than 0"
if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
return shape, nTxs
if len(shape) != 2 and len(shape) != 3:
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)
if len(shape) == 2:
shape_tuple = [dataOut.nChannels]
shape_tuple.extend(shape)
else:
shape_tuple = list(shape)
nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
return shape_tuple, nTxs
def run(self, dataOut, shape=None, nTxs=None):
shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
dataOut.flagNoData = True
profileIndex = None
if dataOut.flagDataAsBlock:
dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
dataOut.flagNoData = False
profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
else:
if self.__nTxs < 1:
self.__appendProfile(dataOut, self.__nTxs)
new_data = self.__getBuffer()
if new_data is not None:
dataOut.data = new_data
dataOut.flagNoData = False
profileIndex = dataOut.profileIndex*nTxs
else:
raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
dataOut.profileIndex = profileIndex
dataOut.ippSeconds /= self.__nTxs
class SplitProfiles(Operation):
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
def run(self, dataOut, n):
dataOut.flagNoData = True
profileIndex = None
if dataOut.flagDataAsBlock:
#nchannels, nprofiles, nsamples
shape = dataOut.data.shape
if shape[2] % n != 0:
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
new_shape = shape[0], shape[1]*n, shape[2]/n
dataOut.data = numpy.reshape(dataOut.data, new_shape)
dataOut.flagNoData = False
profileIndex = int(dataOut.nProfiles/n) - 1
else:
raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
dataOut.nProfiles = int(dataOut.nProfiles*n)
dataOut.profileIndex = profileIndex
dataOut.ippSeconds /= n
class CombineProfiles(Operation):
def __init__(self, **kwargs):
Operation.__init__(self, **kwargs)
self.__remData = None
self.__profileIndex = 0
def run(self, dataOut, n):
dataOut.flagNoData = True
profileIndex = None
if dataOut.flagDataAsBlock:
#nchannels, nprofiles, nsamples
shape = dataOut.data.shape
new_shape = shape[0], shape[1]/n, shape[2]*n
if shape[1] % n != 0:
raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
dataOut.data = numpy.reshape(dataOut.data, new_shape)
dataOut.flagNoData = False
profileIndex = int(dataOut.nProfiles*n) - 1
else:
#nchannels, nsamples
if self.__remData is None:
newData = dataOut.data
else:
newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
self.__profileIndex += 1
if self.__profileIndex < n:
self.__remData = newData
#continue
return
self.__profileIndex = 0
self.__remData = None
dataOut.data = newData
dataOut.flagNoData = False
profileIndex = dataOut.profileIndex/n
deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
dataOut.nProfiles = int(dataOut.nProfiles/n)
dataOut.profileIndex = profileIndex
dataOut.ippSeconds *= n
# import collections
# from scipy.stats import mode
#
# class Synchronize(Operation):
#
# isConfig = False
# __profIndex = 0
#
# def __init__(self, **kwargs):
#
# Operation.__init__(self, **kwargs)
# # self.isConfig = False
# self.__powBuffer = None
# self.__startIndex = 0
# self.__pulseFound = False
#
# def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
#
# #Read data
#
# powerdB = dataOut.getPower(channel = channel)
# noisedB = dataOut.getNoise(channel = channel)[0]
#
# self.__powBuffer.extend(powerdB.flatten())
#
# dataArray = numpy.array(self.__powBuffer)
#
# filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
#
# maxValue = numpy.nanmax(filteredPower)
#
# if maxValue < noisedB + 10:
# #No se encuentra ningun pulso de transmision
# return None
#
# maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
#
# if len(maxValuesIndex) < 2:
# #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
# return None
#
# phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
#
# #Seleccionar solo valores con un espaciamiento de nSamples
# pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
#
# if len(pulseIndex) < 2:
# #Solo se encontro un pulso de transmision con ancho mayor a 1
# return None
#
# spacing = pulseIndex[1:] - pulseIndex[:-1]
#
# #remover senales que se distancien menos de 10 unidades o muestras
# #(No deberian existir IPP menor a 10 unidades)
#
# realIndex = numpy.where(spacing > 10 )[0]
#
# if len(realIndex) < 2:
# #Solo se encontro un pulso de transmision con ancho mayor a 1
# return None
#
# #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
# realPulseIndex = pulseIndex[realIndex]
#
# period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
#
# print "IPP = %d samples" %period
#
# self.__newNSamples = dataOut.nHeights #int(period)
# self.__startIndex = int(realPulseIndex[0])
#
# return 1
#
#
# def setup(self, nSamples, nChannels, buffer_size = 4):
#
# self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
# maxlen = buffer_size*nSamples)
#
# bufferList = []
#
# for i in range(nChannels):
# bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
# maxlen = buffer_size*nSamples)
#
# bufferList.append(bufferByChannel)
#
# self.__nSamples = nSamples
# self.__nChannels = nChannels
# self.__bufferList = bufferList
#
# def run(self, dataOut, channel = 0):
#
# if not self.isConfig:
# nSamples = dataOut.nHeights
# nChannels = dataOut.nChannels
# self.setup(nSamples, nChannels)
# self.isConfig = True
#
# #Append new data to internal buffer
# for thisChannel in range(self.__nChannels):
# bufferByChannel = self.__bufferList[thisChannel]
# bufferByChannel.extend(dataOut.data[thisChannel])
#
# if self.__pulseFound:
# self.__startIndex -= self.__nSamples
#
# #Finding Tx Pulse
# if not self.__pulseFound:
# indexFound = self.__findTxPulse(dataOut, channel)
#
# if indexFound == None:
# dataOut.flagNoData = True
# return
#
# self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
# self.__pulseFound = True
# self.__startIndex = indexFound
#
# #If pulse was found ...
# for thisChannel in range(self.__nChannels):
# bufferByChannel = self.__bufferList[thisChannel]
# #print self.__startIndex
# x = numpy.array(bufferByChannel)
# self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
#
# deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
# dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
# # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
#
# dataOut.data = self.__arrayBuffer
#
# self.__startIndex += self.__newNSamples
#
# return