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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 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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methodToCall = getattr(self, name)
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methodToCall(**kwargs)
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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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object = self.objectList[objId]
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object.run(self.dataOut, **kwargs)
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def call(self, operationConf, **kwargs):
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"""
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Ejecuta la operacion "operationConf.name" con los argumentos "**kwargs". La operacion puede
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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 self.dataIn.isEmpty():
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return None
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if operationConf.type == 'self':
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self.callMethod(operationConf.name, **kwargs)
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return
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if operationConf.type == 'other':
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self.callObject(operationConf.id, **kwargs)
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return
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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 setup(self, dataIn=None, dataOut=None):
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self.dataIn = dataIn
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if self.dataOut == None:
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dataOut = Voltage()
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self.dataOut = dataOut
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return self.dataOut
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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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if self.dataIn.isEmpty():
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return 0
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self.selectChannelsByIndex(channelList)
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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 channel in channelIndexList:
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if channel not in self.dataOut.channelIndexList:
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raise ValueError, "The value %d in channelIndexList is not valid" %channel
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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.channelIndexList = channelIndexList
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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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nCohInt = None
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def __init__(self):
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self.__isConfig = False
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def setup(self, nCohInt=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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nCohInt : Number of coherent integrations
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timeInterval : Time of integration. If the parameter "nCohInt" 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 nCohInt == None and timeInterval == None:
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raise ValueError, "nCohInt or timeInterval should be specified ..."
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if nCohInt != None:
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self.nCohInt = nCohInt
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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.nCohInt = 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
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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 nCohInt then stakcing the data value
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if self.__profIndex < self.nCohInt:
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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 nCohInt 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.nCohInt-1] = data
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self.__profIndex = self.nCohInt
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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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nCohInt = self.__profIndex
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self.__buffer = 0
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self.__profIndex = 0
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return data, nCohInt
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#Integration with Overlapping
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data = numpy.sum(self.__buffer, axis=0)
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nCohInt = self.__profIndex
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return data, nCohInt
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def byProfiles(self, data):
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self.__dataReady = False
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avg_data = None
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nCohInt = None
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self.putData(data)
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if self.__profIndex == self.nCohInt:
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avgdata, nCohInt = self.pushData()
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self.__dataReady = True
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return avgdata, nCohInt
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def byTime(self, data, datatime):
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self.__dataReady = False
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avg_data = None
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nCohInt = None
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self.putData(data)
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if (datatime - self.__initime) >= self.__integrationtime:
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avgdata, nCohInt = self.pushData()
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self.nCohInt = nCohInt
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self.__dataReady = True
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return avgdata, nCohInt
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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
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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, nCohInt=None, timeInterval=None, overlapping=False):
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if not self.__isConfig:
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self.setup(nCohInt, 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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# self.dataOut.timeInterval *= nCohInt
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self.dataOut.flagNoData = True
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if self.__dataReady:
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dataOut.data = avgdata
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dataOut.timeInterval *= self.nCohInt
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dataOut.nCohInt *= self.nCohInt
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dataOut.utctime = avgdatatime
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dataOut.flagNoData = False
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