schain Commit - r1542:9f6529c5475c · Jicamarca Repository

LP Faraday update

rflores -

r1542:9f6529c5475c

parent child

Context file:

r1542:9f6529c5475c

Collapse all files

The requested changes are too big and content was truncated. Show full diff

schainpy/controller.py +1 0

             # Copyright (c) 2012-2020 Jicamarca Radio Observatory
             # All rights reserved.
             #
             # Distributed under the terms of the BSD 3-clause license.
             """API to create signal chain projects
             The API is provide through class: Project
             """
             import re
             import sys
             import ast
             import datetime
             import traceback
             import time
             import multiprocessing
             from multiprocessing import Process, Queue
             from threading import Thread
             from xml.etree.ElementTree import ElementTree, Element, SubElement
             from schainpy.admin import Alarm, SchainWarning
             from schainpy.model import *
             from schainpy.utils import log
             if 'darwin' in sys.platform and sys.version_info[0] == 3 and sys.version_info[1] > 7:
                 multiprocessing.set_start_method('fork')
             class ConfBase():
                 def __init__(self):
                     self.id = '0'
                     self.name = None
                     self.priority = None
                     self.parameters = {}
                     self.object = None
                     self.operations = []
                 def getId(self):
                     return self.id
                 def getNewId(self):
                     return int(self.id) * 10 + len(self.operations) + 1
                 def updateId(self, new_id):
                     self.id = str(new_id)
                     n = 1
                     for conf in self.operations:
                         conf_id = str(int(new_id) * 10 + n)
                         conf.updateId(conf_id)
                         n += 1
                 def getKwargs(self):
                     params = {}
                     for key, value in self.parameters.items():
                         if value not in (None, '', ' '):
                             params[key] = value
                     return params
                 def update(self, **kwargs):
                     for key, value in kwargs.items():
                         self.addParameter(name=key, value=value)
                 def addParameter(self, name, value, format=None):
                     '''
                     '''
                     if isinstance(value, str) and re.search(r'(\d+/\d+/\d+)', value):
                         self.parameters[name] = datetime.date(*[int(x) for x in value.split('/')])
                     elif isinstance(value, str) and re.search(r'(\d+:\d+:\d+)', value):
                         self.parameters[name] = datetime.time(*[int(x) for x in value.split(':')])
                     else:
                         try:
                             self.parameters[name] = ast.literal_eval(value)
                         except:
                             if isinstance(value, str) and ',' in value:
                                 self.parameters[name] = value.split(',')
                             else:
                                 self.parameters[name] = value
                 def getParameters(self):
                     params = {}
                     for key, value in self.parameters.items():
                         s = type(value).__name__
                         if s == 'date':
                             params[key] = value.strftime('%Y/%m/%d')
                         elif s == 'time':
                             params[key] = value.strftime('%H:%M:%S')
                         else:
                             params[key] = str(value)
                     return params
                 def makeXml(self, element):
                     xml = SubElement(element, self.ELEMENTNAME)
                     for label in self.xml_labels:
                         xml.set(label, str(getattr(self, label)))
                     for key, value in self.getParameters().items():
                         xml_param = SubElement(xml, 'Parameter')
                         xml_param.set('name', key)
                         xml_param.set('value', value)
                     for conf in self.operations:
                         conf.makeXml(xml)
                 def __str__(self):
                     if self.ELEMENTNAME == 'Operation':
                         s = '  {}[id={}]\n'.format(self.name, self.id)
                     else:
                         s = '{}[id={}, inputId={}]\n'.format(self.name, self.id, self.inputId)
                     for key, value in self.parameters.items():
                         if self.ELEMENTNAME == 'Operation':
                             s += '    {}: {}\n'.format(key, value)
                         else:
                             s += '  {}: {}\n'.format(key, value)
                     for conf in self.operations:
                         s += str(conf)
                     return s
             class OperationConf(ConfBase):
                 ELEMENTNAME = 'Operation'
                 xml_labels = ['id', 'name']
                 def setup(self, id, name, priority, project_id, err_queue):
                     self.id = str(id)
                     self.project_id = project_id
                     self.name = name
                     self.type = 'other'
                     self.err_queue = err_queue
                 def readXml(self, element, project_id, err_queue):
                     self.id = element.get('id')
                     self.name = element.get('name')
                     self.type = 'other'
                     self.project_id = str(project_id)
                     self.err_queue = err_queue
                     for elm in element.iter('Parameter'):
                         self.addParameter(elm.get('name'), elm.get('value'))
                 def createObject(self):
                     className = eval(self.name)
                     if 'Plot' in self.name or 'Writer' in self.name or 'Send' in self.name or 'print' in self.name:
                         kwargs = self.getKwargs()
                         opObj = className(self.id, self.id, self.project_id, self.err_queue, **kwargs)
                         opObj.start()
                         self.type = 'external'
                     else:
                         opObj = className()
                     self.object = opObj
                     return opObj
             class ProcUnitConf(ConfBase):
                 ELEMENTNAME = 'ProcUnit'
                 xml_labels = ['id', 'inputId', 'name']
                 def setup(self, project_id, id, name, datatype, inputId, err_queue):
                     '''
                     '''
                     if datatype == None and name == None:
                         raise ValueError('datatype or name should be defined')
                     if name == None:
                         if 'Proc' in datatype:
                             name = datatype
                         else:
                             name = '%sProc' % (datatype)
                     if datatype == None:
                         datatype = name.replace('Proc', '')
                     self.id = str(id)
                     self.project_id = project_id
                     self.name = name
                     self.datatype = datatype
                     self.inputId = inputId
                     self.err_queue = err_queue
                     self.operations = []
                     self.parameters = {}
                 def removeOperation(self, id):
                     i = [1 if x.id == id else 0 for x in self.operations]
                     self.operations.pop(i.index(1))
                 def getOperation(self, id):
                     for conf in self.operations:
                         if conf.id == id:
                             return conf
                 def addOperation(self, name, optype='self'):
                     '''
                     '''
                     id = self.getNewId()
                     conf = OperationConf()
                     conf.setup(id, name=name, priority='0', project_id=self.project_id, err_queue=self.err_queue)
                     self.operations.append(conf)
                     return conf
                 def readXml(self, element, project_id, err_queue):
                     self.id = element.get('id')
                     self.name = element.get('name')
                     self.inputId = None if element.get('inputId') == 'None' else element.get('inputId')
                     self.datatype = element.get('datatype', self.name.replace(self.ELEMENTNAME.replace('Unit', ''), ''))
                     self.project_id = str(project_id)
                     self.err_queue = err_queue
                     self.operations = []
                     self.parameters = {}
                     for elm in element:
                         if elm.tag == 'Parameter':
                             self.addParameter(elm.get('name'), elm.get('value'))
                         elif elm.tag == 'Operation':
                             conf = OperationConf()
                             conf.readXml(elm, project_id, err_queue)
                             self.operations.append(conf)
                 def createObjects(self):
                     '''
                     Instancia de unidades de procesamiento.
                     '''
                     className = eval(self.name)
                     kwargs = self.getKwargs()
                     procUnitObj = className()
                     procUnitObj.name = self.name
                     log.success('creating process...', self.name)
                     for conf in self.operations:
                         opObj = conf.createObject()
                         log.success('adding operation: {}, type:{}'.format(
                             conf.name,
                             conf.type), self.name)
                         procUnitObj.addOperation(conf, opObj)
                     self.object = procUnitObj
                 def run(self):
                     '''
                     '''
                     #self.object.call(**self.getKwargs())
                     return self.object.call(**self.getKwargs())
             class ReadUnitConf(ProcUnitConf):
                 ELEMENTNAME = 'ReadUnit'
                 def __init__(self):
                     self.id = None
                     self.datatype = None
                     self.name = None
                     self.inputId = None
                     self.operations = []
                     self.parameters = {}
                 def setup(self, project_id, id, name, datatype, err_queue, path='', startDate='', endDate='',
                           startTime='', endTime='', server=None, **kwargs):
                     if datatype == None and name == None:
                         raise ValueError('datatype or name should be defined')
                     if name == None:
                         if 'Reader' in datatype:
                             name = datatype
                             datatype = name.replace('Reader', '')
                         else:
                             name = '{}Reader'.format(datatype)
                     if datatype == None:
                         if 'Reader' in name:
                             datatype = name.replace('Reader', '')
                         else:
                             datatype = name
                             name = '{}Reader'.format(name)
                     self.id = id
                     self.project_id = project_id
                     self.name = name
                     self.datatype = datatype
                     self.err_queue = err_queue
                     self.addParameter(name='path', value=path)
                     self.addParameter(name='startDate', value=startDate)
                     self.addParameter(name='endDate', value=endDate)
                     self.addParameter(name='startTime', value=startTime)
                     self.addParameter(name='endTime', value=endTime)
                     for key, value in kwargs.items():
                         self.addParameter(name=key, value=value)
             class Project(Process):
                 """API to create signal chain projects"""
                 ELEMENTNAME = 'Project'
                 def __init__(self, name=''):
                     Process.__init__(self)
                     self.id = '1'
                     if name:
                         self.name = '{} ({})'.format(Process.__name__, name)
                     self.filename = None
                     self.description = None
                     self.email = None
                     self.alarm = []
                     self.configurations = {}
                     # self.err_queue = Queue()
                     self.err_queue = None
                     self.started = False
                 def getNewId(self):
                     idList = list(self.configurations.keys())
                     id = int(self.id) * 10
                     while True:
                         id += 1
                         if str(id) in idList:
                             continue
                         break
                     return str(id)
                 def updateId(self, new_id):
                     self.id = str(new_id)
                     keyList = list(self.configurations.keys())
                     keyList.sort()
                     n = 1
                     new_confs = {}
                     for procKey in keyList:
                         conf = self.configurations[procKey]
                         idProcUnit = str(int(self.id) * 10 + n)
                         conf.updateId(idProcUnit)
                         new_confs[idProcUnit] = conf
                         n += 1
                     self.configurations = new_confs
                 def setup(self, id=1, name='', description='', email=None, alarm=[]):
                     self.id = str(id)
                     self.description = description
                     self.email = email
                     self.alarm = alarm
                     if name:
                         self.name = '{} ({})'.format(Process.__name__, name)
                 def update(self, **kwargs):
                     for key, value in kwargs.items():
                         setattr(self, key, value)
                 def clone(self):
                     p = Project()
                     p.id = self.id
                     p.name = self.name
                     p.description = self.description
                     p.configurations = self.configurations.copy()
                     return p
                 def addReadUnit(self, id=None, datatype=None, name=None, **kwargs):
                     '''
                     '''
                     if id is None:
                         idReadUnit = self.getNewId()
                     else:
                         idReadUnit = str(id)
                     conf = ReadUnitConf()
                     conf.setup(self.id, idReadUnit, name, datatype, self.err_queue, **kwargs)
                     self.configurations[conf.id] = conf
                     return conf
                 def addProcUnit(self, id=None, inputId='0', datatype=None, name=None):
                     '''
                     '''
                     if id is None:
                         idProcUnit = self.getNewId()
                     else:
                         idProcUnit = id
                     conf = ProcUnitConf()
                     conf.setup(self.id, idProcUnit, name, datatype, inputId, self.err_queue)
                     self.configurations[conf.id] = conf
                     return conf
                 def removeProcUnit(self, id):
                     if id in self.configurations:
                         self.configurations.pop(id)
                 def getReadUnit(self):
                     for obj in list(self.configurations.values()):
                         if obj.ELEMENTNAME == 'ReadUnit':
                             return obj
                     return None
                 def getProcUnit(self, id):
                     return self.configurations[id]
                 def getUnits(self):
                     keys = list(self.configurations)
                     keys.sort()
                     for key in keys:
                         yield self.configurations[key]
                 def updateUnit(self, id, **kwargs):
                     conf = self.configurations[id].update(**kwargs)
                 def makeXml(self):
                     xml = Element('Project')
                     xml.set('id', str(self.id))
                     xml.set('name', self.name)
                     xml.set('description', self.description)
                     for conf in self.configurations.values():
                         conf.makeXml(xml)
                     self.xml = xml
                 def writeXml(self, filename=None):
                     if filename == None:
                         if self.filename:
                             filename = self.filename
                         else:
                             filename = 'schain.xml'
                     if not filename:
                         print('filename has not been defined. Use setFilename(filename) for do it.')
                         return 0
                     abs_file = os.path.abspath(filename)
                     if not os.access(os.path.dirname(abs_file), os.W_OK):
                         print('No write permission on %s' % os.path.dirname(abs_file))
                         return 0
                     if os.path.isfile(abs_file) and not(os.access(abs_file, os.W_OK)):
                         print('File %s already exists and it could not be overwriten' % abs_file)
                         return 0
                     self.makeXml()
                     ElementTree(self.xml).write(abs_file, method='xml')
                     self.filename = abs_file
                     return 1
                 def readXml(self, filename):
                     abs_file = os.path.abspath(filename)
                     self.configurations = {}
                     try:
                         self.xml = ElementTree().parse(abs_file)
                     except:
                         log.error('Error reading %s, verify file format' % filename)
                         return 0
                     self.id = self.xml.get('id')
                     self.name = self.xml.get('name')
                     self.description = self.xml.get('description')
                     for element in self.xml:
                         if element.tag == 'ReadUnit':
                             conf = ReadUnitConf()
                             conf.readXml(element, self.id, self.err_queue)
                             self.configurations[conf.id] = conf
                         elif element.tag == 'ProcUnit':
                             conf = ProcUnitConf()
                             input_proc = self.configurations[element.get('inputId')]
                             conf.readXml(element, self.id, self.err_queue)
                             self.configurations[conf.id] = conf
                     self.filename = abs_file
                     return 1
                 def __str__(self):
                     text = '\nProject[id=%s, name=%s, description=%s]\n\n' % (
                         self.id,
                         self.name,
                         self.description,
                         )
                     for conf in self.configurations.values():
                         text += '{}'.format(conf)
                     return text
                 def createObjects(self):
                     keys = list(self.configurations.keys())
                     keys.sort()
                     for key in keys:
                         conf = self.configurations[key]
                         conf.createObjects()
                         if conf.inputId is not None:
                             if isinstance(conf.inputId, list):
                                 conf.object.setInput([self.configurations[x].object for x in conf.inputId])
                             else:
                                 conf.object.setInput([self.configurations[conf.inputId].object])
                 def monitor(self):
                     t = Thread(target=self._monitor, args=(self.err_queue, self.ctx))
                     t.start()
                 def _monitor(self, queue, ctx):
                     import socket
                     procs = 0
                     err_msg = ''
                     while True:
                         msg = queue.get()
                         if '#_start_#' in msg:
                             procs += 1
                         elif '#_end_#' in msg:
                             procs -= 1
                         else:
                             err_msg = msg
                         if procs == 0 or 'Traceback' in err_msg:
                             break
                         time.sleep(0.1)
                     if '|' in err_msg:
                         name, err = err_msg.split('|')
                         if 'SchainWarning' in err:
                             log.warning(err.split('SchainWarning:')[-1].split('\n')[0].strip(), name)
                         elif 'SchainError' in err:
                             log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), name)
                         else:
                             log.error(err, name)
                     else:
                         name, err = self.name, err_msg
                     time.sleep(1)
                     ctx.term()
                     message = ''.join(err)
                     if err_msg:
                         subject = 'SChain v%s: Error running %s\n' % (
                             schainpy.__version__, self.name)
                         subtitle = 'Hostname: %s\n' % socket.gethostbyname(
                             socket.gethostname())
                         subtitle += 'Working directory: %s\n' % os.path.abspath('./')
                         subtitle += 'Configuration file: %s\n' % self.filename
                         subtitle += 'Time: %s\n' % str(datetime.datetime.now())
                         readUnitConfObj = self.getReadUnit()
                         if readUnitConfObj:
                             subtitle += '\nInput parameters:\n'
                             subtitle += '[Data path = %s]\n' % readUnitConfObj.parameters['path']
                             subtitle += '[Start date = %s]\n' % readUnitConfObj.parameters['startDate']
                             subtitle += '[End date = %s]\n' % readUnitConfObj.parameters['endDate']
                             subtitle += '[Start time = %s]\n' % readUnitConfObj.parameters['startTime']
                             subtitle += '[End time = %s]\n' % readUnitConfObj.parameters['endTime']
                         a = Alarm(
                             modes=self.alarm,
                             email=self.email,
                             message=message,
                             subject=subject,
                             subtitle=subtitle,
                             filename=self.filename
                         )
                         a.start()
                 def setFilename(self, filename):
                     self.filename = filename
                 def runProcs(self):
                     err = False
                     n = len(self.configurations)
                     #print(n)
                     while not err:
                         #print(self.getUnits())
                         for conf in self.getUnits():
                             #print(conf)
                             ok = conf.run()
                             #print("ok", ok)
                             if ok == 'Error':
                                 n -= 1
                                 continue
                             elif not ok:
                                 break
                         #print("****************************************************end")
+                        #exit(1)
                         if n == 0:
                             err = True
                 def run(self):
                     log.success('\nStarting Project {} [id={}]'.format(self.name, self.id), tag='')
                     self.started = True
                     self.start_time = time.time()
                     self.createObjects()
                     self.runProcs()
                     log.success('{} Done (Time: {:4.2f}s)'.format(
                         self.name,
                         time.time() - self.start_time), '')

schainpy/model/graphics/jroplot_spectra.py +3 -1

             # Copyright (c) 2012-2021 Jicamarca Radio Observatory
             # All rights reserved.
             #
             # Distributed under the terms of the BSD 3-clause license.
             """Classes to plot Spectra data
             """
             import os
             import numpy
             from schainpy.model.graphics.jroplot_base import Plot, plt, log
             class SpectraPlot(Plot):
                 '''
                 Plot for Spectra data
                 '''
                 CODE = 'spc'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 buffering = False
                 def setup(self):
                     self.nplots = len(self.data.channels)
                     self.ncols = int(numpy.sqrt(self.nplots) + 0.9)
                     self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9)
                     self.height = 2.6 * self.nrows
                     self.cb_label = 'dB'
                     if self.showprofile:
                         self.width = 4 * self.ncols
                     else:
                         self.width = 3.5 * self.ncols
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     self.ylabel = 'Range [km]'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor)
                     data['spc'] = spc
                     data['rti'] = dataOut.getPower()
+                    #print("NormFactor: ",dataOut.normFactor)
                     #data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
                     if hasattr(dataOut, 'LagPlot'): #Double Pulse
                         max_hei_id = dataOut.nHeights - 2*dataOut.LagPlot
                         #data['noise'] = 10*numpy.log10(dataOut.getNoise(ymin_index=46,ymax_index=max_hei_id)/dataOut.normFactor)
                         #data['noise'] = 10*numpy.log10(dataOut.getNoise(ymin_index=40,ymax_index=max_hei_id)/dataOut.normFactor)
                         data['noise'] = 10*numpy.log10(dataOut.getNoise(ymin_index=53,ymax_index=max_hei_id)/dataOut.normFactor)
                         data['noise'][0] = 10*numpy.log10(dataOut.getNoise(ymin_index=53)[0]/dataOut.normFactor)
                         #data['noise'][1] = 22.035507
                     else:
                         data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
                     #data['noise'] = 10*numpy.log10(dataOut.getNoise(ymin_index=26,ymax_index=44)/dataOut.normFactor)
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     if self.CODE == 'spc_moments':
                         data['moments'] = dataOut.moments
                     if self.CODE == 'gaussian_fit':
                         data['gaussfit'] = dataOut.DGauFitParams
                     return data, meta
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     if (self.CODE == 'spc_moments') | (self.CODE == 'gaussian_fit'):
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.yrange
                     self.y = y
                     data = self.data[-1]
                     z = data['spc']
                     self.CODE2 = 'spc_oblique'
                     for n, ax in enumerate(self.axes):
                         noise = data['noise'][n]
                         if self.CODE == 'spc_moments':
                             mean = data['moments'][n, 1]
                         if self.CODE == 'gaussian_fit':
                             gau0 = data['gaussfit'][n][2,:,0]
                             gau1 = data['gaussfit'][n][2,:,1]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else numpy.nanmin(x)#-self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(z)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(z)
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
-                                                   cmap=plt.get_cmap(self.colormap)
+                                                   cmap=plt.get_cmap(self.colormap),
                                                    )
                             if self.showprofile:
                                 ax.plt_profile = self.pf_axes[n].plot(
                                     data['rti'][n], y)[0]
                                 ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y,
                                                                     color="k", linestyle="dashed", lw=1)[0]
                             if self.CODE == 'spc_moments':
                                 ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0]
                             if self.CODE == 'gaussian_fit':
                                 ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0]
                                 ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0]
                         else:
                             ax.plt.set_array(z[n].T.ravel())
                             if self.showprofile:
                                 ax.plt_profile.set_data(data['rti'][n], y)
                                 ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y)
                             if self.CODE == 'spc_moments':
                                 ax.plt_mean.set_data(mean, y)
                             if self.CODE == 'gaussian_fit':
                                 ax.plt_gau0.set_data(gau0, y)
                                 ax.plt_gau1.set_data(gau1, y)
                         self.titles.append('CH {}: {:3.2f}dB'.format(n, noise))
             class SpectraObliquePlot(Plot):
                 '''
                 Plot for Spectra data
                 '''
                 CODE = 'spc_oblique'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 def setup(self):
                     self.xaxis = "oblique"
                     self.nplots = len(self.data.channels)
                     self.ncols = int(numpy.sqrt(self.nplots) + 0.9)
                     self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9)
                     self.height = 2.6 * self.nrows
                     self.cb_label = 'dB'
                     if self.showprofile:
                         self.width = 4 * self.ncols
                     else:
                         self.width = 3.5 * self.ncols
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     self.ylabel = 'Range [km]'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor)
                     data['spc'] = spc
                     data['rti'] = dataOut.getPower()
                     data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     data['shift1'] = dataOut.Oblique_params[0][1]
                     data['shift2'] = dataOut.Oblique_params[0][4]
                     data['shift1_error'] = dataOut.Oblique_param_errors[0][1]
                     data['shift2_error'] = dataOut.Oblique_param_errors[0][4]
                     return data, meta
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.yrange
                     self.y = y
                     z = self.data['spc']
                     for n, ax in enumerate(self.axes):
                         noise = self.data['noise'][n][-1]
                         shift1 = self.data['shift1']
                         shift2 = self.data['shift2']
                         err1 = self.data['shift1_error']
                         err2 = self.data['shift2_error']
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(z)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(z)
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                             if self.showprofile:
                                 ax.plt_profile = self.pf_axes[n].plot(
                                     self.data['rti'][n][-1], y)[0]
                                 ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y,
                                                                     color="k", linestyle="dashed", lw=1)[0]
                             self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=0.2, marker='x', linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2)
                             self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2)
                         else:
                             self.ploterr1.remove()
                             self.ploterr2.remove()
                             ax.plt.set_array(z[n].T.ravel())
                             if self.showprofile:
                                 ax.plt_profile.set_data(self.data['rti'][n][-1], y)
                                 ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y)
                                 self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2)
                                 self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2)
                         self.titles.append('CH {}: {:3.2f}dB'.format(n, noise))
             class CrossSpectraPlot(Plot):
                 CODE = 'cspc'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 zmin_coh = None
                 zmax_coh = None
                 zmin_phase = None
                 zmax_phase = None
                 def setup(self):
                     self.ncols = 4
                     self.nplots = len(self.data.pairs) * 2
                     self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9)
                     self.width = 3.1 * self.ncols
                     self.height = 5 * self.nrows
                     self.ylabel = 'Range [km]'
                     self.showprofile = False
                     self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     spc = dataOut.data_spc
                     cspc = dataOut.data_cspc
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     meta['pairs'] = dataOut.pairsList
                     tmp = []
                     for n, pair in enumerate(meta['pairs']):
                         out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]])
                         coh = numpy.abs(out)
                         phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi
                         tmp.append(coh)
                         tmp.append(phase)
                     data['cspc'] = numpy.array(tmp)
                     return data, meta
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.yrange
                     self.y = y
                     data = self.data[-1]
                     cspc = data['cspc']
                     for n in range(len(self.data.pairs)):
                         pair = self.data.pairs[n]
                         coh = cspc[n*2]
                         phase = cspc[n*2+1]
                         ax = self.axes[2 * n]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, coh.T,
                                                    vmin=0,
                                                    vmax=1,
                                                    cmap=plt.get_cmap(self.colormap_coh)
                                                    )
                         else:
                             ax.plt.set_array(coh.T.ravel())
                         self.titles.append(
                             'Coherence Ch{} * Ch{}'.format(pair[0], pair[1]))
                         ax = self.axes[2 * n + 1]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, phase.T,
                                                    vmin=-180,
                                                    vmax=180,
                                                    cmap=plt.get_cmap(self.colormap_phase)
                                                    )
                         else:
                             ax.plt.set_array(phase.T.ravel())
                         self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1]))
             class CrossSpectra4Plot(Plot):
                 CODE = 'cspc'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 zmin_coh = None
                 zmax_coh = None
                 zmin_phase = None
                 zmax_phase = None
                 def setup(self):
                     self.ncols = 4
                     self.nrows = len(self.data.pairs)
                     self.nplots = self.nrows * 4
                     self.width = 3.1 * self.ncols
                     self.height = 5 * self.nrows
                     self.ylabel = 'Range [km]'
                     self.showprofile = False
                     self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08})
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.heights
                     self.y = y
                     nspc = self.data['spc']
                     #print(numpy.shape(self.data['spc']))
                     spc = self.data['cspc'][0]
                     #print(numpy.shape(nspc))
                     #exit()
                     #nspc[1,:,:] = numpy.flip(nspc[1,:,:],axis=0)
                     #print(numpy.shape(spc))
                     #exit()
                     cspc = self.data['cspc'][1]
                     #xflip=numpy.flip(x)
                     #print(numpy.shape(cspc))
                     #exit()
                     for n in range(self.nrows):
                         noise = self.data['noise'][:,-1]
                         pair = self.data.pairs[n]
                         #print(pair)
                         #exit()
                         ax = self.axes[4 * n]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(nspc)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(nspc)
                             ax.plt = ax.pcolormesh(x , y , nspc[pair[0]].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             #print(numpy.shape(nspc[pair[0]].T))
                             #exit()
                             ax.plt.set_array(nspc[pair[0]].T.ravel())
                         self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise[pair[0]]))
                         ax = self.axes[4 * n + 1]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x , y, numpy.flip(nspc[pair[1]],axis=0).T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.plt.set_array(numpy.flip(nspc[pair[1]],axis=0).T.ravel())
                         self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise[pair[1]]))
                         out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]])
                         coh = numpy.abs(out)
                         phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi
                         ax = self.axes[4 * n + 2]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, numpy.flip(coh,axis=0).T,
                                                    vmin=0,
                                                    vmax=1,
                                                    cmap=plt.get_cmap(self.colormap_coh)
                                                    )
                         else:
                             ax.plt.set_array(numpy.flip(coh,axis=0).T.ravel())
                         self.titles.append(
                             'Coherence Ch{} * Ch{}'.format(pair[0], pair[1]))
                         ax = self.axes[4 * n + 3]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, numpy.flip(phase,axis=0).T,
                                                    vmin=-180,
                                                    vmax=180,
                                                    cmap=plt.get_cmap(self.colormap_phase)
                                                    )
                         else:
                             ax.plt.set_array(numpy.flip(phase,axis=0).T.ravel())
                         self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1]))
             class CrossSpectra2Plot(Plot):
                 CODE = 'cspc'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 zmin_coh = None
                 zmax_coh = None
                 zmin_phase = None
                 zmax_phase = None
                 def setup(self):
                     self.ncols = 1
                     self.nrows = len(self.data.pairs)
                     self.nplots = self.nrows * 1
                     self.width = 3.1 * self.ncols
                     self.height = 5 * self.nrows
                     self.ylabel = 'Range [km]'
                     self.showprofile = False
                     self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08})
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.heights
                     self.y = y
                     #nspc = self.data['spc']
                     #print(numpy.shape(self.data['spc']))
                     #spc = self.data['cspc'][0]
                     #print(numpy.shape(spc))
                     #exit()
                     cspc = self.data['cspc'][1]
                     #print(numpy.shape(cspc))
                     #exit()
                     for n in range(self.nrows):
                         noise = self.data['noise'][:,-1]
                         pair = self.data.pairs[n]
                         #print(pair)            #exit()
                         out = cspc[n]# / numpy.sqrt(spc[pair[0]] * spc[pair[1]])
                         #print(out[:,53])
                         #exit()
                         cross = numpy.abs(out)
                         z = cross/self.data.nFactor
                         #print("here")
                         #print(dataOut.data_spc[0,0,0])
                         #exit()
                         cross = 10*numpy.log10(z)
                         #print(numpy.shape(cross))
                         #print(cross[0,:])
                         #print(self.data.nFactor)
                         #exit()
                         #phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi
                         ax = self.axes[1 * n]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(cross)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(cross)
                             ax.plt = ax.pcolormesh(x, y, cross.T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.plt.set_array(cross.T.ravel())
                         self.titles.append(
                             'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1]))
             class CrossSpectra3Plot(Plot):
                 CODE = 'cspc'
                 colormap = 'jet'
                 plot_type = 'pcolor'
                 zmin_coh = None
                 zmax_coh = None
                 zmin_phase = None
                 zmax_phase = None
                 def setup(self):
                     self.ncols = 3
                     self.nrows = len(self.data.pairs)
                     self.nplots = self.nrows * 3
                     self.width = 3.1 * self.ncols
                     self.height = 5 * self.nrows
                     self.ylabel = 'Range [km]'
                     self.showprofile = False
                     self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08})
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.heights
                     self.y = y
                     #nspc = self.data['spc']
                     #print(numpy.shape(self.data['spc']))
                     #spc = self.data['cspc'][0]
                     #print(numpy.shape(spc))
                     #exit()
                     cspc = self.data['cspc'][1]
                     #print(numpy.shape(cspc))
                     #exit()
                     for n in range(self.nrows):
                         noise = self.data['noise'][:,-1]
                         pair = self.data.pairs[n]
                         #print(pair)            #exit()
                         out = cspc[n]# / numpy.sqrt(spc[pair[0]] * spc[pair[1]])
                         #print(out[:,53])
                         #exit()
                         cross = numpy.abs(out)
                         z = cross/self.data.nFactor
                         cross = 10*numpy.log10(z)
                         out_r= out.real/self.data.nFactor
                         #out_r = 10*numpy.log10(out_r)
                         out_i= out.imag/self.data.nFactor
                         #out_i = 10*numpy.log10(out_i)
                         #print(numpy.shape(cross))
                         #print(cross[0,:])
                         #print(self.data.nFactor)
                         #exit()
                         #phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi
                         ax = self.axes[3 * n]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(cross)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(cross)
                             ax.plt = ax.pcolormesh(x, y, cross.T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.plt.set_array(cross.T.ravel())
                         self.titles.append(
                             'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1]))
                         ax = self.axes[3 * n + 1]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(cross)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(cross)
                             ax.plt = ax.pcolormesh(x, y, out_r.T,
                                                    vmin=-1.e6,
                                                    vmax=0,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.plt.set_array(out_r.T.ravel())
                         self.titles.append(
                             'Cross Spectra Real Ch{} * Ch{}'.format(pair[0], pair[1]))
                         ax = self.axes[3 * n + 2]
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.zmin = self.zmin if self.zmin else numpy.nanmin(cross)
                             self.zmax = self.zmax if self.zmax else numpy.nanmax(cross)
                             ax.plt = ax.pcolormesh(x, y, out_i.T,
                                                    vmin=-1.e6,
                                                    vmax=1.e6,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.plt.set_array(out_i.T.ravel())
                         self.titles.append(
                             'Cross Spectra Imag Ch{} * Ch{}'.format(pair[0], pair[1]))
             class RTIPlot(Plot):
                 '''
                 Plot for RTI data
                 '''
                 CODE = 'rti'
                 colormap = 'jet'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = len(self.data.channels)
                     self.nplots = len(self.data.channels)
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time'
                     self.cb_label = 'dB'
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95})
                     self.titles = ['{} Channel {}'.format(
                         self.CODE.upper(), x) for x in range(self.nrows)]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['rti'] = dataOut.getPower()
                     data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data[self.CODE]
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmin = self.zmin if self.zmin else numpy.min(self.z)
                         self.zmax = self.zmax if self.zmax else numpy.max(self.z)
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                             if self.showprofile:
                                 ax.plot_profile = self.pf_axes[n].plot(
                                     self.data['rti'][n][-1], self.y)[0]
                                 ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y,
                                                                      color="k", linestyle="dashed", lw=1)[0]
                         else:
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                             if self.showprofile:
                                 ax.plot_profile.set_data(self.data['rti'][n][-1], self.y)
                                 ax.plot_noise.set_data(numpy.repeat(
                                     self.data['noise'][n][-1], len(self.y)), self.y)
             class SpectrogramPlot(Plot):
                 '''
                 Plot for Spectrogram data
                 '''
                 CODE = 'Spectrogram_Profile'
                 colormap = 'binary'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = len(self.data.channels)
                     self.nplots = len(self.data.channels)
                     self.xlabel = 'Time'
                     #self.cb_label = 'dB'
                     self.plots_adjust.update({'hspace':1.2, 'left': 0.1, 'bottom': 0.12, 'right':0.95})
                     self.titles = []
                     #self.titles = ['{} Channel {} \n H = {} km ({} - {})'.format(
                         #self.CODE.upper(), x, self.data.heightList[self.data.hei], self.data.heightList[self.data.hei],self.data.heightList[self.data.hei]+(self.data.DH*self.data.nProfiles)) for x in range(self.nrows)]
                     self.titles = ['{} Channel {}'.format(
                         self.CODE.upper(), x) for x in range(self.nrows)]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     maxHei = 1620#+12000
                     indb = numpy.where(dataOut.heightList <= maxHei)
                     hei = indb[0][-1]
                     #print(dataOut.heightList)
                     factor = dataOut.nIncohInt
                     z = dataOut.data_spc[:,:,hei] / factor
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     #buffer = 10 * numpy.log10(z)
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     #self.hei = hei
                     #self.heightList = dataOut.heightList
                     #self.DH = (dataOut.heightList[1] - dataOut.heightList[0])/dataOut.step
                     #self.nProfiles = dataOut.nProfiles
                     data['Spectrogram_Profile'] = 10 * numpy.log10(z)
                     data['hei'] = hei
                     data['DH'] = (dataOut.heightList[1] - dataOut.heightList[0])/dataOut.step
                     data['nProfiles'] = dataOut.nProfiles
                     #meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     '''
                     import matplotlib.pyplot as plt
                     plt.plot(10 * numpy.log10(z[0,:]))
                     plt.show()
                     from time import sleep
                     sleep(10)
                     '''
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.z = self.data[self.CODE]
                     self.y = self.data.xrange[0]
                     hei = self.data['hei'][-1]
                     DH = self.data['DH'][-1]
                     nProfiles = self.data['nProfiles'][-1]
                     self.ylabel = "Frequency (kHz)"
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmin = self.zmin if self.zmin else numpy.min(self.z)
                         self.zmax = self.zmax if self.zmax else numpy.max(self.z)
                         data = self.data[-1]
                         if ax.firsttime:
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                     #self.titles.append('Spectrogram')
                     #self.titles.append('{} Channel {} \n H = {} km ({} - {})'.format(
                         #self.CODE.upper(), x, y[hei], y[hei],y[hei]+(DH*nProfiles)))
             class CoherencePlot(RTIPlot):
                 '''
                 Plot for Coherence data
                 '''
                 CODE = 'coh'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = len(self.data.pairs)
                     self.nplots = len(self.data.pairs)
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time'
                     self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1,'right':0.95})
                     if self.CODE == 'coh':
                         self.cb_label = ''
                         self.titles = [
                             'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs]
                     else:
                         self.cb_label = 'Degrees'
                         self.titles = [
                             'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['coh'] = dataOut.getCoherence()
                     meta['pairs'] = dataOut.pairsList
                     return data, meta
             class PhasePlot(CoherencePlot):
                 '''
                 Plot for Phase map data
                 '''
                 CODE = 'phase'
                 colormap = 'seismic'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['phase'] = dataOut.getCoherence(phase=True)
                     meta['pairs'] = dataOut.pairsList
                     return data, meta
             class NoisePlot(Plot):
                 '''
                 Plot for noise
                 '''
                 CODE = 'noise'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Intensity [dB]'
                     self.xlabel = 'Time'
                     self.titles = ['Noise']
                     self.colorbar = False
                     self.plots_adjust.update({'right': 0.85 })
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1)
                     meta['yrange'] = numpy.array([])
                     return data, meta
                 def plot(self):
                     x = self.data.times
                     xmin = self.data.min_time
                     xmax = xmin + self.xrange * 60 * 60
                     Y = self.data['noise']
                     if self.axes[0].firsttime:
                         self.ymin = numpy.nanmin(Y) - 5
                         self.ymax = numpy.nanmax(Y) + 5
                         for ch in self.data.channels:
                             y = Y[ch]
                             self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch))
                         plt.legend(bbox_to_anchor=(1.18, 1.0))
                     else:
                         for ch in self.data.channels:
                             y = Y[ch]
                             self.axes[0].lines[ch].set_data(x, y)
                     self.ymin = numpy.nanmin(Y) - 5
                     self.ymax = numpy.nanmax(Y) + 10
             class PowerProfilePlot(Plot):
                 CODE = 'pow_profile'
                 plot_type = 'scatter'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.height = 4
                     self.width = 3
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Intensity [dB]'
                     self.titles = ['Power Profile']
                     self.colorbar = False
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data[self.CODE] = dataOut.getPower()
                     return data, meta
                 def plot(self):
                     y = self.data.yrange
                     self.y = y
                     x = self.data[-1][self.CODE]
                     if self.xmin is None: self.xmin = numpy.nanmin(x)*0.9
                     if self.xmax is None: self.xmax = numpy.nanmax(x)*1.1
                     if self.axes[0].firsttime:
                         for ch in self.data.channels:
                             self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch))
                         plt.legend()
                     else:
                         for ch in self.data.channels:
                             self.axes[0].lines[ch].set_data(x[ch], y)
             class SpectraCutPlot(Plot):
                 CODE = 'spc_cut'
                 plot_type = 'scatter'
                 buffering = False
                 def setup(self):
                     self.nplots = len(self.data.channels)
                     self.ncols = int(numpy.sqrt(self.nplots) + 0.9)
                     self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9)
                     self.width = 3.4 * self.ncols + 1.5
                     self.height = 3 * self.nrows
                     self.ylabel = 'Power [dB]'
                     self.colorbar = False
                     self.plots_adjust.update({'left':0.1, 'hspace':0.3, 'right': 0.75, 'bottom':0.08})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor)
                     data['spc'] = spc
                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     if self.CODE == 'cut_gaussian_fit':
                         data['gauss_fit0'] = 10*numpy.log10(dataOut.GaussFit0/dataOut.normFactor)
                         data['gauss_fit1'] = 10*numpy.log10(dataOut.GaussFit1/dataOut.normFactor)
                     return data, meta
                 def plot(self):
                     if self.xaxis == "frequency":
                         x = self.data.xrange[0][1:]
                         self.xlabel = "Frequency (kHz)"
                     elif self.xaxis == "time":
                         x = self.data.xrange[1]
                         self.xlabel = "Time (ms)"
                     else:
                         x = self.data.xrange[2][:-1]
                         self.xlabel = "Velocity (m/s)"
                     if self.CODE == 'cut_gaussian_fit':
                         x = self.data.xrange[2][:-1]
                         self.xlabel = "Velocity (m/s)"
                     self.titles = []
                     y = self.data.yrange
                     data = self.data[-1]
                     z = data['spc']
                     if self.height_index:
                         index = numpy.array(self.height_index)
                     else:
                         index = numpy.arange(0, len(y), int((len(y))/9))
                     for n, ax in enumerate(self.axes):
                         if self.CODE == 'cut_gaussian_fit':
                             gau0 = data['gauss_fit0']
                             gau1 = data['gauss_fit1']
                         if ax.firsttime:
                             self.xmax = self.xmax if self.xmax else numpy.nanmax(x)
                             self.xmin = self.xmin if self.xmin else -self.xmax
                             self.ymin = self.ymin if self.ymin else numpy.nanmin(z[:,:,index])
                             self.ymax = self.ymax if self.ymax else numpy.nanmax(z[:,:,index])
                             #print(self.ymax)
                             #print(z[n, :, index])
                             ax.plt = ax.plot(x, z[n, :, index].T, lw=0.25)
                             if self.CODE == 'cut_gaussian_fit':
                                 ax.plt_gau0 = ax.plot(x, gau0[n, :, index].T, lw=1, linestyle='-.')
                                 for i, line in enumerate(ax.plt_gau0):
                                     line.set_color(ax.plt[i].get_color())
                                 ax.plt_gau1 = ax.plot(x, gau1[n, :, index].T, lw=1, linestyle='--')
                                 for i, line in enumerate(ax.plt_gau1):
                                     line.set_color(ax.plt[i].get_color())
                             labels = ['Range = {:2.1f}km'.format(y[i]) for i in index]
                             self.figures[0].legend(ax.plt, labels, loc='center right')
                         else:
                             for i, line in enumerate(ax.plt):
                                 line.set_data(x, z[n, :, index[i]].T)
                             for i, line in enumerate(ax.plt_gau0):
                                 line.set_data(x, gau0[n, :, index[i]].T)
                                 line.set_color(ax.plt[i].get_color())
                             for i, line in enumerate(ax.plt_gau1):
                                 line.set_data(x, gau1[n, :, index[i]].T)
                                 line.set_color(ax.plt[i].get_color())
                         self.titles.append('CH {}'.format(n))
             class BeaconPhase(Plot):
                 __isConfig = None
                 __nsubplots = None
                 PREFIX = 'beacon_phase'
                 def __init__(self):
                     Plot.__init__(self)
                     self.timerange = 24*60*60
                     self.isConfig = False
                     self.__nsubplots = 1
                     self.counter_imagwr = 0
                     self.WIDTH = 800
                     self.HEIGHT = 400
                     self.WIDTHPROF = 120
                     self.HEIGHTPROF = 0
                     self.xdata = None
                     self.ydata = None
                     self.PLOT_CODE = BEACON_CODE
                     self.FTP_WEI = None
                     self.EXP_CODE = None
                     self.SUB_EXP_CODE = None
                     self.PLOT_POS = None
                     self.filename_phase = None
                     self.figfile = None
                     self.xmin = None
                     self.xmax = None
                 def getSubplots(self):
                     ncol = 1
                     nrow = 1
                     return nrow, ncol
                 def setup(self, id, nplots, wintitle, showprofile=True, show=True):
                     self.__showprofile = showprofile
                     self.nplots = nplots
                     ncolspan = 7
                     colspan = 6
                     self.__nsubplots = 2
                     self.createFigure(id = id,
                                       wintitle = wintitle,
                                       widthplot = self.WIDTH+self.WIDTHPROF,
                                       heightplot = self.HEIGHT+self.HEIGHTPROF,
                                       show=show)
                     nrow, ncol = self.getSubplots()
                     self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1)
                 def save_phase(self, filename_phase):
                     f = open(filename_phase,'w+')
                     f.write('\n\n')
                     f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n')
                     f.write('DD MM YYYY  HH MM SS   pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n' )
                     f.close()
                 def save_data(self, filename_phase, data, data_datetime):
                     f=open(filename_phase,'a')
                     timetuple_data = data_datetime.timetuple()
                     day = str(timetuple_data.tm_mday)
                     month = str(timetuple_data.tm_mon)
                     year = str(timetuple_data.tm_year)
                     hour = str(timetuple_data.tm_hour)
                     minute = str(timetuple_data.tm_min)
                     second = str(timetuple_data.tm_sec)
                     f.write(day+' '+month+' '+year+'  '+hour+' '+minute+' '+second+'   '+str(data[0])+'   '+str(data[1])+'   '+str(data[2])+'   '+str(data[3])+'\n')
                     f.close()
                 def plot(self):
                     log.warning('TODO: Not yet implemented...')
                 def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True',
                         xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None,
                         timerange=None,
                         save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1,
                         server=None, folder=None, username=None, password=None,
                         ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0):
                     if dataOut.flagNoData:
                         return dataOut
                     if not isTimeInHourRange(dataOut.datatime, xmin, xmax):
                         return
                     if pairsList == None:
                         pairsIndexList = dataOut.pairsIndexList[:10]
                     else:
                         pairsIndexList = []
                         for pair in pairsList:
                             if pair not in dataOut.pairsList:
                                 raise ValueError("Pair %s is not in dataOut.pairsList" %(pair))
                             pairsIndexList.append(dataOut.pairsList.index(pair))
                     if pairsIndexList == []:
                         return
              #         if len(pairsIndexList) > 4:
              #             pairsIndexList = pairsIndexList[0:4]
                     hmin_index = None
                     hmax_index = None
                     if hmin != None and hmax != None:
                         indexes = numpy.arange(dataOut.nHeights)
                         hmin_list = indexes[dataOut.heightList >= hmin]
                         hmax_list = indexes[dataOut.heightList <= hmax]
                         if hmin_list.any():
                             hmin_index = hmin_list[0]
                         if hmax_list.any():
                             hmax_index = hmax_list[-1]+1
                     x = dataOut.getTimeRange()
                     thisDatetime = dataOut.datatime
                     title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y"))
                     xlabel = "Local Time"
                     ylabel = "Phase (degrees)"
                     update_figfile = False
                     nplots = len(pairsIndexList)
                     #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList)))
                     phase_beacon = numpy.zeros(len(pairsIndexList))
                     for i in range(nplots):
                         pair = dataOut.pairsList[pairsIndexList[i]]
                         ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0)
                         powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0)
                         powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0)
                         avgcoherenceComplex = ccf/numpy.sqrt(powa*powb)
                         phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi
                         if dataOut.beacon_heiIndexList:
                             phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList])
                         else:
                             phase_beacon[i] = numpy.average(phase)
                     if not self.isConfig:
                         nplots = len(pairsIndexList)
                         self.setup(id=id,
                                    nplots=nplots,
                                    wintitle=wintitle,
                                    showprofile=showprofile,
                                    show=show)
                         if timerange != None:
                             self.timerange = timerange
                         self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange)
                         if ymin == None: ymin = 0
                         if ymax == None: ymax = 360
                         self.FTP_WEI = ftp_wei
                         self.EXP_CODE = exp_code
                         self.SUB_EXP_CODE = sub_exp_code
                         self.PLOT_POS = plot_pos
                         self.name = thisDatetime.strftime("%Y%m%d_%H%M%S")
                         self.isConfig = True
                         self.figfile = figfile
                         self.xdata = numpy.array([])
                         self.ydata = numpy.array([])
                         update_figfile = True
                         #open file beacon phase
                         path = '%s%03d' %(self.PREFIX, self.id)
                         beacon_file = os.path.join(path,'%s.txt'%self.name)
                         self.filename_phase = os.path.join(figpath,beacon_file)
                         #self.save_phase(self.filename_phase)
                     #store data beacon phase
                     #self.save_data(self.filename_phase, phase_beacon, thisDatetime)
                     self.setWinTitle(title)
                     title = "Phase Plot %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S"))
                     legendlabels = ["Pair (%d,%d)"%(pair[0], pair[1]) for pair in dataOut.pairsList]
                     axes = self.axesList[0]
                     self.xdata = numpy.hstack((self.xdata, x[0:1]))
                     if len(self.ydata)==0:
                         self.ydata = phase_beacon.reshape(-1,1)
                     else:
                         self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1)))
                     axes.pmultilineyaxis(x=self.xdata, y=self.ydata,
                                 xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax,
                                 xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid",
                                 XAxisAsTime=True, grid='both'
                                 )
                     self.draw()
                     if dataOut.ltctime >= self.xmax:
                         self.counter_imagwr = wr_period
                         self.isConfig = False
                         update_figfile = True
                     self.save(figpath=figpath,
                               figfile=figfile,
                               save=save,
                               ftp=ftp,
                               wr_period=wr_period,
                               thisDatetime=thisDatetime,
                               update_figfile=update_figfile)
                     return dataOut

schainpy/model/graphics/jroplot_voltage_lags.py +2 -4

             import os
             import time
             import math
             import datetime
             import numpy
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator  #YONG
             from .jroplot_spectra import RTIPlot, NoisePlot
             from schainpy.utils import log
             from .plotting_codes import *
             from schainpy.model.graphics.jroplot_base import Plot, plt
             import matplotlib.pyplot as plt
             import matplotlib.colors as colors
             from matplotlib.ticker import MultipleLocator
             class RTIDPPlot(RTIPlot):
                 '''Plot for RTI Double Pulse Experiment
                 '''
                 CODE = 'RTIDP'
                 colormap = 'jet'
                 plot_name = 'RTI'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = 3
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.cb_label = 'Intensity (dB)'
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95})
                     self.titles = ['{} Channel {}'.format(
                         self.plot_name.upper(), '0x1'),'{} Channel {}'.format(
                             self.plot_name.upper(), '0'),'{} Channel {}'.format(
                                 self.plot_name.upper(), '1')]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['rti'] = dataOut.data_for_RTI_DP
                     data['NDP'] = dataOut.NDP
                     return data, meta
                 def plot(self):
                     NDP = self.data['NDP'][-1]
                     self.x = self.data.times
                     self.y = self.data.yrange[0:NDP]
                     self.z = self.data['rti']
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmax = self.zmax if self.zmax is not None else numpy.max(
                             self.z[1][0,12:40])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[1][0,12:40])
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             #if self.zlimits is not None:
                                 #self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
             class RTILPPlot(RTIPlot):
                 '''
                    Plot for RTI Long Pulse
                 '''
                 CODE = 'RTILP'
                 colormap = 'jet'
                 plot_name = 'RTI LP'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = 4
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.cb_label = 'Intensity (dB)'
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95})
                     self.titles = ['{} Channel {}'.format(
                         self.plot_name.upper(), '0'),'{} Channel {}'.format(
                             self.plot_name.upper(), '1'),'{} Channel {}'.format(
                                 self.plot_name.upper(), '2'),'{} Channel {}'.format(
                                     self.plot_name.upper(), '3')]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['rti'] = dataOut.data_for_RTI_LP
                     data['NRANGE'] = dataOut.NRANGE
                     return data, meta
                 def plot(self):
                     NRANGE = self.data['NRANGE'][-1]
                     self.x = self.data.times
                     self.y = self.data.yrange[0:NRANGE]
                     self.z = self.data['rti']
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmax = self.zmax if self.zmax is not None else numpy.max(
                             self.z[1][0,12:40])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[1][0,12:40])
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         else:
                             #if self.zlimits is not None:
                                 #self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
             class DenRTIPlot(RTIPlot):
                 '''
                    Plot for Den
                 '''
                 CODE = 'denrti'
                 colormap = 'jet'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = self.data.shape(self.CODE)[0]
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     if self.CODE == 'denrti':
                         self.cb_label = r'$\mathrm{N_e}$ Electron Density ($\mathrm{1/cm^3}$)'
                     self.titles = ['Electron Density RTI']
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['denrti'] = dataOut.DensityFinal
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data[self.CODE]
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmax = self.zmax if self.zmax is not None else numpy.max(
                             self.z[n])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[n])
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             if numpy.log10(self.zmin)<0:
                                 self.zmin=1
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n],
                                                    norm=colors.LogNorm()
                                                    )
                         else:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n],
                                                    norm=colors.LogNorm()
                                                    )
             class ETempRTIPlot(RTIPlot):
                 '''
                    Plot for Electron Temperature
                 '''
                 CODE = 'ETemp'
                 colormap = 'jet'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = self.data.shape(self.CODE)[0]
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     if self.CODE == 'ETemp':
                         self.cb_label = 'Electron Temperature (K)'
                         self.titles = ['Electron Temperature RTI']
                     if self.CODE == 'ITemp':
                         self.cb_label = 'Ion Temperature (K)'
                         self.titles = ['Ion Temperature RTI']
                     if self.CODE == 'HeFracLP':
                         self.cb_label='He+ Fraction'
                         self.titles = ['He+ Fraction RTI']
                         self.zmax=0.16
                     if self.CODE== 'HFracLP':
                         self.cb_label='H+ Fraction'
                         self.titles = ['H+ Fraction RTI']
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ETemp'] = dataOut.ElecTempFinal
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data[self.CODE]
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmax = self.zmax if self.zmax is not None else numpy.max(
                             self.z[n])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[n])
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n]
                                                    )
                             #plt.tight_layout()
                         else:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n]
                                                    )
             class ITempRTIPlot(ETempRTIPlot):
                 '''
                    Plot for Ion Temperature
                 '''
                 CODE = 'ITemp'
                 colormap = 'jet'
                 plot_name = 'Ion Temperature'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ITemp'] = dataOut.IonTempFinal
                     return data, meta
             class HFracRTIPlot(ETempRTIPlot):
                 '''
                    Plot for H+ LP
                 '''
                 CODE = 'HFracLP'
                 colormap = 'jet'
                 plot_name = 'H+ Frac'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['HFracLP'] = dataOut.PhyFinal
                     return data, meta
             class HeFracRTIPlot(ETempRTIPlot):
                 '''
                    Plot for He+ LP
                 '''
                 CODE = 'HeFracLP'
                 colormap = 'jet'
                 plot_name = 'He+ Frac'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['HeFracLP'] = dataOut.PheFinal
                     return data, meta
             class TempsDPPlot(Plot):
                 '''
                 Plot for Electron - Ion Temperatures
                 '''
                 CODE = 'tempsDP'
                 #plot_name = 'Temperatures'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Temperature (K)'
                     self.titles = ['Electron/Ion Temperatures']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['Te'] = dataOut.te2
                     data['Ti'] = dataOut.ti2
                     data['Te_error'] = dataOut.ete2
                     data['Ti_error'] = dataOut.eti2
                     meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     y = self.data.yrange
                     self.xmin = -100
                     self.xmax = 5000
                     ax = self.axes[0]
                     data = self.data[-1]
                     Te = data['Te']
                     Ti = data['Ti']
                     errTe = data['Te_error']
                     errTi = data['Ti_error']
                     if ax.firsttime:
                         ax.errorbar(Te, y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         self.ystep_given = 50
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(Te, y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class TempsHPPlot(Plot):
                 '''
                 Plot for Temperatures Hybrid Experiment
                 '''
                 CODE = 'temps_LP'
                 #plot_name = 'Temperatures'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Temperature (K)'
                     self.titles = ['Electron/Ion Temperatures']
                     self.width = 3.5
                     self.height = 6.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['Te'] = numpy.concatenate((dataOut.te2[:dataOut.cut],dataOut.te[dataOut.cut:]))
                     data['Ti'] = numpy.concatenate((dataOut.ti2[:dataOut.cut],dataOut.ti[dataOut.cut:]))
                     data['Te_error'] = numpy.concatenate((dataOut.ete2[:dataOut.cut],dataOut.ete[dataOut.cut:]))
                     data['Ti_error'] = numpy.concatenate((dataOut.eti2[:dataOut.cut],dataOut.eti[dataOut.cut:]))
                     meta['yrange'] = dataOut.heightList[0:dataOut.NACF]
                     return data, meta
                 def plot(self):
                     self.y = self.data.yrange
                     self.xmin = -100
                     self.xmax = 4500
                     ax = self.axes[0]
                     data = self.data[-1]
                     Te = data['Te']
                     Ti = data['Ti']
                     errTe = data['Te_error']
                     errTi = data['Ti_error']
                     if ax.firsttime:
                         ax.errorbar(Te, self.y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, self.y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(Te, self.y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, self.y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
+                        ax.grid(which='minor')
             class FracsHPPlot(Plot):
                 '''
                 Plot for Composition LP
                 '''
                 CODE = 'fracs_LP'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Frac'
                     self.titles = ['Composition']
                     self.width = 3.5
                     self.height = 6.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     #aux_nan=numpy.zeros(dataOut.cut,'float32')
                     #aux_nan[:]=numpy.nan
                     #data['ph'] = numpy.concatenate((aux_nan,dataOut.ph[dataOut.cut:]))
                     #data['eph'] = numpy.concatenate((aux_nan,dataOut.eph[dataOut.cut:]))
                     data['ph'] = dataOut.ph[dataOut.cut:]
                     data['eph'] = dataOut.eph[dataOut.cut:]
                     data['phe'] = dataOut.phe[dataOut.cut:]
                     data['ephe'] = dataOut.ephe[dataOut.cut:]
                     data['cut'] = dataOut.cut
                     meta['yrange'] = dataOut.heightList[0:dataOut.NACF]
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     ph = data['ph']
                     eph = data['eph']
                     phe = data['phe']
                     ephe = data['ephe']
                     cut = data['cut']
                     self.y = self.data.yrange
                     self.xmin = 0
                     self.xmax = 1
                     ax = self.axes[0]
                     if ax.firsttime:
                         ax.errorbar(ph, self.y[cut:], xerr=eph, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='H+')
                         ax.errorbar(phe, self.y[cut:], fmt='k^', xerr=ephe,elinewidth=1.0,color='b',linewidth=2.0, label='He+')
                         plt.legend(loc='lower right')
                         self.xstep_given = 0.2
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(ph, self.y[cut:], xerr=eph, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='H+')
                         ax.errorbar(phe, self.y[cut:], fmt='k^', xerr=ephe,elinewidth=1.0,color='b',linewidth=2.0, label='He+')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class EDensityPlot(Plot):
                 '''
                 Plot for electron density
                 '''
                 CODE = 'den'
                 #plot_name = 'Electron Density'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = r'$\mathrm{N_e}$ Electron Density ($\mathrm{1/cm^3}$)'
                     self.titles = ['Electron Density']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['den_power'] = dataOut.ph2[:dataOut.NSHTS]
                     data['den_Faraday'] = dataOut.dphi[:dataOut.NSHTS]
                     data['den_error'] = dataOut.sdp2[:dataOut.NSHTS]
                     #data['err_Faraday'] = dataOut.sdn1[:dataOut.NSHTS]
                     data['NSHTS'] = dataOut.NSHTS
                     meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     y = self.data.yrange
                     self.xmin = 1e3
                     self.xmax = 1e7
                     ax = self.axes[0]
                     data = self.data[-1]
                     DenPow = data['den_power']
                     DenFar = data['den_Faraday']
                     errDenPow = data['den_error']
                     #errFaraday = data['err_Faraday']
                     NSHTS = data['NSHTS']
                     if self.CODE == 'denLP':
                         DenPowLP = data['den_LP']
                         errDenPowLP = data['den_LP_error']
                         cut = data['cut']
                     if ax.firsttime:
                         self.autoxticks=False
                         #ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday Profile',markersize=2)
                         ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday',markersize=2)
                         #ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power Profile',markersize=2)
                         ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2)
                         if self.CODE=='denLP':
                             ax.errorbar(DenPowLP[cut:], y[cut:], xerr=errDenPowLP[cut:], fmt='r^-',elinewidth=1.0,color='r',linewidth=1.0, label='LP Profile',markersize=2)
                         plt.legend(loc='upper left',fontsize=8.5)
                         #plt.legend(loc='lower left',fontsize=8.5)
                         ax.set_xscale("log", nonposx='clip')
                         grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
                         self.ystep_given=100
                         if self.CODE=='denLP':
                             self.ystep_given=200
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
                     else:
                         dataBefore = self.data[-2]
                         DenPowBefore = dataBefore['den_power']
                         self.clear_figures()
                         #ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday Profile',markersize=2)
                         ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday',markersize=2)
                         #ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power Profile',markersize=2)
                         ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2)
                         ax.errorbar(DenPowBefore, y[:NSHTS], elinewidth=1.0,color='r',linewidth=0.5,linestyle="dashed")
                         if self.CODE=='denLP':
                             ax.errorbar(DenPowLP[cut:], y[cut:], fmt='r^-', xerr=errDenPowLP[cut:],elinewidth=1.0,color='r',linewidth=1.0, label='LP Profile',markersize=2)
                         ax.set_xscale("log", nonposx='clip')
                         grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
                         plt.legend(loc='upper left',fontsize=8.5)
                         #plt.legend(loc='lower left',fontsize=8.5)
             class FaradayAnglePlot(Plot):
                 '''
                 Plot for electron density
                 '''
                 CODE = 'angle'
                 plot_name = 'Faraday Angle'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Faraday Angle (º)'
                     self.titles = ['Electron Density']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['angle'] = numpy.degrees(dataOut.phi)
                     #'''
                     print(dataOut.phi_uwrp)
                     print(data['angle'])
                     exit(1)
                     #'''
                     data['dphi'] = dataOut.dphi_uc*10
                     #print(dataOut.dphi)
                     #data['NSHTS'] = dataOut.NSHTS
                     #meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     self.x = data[self.CODE]
                     dphi = data['dphi']
                     self.y = self.data.yrange
                     self.xmin = -360#-180
                     self.xmax = 360#180
                     ax = self.axes[0]
                     if ax.firsttime:
                         self.autoxticks=False
                         #if self.CODE=='den':
                         ax.plot(self.x, self.y,marker='o',color='g',linewidth=1.0,markersize=2)
                         ax.plot(dphi, self.y,marker='o',color='blue',linewidth=1.0,markersize=2)
                         grid_y_ticks=numpy.arange(numpy.nanmin(self.y),numpy.nanmax(self.y),50)
                         self.ystep_given=100
                         if self.CODE=='denLP':
                             self.ystep_given=200
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
                         #plt.tight_layout()
                     else:
                         self.clear_figures()
                         #if self.CODE=='den':
                         #print(numpy.shape(self.x))
                         ax.plot(self.x, self.y, marker='o',color='g',linewidth=1.0, markersize=2)
                         ax.plot(dphi, self.y,marker='o',color='blue',linewidth=1.0,markersize=2)
                         grid_y_ticks=numpy.arange(numpy.nanmin(self.y),numpy.nanmax(self.y),50)
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
             class EDensityHPPlot(EDensityPlot):
                 '''
                    Plot for Electron Density Hybrid Experiment
                 '''
                 CODE = 'denLP'
                 plot_name = 'Electron Density'
                 plot_type = 'scatterbuffer'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['den_power'] = dataOut.ph2[:dataOut.NSHTS]
                     data['den_Faraday']=dataOut.dphi[:dataOut.NSHTS]
                     data['den_error']=dataOut.sdp2[:dataOut.NSHTS]
                     data['den_LP']=dataOut.ne[:dataOut.NACF]
                     data['den_LP_error']=dataOut.ene[:dataOut.NACF]*dataOut.ne[:dataOut.NACF]*0.434
                     #self.ene=10**dataOut.ene[:dataOut.NACF]
                     data['NSHTS']=dataOut.NSHTS
                     data['cut']=dataOut.cut
                     return data, meta
             class ACFsPlot(Plot):
                 '''
                 Plot for ACFs Double Pulse Experiment
                 '''
                 CODE = 'acfs'
                 #plot_name = 'ACF'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Lag (ms)'
                     self.titles = ['ACFs']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ACFs'] = dataOut.acfs_to_plot
                     data['ACFs_error'] = dataOut.acfs_error_to_plot
                     data['lags'] = dataOut.lags_to_plot
                     data['Lag_contaminated_1'] = dataOut.x_igcej_to_plot
                     data['Lag_contaminated_2'] = dataOut.x_ibad_to_plot
                     data['Height_contaminated_1'] = dataOut.y_igcej_to_plot
                     data['Height_contaminated_2'] = dataOut.y_ibad_to_plot
                     meta['yrange'] = numpy.array([])
                     #meta['NSHTS'] = dataOut.NSHTS
                     #meta['DPL'] = dataOut.DPL
                     data['NSHTS'] = dataOut.NSHTS #This is metadata
                     data['DPL'] = dataOut.DPL #This is metadata
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     #NSHTS = self.meta['NSHTS']
                     #DPL = self.meta['DPL']
                     NSHTS = data['NSHTS'] #This is metadata
                     DPL = data['DPL'] #This is metadata
                     lags = data['lags']
                     ACFs = data['ACFs']
                     errACFs = data['ACFs_error']
                     BadLag1 = data['Lag_contaminated_1']
                     BadLag2 = data['Lag_contaminated_2']
                     BadHei1 = data['Height_contaminated_1']
                     BadHei2 = data['Height_contaminated_2']
                     self.xmin = 0.0
                     self.xmax = 2.0
                     self.y = ACFs
                     ax = self.axes[0]
                     if ax.firsttime:
                         for i in range(NSHTS):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             x_igcej_aux = numpy.isfinite(BadLag1[i,:])
                             y_igcej_aux = numpy.isfinite(BadHei1[i,:])
                             x_ibad_aux = numpy.isfinite(BadLag2[i,:])
                             y_ibad_aux = numpy.isfinite(BadHei2[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',marker='o',linewidth=1.0,markersize=2)
                             ax.plot(BadLag1[i,x_igcej_aux],BadHei1[i,y_igcej_aux],'x',color='red',markersize=2)
                             ax.plot(BadLag2[i,x_ibad_aux],BadHei2[i,y_ibad_aux],'X',color='red',markersize=2)
                         self.xstep_given = (self.xmax-self.xmin)/(DPL-1)
                         self.ystep_given = 50
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         for i in range(NSHTS):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             x_igcej_aux = numpy.isfinite(BadLag1[i,:])
                             y_igcej_aux = numpy.isfinite(BadHei1[i,:])
                             x_ibad_aux = numpy.isfinite(BadLag2[i,:])
                             y_ibad_aux = numpy.isfinite(BadHei2[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],linewidth=1.0,markersize=2,color='b',marker='o')
                             ax.plot(BadLag1[i,x_igcej_aux],BadHei1[i,y_igcej_aux],'x',color='red',markersize=2)
                             ax.plot(BadLag2[i,x_ibad_aux],BadHei2[i,y_ibad_aux],'X',color='red',markersize=2)
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class ACFsLPPlot(Plot):
                 '''
                 Plot for ACFs Double Pulse Experiment
                 '''
                 CODE = 'acfs_LP'
                 #plot_name = 'ACF'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Lag (ms)'
                     self.titles = ['ACFs']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     aux=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     errors=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     lags_LP_to_plot=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     for i in range(dataOut.NACF):
                         for j in range(dataOut.IBITS):
                             if numpy.abs(dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0])<1.0:
                                 aux[i,j]=dataOut.output_LP_integrated.real[j,i,0]/dataOut.output_LP_integrated.real[0,i,0]
                                 aux[i,j]=max(min(aux[i,j],1.0),-1.0)*dataOut.DH+dataOut.heightList[i]
                                 lags_LP_to_plot[i,j]=dataOut.lags_LP[j]
                                 errors[i,j]=dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0]*dataOut.DH
                             else:
                                 aux[i,j]=numpy.nan
                                 lags_LP_to_plot[i,j]=numpy.nan
                                 errors[i,j]=numpy.nan
                     data['ACFs'] = aux
                     data['ACFs_error'] = errors
                     data['lags'] = lags_LP_to_plot
                     meta['yrange'] = numpy.array([])
                     #meta['NACF'] = dataOut.NACF
                     #meta['NLAG'] = dataOut.NLAG
                     data['NACF'] = dataOut.NACF #This is metadata
                     data['NLAG'] = dataOut.NLAG #This is metadata
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     #NACF = self.meta['NACF']
                     #NLAG = self.meta['NLAG']
                     NACF = data['NACF'] #This is metadata
                     NLAG = data['NLAG'] #This is metadata
                     lags = data['lags']
                     ACFs = data['ACFs']
                     errACFs = data['ACFs_error']
                     self.xmin = 0.0
                     self.xmax = 1.5
                     self.y = ACFs
                     ax = self.axes[0]
                     if ax.firsttime:
                         for i in range(NACF):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',linewidth=1.0,markersize=2,ecolor='r')
                         #self.xstep_given = (self.xmax-self.xmin)/(self.data.NLAG-1)
                         self.xstep_given=0.3
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         for i in range(NACF):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',linewidth=1.0,markersize=2,ecolor='r')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class CrossProductsPlot(Plot):
                 '''
                 Plot for cross products
                 '''
                 CODE = 'crossprod'
                 plot_name = 'Cross Products'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 3
                     self.nrows = 1
                     self.nplots = 3
                     self.ylabel = 'Range [km]'
                     self.titles = []
                     self.width = 3.5*self.nplots
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'wspace':.3, 'left': 0.12, 'right': 0.92, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['crossprod'] = dataOut.crossprods
                     data['NDP'] = dataOut.NDP
                     return data, meta
                 def plot(self):
                     NDP = self.data['NDP'][-1]
                     x = self.data['crossprod'][:,-1,:,:,:,:]
                     y = self.data.yrange[0:NDP]
                     for n, ax in enumerate(self.axes):
                         self.xmin=numpy.min(numpy.concatenate((x[n][0,20:30,0,0],x[n][1,20:30,0,0],x[n][2,20:30,0,0],x[n][3,20:30,0,0])))
                         self.xmax=numpy.max(numpy.concatenate((x[n][0,20:30,0,0],x[n][1,20:30,0,0],x[n][2,20:30,0,0],x[n][3,20:30,0,0])))
                         if ax.firsttime:
                             self.autoxticks=False
                             if n==0:
                                 label1='kax'
                                 label2='kay'
                                 label3='kbx'
                                 label4='kby'
                                 self.xlimits=[(self.xmin,self.xmax)]
                             elif n==1:
                                 label1='kax2'
                                 label2='kay2'
                                 label3='kbx2'
                                 label4='kby2'
                                 self.xlimits.append((self.xmin,self.xmax))
                             elif n==2:
                                 label1='kaxay'
                                 label2='kbxby'
                                 label3='kaxbx'
                                 label4='kaxby'
                                 self.xlimits.append((self.xmin,self.xmax))
                             ax.plotline1 = ax.plot(x[n][0,:,0,0], y, color='r',linewidth=2.0, label=label1)
                             ax.plotline2 = ax.plot(x[n][1,:,0,0], y, color='k',linewidth=2.0, label=label2)
                             ax.plotline3 = ax.plot(x[n][2,:,0,0], y, color='b',linewidth=2.0, label=label3)
                             ax.plotline4 = ax.plot(x[n][3,:,0,0], y, color='m',linewidth=2.0, label=label4)
                             ax.legend(loc='upper right')
                             ax.set_xlim(self.xmin, self.xmax)
                             self.titles.append('{}'.format(self.plot_name.upper()))
                         else:
                             if n==0:
                                 self.xlimits=[(self.xmin,self.xmax)]
                             else:
                                 self.xlimits.append((self.xmin,self.xmax))
                             ax.set_xlim(self.xmin, self.xmax)
                             ax.plotline1[0].set_data(x[n][0,:,0,0],y)
                             ax.plotline2[0].set_data(x[n][1,:,0,0],y)
                             ax.plotline3[0].set_data(x[n][2,:,0,0],y)
                             ax.plotline4[0].set_data(x[n][3,:,0,0],y)
                             self.titles.append('{}'.format(self.plot_name.upper()))
             class CrossProductsLPPlot(Plot):
                 '''
                 Plot for cross products LP
                 '''
                 CODE = 'crossprodslp'
                 plot_name = 'Cross Products LP'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 2
                     self.nrows = 1
                     self.nplots = 2
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'dB'
                     self.width = 3.5*self.nplots
                     self.height = 5.5
                     self.colorbar = False
                     self.titles = []
                     self.plots_adjust.update({'wspace': .8 ,'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['crossprodslp'] = 10*numpy.log10(numpy.abs(dataOut.output_LP))
                     data['NRANGE'] = dataOut.NRANGE #This is metadata
                     data['NLAG'] = dataOut.NLAG #This is metadata
                     return data, meta
                 def plot(self):
                     NRANGE = self.data['NRANGE'][-1]
                     NLAG = self.data['NLAG'][-1]
                     x = self.data[self.CODE][:,-1,:,:]
                     self.y = self.data.yrange[0:NRANGE]
                     label_array=numpy.array(['lag '+ str(x) for x in range(NLAG)])
                     color_array=['r','k','g','b','c','m','y','orange','steelblue','purple','peru','darksalmon','grey','limegreen','olive','midnightblue']
                     for n, ax in enumerate(self.axes):
                         self.xmin=28#30
                         self.xmax=70#70
                         #self.xmin=numpy.min(numpy.concatenate((self.x[0,:,n],self.x[1,:,n])))
                         #self.xmax=numpy.max(numpy.concatenate((self.x[0,:,n],self.x[1,:,n])))
                         if ax.firsttime:
                             self.autoxticks=False
                             if n == 0:
                                 self.plotline_array=numpy.zeros((2,NLAG),dtype=object)
                             for i in range(NLAG):
                                 self.plotline_array[n,i], = ax.plot(x[i,:,n], self.y, color=color_array[i],linewidth=1.0, label=label_array[i])
                             ax.legend(loc='upper right')
                             ax.set_xlim(self.xmin, self.xmax)
                             if n==0:
                                 self.titles.append('{} CH0'.format(self.plot_name.upper()))
                             if n==1:
                                 self.titles.append('{} CH1'.format(self.plot_name.upper()))
                         else:
                             for i in range(NLAG):
                                 self.plotline_array[n,i].set_data(x[i,:,n],self.y)
                             if n==0:
                                 self.titles.append('{} CH0'.format(self.plot_name.upper()))
                             if n==1:
                                 self.titles.append('{} CH1'.format(self.plot_name.upper()))
             class NoiseDPPlot(NoisePlot):
                 '''
                 Plot for noise Double Pulse
                 '''
                 CODE = 'noise'
                 #plot_name = 'Noise'
                 #plot_type = 'scatterbuffer'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['noise'] = 10*numpy.log10(dataOut.noise_final)
                     return data, meta
             class XmitWaveformPlot(Plot):
                 '''
                 Plot for xmit waveform
                 '''
                 CODE = 'xmit'
                 plot_name = 'Xmit Waveform'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = ''
                     self.xlabel = 'Number of Lag'
                     self.width = 5.5
                     self.height = 3.5
                     self.colorbar = False
                     self.plots_adjust.update({'right': 0.85 })
                     self.titles = [self.plot_name]
                     #self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                     #if not self.titles:
                         #self.titles = self.data.parameters \
                             #if self.data.parameters else ['{}'.format(self.plot_name.upper())]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     y_1=numpy.arctan2(dataOut.output_LP[:,0,2].imag,dataOut.output_LP[:,0,2].real)* 180 / (numpy.pi*10)
                     y_2=numpy.abs(dataOut.output_LP[:,0,2])
                     norm=numpy.max(y_2)
                     norm=max(norm,0.1)
                     y_2=y_2/norm
                     meta['yrange'] = numpy.array([])
                     data['xmit'] = numpy.vstack((y_1,y_2))
                     data['NLAG'] = dataOut.NLAG
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     NLAG = data['NLAG']
                     x = numpy.arange(0,NLAG,1,'float32')
                     y = data['xmit']
                     self.xmin = 0
                     self.xmax = NLAG-1
                     self.ymin = -1.0
                     self.ymax = 1.0
                     ax = self.axes[0]
                     if ax.firsttime:
                         ax.plotline0=ax.plot(x,y[0,:],color='blue')
                         ax.plotline1=ax.plot(x,y[1,:],color='red')
                         secax=ax.secondary_xaxis(location=0.5)
                         secax.xaxis.tick_bottom()
                         secax.tick_params( labelleft=False, labeltop=False,
                                   labelright=False, labelbottom=False)
                         self.xstep_given = 3
                         self.ystep_given = .25
                         secax.set_xticks(numpy.linspace(self.xmin, self.xmax, 6)) #only works on matplotlib.version>3.2
                     else:
                         ax.plotline0[0].set_data(x,y[0,:])
                         ax.plotline1[0].set_data(x,y[1,:])

schainpy/model/proc/jroproc_base.py +9 -5

             '''
             Base clases to create Processing units and operations, the MPDecorator
             must be used in plotting and writing operations to allow to run as an
             external process.
             '''
             import os
             import inspect
             import zmq
             import time
             import pickle
             import traceback
             from threading import Thread
             from multiprocessing import Process, Queue
             from schainpy.utils import log
             import copy
             QUEUE_SIZE = int(os.environ.get('QUEUE_MAX_SIZE', '10'))
             class ProcessingUnit(object):
                 '''
                 Base class to create Signal Chain Units
                 '''
                 proc_type = 'processing'
                 def __init__(self):
                     self.dataIn = None
                     self.dataOut = None
                     self.isConfig = False
                     self.operations = []
                     self.name = 'Test'
                     self.inputs = []
                 def setInput(self, unit):
                     attr = 'dataIn'
                     for i, u in enumerate(unit):
                         if i==0:
                             #print(u.dataOut.flagNoData)
                             #exit(1)
                             self.dataIn = u.dataOut#.copy()
                             self.inputs.append('dataIn')
                         else:
                             setattr(self, 'dataIn{}'.format(i), u.dataOut)#.copy())
                             self.inputs.append('dataIn{}'.format(i))
                 def getAllowedArgs(self):
                     if hasattr(self, '__attrs__'):
                         return self.__attrs__
                     else:
                         return inspect.getargspec(self.run).args
                 def addOperation(self, conf, operation):
                     '''
                     '''
                     self.operations.append((operation, conf.type, conf.getKwargs()))
                 def getOperationObj(self, objId):
                     if objId not in list(self.operations.keys()):
                         return None
                     return self.operations[objId]
                 def call(self, **kwargs):
                     '''
                     '''
+                    #print("call")
                     try:
-                        #print("try")
                         if self.dataIn is not None and self.dataIn.flagNoData and not self.dataIn.error:
-                            #print("Try")
+                        #if self.dataIn is not None and self.dataIn.flagNoData and not self.dataIn.error and not self.dataIn.runNextUnit:
-                            #print(self.dataIn)
+                            if self.dataIn.runNextUnit:
-                            #print(self.dataIn.flagNoData)
+                                #print("SUCCESSSSSSS")
+                                #exit(1)
+                                return not self.dataIn.isReady()
+                            else:
                                 return self.dataIn.isReady()
                         elif self.dataIn is None or not self.dataIn.error:
                             #print([getattr(self, at) for at in self.inputs])
                             #print("Elif 1")
                             self.run(**kwargs)
                         elif self.dataIn.error:
                             #print("Elif 2")
                             self.dataOut.error = self.dataIn.error
                             self.dataOut.flagNoData = True
                     except:
                         #print("Except")
                         err = traceback.format_exc()
                         if 'SchainWarning' in err:
                             log.warning(err.split('SchainWarning:')[-1].split('\n')[0].strip(), self.name)
                         elif 'SchainError' in err:
                             log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), self.name)
                         else:
                             log.error(err, self.name)
                         self.dataOut.error = True
                     #print("before op")
                     for op, optype, opkwargs in self.operations:
                         aux = self.dataOut.copy()
                         #aux = copy.deepcopy(self.dataOut)
                         #print("**********************Before",op)
                         if optype == 'other' and not self.dataOut.flagNoData:
                             #print("**********************Other",op)
                             #print(self.dataOut.flagNoData)
                             self.dataOut = op.run(self.dataOut, **opkwargs)
                         elif optype == 'external' and not self.dataOut.flagNoData:
                             op.queue.put(aux)
                         elif optype == 'external' and self.dataOut.error:
                             op.queue.put(aux)
                         #elif optype == 'external' and self.dataOut.isReady():
                             #op.queue.put(copy.deepcopy(self.dataOut))
                     #print(not self.dataOut.isReady())
                     try:
                         if self.dataOut.runNextUnit:
                             runNextUnit = self.dataOut.runNextUnit
                             #print(self.operations)
                             #print("Tru")
                         else:
                             runNextUnit = self.dataOut.isReady()
                     except:
                         runNextUnit = self.dataOut.isReady()
                     #if  not self.dataOut.isReady():
                         #return 'Error' if self.dataOut.error else input()
                     #print("NexT",runNextUnit)
+                    #print("error: ",self.dataOut.error)
                     return 'Error' if self.dataOut.error else runNextUnit# self.dataOut.isReady()
                 def setup(self):
                     raise NotImplementedError
                 def run(self):
                     raise NotImplementedError
                 def close(self):
                     return
             class Operation(object):
                 '''
                 '''
                 proc_type = 'operation'
                 def __init__(self):
                     self.id = None
                     self.isConfig = False
                     if not hasattr(self, 'name'):
                         self.name = self.__class__.__name__
                 def getAllowedArgs(self):
                     if hasattr(self, '__attrs__'):
                         return self.__attrs__
                     else:
                         return inspect.getargspec(self.run).args
                 def setup(self):
                     self.isConfig = True
                     raise NotImplementedError
                 def run(self, dataIn, **kwargs):
                     """
                     Realiza las operaciones necesarias sobre la dataIn.data y actualiza los
                     atributos del objeto dataIn.
                     Input:
                         dataIn    :    objeto del tipo JROData
                     Return:
                         None
                     Affected:
                         __buffer    :    buffer de recepcion de datos.
                     """
                     if not self.isConfig:
                         self.setup(**kwargs)
                     raise NotImplementedError
                 def close(self):
                     return
             def MPDecorator(BaseClass):
                 """
                 Multiprocessing class decorator
                 This function add multiprocessing features to a BaseClass.
                 """
                 class MPClass(BaseClass, Process):
                     def __init__(self, *args, **kwargs):
                         super(MPClass, self).__init__()
                         Process.__init__(self)
                         self.args = args
                         self.kwargs = kwargs
                         self.t = time.time()
                         self.op_type = 'external'
                         self.name = BaseClass.__name__
                         self.__doc__ = BaseClass.__doc__
                         if 'plot' in self.name.lower() and not self.name.endswith('_'):
                             self.name = '{}{}'.format(self.CODE.upper(), 'Plot')
                         self.start_time = time.time()
                         self.err_queue = args[3]
                         self.queue = Queue(maxsize=QUEUE_SIZE)
                         self.myrun = BaseClass.run
                     def run(self):
                         while True:
                             dataOut = self.queue.get()
                             if not dataOut.error:
                                 try:
                                     BaseClass.run(self, dataOut, **self.kwargs)
                                 except:
                                     err = traceback.format_exc()
                                     log.error(err, self.name)
                             else:
                                 break
                         self.close()
                     def close(self):
                         BaseClass.close(self)
                         log.success('Done...(Time:{:4.2f} secs)'.format(time.time() - self.start_time), self.name)
                 return MPClass

schainpy/model/proc/jroproc_parameters.py 0 0

	1	NO CONTENT: modified file			NO CONTENT: modified file
The requested commit or file is too big and content was truncated. Show full diff

schainpy/model/proc/jroproc_spectra_acf.py +854 -5

             import numpy
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jrodata import Spectra
             from schainpy.model.data.jrodata import hildebrand_sekhon
-            class SpectraAFCProc(ProcessingUnit):
+            class SpectraAFCProc_V0(ProcessingUnit):
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Spectra()
                     self.id_min = None
                     self.id_max = None
                 def __updateSpecFromVoltage(self):
                     self.dataOut.plotting = "spectra_acf"
                     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.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
                     self.dataOut.nBaud = self.dataIn.nBaud
                     self.dataOut.nCode = self.dataIn.nCode
                     self.dataOut.code = self.dataIn.code
             #         self.dataOut.nProfiles = self.dataOut.nFFTPoints
                     self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
                     self.dataOut.utctime = self.firstdatatime
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
                     self.dataOut.flagShiftFFT = False
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.nIncohInt = 1
                     self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.realtime = self.dataIn.realtime
                     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
                 def __decodeData(self, nProfiles, code):
                     if code is None:
                         return
                     for i in range(nProfiles):
                         self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i]
                 def __getFft(self):
                     """
                     Convierte valores de Voltaje a Spectra
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                         self.profIndex
                         self.buffer
                         self.dataOut.flagNoData
                     """
                     nsegments = self.dataOut.nHeights
                     _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex')
                     for i in range(nsegments):
                         try:
                             _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles]
                             if self.code is not None:
                                 _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0]
                         except:
                             pass
                     fft_volt = numpy.fft.fft(_fft_buffer, n=self.dataOut.nFFTPoints, axis=1)
                     fft_volt = fft_volt.astype(numpy.dtype('complex'))
                     dc = fft_volt[:,0,:]
                     #calculo de self-spectra
                     spc = fft_volt * numpy.conjugate(fft_volt)
                     data = numpy.fft.ifft(spc, axis=1)
                     data = numpy.fft.fftshift(data, axes=(1,))
                     spc = data
                     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:
                             if pair[0] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)))
                             if pair[1] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)))
                             chan_index0 = self.dataOut.channelList.index(pair[0])
                             chan_index1 = self.dataOut.channelList.index(pair[1])
                             cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:])
                             pairIndex += 1
                         blocksize += cspc.size
                     self.dataOut.data_spc = spc
                     self.dataOut.data_cspc = cspc
                     self.dataOut.data_dc = dc
                     self.dataOut.blockSize = blocksize
                     self.dataOut.flagShiftFFT = True
                 def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1,real= None, imag=None):
                     self.dataOut.flagNoData = True
                     if self.dataIn.type == "Spectra":
                         self.dataOut.copy(self.dataIn)
+                        #print(self.dataOut.data.shape)
+                        #exit(1)
                         spc         =  self.dataOut.data_spc
                         data        =  numpy.fft.fftshift( spc, axes=(1,))
                         data        =  numpy.fft.ifft(data, axis=1)
-                        #data        =  numpy.fft.fftshift( data, axes=(1,))
+                        data        =  numpy.fft.fftshift( data, axes=(1,))
-                        #acf         =  numpy.abs(data)     #  Autocorrelacion LLAMAR A ESTE VALOR ACF
+                        acf         =  numpy.abs(data)     #  Autocorrelacion LLAMAR A ESTE VALOR ACF
-                        acf = data
+                        acf = data #Comentarlo?
+                        print("acf",acf[0,:,150])
+                        exit(1)
                         #'''
                         if real:
                             acf     = data.real
                         if imag:
                             acf     = data.imag
                             #'''
                         shape       =  acf.shape            #  nchannels, nprofiles, nsamples //nchannles, lags , alturas
                         '''
                         for j in range(shape[0]):
                             for i in range(shape[2]):
                                tmp = int(shape[1]/2)
                                #print(i,j,tmp)
                                value  = (acf[j,:,i][tmp-1]+acf[j,:,i][tmp+1])/2.0
                                acf[j,:,i][tmp] = value
                         # Normalizando
                         for i in range(shape[0]):
                             for j in range(shape[2]):
                                 acf[i,:,j]= acf[i,:,j] / numpy.max(numpy.abs(acf[i,:,j]))
                                 '''
                         self.dataOut.data_acf = acf
-                        self.dataOut.data_spc = acf
+                        self.dataOut.data_spc = acf.real
                         #print(self.dataOut.data_acf[0,:,0])
                         #exit(1)
                         '''
                         shape = self.dataOut.data_acf.shape
                         resFactor = 5
                         z = self.dataOut.data_acf.copy()
                         min = numpy.min(z[0,:,0])
                         max =numpy.max(z[0,:,0])
                         deltaHeight = self.dataOut.heightList[1]-self.dataOut.heightList[0]
                         for i in range(shape[0]):
                             for j in range(shape[2]):
                                 z[i,:,j]= (((z[i,:,j]-min)/(max-min))*deltaHeight*resFactor + j*deltaHeight)
                         #print(self.dataOut.data_spc.shape)
                         #print(self.dataOut.data_acf.shape)
                         '''
+                        '''
                         import matplotlib.pyplot as plt
                         hei = 10
                         #print(self.dataOut.heightList)
                         print(self.dataOut.heightList[hei])
                         #plt.plot(z[0,0,:],self.dataOut.heightList)
                         aux =  self.dataOut.data_acf[0,0,:]
                         power = aux*numpy.conjugate(aux)
                         print(power)
                         powerdb = numpy.log10(power)
                         plt.plot(powerdb,self.dataOut.heightList)
                         #plt.plot(self.dataOut.data_acf[0,:,1])
                         plt.ylim(0,1000)
                         plt.show()
                         exit(1)
+                        '''
+                        return True
+                    if code is not None:
+                        self.code = numpy.array(code).reshape(nCode,nBaud)
+                    else:
+                        self.code = None
+                    if self.dataIn.type == "Voltage":
+                        if nFFTPoints == None:
+                            raise ValueError("This SpectraProc.run() need nFFTPoints input variable")
+                        if nProfiles == None:
+                            nProfiles = nFFTPoints
+                        self.dataOut.ippFactor = 1
+                        self.dataOut.nFFTPoints = nFFTPoints
+                        self.dataOut.nProfiles = nProfiles
+                        self.dataOut.pairsList = pairsList
+            #             if self.buffer is None:
+            #                 self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights),
+            #                                           dtype='complex')
+                        if not self.dataIn.flagDataAsBlock:
+                            self.buffer = self.dataIn.data.copy()
+            #                 for i in range(self.dataIn.nHeights):
+            #                     self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex]
+            #
+            #                 self.profIndex += 1
+                        else:
+                            raise ValueError("")
+                        self.firstdatatime = self.dataIn.utctime
+                        self.profIndex == nProfiles
+                        self.__updateSpecFromVoltage()
+                        self.__getFft()
+                        self.dataOut.flagNoData = False
+                        return True
+                    raise ValueError("The type of input object '%s' is not valid"%(self.dataIn.type))
+                def __selectPairs(self, pairsList):
+                    if channelList == None:
+                        return
+                    pairsIndexListSelected = []
+                    for thisPair in pairsList:
+                        if thisPair not in self.dataOut.pairsList:
+                            continue
+                        pairIndex = self.dataOut.pairsList.index(thisPair)
+                        pairsIndexListSelected.append(pairIndex)
+                    if not pairsIndexListSelected:
+                        self.dataOut.data_cspc = None
+                        self.dataOut.pairsList = []
+                        return
+                    self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
+                    self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
+                    return
+                def __selectPairsByChannel(self, channelList=None):
+                    if channelList == None:
+                        return
+                    pairsIndexListSelected = []
+                    for pairIndex in self.dataOut.pairsIndexList:
+                        #First pair
+                        if self.dataOut.pairsList[pairIndex][0] not in channelList:
+                            continue
+                        #Second pair
+                        if self.dataOut.pairsList[pairIndex][1] not in channelList:
+                            continue
+                        pairsIndexListSelected.append(pairIndex)
+                    if not pairsIndexListSelected:
+                        self.dataOut.data_cspc = None
+                        self.dataOut.pairsList = []
+                        return
+                    self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
+                    self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
+                    return
+                def selectChannels(self, channelList):
+                    channelIndexList = []
+                    for channel in channelList:
+                        if channel not in self.dataOut.channelList:
+                            raise ValueError("Error selecting channels, Channel %d is not valid.\nAvailable channels = %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_spc
+                        self.dataOut.channelIndexList
+                        self.dataOut.nChannels
+                    Return:
+                        None
+                    """
+                    for channelIndex in channelIndexList:
+                        if channelIndex not in self.dataOut.channelIndexList:
+                            raise ValueError("Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList))
+            #         nChannels = len(channelIndexList)
+                    data_spc = self.dataOut.data_spc[channelIndexList,:]
+                    data_dc = self.dataOut.data_dc[channelIndexList,:]
+                    self.dataOut.data_spc = data_spc
+                    self.dataOut.data_dc = data_dc
+                    self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
+            #        self.dataOut.nChannels = nChannels
+                    self.__selectPairsByChannel(self.dataOut.channelList)
+                    return 1
+                def selectHeights(self, minHei, maxHei):
+                    """
+                    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:
+si el metodo se ejecuto con exito caso contrario devuelve 0
+                    """
+                    if (minHei > maxHei):
+                        raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei))
+                    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 getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
+                    newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
+                    if hei_ref != None:
+                        newheis = numpy.where(self.dataOut.heightList>hei_ref)
+                    minIndex = min(newheis[0])
+                    maxIndex = max(newheis[0])
+                    data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
+                    heightList = self.dataOut.heightList[minIndex:maxIndex+1]
+                    # determina indices
+                    nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
+                    avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
+                    beacon_dB = numpy.sort(avg_dB)[-nheis:]
+                    beacon_heiIndexList = []
+                    for val in avg_dB.tolist():
+                        if val >= beacon_dB[0]:
+                            beacon_heiIndexList.append(avg_dB.tolist().index(val))
+                    #data_spc = data_spc[:,:,beacon_heiIndexList]
+                    data_cspc = None
+                    if self.dataOut.data_cspc is not None:
+                        data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
+                        #data_cspc = data_cspc[:,:,beacon_heiIndexList]
+                    data_dc = None
+                    if self.dataOut.data_dc is not None:
+                        data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
+                        #data_dc = data_dc[:,beacon_heiIndexList]
+                    self.dataOut.data_spc = data_spc
+                    self.dataOut.data_cspc = data_cspc
+                    self.dataOut.data_dc = data_dc
+                    self.dataOut.heightList = heightList
+                    self.dataOut.beacon_heiIndexList = beacon_heiIndexList
+                    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_spc
+                        self.dataOut.data_cspc
+                        self.dataOut.data_dc
+                        self.dataOut.heightList
+                    Return:
+si el metodo se ejecuto con exito caso contrario devuelve 0
+                    """
+                    if (minIndex < 0) or (minIndex > maxIndex):
+                        raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))
+                    if (maxIndex >= self.dataOut.nHeights):
+                        maxIndex = self.dataOut.nHeights-1
+                    #Spectra
+                    data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
+                    data_cspc = None
+                    if self.dataOut.data_cspc is not None:
+                        data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
+                    data_dc = None
+                    if self.dataOut.data_dc is not None:
+                        data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
+                    self.dataOut.data_spc = data_spc
+                    self.dataOut.data_cspc = data_cspc
+                    self.dataOut.data_dc = data_dc
+                    self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
+                    return 1
+                def removeDC(self, mode = 2):
+                    jspectra = self.dataOut.data_spc
+                    jcspectra = self.dataOut.data_cspc
+                    num_chan = jspectra.shape[0]
+                    num_hei = jspectra.shape[2]
+                    if jcspectra is not None:
+                        jcspectraExist = True
+                        num_pairs = jcspectra.shape[0]
+                    else:   jcspectraExist = False
+                    freq_dc = jspectra.shape[1]/2
+                    ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
+                    if ind_vel[0]<0:
+                        ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof
+                    if mode == 1:
+                        jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION
+                        if jcspectraExist:
+                            jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2
+                    if mode == 2:
+                        vel = numpy.array([-2,-1,1,2])
+                        xx = numpy.zeros([4,4])
+                        for fil in range(4):
+                            xx[fil,:] = vel[fil]**numpy.asarray(range(4))
+                        xx_inv = numpy.linalg.inv(xx)
+                        xx_aux = xx_inv[0,:]
+                        for ich in range(num_chan):
+                            yy = jspectra[ich,ind_vel,:]
+                            jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)
+                            junkid = jspectra[ich,freq_dc,:]<=0
+                            cjunkid = sum(junkid)
+                            if cjunkid.any():
+                                jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2
+                        if jcspectraExist:
+                            for ip in range(num_pairs):
+                                yy = jcspectra[ip,ind_vel,:]
+                                jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)
+                    self.dataOut.data_spc = jspectra
+                    self.dataOut.data_cspc = jcspectra
+                    return 1
+                def removeInterference(self,  interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
+                    jspectra = self.dataOut.data_spc
+                    jcspectra = self.dataOut.data_cspc
+                    jnoise = self.dataOut.getNoise()
+                    num_incoh = self.dataOut.nIncohInt
+                    num_channel  = jspectra.shape[0]
+                    num_prof  = jspectra.shape[1]
+                    num_hei   = jspectra.shape[2]
+                    #hei_interf
+                    if hei_interf is None:
+                        count_hei = num_hei/2   #Como es entero no importa
+                        hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
+                        hei_interf = numpy.asarray(hei_interf)[0]
+                    #nhei_interf
+                    if (nhei_interf == None):
+                        nhei_interf = 5
+                    if (nhei_interf < 1):
+                        nhei_interf = 1
+                    if (nhei_interf > count_hei):
+                        nhei_interf = count_hei
+                    if (offhei_interf == None):
+                        offhei_interf = 0
+                    ind_hei = range(num_hei)
+            #         mask_prof = numpy.asarray(range(num_prof - 2)) + 1
+            #         mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
+                    mask_prof = numpy.asarray(range(num_prof))
+                    num_mask_prof = mask_prof.size
+                    comp_mask_prof = [0, num_prof/2]
+                    #noise_exist:    Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
+                    if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
+                        jnoise = numpy.nan
+                    noise_exist = jnoise[0] < numpy.Inf
+                    #Subrutina de Remocion de la Interferencia
+                    for ich in range(num_channel):
+                        #Se ordena los espectros segun su potencia (menor a mayor)
+                        power = jspectra[ich,mask_prof,:]
+                        power = power[:,hei_interf]
+                        power = power.sum(axis = 0)
+                        psort = power.ravel().argsort()
+                        #Se estima la interferencia promedio en los Espectros de Potencia empleando
+                        junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]
+                        if noise_exist:
+                        #    tmp_noise = jnoise[ich] / num_prof
+                            tmp_noise = jnoise[ich]
+                        junkspc_interf = junkspc_interf - tmp_noise
+                        #junkspc_interf[:,comp_mask_prof] = 0
+                        jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
+                        jspc_interf = jspc_interf.transpose()
+                        #Calculando el espectro de interferencia promedio
+                        noiseid =  numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh))
+                        noiseid = noiseid[0]
+                        cnoiseid = noiseid.size
+                        interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh))
+                        interfid = interfid[0]
+                        cinterfid = interfid.size
+                        if (cnoiseid > 0):   jspc_interf[noiseid] = 0
+                        #Expandiendo los perfiles a limpiar
+                        if (cinterfid > 0):
+                            new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
+                            new_interfid = numpy.asarray(new_interfid)
+                            new_interfid = {x for x in new_interfid}
+                            new_interfid = numpy.array(list(new_interfid))
+                            new_cinterfid = new_interfid.size
+                        else: new_cinterfid = 0
+                        for ip in range(new_cinterfid):
+                            ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
+                            jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]
+                        jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices
+                        #Removiendo la interferencia del punto de mayor interferencia
+                        ListAux = jspc_interf[mask_prof].tolist()
+                        maxid = ListAux.index(max(ListAux))
+                        if cinterfid > 0:
+                            for ip in range(cinterfid*(interf == 2) - 1):
+                                ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero()
+                                cind = len(ind)
+                                if (cind > 0):
+                                    jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh))
+                            ind = numpy.array([-2,-1,1,2])
+                            xx = numpy.zeros([4,4])
+                            for id1 in range(4):
+                                xx[:,id1] = ind[id1]**numpy.asarray(range(4))
+                            xx_inv = numpy.linalg.inv(xx)
+                            xx = xx_inv[:,0]
+                            ind = (ind + maxid + num_mask_prof)%num_mask_prof
+                            yy = jspectra[ich,mask_prof[ind],:]
+                            jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
+                        indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero()
+                        jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh))
+                    #Remocion de Interferencia en el Cross Spectra
+                    if jcspectra is None: return jspectra, jcspectra
+                    num_pairs = jcspectra.size/(num_prof*num_hei)
+                    jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
+                    for ip in range(num_pairs):
+                        #-------------------------------------------
+                        cspower = numpy.abs(jcspectra[ip,mask_prof,:])
+                        cspower = cspower[:,hei_interf]
+                        cspower = cspower.sum(axis = 0)
+                        cspsort = cspower.ravel().argsort()
+                        junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
+                        junkcspc_interf = junkcspc_interf.transpose()
+                        jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf
+                        ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
+                        median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
+                        median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
+                        junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)
+                        for iprof in range(num_prof):
+                            ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
+                            jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]
+                        #Removiendo la Interferencia
+                        jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf
+                        ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
+                        maxid = ListAux.index(max(ListAux))
+                        ind = numpy.array([-2,-1,1,2])
+                        xx = numpy.zeros([4,4])
+                        for id1 in range(4):
+                            xx[:,id1] = ind[id1]**numpy.asarray(range(4))
+                        xx_inv = numpy.linalg.inv(xx)
+                        xx = xx_inv[:,0]
+                        ind = (ind + maxid + num_mask_prof)%num_mask_prof
+                        yy = jcspectra[ip,mask_prof[ind],:]
+                        jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
+                    #Guardar Resultados
+                    self.dataOut.data_spc = jspectra
+                    self.dataOut.data_cspc = jcspectra
+                    return 1
+                def setRadarFrequency(self, frequency=None):
+                    if frequency != None:
+                        self.dataOut.frequency = frequency
+                    return 1
+                def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
+                    #validacion de rango
+                    if minHei == None:
+                        minHei = self.dataOut.heightList[0]
+                    if maxHei == None:
+                        maxHei = self.dataOut.heightList[-1]
+                    if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
+                        print('minHei: %.2f is out of the heights range'%(minHei))
+                        print('minHei is setting to %.2f'%(self.dataOut.heightList[0]))
+                        minHei = self.dataOut.heightList[0]
+                    if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
+                        print('maxHei: %.2f is out of the heights range'%(maxHei))
+                        print('maxHei is setting to %.2f'%(self.dataOut.heightList[-1]))
+                        maxHei = self.dataOut.heightList[-1]
+                    # validacion de velocidades
+                    velrange = self.dataOut.getVelRange(1)
+                    if minVel == None:
+                        minVel = velrange[0]
+                    if maxVel == None:
+                        maxVel = velrange[-1]
+                    if (minVel < velrange[0]) or (minVel > maxVel):
+                        print('minVel: %.2f is out of the velocity range'%(minVel))
+                        print('minVel is setting to %.2f'%(velrange[0]))
+                        minVel = velrange[0]
+                    if (maxVel > velrange[-1]) or (maxVel < minVel):
+                        print('maxVel: %.2f is out of the velocity range'%(maxVel))
+                        print('maxVel is setting to %.2f'%(velrange[-1]))
+                        maxVel = velrange[-1]
+                    # seleccion de indices para rango
+                    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)
+                    if (minIndex < 0) or (minIndex > maxIndex):
+                        raise ValueError("some value in (%d,%d) is not valid" % (minIndex, maxIndex))
+                    if (maxIndex >= self.dataOut.nHeights):
+                        maxIndex = self.dataOut.nHeights-1
+                    # seleccion de indices para velocidades
+                    indminvel = numpy.where(velrange >= minVel)
+                    indmaxvel = numpy.where(velrange <= maxVel)
+                    try:
+                        minIndexVel = indminvel[0][0]
+                    except:
+                        minIndexVel = 0
+                    try:
+                        maxIndexVel = indmaxvel[0][-1]
+                    except:
+                        maxIndexVel = len(velrange)
+                    #seleccion del espectro
+                    data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
+                    #estimacion de ruido
+                    noise = numpy.zeros(self.dataOut.nChannels)
+                    for channel in range(self.dataOut.nChannels):
+                        daux = data_spc[channel,:,:]
+                        noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
+                    self.dataOut.noise_estimation = noise.copy()
+                    return 1
+            class SpectraAFCProc(ProcessingUnit):
+                def __init__(self, **kwargs):
+                    ProcessingUnit.__init__(self, **kwargs)
+                    self.buffer = None
+                    self.firstdatatime = None
+                    self.profIndex = 0
+                    self.dataOut = Spectra()
+                    self.id_min = None
+                    self.id_max = None
+                def __updateSpecFromVoltage(self):
+                    self.dataOut.plotting = "spectra_acf"
+                    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.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
+                    self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
+                    self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15
+                    self.dataOut.channelList = self.dataIn.channelList
+                    self.dataOut.heightList = self.dataIn.heightList
+                    self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
+                    self.dataOut.nBaud = self.dataIn.nBaud
+                    self.dataOut.nCode = self.dataIn.nCode
+                    self.dataOut.code = self.dataIn.code
+            #         self.dataOut.nProfiles = self.dataOut.nFFTPoints
+                    self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
+                    self.dataOut.utctime = self.firstdatatime
+                    self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
+                    self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
+                    self.dataOut.flagShiftFFT = False
+                    self.dataOut.nCohInt = self.dataIn.nCohInt
+                    self.dataOut.nIncohInt = 1
+                    self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
+                    self.dataOut.frequency = self.dataIn.frequency
+                    self.dataOut.realtime = self.dataIn.realtime
+                    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
+                def __decodeData(self, nProfiles, code):
+                    if code is None:
+                        return
+                    for i in range(nProfiles):
+                        self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i]
+                def __getFft(self):
+                    """
+                    Convierte valores de Voltaje a Spectra
+                    Affected:
+                        self.dataOut.data_spc
+                        self.dataOut.data_cspc
+                        self.dataOut.data_dc
+                        self.dataOut.heightList
+                        self.profIndex
+                        self.buffer
+                        self.dataOut.flagNoData
+                    """
+                    nsegments = self.dataOut.nHeights
+                    _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex')
+                    for i in range(nsegments):
+                        try:
+                            _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles]
+                            if self.code is not None:
+                                _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0]
+                        except:
+                            pass
+                    fft_volt = numpy.fft.fft(_fft_buffer, n=self.dataOut.nFFTPoints, axis=1)
+                    fft_volt = fft_volt.astype(numpy.dtype('complex'))
+                    dc = fft_volt[:,0,:]
+                    #calculo de self-spectra
+                    spc = fft_volt * numpy.conjugate(fft_volt)
+                    data = numpy.fft.ifft(spc, axis=1)
+                    data = numpy.fft.fftshift(data, axes=(1,))
+                    spc = data
+                    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:
+                            if pair[0] not in self.dataOut.channelList:
+                                raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)))
+                            if pair[1] not in self.dataOut.channelList:
+                                raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)))
+                            chan_index0 = self.dataOut.channelList.index(pair[0])
+                            chan_index1 = self.dataOut.channelList.index(pair[1])
+                            cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:])
+                            pairIndex += 1
+                        blocksize += cspc.size
+                    self.dataOut.data_spc = spc
+                    self.dataOut.data_cspc = cspc
+                    self.dataOut.data_dc = dc
+                    self.dataOut.blockSize = blocksize
+                    self.dataOut.flagShiftFFT = True
+                def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1):
+                    #self.dataOut.flagNoData = True
+                    #self.dataIn.runNextUnit = runNextUnit
+                    #print("here")
+                    if self.dataIn.type == "Spectra":
+                        self.dataOut.copy(self.dataIn)
+                        spc         =  self.dataOut.data_spc
+                        data        =  numpy.fft.fftshift( spc, axes=(1,))
+                        data        =  numpy.fft.ifft(data, axis=1)
+                        #data        =  numpy.fft.ifft(data, axis=1, n = 32)
+                        #data        =  numpy.fft.fftshift( data, axes=(1,))
+                        #acf         =  numpy.abs(data)
+                        acf = data[:,:16,:]
+                        #acf = data[:,16:,:]
+                        #print("SUM: ",numpy.sum(acf))
+                        #print(acf.shape)
+                        '''
+                        hei_id = 35
+                        aux2 = numpy.fft.fft(self.dataOut.data[0,:,hei_id],n = 16)
+                        aux2 = numpy.fft.fftshift(aux2)
+                        aux2 = aux2*numpy.conjugate(aux2)
+                        aux2 = aux2.real #Este valor es el que da SCh
+                        print("spc 2: ",numpy.sum(aux2))
+                        aux2        =  numpy.fft.fftshift(aux2)
+                        aux2        =  numpy.fft.ifft(aux2)
+                        print("Rate_Right?: ",aux2[0]/corr[0,0,hei_id])
+                        print("AFC sum: ",numpy.sum(acf[0,:,hei_id]))
+                        print("aux2 sum: ",numpy.sum(aux2))
+                        print("Rate: ",acf[0,0,hei_id]/corr[0,0,hei_id])
+                        print("Rate aux: ",acf[0,0,hei_id]/corr_aux[0,-1,hei_id])
+                        '''
+                        #print(acf[0,:,150])
+                        #exit(1)
+                        '''
+                        if real:
+                            acf     = data.real
+                        if imag:
+                            acf     = data.imag
+                            '''
+                        #shape       =  acf.shape            #  nchannels, nprofiles, nsamples //nchannles, lags , alturas
+                        '''
+                        for j in range(shape[0]):
+                            for i in range(shape[2]):
+                               tmp = int(shape[1]/2)
+                               #print(i,j,tmp)
+                               value  = (acf[j,:,i][tmp-1]+acf[j,:,i][tmp+1])/2.0
+                               acf[j,:,i][tmp] = value
+                               '''
+                        #acf        =  numpy.fft.fftshift( acf, axes=(1,))
+                        '''
+                        # Normalizando
+                        for i in range(shape[0]):
+                            for j in range(shape[2]):
+                                acf[i,:,j]= acf[i,:,j] / numpy.max(numpy.abs(acf[i,:,j]))
+                                '''
+                        #self.dataOut.data_acf = acf[:,:16,:]#*2
+                        #self.dataOut.data_spc = acf[:,:16,:].real#*2
+                        self.dataOut.data_acf = acf
+                        '''
+                        self.dataOut.data_spc = data.imag
+                        print("Real: ",data[0,:,26].real)
+                        print("Real dB:",10*numpy.log10(data[0,:,26].real))
+                        print("Imag: ",data[0,:,26].imag)
+                        print("Imag dB:",10*numpy.log10(data[0,:,26].imag))
+                        exit(1)
+                        '''
+                        #print("AFC",self.dataOut.flagNoData)
+                        #'''
+                        #print("acf 0: ", self.dataOut.data_acf[0,0,100])
+                        #print("spc: ",numpy.mean(self.dataOut.data_spc[0,:,100]))
+                        #print("spc 0: ",numpy.fft.fftshift(self.dataOut.data_spc[0,:,100])[0])
+                        #exit(1)
+                        #self.dataOut.data_spc = acf.real
+                        #print(self.dataOut.data_acf[0,:,0])
+                        #exit(1)
+                        #'''
+                        '''
+                        shape = self.dataOut.data_acf.shape
+                        resFactor = 5
+                        z = self.dataOut.data_acf.copy()
+                        min = numpy.min(z[0,:,0])
+                        max =numpy.max(z[0,:,0])
+                        deltaHeight = self.dataOut.heightList[1]-self.dataOut.heightList[0]
+                        for i in range(shape[0]):
+                            for j in range(shape[2]):
+                                z[i,:,j]= (((z[i,:,j]-min)/(max-min))*deltaHeight*resFactor + j*deltaHeight)
+                        #print(self.dataOut.data_spc.shape)
+                        #print(self.dataOut.data_acf.shape)
+                        '''
+                        '''
+                        import matplotlib.pyplot as plt
+                        #hei = 10
+                        #print(self.dataOut.heightList)
+                        #print(self.dataOut.heightList[hei])
+                        #plt.plot(z[0,0,:],self.dataOut.heightList)
+                        aux =  self.dataOut.data_acf[0,:,100]
+                        power = aux*numpy.conjugate(aux)
+                        #print(power)
+                        powerdb = numpy.log10(power)
+                        #plt.plot(powerdb,self.dataOut.heightList)
+                        #plt.plot(self.dataOut.data_acf[0,:,33])
+                        #plt.plot(corr)
+                        #plt.imshow(corr.real[0])
+                        plt.imshow(self.dataOut.data_acf.real[0])
+                        #plt.ylim(0,1000)
+                        plt.grid()
+                        plt.show()
+                        exit(1)
+                        '''
                         return True
                     if code is not None:
                         self.code = numpy.array(code).reshape(nCode,nBaud)
                     else:
                         self.code = None
                     if self.dataIn.type == "Voltage":
                         if nFFTPoints == None:
                             raise ValueError("This SpectraProc.run() need nFFTPoints input variable")
                         if nProfiles == None:
                             nProfiles = nFFTPoints
                         self.dataOut.ippFactor = 1
                         self.dataOut.nFFTPoints = nFFTPoints
                         self.dataOut.nProfiles = nProfiles
                         self.dataOut.pairsList = pairsList
             #             if self.buffer is None:
             #                 self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights),
             #                                           dtype='complex')
                         if not self.dataIn.flagDataAsBlock:
                             self.buffer = self.dataIn.data.copy()
             #                 for i in range(self.dataIn.nHeights):
             #                     self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex]
             #
             #                 self.profIndex += 1
                         else:
                             raise ValueError("")
                         self.firstdatatime = self.dataIn.utctime
                         self.profIndex == nProfiles
                         self.__updateSpecFromVoltage()
                         self.__getFft()
                         self.dataOut.flagNoData = False
                         return True
                     raise ValueError("The type of input object '%s' is not valid"%(self.dataIn.type))
                 def __selectPairs(self, pairsList):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for thisPair in pairsList:
                         if thisPair not in self.dataOut.pairsList:
                             continue
                         pairIndex = self.dataOut.pairsList.index(thisPair)
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
                     return
                 def __selectPairsByChannel(self, channelList=None):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for pairIndex in self.dataOut.pairsIndexList:
                         #First pair
                         if self.dataOut.pairsList[pairIndex][0] not in channelList:
                             continue
                         #Second pair
                         if self.dataOut.pairsList[pairIndex][1] not in channelList:
                             continue
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
                     return
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError("Error selecting channels, Channel %d is not valid.\nAvailable channels = %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_spc
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             raise ValueError("Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList))
             #         nChannels = len(channelIndexList)
                     data_spc = self.dataOut.data_spc[channelIndexList,:]
                     data_dc = self.dataOut.data_dc[channelIndexList,:]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
             #        self.dataOut.nChannels = nChannels
                     self.__selectPairsByChannel(self.dataOut.channelList)
                     return 1
                 def selectHeights(self, minHei, maxHei):
                     """
                     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:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minHei > maxHei):
                         raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei))
                     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 getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
                     newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
                     if hei_ref != None:
                         newheis = numpy.where(self.dataOut.heightList>hei_ref)
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
                     heightList = self.dataOut.heightList[minIndex:maxIndex+1]
                     # determina indices
                     nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
                     avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
                     beacon_dB = numpy.sort(avg_dB)[-nheis:]
                     beacon_heiIndexList = []
                     for val in avg_dB.tolist():
                         if val >= beacon_dB[0]:
                             beacon_heiIndexList.append(avg_dB.tolist().index(val))
                     #data_spc = data_spc[:,:,beacon_heiIndexList]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
                         #data_cspc = data_cspc[:,:,beacon_heiIndexList]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
                         #data_dc = data_dc[:,beacon_heiIndexList]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = heightList
                     self.dataOut.beacon_heiIndexList = beacon_heiIndexList
                     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_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights-1
                     #Spectra
                     data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
                     return 1
                 def removeDC(self, mode = 2):
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     if jcspectra is not None:
                         jcspectraExist = True
                         num_pairs = jcspectra.shape[0]
                     else:   jcspectraExist = False
                     freq_dc = jspectra.shape[1]/2
                     ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
                     if ind_vel[0]<0:
                         ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof
                     if mode == 1:
                         jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION
                         if jcspectraExist:
                             jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2
                     if mode == 2:
                         vel = numpy.array([-2,-1,1,2])
                         xx = numpy.zeros([4,4])
                         for fil in range(4):
                             xx[fil,:] = vel[fil]**numpy.asarray(range(4))
                         xx_inv = numpy.linalg.inv(xx)
                         xx_aux = xx_inv[0,:]
                         for ich in range(num_chan):
                             yy = jspectra[ich,ind_vel,:]
                             jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)
                             junkid = jspectra[ich,freq_dc,:]<=0
                             cjunkid = sum(junkid)
                             if cjunkid.any():
                                 jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2
                         if jcspectraExist:
                             for ip in range(num_pairs):
                                 yy = jcspectra[ip,ind_vel,:]
                                 jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     return 1
                 def removeInterference(self,  interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     jnoise = self.dataOut.getNoise()
                     num_incoh = self.dataOut.nIncohInt
                     num_channel  = jspectra.shape[0]
                     num_prof  = jspectra.shape[1]
                     num_hei   = jspectra.shape[2]
                     #hei_interf
                     if hei_interf is None:
                         count_hei = num_hei/2   #Como es entero no importa
                         hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
                         hei_interf = numpy.asarray(hei_interf)[0]
                     #nhei_interf
                     if (nhei_interf == None):
                         nhei_interf = 5
                     if (nhei_interf < 1):
                         nhei_interf = 1
                     if (nhei_interf > count_hei):
                         nhei_interf = count_hei
                     if (offhei_interf == None):
                         offhei_interf = 0
                     ind_hei = range(num_hei)
             #         mask_prof = numpy.asarray(range(num_prof - 2)) + 1
             #         mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
                     mask_prof = numpy.asarray(range(num_prof))
                     num_mask_prof = mask_prof.size
                     comp_mask_prof = [0, num_prof/2]
                     #noise_exist:    Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
                     if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
                         jnoise = numpy.nan
                     noise_exist = jnoise[0] < numpy.Inf
                     #Subrutina de Remocion de la Interferencia
                     for ich in range(num_channel):
                         #Se ordena los espectros segun su potencia (menor a mayor)
                         power = jspectra[ich,mask_prof,:]
                         power = power[:,hei_interf]
                         power = power.sum(axis = 0)
                         psort = power.ravel().argsort()
                         #Se estima la interferencia promedio en los Espectros de Potencia empleando
                         junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]
                         if noise_exist:
                         #    tmp_noise = jnoise[ich] / num_prof
                             tmp_noise = jnoise[ich]
                         junkspc_interf = junkspc_interf - tmp_noise
                         #junkspc_interf[:,comp_mask_prof] = 0
                         jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
                         jspc_interf = jspc_interf.transpose()
                         #Calculando el espectro de interferencia promedio
                         noiseid =  numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh))
                         noiseid = noiseid[0]
                         cnoiseid = noiseid.size
                         interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh))
                         interfid = interfid[0]
                         cinterfid = interfid.size
                         if (cnoiseid > 0):   jspc_interf[noiseid] = 0
                         #Expandiendo los perfiles a limpiar
                         if (cinterfid > 0):
                             new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
                             new_interfid = numpy.asarray(new_interfid)
                             new_interfid = {x for x in new_interfid}
                             new_interfid = numpy.array(list(new_interfid))
                             new_cinterfid = new_interfid.size
                         else: new_cinterfid = 0
                         for ip in range(new_cinterfid):
                             ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
                             jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]
                         jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices
                         #Removiendo la interferencia del punto de mayor interferencia
                         ListAux = jspc_interf[mask_prof].tolist()
                         maxid = ListAux.index(max(ListAux))
                         if cinterfid > 0:
                             for ip in range(cinterfid*(interf == 2) - 1):
                                 ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero()
                                 cind = len(ind)
                                 if (cind > 0):
                                     jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh))
                             ind = numpy.array([-2,-1,1,2])
                             xx = numpy.zeros([4,4])
                             for id1 in range(4):
                                 xx[:,id1] = ind[id1]**numpy.asarray(range(4))
                             xx_inv = numpy.linalg.inv(xx)
                             xx = xx_inv[:,0]
                             ind = (ind + maxid + num_mask_prof)%num_mask_prof
                             yy = jspectra[ich,mask_prof[ind],:]
                             jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
                         indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero()
                         jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh))
                     #Remocion de Interferencia en el Cross Spectra
                     if jcspectra is None: return jspectra, jcspectra
                     num_pairs = jcspectra.size/(num_prof*num_hei)
                     jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
                     for ip in range(num_pairs):
                         #-------------------------------------------
                         cspower = numpy.abs(jcspectra[ip,mask_prof,:])
                         cspower = cspower[:,hei_interf]
                         cspower = cspower.sum(axis = 0)
                         cspsort = cspower.ravel().argsort()
                         junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
                         junkcspc_interf = junkcspc_interf.transpose()
                         jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf
                         ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
                         median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
                         median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
                         junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)
                         for iprof in range(num_prof):
                             ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
                             jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]
                         #Removiendo la Interferencia
                         jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf
                         ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
                         maxid = ListAux.index(max(ListAux))
                         ind = numpy.array([-2,-1,1,2])
                         xx = numpy.zeros([4,4])
                         for id1 in range(4):
                             xx[:,id1] = ind[id1]**numpy.asarray(range(4))
                         xx_inv = numpy.linalg.inv(xx)
                         xx = xx_inv[:,0]
                         ind = (ind + maxid + num_mask_prof)%num_mask_prof
                         yy = jcspectra[ip,mask_prof[ind],:]
                         jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
                     #Guardar Resultados
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     return 1
                 def setRadarFrequency(self, frequency=None):
                     if frequency != None:
                         self.dataOut.frequency = frequency
                     return 1
                 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
                     #validacion de rango
                     if minHei == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
                         print('minHei: %.2f is out of the heights range'%(minHei))
                         print('minHei is setting to %.2f'%(self.dataOut.heightList[0]))
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
                         print('maxHei: %.2f is out of the heights range'%(maxHei))
                         print('maxHei is setting to %.2f'%(self.dataOut.heightList[-1]))
                         maxHei = self.dataOut.heightList[-1]
                     # validacion de velocidades
                     velrange = self.dataOut.getVelRange(1)
                     if minVel == None:
                         minVel = velrange[0]
                     if maxVel == None:
                         maxVel = velrange[-1]
                     if (minVel < velrange[0]) or (minVel > maxVel):
                         print('minVel: %.2f is out of the velocity range'%(minVel))
                         print('minVel is setting to %.2f'%(velrange[0]))
                         minVel = velrange[0]
                     if (maxVel > velrange[-1]) or (maxVel < minVel):
                         print('maxVel: %.2f is out of the velocity range'%(maxVel))
                         print('maxVel is setting to %.2f'%(velrange[-1]))
                         maxVel = velrange[-1]
                     # seleccion de indices para rango
                     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)
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("some value in (%d,%d) is not valid" % (minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights-1
                     # seleccion de indices para velocidades
                     indminvel = numpy.where(velrange >= minVel)
                     indmaxvel = numpy.where(velrange <= maxVel)
                     try:
                         minIndexVel = indminvel[0][0]
                     except:
                         minIndexVel = 0
                     try:
                         maxIndexVel = indmaxvel[0][-1]
                     except:
                         maxIndexVel = len(velrange)
                     #seleccion del espectro
                     data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
                     #estimacion de ruido
                     noise = numpy.zeros(self.dataOut.nChannels)
                     for channel in range(self.dataOut.nChannels):
                         daux = data_spc[channel,:,:]
                         noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
                     self.dataOut.noise_estimation = noise.copy()
                     return 1

schainpy/model/proc/jroproc_spectra_lags_faraday.py 0 0

	1	NO CONTENT: modified file			NO CONTENT: modified file
The requested commit or file is too big and content was truncated. Show full diff

schainpy/model/proc/jroproc_voltage.py 0 0

	1	NO CONTENT: modified file			NO CONTENT: modified file
The requested commit or file is too big and content was truncated. Show full diff

setup.py 0 0

	1	NO CONTENT: modified file			NO CONTENT: modified file
The requested commit or file is too big and content was truncated. Show full diff

General Comments 0

Write
Preview

You need to be logged in to leave comments. Login now

No TODOs yet

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages