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'''
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$Author: dsuarez $
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$Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
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'''
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import os
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import numpy
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import datetime
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import time
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from jrodata import *
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from jrodataIO import *
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from jroplot import *
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class ProcessingUnit:
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"""
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Esta es la clase base para el procesamiento de datos.
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Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
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- Metodos internos (callMethod)
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- Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
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tienen que ser agreagados con el metodo "add".
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"""
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# objeto de datos de entrada (Voltage, Spectra o Correlation)
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dataIn = None
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# objeto de datos de entrada (Voltage, Spectra o Correlation)
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dataOut = None
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objectDict = None
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def __init__(self):
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self.objectDict = {}
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def init(self):
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raise ValueError, "Not implemented"
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def addOperation(self, object, objId):
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"""
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Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
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identificador asociado a este objeto.
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Input:
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object : objeto de la clase "Operation"
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Return:
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objId : identificador del objeto, necesario para ejecutar la operacion
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"""
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self.objectDict[objId] = object
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return objId
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def operation(self, **kwargs):
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"""
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Operacion directa sobre la data (dataout.data). Es necesario actualizar los valores de los
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atributos del objeto dataOut
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Input:
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**kwargs : Diccionario de argumentos de la funcion a ejecutar
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"""
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raise ValueError, "ImplementedError"
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def callMethod(self, name, **kwargs):
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"""
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Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
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Input:
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name : nombre del metodo a ejecutar
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**kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
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"""
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if name != 'run':
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if name == 'init' and self.dataIn.isEmpty():
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self.dataOut.flagNoData = True
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return False
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if name != 'init' and self.dataOut.isEmpty():
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return False
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methodToCall = getattr(self, name)
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methodToCall(**kwargs)
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if name != 'run':
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return True
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if self.dataOut.isEmpty():
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return False
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return True
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def callObject(self, objId, **kwargs):
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"""
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Ejecuta la operacion asociada al identificador del objeto "objId"
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Input:
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objId : identificador del objeto a ejecutar
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**kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
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Return:
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None
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"""
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if self.dataOut.isEmpty():
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return False
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object = self.objectDict[objId]
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object.run(self.dataOut, **kwargs)
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return True
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def call(self, operationConf, **kwargs):
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"""
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Return True si ejecuta la operacion "operationConf.name" con los
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argumentos "**kwargs". False si la operacion no se ha ejecutado.
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La operacion puede ser de dos tipos:
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1. Un metodo propio de esta clase:
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operation.type = "self"
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2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
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operation.type = "other".
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Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
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"addOperation" e identificado con el operation.id
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con el id de la operacion.
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Input:
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Operation : Objeto del tipo operacion con los atributos: name, type y id.
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"""
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if operationConf.type == 'self':
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sts = self.callMethod(operationConf.name, **kwargs)
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if operationConf.type == 'other':
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sts = self.callObject(operationConf.id, **kwargs)
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return sts
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def setInput(self, dataIn):
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self.dataIn = dataIn
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def getOutput(self):
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return self.dataOut
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class Operation():
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"""
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Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
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y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
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acumulacion dentro de esta clase
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Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
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"""
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__buffer = None
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__isConfig = False
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def __init__(self):
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pass
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def run(self, dataIn, **kwargs):
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"""
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Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
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Input:
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dataIn : objeto del tipo JROData
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Return:
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None
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Affected:
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__buffer : buffer de recepcion de datos.
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"""
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raise ValueError, "ImplementedError"
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class VoltageProc(ProcessingUnit):
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def __init__(self):
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self.objectDict = {}
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self.dataOut = Voltage()
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def init(self):
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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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# la copia deberia hacerse por cada nuevo bloque de datos
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def selectChannels(self, channelList):
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channelIndexList = []
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for channel in channelList:
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index = self.dataOut.channelList.index(channel)
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channelIndexList.append(index)
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self.selectChannelsByIndex(channelIndexList)
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def selectChannelsByIndex(self, channelIndexList):
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"""
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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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channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
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Affected:
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self.dataOut.data
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self.dataOut.channelIndexList
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self.dataOut.nChannels
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self.dataOut.m_ProcessingHeader.totalSpectra
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self.dataOut.systemHeaderObj.numChannels
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self.dataOut.m_ProcessingHeader.blockSize
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Return:
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None
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"""
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for channelIndex in channelIndexList:
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if channelIndex not in self.dataOut.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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nChannels = len(channelIndexList)
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data = self.dataOut.data[channelIndexList,:]
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self.dataOut.data = data
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self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
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# self.dataOut.nChannels = nChannels
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return 1
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class CohInt(Operation):
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__profIndex = 0
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__withOverapping = False
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__byTime = False
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__initime = None
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__lastdatatime = None
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__integrationtime = None
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__buffer = None
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__dataReady = False
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n = None
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def __init__(self):
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self.__isConfig = False
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def setup(self, n=None, timeInterval=None, overlapping=False):
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"""
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Set the parameters of the integration class.
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Inputs:
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n : Number of coherent integrations
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timeInterval : Time of integration. If the parameter "n" is selected this one does not work
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overlapping :
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"""
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self.__initime = None
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self.__lastdatatime = 0
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self.__buffer = None
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self.__dataReady = False
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if n == None and timeInterval == None:
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raise ValueError, "n or timeInterval should be specified ..."
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if n != None:
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self.n = n
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self.__byTime = False
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else:
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self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
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self.n = 9999
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self.__byTime = True
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if overlapping:
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self.__withOverapping = True
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self.__buffer = None
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else:
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self.__withOverapping = False
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self.__buffer = 0
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self.__profIndex = 0
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def putData(self, data):
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"""
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Add a profile to the __buffer and increase in one the __profileIndex
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"""
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if not self.__withOverapping:
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self.__buffer += data.copy()
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self.__profIndex += 1
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return
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#Overlapping data
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nChannels, nHeis = data.shape
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data = numpy.reshape(data, (1, nChannels, nHeis))
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#If the buffer is empty then it takes the data value
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if self.__buffer == None:
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self.__buffer = data
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self.__profIndex += 1
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return
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#If the buffer length is lower than n then stakcing the data value
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if self.__profIndex < self.n:
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self.__buffer = numpy.vstack((self.__buffer, data))
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self.__profIndex += 1
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return
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#If the buffer length is equal to n then replacing the last buffer value with the data value
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self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
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self.__buffer[self.n-1] = data
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self.__profIndex = self.n
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return
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def pushData(self):
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"""
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Return the sum of the last profiles and the profiles used in the sum.
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Affected:
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self.__profileIndex
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"""
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if not self.__withOverapping:
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data = self.__buffer
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n = self.__profIndex
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self.__buffer = 0
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self.__profIndex = 0
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return data, n
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#Integration with Overlapping
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data = numpy.sum(self.__buffer, axis=0)
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n = self.__profIndex
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return data, n
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def byProfiles(self, data):
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self.__dataReady = False
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avgdata = None
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n = None
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self.putData(data)
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if self.__profIndex == self.n:
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avgdata, n = self.pushData()
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self.__dataReady = True
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return avgdata
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def byTime(self, data, datatime):
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self.__dataReady = False
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avgdata = None
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n = None
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self.putData(data)
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if (datatime - self.__initime) >= self.__integrationtime:
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avgdata, n = self.pushData()
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self.n = n
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self.__dataReady = True
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return avgdata
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def integrate(self, data, datatime=None):
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if self.__initime == None:
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self.__initime = datatime
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if self.__byTime:
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avgdata = self.byTime(data, datatime)
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else:
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avgdata = self.byProfiles(data)
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self.__lastdatatime = datatime
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if avgdata == None:
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return None, None
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avgdatatime = self.__initime
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deltatime = datatime -self.__lastdatatime
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if not self.__withOverapping:
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self.__initime = datatime
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else:
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self.__initime += deltatime
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return avgdata, avgdatatime
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def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
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if not self.__isConfig:
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self.setup(n, timeInterval, overlapping)
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self.__isConfig = True
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avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
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# dataOut.timeInterval *= n
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dataOut.flagNoData = True
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if self.__dataReady:
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dataOut.data = avgdata
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dataOut.nCohInt *= self.n
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dataOut.utctime = avgdatatime
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dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
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dataOut.flagNoData = False
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class SpectraProc(ProcessingUnit):
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def __init__(self):
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self.objectDict = {}
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self.buffer = None
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self.firstdatatime = None
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self.profIndex = 0
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self.dataOut = Spectra()
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def __updateObjFromInput(self):
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self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
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self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
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self.dataOut.channelList = self.dataIn.channelList
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self.dataOut.heightList = self.dataIn.heightList
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self.dataOut.dtype = self.dataIn.dtype
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self.dataOut.nHeights = self.dataIn.nHeights
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# self.dataOut.nChannels = self.dataIn.nChannels
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self.dataOut.nBaud = self.dataIn.nBaud
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self.dataOut.nCode = self.dataIn.nCode
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self.dataOut.code = self.dataIn.code
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self.dataOut.nProfiles = self.dataOut.nFFTPoints
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# self.dataOut.channelIndexList = self.dataIn.channelIndexList
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self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
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self.dataOut.utctime = self.firstdatatime
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self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
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self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
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self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
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self.dataOut.nCohInt = self.dataIn.nCohInt
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self.dataOut.nIncohInt = 1
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self.dataOut.ippSeconds = self.dataIn.ippSeconds
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self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints
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def __getFft(self):
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"""
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Convierte valores de Voltaje a Spectra
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Affected:
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self.dataOut.data_spc
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self.dataOut.data_cspc
|
|
|
self.dataOut.data_dc
|
|
|
self.dataOut.heightList
|
|
|
self.dataOut.m_BasicHeader
|
|
|
self.dataOut.m_ProcessingHeader
|
|
|
self.dataOut.radarControllerHeaderObj
|
|
|
self.dataOut.systemHeaderObj
|
|
|
self.profIndex
|
|
|
self.buffer
|
|
|
self.dataOut.flagNoData
|
|
|
self.dataOut.dtype
|
|
|
self.dataOut.nPairs
|
|
|
self.dataOut.nChannels
|
|
|
self.dataOut.nProfiles
|
|
|
self.dataOut.systemHeaderObj.numChannels
|
|
|
self.dataOut.m_ProcessingHeader.totalSpectra
|
|
|
self.dataOut.m_ProcessingHeader.profilesPerBlock
|
|
|
self.dataOut.m_ProcessingHeader.numHeights
|
|
|
self.dataOut.m_ProcessingHeader.spectraComb
|
|
|
self.dataOut.m_ProcessingHeader.shif_fft
|
|
|
"""
|
|
|
fft_volt = numpy.fft.fft(self.buffer,axis=1)
|
|
|
dc = fft_volt[:,0,:]
|
|
|
|
|
|
#calculo de self-spectra
|
|
|
fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
|
|
|
spc = fft_volt * numpy.conjugate(fft_volt)
|
|
|
spc = spc.real
|
|
|
|
|
|
blocksize = 0
|
|
|
blocksize += dc.size
|
|
|
blocksize += spc.size
|
|
|
|
|
|
cspc = None
|
|
|
pairIndex = 0
|
|
|
if self.dataOut.pairsList != None:
|
|
|
#calculo de cross-spectra
|
|
|
cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
|
|
|
for pair in self.dataOut.pairsList:
|
|
|
cspc[pairIndex,:,:] = numpy.abs(fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]))
|
|
|
pairIndex += 1
|
|
|
blocksize += cspc.size
|
|
|
|
|
|
self.dataOut.data_spc = spc
|
|
|
self.dataOut.data_cspc = cspc
|
|
|
self.dataOut.data_dc = dc
|
|
|
self.dataOut.blockSize = blocksize
|
|
|
|
|
|
def init(self, nFFTPoints=None, pairsList=None):
|
|
|
|
|
|
if self.dataIn.type == "Spectra":
|
|
|
self.dataOut.copy(self.dataIn)
|
|
|
return
|
|
|
|
|
|
if self.dataIn.type == "Voltage":
|
|
|
|
|
|
if nFFTPoints == None:
|
|
|
raise ValueError, "This SpectraProc.setup() need nFFTPoints input variable"
|
|
|
|
|
|
if pairsList == None:
|
|
|
nPairs = 0
|
|
|
else:
|
|
|
nPairs = len(pairsList)
|
|
|
|
|
|
self.dataOut.nFFTPoints = nFFTPoints
|
|
|
self.dataOut.pairsList = pairsList
|
|
|
self.dataOut.nPairs = nPairs
|
|
|
|
|
|
if self.buffer == None:
|
|
|
self.buffer = numpy.zeros((self.dataIn.nChannels,
|
|
|
self.dataOut.nFFTPoints,
|
|
|
self.dataIn.nHeights),
|
|
|
dtype='complex')
|
|
|
|
|
|
|
|
|
self.buffer[:,self.profIndex,:] = self.dataIn.data
|
|
|
self.profIndex += 1
|
|
|
|
|
|
if self.firstdatatime == None:
|
|
|
self.firstdatatime = self.dataIn.utctime
|
|
|
|
|
|
if self.profIndex == self.dataOut.nFFTPoints:
|
|
|
self.__updateObjFromInput()
|
|
|
self.__getFft()
|
|
|
|
|
|
self.dataOut.flagNoData = False
|
|
|
|
|
|
self.buffer = None
|
|
|
self.firstdatatime = None
|
|
|
self.profIndex = 0
|
|
|
|
|
|
return
|
|
|
|
|
|
raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
|
|
|
|
|
|
def selectChannels(self, channelList):
|
|
|
|
|
|
channelIndexList = []
|
|
|
|
|
|
for channel in 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_spc
|
|
|
self.dataOut.channelIndexList
|
|
|
self.dataOut.nChannels
|
|
|
|
|
|
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
|
|
|
|
|
|
nChannels = len(channelIndexList)
|
|
|
|
|
|
data_spc = self.dataOut.data_spc[channelIndexList,:]
|
|
|
|
|
|
self.dataOut.data_spc = data_spc
|
|
|
self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
|
|
|
# self.dataOut.nChannels = nChannels
|
|
|
|
|
|
return 1
|
|
|
|
|
|
|
|
|
class IncohInt(Operation):
|
|
|
|
|
|
|
|
|
__profIndex = 0
|
|
|
__withOverapping = False
|
|
|
|
|
|
__byTime = False
|
|
|
__initime = None
|
|
|
__lastdatatime = None
|
|
|
__integrationtime = None
|
|
|
|
|
|
__buffer = None
|
|
|
|
|
|
__dataReady = False
|
|
|
|
|
|
n = None
|
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
|
self.__isConfig = False
|
|
|
|
|
|
def setup(self, n=None, timeInterval=None, overlapping=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
|
|
|
|
|
|
|
|
|
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.__withOverapping = True
|
|
|
self.__buffer = None
|
|
|
else:
|
|
|
self.__withOverapping = 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.__withOverapping:
|
|
|
self.__buffer += data.copy()
|
|
|
self.__profIndex += 1
|
|
|
return
|
|
|
|
|
|
#Overlapping data
|
|
|
nChannels, nFFTPoints, nHeis = data.shape
|
|
|
data = numpy.reshape(data, (1, nChannels, nFFTPoints, nHeis))
|
|
|
|
|
|
#If the buffer is empty then it takes the data value
|
|
|
if self.__buffer == 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.__withOverapping:
|
|
|
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)
|
|
|
n = self.__profIndex
|
|
|
|
|
|
return data, n
|
|
|
|
|
|
def byProfiles(self, data):
|
|
|
|
|
|
self.__dataReady = False
|
|
|
avgdata = None
|
|
|
n = None
|
|
|
|
|
|
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 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 == None:
|
|
|
return None, None
|
|
|
|
|
|
avgdatatime = self.__initime
|
|
|
|
|
|
deltatime = datatime -self.__lastdatatime
|
|
|
|
|
|
if not self.__withOverapping:
|
|
|
self.__initime = datatime
|
|
|
else:
|
|
|
self.__initime += deltatime
|
|
|
|
|
|
return avgdata, avgdatatime
|
|
|
|
|
|
def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
|
|
|
|
|
|
if not self.__isConfig:
|
|
|
self.setup(n, timeInterval, overlapping)
|
|
|
self.__isConfig = True
|
|
|
|
|
|
avgdata, avgdatatime = self.integrate(dataOut.data_spc, dataOut.utctime)
|
|
|
|
|
|
# dataOut.timeInterval *= n
|
|
|
dataOut.flagNoData = True
|
|
|
|
|
|
if self.__dataReady:
|
|
|
dataOut.data_spc = avgdata
|
|
|
dataOut.nIncohInt *= self.n
|
|
|
dataOut.utctime = avgdatatime
|
|
|
dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt
|
|
|
dataOut.flagNoData = False
|
|
|
|
