From 7e9c8c0fa5ebb48f0a6e723d069f384083bc409a Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Thu, 29 Jun 2017 11:55:14 -0400
Subject: [PATCH 01/10]  getrawfitspath() will return url; TODO listed.

---
 py3/SciServer/SkyServer.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/py3/SciServer/SkyServer.py b/py3/SciServer/SkyServer.py
index a664723..30612f0 100644
--- a/py3/SciServer/SkyServer.py
+++ b/py3/SciServer/SkyServer.py
@@ -6,8 +6,9 @@
 
 from io import StringIO
 from io import BytesIO
-
+from astropy.io import fits
 from SciServer import Authentication, Config
+from SciServer import CasJobs
 
 def sqlSearch(sql, dataRelease=None):
     """
@@ -340,3 +341,25 @@ def objectSearch(objId=None, specObjId=None, apogee_id=None, apstar_id=None, ra=
     #r = json.loads(response.content.decode())
     #return pandas.read_csv(StringIO(r), comment='#')
     return r;
+
+    # TODO: get a list of FITS path given a list of object ID and
+    # TODO: make a new function that return a hdu object from FITS file after solve the fits.open() warning problem
+
+def getRawFITSPath(specObjId, context=None, URL = True):
+    def PathAlter(url):#TODO: turn https address to local path on DAS package
+        localPath = url
+        return localPath
+    sql = "select dbo.fGetUrlFitsSpectrum(%d)" % specObjId
+    if(context):
+        pass
+    else:
+        context = 'dr13'
+    dataFrame = CasJobs.executeQuery(sql, context=context, format='pandas')
+    dataInCSV = dataFrame.to_CSV()
+    url = dataInCSV['Colunm1'][0]
+    if(URL):
+        return url
+    else:
+        localPath = PathAlter(url)
+        return localPath
+

From 1387257e1ccf7797dba1b461390b588299ce6196 Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Thu, 13 Jul 2017 15:37:37 -0400
Subject: [PATCH 02/10] get fits raw path from DAS done

---
 py3/SciServer/SkyServer.py | 46 +++++++++++++++++++++-----------------
 1 file changed, 25 insertions(+), 21 deletions(-)

diff --git a/py3/SciServer/SkyServer.py b/py3/SciServer/SkyServer.py
index 30612f0..ebef399 100644
--- a/py3/SciServer/SkyServer.py
+++ b/py3/SciServer/SkyServer.py
@@ -7,8 +7,9 @@
 from io import StringIO
 from io import BytesIO
 from astropy.io import fits
-from SciServer import Authentication, Config
-from SciServer import CasJobs
+from py3.SciServer import Authentication, Config
+from py3.SciServer import CasJobs
+from urllib.parse import urlparse
 
 def sqlSearch(sql, dataRelease=None):
     """
@@ -344,22 +345,25 @@ def objectSearch(objId=None, specObjId=None, apogee_id=None, apstar_id=None, ra=
 
     # TODO: get a list of FITS path given a list of object ID and
     # TODO: make a new function that return a hdu object from FITS file after solve the fits.open() warning problem
-
-def getRawFITSPath(specObjId, context=None, URL = True):
-    def PathAlter(url):#TODO: turn https address to local path on DAS package
-        localPath = url
-        return localPath
-    sql = "select dbo.fGetUrlFitsSpectrum(%d)" % specObjId
-    if(context):
-        pass
-    else:
-        context = 'dr13'
-    dataFrame = CasJobs.executeQuery(sql, context=context, format='pandas')
-    dataInCSV = dataFrame.to_CSV()
-    url = dataInCSV['Colunm1'][0]
-    if(URL):
-        return url
-    else:
-        localPath = PathAlter(url)
-        return localPath
-
+#1: use casjobs to query for the specobjid by the coordinate
+#2: use dr7.fgeturlfitsspectrum(specobjid) to have the http url:
+# like this http://das.sdss.org/spectro/1d_26/1472/1d/spSpec-52913-1472-269.fit
+# then append sdss_das/das2/ in the front
+
+
+def getSpecObjID(ra, dec, width, height, context=None, URL=True):
+    leftBoundry = ra - width / 2
+    rightBoundry = ra + width / 2
+    upperBoundry = dec + height / 2
+    lowerBoundry = dec - height / 2
+    sqlQuery = 'select specObjID from dr7.specobjall Where (ra >' + str(leftBoundry) + ' and ra<' + str(
+        rightBoundry) + ') and (dec>' + str(lowerBoundry) + ' and dec<' + str(upperBoundry) + ')'
+    return CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+def getRawFITSPathInDAS(SpecObjID):
+    sqlQuery = 'select dr7.fGetUrlFitsSpectrum('+SpecObjID+')'
+    response = CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+    url= response['Column1'][0]
+    parseResult = urlparse(url)
+    prefix = '/home/idies/workspace/sdss_das/das2'
+    path = prefix+parseResult.path
+    return path
\ No newline at end of file

From d5dcd1661dae509115b80ef32c638729bb6416f3 Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Sun, 29 Oct 2017 17:41:27 -0400
Subject: [PATCH 03/10] spectrum viewer v1

the first prototype of spectrum viewer as part of sciserver. need to config together with sciserver
---
 SpectrumViewer/CoaddObj.py                    |  17 ++
 SpectrumViewer/InlineViewer.py                |   0
 SpectrumViewer/SDSSDriver.py                  |  52 +++++
 SpectrumViewer/SpecUtil.py                    | 131 +++++++++++
 SpectrumViewer/WindowViewer.py                | 137 +++++++++++
 SpectrumViewer/ZObj.py                        |   9 +
 SpectrumViewer/__init__.py                    |   0
 SpectrumViewer/example.py                     |   6 +
 SpectrumViewer/example_lite.fits              | 218 ++++++++++++++++++
 SpectrumViewer/test.py                        |   9 +
 .../viewer screenshot/emission line only.PNG  | Bin 0 -> 33844 bytes
 .../flux and model fit only.PNG               | Bin 0 -> 39757 bytes
 SpectrumViewer/viewer screenshot/showall.PNG  | Bin 0 -> 55227 bytes
 13 files changed, 579 insertions(+)
 create mode 100644 SpectrumViewer/CoaddObj.py
 create mode 100644 SpectrumViewer/InlineViewer.py
 create mode 100644 SpectrumViewer/SDSSDriver.py
 create mode 100644 SpectrumViewer/SpecUtil.py
 create mode 100644 SpectrumViewer/WindowViewer.py
 create mode 100644 SpectrumViewer/ZObj.py
 create mode 100644 SpectrumViewer/__init__.py
 create mode 100644 SpectrumViewer/example.py
 create mode 100644 SpectrumViewer/example_lite.fits
 create mode 100644 SpectrumViewer/test.py
 create mode 100644 SpectrumViewer/viewer screenshot/emission line only.PNG
 create mode 100644 SpectrumViewer/viewer screenshot/flux and model fit only.PNG
 create mode 100644 SpectrumViewer/viewer screenshot/showall.PNG

diff --git a/SpectrumViewer/CoaddObj.py b/SpectrumViewer/CoaddObj.py
new file mode 100644
index 0000000..fdb2987
--- /dev/null
+++ b/SpectrumViewer/CoaddObj.py
@@ -0,0 +1,17 @@
+# This class init with a fits file in the directory and store all the data in the RAM
+import numpy as np
+class CoaddObj:
+    def __init__(self,flux,loglam=None,ivar=None,andMask=None,orMask=None
+                 ,wdisp=None,sky=None,model=None):#type can be pfs
+        self.flux = flux
+        self.loglam = loglam
+        self.ivar =ivar
+        self.andMask =andMask
+        self.orMask = orMask
+        self.wdisp = wdisp
+        self.sky =sky
+        self.model = model
+        self.lam = np.power(10,self.loglam)
+
+
+
diff --git a/SpectrumViewer/InlineViewer.py b/SpectrumViewer/InlineViewer.py
new file mode 100644
index 0000000..e69de29
diff --git a/SpectrumViewer/SDSSDriver.py b/SpectrumViewer/SDSSDriver.py
new file mode 100644
index 0000000..12bcf6e
--- /dev/null
+++ b/SpectrumViewer/SDSSDriver.py
@@ -0,0 +1,52 @@
+#load SDSS into workable object
+from astropy.io import fits
+from py3.SciServer import Authentication, Config
+from py3.SciServer import CasJobs
+from urllib.parse import urlparse
+import SpectrumViewer.CoaddObj as Coadd
+import SpectrumViewer.ZObj as Z
+def getSpecObjID(ra, dec, width, height, context=None, URL=True):
+    leftBoundry = ra - width / 2
+    rightBoundry = ra + width / 2
+    upperBoundry = dec + height / 2
+    lowerBoundry = dec - height / 2
+    sqlQuery = 'select specObjID from dr7.specobjall Where (ra >' + str(leftBoundry) + ' and ra<' + str(
+        rightBoundry) + ') and (dec>' + str(lowerBoundry) + ' and dec<' + str(upperBoundry) + ')'
+    return CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+def getRawFITSPathInDAS(SpecObjID):
+    sqlQuery = 'select dr7.fGetUrlFitsSpectrum('+SpecObjID+')'
+    response = CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+    url= response['Column1'][0]
+    parseResult = urlparse(url)
+    prefix = '/home/idies/workspace/sdss_das/das2'
+    path = prefix+parseResult.path
+    return path
+
+def loadFITS(filename,fileSource):
+
+    if fileSource=='SDSS':
+        coaddData =1
+        zData =3
+        hdulist = fits.open(filename)
+        c=hdulist[coaddData].data
+        z=hdulist[zData].data
+        coaddObj = Coadd.CoaddObj(
+            flux=c['flux'],
+            loglam=c['loglam'],
+            ivar=c['ivar'],
+            andMask=c['and_Mask'],
+            orMask=c['or_Mask'],
+            wdisp=c['wdisp'],
+            sky = c['sky'],
+            model=c['model'])
+        zObj = Z.ZObj(
+            LINENAME=z['LINENAME'],
+            LINEWAVE=z['LINEWAVE'],
+            LINEZ=z['LINEZ'],
+            LINEEW=z['LINEEW'],
+            LINEZ_ERR=z['LINEZ_ERR'],
+            LINEEW_ERR=z['LINEEW_ERR'])
+        return coaddObj,zObj
+
+    else:
+        print('Sorry, we are loyal to SDSS ONLY, for the time being. ')
\ No newline at end of file
diff --git a/SpectrumViewer/SpecUtil.py b/SpectrumViewer/SpecUtil.py
new file mode 100644
index 0000000..0309f4e
--- /dev/null
+++ b/SpectrumViewer/SpecUtil.py
@@ -0,0 +1,131 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+import numpy as np
+class SpecUtil:
+    def __init__(self,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        #self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.totalAx = self.totalFig.add_subplot(111)
+        #fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        #skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        self.shownAx = self.shownFig.add_subplot(111)
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError: # dict is longer than plot numbers in totalax
+                    pass
+        self.shownAx.legend()
+        #self.shownAx = self.totalAx
+    def updateFig(self):
+        #self.shownAx.lines=[]
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx,key)
+                except IndexError:
+                    pass
+        #line1, = ax.plot(x, y, 'b-')
+        self.shownAx.legend()
+        #for phase in np.linspace(0, 10 * np.pi, 100):
+         #   line1.set_ydata(np.sin(0.5 * x + phase))
+          #  fig.canvas.draw()
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer/WindowViewer.py b/SpectrumViewer/WindowViewer.py
new file mode 100644
index 0000000..557c4b3
--- /dev/null
+++ b/SpectrumViewer/WindowViewer.py
@@ -0,0 +1,137 @@
+# show plotting result in a new window interactively
+# this is actual user interface all the object are hide away from user
+import SpectrumViewer.ZObj as ZObj
+import SpectrumViewer.CoaddObj as CoaddObj
+import matplotlib
+matplotlib.use("TkAgg")
+import SpectrumViewer.SDSSDriver as driver
+import SpectrumViewer.SpecUtil as specutil
+from matplotlib.figure import Figure
+from numpy import arange, sin, pi
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
+# implement the default mpl key bindings
+from matplotlib.backend_bases import key_press_handler
+import tkinter as Tk
+
+
+def view(fileName,fileSource):
+    coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+    #construn tk window and all components
+
+
+    root = Tk.Tk()
+    root.wm_title("Embedding in TK")
+    app = SpecController(root,coaddObj,zObj)
+    root.mainloop()
+
+class SpecController:
+    #take objs to construct specmodel and specview
+    def __init__(self,root,coaddObj,zObj):
+        self.model = SpecModel(coaddObj,zObj)
+        self.view = SpecView(root,self.model.specUtilObj.shownFig)
+        self.view.sidepanel.fluxTogg.bind("<Button>",self.fluxTogg)
+        self.view.sidepanel.skylineTogg.bind("<Button>", self.skylineTogg)
+        self.view.sidepanel.emissionTogg.bind("<Button>", self.emissionTogg)
+        self.view.sidepanel.absorbTogg.bind("<Button>", self.absorbTogg)
+        self.view.sidepanel.modelTogg.bind("<Button>", self.modelTogg)
+
+        #self.view.updateFigure(self.model.f)
+        # add buttons manage lau out
+    # def function change model and tell view.updatefigure(model.figure)
+    def fluxTogg(self,event):
+        if self.view.sidepanel.fluxTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeFlux()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.fluxTogg.config(text='FLUX:OFF')
+        else:
+            self.model.specUtilObj.addFlux()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.fluxTogg.config(text='FLUX:ON')
+    def skylineTogg(self,event):
+        if self.view.sidepanel.skylineTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeSkyline()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.skylineTogg.config(text='SKYLINE:OFF')
+        else:
+            self.model.specUtilObj.addSkyline()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.skylineTogg.config(text='SKYLINE:ON')
+    def emissionTogg(self,event):
+        if self.view.sidepanel.emissionTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeEmissionLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.emissionTogg.config(text='EMISSION LINE:OFF')
+        else:
+            self.model.specUtilObj.addEmissionLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.emissionTogg.config(text='EMISSION LINE:ON')
+    def absorbTogg(self,event):
+        if self.view.sidepanel.absorbTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeAbsorbLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.absorbTogg.config(text='ABSORPTION LINE:OFF')
+        else:
+            self.model.specUtilObj.addAbsorbLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.absorbTogg.config(text='ABSORPTION LINE:ON')
+    def modelTogg(self,event):
+        if self.view.sidepanel.modelTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeModel()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.modelTogg.config(text='BEST FIT MODEL:OFF')
+        else:
+            self.model.specUtilObj.addModel()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.modelTogg.config(text='BEST FIT MODEL:ON')
+
+class SpecModel:
+    def __init__(self,coaddObj,zObj):
+        self.specUtilObj = specutil.SpecUtil(coaddObj,zObj)
+    #take data to self construct
+    # this is where plot happens
+    #Figure composing
+class SidePanel():
+    def __init__(self, root):
+        self.frame2 = Tk.Frame(root)
+        self.frame2.pack(side=Tk.LEFT, fill=Tk.BOTH, expand=1)
+
+
+        #TODO:put in toggle button for all the function
+        self.fluxTogg = Tk.Button(self.frame2, text="FLUX:ON")
+        self.fluxTogg.pack(side="top", fill=Tk.BOTH)
+        self.skylineTogg = Tk.Button(self.frame2, text="SKYLINE:ON")
+        self.skylineTogg.pack(side="top", fill=Tk.BOTH)
+        self.emissionTogg = Tk.Button(self.frame2, text="EMISSION LINE:ON")
+        self.emissionTogg.pack(side="top", fill=Tk.BOTH)
+        self.absorbTogg = Tk.Button(self.frame2, text="ABSORPTION LINE:ON")
+        self.absorbTogg.pack(side="top", fill=Tk.BOTH)
+        self.modelTogg = Tk.Button(self.frame2, text="BEST FIT MODEL:ON")
+        self.modelTogg.pack(side="top", fill=Tk.BOTH)
+class SpecView(Tk.Toplevel):
+    def __init__(self,master,figure):
+        self.sidepanel = SidePanel(master)
+        self.figure =figure
+        self.canvas = FigureCanvasTkAgg(self.figure, master=master)
+        self.canvas.show()
+        self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+
+        toolbar = NavigationToolbar2TkAgg(self.canvas, master)
+
+        toolbar.update()
+        self.canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+        self.connectKey()
+
+    def updateFigure(self,figure):
+        self.figure = figure
+        self.figure.canvas.draw()
+    def on_key_event(self,event):
+        print('you pressed %s' % event.key)
+        key_press_handler(event, self.canvas, self.toolbar)
+    def connectKey(self):
+        self.canvas.mpl_connect('key_press_event', self.on_key_event)
+    # generate
diff --git a/SpectrumViewer/ZObj.py b/SpectrumViewer/ZObj.py
new file mode 100644
index 0000000..d3faa0b
--- /dev/null
+++ b/SpectrumViewer/ZObj.py
@@ -0,0 +1,9 @@
+class ZObj:
+    def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW=None,LINEEW_ERR=None):
+        self.LINENAME = LINENAME
+
+        self.LINEWAVE = LINEWAVE
+        self.LINEZ = LINEZ
+        self.LINEZ_ERR = LINEZ_ERR
+        self.LINEEW = LINEEW
+        self.LINEER_ERR = LINEEW_ERR
diff --git a/SpectrumViewer/__init__.py b/SpectrumViewer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/SpectrumViewer/example.py b/SpectrumViewer/example.py
new file mode 100644
index 0000000..000f747
--- /dev/null
+++ b/SpectrumViewer/example.py
@@ -0,0 +1,6 @@
+import SciServer.SepctrumViewer.SDSSDriver as driver
+import SciServer.SepctrumViewer.WindowViewer as viewer
+objID= 'please replace this with an objID from SDSS DR7'
+filesource='SDSS'
+path=driver.getRawFITSPathInDAS(objID)
+viewer.view(path,filesource)
diff --git a/SpectrumViewer/example_lite.fits b/SpectrumViewer/example_lite.fits
new file mode 100644
index 0000000..5347b30
--- /dev/null
+++ b/SpectrumViewer/example_lite.fits
@@ -0,0 +1,218 @@
+SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  TAI     =        4510856565.27 / 1st row - Number of seconds since Nov 17 1858  RA      =            34.081409 / 1st row - Right ascension of telescope boresighDEC     =           -0.015930  / 1st row - Declination of telescope boresight (dEQUINOX =              2000.00 /                                                RADECSYS= 'FK5     '           /                                                TAIHMS  = '09:39:25.74'        / 1st row - TAI time (HH:MM:SS.SS) (TAI-UT = apprTIMESYS = 'tai     '           / TAI, not UTC                                   MJD     =                52209 / MJD of observation                             MJDLIST = '52208 52209'        /                                                VERSION = 'NOCVS:v3_58_2:/p/iop/v3_58_2' / version of IOP                       CAMVER  = 'SPEC1 v4_7'         / Camera code version                            OBSERVER= 'snedden '                                                            OBSCOMM = 'science '                                                            TELESCOP= 'SDSS 2.5-M'         / Sloan Digital Sky Survey                       BESTEXP =                11592 /                                                NEXP_B1 =                    5 / b1 camera number of exposures                  NEXP_R1 =                    5 / r1 camera number of exposures                  EXPT_B1 =              6004.00 / b1 camera exposure time (seconds)              EXPT_R1 =              6004.00 / r1 camera exposure time (seconds)              EXPTIME =              6004.00 / Minimum of exposure times for all cameras      SPCOADD = 'Tue Feb 26 00:42:17 2008' / SPCOADD finished                         FLAVOR  = 'science '           / Flavor of this sequence (e.g., dome flat, etc.)QUALITY = 'excellent'          / night/image quality                            RADEG   = 3.40813400000000E+01 / Right Ascension. (degrees)                     DECDEG  = -1.6159310000000E-02 / Declination. (degrees)                         PLATEID =                  703 / Plate Id                                       TILEID  =                  471 / Tile Id                                        CARTID  =                    6 /Cartridge used in this plugging                 REDDEN  = '0.1276 0.0939 0.0681 0.0516 0.0366' / Median reddening vector for plaMAPID   =              5220601 / Map Id                                         NAME    = '0703-52206-01'      / Name of target; PlateId-MJD-Mapper ReRun       COLBIN  =                    1 / Binning factor perpendicular to the columns    ROWBIN  =                    1 / Binning factor perpendicular to the rows       PROGRAM = 'binning 1   1 '     / Identifying name for CCD program               SECFOCUS= -1.2000000000000E+02 / Secondary Mirror Position as reported by TCC   GUIDEIMG= '/data/spectro/guider/gimg0701.fits' / Last archived guider image     WTIME   =        1004139555.20 / weatherData(timeStamp)                         AIRTEMP =        9.40000000000 / weatherData(airtemp)                           DEWPOINT=       -5.72000000000 / weatherData(dewpoint)                          DEWDEP  =        15.1200000000 / weatherData(dewpointDep)                       DUSTA   =        23922.4000000 / weatherData(dusta)                             DUSTB   =        692.600000000 / weatherData(dustb)                             DUSTC   =        33007.0000000 / weatherData(dustc)                             DUSTD   =        1029.40000000 / weatherData(dustd)                             GUSTD   = 1.80000000000000E+02 / weatherData(gustd)                             GUSTS   =        5.52000000000 / weatherData(gusts)                             HUMIDITY=        34.1200000000 / weatherData(humidity)                          HUMIDOUT=        33.8000000000 / weatherData(humidout)                          PRESSURE=        21.7240000000 / weatherData(pressure)                          WINDD   =        184.600000000 / weatherData(windd)                             WINDS   =        3.56000000000 / weatherData(winds)                             TEMP01  =       -93.9840000000 / CCD 01 Temperature                             TEMP02  =       -93.5160000000 / CCD 02 Temperature                             TEMP03  =       -92.6580000000 / CCD 03 Temperature                             TEMP04  =       -93.0480000000 / CCD 04 Temperature                             VERSIDL = '6.3     '           / Version of IDL                                 VERSUTIL= 'v5_3_0  '           / Version of idlutils                            VERSREAD= 'v5_3_12 '           / Version of idlspec2d for pre-processing raw datVERS2D  = 'v5_3_12 '           / Version of idlspec2d for 2D reduction          VERSCOMB= 'v5_3_12 '           / Version of idlspec2d for combining multiple speVERSLOG = 'fermi-CVS 02/25/08' / Version of SPECLOG product                     VERSFLAT= 'v1_7    '           / Version of SPECFLAT product                    OPBC    = 'opBC-51813.par'     /                                                OPCONFIG= 'opConfig-50000.par' /                                                OPECALIB= 'opECalib-51577.par' /                                                PLUGFILE= 'plPlugMapM-0703-52206-01.par' /                                      HELIO_RV=        1.55079685031 / Heliocentric correction (added to velocities)  VACUUM  =                    T / Wavelengths are in vacuum                      SFLATTEN=                    T / Superflat has been applied                     PSFSKY  =                    3 / Order of PSF skysubtraction                    SKYCHI2 =        1.22384130844 / Mean chi^2 of sky-subtraction                  SCHI2MIN=        1.06626973255 /                                                SCHI2MAX=        1.49250118624 /                                                PREJECT =             0.200000 / Profile area rejection threshold               SPEC1_G =              23.2480 /(S/N)^2 for spec  1 at mag 20.20                SPEC1_R =              24.7649 /(S/N)^2 for spec  1 at mag 20.25                SPEC1_I =              23.1359 /(S/N)^2 for spec  1 at mag 19.90                SPEC2_G =              32.4262 /(S/N)^2 for spec  2 at mag 20.20                SPEC2_R =              32.4642 /(S/N)^2 for spec  2 at mag 20.25                SPEC2_I =              27.2296 /(S/N)^2 for spec  2 at mag 19.90                NSTD    =                   16 / Number of (good) std stars                     GOFFSTD =            0.0268574 / Spectrophoto offset for std stars in G-band    GRMSSTD =            0.0683525 / Spectrophoto RMS for std stars in G-band       ROFFSTD =            0.0235081 / Spectrophoto offset for std stars in R-band    RRMSSTD =            0.0559933 / Spectrophoto RMS for std stars in R-band       IOFFSTD =            0.0508566 / Spectrophoto offset for std stars in I-band    IRMSSTD =            0.0564636 / Spectrophoto RMS for std stars in I-band       GROFFSTD=            0.0197706 / Spectrophoto offset for std stars in (GR)      GRRMSSTD=            0.0564266 / Spectrophoto RMS for std stars in (GR)         RIOFFSTD=           -0.0232182 / Spectrophoto offset for std stars in (RI)      RIRMSSTD=            0.0238882 / Spectrophoto RMS for std stars in (RI)         NGAL    =                   64 / Number of (good) main galaxies                 GOFFGAL =            0.0610857 / Spectrophoto offset for main galaxies in G-bandGRMSGAL =             0.132475 / Spectrophoto RMS for main galaxies in G-band   ROFFGAL =            0.0628061 / Spectrophoto offset for main galaxies in R-bandRRMSGAL =            0.0622986 / Spectrophoto RMS for main galaxies in R-band   IOFFGAL =            0.0700979 / Spectrophoto offset for main galaxies in I-bandIRMSGAL =            0.0636804 / Spectrophoto RMS for main galaxies in I-band   GROFFGAL=            0.0133495 / Spectrophoto offset for main galaxies in (GR)  GRRMSGAL=            0.0472521 / Spectrophoto RMS for main galaxies in (GR)     RIOFFGAL=           -0.0103455 / Spectrophoto offset for main galaxies in (RI)  RIRMSGAL=            0.0216159 / Spectrophoto RMS for main galaxies in (RI)     LOWREJ  =                    5 / Extraction: low rejection                      HIGHREJ =                    8 / Extraction: high rejection                     SCATPOLY=                    0 / Extraction: Order of scattered light polynomialPROFTYPE=                    3 / Extraction profile: 1=Gaussian                 NFITPOLY=                    1 / Extraction: Number of parameters in each profilXCHI2   =              1.44428 / Extraction: Mean chi^2                         XCHI2MIN=              1.15894 /                                                XCHI2MAX=              1.77136 /                                                SHIFTGUE=   -0.000812781513105 /                                                SHIFT   =   -0.000812781513105 /Angstroms, at 4000AA                            SHIFTERR=   -0.000812781513105 /                                                NWORDER =                    2 / Linear-log10 coefficients                      WFITTYPE= 'LOG-LINEAR'         / Linear-log10 dispersion                        COEFF0  =        3.57930000000 / Central wavelength (log10) of first pixel      COEFF1  =    0.000100000000000 / Log10 dispersion per pixel                     FBADPIX =            0.0754441 / Fraction of bad pixels                         FBADPIX1=            0.0759304 / Fraction of bad pixels on spectro-1            FBADPIX2=            0.0749579 / Fraction of bad pixels on spectro-2            WAT0_001= 'system=linear'      /                                                WAT1_001= 'wtype=linear label=Wavelength units=Angstroms' /                     DC-FLAG =                    1 / Log-linear flag                                BUNIT   = '1E-17 erg/cm^2/s/Ang' /                                              NEXP    =                   10 / Number of individual exposures                 EXPID01 = 'b1-00011558-00011561-00011562'                                       EXPID02 = 'b1-00011559-00011561-00011562'                                       EXPID03 = 'b1-00011591-00011596-00011597'                                       EXPID04 = 'b1-00011592-00011596-00011597'                                       EXPID05 = 'b1-00011593-00011596-00011597'                                       EXPID06 = 'r1-00011558-00011561-00011562'                                       EXPID07 = 'r1-00011559-00011561-00011562'                                       EXPID08 = 'r1-00011591-00011596-00011597'                                       EXPID09 = 'r1-00011592-00011596-00011597'                                       EXPID10 = 'r1-00011593-00011596-00011597'                                       PLUG_RA =            34.928021 / RA of object [deg]                             PLUG_DEC=           -1.1370997 / dec of object [deg]                            SPEC_ID = '    791507946449168384' / Unique object identifier                   FIBERID =                    1 / Fiber number (1-640)                           END                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                   32 / length of dimension 1                          NAXIS2  =                 3830 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                    8 / number of table fields                         TTYPE1  = 'flux    '                                                            TFORM1  = 'E       '                                                            TTYPE2  = 'loglam  '                                                            TFORM2  = 'E       '                                                            TTYPE3  = 'ivar    '                                                            TFORM3  = 'E       '                                                            TTYPE4  = 'and_mask'                                                            TFORM4  = 'J       '                                                            TTYPE5  = 'or_mask '                                                            TFORM5  = 'J       '                                                            TTYPE6  = 'wdisp   '                                                            TFORM6  = 'E       '                                                            TTYPE7  = 'sky     '                                                            TFORM7  = 'E       '                                                            TTYPE8  = 'model   '                                                            TFORM8  = 'E       '                                                            EXTNAME = 'COADD   '                                                            COMMENT Coadded spectrum                                                        END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        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+@}�C?�CM?8�@׉@.�0@��@}��?�z?8��@�d�@Bh@���@}��?��?8��@�U�@:ɐ@�X@}�-?�3�?9J@�G@3 �@u�Y@}��?�:U?9��@�|,@��@H�\@}�t?���?9��@��>@%��?LX@}�?�߿?9��A��@/�@0��@}��?�)?9��Aޘ@l�>�g�@}�^?�x�?:�A!(@&l@XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                 1068 / length of dimension 1                          NAXIS2  =                    1 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                  126 / number of table fields                         TTYPE1  = 'SURVEY  '                                                            TFORM1  = '6A      '                                                            TTYPE2  = 'INSTRUMENT'                                                          TFORM2  = '4A      '                                                            TTYPE3  = 'CHUNK   '                                                            TFORM3  = '16A     '                                                            TTYPE4  = 'PROGRAMNAME'                                                         TFORM4  = '23A     '                                                            TTYPE5  = 'PLATERUN'                                                            TFORM5  = '16A     '                                                            TTYPE6  = 'PLATEQUALITY'                                                        TFORM6  = '8A      '                                                            TTYPE7  = 'PLATESN2'                                                            TFORM7  = 'E       '                                                            TTYPE8  = 'DEREDSN2'                                                            TFORM8  = 'E       '                                                            TTYPE9  = 'LAMBDA_EFF'                                                          TFORM9  = 'E       '                                                            TTYPE10 = 'BLUEFIBER'                                                           TFORM10 = 'J       '                                                            TTYPE11 = 'ZOFFSET '                                                            TFORM11 = 'E       '                                                            TTYPE12 = 'SNTURNOFF'                                                           TFORM12 = 'E       '                                                            TTYPE13 = 'NTURNOFF'                                                            TFORM13 = 'J       '                                                            TTYPE14 = 'SPECPRIMARY'                                                         TFORM14 = 'B       '                                                            TTYPE15 = 'SPECLEGACY'                                                          TFORM15 = 'B       '                                                            TTYPE16 = 'SPECSEGUE'                                                           TFORM16 = 'B       '                                                            TTYPE17 = 'SPECSEGUE1'                                                          TFORM17 = 'B       '                                                            TTYPE18 = 'SPECSEGUE2'                                                          TFORM18 = 'B       '                                                            TTYPE19 = 'SPECBOSS'                                                            TFORM19 = 'B       '                                                            TTYPE20 = 'BOSS_SPECOBJ_ID'                                                     TFORM20 = 'J       '                                                            TTYPE21 = 'SPECOBJID'                                                           TFORM21 = '22A     '                                                            TTYPE22 = 'FLUXOBJID'                                                           TFORM22 = '19A     '                                                            TTYPE23 = 'BESTOBJID'                                                           TFORM23 = '19A     '                                                            TTYPE24 = 'TARGETOBJID'                                                         TFORM24 = '22A     '                                                            TTYPE25 = 'PLATEID '                                                            TFORM25 = '19A     '                                                            TTYPE26 = 'NSPECOBS'                                                            TFORM26 = 'I       '                                                            TTYPE27 = 'FIRSTRELEASE'                                                        TFORM27 = '3A      '                                                            TTYPE28 = 'RUN2D   '                                                            TFORM28 = '3A      '                                                            TTYPE29 = 'RUN1D   '                                                            TFORM29 = '1A      '                                                            TTYPE30 = 'DESIGNID'                                                            TFORM30 = 'J       '                                                            TTYPE31 = 'CX      '                                                            TFORM31 = 'D       '                                                            TTYPE32 = 'CY      '                                                            TFORM32 = 'D       '                                                            TTYPE33 = 'CZ      '                                                            TFORM33 = 'D       '                                                            TTYPE34 = 'XFOCAL  '                                                            TFORM34 = 'E       '                                                            TTYPE35 = 'YFOCAL  '                                                            TFORM35 = 'E       '                                                            TTYPE36 = 'SOURCETYPE'                                                          TFORM36 = '19A     '                                                            TTYPE37 = 'TARGETTYPE'                                                          TFORM37 = '8A      '                                                            TTYPE38 = 'PRIMTARGET'                                                          TFORM38 = 'J       '                                                            TTYPE39 = 'SECTARGET'                                                           TFORM39 = 'J       '                                                            TTYPE40 = 'LEGACY_TARGET1'                                                      TFORM40 = 'J       '                                                            TTYPE41 = 'LEGACY_TARGET2'                                                      TFORM41 = 'J       '                                                            TTYPE42 = 'SPECIAL_TARGET1'                                                     TFORM42 = 'K       '                                                            TTYPE43 = 'SPECIAL_TARGET2'                                                     TFORM43 = 'K       '                                                            TTYPE44 = 'SEGUE1_TARGET1'                                                      TFORM44 = 'J       '                                                            TTYPE45 = 'SEGUE1_TARGET2'                                                      TFORM45 = 'J       '                                                            TTYPE46 = 'SEGUE2_TARGET1'                                                      TFORM46 = 'J       '                                                            TTYPE47 = 'SEGUE2_TARGET2'                                                      TFORM47 = 'J       '                                                            TTYPE48 = 'MARVELS_TARGET1'                                                     TFORM48 = 'J       '                                                            TTYPE49 = 'MARVELS_TARGET2'                                                     TFORM49 = 'J       '                                                            TTYPE50 = 'BOSS_TARGET1'                                                        TFORM50 = 'K       '                                                            TTYPE51 = 'BOSS_TARGET2'                                                        TFORM51 = 'K       '                                                            TTYPE52 = 'ANCILLARY_TARGET1'                                                   TFORM52 = 'K       '                                                            TTYPE53 = 'ANCILLARY_TARGET2'                                                   TFORM53 = 'K       '                                                            TTYPE54 = 'SPECTROGRAPHID'                                                      TFORM54 = 'I       '                                                            TTYPE55 = 'PLATE   '                                                            TFORM55 = 'J       '                                                            TTYPE56 = 'TILE    '                                                            TFORM56 = 'J       '                                                            TTYPE57 = 'MJD     '                                                            TFORM57 = 'J       '                                                            TTYPE58 = 'FIBERID '                                                            TFORM58 = 'J       '                                                            TTYPE59 = 'OBJID   '                                                            TFORM59 = '5J      '                                                            TTYPE60 = 'PLUG_RA '                                                            TFORM60 = 'D       '                                                            TTYPE61 = 'PLUG_DEC'                                                            TFORM61 = 'D       '                                                            TTYPE62 = 'CLASS   '                                                            TFORM62 = '6A      '                                                            TTYPE63 = 'SUBCLASS'                                                            TFORM63 = '21A     '                                                            TTYPE64 = 'Z       '                                                            TFORM64 = 'E       '                                                            TTYPE65 = 'Z_ERR   '                                                            TFORM65 = 'E       '                                                            TTYPE66 = 'RCHI2   '                                                            TFORM66 = 'E       '                                                            TTYPE67 = 'DOF     '                                                            TFORM67 = 'J       '                                                            TTYPE68 = 'RCHI2DIFF'                                                           TFORM68 = 'E       '                                                            TTYPE69 = 'TFILE   '                                                            TFORM69 = '24A     '                                                            TTYPE70 = 'TCOLUMN '                                                            TFORM70 = '10J     '                                                            TTYPE71 = 'NPOLY   '                                                            TFORM71 = 'J       '                                                            TTYPE72 = 'THETA   '                                                            TFORM72 = '10E     '                                                            TTYPE73 = 'VDISP   '                                                            TFORM73 = 'E       '                                                            TTYPE74 = 'VDISP_ERR'                                                           TFORM74 = 'E       '                                                            TTYPE75 = 'VDISPZ  '                                                            TFORM75 = 'E       '                                                            TTYPE76 = 'VDISPZ_ERR'                                                          TFORM76 = 'E       '                                                            TTYPE77 = 'VDISPCHI2'                                                           TFORM77 = 'E       '                                                            TTYPE78 = 'VDISPNPIX'                                                           TFORM78 = 'E       '                                                            TTYPE79 = 'VDISPDOF'                                                            TFORM79 = 'J       '                                                            TTYPE80 = 'WAVEMIN '                                                            TFORM80 = 'E       '                                                            TTYPE81 = 'WAVEMAX '                                                            TFORM81 = 'E       '                                                            TTYPE82 = 'WCOVERAGE'                                                           TFORM82 = 'E       '                                                            TTYPE83 = 'ZWARNING'                                                            TFORM83 = 'J       '                                                            TTYPE84 = 'SN_MEDIAN_ALL'                                                       TFORM84 = 'E       '                                                            TTYPE85 = 'SN_MEDIAN'                                                           TFORM85 = '5E      '                                                            TTYPE86 = 'CHI68P  '                                                            TFORM86 = 'E       '                                                            TTYPE87 = 'FRACNSIGMA'                                                          TFORM87 = '10E     '                                                            TTYPE88 = 'FRACNSIGHI'                                                          TFORM88 = '10E     '                                                            TTYPE89 = 'FRACNSIGLO'                                                          TFORM89 = '10E     '                                                            TTYPE90 = 'SPECTROFLUX'                                                         TFORM90 = '5E      '                                                            TTYPE91 = 'SPECTROFLUX_IVAR'                                                    TFORM91 = '5E      '                                                            TTYPE92 = 'SPECTROSYNFLUX'                                                      TFORM92 = '5E      '                                                            TTYPE93 = 'SPECTROSYNFLUX_IVAR'                                                 TFORM93 = '5E      '                                                            TTYPE94 = 'SPECTROSKYFLUX'                                                      TFORM94 = '5E      '                                                            TTYPE95 = 'ANYANDMASK'                                                          TFORM95 = 'J       '                                                            TTYPE96 = 'ANYORMASK'                                                           TFORM96 = 'J       '                                                            TTYPE97 = 'SPEC1_G '                                                            TFORM97 = 'E       '                                                            TTYPE98 = 'SPEC1_R '                                                            TFORM98 = 'E       '                                                            TTYPE99 = 'SPEC1_I '                                                            TFORM99 = 'E       '                                                            TTYPE100= 'SPEC2_G '                                                            TFORM100= 'E       '                                                            TTYPE101= 'SPEC2_R '                                                            TFORM101= 'E       '                                                            TTYPE102= 'SPEC2_I '                                                            TFORM102= 'E       '                                                            TTYPE103= 'ELODIE_FILENAME'                                                     TFORM103= '25A     '                                                            TTYPE104= 'ELODIE_OBJECT'                                                       TFORM104= '21A     '                                                            TTYPE105= 'ELODIE_SPTYPE'                                                       TFORM105= '10A     '                                                            TTYPE106= 'ELODIE_BV'                                                           TFORM106= 'E       '                                                            TTYPE107= 'ELODIE_TEFF'                                                         TFORM107= 'E       '                                                            TTYPE108= 'ELODIE_LOGG'                                                         TFORM108= 'E       '                                                            TTYPE109= 'ELODIE_FEH'                                                          TFORM109= 'E       '                                                            TTYPE110= 'ELODIE_Z'                                                            TFORM110= 'E       '                                                            TTYPE111= 'ELODIE_Z_ERR'                                                        TFORM111= 'E       '                                                            TTYPE112= 'ELODIE_Z_MODELERR'                                                   TFORM112= 'E       '                                                            TTYPE113= 'ELODIE_RCHI2'                                                        TFORM113= 'E       '                                                            TTYPE114= 'ELODIE_DOF'                                                          TFORM114= 'J       '                                                            TTYPE115= 'Z_NOQSO '                                                            TFORM115= 'E       '                                                            TTYPE116= 'Z_ERR_NOQSO'                                                         TFORM116= 'E       '                                                            TTYPE117= 'ZWARNING_NOQSO'                                                      TFORM117= 'J       '                                                            TTYPE118= 'CLASS_NOQSO'                                                         TFORM118= '1A      '                                                            TTYPE119= 'SUBCLASS_NOQSO'                                                      TFORM119= '1A      '                                                            TTYPE120= 'RCHI2DIFF_NOQSO'                                                     TFORM120= 'E       '                                                            TTYPE121= 'Z_PERSON'                                                            TFORM121= 'E       '                                                            TTYPE122= 'CLASS_PERSON'                                                        TFORM122= 'J       '                                                            TTYPE123= 'Z_CONF_PERSON'                                                       TFORM123= 'J       '                                                            TTYPE124= 'COMMENTS_PERSON'                                                     TFORM124= '1A      '                                                            TTYPE125= 'CALIBFLUX'                                                           TFORM125= '5E      '                                                            TTYPE126= 'CALIBFLUX_IVAR'                                                      TFORM126= '5E      '                                                            EXTNAME = 'SPECOBJ '                                                            COMMENT Metadata from specObj row                                               END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             sdss  SDSSchunk22         southern               dr2001.8.2      marginalA�SE�@�����<����
    79150794644916838412376637826020803741237663782602080374         7538244386924791507671571261440 
dr726  ����?�;��!?�Qao�Oe��R4%��C8�W�tcDNONLEGACY          SCIENCE ��
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al@Av�db�(��1�t|��GALAXYSTARBURST            >��r6�x4@zsU
�A��spEigenGal-53724.fits   
������������������������:<C�loG<�b%>3�AYK�6��#�qC�B��E<ħE�EEn~�Fɂ>�@ϲ�?�(�@�gpA�@���@��H?��*?��>K[�=�Ò<��</`�;���;�t�;o��;K�;K�>�A1=�V�=�2<A��;�L�;�9�;K�;8��;&%�;&%�>z`=�`}=
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9�:���:]��:��:��@�]A)�/A�hA�DbB@6?�w!@'��?�V�?��T?z0@��A-�+A��A�/�A���@5&@'|??�/�?#��AǏ8A���Byy3B�8IC�I
��A���A��A�SB
�nB
�WA��9                                                           @�U8A�AA�AF�A�j�A"�B��A�z�A*A-?��XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                   89 / length of dimension 1                          NAXIS2  =                   29 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   19 / number of table fields                         TTYPE1  = 'PLATE   '                                                            TFORM1  = 'J       '                                                            TTYPE2  = 'MJD     '                                                            TFORM2  = 'J       '                                                            TTYPE3  = 'FIBERID '                                                            TFORM3  = 'J       '                                                            TTYPE4  = 'LINENAME'                                                            TFORM4  = '13A     '                                                            TTYPE5  = 'LINEWAVE'                                                            TFORM5  = 'D       '                                                            TTYPE6  = 'LINEZ   '                                                            TFORM6  = 'E       '                                                            TTYPE7  = 'LINEZ_ERR'                                                           TFORM7  = 'E       '                                                            TTYPE8  = 'LINESIGMA'                                                           TFORM8  = 'E       '                                                            TTYPE9  = 'LINESIGMA_ERR'                                                       TFORM9  = 'E       '                                                            TTYPE10 = 'LINEAREA'                                                            TFORM10 = 'E       '                                                            TTYPE11 = 'LINEAREA_ERR'                                                        TFORM11 = 'E       '                                                            TTYPE12 = 'LINEEW  '                                                            TFORM12 = 'E       '                                                            TTYPE13 = 'LINEEW_ERR'                                                          TFORM13 = 'E       '                                                            TTYPE14 = 'LINECONTLEVEL'                                                       TFORM14 = 'E       '                                                            TTYPE15 = 'LINECONTLEVEL_ERR'                                                   TFORM15 = 'E       '                                                            TTYPE16 = 'LINENPIXLEFT'                                                        TFORM16 = 'J       '                                                            TTYPE17 = 'LINENPIXRIGHT'                                                       TFORM17 = 'J       '                                                            TTYPE18 = 'LINEDOF '                                                            TFORM18 = 'E       '                                                            TTYPE19 = 'LINECHI2'                                                            TFORM19 = 'E       '                                                            EXTNAME = 'SPZLINE '                                                            COMMENT Line fits from spZline                                                  END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ���
Ly_alpha     @���z�H���������������
N_V 1240     @�c=p��
+���������������
C_IV 1549    @�5��R���������������
He_II 1640   @���z�H���������������
C_III] 1908  @��-���������������
Mg_II 2799   @��_������������������
[O_II] 3725  @�.�Л>���6��B�h?!+CM^�@jMB=R6��@���=��T@�ĆBR����
[O_II] 3727  @�#�:�J�>���6��B�h?!+C}�L@{]Bj����@�F\=��@�ĆB)����
[Ne_III] 3868@�;�� L�>���6��B�h?!+A�te?��c@�~�>�@��)=�Ĝ@�ĆA	�y���
H_epsilon    @�dMZX�k>���6��B���?BA��E?���@�k�>��!@�L�=�a�@֋�A�M����
[Ne_III] 3970@�?d�F>���6��B�h?!+A���?��@W�
>�ٽ@��X=��@�ĆA������
H_delta      @��A��>���6��B���?BBs�@�@�>�@���=��@֋�Ad;����
H_gamma      @���/!�>���6��B�h?!+B��@!�GAj.&>搽@��<u6@�ĆB�~���
[O_III] 4363 @�oo��D>���6��B�h?!+?�М@�_>��r>���@�ϐ<P�g@�ĆA.����
He_II 4685   @�N��Ij>���6��B�h?!+?�3?�4>��K>�"�@�fV<i�Y@�Ć@̴����
H_beta       @���ح[>���6��B���?BCC�>@.2�B4�?>���@�Ν<OZ�@֋�A2�;���
[O_III] 4959 @�`K~�E3>���6��B�h?!+C�?�!�B�>(�@�l�<H��@�ĆA�#����
[O_III] 5007 @��=X��1>���6��B�h?!+Cާ�@X��B�+X>�u@��2<I�@�ĆB�l7���
He_II 5411   @�%@��H>���6��B�h?!+@,��?ä�?/7A>ŧ�@|�<<I�@�ĆA����
[O_I] 5577   @���@�P�>��
6��B�h?!+��B?��R�T=>�'@t�o<6�T@�ĆASi����
[O_I] 6300   @���X]Q>��6��B�h?!+A�0v@,p@�6�?U�@H�-<��@�ĆAA����
[S_III] 6312 @���7g��>���6��B�h?!+>[O�?�]=���?@Or<�v@�ĆA�����
[O_I] 6363   @�݉A�>���6��B�h?!+A1dz@�
+�@b��?�-e@Hɍ<��@9�
>ү����
[N_II] 6548  @����ɞ�>���6��B�h?!+B,+�@WAY��?54�@Js�<6�@�ĆAb����
H_alpha      @���(-Ԩ>���6��B���?BDC�Ab@C�k@"k@" �;�0�@֋�B��N���
[N_II] 6583  @��D���q>���6��B�h?!+C��@M
�B#�?X�@T/N<{�@�ĆA������
[S_II] 6716  @�>KQ9�>���6��B�h?!+B��	@V�A�8�?ZU?@e@F<+;@�ĆA������
[S_II] 6730  @�L��i.>���6��B�h?!+B�E�A=�A��!@ ��@q�F<4��@�ĆA������
[Ar_III] 7135@���ц.R������������
\ No newline at end of file
diff --git a/SpectrumViewer/test.py b/SpectrumViewer/test.py
new file mode 100644
index 0000000..e01ee36
--- /dev/null
+++ b/SpectrumViewer/test.py
@@ -0,0 +1,9 @@
+import SpectrumViewer.ZObj as ZObj
+import SpectrumViewer.CoaddObj as CoaddObj
+import SpectrumViewer.WindowViewer as viewer
+fileName = 'example_lite.fits'
+fileSource = 'SDSS'
+viewer.view(fileName,fileSource)
+
+
+
diff --git a/SpectrumViewer/viewer screenshot/emission line only.PNG b/SpectrumViewer/viewer screenshot/emission line only.PNG
new file mode 100644
index 0000000000000000000000000000000000000000..2a1b6c4306f176ba84395f8f692b389e11961019
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literal 0
HcmV?d00001


From 89f7b20731c9873674ca887a1e96703ddfd90ec3 Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Fri, 10 Nov 2017 14:52:03 -0500
Subject: [PATCH 04/10] inline viewer completed

jupyter notebook compatible viewer completed demo 1/2 added
---
 .../demo 1-checkpoint.ipynb                   |   6 +
 .../demo 2-checkpoint.ipynb                   |  62 ++
 SpectrumViewer/InlineViewer.py                |   6 +
 SpectrumViewer/SDSSDriver.py                  |  36 +-
 SpectrumViewer/SpecUtil_inline.py             | 170 ++++
 SpectrumViewer/SpecUtil_interactive.py        | 142 +++
 SpectrumViewer/WindowViewer.py                |  11 +-
 SpectrumViewer/ZObj.py                        |   1 +
 .../__pycache__/CoaddObj.cpython-36.pyc       | Bin 0 -> 643 bytes
 .../__pycache__/InlineViewer.cpython-36.pyc   | Bin 0 -> 495 bytes
 .../__pycache__/SDSSDriver.cpython-36.pyc     | Bin 0 -> 1020 bytes
 .../__pycache__/SpecUtil.cpython-36.pyc       | Bin 0 -> 3941 bytes
 .../SpecUtil_inline.cpython-36.pyc            | Bin 0 -> 5467 bytes
 .../SpecUtil_interactive.cpython-36.pyc       | Bin 0 -> 4357 bytes
 .../__pycache__/WindowViewer.cpython-36.pyc   | Bin 0 -> 5281 bytes
 .../__pycache__/ZObj.cpython-36.pyc           | Bin 0 -> 545 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 149 bytes
 SpectrumViewer/debug.py                       | 176 ++++
 SpectrumViewer/demo 1.ipynb                   | 843 ++++++++++++++++++
 SpectrumViewer/test.py                        |  11 +-
 .../emission line only.PNG                    | Bin
 .../flux and model fit only.PNG               | Bin
 .../showall.PNG                               | Bin
 inline viewer demo 2.ipynb                    | 839 +++++++++++++++++
 24 files changed, 2272 insertions(+), 31 deletions(-)
 create mode 100644 SpectrumViewer/.ipynb_checkpoints/demo 1-checkpoint.ipynb
 create mode 100644 SpectrumViewer/.ipynb_checkpoints/demo 2-checkpoint.ipynb
 create mode 100644 SpectrumViewer/SpecUtil_inline.py
 create mode 100644 SpectrumViewer/SpecUtil_interactive.py
 create mode 100644 SpectrumViewer/__pycache__/CoaddObj.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/InlineViewer.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/SDSSDriver.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/SpecUtil.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/SpecUtil_interactive.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/WindowViewer.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/ZObj.cpython-36.pyc
 create mode 100644 SpectrumViewer/__pycache__/__init__.cpython-36.pyc
 create mode 100644 SpectrumViewer/debug.py
 create mode 100644 SpectrumViewer/demo 1.ipynb
 rename SpectrumViewer/{viewer screenshot => window viewer screenshot}/emission line only.PNG (100%)
 rename SpectrumViewer/{viewer screenshot => window viewer screenshot}/flux and model fit only.PNG (100%)
 rename SpectrumViewer/{viewer screenshot => window viewer screenshot}/showall.PNG (100%)
 create mode 100644 inline viewer demo 2.ipynb

diff --git a/SpectrumViewer/.ipynb_checkpoints/demo 1-checkpoint.ipynb b/SpectrumViewer/.ipynb_checkpoints/demo 1-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/SpectrumViewer/.ipynb_checkpoints/demo 1-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer/.ipynb_checkpoints/demo 2-checkpoint.ipynb b/SpectrumViewer/.ipynb_checkpoints/demo 2-checkpoint.ipynb
new file mode 100644
index 0000000..3e3479f
--- /dev/null
+++ b/SpectrumViewer/.ipynb_checkpoints/demo 2-checkpoint.ipynb	
@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'SpectrumViewer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-5fc1ec971204>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mInlineViewer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib notebook'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfileName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'example_lite.fits'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfileSource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'SDSS'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mInlineViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfileSource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Users\\wzuo\\Developer\\Spectrum Viewer\\SpectrumViewer\\InlineViewer.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpecUtil_inline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSDSSDriver\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilesource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mcoaddObj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mzObj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdriver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloadFITS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilesource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpecUtil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoaddObj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzObj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'SpectrumViewer'"
+     ]
+    }
+   ],
+   "source": [
+    "import InlineViewer\n",
+    "%matplotlib notebook\n",
+    "fileName = 'example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "InlineViewer.view(fileName, fileSource)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/SpectrumViewer/InlineViewer.py b/SpectrumViewer/InlineViewer.py
index e69de29..8822b92 100644
--- a/SpectrumViewer/InlineViewer.py
+++ b/SpectrumViewer/InlineViewer.py
@@ -0,0 +1,6 @@
+import SpectrumViewer.SpecUtil_inline as inline
+import SpectrumViewer.SDSSDriver as driver
+def view(filename, filesource):
+    coaddObj,zObj=driver.loadFITS(filename, filesource)
+    spec = inline.SpecUtil(filename, coaddObj, zObj)
+    spec.showfig()
diff --git a/SpectrumViewer/SDSSDriver.py b/SpectrumViewer/SDSSDriver.py
index 12bcf6e..fb36ef1 100644
--- a/SpectrumViewer/SDSSDriver.py
+++ b/SpectrumViewer/SDSSDriver.py
@@ -1,26 +1,26 @@
 #load SDSS into workable object
 from astropy.io import fits
-from py3.SciServer import Authentication, Config
-from py3.SciServer import CasJobs
+#from py3.SciServer import Authentication, Config
+#from py3.SciServer import CasJobs
 from urllib.parse import urlparse
 import SpectrumViewer.CoaddObj as Coadd
 import SpectrumViewer.ZObj as Z
-def getSpecObjID(ra, dec, width, height, context=None, URL=True):
-    leftBoundry = ra - width / 2
-    rightBoundry = ra + width / 2
-    upperBoundry = dec + height / 2
-    lowerBoundry = dec - height / 2
-    sqlQuery = 'select specObjID from dr7.specobjall Where (ra >' + str(leftBoundry) + ' and ra<' + str(
-        rightBoundry) + ') and (dec>' + str(lowerBoundry) + ' and dec<' + str(upperBoundry) + ')'
-    return CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
-def getRawFITSPathInDAS(SpecObjID):
-    sqlQuery = 'select dr7.fGetUrlFitsSpectrum('+SpecObjID+')'
-    response = CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
-    url= response['Column1'][0]
-    parseResult = urlparse(url)
-    prefix = '/home/idies/workspace/sdss_das/das2'
-    path = prefix+parseResult.path
-    return path
+# def getSpecObjID(ra, dec, width, height, context=None, URL=True):
+#     leftBoundry = ra - width / 2
+#     rightBoundry = ra + width / 2
+#     upperBoundry = dec + height / 2
+#     lowerBoundry = dec - height / 2
+#     sqlQuery = 'select specObjID from dr7.specobjall Where (ra >' + str(leftBoundry) + ' and ra<' + str(
+#         rightBoundry) + ') and (dec>' + str(lowerBoundry) + ' and dec<' + str(upperBoundry) + ')'
+#     return CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+# def getRawFITSPathInDAS(SpecObjID):
+#     sqlQuery = 'select dr7.fGetUrlFitsSpectrum('+SpecObjID+')'
+#     response = CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+#     url= response['Column1'][0]
+#     parseResult = urlparse(url)
+#     prefix = '/home/idies/workspace/sdss_das/das2'
+#     path = prefix+parseResult.path
+#     return path
 
 def loadFITS(filename,fileSource):
 
diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
new file mode 100644
index 0000000..eff5af8
--- /dev/null
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -0,0 +1,170 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button
+from astropy.io import fits
+class SpecUtil(object):
+    def __init__(self,filename,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.updateFig()
+        self.figure = plt.figure(filename,figsize=(10, 8), dpi=100)
+        self.shownAx = self.figure.add_subplot(111)
+        plt.subplots_adjust(bottom=0.2)
+
+        self.axfluxbutton = plt.axes([0.1, 0.05, 0.1, 0.075])
+        self.fluxbutton = Button(self.axfluxbutton, 'flux')
+        self.fluxbutton.on_clicked(self.fluxClicked)
+
+        self.axskylinebutton = plt.axes([0.2, 0.05, 0.1, 0.075])
+        self.skylinebutton = Button(self.axskylinebutton, 'skyline')
+        self.skylinebutton.on_clicked(self.skylineClicked)
+
+        self.axabsorbbutton = plt.axes([0.3, 0.05, 0.1, 0.075])
+        self.absorbbutton = Button(self.axabsorbbutton, 'absorption')
+        self.absorbbutton.on_clicked(self.absorbClicked)
+
+        self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
+        self.emissionbutton = Button(self.axemission, 'emission')
+        self.emissionbutton.on_clicked(self.emissionClicked)
+
+        self.axmodelbutton = plt.axes([0.5, 0.05, 0.1, 0.075])
+        self.modelbutton = Button(self.axmodelbutton, 'model')
+        self.modelbutton.on_clicked(self.modelClicked)
+
+    def showfig(self):
+        self.updateFig()
+    def updateFig(self):
+
+        # figure = plt.figure(figsize=(8, 4), dpi=100)
+        # shownAx=figure.add_subplot(111)
+        # plt.subplots_adjust(bottom=0.2)
+
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx,key)
+                except IndexError:
+                    pass
+
+        self.shownAx.legend()
+        plt.show()
+    def fluxClicked(self,event):
+        if self.plotDict[0]:
+            self.plotDict[0]=False
+        else:
+            self.plotDict[0] = True
+
+        self.updateFig()
+    def skylineClicked(self,event):
+        if self.plotDict[1]:
+            self.plotDict[1]=False
+        else:
+            self.plotDict[1] = True
+        self.updateFig()
+    def emissionClicked(self, event):
+        if self.plotDict[2]:
+            self.plotDict[2] = False
+        else:
+            self.plotDict[2] = True
+        self.updateFig()
+    def absorbClicked(self, event):
+        if self.plotDict[3]:
+            self.plotDict[3] = False
+        else:
+            self.plotDict[3] = True
+        self.updateFig()
+    def modelClicked(self, event):
+        if self.plotDict[4]:
+            self.plotDict[4] = False
+        else:
+            self.plotDict[4] = True
+        self.updateFig()
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer/SpecUtil_interactive.py b/SpectrumViewer/SpecUtil_interactive.py
new file mode 100644
index 0000000..fc610c6
--- /dev/null
+++ b/SpectrumViewer/SpecUtil_interactive.py
@@ -0,0 +1,142 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+import numpy as np
+from ipywidgets import interactive
+class SpecUtil:
+    def __init__(self,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        #self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        #self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        #self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.totalAx = self.totalFig.add_subplot(111)
+        #fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        #skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        #self.shownAx = self.shownFig.add_subplot(111)
+        #self.updateFig()
+        #self.shownAx = self.totalAx
+        self.interactive_plot = interactive(self.showfig, flux=True,skyline=True,emission=True,absorb=True,model=True)
+        self.interactive_plot.children[0].layout.display = 'flex'
+        output = self.interactive_plot.children[-1]
+        output.layout.height = '550px'
+
+    def showfig(self,flux,skyline,emission,absorb,model):
+        self.plotDict[0] = flux
+        self.plotDict[1] =skyline
+        self.plotDict[2] =emission
+        self.plotDict[3] =absorb
+        self.plotDict[4] =model
+        self.updateFig()
+    def updateFig(self):
+        #self.shownAx.lines=[]
+        figure = plt.figure(figsize=(8, 4), dpi=100)
+        shownAx=figure.add_subplot(111)
+
+        #self.shownAx.clear()
+        shownAx.set_xlabel('lambda/Å')
+        shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(shownAx,key)
+                except IndexError:
+                    pass
+        #line1, = ax.plot(x, y, 'b-')
+        shownAx.legend()
+        #for phase in np.linspace(0, 10 * np.pi, 100):
+         #   line1.set_ydata(np.sin(0.5 * x + phase))
+          #  fig.canvas.draw()
+
+        plt.show()
+    def format_coord(self,x,y):
+        return 'x=%1.4f, y=%1.4f'%(x, y)
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer/WindowViewer.py b/SpectrumViewer/WindowViewer.py
index 557c4b3..93ffcd0 100644
--- a/SpectrumViewer/WindowViewer.py
+++ b/SpectrumViewer/WindowViewer.py
@@ -1,19 +1,14 @@
 # show plotting result in a new window interactively
 # this is actual user interface all the object are hide away from user
-import SpectrumViewer.ZObj as ZObj
-import SpectrumViewer.CoaddObj as CoaddObj
 import matplotlib
 matplotlib.use("TkAgg")
-import SpectrumViewer.SDSSDriver as driver
-import SpectrumViewer.SpecUtil as specutil
-from matplotlib.figure import Figure
-from numpy import arange, sin, pi
+from SpectrumViewer import SDSSDriver as driver
 from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
 # implement the default mpl key bindings
 from matplotlib.backend_bases import key_press_handler
 import tkinter as Tk
 
-
+from SpectrumViewer import SpecUtil as SpecUtil
 def view(fileName,fileSource):
     coaddObj, zObj = driver.loadFITS(fileName, fileSource)
     #construn tk window and all components
@@ -91,7 +86,7 @@ def modelTogg(self,event):
 
 class SpecModel:
     def __init__(self,coaddObj,zObj):
-        self.specUtilObj = specutil.SpecUtil(coaddObj,zObj)
+        self.specUtilObj = SpecUtil.SpecUtil(coaddObj, zObj)
     #take data to self construct
     # this is where plot happens
     #Figure composing
diff --git a/SpectrumViewer/ZObj.py b/SpectrumViewer/ZObj.py
index d3faa0b..7409f03 100644
--- a/SpectrumViewer/ZObj.py
+++ b/SpectrumViewer/ZObj.py
@@ -7,3 +7,4 @@ def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW
         self.LINEZ_ERR = LINEZ_ERR
         self.LINEEW = LINEEW
         self.LINEER_ERR = LINEEW_ERR
+
diff --git a/SpectrumViewer/__pycache__/CoaddObj.cpython-36.pyc b/SpectrumViewer/__pycache__/CoaddObj.cpython-36.pyc
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diff --git a/SpectrumViewer/debug.py b/SpectrumViewer/debug.py
new file mode 100644
index 0000000..e11efe3
--- /dev/null
+++ b/SpectrumViewer/debug.py
@@ -0,0 +1,176 @@
+import SDSSDriver as driver
+from matplotlib.widgets import Button
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+
+
+class SpecUtil:
+    def __init__(self, coaddObj, zObj):
+        self.coaddObj = coaddObj
+        self.zObj = zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        # self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        # 0 flux 1 skyline 2 emission 3 absorb 4 model
+        # self.totalAx = self.totalFig.add_subplot(111)
+        # fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        # skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        self.shownAx = self.shownFig.add_subplot(111)
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError:  # dict is longer than plot numbers in totalax
+                    pass
+        self.shownAx.legend()
+        # self.shownAx = self.totalAx
+
+    def updateFig(self):
+        # self.shownAx.lines=[]
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError:
+                    pass
+        # line1, = ax.plot(x, y, 'b-')
+        self.shownAx.legend()
+        # for phase in np.linspace(0, 10 * np.pi, 100):
+        #   line1.set_ydata(np.sin(0.5 * x + phase))
+        #  fig.canvas.draw()
+
+    def addFlux(self):
+        self.plotDict[0] = True
+        self.updateFig()
+
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+
+    def plotLines(self, axes, actionIndex):
+        if actionIndex == 0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux, 'C0', label='flux')
+        elif actionIndex == 1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky, 'C1', label='skyline')
+        elif actionIndex == 2:
+            # plot emission line
+            self.plotSegment(axes, actionIndex)
+
+        elif actionIndex == 3:
+            # plot absorption line
+            self.plotSegment(axes, actionIndex)
+
+
+        elif actionIndex == 4:
+
+            # plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+
+    def plotSegment(self, axes, actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex == 2:  # emissiaon line
+            for lineIndex in range(0, len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width > 0:
+                    # print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position - width / 2
+                    endPostition = position + width / 2
+                    name = self.zObj.LINENAME[lineIndex]
+                    # print(name)
+                    axes.axvspan(xmin=startPostition, xmax=endPostition, facecolor='C4')
+                    axes.text(position, 0, name, rotation=90)
+        elif actionIndex == 3:  # absorption line
+            for lineIndex in range(0, len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width > 0:
+                    # print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position - width / 2
+                    endPostition = position + width / 2
+                    name = self.zObj.LINENAME[lineIndex]
+                    # print(name)
+                    axes.axvspan(xmin=startPostition, xmax=endPostition, facecolor='C4')
+                    axes.text(position, 0, name, rotation=90)
+
+
+class Callback(object):
+    def __init__(self, specObj):
+        self.specObj = specObj
+
+    def fluxToggle(self, event):
+        self.specObj.removeFlux()
+        self.specObj.shownFig.draw()
+
+
+def view(fileName, fileSource):
+    coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+    # just show stuff
+    specObj = SpecUtil(coaddObj, zObj)
+    # specObj.shownFig.show()
+    callback = Callback(specObj)
+    axFluxToggle = plt.axes([0.81, 0.05, 0.1, 0.075])
+    bFluxToggle = Button(axFluxToggle, 'flux')
+    bFluxToggle.on_clicked(callback.fluxToggle)
+
+    # def skylineToggle(self,event):
+
+# import InlineViewer as viewer
+
+fileName = 'example_lite.fits'
+fileSource = 'SDSS'
+view(fileName, fileSource)
\ No newline at end of file
diff --git a/SpectrumViewer/demo 1.ipynb b/SpectrumViewer/demo 1.ipynb
new file mode 100644
index 0000000..32152f3
--- /dev/null
+++ b/SpectrumViewer/demo 1.ipynb	
@@ -0,0 +1,843 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"1000\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import SpecUtil_inline as inline\n",
+    "\n",
+    "import SDSSDriver as driver\n",
+    "%matplotlib notebook\n",
+    "fileName = 'example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "coaddObj, zObj = driver.loadFITS(fileName, fileSource)\n",
+    "spec=inline.SpecUtil(fileName, coaddObj, zObj)\n",
+    "spec.showfig()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer/test.py b/SpectrumViewer/test.py
index e01ee36..546d82b 100644
--- a/SpectrumViewer/test.py
+++ b/SpectrumViewer/test.py
@@ -1,9 +1,10 @@
-import SpectrumViewer.ZObj as ZObj
-import SpectrumViewer.CoaddObj as CoaddObj
-import SpectrumViewer.WindowViewer as viewer
+import SpectrumViewer.SpecUtil_inline as inline
+
+import SpectrumViewer.SDSSDriver as driver
 fileName = 'example_lite.fits'
 fileSource = 'SDSS'
-viewer.view(fileName,fileSource)
-
+coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+spec=inline.SpecUtil(coaddObj, zObj)
+spec.showfig()
 
 
diff --git a/SpectrumViewer/viewer screenshot/emission line only.PNG b/SpectrumViewer/window viewer screenshot/emission line only.PNG
similarity index 100%
rename from SpectrumViewer/viewer screenshot/emission line only.PNG
rename to SpectrumViewer/window viewer screenshot/emission line only.PNG
diff --git a/SpectrumViewer/viewer screenshot/flux and model fit only.PNG b/SpectrumViewer/window viewer screenshot/flux and model fit only.PNG
similarity index 100%
rename from SpectrumViewer/viewer screenshot/flux and model fit only.PNG
rename to SpectrumViewer/window viewer screenshot/flux and model fit only.PNG
diff --git a/SpectrumViewer/viewer screenshot/showall.PNG b/SpectrumViewer/window viewer screenshot/showall.PNG
similarity index 100%
rename from SpectrumViewer/viewer screenshot/showall.PNG
rename to SpectrumViewer/window viewer screenshot/showall.PNG
diff --git a/inline viewer demo 2.ipynb b/inline viewer demo 2.ipynb
new file mode 100644
index 0000000..6ff334f
--- /dev/null
+++ b/inline viewer demo 2.ipynb	
@@ -0,0 +1,839 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"1000\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import SpectrumViewer.InlineViewer as inline\n",
+    "%matplotlib notebook\n",
+    "fileName = 'SpectrumViewer/example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "inline.view(fileName, fileSource)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 0f04e0b1cc79b040f2d5d473ff61b8368090db8e Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Thu, 18 Jan 2018 14:09:17 -0500
Subject: [PATCH 05/10] changed segment to line, added redshift table

---
 .../inline viewer demo 2-checkpoint.ipynb     | 879 ++++++++++++++++++
 .../demo 1-checkpoint.ipynb                   |   6 +
 .../demo 2-checkpoint.ipynb                   |  62 ++
 SpectrumViewer - Copy/CoaddObj.py             |  17 +
 SpectrumViewer - Copy/InlineViewer.py         |   6 +
 SpectrumViewer - Copy/SDSSDriver.py           |  52 ++
 SpectrumViewer - Copy/SpecUtil.py             | 131 +++
 SpectrumViewer - Copy/SpecUtil_inline.py      | 170 ++++
 SpectrumViewer - Copy/SpecUtil_interactive.py | 142 +++
 SpectrumViewer - Copy/WindowViewer.py         | 132 +++
 SpectrumViewer - Copy/ZObj.py                 |  10 +
 SpectrumViewer - Copy/__init__.py             |   0
 .../__pycache__/CoaddObj.cpython-36.pyc       | Bin 0 -> 643 bytes
 .../__pycache__/InlineViewer.cpython-36.pyc   | Bin 0 -> 495 bytes
 .../__pycache__/SDSSDriver.cpython-36.pyc     | Bin 0 -> 1020 bytes
 .../__pycache__/SpecUtil.cpython-36.pyc       | Bin 0 -> 3941 bytes
 .../SpecUtil_inline.cpython-36.pyc            | Bin 0 -> 5467 bytes
 .../SpecUtil_interactive.cpython-36.pyc       | Bin 0 -> 4357 bytes
 .../__pycache__/WindowViewer.cpython-36.pyc   | Bin 0 -> 5281 bytes
 .../__pycache__/ZObj.cpython-36.pyc           | Bin 0 -> 545 bytes
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 149 bytes
 SpectrumViewer - Copy/debug.py                | 176 ++++
 SpectrumViewer - Copy/demo 1.ipynb            | 843 +++++++++++++++++
 SpectrumViewer - Copy/example.py              |   6 +
 SpectrumViewer - Copy/example_lite.fits       | 218 +++++
 SpectrumViewer - Copy/test.py                 |  10 +
 .../emission line only.PNG                    | Bin 0 -> 33844 bytes
 .../flux and model fit only.PNG               | Bin 0 -> 39757 bytes
 .../window viewer screenshot/showall.PNG      | Bin 0 -> 55227 bytes
 .../Untitled-checkpoint.ipynb                 |   6 +
 SpectrumViewer/SDSSDriver.py                  |   1 +
 SpectrumViewer/SpecUtil_inline.py             |  86 +-
 SpectrumViewer/Untitled.ipynb                 |  62 ++
 .../__pycache__/SDSSDriver.cpython-36.pyc     | Bin 1020 -> 1021 bytes
 .../SpecUtil_inline.cpython-36.pyc            | Bin 5467 -> 7068 bytes
 SpectrumViewer/demo 1.ipynb                   | 799 +---------------
 inline viewer demo 2.ipynb                    | 133 ++-
 37 files changed, 3147 insertions(+), 800 deletions(-)
 create mode 100644 .ipynb_checkpoints/inline viewer demo 2-checkpoint.ipynb
 create mode 100644 SpectrumViewer - Copy/.ipynb_checkpoints/demo 1-checkpoint.ipynb
 create mode 100644 SpectrumViewer - Copy/.ipynb_checkpoints/demo 2-checkpoint.ipynb
 create mode 100644 SpectrumViewer - Copy/CoaddObj.py
 create mode 100644 SpectrumViewer - Copy/InlineViewer.py
 create mode 100644 SpectrumViewer - Copy/SDSSDriver.py
 create mode 100644 SpectrumViewer - Copy/SpecUtil.py
 create mode 100644 SpectrumViewer - Copy/SpecUtil_inline.py
 create mode 100644 SpectrumViewer - Copy/SpecUtil_interactive.py
 create mode 100644 SpectrumViewer - Copy/WindowViewer.py
 create mode 100644 SpectrumViewer - Copy/ZObj.py
 create mode 100644 SpectrumViewer - Copy/__init__.py
 create mode 100644 SpectrumViewer - Copy/__pycache__/CoaddObj.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/InlineViewer.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/SDSSDriver.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/SpecUtil.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/SpecUtil_inline.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/SpecUtil_interactive.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/WindowViewer.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/ZObj.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/__pycache__/__init__.cpython-36.pyc
 create mode 100644 SpectrumViewer - Copy/debug.py
 create mode 100644 SpectrumViewer - Copy/demo 1.ipynb
 create mode 100644 SpectrumViewer - Copy/example.py
 create mode 100644 SpectrumViewer - Copy/example_lite.fits
 create mode 100644 SpectrumViewer - Copy/test.py
 create mode 100644 SpectrumViewer - Copy/window viewer screenshot/emission line only.PNG
 create mode 100644 SpectrumViewer - Copy/window viewer screenshot/flux and model fit only.PNG
 create mode 100644 SpectrumViewer - Copy/window viewer screenshot/showall.PNG
 create mode 100644 SpectrumViewer/.ipynb_checkpoints/Untitled-checkpoint.ipynb
 create mode 100644 SpectrumViewer/Untitled.ipynb

diff --git a/.ipynb_checkpoints/inline viewer demo 2-checkpoint.ipynb b/.ipynb_checkpoints/inline viewer demo 2-checkpoint.ipynb
new file mode 100644
index 0000000..e5c98e3
--- /dev/null
+++ b/.ipynb_checkpoints/inline viewer demo 2-checkpoint.ipynb	
@@ -0,0 +1,879 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"1000\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import SpectrumViewer.InlineViewer as inline\n",
+    "%matplotlib notebook\n",
+    "fileName = 'SpectrumViewer/example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "inline.view(fileName, fileSource)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import SpectrumViewer.InlineViewer as inline\n",
+    "%matplotlib notebook\n",
+    "fileName = 'SpectrumViewer/example_lite.fits'\n",
+    "fileSource = 'SDSS'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'filename' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-ab60c62b48dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mcoaddData\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mzData\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mhdulist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhdulist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcoaddData\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhdulist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mzData\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'filename' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from astropy.io import fits\n",
+    "coaddData =1\n",
+    "zData =3\n",
+    "hdulist = fits.open(fileName)\n",
+    "c=hdulist[coaddData].data\n",
+    "z=hdulist[zData].data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/SpectrumViewer - Copy/.ipynb_checkpoints/demo 1-checkpoint.ipynb b/SpectrumViewer - Copy/.ipynb_checkpoints/demo 1-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/SpectrumViewer - Copy/.ipynb_checkpoints/demo 1-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer - Copy/.ipynb_checkpoints/demo 2-checkpoint.ipynb b/SpectrumViewer - Copy/.ipynb_checkpoints/demo 2-checkpoint.ipynb
new file mode 100644
index 0000000..3e3479f
--- /dev/null
+++ b/SpectrumViewer - Copy/.ipynb_checkpoints/demo 2-checkpoint.ipynb	
@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'SpectrumViewer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-5fc1ec971204>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mInlineViewer\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib notebook'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfileName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'example_lite.fits'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mfileSource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'SDSS'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mInlineViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileName\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfileSource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Users\\wzuo\\Developer\\Spectrum Viewer\\SpectrumViewer\\InlineViewer.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpecUtil_inline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSDSSDriver\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilesource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mcoaddObj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mzObj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdriver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloadFITS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilesource\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSpecUtil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcoaddObj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzObj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'SpectrumViewer'"
+     ]
+    }
+   ],
+   "source": [
+    "import InlineViewer\n",
+    "%matplotlib notebook\n",
+    "fileName = 'example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "InlineViewer.view(fileName, fileSource)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/SpectrumViewer - Copy/CoaddObj.py b/SpectrumViewer - Copy/CoaddObj.py
new file mode 100644
index 0000000..fdb2987
--- /dev/null
+++ b/SpectrumViewer - Copy/CoaddObj.py	
@@ -0,0 +1,17 @@
+# This class init with a fits file in the directory and store all the data in the RAM
+import numpy as np
+class CoaddObj:
+    def __init__(self,flux,loglam=None,ivar=None,andMask=None,orMask=None
+                 ,wdisp=None,sky=None,model=None):#type can be pfs
+        self.flux = flux
+        self.loglam = loglam
+        self.ivar =ivar
+        self.andMask =andMask
+        self.orMask = orMask
+        self.wdisp = wdisp
+        self.sky =sky
+        self.model = model
+        self.lam = np.power(10,self.loglam)
+
+
+
diff --git a/SpectrumViewer - Copy/InlineViewer.py b/SpectrumViewer - Copy/InlineViewer.py
new file mode 100644
index 0000000..8822b92
--- /dev/null
+++ b/SpectrumViewer - Copy/InlineViewer.py	
@@ -0,0 +1,6 @@
+import SpectrumViewer.SpecUtil_inline as inline
+import SpectrumViewer.SDSSDriver as driver
+def view(filename, filesource):
+    coaddObj,zObj=driver.loadFITS(filename, filesource)
+    spec = inline.SpecUtil(filename, coaddObj, zObj)
+    spec.showfig()
diff --git a/SpectrumViewer - Copy/SDSSDriver.py b/SpectrumViewer - Copy/SDSSDriver.py
new file mode 100644
index 0000000..fb36ef1
--- /dev/null
+++ b/SpectrumViewer - Copy/SDSSDriver.py	
@@ -0,0 +1,52 @@
+#load SDSS into workable object
+from astropy.io import fits
+#from py3.SciServer import Authentication, Config
+#from py3.SciServer import CasJobs
+from urllib.parse import urlparse
+import SpectrumViewer.CoaddObj as Coadd
+import SpectrumViewer.ZObj as Z
+# def getSpecObjID(ra, dec, width, height, context=None, URL=True):
+#     leftBoundry = ra - width / 2
+#     rightBoundry = ra + width / 2
+#     upperBoundry = dec + height / 2
+#     lowerBoundry = dec - height / 2
+#     sqlQuery = 'select specObjID from dr7.specobjall Where (ra >' + str(leftBoundry) + ' and ra<' + str(
+#         rightBoundry) + ') and (dec>' + str(lowerBoundry) + ' and dec<' + str(upperBoundry) + ')'
+#     return CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+# def getRawFITSPathInDAS(SpecObjID):
+#     sqlQuery = 'select dr7.fGetUrlFitsSpectrum('+SpecObjID+')'
+#     response = CasJobs.executeQuery(sqlQuery, context='dr7', format='pandas')
+#     url= response['Column1'][0]
+#     parseResult = urlparse(url)
+#     prefix = '/home/idies/workspace/sdss_das/das2'
+#     path = prefix+parseResult.path
+#     return path
+
+def loadFITS(filename,fileSource):
+
+    if fileSource=='SDSS':
+        coaddData =1
+        zData =3
+        hdulist = fits.open(filename)
+        c=hdulist[coaddData].data
+        z=hdulist[zData].data
+        coaddObj = Coadd.CoaddObj(
+            flux=c['flux'],
+            loglam=c['loglam'],
+            ivar=c['ivar'],
+            andMask=c['and_Mask'],
+            orMask=c['or_Mask'],
+            wdisp=c['wdisp'],
+            sky = c['sky'],
+            model=c['model'])
+        zObj = Z.ZObj(
+            LINENAME=z['LINENAME'],
+            LINEWAVE=z['LINEWAVE'],
+            LINEZ=z['LINEZ'],
+            LINEEW=z['LINEEW'],
+            LINEZ_ERR=z['LINEZ_ERR'],
+            LINEEW_ERR=z['LINEEW_ERR'])
+        return coaddObj,zObj
+
+    else:
+        print('Sorry, we are loyal to SDSS ONLY, for the time being. ')
\ No newline at end of file
diff --git a/SpectrumViewer - Copy/SpecUtil.py b/SpectrumViewer - Copy/SpecUtil.py
new file mode 100644
index 0000000..0309f4e
--- /dev/null
+++ b/SpectrumViewer - Copy/SpecUtil.py	
@@ -0,0 +1,131 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+import numpy as np
+class SpecUtil:
+    def __init__(self,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        #self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.totalAx = self.totalFig.add_subplot(111)
+        #fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        #skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        self.shownAx = self.shownFig.add_subplot(111)
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError: # dict is longer than plot numbers in totalax
+                    pass
+        self.shownAx.legend()
+        #self.shownAx = self.totalAx
+    def updateFig(self):
+        #self.shownAx.lines=[]
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx,key)
+                except IndexError:
+                    pass
+        #line1, = ax.plot(x, y, 'b-')
+        self.shownAx.legend()
+        #for phase in np.linspace(0, 10 * np.pi, 100):
+         #   line1.set_ydata(np.sin(0.5 * x + phase))
+          #  fig.canvas.draw()
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer - Copy/SpecUtil_inline.py b/SpectrumViewer - Copy/SpecUtil_inline.py
new file mode 100644
index 0000000..eff5af8
--- /dev/null
+++ b/SpectrumViewer - Copy/SpecUtil_inline.py	
@@ -0,0 +1,170 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button
+from astropy.io import fits
+class SpecUtil(object):
+    def __init__(self,filename,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.updateFig()
+        self.figure = plt.figure(filename,figsize=(10, 8), dpi=100)
+        self.shownAx = self.figure.add_subplot(111)
+        plt.subplots_adjust(bottom=0.2)
+
+        self.axfluxbutton = plt.axes([0.1, 0.05, 0.1, 0.075])
+        self.fluxbutton = Button(self.axfluxbutton, 'flux')
+        self.fluxbutton.on_clicked(self.fluxClicked)
+
+        self.axskylinebutton = plt.axes([0.2, 0.05, 0.1, 0.075])
+        self.skylinebutton = Button(self.axskylinebutton, 'skyline')
+        self.skylinebutton.on_clicked(self.skylineClicked)
+
+        self.axabsorbbutton = plt.axes([0.3, 0.05, 0.1, 0.075])
+        self.absorbbutton = Button(self.axabsorbbutton, 'absorption')
+        self.absorbbutton.on_clicked(self.absorbClicked)
+
+        self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
+        self.emissionbutton = Button(self.axemission, 'emission')
+        self.emissionbutton.on_clicked(self.emissionClicked)
+
+        self.axmodelbutton = plt.axes([0.5, 0.05, 0.1, 0.075])
+        self.modelbutton = Button(self.axmodelbutton, 'model')
+        self.modelbutton.on_clicked(self.modelClicked)
+
+    def showfig(self):
+        self.updateFig()
+    def updateFig(self):
+
+        # figure = plt.figure(figsize=(8, 4), dpi=100)
+        # shownAx=figure.add_subplot(111)
+        # plt.subplots_adjust(bottom=0.2)
+
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx,key)
+                except IndexError:
+                    pass
+
+        self.shownAx.legend()
+        plt.show()
+    def fluxClicked(self,event):
+        if self.plotDict[0]:
+            self.plotDict[0]=False
+        else:
+            self.plotDict[0] = True
+
+        self.updateFig()
+    def skylineClicked(self,event):
+        if self.plotDict[1]:
+            self.plotDict[1]=False
+        else:
+            self.plotDict[1] = True
+        self.updateFig()
+    def emissionClicked(self, event):
+        if self.plotDict[2]:
+            self.plotDict[2] = False
+        else:
+            self.plotDict[2] = True
+        self.updateFig()
+    def absorbClicked(self, event):
+        if self.plotDict[3]:
+            self.plotDict[3] = False
+        else:
+            self.plotDict[3] = True
+        self.updateFig()
+    def modelClicked(self, event):
+        if self.plotDict[4]:
+            self.plotDict[4] = False
+        else:
+            self.plotDict[4] = True
+        self.updateFig()
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer - Copy/SpecUtil_interactive.py b/SpectrumViewer - Copy/SpecUtil_interactive.py
new file mode 100644
index 0000000..fc610c6
--- /dev/null
+++ b/SpectrumViewer - Copy/SpecUtil_interactive.py	
@@ -0,0 +1,142 @@
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+import numpy as np
+from ipywidgets import interactive
+class SpecUtil:
+    def __init__(self,coaddObj,zObj):
+        self.coaddObj = coaddObj
+        self.zObj=zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        #self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        #self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        #self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model
+        #self.totalAx = self.totalFig.add_subplot(111)
+        #fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        #skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        #self.shownAx = self.shownFig.add_subplot(111)
+        #self.updateFig()
+        #self.shownAx = self.totalAx
+        self.interactive_plot = interactive(self.showfig, flux=True,skyline=True,emission=True,absorb=True,model=True)
+        self.interactive_plot.children[0].layout.display = 'flex'
+        output = self.interactive_plot.children[-1]
+        output.layout.height = '550px'
+
+    def showfig(self,flux,skyline,emission,absorb,model):
+        self.plotDict[0] = flux
+        self.plotDict[1] =skyline
+        self.plotDict[2] =emission
+        self.plotDict[3] =absorb
+        self.plotDict[4] =model
+        self.updateFig()
+    def updateFig(self):
+        #self.shownAx.lines=[]
+        figure = plt.figure(figsize=(8, 4), dpi=100)
+        shownAx=figure.add_subplot(111)
+
+        #self.shownAx.clear()
+        shownAx.set_xlabel('lambda/Å')
+        shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(shownAx,key)
+                except IndexError:
+                    pass
+        #line1, = ax.plot(x, y, 'b-')
+        shownAx.legend()
+        #for phase in np.linspace(0, 10 * np.pi, 100):
+         #   line1.set_ydata(np.sin(0.5 * x + phase))
+          #  fig.canvas.draw()
+
+        plt.show()
+    def format_coord(self,x,y):
+        return 'x=%1.4f, y=%1.4f'%(x, y)
+    def addFlux(self):
+        self.plotDict[0]=True
+        self.updateFig()
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+    def plotLines(self,axes,actionIndex):
+        if actionIndex==0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+        elif actionIndex==1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+        elif actionIndex==2:
+            #plot emission line
+            self.plotSegment(axes,actionIndex)
+
+        elif actionIndex ==3:
+            #plot absorption line
+            self.plotSegment(axes,actionIndex)
+
+
+        elif actionIndex ==4:
+
+            #plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+    def plotSegment(self,axes,actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position-width/2
+                    endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
diff --git a/SpectrumViewer - Copy/WindowViewer.py b/SpectrumViewer - Copy/WindowViewer.py
new file mode 100644
index 0000000..93ffcd0
--- /dev/null
+++ b/SpectrumViewer - Copy/WindowViewer.py	
@@ -0,0 +1,132 @@
+# show plotting result in a new window interactively
+# this is actual user interface all the object are hide away from user
+import matplotlib
+matplotlib.use("TkAgg")
+from SpectrumViewer import SDSSDriver as driver
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
+# implement the default mpl key bindings
+from matplotlib.backend_bases import key_press_handler
+import tkinter as Tk
+
+from SpectrumViewer import SpecUtil as SpecUtil
+def view(fileName,fileSource):
+    coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+    #construn tk window and all components
+
+
+    root = Tk.Tk()
+    root.wm_title("Embedding in TK")
+    app = SpecController(root,coaddObj,zObj)
+    root.mainloop()
+
+class SpecController:
+    #take objs to construct specmodel and specview
+    def __init__(self,root,coaddObj,zObj):
+        self.model = SpecModel(coaddObj,zObj)
+        self.view = SpecView(root,self.model.specUtilObj.shownFig)
+        self.view.sidepanel.fluxTogg.bind("<Button>",self.fluxTogg)
+        self.view.sidepanel.skylineTogg.bind("<Button>", self.skylineTogg)
+        self.view.sidepanel.emissionTogg.bind("<Button>", self.emissionTogg)
+        self.view.sidepanel.absorbTogg.bind("<Button>", self.absorbTogg)
+        self.view.sidepanel.modelTogg.bind("<Button>", self.modelTogg)
+
+        #self.view.updateFigure(self.model.f)
+        # add buttons manage lau out
+    # def function change model and tell view.updatefigure(model.figure)
+    def fluxTogg(self,event):
+        if self.view.sidepanel.fluxTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeFlux()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.fluxTogg.config(text='FLUX:OFF')
+        else:
+            self.model.specUtilObj.addFlux()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.fluxTogg.config(text='FLUX:ON')
+    def skylineTogg(self,event):
+        if self.view.sidepanel.skylineTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeSkyline()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.skylineTogg.config(text='SKYLINE:OFF')
+        else:
+            self.model.specUtilObj.addSkyline()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.skylineTogg.config(text='SKYLINE:ON')
+    def emissionTogg(self,event):
+        if self.view.sidepanel.emissionTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeEmissionLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.emissionTogg.config(text='EMISSION LINE:OFF')
+        else:
+            self.model.specUtilObj.addEmissionLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.emissionTogg.config(text='EMISSION LINE:ON')
+    def absorbTogg(self,event):
+        if self.view.sidepanel.absorbTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeAbsorbLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.absorbTogg.config(text='ABSORPTION LINE:OFF')
+        else:
+            self.model.specUtilObj.addAbsorbLine()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.absorbTogg.config(text='ABSORPTION LINE:ON')
+    def modelTogg(self,event):
+        if self.view.sidepanel.modelTogg.config('text')[-1].endswith('ON'):
+            #update the model
+            self.model.specUtilObj.removeModel()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.modelTogg.config(text='BEST FIT MODEL:OFF')
+        else:
+            self.model.specUtilObj.addModel()
+            self.view.updateFigure(self.model.specUtilObj.shownFig)
+            self.view.sidepanel.modelTogg.config(text='BEST FIT MODEL:ON')
+
+class SpecModel:
+    def __init__(self,coaddObj,zObj):
+        self.specUtilObj = SpecUtil.SpecUtil(coaddObj, zObj)
+    #take data to self construct
+    # this is where plot happens
+    #Figure composing
+class SidePanel():
+    def __init__(self, root):
+        self.frame2 = Tk.Frame(root)
+        self.frame2.pack(side=Tk.LEFT, fill=Tk.BOTH, expand=1)
+
+
+        #TODO:put in toggle button for all the function
+        self.fluxTogg = Tk.Button(self.frame2, text="FLUX:ON")
+        self.fluxTogg.pack(side="top", fill=Tk.BOTH)
+        self.skylineTogg = Tk.Button(self.frame2, text="SKYLINE:ON")
+        self.skylineTogg.pack(side="top", fill=Tk.BOTH)
+        self.emissionTogg = Tk.Button(self.frame2, text="EMISSION LINE:ON")
+        self.emissionTogg.pack(side="top", fill=Tk.BOTH)
+        self.absorbTogg = Tk.Button(self.frame2, text="ABSORPTION LINE:ON")
+        self.absorbTogg.pack(side="top", fill=Tk.BOTH)
+        self.modelTogg = Tk.Button(self.frame2, text="BEST FIT MODEL:ON")
+        self.modelTogg.pack(side="top", fill=Tk.BOTH)
+class SpecView(Tk.Toplevel):
+    def __init__(self,master,figure):
+        self.sidepanel = SidePanel(master)
+        self.figure =figure
+        self.canvas = FigureCanvasTkAgg(self.figure, master=master)
+        self.canvas.show()
+        self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+
+        toolbar = NavigationToolbar2TkAgg(self.canvas, master)
+
+        toolbar.update()
+        self.canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+        self.connectKey()
+
+    def updateFigure(self,figure):
+        self.figure = figure
+        self.figure.canvas.draw()
+    def on_key_event(self,event):
+        print('you pressed %s' % event.key)
+        key_press_handler(event, self.canvas, self.toolbar)
+    def connectKey(self):
+        self.canvas.mpl_connect('key_press_event', self.on_key_event)
+    # generate
diff --git a/SpectrumViewer - Copy/ZObj.py b/SpectrumViewer - Copy/ZObj.py
new file mode 100644
index 0000000..7409f03
--- /dev/null
+++ b/SpectrumViewer - Copy/ZObj.py	
@@ -0,0 +1,10 @@
+class ZObj:
+    def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW=None,LINEEW_ERR=None):
+        self.LINENAME = LINENAME
+
+        self.LINEWAVE = LINEWAVE
+        self.LINEZ = LINEZ
+        self.LINEZ_ERR = LINEZ_ERR
+        self.LINEEW = LINEEW
+        self.LINEER_ERR = LINEEW_ERR
+
diff --git a/SpectrumViewer - Copy/__init__.py b/SpectrumViewer - Copy/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/SpectrumViewer - Copy/__pycache__/CoaddObj.cpython-36.pyc b/SpectrumViewer - Copy/__pycache__/CoaddObj.cpython-36.pyc
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diff --git a/SpectrumViewer - Copy/__pycache__/__init__.cpython-36.pyc b/SpectrumViewer - Copy/__pycache__/__init__.cpython-36.pyc
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diff --git a/SpectrumViewer - Copy/debug.py b/SpectrumViewer - Copy/debug.py
new file mode 100644
index 0000000..e11efe3
--- /dev/null
+++ b/SpectrumViewer - Copy/debug.py	
@@ -0,0 +1,176 @@
+import SDSSDriver as driver
+from matplotlib.widgets import Button
+import matplotlib.pyplot as plt
+from matplotlib.widgets import Button
+# this file hold utility functions that generate the plot and actual data
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+
+
+class SpecUtil:
+    def __init__(self, coaddObj, zObj):
+        self.coaddObj = coaddObj
+        self.zObj = zObj
+        # self.totalFig = plt.figure()
+        # self.shownFig = plt.figure()
+        # self.totalFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = Figure(figsize=(5, 4), dpi=100)
+        self.shownFig = plt.figure()
+        self.plotDict = {0: True,
+                         1: True,
+                         2: True,
+                         3: True,
+                         4: True}
+        # 0 flux 1 skyline 2 emission 3 absorb 4 model
+        # self.totalAx = self.totalFig.add_subplot(111)
+        # fluxLine,=self.totalAx.plot(self.coaddObj.lam,self.coaddObj.flux)
+        # skyLine, = self.totalAx.plot(self.coaddObj.lam,self.coaddObj.sky)
+
+        self.shownAx = self.shownFig.add_subplot(111)
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError:  # dict is longer than plot numbers in totalax
+                    pass
+        self.shownAx.legend()
+        # self.shownAx = self.totalAx
+
+    def updateFig(self):
+        # self.shownAx.lines=[]
+        self.shownAx.clear()
+        self.shownAx.set_xlabel('lambda/Å')
+        self.shownAx.set_ylabel('10-17 ergs/s/cm2/Å')
+        for key in self.plotDict.keys():
+            if self.plotDict[key]:
+                try:
+                    self.plotLines(self.shownAx, key)
+                except IndexError:
+                    pass
+        # line1, = ax.plot(x, y, 'b-')
+        self.shownAx.legend()
+        # for phase in np.linspace(0, 10 * np.pi, 100):
+        #   line1.set_ydata(np.sin(0.5 * x + phase))
+        #  fig.canvas.draw()
+
+    def addFlux(self):
+        self.plotDict[0] = True
+        self.updateFig()
+
+    def removeFlux(self):
+        self.plotDict[0] = False
+        self.updateFig()
+
+    def addSkyline(self):
+        self.plotDict[1] = True
+        self.updateFig()
+
+    def removeSkyline(self):
+        self.plotDict[1] = False
+        self.updateFig()
+
+    def addEmissionLine(self):
+        self.plotDict[2] = True
+        self.updateFig()
+
+    def removeEmissionLine(self):
+        self.plotDict[2] = False
+        self.updateFig()
+
+    def addAbsorbLine(self):
+        self.plotDict[3] = True
+        self.updateFig()
+
+    def removeAbsorbLine(self):
+        self.plotDict[3] = False
+        self.updateFig()
+
+    def addModel(self):
+        self.plotDict[4] = True
+        self.updateFig()
+
+    def removeModel(self):
+        self.plotDict[4] = False
+        self.updateFig()
+
+    def plotLines(self, axes, actionIndex):
+        if actionIndex == 0:
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux, 'C0', label='flux')
+        elif actionIndex == 1:
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky, 'C1', label='skyline')
+        elif actionIndex == 2:
+            # plot emission line
+            self.plotSegment(axes, actionIndex)
+
+        elif actionIndex == 3:
+            # plot absorption line
+            self.plotSegment(axes, actionIndex)
+
+
+        elif actionIndex == 4:
+
+            # plot best fit model
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+        else:
+
+            pass
+
+    def plotSegment(self, axes, actionIndex):
+
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex == 2:  # emissiaon line
+            for lineIndex in range(0, len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width > 0:
+                    # print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position - width / 2
+                    endPostition = position + width / 2
+                    name = self.zObj.LINENAME[lineIndex]
+                    # print(name)
+                    axes.axvspan(xmin=startPostition, xmax=endPostition, facecolor='C4')
+                    axes.text(position, 0, name, rotation=90)
+        elif actionIndex == 3:  # absorption line
+            for lineIndex in range(0, len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width > 0:
+                    # print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    startPostition = position - width / 2
+                    endPostition = position + width / 2
+                    name = self.zObj.LINENAME[lineIndex]
+                    # print(name)
+                    axes.axvspan(xmin=startPostition, xmax=endPostition, facecolor='C4')
+                    axes.text(position, 0, name, rotation=90)
+
+
+class Callback(object):
+    def __init__(self, specObj):
+        self.specObj = specObj
+
+    def fluxToggle(self, event):
+        self.specObj.removeFlux()
+        self.specObj.shownFig.draw()
+
+
+def view(fileName, fileSource):
+    coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+    # just show stuff
+    specObj = SpecUtil(coaddObj, zObj)
+    # specObj.shownFig.show()
+    callback = Callback(specObj)
+    axFluxToggle = plt.axes([0.81, 0.05, 0.1, 0.075])
+    bFluxToggle = Button(axFluxToggle, 'flux')
+    bFluxToggle.on_clicked(callback.fluxToggle)
+
+    # def skylineToggle(self,event):
+
+# import InlineViewer as viewer
+
+fileName = 'example_lite.fits'
+fileSource = 'SDSS'
+view(fileName, fileSource)
\ No newline at end of file
diff --git a/SpectrumViewer - Copy/demo 1.ipynb b/SpectrumViewer - Copy/demo 1.ipynb
new file mode 100644
index 0000000..32152f3
--- /dev/null
+++ b/SpectrumViewer - Copy/demo 1.ipynb	
@@ -0,0 +1,843 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"1000\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import SpecUtil_inline as inline\n",
+    "\n",
+    "import SDSSDriver as driver\n",
+    "%matplotlib notebook\n",
+    "fileName = 'example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "coaddObj, zObj = driver.loadFITS(fileName, fileSource)\n",
+    "spec=inline.SpecUtil(fileName, coaddObj, zObj)\n",
+    "spec.showfig()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer - Copy/example.py b/SpectrumViewer - Copy/example.py
new file mode 100644
index 0000000..000f747
--- /dev/null
+++ b/SpectrumViewer - Copy/example.py	
@@ -0,0 +1,6 @@
+import SciServer.SepctrumViewer.SDSSDriver as driver
+import SciServer.SepctrumViewer.WindowViewer as viewer
+objID= 'please replace this with an objID from SDSS DR7'
+filesource='SDSS'
+path=driver.getRawFITSPathInDAS(objID)
+viewer.view(path,filesource)
diff --git a/SpectrumViewer - Copy/example_lite.fits b/SpectrumViewer - Copy/example_lite.fits
new file mode 100644
index 0000000..5347b30
--- /dev/null
+++ b/SpectrumViewer - Copy/example_lite.fits	
@@ -0,0 +1,218 @@
+SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  TAI     =        4510856565.27 / 1st row - Number of seconds since Nov 17 1858  RA      =            34.081409 / 1st row - Right ascension of telescope boresighDEC     =           -0.015930  / 1st row - Declination of telescope boresight (dEQUINOX =              2000.00 /                                                RADECSYS= 'FK5     '           /                                                TAIHMS  = '09:39:25.74'        / 1st row - TAI time (HH:MM:SS.SS) (TAI-UT = apprTIMESYS = 'tai     '           / TAI, not UTC                                   MJD     =                52209 / MJD of observation                             MJDLIST = '52208 52209'        /                                                VERSION = 'NOCVS:v3_58_2:/p/iop/v3_58_2' / version of IOP                       CAMVER  = 'SPEC1 v4_7'         / Camera code version                            OBSERVER= 'snedden '                                                            OBSCOMM = 'science '                                                            TELESCOP= 'SDSS 2.5-M'         / Sloan Digital Sky Survey                       BESTEXP =                11592 /                                                NEXP_B1 =                    5 / b1 camera number of exposures                  NEXP_R1 =                    5 / r1 camera number of exposures                  EXPT_B1 =              6004.00 / b1 camera exposure time (seconds)              EXPT_R1 =              6004.00 / r1 camera exposure time (seconds)              EXPTIME =              6004.00 / Minimum of exposure times for all cameras      SPCOADD = 'Tue Feb 26 00:42:17 2008' / SPCOADD finished                         FLAVOR  = 'science '           / Flavor of this sequence (e.g., dome flat, etc.)QUALITY = 'excellent'          / night/image quality                            RADEG   = 3.40813400000000E+01 / Right Ascension. (degrees)                     DECDEG  = -1.6159310000000E-02 / Declination. (degrees)                         PLATEID =                  703 / Plate Id                                       TILEID  =                  471 / Tile Id                                        CARTID  =                    6 /Cartridge used in this plugging                 REDDEN  = '0.1276 0.0939 0.0681 0.0516 0.0366' / Median reddening vector for plaMAPID   =              5220601 / Map Id                                         NAME    = '0703-52206-01'      / Name of target; PlateId-MJD-Mapper ReRun       COLBIN  =                    1 / Binning factor perpendicular to the columns    ROWBIN  =                    1 / Binning factor perpendicular to the rows       PROGRAM = 'binning 1   1 '     / Identifying name for CCD program               SECFOCUS= -1.2000000000000E+02 / Secondary Mirror Position as reported by TCC   GUIDEIMG= '/data/spectro/guider/gimg0701.fits' / Last archived guider image     WTIME   =        1004139555.20 / weatherData(timeStamp)                         AIRTEMP =        9.40000000000 / weatherData(airtemp)                           DEWPOINT=       -5.72000000000 / weatherData(dewpoint)                          DEWDEP  =        15.1200000000 / weatherData(dewpointDep)                       DUSTA   =        23922.4000000 / weatherData(dusta)                             DUSTB   =        692.600000000 / weatherData(dustb)                             DUSTC   =        33007.0000000 / weatherData(dustc)                             DUSTD   =        1029.40000000 / weatherData(dustd)                             GUSTD   = 1.80000000000000E+02 / weatherData(gustd)                             GUSTS   =        5.52000000000 / weatherData(gusts)                             HUMIDITY=        34.1200000000 / weatherData(humidity)                          HUMIDOUT=        33.8000000000 / weatherData(humidout)                          PRESSURE=        21.7240000000 / weatherData(pressure)                          WINDD   =        184.600000000 / weatherData(windd)                             WINDS   =        3.56000000000 / weatherData(winds)                             TEMP01  =       -93.9840000000 / CCD 01 Temperature                             TEMP02  =       -93.5160000000 / CCD 02 Temperature                             TEMP03  =       -92.6580000000 / CCD 03 Temperature                             TEMP04  =       -93.0480000000 / CCD 04 Temperature                             VERSIDL = '6.3     '           / Version of IDL                                 VERSUTIL= 'v5_3_0  '           / Version of idlutils                            VERSREAD= 'v5_3_12 '           / Version of idlspec2d for pre-processing raw datVERS2D  = 'v5_3_12 '           / Version of idlspec2d for 2D reduction          VERSCOMB= 'v5_3_12 '           / Version of idlspec2d for combining multiple speVERSLOG = 'fermi-CVS 02/25/08' / Version of SPECLOG product                     VERSFLAT= 'v1_7    '           / Version of SPECFLAT product                    OPBC    = 'opBC-51813.par'     /                                                OPCONFIG= 'opConfig-50000.par' /                                                OPECALIB= 'opECalib-51577.par' /                                                PLUGFILE= 'plPlugMapM-0703-52206-01.par' /                                      HELIO_RV=        1.55079685031 / Heliocentric correction (added to velocities)  VACUUM  =                    T / Wavelengths are in vacuum                      SFLATTEN=                    T / Superflat has been applied                     PSFSKY  =                    3 / Order of PSF skysubtraction                    SKYCHI2 =        1.22384130844 / Mean chi^2 of sky-subtraction                  SCHI2MIN=        1.06626973255 /                                                SCHI2MAX=        1.49250118624 /                                                PREJECT =             0.200000 / Profile area rejection threshold               SPEC1_G =              23.2480 /(S/N)^2 for spec  1 at mag 20.20                SPEC1_R =              24.7649 /(S/N)^2 for spec  1 at mag 20.25                SPEC1_I =              23.1359 /(S/N)^2 for spec  1 at mag 19.90                SPEC2_G =              32.4262 /(S/N)^2 for spec  2 at mag 20.20                SPEC2_R =              32.4642 /(S/N)^2 for spec  2 at mag 20.25                SPEC2_I =              27.2296 /(S/N)^2 for spec  2 at mag 19.90                NSTD    =                   16 / Number of (good) std stars                     GOFFSTD =            0.0268574 / Spectrophoto offset for std stars in G-band    GRMSSTD =            0.0683525 / Spectrophoto RMS for std stars in G-band       ROFFSTD =            0.0235081 / Spectrophoto offset for std stars in R-band    RRMSSTD =            0.0559933 / Spectrophoto RMS for std stars in R-band       IOFFSTD =            0.0508566 / Spectrophoto offset for std stars in I-band    IRMSSTD =            0.0564636 / Spectrophoto RMS for std stars in I-band       GROFFSTD=            0.0197706 / Spectrophoto offset for std stars in (GR)      GRRMSSTD=            0.0564266 / Spectrophoto RMS for std stars in (GR)         RIOFFSTD=           -0.0232182 / Spectrophoto offset for std stars in (RI)      RIRMSSTD=            0.0238882 / Spectrophoto RMS for std stars in (RI)         NGAL    =                   64 / Number of (good) main galaxies                 GOFFGAL =            0.0610857 / Spectrophoto offset for main galaxies in G-bandGRMSGAL =             0.132475 / Spectrophoto RMS for main galaxies in G-band   ROFFGAL =            0.0628061 / Spectrophoto offset for main galaxies in R-bandRRMSGAL =            0.0622986 / Spectrophoto RMS for main galaxies in R-band   IOFFGAL =            0.0700979 / Spectrophoto offset for main galaxies in I-bandIRMSGAL =            0.0636804 / Spectrophoto RMS for main galaxies in I-band   GROFFGAL=            0.0133495 / Spectrophoto offset for main galaxies in (GR)  GRRMSGAL=            0.0472521 / Spectrophoto RMS for main galaxies in (GR)     RIOFFGAL=           -0.0103455 / Spectrophoto offset for main galaxies in (RI)  RIRMSGAL=            0.0216159 / Spectrophoto RMS for main galaxies in (RI)     LOWREJ  =                    5 / Extraction: low rejection                      HIGHREJ =                    8 / Extraction: high rejection                     SCATPOLY=                    0 / Extraction: Order of scattered light polynomialPROFTYPE=                    3 / Extraction profile: 1=Gaussian                 NFITPOLY=                    1 / Extraction: Number of parameters in each profilXCHI2   =              1.44428 / Extraction: Mean chi^2                         XCHI2MIN=              1.15894 /                                                XCHI2MAX=              1.77136 /                                                SHIFTGUE=   -0.000812781513105 /                                                SHIFT   =   -0.000812781513105 /Angstroms, at 4000AA                            SHIFTERR=   -0.000812781513105 /                                                NWORDER =                    2 / Linear-log10 coefficients                      WFITTYPE= 'LOG-LINEAR'         / Linear-log10 dispersion                        COEFF0  =        3.57930000000 / Central wavelength (log10) of first pixel      COEFF1  =    0.000100000000000 / Log10 dispersion per pixel                     FBADPIX =            0.0754441 / Fraction of bad pixels                         FBADPIX1=            0.0759304 / Fraction of bad pixels on spectro-1            FBADPIX2=            0.0749579 / Fraction of bad pixels on spectro-2            WAT0_001= 'system=linear'      /                                                WAT1_001= 'wtype=linear label=Wavelength units=Angstroms' /                     DC-FLAG =                    1 / Log-linear flag                                BUNIT   = '1E-17 erg/cm^2/s/Ang' /                                              NEXP    =                   10 / Number of individual exposures                 EXPID01 = 'b1-00011558-00011561-00011562'                                       EXPID02 = 'b1-00011559-00011561-00011562'                                       EXPID03 = 'b1-00011591-00011596-00011597'                                       EXPID04 = 'b1-00011592-00011596-00011597'                                       EXPID05 = 'b1-00011593-00011596-00011597'                                       EXPID06 = 'r1-00011558-00011561-00011562'                                       EXPID07 = 'r1-00011559-00011561-00011562'                                       EXPID08 = 'r1-00011591-00011596-00011597'                                       EXPID09 = 'r1-00011592-00011596-00011597'                                       EXPID10 = 'r1-00011593-00011596-00011597'                                       PLUG_RA =            34.928021 / RA of object [deg]                             PLUG_DEC=           -1.1370997 / dec of object [deg]                            SPEC_ID = '    791507946449168384' / Unique object identifier                   FIBERID =                    1 / Fiber number (1-640)                           END                                                                             XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                   32 / length of dimension 1                          NAXIS2  =                 3830 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                    8 / number of table fields                         TTYPE1  = 'flux    '                                                            TFORM1  = 'E       '                                                            TTYPE2  = 'loglam  '                                                            TFORM2  = 'E       '                                                            TTYPE3  = 'ivar    '                                                            TFORM3  = 'E       '                                                            TTYPE4  = 'and_mask'                                                            TFORM4  = 'J       '                                                            TTYPE5  = 'or_mask '                                                            TFORM5  = 'J       '                                                            TTYPE6  = 'wdisp   '                                                            TFORM6  = 'E       '                                                            TTYPE7  = 'sky     '                                                            TFORM7  = 'E       '                                                            TTYPE8  = 'model   '                                                            TFORM8  = 'E       '                                                            EXTNAME = 'COADD   '                                                            COMMENT Coadded spectrum                                                        END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        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TTYPE2  = 'INSTRUMENT'                                                          TFORM2  = '4A      '                                                            TTYPE3  = 'CHUNK   '                                                            TFORM3  = '16A     '                                                            TTYPE4  = 'PROGRAMNAME'                                                         TFORM4  = '23A     '                                                            TTYPE5  = 'PLATERUN'                                                            TFORM5  = '16A     '                                                            TTYPE6  = 'PLATEQUALITY'                                                        TFORM6  = '8A      '                                                            TTYPE7  = 'PLATESN2'                                                            TFORM7  = 'E       '                                                            TTYPE8  = 'DEREDSN2'                                                            TFORM8  = 'E       '                                                            TTYPE9  = 'LAMBDA_EFF'                                                          TFORM9  = 'E       '                                                            TTYPE10 = 'BLUEFIBER'                                                           TFORM10 = 'J       '                                                            TTYPE11 = 'ZOFFSET '                                                            TFORM11 = 'E       '                                                            TTYPE12 = 'SNTURNOFF'                                                           TFORM12 = 'E       '                                                            TTYPE13 = 'NTURNOFF'                                                            TFORM13 = 'J       '                                                            TTYPE14 = 'SPECPRIMARY'                                                         TFORM14 = 'B       '                                                            TTYPE15 = 'SPECLEGACY'                                                          TFORM15 = 'B       '                                                            TTYPE16 = 'SPECSEGUE'                                                           TFORM16 = 'B       '                                                            TTYPE17 = 'SPECSEGUE1'                                                          TFORM17 = 'B       '                                                            TTYPE18 = 'SPECSEGUE2'                                                          TFORM18 = 'B       '                                                            TTYPE19 = 'SPECBOSS'                                                            TFORM19 = 'B       '                                                            TTYPE20 = 'BOSS_SPECOBJ_ID'                                                     TFORM20 = 'J       '                                                            TTYPE21 = 'SPECOBJID'                                                           TFORM21 = '22A     '                                                            TTYPE22 = 'FLUXOBJID'                                                           TFORM22 = '19A     '                                                            TTYPE23 = 'BESTOBJID'                                                           TFORM23 = '19A     '                                                            TTYPE24 = 'TARGETOBJID'                                                         TFORM24 = '22A     '                                                            TTYPE25 = 'PLATEID '                                                            TFORM25 = '19A     '                                                            TTYPE26 = 'NSPECOBS'                                                            TFORM26 = 'I       '                                                            TTYPE27 = 'FIRSTRELEASE'                                                        TFORM27 = '3A      '                                                            TTYPE28 = 'RUN2D   '                                                            TFORM28 = '3A      '                                                            TTYPE29 = 'RUN1D   '                                                            TFORM29 = '1A      '                                                            TTYPE30 = 'DESIGNID'                                                            TFORM30 = 'J       '                                                            TTYPE31 = 'CX      '                                                            TFORM31 = 'D       '                                                            TTYPE32 = 'CY      '                                                            TFORM32 = 'D       '                                                            TTYPE33 = 'CZ      '                     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TFORM39 = 'J       '                                                            TTYPE40 = 'LEGACY_TARGET1'                                                      TFORM40 = 'J       '                                                            TTYPE41 = 'LEGACY_TARGET2'                                                      TFORM41 = 'J       '                                                            TTYPE42 = 'SPECIAL_TARGET1'                                                     TFORM42 = 'K       '                                                            TTYPE43 = 'SPECIAL_TARGET2'                                                     TFORM43 = 'K       '                                                            TTYPE44 = 'SEGUE1_TARGET1'                                                      TFORM44 = 'J       '                                                            TTYPE45 = 'SEGUE1_TARGET2'                                                      TFORM45 = 'J       '                                                            TTYPE46 = 'SEGUE2_TARGET1'                                                      TFORM46 = 'J       '                                                            TTYPE47 = 'SEGUE2_TARGET2'                                                      TFORM47 = 'J       '                                                            TTYPE48 = 'MARVELS_TARGET1'                                                     TFORM48 = 'J       '                                                            TTYPE49 = 'MARVELS_TARGET2'                                                     TFORM49 = 'J       '                                                            TTYPE50 = 'BOSS_TARGET1'                                                        TFORM50 = 'K       '                                                            TTYPE51 = 'BOSS_TARGET2'                                                        TFORM51 = 'K       '                                                            TTYPE52 = 'ANCILLARY_TARGET1'                                                   TFORM52 = 'K       '                                                            TTYPE53 = 'ANCILLARY_TARGET2'                                                   TFORM53 = 'K       '                                                            TTYPE54 = 'SPECTROGRAPHID'                                                      TFORM54 = 'I       '                                                            TTYPE55 = 'PLATE   '                                                            TFORM55 = 'J       '                                                            TTYPE56 = 'TILE    '                                                            TFORM56 = 'J       '                                                            TTYPE57 = 'MJD     '                                                            TFORM57 = 'J       '                                                            TTYPE58 = 'FIBERID '                                                            TFORM58 = 'J       '                                                            TTYPE59 = 'OBJID   '                                                            TFORM59 = '5J      '                                                            TTYPE60 = 'PLUG_RA '                                                            TFORM60 = 'D       '                                                            TTYPE61 = 'PLUG_DEC'                                                            TFORM61 = 'D       '                                                            TTYPE62 = 'CLASS   '                                                            TFORM62 = '6A      '                                                            TTYPE63 = 'SUBCLASS'                                                            TFORM63 = '21A     '                                                            TTYPE64 = 'Z       '                                                            TFORM64 = 'E       '                                                            TTYPE65 = 'Z_ERR   '                                                            TFORM65 = 'E       '                                                            TTYPE66 = 'RCHI2   '                                                            TFORM66 = 'E       '                                                            TTYPE67 = 'DOF     '                                                            TFORM67 = 'J       '                                                            TTYPE68 = 'RCHI2DIFF'                                                           TFORM68 = 'E       '                                                            TTYPE69 = 'TFILE   '                                                            TFORM69 = '24A     '                                                            TTYPE70 = 'TCOLUMN '                                                            TFORM70 = '10J     '                                                            TTYPE71 = 'NPOLY   '                                                            TFORM71 = 'J       '                                                            TTYPE72 = 'THETA   '                                                            TFORM72 = '10E     '                                                            TTYPE73 = 'VDISP   '                                                            TFORM73 = 'E       '                                                            TTYPE74 = 'VDISP_ERR'                                                           TFORM74 = 'E       '                                                            TTYPE75 = 'VDISPZ  '                                                            TFORM75 = 'E       '                                                            TTYPE76 = 'VDISPZ_ERR'                                                          TFORM76 = 'E       '                                                            TTYPE77 = 'VDISPCHI2'                                                           TFORM77 = 'E       '                                                            TTYPE78 = 'VDISPNPIX'                                                           TFORM78 = 'E       '                                                            TTYPE79 = 'VDISPDOF'                                                            TFORM79 = 'J       '                                                            TTYPE80 = 'WAVEMIN '                                                            TFORM80 = 'E       '                                                            TTYPE81 = 'WAVEMAX '                                                            TFORM81 = 'E       '                                                            TTYPE82 = 'WCOVERAGE'                                                           TFORM82 = 'E       '                                                            TTYPE83 = 'ZWARNING'                                                            TFORM83 = 'J       '                                                            TTYPE84 = 'SN_MEDIAN_ALL'                                                       TFORM84 = 'E       '                                                            TTYPE85 = 'SN_MEDIAN'                                                           TFORM85 = '5E      '                                                            TTYPE86 = 'CHI68P  '                                                            TFORM86 = 'E       '                                                            TTYPE87 = 'FRACNSIGMA'                                                          TFORM87 = '10E     '                                                            TTYPE88 = 'FRACNSIGHI'                                                          TFORM88 = '10E     '                                                            TTYPE89 = 'FRACNSIGLO'                                                          TFORM89 = '10E     '                                                            TTYPE90 = 'SPECTROFLUX'                                                         TFORM90 = '5E      '                                                            TTYPE91 = 'SPECTROFLUX_IVAR'                                                    TFORM91 = '5E      '                                                            TTYPE92 = 'SPECTROSYNFLUX'                                                      TFORM92 = '5E      '                                                            TTYPE93 = 'SPECTROSYNFLUX_IVAR'                                                 TFORM93 = '5E      '                                                            TTYPE94 = 'SPECTROSKYFLUX'                                                      TFORM94 = '5E      '                                                            TTYPE95 = 'ANYANDMASK'                                                          TFORM95 = 'J       '                                                            TTYPE96 = 'ANYORMASK'                                                           TFORM96 = 'J       '                                                            TTYPE97 = 'SPEC1_G '                                                            TFORM97 = 'E       '                                                            TTYPE98 = 'SPEC1_R '                                                            TFORM98 = 'E       '                                                            TTYPE99 = 'SPEC1_I '                                                            TFORM99 = 'E       '                                                            TTYPE100= 'SPEC2_G '                                                            TFORM100= 'E       '                                                            TTYPE101= 'SPEC2_R '                                                            TFORM101= 'E       '                                                            TTYPE102= 'SPEC2_I '                                                            TFORM102= 'E       '                                                            TTYPE103= 'ELODIE_FILENAME'                                                     TFORM103= '25A     '                                                            TTYPE104= 'ELODIE_OBJECT'                                                       TFORM104= '21A     '                                                            TTYPE105= 'ELODIE_SPTYPE'                                                       TFORM105= '10A     '                                                            TTYPE106= 'ELODIE_BV'                                                           TFORM106= 'E       '                                                            TTYPE107= 'ELODIE_TEFF'                                                         TFORM107= 'E       '                                                            TTYPE108= 'ELODIE_LOGG'                                                         TFORM108= 'E       '                                                            TTYPE109= 'ELODIE_FEH'                                                          TFORM109= 'E       '                                                            TTYPE110= 'ELODIE_Z'                                                            TFORM110= 'E       '                                                            TTYPE111= 'ELODIE_Z_ERR'                                                        TFORM111= 'E       '                                                            TTYPE112= 'ELODIE_Z_MODELERR'                                                   TFORM112= 'E       '                                                            TTYPE113= 'ELODIE_RCHI2'                                                        TFORM113= 'E       '                                                            TTYPE114= 'ELODIE_DOF'                                                          TFORM114= 'J       '                                                            TTYPE115= 'Z_NOQSO '                                                            TFORM115= 'E       '                                                            TTYPE116= 'Z_ERR_NOQSO'                                                         TFORM116= 'E       '                                                            TTYPE117= 'ZWARNING_NOQSO'                                                      TFORM117= 'J       '                                                            TTYPE118= 'CLASS_NOQSO'                                                         TFORM118= '1A      '                                                            TTYPE119= 'SUBCLASS_NOQSO'                                                      TFORM119= '1A      '                                                            TTYPE120= 'RCHI2DIFF_NOQSO'                                                     TFORM120= 'E       '                                                            TTYPE121= 'Z_PERSON'                                                            TFORM121= 'E       '                                                            TTYPE122= 'CLASS_PERSON'                                                        TFORM122= 'J       '                                                            TTYPE123= 'Z_CONF_PERSON'                                                       TFORM123= 'J       '                                                            TTYPE124= 'COMMENTS_PERSON'                                                     TFORM124= '1A      '                                                            TTYPE125= 'CALIBFLUX'                                                           TFORM125= '5E      '                                                            TTYPE126= 'CALIBFLUX_IVAR'                                                      TFORM126= '5E      '                                                            EXTNAME = 'SPECOBJ '                                                            COMMENT Metadata from specObj row                                               END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             sdss  SDSSchunk22         southern               dr2001.8.2      marginalA�SE�@�����<����
    79150794644916838412376637826020803741237663782602080374         7538244386924791507671571261440 
dr726  ����?�;��!?�Qao�Oe��R4%��C8�W�tcDNONLEGACY          SCIENCE ��
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al@Av�db�(��1�t|��GALAXYSTARBURST            >��r6�x4@zsU
�A��spEigenGal-53724.fits   
������������������������:<C�loG<�b%>3�AYK�6��#�qC�B��E<ħE�EEn~�Fɂ>�@ϲ�?�(�@�gpA�@���@��H?��*?��>K[�=�Ò<��</`�;���;�t�;o��;K�;K�>�A1=�V�=�2<A��;�L�;�9�;K�;8��;&%�;&%�>z`=�`}=
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9�:���:]��:��:��@�]A)�/A�hA�DbB@6?�w!@'��?�V�?��T?z0@��A-�+A��A�/�A���@5&@'|??�/�?#��AǏ8A���Byy3B�8IC�I
��A���A��A�SB
�nB
�WA��9                                                           @�U8A�AA�AF�A�j�A"�B��A�z�A*A-?��XTENSION= 'BINTABLE'           / binary table extension                         BITPIX  =                    8 / array data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                   89 / length of dimension 1                          NAXIS2  =                   29 / length of dimension 2                          PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS =                   19 / number of table fields                         TTYPE1  = 'PLATE   '                                                            TFORM1  = 'J       '                                                            TTYPE2  = 'MJD     '                                                            TFORM2  = 'J       '                                                            TTYPE3  = 'FIBERID '                                                            TFORM3  = 'J       '                                                            TTYPE4  = 'LINENAME'                                                            TFORM4  = '13A     '                                                            TTYPE5  = 'LINEWAVE'                                                            TFORM5  = 'D       '                                                            TTYPE6  = 'LINEZ   '                                                            TFORM6  = 'E       '                                                            TTYPE7  = 'LINEZ_ERR'                                                           TFORM7  = 'E       '                                                            TTYPE8  = 'LINESIGMA'                                                           TFORM8  = 'E       '                                                            TTYPE9  = 'LINESIGMA_ERR'                                                       TFORM9  = 'E       '                                                            TTYPE10 = 'LINEAREA'                                                            TFORM10 = 'E       '                                                            TTYPE11 = 'LINEAREA_ERR'                                                        TFORM11 = 'E       '                                                            TTYPE12 = 'LINEEW  '                                                            TFORM12 = 'E       '                                                            TTYPE13 = 'LINEEW_ERR'                                                          TFORM13 = 'E       '                                                            TTYPE14 = 'LINECONTLEVEL'                                                       TFORM14 = 'E       '                                                            TTYPE15 = 'LINECONTLEVEL_ERR'                                                   TFORM15 = 'E       '                                                            TTYPE16 = 'LINENPIXLEFT'                                                        TFORM16 = 'J       '                                                            TTYPE17 = 'LINENPIXRIGHT'                                                       TFORM17 = 'J       '                                                            TTYPE18 = 'LINEDOF '                                                            TFORM18 = 'E       '                                                            TTYPE19 = 'LINECHI2'                                                            TFORM19 = 'E       '                                                            EXTNAME = 'SPZLINE '                                                            COMMENT Line fits from spZline                                                  END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ���
Ly_alpha     @���z�H���������������
N_V 1240     @�c=p��
+���������������
C_IV 1549    @�5��R���������������
He_II 1640   @���z�H���������������
C_III] 1908  @��-���������������
Mg_II 2799   @��_������������������
[O_II] 3725  @�.�Л>���6��B�h?!+CM^�@jMB=R6��@���=��T@�ĆBR����
[O_II] 3727  @�#�:�J�>���6��B�h?!+C}�L@{]Bj����@�F\=��@�ĆB)����
[Ne_III] 3868@�;�� L�>���6��B�h?!+A�te?��c@�~�>�@��)=�Ĝ@�ĆA	�y���
H_epsilon    @�dMZX�k>���6��B���?BA��E?���@�k�>��!@�L�=�a�@֋�A�M����
[Ne_III] 3970@�?d�F>���6��B�h?!+A���?��@W�
>�ٽ@��X=��@�ĆA������
H_delta      @��A��>���6��B���?BBs�@�@�>�@���=��@֋�Ad;����
H_gamma      @���/!�>���6��B�h?!+B��@!�GAj.&>搽@��<u6@�ĆB�~���
[O_III] 4363 @�oo��D>���6��B�h?!+?�М@�_>��r>���@�ϐ<P�g@�ĆA.����
He_II 4685   @�N��Ij>���6��B�h?!+?�3?�4>��K>�"�@�fV<i�Y@�Ć@̴����
H_beta       @���ح[>���6��B���?BCC�>@.2�B4�?>���@�Ν<OZ�@֋�A2�;���
[O_III] 4959 @�`K~�E3>���6��B�h?!+C�?�!�B�>(�@�l�<H��@�ĆA�#����
[O_III] 5007 @��=X��1>���6��B�h?!+Cާ�@X��B�+X>�u@��2<I�@�ĆB�l7���
He_II 5411   @�%@��H>���6��B�h?!+@,��?ä�?/7A>ŧ�@|�<<I�@�ĆA����
[O_I] 5577   @���@�P�>��
6��B�h?!+��B?��R�T=>�'@t�o<6�T@�ĆASi����
[O_I] 6300   @���X]Q>��6��B�h?!+A�0v@,p@�6�?U�@H�-<��@�ĆAA����
[S_III] 6312 @���7g��>���6��B�h?!+>[O�?�]=���?@Or<�v@�ĆA�����
[O_I] 6363   @�݉A�>���6��B�h?!+A1dz@�
+�@b��?�-e@Hɍ<��@9�
>ү����
[N_II] 6548  @����ɞ�>���6��B�h?!+B,+�@WAY��?54�@Js�<6�@�ĆAb����
H_alpha      @���(-Ԩ>���6��B���?BDC�Ab@C�k@"k@" �;�0�@֋�B��N���
[N_II] 6583  @��D���q>���6��B�h?!+C��@M
�B#�?X�@T/N<{�@�ĆA������
[S_II] 6716  @�>KQ9�>���6��B�h?!+B��	@V�A�8�?ZU?@e@F<+;@�ĆA������
[S_II] 6730  @�L��i.>���6��B�h?!+B�E�A=�A��!@ ��@q�F<4��@�ĆA������
[Ar_III] 7135@���ц.R������������
\ No newline at end of file
diff --git a/SpectrumViewer - Copy/test.py b/SpectrumViewer - Copy/test.py
new file mode 100644
index 0000000..546d82b
--- /dev/null
+++ b/SpectrumViewer - Copy/test.py	
@@ -0,0 +1,10 @@
+import SpectrumViewer.SpecUtil_inline as inline
+
+import SpectrumViewer.SDSSDriver as driver
+fileName = 'example_lite.fits'
+fileSource = 'SDSS'
+coaddObj, zObj = driver.loadFITS(fileName, fileSource)
+spec=inline.SpecUtil(coaddObj, zObj)
+spec.showfig()
+
+
diff --git a/SpectrumViewer - Copy/window viewer screenshot/emission line only.PNG b/SpectrumViewer - Copy/window viewer screenshot/emission line only.PNG
new file mode 100644
index 0000000000000000000000000000000000000000..2a1b6c4306f176ba84395f8f692b389e11961019
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literal 0
HcmV?d00001

diff --git a/SpectrumViewer/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/SpectrumViewer/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/SpectrumViewer/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer/SDSSDriver.py b/SpectrumViewer/SDSSDriver.py
index fb36ef1..2a55464 100644
--- a/SpectrumViewer/SDSSDriver.py
+++ b/SpectrumViewer/SDSSDriver.py
@@ -43,6 +43,7 @@ def loadFITS(filename,fileSource):
             LINENAME=z['LINENAME'],
             LINEWAVE=z['LINEWAVE'],
             LINEZ=z['LINEZ'],
+
             LINEEW=z['LINEEW'],
             LINEZ_ERR=z['LINEZ_ERR'],
             LINEEW_ERR=z['LINEEW_ERR'])
diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
index eff5af8..faa6a94 100644
--- a/SpectrumViewer/SpecUtil_inline.py
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -2,20 +2,27 @@
 import matplotlib.pyplot as plt
 from matplotlib.widgets import Button
 from astropy.io import fits
+#TODO make the redshift in a table below the chart
+#   having a button switch off the table
+# ask about the "A best-fit stellar absorption-line template has been subtracted."
 class SpecUtil(object):
     def __init__(self,filename,coaddObj,zObj):
         self.coaddObj = coaddObj
         self.zObj=zObj
         self.plotDict = {0: True,
-                         1: True,
+                         1: False,
                          2: True,
                          3: True,
-                         4: True}
-        #0 flux 1 skyline 2 emission 3 absorb 4 model
+                         4: True,
+                         5: True}
+        #0 flux 1 skyline 2 emission 3 absorb 4 model 5 redshift table
+
         #self.updateFig()
         self.figure = plt.figure(filename,figsize=(10, 8), dpi=100)
+        #self.figure1 = plt.figure(filename, figsize=(10, 8), dpi=100)
         self.shownAx = self.figure.add_subplot(111)
-        plt.subplots_adjust(bottom=0.2)
+
+        plt.subplots_adjust(bottom=0.2,right=0.7)
 
         self.axfluxbutton = plt.axes([0.1, 0.05, 0.1, 0.075])
         self.fluxbutton = Button(self.axfluxbutton, 'flux')
@@ -37,6 +44,11 @@ def __init__(self,filename,coaddObj,zObj):
         self.modelbutton = Button(self.axmodelbutton, 'model')
         self.modelbutton.on_clicked(self.modelClicked)
 
+        self.axzbutton = plt.axes([0.6, 0.05, 0.1, 0.075])
+        self.zbutton = Button(self.axzbutton, 'redshift')
+        self.zbutton.on_clicked(self.zClicked)
+
+
     def showfig(self):
         self.updateFig()
     def updateFig(self):
@@ -57,9 +69,18 @@ def updateFig(self):
 
         self.shownAx.legend()
         plt.show()
+    def zClicked(self,event):
+        if self.plotDict[5]:
+            self.plotDict[5]=False
+
+        else:
+            self.plotDict[5] = True
+
+        self.updateFig()
     def fluxClicked(self,event):
         if self.plotDict[0]:
             self.plotDict[0]=False
+
         else:
             self.plotDict[0] = True
 
@@ -126,20 +147,67 @@ def plotLines(self,axes,actionIndex):
             axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
         elif actionIndex==2:
             #plot emission line
-            self.plotSegment(axes,actionIndex)
-
+            #self.plotSegment(axes,actionIndex)
+            self.plotVLine(axes, actionIndex)
         elif actionIndex ==3:
             #plot absorption line
-            self.plotSegment(axes,actionIndex)
-
+            #self.plotSegment(axes,actionIndex)
+            self.plotVLine(axes,actionIndex)
 
         elif actionIndex ==4:
 
             #plot best fit model
             axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
-        else:
+        elif actionIndex==5:
+            #plot the redshift table
+            self.plotZTable(axes)
 
+        else:
             pass
+
+    def plotZTable(self,axes):
+        #col_labels = ['col1', 'col2', 'col3']
+        col_labels = ['redshift','error']
+        #row_labels = ['row1', 'row2', 'row3']
+        row_labels = [name for name in self.zObj.LINENAME]
+        table_vals = [[self.zObj.LINEZ[i],self.zObj.LINEZ_ERR[i]] for i in range(len(self.zObj.LINENAME))]
+        # the rectangle is where I want to place the table
+        axes.table(cellText=table_vals,
+                   colWidths=[0.1] * 3,
+                   rowLabels=row_labels,
+                   colLabels=col_labels,
+                   loc='right',bbox=[1.1, 0, 0.3, 0.04*len(self.zObj.LINENAME)])
+        axes.text(12, 3.4, 'redshift and error', size=8)
+    def plotVLine(self,axes,actionIndex):
+        # read e/a line from zobj and plot it on axes with function from axhspan
+        if actionIndex==2:#emissiaon line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    # startPostition = position-width/2
+                    # endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    #axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.axvline(position,c='b')
+                    axes.text(position-5,5,name , rotation=90)
+        elif actionIndex==3:#absorption line
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if width >0:
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    #startPostition = position-width/2
+                    #endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvline(position,c='b')
+                    #axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position,0,name , rotation=90)
+
     def plotSegment(self,axes,actionIndex):
 
         # read e/a line from zobj and plot it on axes with function from axhspan
diff --git a/SpectrumViewer/Untitled.ipynb b/SpectrumViewer/Untitled.ipynb
new file mode 100644
index 0000000..7db7bb1
--- /dev/null
+++ b/SpectrumViewer/Untitled.ipynb
@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'SpectrumViewer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-ded0ca7b2600>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpecUtil_inline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSDSSDriver\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib notebook'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfileName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'example_lite.fits'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Users\\wzuo\\Developer\\SciScript-Python\\SpectrumViewer\\SDSSDriver.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#from py3.SciServer import CasJobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0murlparse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCoaddObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mCoadd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mZObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# def getSpecObjID(ra, dec, width, height, context=None, URL=True):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'SpectrumViewer'"
+     ]
+    }
+   ],
+   "source": [
+    "import SpecUtil_inline as inline\n",
+    "\n",
+    "import SDSSDriver as driver\n",
+    "%matplotlib notebook\n",
+    "fileName = 'example_lite.fits'\n",
+    "fileSource = 'SDSS'\n",
+    "coaddObj, zObj = driver.loadFITS(fileName, fileSource)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SpectrumViewer/__pycache__/SDSSDriver.cpython-36.pyc b/SpectrumViewer/__pycache__/SDSSDriver.cpython-36.pyc
index 10ea6206127f1667ab9447ca6b60490f1161f1f6..d2b354aae9b330b4cf818b2385a637ba60b3f430 100644
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diff --git a/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc b/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc
index 12d8e1e4bfdeefeceb86c887838d2e5ed9a61a2f..3e30e49771d155f22700101f8f8458346b15ad89 100644
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diff --git a/SpectrumViewer/demo 1.ipynb b/SpectrumViewer/demo 1.ipynb
index 32152f3..68979a6 100644
--- a/SpectrumViewer/demo 1.ipynb	
+++ b/SpectrumViewer/demo 1.ipynb	
@@ -2,799 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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0BZ592WO1Vg3go6gFzmP6csvgdgag6D7zY7KIAABCHglgKD7SwZB70wmMUEP3zW/WtICSW/JSgwn5z/sI9vhj8Ip+6WSPjrIrXxW0mf6/hmCXnzwsc1z8SN294igI+hWc4t+fRGIrTEIuq+8qAYCEIBACgQQdH8pIeidyWQoQQ8vjPu1pF0kzclOz0OVrQo6J+glvTwttnm2mmoIOoJuNbfo1xeB2BqDoPvKi2ogUAUCK1ev1T2PP6ODdt5CI0eiDVXItO89IOj+UuW/tM5kMpigBzk/J3uTe3ik/amG8lp9xL3vHfIddKPMY5tno2E5Qedn1qymFv06IxBbYxB0Z4FRDgQqQOD4712nq/76pE57zQE6YcbuFbgjbgFB9z8HEPTOZDSQoNfl/EWSjsxeCNdYXf0lcW/KTtjDn+2TPQbPS+IGyTG2oS0y/jLHaqybE3RO0Iucx/Tll0BsjUHQ/WZHZRBIlcDuHzu3Vvre22+mCz4wO9XboO4hCHCC7m96IOjlZbKZpL2y4W6W9MHsu+OLJT2WvZl9kqRX9fld8/Dnq7Lrws+shd9KDz+zFl4c95/Z/31GE7fBCXoTsJppGts8N9NXM20RdAS9mflC23QJxNYYBD3dbKkcAl4J1AV9/x3Had77jvBaJnW1QQBBbwOe0aUIuhHYAboN3ycPL3Pr+zlbUniR2/2DlBJO0y/L/iy8PO50SeEUPXy3/HxJJ0t6vInbQNCbgNVM09jmuZm+mmmLoCPozcwX2qZLILbGIOjpZkvlEPBKoC7oE3bZQr//58O9lkldbRBA0NuAZ3Qpgm4E1nG3CLpROLHNs9GwfAed76BbTS36dUYgtsYg6M4CoxwIVIBAXdAnvWC8fnPyzArcEbfQlwCC7m9OIOj+MrGuCEE3IhzbPBsNi6Aj6FZTi36dEYitMQi6s8AoBwIVIFAX9Gkv3ErnvCe88ohP1Qgg6P4SRdD9ZWJdEYJuRDi2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQqQKAu6DP23Fo/f9dhFbgjboETdP9zAEH3n1HRFSLoRRPN+ottno2GRdARdKupRb/OCMTWGATdWWCUA4EKEKgL+qy9t9WP3zGtAnfELSDo/ucAgu4/o6IrRNCLJoqgGxHN3239RXl9rzjlrLmDdjLYNXlGHarfPNfTBgKtEEDQW6HGNRCAQDsE6oL+4n230/dPnNpOV1zrlACPuPsLBkH3l4l1RQi6EeHY5tloWE7QOUG3mlr064xAbI3hBN1ZYJQDgQoQqAv6Sw/YXv/91ikVuCNuoS8BBN3fnEDQ/WViXRGCbkQ4tnk2GhZBR9Ctphb9OiMQW2MQdGeBUQ4EKkCgLuivPGhHnfmWSRW4I24BQfc/BxB0/xkVXSGCXjTRrL/Y5tloWAQdQbeaWvTrjEBsjUHQnQVGORCoAIG6oL/2kJ30zTdOrMAdcQsIuv85gKD7z6joChH0ooki6EZE83fLd9Dzs6JlugQQ9HSzo3IIpEqgLujHTtpZXzvukFRvg7qHIMAj7v6mB4LuLxPrihB0I8KxzbPRsJygc4JuNbXo1xmB2BrDCbqzwCgHAhUgUBf046bsov/4x4MrcEfcAifo/ucAgu4/o6IrRNCLJsoJuhHR/N1ygp6fFS3TJYCgp5sdlUMgVQIIeqrJ5a+bE/T8rMpqiaCXRdrPOAi6URaxzbPRsJygc4JuNbXo1xmB2BrDCbqzwCgHAhUggKBXIMTILSDo/jJG0P1lYl0Rgm5EOLZ5NhoWQUfQraYW/TojEFtjEHRngVEOBCpAAEGvQIgIenIhIujJRdZ2wQh62wgH7iC2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQqQABBr0CICHpyISLoyUXWdsEIetsIEXQjhC13y3fQW0bHhQkRQNATCotSIVARAnVBf/3kXfSV1/OSuIrE2us2eMTdX6oIur9MrCtC0I0IxzbPRsNygs4JutXUol9nBGJrDCfozgKjHAhUgACCXoEQOUFPLkQEPbnI2i4YQW8bISfoRghb7pYT9JbRcWFCBBD0hMKiVAhUhACCXpEgh7gNTtD9ZYyg+8vEuiIE3YhwbPNsNCwn6JygW00t+nVGILbGcILuLDDKgUAFCCDoFQiRE/TkQkTQk4us7YIR9LYRcoJuhLDlbjlBbxkdFyZEAEFPKCxKhUBFCNQF/R8n76Kv8h30iqTa+zY4QfcXK4LuLxPrihB0I8KxzbPRsJygc4JuNbXo1xmB2BrDCbqzwCgHAhUggKBXIERO0JMLEUFPLrK2C0bQ20bICboRwpa75QS9ZXRcmBABBD2hsCgVAhUhwCPuFQlyiNvgBN1fxgi6v0ysK0LQjQjHNs9Gw3KCzgm61dSiX2cEYmsMJ+jOAqMcCFSAACfoFQiRE/TkQkTQk4us7YIR9LYRcoJuhLDlbjlBbxkdFyZEAEFPKCxKhUBFCCDoFQmSE/SkgkTQk4qrkGIR9EIw9u8ktnk2GpYTdE7QraYW/TojEFtjOEF3FhjlQKACBBD0CoTICXpyISLoyUXWdsEIetsIOUE3Qthyt5ygt4yOCxMigKAnFBalQqAiBBD0igTJCXpSQSLoScVVSLEIeiEYOUE3wthStwh6S9i4KDECCHpigVEuBCpAAEGvQIicoCcXIoKeXGRtF4ygt42QE3QjhC13i6C3jI4LEyKAoCcUFqVCoCIEEPSKBMkJelJBIuhJxVVIsQh6IRg5QTfC2FK3CHpL2LgoMQIIemKBUS4EKkCgLuj/MGkXnX7cwRW4I26hLwF+Zs3fnEDQ/WViXRGCbkQ4tnk2GpaXxPGSOKupRb/OCMTWGF4S5ywwyoFABQgg6BUIMXILCLq/jBF0f5lYV4SgGxGObZ6NhkXQEXSrqUW/zgjE1hgE3VlglAOBChDgEfcKhIigJxcigp5cZG0XjKC3jXDgDmKbZ6NhEXQE3Wpq0a8zArE1BkF3FhjlQKACBDhBr0CICHpyISLoyUXWdsEIetsIEXQjhC13y3fQW0bHhQkRQNATCotSIVARAgh6RYIc4jZ4xN1fxgi6v0ysK0LQjQjHNs9Gw3KCzgm61dSiX2cEYmsMJ+jOAqMcCFSAAIJegRA5QU8uRAQ9ucjaLhhBbxshJ+hGCFvulhP0ltFxYUIEEPSEwqJUCFSEAIJekSA5QU8qSAQ9qbgKKRZBLwRj/05im2ejYTlB5wTdamrRrzMCsTWGE3RngVEOBCpAoC7ox07aWV877pAK3BG30JcAj7j7mxMIur9MrCtC0I0IxzbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQAUIIOgVCDFyCwi6v4wRdH+ZWFeEoBsRjm2ejYZF0BF0q6lFv84IxNYYBN1ZYJQDgQoQQNArECKCnlyICHpykbVdMILeNsKBO4htno2GRdARdKupRb/OCMTWGATdWWCUA4EKEEDQKxAigp5ciAh6cpG1XTCC3jZCBN0IYcvd8pK4ltFxYUIEEPSEwqJUCFSEAIJekSCHuA0ecfeXMYLuLxPrihB0I8KxzbPRsJygc4JuNbXo1xmB2BrDCbqzwCgHAhUggKBXIERO0JMLEUFPLrK2C0bQ20bICboRwpa75QS9ZXRcmBABBD2hsCgVAhUhgKBXJEhO0JMKEkFPKq5CikXQC8HYv5PY5tloWE7QOUG3mlr064xAbI3hBN1ZYJQDgQoQCII+eu1qvXrK7vraG/iZtQpE2u8WeMTdX6oIur9MrCtC0I0IxzbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQAUIfOJ179NbF1ygc0/4pD788eMrcEfcQl8CCLq/OYGg+8vEuiIE3YhwbPNsNCyCjqBbTS36dUYgtsYg6M4CoxwIVIDA3fvuV7uLp7fZSTOuurgCd8QtIOj+5wCC7j+joitE0IsmmvUX2zwbDYugI+hWU4t+nRGIrTEIurPAKAcCFSDQI+jb7qwZV15UgTviFhB0/3MAQfefUdEVIuhFE0XQjYjm75aXxOVnRct0CSDo6WZH5RBIlQCCnmpy+evmEff8rMpqiaCXRdrPOAi6URaxzbPRsJygc4JuNbXo1xmB2BrDCbqzwCgHAhUggKBXIMTILSDo/jJG0P1lYl0Rgm5EOLZ5NhoWQUfQraYW/TojEFtjEHRngVEOBCpAAEGvQIgIenIhIujJRdZ2wQh62wgH7iC2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQqQKAu6Iu33Vkz+Q56BRLtfwucoPuLFUH3l4l1RQi6EeHY5tloWAQdQbeaWvTrjEBsjUHQnQVGORCoAAFO0CsQIifoyYWIoCcXWdsFI+htI+QE3Qhhy93ykriW0XFhQgQQ9ITColQIVIQAJ+gVCXKI2+AE3V/GCLq/TKwrQtCNCMc2z0bDcoLOCbrV1KJfZwRiawwn6M4CoxwIVIAAgl6BEDlBTy5EBD25yNouGEFvGyEn6EYIW+6WE/SW0XFhQgQQ9ITColQIVIRAj6Bvt4tmXnFhRe6K22gkwAm6v/mAoJeXySxJp0qaLGlHScdI+m3D8CGL0yS9S9J4SVdLOknSvQ1txkg6XdIbJXVJOl/SyZIWNnEbCHoTsJppGts8N9NXM23rcnrKWXObuaxSbRH0SsXJzQxCILbGcILO1IEABIomsEHQd9XMKy4ounv6c0AAQXcQQp8SEPTyMnm5pJmSbpT0mwEE/aOSPi7pBEn3S/q8pIMk7S9pZVbmtyW9UtKJkpZKOkPSuqzfvHeCoOcl1WS72Oa5ye5yN0fQ1fOYf19oQ/2lxWBSnwf8cP7LkDx8aGNDILbGIOg23OkVAsOZQF3Qn9puVx2OoFdyKiDo/mJF0DuTyfo+gh5yeDQ7Hf9qVtIW2cl4kPFfSgr/+wlJb5b0q6zNvpLuljRd0rU5bwVBzwmq2WaxzXOz/eVtj6Aj6HnnCu3SJhBbYxD0tPOlegh4JICge0yl2JoQ9GJ5FtEbgl4Exeb76Cvoe0i6T9JESbc0dHd59r/fJyk8v3yxpC0lLWlo84Ckb0j6+iBlhEfhwz/1z+aSHl66dKnGjQuu3tlP7BSz3ZPK2Ia2yLsvc6zGuhF0BL3IeUxffgnE1hgE3W92VAaBVAkg6Kkml79uBD0/q7JaIuhlke49Tl9Bn5F953wnSY81ND1HUmj7huzk/Id9ZDs0vV7SpZLCI/IDfT4r6TN9/wBBLz742Oa5+BG7e0TQEXSruUW/vgjE1hgE3VdeVAOBKhBA0KuQ4tD3gKD7yxhB70wmZQo6J+glvTwttnm2mmoIOoJuNbfo1xeB2BqDoPvKi2ogUAUCCHoVUkTQU0sRQe9MYmU+4t73DvkOulHmsc2z0bCcoPM76FZTi36dEYitMQi6s8AoBwIVIICgVyDEyC1wgu4vYwS9M5kM9pK48IK48DNq4RNEelH2xvbGl8S9SdKvszb7SFrAS+IGDzG2oS0y/jLHaqybE3RO0Iucx/Tll0BsjUHQ/WZHZRBIlQCCnmpy+etG0POzKqslgl4WaWkzSXtlw90s6YPZd8cXS3ow+w75x/r8zNqEAX5m7RWZtC+T9J9Zf+E77Hk/nKDnJdVku9jmucnucjdH0BH03JOFhkkTiK0xCHrS8VI8BFwSQNBdxlJoUQh6oTgL6QxBLwRjrk7mZELet/HZmXCHLE6T9G5J4yVdJelkSX9puGBMdsIeTtHDd8vPz9o8nquC7kYIehOwmmka2zw301czbRF0BL2Z+ULbdAnE1hgEPd1sqRwCXgn0CPr2L9Dhl4dtJ5+qEUDQ/SWKoPvLxLoiBN2IcGzzbDQs30HnO+hWU4t+nRGIrTEIurPAKAcCFSBQF/Qnt3+BjkDQK5Bo/1tA0P3FiqD7y8S6IgTdiHBs82w0LIKOoFtNLfp1RiC2xiDozgKjHAhUgMAGQd9NR1x+XgXuiFvoSwBB9zcnEHR/mVhXhKAbEY5tno2GRdARdKupRb/OCMTWGATdWWCUA4EKEEDQKxBi5BYQdH8ZI+j+MrGuCEE3IumQY3EAACAASURBVBzbPBsNi6Aj6FZTi36dEYitMQi6s8AoBwIVINAj6DvspiMu4wS9ApH2uwUE3V+qCLq/TKwrQtCNCMc2z0bDIugIutXUol9nBGJrDILuLDDKgUAFCNQF/YkddtMsBL0Cifa/BQTdX6wIur9MrCtC0I0IxzbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQAUIIOgVCDFyCwi6v4wRdH+ZWFeEoBsRjm2ejYZF0BF0q6lFv84IxNYYBN1ZYJQDgQoQ4BH3CoSIoCcXIoKeXGRtF4ygt41w4A5im2ejYRF0BN1qatGvMwKxNQZBdxYY5UCgAgQ4Qa9AiAh6ciEi6MlF1nbBCHrbCBF0I4QtdxvEZKDPKWfNHbTPwa7JU8RQ/ea5njYQaIUAgt4KNa6BAATaIbBB0HfXrMvmt9MV1zolwCPu/oJB0P1lYl0Rgm5EOLZ5NhqWE3RO0K2mFv06IxBbYzhBdxYY5UCgAgQQ9AqEGLkFBN1fxgi6v0ysK0LQjQjHNs9GwyLoCLrV1KJfZwRiawyC7iwwyoFABQgg6BUIEUFPLkQEPbnI2i4YQW8b4cAdxDbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQAUIIOgVCBFBTy5EBD25yNouGEFvGyGCboSw5W75DnrL6LgwIQIIekJhUSoEKkKgR9B33F2zLuU76BWJtddt8Ii7v1QRdH+ZWFeEoBsRjm2ejYblBJ0TdKupRb/OCMTWGE7QnQVGORCoAIG6oC/a8YWafem8CtwRt9CXAILub04g6P4ysa4IQTciHNs8Gw2LoCPoVlOLfp0RiK0xCLqzwCgHAhUggKBXIMTILSDo/jJG0P1lYl0Rgm5EOLZ5NhoWQUfQraYW/TojEFtjEHRngVEOBCpAAEGvQIgIenIhIujJRdZ2wQh62wgH7iC2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQqQABBr0CICHpyISLoyUXWdsEIetsIEXQjhC13y0viWkbHhQkRQNATCotSIVARAj2CvtMemn3JuRW5K26jkQCPuPubDwi6v0ysK0LQjQjHNs9Gw3KCzgm61dSiX2cEYmsMJ+jOAqMcCFSAAIJegRA5QU8uRAQ9ucjaLhhBbxshJ+hGCFvulhP0ltFxYUIEEPSEwqJUCFSEAIJekSCHuA1O0P1ljKD7y8S6IgTdiHBs82w0LCfonKBbTS36dUYgtsZwgu4sMMqBQOIE1q9frwX77V+7i4U77aE5POKeeKIDl4+g+4sVQfeXiXVFCLoR4djm2WhYBB1Bt5pa9OuMQGyNQdCdBUY5EEicAIKeeIA5y0fQc4IqsRmCXiJsJ0Mh6EZBxDbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQOIEegv6nppzyR8TvyPKH4gAgu5vXiDo/jKxrghBNyIc2zwbDYugI+hWU4t+nRGIrTEIurPAKAcCiRNYt2697tm//og7gp54nIOWj6D7SxZB95eJdUUIuhHh2ObZaFgEHUG3mlr064xAbI1B0J0FRjkQSJwAgp54gDnLR9BzgiqxGYJeImwnQyHoRkHENs9GwyLoCLrV1KJfZwRiawyC7iwwyoFA4gQQ9MQDzFk+gp4TVInNEPQSYTsZCkE3CiK2eTYaFkFH0K2mFv06IxBbY4oU9KdGrtNlY1drxsrR2nHtyNJInHLW3NLGYiAIQGBoAmvXrddfskfcH995Tx15Md9Br+KcQdD9pYqg+8vEuiIE3YhwbPNsNCyCjqBbTS36dUYgtsYUKejf2Xyllo5aXyNw6pKxpZFA0EtDzUAQiBJA0KOIKtEAQfcXI4LuLxPrihB0I8KxzbPRsAg6gm41tejXGYHYGlOkoH9l/Iqeu0fQnU0EyoFASQQQ9JJAd3gYBL3DAQwwPILuLxPrihB0I8KxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOAEEPfEAc5aPoOcEVWIzBL1E2E6GQtCNgohtno2GRdARdKupRb/OCMTWGATdWWCUA4HECaxZu073HnBA7S74DnriYQ5RPoLuL1sE3V8m1hUh6EaEY5tno2ERdATdamrRrzMCsTUGQXcWGOVAIHECvQV9Lx158R8SvyPKH4gAgu5vXiDo/jKxrghBNyIc2zwbDYugI+hWU4t+nRGIrTEIurPAKAcCiRNA0BMPMGf5CHpOUCU2Q9BLhO1kKATdKIjY5tloWAQdQbeaWvTrjEBsjUHQnQVGORBInMDqtev01+wR98d2eZHmXvT7xO+I8jlBT2MOIOhp5FRklQh6kTQb+optno2GRdARdKupRb/OCMTWGATdWWCUA4HECSDoiQeYs3xO0HOCKrEZgl4ibCdDIehGQcQ2z0bDIugIutXUol9nBGJrDILuLDDKgUDiBBD0xAPMWT6CnhNUic0Q9BJhOxkKQTcKIrZ5NhoWQUfQraYW/TojEFtjEHRngVEOBBInsGrNOt13YPdb3HnEPfEwhygfQfeXLYLuLxPrihB0I8KxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOAEEPfEAc5aPoOcEVWIzBL1E2E6GQtCNgohtno2GRdARdKupRb/OCMTWGATdWWCUA4HECSDoiQeYs3wEPSeoEpsh6CXCdjIUgm4URGzzbDQsgo6gW00t+nVGILbGIOjOAqMcCCRO4Pk1a/W3Aw+s3QWPuCce5hDlI+j+skXQ/WViXRGCbkQ4tnk2GhZBR9Ctphb9OiMQW2MQdGeBUQ4EEifQKOiP7vIivZifWUs80YHLR9D9xYqg+8vEuiIE3YhwbPNsNCyCjqBbTS36dUYgtsYg6M4CoxwIJE6AE/TEA8xZPoKeE1SJzRD0EmE7GQpBNwoitnk2GhZBR9Ctphb9OiMQW2MQdGeBUQ4EEieAoCceYM7yEfScoEpshqCXCNvJUAi6URCxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOIGVq9fq/oP4DnriMUbLR9CjiEpvgKCXjrzjAyLoRhHENs9GwyLoCLrV1KJfZwRiawyC7iwwyoFA4gQQ9MQDzFk+gp4TVInNEPQSYTsZCkE3CiK2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQSJ9BL0HfeS3Mv/kPid0T5AxFA0P3NCwTdXybWFSHoRoRjm2ejYRF0BN1qatGvMwKxNQZBdxYY5UAgcQKNgv74znvqyIv/mPgdUT6CnsYcQNDTyKnIKhH0Imk29BXbPBsNi6Aj6FZTi36dEYitMQi6s8AoBwKJE0DQEw8wZ/mcoOcEVWIzBL1E2E6GQtCNgohtno2GRdARdKupRb/OCMTWGATdWWCUA4HECSDoiQeYs3wEPSeoEpsh6CXCdjIUgm4URGzzbDQsgo6gW00t+nVGILbGIOjOAqMcCCROYMWqtfr7hO63uPOIe+JhDlE+gu4vWwTdXybWFSHoRoRjm2ejYRF0BN1qatGvMwKxNQZBdxYY5UAgcQK9BH2nPXXkJXwHPfFIBywfQfeXKoLuLxPrihB0I8KxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOAEEPfEAc5aPoOcEVWIzBL1E2DmGGiXps5KOl7SDpEcl/UjSFyStz64PmZ0m6V2Sxku6WtJJku7N0X9ogqDnBNVss9jmudn+8rYP44bPKWfNzXtJ5drVGfS9saGYDHZNHjjDmXUePrSxIRBbYxB0G+70CoHhSmD5qjV6YMJBtdt/nBP0yk4DBN1ftAi6r0w+IemDkk6QdKekKZJ+KOmTkr6VlfpRSR/P2twv6fOSwuq5v6SVOW4HQc8BqZUmsc1zK33muQZBV89TBAh6nhlDm1QJxNYYBD3VZKkbAj4JIOg+cym6KgS9aKLt94egt8+wyB7Cl3sWSvqnhk5/LWlFdqoe8gqn6qdL+mrWZovsmhMl/TJHMQh6DkitNIltnlvpM881CDqCnmee0CZ9ArE1BkFPP2PuAAKeCPQS9B330JGXnuupPGopiACCXhDIArtB0AuEWUBX4QT93ZKOlvQXSQdLuiA7Vf+ZpD0k3SdpoqRbGsa7PPvf78tRA4KeA1IrTWKb51b6zHMNgo6g55kntEmfQGyNQdDTz5g7gIAnAgi6pzTsakHQ7di22jOC3io5m+tGSvo3SR+RtFZS+E56eLz9S9lwM7LvnO8k6bGGEs7JvqP+hgHK6pIU/ql/Npf08NKlSzVuXHD1zn5i3wNu97u+sQ1tkXdf5liNdSPoCHqR85i+/BKIrTEIut/sqAwCKRJ47vk1evDg7u+gL9zxhZpz6bwUb4OaIwQQdH9TBEH3lckbJX1F0qnZd9APkfSN7AT9bEmtCHp46dxn+t4mgl588LHNc/EjdveIoCPoVnOLfn0RiK0xCLqvvKgGAqkTQNBTTzBf/Qh6Pk5ltkLQy6QdH+shSV+WdEZD009l3z/ft8VH3DlBL+nt5rHNczz+1log6Ah6azOHq1IjEFtjEPTUEqVeCPgmgKD7zqeo6hD0okgW1w+CXhzLInp6Knuk/ayGzsIb298uaW9J9ZfEhRfEhRfFhU94Tn2RJF4SN0ACsQ1tEaHV+yhzrMa6EXQEvch5TF9+CcTWGATdb3ZUBoEUCTz7/Bo9xCPuKUbXVM0IelO4SmmMoJeCOfcg4TfPXyLpPdkj7uFlcN+R9ANJ4efVwif8vx/r8zNrE/iZtYEZxza0uZPJ0bDMsRD03oEM9i4Dfgc9x8SlSTIEYmsMgp5MlBQKgSQI9BL0HV6oOZfxHfQkgmuySAS9SWAlNEfQS4DcxBDhBW7hd82PkbRd9pNqv5D0OUmrsn5CZqdlb3sfL+kqSSdnb33PMxRvcc9DqYU2sc1zC13muoQTdE7Qc00UGiVPILbGIOjJR8wNQMAVAQTdVRxmxSDoZmhb7hhBbxldshci6EbRxTbPRsPykrjsRXnLR6zXmPXSyNo3Qbo/nKBbzTr67QSB2BqDoHciFcaEQHUJPLNytR4+JDykKS3kBL2yQSPo/qJF0P1lYl0Rgm5EOLZ5NhoWQZf0r6dcpJ9s/rx2Wz1Sxz234VcFEXSrWUe/nSAQW2MQ9E6kwpgQqC6B3oK+u+ZcNr+6NzuM7wxB9xc+gu4vE+uKEHQjwrHNs9GwCLqkV3/gPN3etbaG+NQlY3tQI+hWs45+O0EgtsYg6J1IhTEhUF0CCHp1s228MwTdX84Iur9MrCtC0I0IxzbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQOIElq1crUd6HnHnBD3xOActH0H3lyyC7i8T64oQdCPCsc2z0bAIOoJuNbXo1xmB2BqDoDsLjHIgkDgBBD3xAHOWj6DnBFViMwS9RNhOhkLQjYKIbZ6NhkXQEXSrqUW/zgjE1hgE3VlglAOBxAkg6IkHmLN8BD0nqBKbIeglwnYyFIJuFERs82w0LIKOoFtNLfp1RiC2xiDozgKjHAgkTmDpitV6dGL9Le484p54nIOWj6D7SxZB95eJdUUIuhHh2ObZaFgEHUG3mlr064xAbI1B0J0FRjkQSJxAL0HffjfNufy8xO+I8gcigKD7mxcIur9MrCtC0I0IxzbPRsMi6Ai61dSiX2cEYmsMgu4sMMqBQOIEEPTEA8xZPoKeE1SJzRD0EmE7GQpBNwoitnk2GhZBR9Ctphb9OiMQW2MQdGeBUQ4EEieAoCceYM7yEfScoEpshqCXCNvJUAi6URCxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOIGly1fr0UnZd9B5xD3xNAcvH0H3Fy2C7i8T64oQdCPCsc2z0bAIOoJuNbXo1xmB2BqDoDsLjHIgkDgBBD3xAHOWj6DnBFViMwS9RNhOhkLQjYKIbZ6NhkXQJb3mA+fptq61NcSnLhnbg/qUs+YOij3k1epnqH5b7ZPrIBAjEFtjEPQYQf4cAhBohgCC3gytdNsi6P6yQ9D9ZWJdEYJuRDi2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQSJ7Bk+So9Nung2l0s5BH3xNMcvHwE3V+0CLq/TKwrQtCNCMc2z0bDIug84m41tejXGYHYGoOgOwuMciCQOIFGQV+0/W6azc+sJZ7owOUj6P5iRdD9ZWJdEYJuRDi2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQSJ4CgJx5gzvIR9JygSmyGoJcI28lQCLpRELHNs9GwCDqPuFtNLfp1RiC2xiDozgKjHAgkTuDp51bp8cndj7hzgp54mEOUj6D7yxZB95eJdUUIuhHh2ObZaFgEHUG3mlr064xAbI1B0J0FRjkQSJwAgp54gDnLR9BzgiqxGYJeImwnQyHoRkHENs9GwyLoCLrV1KJfZwRiawyC7iwwyoFA4gQQ9MQDzFk+gp4TVInNEPQSYTsZCkE3CiK2eTYaFkFH0K2mFv06IxBbYxB0Z4FRDgQSJ7D4uVVayCPuiacYLx9BjzMquwWCXjbxzo+HoBtlENs8Gw2LoCPoVlOLfp0RiK0xCLqzwCgHAokT6CXo271As684P/E7ovyBCCDo/uYFgu4vE+uKEHQjwrHNs9GwCDqCbjW16NcZgdgag6A7C4xyIJA4AQQ98QBzlo+g5wRVYjMEvUTYToZC0I2CiG2ejYZF0BF0q6lFv84IxNYYBN1ZYJQDgcQJIOiJB5izfAQ9J6gSmyHoJcJ2MhSCbhREbPNsNCyCjqBbTS36dUYgtsYg6M4CoxwIJE7gqWef16Iph9TuYhGPuCee5uDlI+j+okXQ/WViXRGCbkQ4tnk2GhZBR9Ctphb9OiMQW2MQdGeBUQ4EEieAoCceYM7yEfScoEpshqCXCNvJUAi6URCxzbPRsAg6gm41tejXGYHYGoOgOwuMciCQOIEnn1mpJ6ZO5AQ98Rxj5SPoMULl/zmCXj7zTo+IoBslENs8Gw2LoCPoVlOLfp0RiK0xCLqzwCgHAokTeOKZlXoSQU88xXj5CHqcUdktEPSyiXd+PATdKIPY5tloWAQdQbeaWvTrjEBsjUHQnQVGORBInMATy1boyWmTshP0XTX7igsSvyPKH4gAgu5vXiDo/jKxrghBNyIc2zwbDYugI+hWU4t+nRGIrTEIurPAKAcCiRNA0BMPMGf5CHpOUCU2Q9BLhO1kKATdKIjY5tloWAQdQbeaWvTrjEBsjUHQnQVGORBInMCiJcv11GGTu0/Qt91Vs6/kBD3xSAcsH0H3lyqC7i8T64oQdCPCsc2z0bAIOoJuNbXo1xmB2BqDoDsLjHIgkDgBBD3xAHOWj6DnBFViMwS9RNhOhkLQjYKIbZ6NhkXQEXSrqUW/zgjE1hgE3VlglAOBxAksevo5PTV9CifoiecYKx9BjxEq/88R9PKZd3pEBN0ogdjm2WhYBB1Bt5pa9OuMQGyNQdCdBUY5EEicwMKnn9Xi6VMR9MRzjJWPoMcIlf/nCHr5zDs9IoJulEBs82w0LIKOoFtNLfp1RiC2xiDozgKjHAgkTmDh089o8fRptbt4YttdNYvvoCee6MDlI+j+YkXQ/WViXRGCbkQ4tnk2GhZBl/TqD5yn27vW1hCfumRsD+pTzpo7KPaQV6ufofpttU+ug0CMQGyNQdBjBPlzCECgGQKNgr5o2100+8oLm7mctokQQND9BYWg+8vEuiIE3YhwbPNsNCyCjqBbTS36dUYgtsYg6M4CoxwIJE5g4VPPaPHM7hN0BD3xMIcoH0H3ly2C7i8T64oQdCPCsc2z0bAIOoJuNbXo1xmB2BqDoDsLjHIgkDgBBD3xAHOWj6DnBFViMwS9RNhOhkLQjYKIbZ6NhkXQg6C/f75WL/+bHt5sO520fOse1DzibjXr6LcTBGJrDILeiVQYEwLVJbDwyWVafPihnKBXN+LanSHo/gJG0P1lYl0Rgm5EOLZ5NhoWQQ/fO3/zV/WOm76vp7s2083Tv4ygW002+u0ogdgag6B3NB4Gh0DlCCDolYt0wBtC0P3ljKD7y8S6IgTdiHBs82w0LIIu6ccvPkFTH7m+hviSOWci6FaTjX47SiC2xiDoHY2HwSFQOQKPP7FUTx9xWO2++A565eLtuSEE3V+2CLq/TKwrQtCNCMc2z0bDIuiSfvLit2nKI39G0K0mGf26IBBbYxB0FzFRBAQqQwBBr0yUQ94Igu4vZwTdXybWFSHoRoRjm2ejYRH0mqC/VVMeuQFBt5pk9OuCQGyNQdBdxEQREKgMgV6Cvs0umn0VP7NWmXAbbgRB95cqgu4vE+uKEHQjwrHNs9GwCLqkn849XpMfvRFBt5pk9OuCQGyNQdBdxEQREKgMgccWLtGS2dNr97MIQa9Mrn1vBEH3Fy2C7i8T64oQdCPCsc2z0bAIuqSfzX2LJj16E4JuNcno1wWB2BqDoLuIiSIgUBkCCHplohzyRhB0fzkj6P4ysa4IQTciHNs8Gw2LoEv6+ZFv1sTHbkbQrSYZ/bogEFtjEHQXMVEEBCpD4LGFT2vJ7BmcoFcm0YFvBEH3FzCC7i8T64oQdCPCsc2z0bAIuqRfHPkmHfLYLQi61SSjXxcEYmsMgu4iJoqAQGUIPLpwsZbOnomgVyZRBD2VKBH0VJIqrk4EvTiWvXqKbZ6NhkXQg6DPeaMOefxWBN1qktGvCwKxNQZBdxETRUCgMgQefXyxls7pFvQnttlZs666qDL3xo1sIMAJur/ZgKD7y8S6IgTdiHBs82w0LIIu6Zdz3qCDH78NQbeaZPTrgkBsjUHQXcREERCoDIFGQV+09c6afTWCXplwG24EQfeXKoLuLxPrihB0I8KxzbPRsAi6pP+ZfZwmLLwdQbeaZPTrgkBsjUHQXcREERCoDIFHHn1Ky+YeXrsfBL0ysfa7EQTdX7YIur9MrCtC0I0IxzbPRsMi6JLOmX2cDkLQraYY/TohEFtjEHQnQVEGBCpCAEGvSJCR20DQ/eWMoPvLxLoiBN2IcGzzbDQsgi7pf2e9XgcuuoMTdKtJRr8uCMTWGATdRUwUAYHKEHjkkSe17MVHcIJemUQHvhEE3V/ACLq/TKwrQtCNCMc2z0bDIuiSfjXrH3TAorsQdKtJRr8uCMTWGATdRUwUAYHKEEDQKxPlkDeCoPvLGUFvLpOJkrp/bDndD4JulF1s82w0LIIu6ddHHKv9n7gbQbeaZPTrgkBsjUHQXcREERCoDIGHH3lCz7x4Vu1++A56ZWLtdyMIur9sEfR4JltLOl7SOyQdKGlU/JK2Wuws6cuSXi5pE0l/lfR2STdkvYbMTpP0LknjJV0t6SRJ9+YcFUHPCarZZrHNc7P95W0fxg2fU86am/eSyrX7zRHHaj8EvXK5ckO9CcTWGASdGQMBCBRJAEEvkqbfvhB0f9kg6ANnMlLSKzMxfoWk+8MhXfaP5Qn6ltkJ/aWSvh1+dlLSiyTdl/0Tqv2opI9LOiGr6/OSDpK0v6SVOaYYgp4DUitNYpvnVvrMcw2CLv3m8GO035MLargumXNmD7ah/tKizi0P475thvNfhrTCi2uKIRBbYxD0YjjTCwQg0E3g4YcX6ZmXzK79+6Ktd9Lsqy8GTQUJIOj+Qh3ugr6NpA9JWizpG5L2zqQ8nJgHNudIeq+kgyV1f8HV9vPvkmZK6n4jR/9PqOlRSadL+mr2x1tIWijpxPBz0DnKQ9BzQGqlSWzz3Eqfea5B0KXfHv5a7fPkX2q4EPQ8s4Y2KRKIrTEIeoqpUjME/BJ46KGFevaoOQi634gKqQxBLwRjoZ0Md0G/UFJ49fMjkk6VtKmkP0j6qaTzJK2VtLpEQQ9/CXC+pF0khb+yDHX9l6TvZqnvkZ2kh+/C39IwEy7P/vf7cswOBD0HpFaaxDbPrfSZ5xoEXfrtzNdqn6cQ9DzzhTbpEoitMQh6utlSOQQ8EnjowYV69mgE3WM2RdaEoBdJs5i+hrugPyTpWEl/y06hv5U9Wt74fe4yBb3+iPrXwi9HSZoq6ZvZKf7ZkmZk3znfSdJjDVMgnPSvl/SGAaZFl6TwT/2zeXhqaenSpRo3Lrh6Zz+xx4zbfZQ4tqEt8u7LHKuxbgRd+v3MV+tFT4XXNXCCXuScpi9fBGJrDILuKy+qgUDqBBD01BPMVz+Cno9Tma2Gu6C/R9KnJT0t6UeZAIfvnofT6Z9kj4wHES7rEfdV2cvggojXP+EvDYKoT29R0D8r6TN9JxWCXvx/ZrHNc/EjdveIoEt/mPEq7bU4vKoBQbeaZ/TbeQKxNQZB73xGVACBKhF48O+P67mXHVm7pUVb7aTZ1/Ad9CrlW78XBN1fqsNd0EMim0l6PnuUPfzvbbO3tofvdIcXr4UXxn1Q0g8kPWMc4QOSwmP372wYJ7yh/VOSwtvdW3nEnRP0kt5uHts8W80dBB1Bt5pb9OuLQGyNQdB95UU1EEidQKOgP7HVjpp1TfevxvCpFgEE3V+eCPrQmUzOXhr3puwnz4I8v8Ywxp9L2rXPS+K+LunQ7PS8/pK48IK48KK48AnPqS/iJXEDpxLb0BaZZZljNdaNoCPoRc5j+vJLILbGIOh+s6MyCKRIoPcJ+o6ajaCnGGO0ZgQ9iqj0Bgh6PuQbS3pdJsHhZ9esPuFR9muyR9LD98qnZS+Ie7ekn2WDhp9Z+1ifn1mbwM+sIejtfl/falKX0e8fpr9Sez0dXiXBI+5l8GaMzhBA0DvDnVEhMFwJPPj3x/Tcy+bWbn/RVgh6VecBgu4vWQR9QyYPSvp99k94hmdNh+J6laQvZb9/Hn5/Pbwwrv4W91BSyOw0SUHax0u6StLJkrpfYR3/8Bb3OKOWWsQ2zy11muMiTtAlBD3HRKFJ8gRiawwn6MlHzA1AwBUBBN1VHGbFIOhmaFvuGEHfgC78rFl4fD38E76HHn7uLAj7uZKWtEzY34UIulEmsc2z0bC8JE7SH6e/Qns+Hf4+ixN0q3lGv50nEFtjEPTOZ0QFEKgSgQfvf1TPvfzFtVviBL1Kyfa+FwTdX7YI+sCZHJCJ+mslFqRNmgAAIABJREFUHZI9dl4/Xe9+jjbdD4JulF1s82w0LIIe/hZt+iu0B4JuNcXo1wmB2BqDoDsJijIgUBECCHpFgozcBoLuL2cEPZ7JDpJenQl7+GvEIOjhe+DhZD3FD4JulFps82w0LIJeE/SXa4+n/15DfMmcM3tQD/W9/PpXA1rJZTh/378VXlxTDIHYGoOgF8OZXiAAgW4CD9z3iJa/8iW1f+cEvbqzAkH3ly2C3lwmm0o6Ovu5tYuau9RNawTdKIrY5tloWAS99oj7y7VnJugXzzlDI2qvapAQdKtZR7+dIBBbYxD0TqTCmBCoLgEEvbrZNt4Zgu4vZwTdXybWFSHoRoRjm2ejYRH0IOiHvVx7Luk+QUfQrWYa/XaaQGyNQdA7nRDjQ6BaBB746yNa/qruE/QnttxRs/7E76BXK+Huu0HQ/aWKoPfOJLwN/VhJiyX9d9jrN/zxNpKul7SHvxibqghBbwpX/saxzXP+nppryVvcg6C/THsueQBBb27q0DoxArE1BkFPLFDKhYBzAn//68Na8aqjEHTnObVbHoLeLsHir0fQNzD9f9nPm/1Q0haSjpP02ez/FlptL+lRSaOKj6HUHhF0I9yxzbPRsJygS/rDYS/TXpmgXzTnDI3kEXer6Ua/HSQQW2MQ9A6Gw9AQqCCBv//1Ia14Vfhmp7Royx01mxP0CqbMCbrHUBH0DancKemLkn6e/Z9mSPqtpLMkfRpBt5m+sRd1tfsyrtiGtsi7KnOsxro5QQ+C/lLtteTBGhYEvchZTV+eCMTWGATdU1rUAoH0CTQK+hNb7qBZf7o0/ZviDvoR4ATd36RA0DdkslzS/pK6v8ja/Tkw7PclhVP1b3CCXvwERtDbZ4qgh0fcX6o9EfT2JxM9uCaAoLuOh+IgUDkC9//lQa18zUtr94WgVy7enhtC0P1li6BvyCQcv71F0pV9YgrSHt6Kcb6k43nEvdhJjKC3zxNBR9Dbn0X0kAIBBD2FlKgRAtUh0EvQx2+vWddeVp2b404QdMdzAEHfEE54tH2hpA8MkNcBksJzPVsj6MXOZgS9fZ4Iem9Bv3DOGRrFd9Dbn1j04I4Agu4uEgqCQKUJ3H/PA1r52pd1n6Aj6JXNmhN0f9Ei6BsymSBpcvY4+0BJhcfd/0HSaf5ibKoiXhLXFK78jWOb5/w9NdcSQZfOPfRo7bH0oRo4BL25+UPrdAjE1hi+g55OllQKgRQI/O2eB/R8JuhPjt9eR3CCnkJsTdeIoDeNzPwCBN0csbsBEHSjSGKbZ6NheYu7EHSruUW/vgjE1hgE3VdeVAOB1An8bcEDev513SfoT26xnY647vLUb4n6ByCAoPubFgj6wJmMkRRO1LeTNLKhyfrwi07+YmyqIgS9KVz5G8c2z/l7aq4lJ+i9Bf2COWdoIx5xb24S0ToJArE1BkFPIkaKhEAyBBD0ZKJqq1AEvS18Jhcj6P2xhr8q/LGkbQYgHgSd30EvcCryHfT2YSLo0rxDj9ILlz5cg4mgtz+n6MEnAQTdZy5UBYGqErjv7r9r1TEvr93eU1tsq8Ovu6Kqtzqs7wtB9xc/gt4/k3vDHl/S57KXxvlLrb2KOEFvj9+gV8c2z0bD8oi7pPmHHqXdEXSrKUa/TgjE1hhO0J0ERRkQqAiB++6+X6uOeUXtbp7cYlsdgaBXJNnet4Gg+4sVQe+fyTJJEyXd5y+uQipC0AvB2L+T2ObZaFgEvY+gnz/nDI3mEXer6Ua/HSQQW2MQ9A6Gw9AQqCCBRkF/aty2Ovx6TtArGLMQdH+pIuj9M/mBpKslfd9fXIVUhKAXghFBN8LYUrfzp71Euy97pHYtgt4SQi5KgACCnkBIlAiBChG4766/adWxr6zd0VPjttHh119ZobvjVuoEEHR/cwFB75/JJpL+N/zko6TbJa3u0+Rb/mJsqiIEvSlc+RvHNs/5e2quJd9Bl86b9mLttuxRBL25qUPrxAjE1hhO0BMLlHIh4JzAX+/6m1Yj6M5Tar88BL19hkX3gKD3J/pPks6StDL8haGk8GK4+if8+x5Fh1Byfwi6EfDY5tloWB5xF4JuNbfo1xeB2BqDoPvKi2ogkDqBv955n1b/w6tqt7F48601889XpX5L1D8AAQTd37RA0Ptn8rikcEr+75LW+Yus7YoQ9LYRDtxBbPNsNCyC3kfQz5tzhjbmO+hW041+O0ggtsYg6B0Mh6EhUEECCHoFQ0XQkwgVQe8f02JJU3lJXDnzl59Za58zj7hL50+dqxc881gNJoLe/pyiB58EEHSfuVAVBKpK4N7b/6o1r3917fY4Qa9qyuIlcQ6jRdD7h/L17Pvn/+YwryJK4gS9CIoD9BHbPBsNywl6eDEcgm41vejXEYHYGsMJuqOwKAUCFSDQKOhPb7aVZtwQ3qHMp2oEeMTdX6IIev9MwuPtb5N0q6TbBnhJ3Af9xdhURQh6U7jyN45tnvP31FxLTtClC6bO1a71E/TZ/6mNR4ysQTzlrLmDwow9vTFUCkP121x6tIZAfgKxNQZBz8+SlhCAQJwAgh5nVIUWCLq/FBH0/plcOkRM4SVxg+/4/eU7UEUIulFOsc2z0bCcoKuvoH9LG48YhaBbTTj67RiB2BqDoHcsGgaGQCUJ3Hv7vVrz+tfU7u3pzbbUjBuuqeR9DvebQtD9zQAE3V8m1hUh6EaEY5tno2ER9JqgH6ldnwnvd5Tmz/6WuhB0q+lGvx0kEFtjEPQOhsPQEKgggXtv+4vWHPfa2p0t2WxLTUfQK5gy30H3GCqC3j+VLSSF47fwsrjGz1aS1kha5jHIJmpC0JuA1UzT2Oa5mb6aacsj7tKFU4/ULgh6M9OGtgkSiK0xCHqCoVIyBBwT6CXom26p6Tdygu44rpZL4wS9ZXRmFyLo/dHOl/S77LfQG//0vZLCcz6vMEujnI4RdCPOsc2z0bCcoKu3oJ8365vaeORGNdx8B91q1tFvJwjE1hgEvROpMCYEqkvgL7fco7VvfF3tBpdsOl7Tb/xTdW92GN8Zgu4vfAS9fybh5Hy6pHv6/NG+ksLrK7f2F2NTFSHoTeHK3zi2ec7fU3MtOUHve4L+TXWNQNCbm0W0ToFAbI1B0FNIkRohkA4BBD2drNqpFEFvh57NtQh6f67PSTpM0u19/uggSddJ2sQmitJ6RdCNUMc2z0bDcoIu6aIpc7TzswtriOfPRtCt5hr9dpZAbI1B0DubD6NDoGoE/nLLAq194zG121q66Xgdxgl61SKu3Q+C7i9WBL1/JuEt7ndI+pc+f3SmpAmSjvAXY1MVIehN4crfOLZ5zt9Tcy05Qe8r6N9Q14jRNYg84t7cXKK1bwKxNQZB950f1UEgNQK9BH2TLXTYTdemdgvUm4MAgp4DUslNEPT+wGeGAzlJf5Z0cfbHL5Y0VdLRkq4sOaOih0PQiyaa9RfbPBsNywl6nxP082Z/XRuP2BhBt5pw9NsxArE1BkHvWDQMDIFKErjn5gVa96bsBB1Br2TG4aYQdH/RIugDZ3KIpFMlhf93haTbJH1J0r3+Imy6IgS9aWT5LohtnvP10nwrTtCli6fM1k7PLqrBmz/76+pC0JufSFzhnkBsjUHQ3UdIgRBIisCCmxZo/Zu7BX3ZJuN06E3hm558qkYAQfeXKIK+IZO5ki6XtNZfTIVWhKAXinNDZ7HNs9GwnKCHR10mz9JOzz3RLeizvq6ukZygW803+u0cgdgaU6Sgf2fsE3rl/dfowhdM1Ymrdirtpof6WkppRTAQBCBQI9BL0MeO06E3I+hVnBoIur9UEfQNmfxN0nhJ52U/sxZ+bi313zwfaMYh6Eb/HcY2z0bDIuj9BP1r6hrZVcPNd9CtZh39doJAbI0pUtC3vPMbmvjEvXp40230l6mnlXa7CHppqBkIAlECd994l/SWf6i1W4agR3ml2gBB95ccgt47k/ASuPBb5+Gf8Nb2qyT9PhP2B/3F11JFCHpL2OIXxTbP8R5aa8Ej7tIlk2dpx+wEfd6sr2kMgt7aZOIq1wRia0yRgj73slN6WFwyJ7wjtZwPgl4OZ0aBQB4Cd99wl3R8t6A/M3ZzTbv5+jyX0SYxAgi6v8AQ9MEzCc/01WX9yOx30YOsh39u8Bdl7ooQ9NyommsY2zw311v+1gi6dOnkI7TDc0/WoCHo+ecOLdMiEFtjEPS08qRaCHgncPcNd0rH/yOC7j2oNutD0NsEaHA5gp4P6qaSXp4J+yskfU3Sv+W71F0rBN0oktjm2WhYHnFXb0GfP+t0dY0cU8PNI+5Ws45+O0EgtsYg6J1IhTEhUF0Cd/35To14ayboYzbXtFs4Qa9i2gi6v1QR9HyZhO+mL8majpK0laTuN1Kl90HQjTKLbZ6NhkXQg6BPOkI7LO8+QZ9/xOnqGoWgW803+u0cgdgag6B3LhtGhkAVCdx5/e0a+bbjarf27JjNNPWW8AvEfKpGAEH3lyiC3j+Tj0r6u6T/yf7oHEnhrw8flRROz8NPrqX8QdCN0ottno2GRdAlXTbpcG2//Kka4nlHnK4xCLrVdKPfDhKIrTEIegfDYWgIVJAAgl7BUAe4JQTdX84Iev9M7pf0FknXSDpKUhD0N0gKf4X4AklH+4uxqYoQ9KZw5W8c2zzn76m5lnwHva+gf1VjRo2tQeQR9+bmEq19E4itMQi67/yoDgKpEbjj2ts16sT6CfqmmnpLyq9gSo1+efUi6OWxzjsSgt6f1ApJe0t6SNI3JYVnZd+T/d/CD0BumReu03YIulEwsc2z0bCcoPc5QZ9/xFfUNWoTBN1qwtFvxwjE1hgEvWPRMDAEKkkAQa9krP1uCkH3lzOC3j+T8Ch7eKQ9nKDfI+lTkv5X0j6SwpdvguCm/EHQjdKLbZ6NhkXQ+z3i/hWNQdCtphv9dpBAbI1B0DsYDkNDoIIE7rj2No06MTxEKj3Xtamm3MoJegVjFoLuL1UEvX8mZ0h6laR7JU2UtHt4N4akN0r6iKRJ/mJsqiIEvSlc+RvHNs/5e2quJY+4S5dPmqntli+ugZt3BILe3AyidSoEYmsMgp5KktQJgTQI3P6n27TR2+uCvomm3HpjGoVTZVMEEPSmcJXSGEHvj3m0pPdJ2lXSjyTdnDX5gKRnJH2vlGTsBkHQjdjGNs9Gw3KCLumKiTO17YpM0A//D43ZKPwyIt9Bt5pz9NsZAn3XmNVr12n0qJE9xSDoncmFUSFQVQK3X3OrNnpHOJ8KJ+gIelVzRtD9JYugb8jkc5J+J6nqfz2IoBv9d4igG4HN0W2joM8//D/UhaDnoEaT1Ag0rjHfuvhehX9+c/IMTdgl/BKoan9ZN9CLEetP2TRzv3MvO6Wn+SVzzmzm0rbaDvVix7Y65mIIQKBpArddc4tGv+NNteuWb7yJJt9W9S1y04gqcQGC7i9GBH1DJj/IHm1fJekPmaxfIin87yp9EHSjNBF0I7A5ur1i4gxtu+LpWksEPQcwmiRJoHGN2f1j59buYcpuW+pXJ81A0JNMlKIh4JvArVfdoo3fWRf0sZp8202+C6a6lggg6C1hM70IQe+NNzwrOFPSqyW9VtKOki7MZP2PkrqfoU37g6Ab5YegG4HN0e2VE2dom0zQ5x3+ZY3ZaLPaVfzMWg54NEmGwECCPmPPrfXzdx2GoCeTIoVCIB0CCHo6WbVTKYLeDj2baxH0obnu1yDrkyVdL+n3kn4h6RGbSMx7RdCNECPoRmBzdNtb0P9dYzbaHEHPwY0maREYSNBn772tzn7HNAQ9rSipFgJJELj1qpu18TvfXKt1xegxmnR7/bVMSZRPkTkJIOg5QZXYDEHPD3tbSa/J/rlS0lfzX+qqJYJuFAeCbgQ2R7eNgj5/5r+razSCngMbTRIjMJCgH7X/9vru26Yg6IllSbkQSIEAgp5CSu3XiKC3z7DoHhD0ONEgtHMlLcj+iV/huwWCbpQPgm4ENke3Vx4yXdusXFJriaDnAEaTJAkMJOivPGhHnfmW7l//5CVxScZK0RBwS+CWK25U17uPr9XHCbrbmNouDEFvG2HhHSDo/ZGeE361SVL4PfSxkm7Nfgs9sAq/NfHrwlMot0ME3Yg3gm4ENke3jYI+b+a/aczoLWpX8R30HPBokgyBgQT9mIk76+tvOARBTyZFCoVAOgRuvuJGjckEfeXoLk28/ZZ0iqfS3AQQ9NyoSmuIoPdH/bikl2ZiHr54c5qkgyWdIOndkiaWlo7NQAi6DddBT6+Mhuvptv4TSsP554muOuQwbb1yaY0Jgm494+i/UwQGEvTXT95FX3l9+P+iOEHvVC6MC4GqErj58hs05j1vrd0egl7VlCUE3V+2CHr/TFZI2lvSQ5J+LOlRSR+T9AJJd0nqfj20/SeM+SVJ35T0/my4kFf4C4N3SQo/fHu1pJMk3dtEOQh6E7CaacoJejO0im179SGHaatM0OfP/KK6Rnf/LjQn6MVyprfOEhhI0I+bsov+4x8R9M4mw+gQqCaBmy67QWPfmwn6Rl2aeAcn6FVMGkH3lyqC3j+Tv0j6lKTwI7P3Z4+1h99DDzugiyVtU0KMUyWFR+2XSbq0QdA/Kunj2Wl+qO3zkg6StH/4y82cdSHoOUE12wxBb5ZYce17n6B/UWMQ9OLg0pMbAgi6mygoBALDgsBNl16vsSeFB0il5zfaWIfcEb71yadqBBB0f4ki6P0zOTk7tX5W0gOSwtt31kn6F0nHSjrSOMZwQn+TpFBH+IuC8NeV4QQ9ZBVO809veIN8+KLtQkknSvplzroQ9Jygmm2GoDdLrLj2Vx9yqLZaGf4+S5o344saszEn6MXRpScvBBB0L0lQBwSGBwEEfXjkjKD7yxlBHziT8Js1u0q6UFIQ9fB5paTwmujwWLnl52xJiyV9QNJlDYK+h6T7su/ANz5jdHnW5n2DFNUlKfxT/4Tfn3p46dKlGjcuuHpnP/XvTw9WRbvfqy5Tmsscq5EX30GXrjn4UG35fCboM7+gMaO3rCHiEffO/vfN6MUSQNCL5UlvEIDA0ARuuuQ6jT05nAFJq0aN1sF33gayChJA0P2FiqD7yiS8Jf6TksIj7uGR9UZBn5H95cBOkh5rKDs8Cr9e0hsGuZXPSvpM3z9D0IsPHkEvnmneHq85eJq2fP6ZWvN5CHpebLRLjACCnlhglAuBxAncePG12uSUtyPoiecYKx9BjxEq/88R9P7MvzZIDEGCgzT/VdLvslPuIhMLJ/Y3SDpKUv2vKIsQdE7Qzwo/Y2//QdDtGQ82AoLeOfaMXB4BBL081owEAQhIN150rTb5ZwS96nMBQfeXMILeP5PwUrbwU2obSbon++PwVve1khZI2ic7sT48e6t7Uam+TtL/ZePU+xyVjRW+Ax/GDX85EGpr5hH3vvXxHfSiEuvTD4JuBDZHt386eJrG95ygf15jRm9Vu4pH3HPAo0kyBBD0ZKKiUAhUgsANF16rTf+lW9BXj9pIE+68vRL3xU30JoCg+5sRCHr/TMLL4OZICitS95dapfAytu9JukrSdyX9XNLY7PfSi0o1fDd8tz6d/TD7S4EvS7oze0ncV7MXxYWmQbYX8ZK4wSMoU5rLHKvxjvkOunTthKnaYlX36yLmz/icujbeGkEvamWiHzcEEHQ3UVAIBIYFgRsu/JM2/Zd3IOgVTxtB9xcwgt4/k/D75y8d4HT8AEkXSNo5e7N7+Hfrn1xrfMQ9VBp+Zi38Pnr4zYv6z6xN4GfWEPRAoN0X6vlbnvJX1Cjo82Z8TmMQ9PzwaJkMAQQ9magoFAKVIPDn86/WZu97Z7egj9xIE+7iBL0Swfa5CQTdX6oIev9MwjHcq7IXtDX+aThV/4OkcNId3qgeHjO3fg16X0EPeZ0m6d2Swu9IhRP98HNs4bfb8354xD0vqSbbcYLeJLACmyPoBcKkK7cEGteYz73qJE148j79+Z9P0xePn16rebA1KPZrGQPd8NzLTun5P18y58zSmAznv2gsDTIDQSAngT+fd7U2ez+CnhNXss0QdH/RIej9M/mZpLDb+ZCkP2d/HN6qHh4tv0bSWyWFt61/WFL4ObbUPgi6UWIIuhHYHN1eN2GKxq16rtZy3ozTNGbj7odb+A56Dng0SYZA4xpz97771eq+dfbr9Mb//hKCnkyKFAqBdAg0CvqakaN00F13pFM8leYmgKDnRlVaQwS9P+rNJH1d0tuyF8WFFmskhd8nD79NHizgkOyyxpe1lRZamwMh6G0CHOxyBN0IbI5urztossatXo6g52BFk3QJDCToC6a9RMf8+D8R9HRjpXIIuCVw/fyrtPkH3lWrb82IkTro7vA6JD5VI4Cg+0sUQR88kyDq4VH28PmbpO43UKX/QdCNMkTQjcDm6Pb6gyZr80zQ508/TV1dnKDnwEaTxAgMJOh3TztKx/74Wwh6YllSLgRSIHDdvCs07oPvQdBTCKuNGhH0NuAZXYqg9wY7WtKK7IS8qs/xIOhG/zEh6EZgc3T754MmabPV4T9dad70z2pM17a1f+cR9xzwWmzy+T/epVseWqJfvOswbbzRyBZ74bJmCCDozdCiLQQg0C6B6869XOM+9N5aN2tHjNSBnKC3i9Tl9Qi6v1gQ9P6ZhNPyY8JX+/zFVUhFCHohGPt3gqAbgc3RbW9B/4zGdG2HoOfg1k6T3T92bu3yb79lkl5+0I7tdMW1OQkM/Ij7UTqGE/ScBGkGAQg0Q6BR0NeNGKED7r6rmctpmwgBBN1fUAh6/0z+SdKx2cvgFvuLrO2KEPS2EQ7cAYJuBDZHtwh6DkgFN6kL+n++aaJeffBOBfdOdwMRQNCZFxCAQJkErv3j5driw90n6Ah6meTLHQtBL5d3ntEQ9P6Ubpa0l6TwuPsD2UvhGltNygPWcRsE3SgcBN0IbI5ubzhwojZds7LWct50TtBzIGu7SV3Qz3jzRL1qAoLeNtAcHSDoOSDRBAIQKIzAdX+4TONOPalb0DVCByzgBL0wuI46QtAdhZGVgqD3z+QzkZjC75Cn/EHQjdJD0I3A5uj2xgMnapO6oB/2aY0Zs33tKr6DngNei03qgn7mmyfplRN4xL1FjE1dhqA3hYvGEIBAmwSu/f2l2uIjJyPobXL0fjmC7i8hBN1fJtYVIehGhBF0I7A5ukXQc0AquEld0P/rLZP0Cr6DXjDdgbtD0EvBzCAQgEBG4NrfXaItPnpKD4/9FtwNmwoSQND9hYqgD5zJeEn/KGlPSV+RFL6LHh5tXyjpEX8xNlURgt4UrvyNEfT8rIpueeOBh2iTNc/Xup3HCXrReAfsj5fElYK51yAIevnMGRECw5nAn353icYj6JWfAgi6v4gR9P6ZTJB0kaSlknaXtE/2O+hfkPQCSW/zF2NTFSHoTeHK3xhBz8+q6JY3HXCIxq6tC/q/asyYHWpD8Ih70aQ39Ieg27EdrGcEvXzmjAiB4Uzgmt9erC0/9s89CDhBr+ZsQND95Yqg988kyPlNkj4i6RlJB2eCPkPSzzNp95dk/ooQ9PysmmqJoDeFq9DGNx9wsMasXVXrc95hCHqhcAfprC7oZx0/SS87kO+gl8EcQS+DMmNAAAJ1An/6v4s0/uP/0gNk37vv0ogRqEPVZgiC7i9R/ivrn0k4OQ+Ps9/XR9B3k3SPpDH+YmyqIgS9KVz5GyPo+VkV3fLmAyZozNrVCHrRYIfob4OgT9bLDux+YoGPLYGBBP3uQ4/WsWd/szbwYGtQ+L/3/SwduU7Xda3RlOc30lbrRvb787mXbfje6SVzzrS9sYbeh3rqpbQiGAgCEKgRuOY3F2rLT/y/HhoIejUnBoLuL1cEvX8miyS9VFL4ubXGE/SjJP1A0q7+YmyqIgS9KVz5GyPo+VkV3bKXoB/6KY0Z232iyyPuRZPe0F9d0P/7rZP10gMQdDvSG3ouUtB/uPlKPTlqvTZZJ52ybCyCXkaAjAGBxAhc/ZsLtVWjoN91p0aM7P8XeondFuX2IYCg+5sSCHr/TL4naWtJx2UvhwvfSV8r6beSrpD0fn8xNlURgt4UrvyNEfT8rIpuecsBE9RVP0E/9JMaM7b7d7kR9KJJ9xf077x1so5G0O1AN/RcpKB/ZfyKnp5PXYKglxIgg0AgMQJX//oCbfXJ9/VUve+dd2jEqFGJ3QXlxggg6DFC5f85gt6f+RaSfiVpiqTNJT0qKRwP/UnSKyQ9V35MhY6IoBeKc0NnCLoR2Bzd3rL/Qepat6bWch6CnoNY+03qJ+jffdsUHbV/9+/O87ElgKDb8qV3CECgN4Grf3W+tvrUhnMpBL2aMwRB95crgj54JjOzF8Rtlr00Lrw8rgofBN0oRQTdCGyObm/d/yBtjKDnIFVcEwS9OJZ5e0LQ85KiHQQgUASBq//3PG31rx/o6WrfO27XiI02KqJr+nBEAEF3FEZWCoLuLxPrihB0I8IIuhHYHN32FvRPaMzYnWtX8Yh7DngtNqkL+vfeNkUv4QS9RYrNXYagN8eL1hCAQHsE+gn67bdpxOjR7XXK1e4IIOjuIhGC7i8T64oQdCPCCLoR2Bzd3rb/gRq9LrwqQpo37RMaswmCngNbW03qgv79E6boxfvxiHtbMHNeXOTPrDXzHfSL55yhESVtF3iLe87JQDMIlEDgqnPma+tPf7BnpH0R9BKolz8Egl4+89iICHqMUPX+HEE3yhRBNwKbo9vb9jtQo9cj6DlQFdakLug/OHGK5u6LoBcGdoiOEPQyKDMGBCBQJ3DV/8zT1p/50AZBv+1Wjdh4YwBVjACC7i9QBN1fJtYVIehGhBF0I7A5ur19vwO00fp1tZbzDv24xozdpfbvPOKeA16LTeqC/sMTp+rIfbdrsRcua4ZAfY1Zv369Fuy3f+3SBdOO0jE//lbt35v5HXRO0JshT1sIDE8CV/7yXG3z2Q8j6BWPH0H3FzCC7i8T64oQdCPCCLoR2BzdIug5IBXcBEGygxKhAAAgAElEQVQvGGiO7uprzLp163XP/uUJ+kVzztBIHnHPkRBNIFAtAlf+4lxtc9oGQd/ntls1khP0aoUsCUH3FymCviGTbSQ96S+iwitC0AtH2t0hgm4ENke3d+x3gEZlJ+jzp31MXZvsWruKE/Qc8Fps0iPob5+qI/fhBL1FjE1dVl9j1q5br78g6E2xozEEINA8gSt/fq62+VyDoN96i0Z2dTXfEVe4JoCg+4sHQd+QSfgC62WSvi/p15Ke9xdXIRUh6IVg7N8Jgm4ENke3d+y7v0Zpfa0lgp4DWAFN6oL+o7dP1RwEvQCi8S7qa8yatet07wEH1C5YcOjROubsb9b+3eoRd07Q49nQAgJVJHDFz/+obT93as+t7YOgVzFmTtAdpoqgbwglfIH1fElzJT0n6WeZrN/iMLd2SkLQ26E3xLUIuhHYHN3eue/+Gtkj6B9V1yYvqF3FCXoOeC02qQv62e+Yptl7b9tiL1zWDIH6GrN67Tr9tURBv3DOGRrFI+7NREVbCFSCwBU//b22/cJHNwj6LTdr5Jgxlbg3bmIDAU7Q/c0GBL23oO8gKYj6CZLeIWlfSUHQv5cJ+zJ/ETZdEYLeNLJ8FyDo+ThZtLp73/16up037aMag6BbYO7VZ13Qf/yOaZqFoJvzDgPU15hVa9bpvgO7T9DvPvRoHWt8go6glxIvg0DAHYF+gn7TjXr881/Qxrvtpm3e+x539VJQawQQ9Na4WV6FoPcX9EUNwKdLeqek10salT36/jbLQEroG0E3goygG4HN0S2CngNSgU3CW8Rf+PF5tR5/8k/TdMSLOEEvEO+gXdXXmOfXrNXfDjwQQS8DOmNAYBgTuPynv9d2DSfou5z1bT383pNqRPZbcPcwJlOtW0fQ/eWJoG/IJHwHfUdJjYJe/9NNJb0xO1Wf6S/GpipC0JvClb8xgp6fVdEtGwV9/rSPqGuT3WpD8Ih70aS7+wtvEd/jEwi6Dd3Be0XQyybOeBAY3gQu/8nvtN0XP9YDYYfTTtPjn/kMgl6xaYGg+wsUQd+QSXi0PTziPpCg+0uu9YoQ9NbZDXklgm4ENtJt429Ch6bzpn5EYzZF0C3TCG8R3zMT9J/+06E6/EXhRzD4WBOorzErVq3R3yccVBuujEfcL5hzhjbiO+jW8dI/BNwRuOzHv9X2//bxnrq2+/CHtOirpyPo7pJqryAEvT1+Flcj6Buohu+d/7LCb2+v3ymCbvFfEj+zZkQ13u36deu0YP/u7+OGz/ypp6pr091r/84JepxfKy3CW8T3+uT82qU/e+ehmrkXgt4Kx2av6RH051fr7wdPQNCbBUh7CECgKQKXn/1/2u5Ln+i5Zut3v1tPfec7CHpTFP03RtD9ZYSg+8vEuiIE3YgwJ+hGYCPdIujlcw9vEX9RJug/f+ehmoGglxJCfY1ZvmKVHph4MIJeCnUGgcDwJdBX0Ld6+9u1+Ic/RNArNiUQdH+BIuj5MwnfQ58s6Yr8l7hsiaAbxYKgG4GNCfratVpwQPcLs8KHE3T7HMJbxPf+VPcJ+s/fdahm7MkJuj31DW9xf275Sj04aWJpgn7+nDM0mkfcy4iYMSDgisBlP/qNtv/3T/bUhKC7iqewYhD0wlAW1hGCnh9lOK64KXube/6r/LVE0I0yQdCNwMYEfc0aLTiw+/u43YL+YXVt+sLav/OIu00m4S3i+3zqvFrnv3jXYZq+59Y2A9FrLwL1NebZZ1fooSmTEHTmBwQgYErg0h/+Wjt8+VMbBP3EE7X4Rz+q/W/e4m6KvtTOEfRScecaDEHPhanWCEHPzyp3y7DhHOozlGDlGaRMaS5zrMZ7rzNsl1Uenh7brF+9WgsO6v4+bvgg6PYprVy9Vvv+K4JuT7r3CPU15plnluvhqeGBrvCSuJfq2LO/Ufv3wdaggdbZr4xf0dP5qUvG9ruVuZed0vN/4wS97KQZDwI+CFz2g19p+//4155itmoQ9H3vvksjRqARPpJqrwoEvT1+FlfzX9YGqosjgMPvoG/GCXqx0xBBb5/nsBf0Vau0YEL393Frgj7lQ+rabI/av3OC3v78GqiHRkH/5bsP02F7cIJuQ3pgQV+29Fk9cujU0gT9vDlnaGMecS8jYsaAgCsCl/7gV9qhUdBPOEGLzz67VuO+d96hEaPC1phP6gQQdH8JIugbMnlO0rcl3T5ITOF3m8KPP6a+GvGIu9F/h5ygG4GNdLu+n6B/UF2b7YmgG8axYtVa7ffp7hP0/3n3YToUQTekvaHr+hqzdMkzevSwaQh6KdQZBALDl8Cl3/+VdvhKwwn6CW/T4rN/3C3ot9+mEaNHD184FbpzBN1fmAj6hkyulnSOpG8OEhOPuBvMX07Q24c63E/Q161apXt6naB/QF2b7YWgtz+1Bu1h+ao12v/T59f+/Jz3TNe0F25lOBpd1wnUBf3pxcv0+IxDEXSmBgQgYErgku+dox2/Gs6muj9bvu2tevrHP6n9+z633qKRXV2m49N5OQQQ9HI4NzMKgr6BVvihx/BXgacNAnBXSZ+T9PZmADtsywm6USicoBuBjXS77vnndc/Bh/S0mj8FQbdO4rnn1+iAz3QL+v++d7qm7o6gWzMP/fcI+lNL9fjMwxD0MqAzBgSGMYFLvnuOdjx9EEG/+SaNHNv//RXDGFeyt46g+4sOQfeXiXVFCLoRYQTdCGxM0Feu1D2HdP/kVPgg6PY5PPv8Gh2IoNuD7jNCfY1Z/OQSLTx8OoJeegIMCIHhRaCfoL/1rXr6J9kJ+o03aOSm4ReI+aROAEH3lyCC7i8T64oQdCPCCLoR2GYFffL71bX5i2pX8ZI4m0yeWblaB332glrnv3rvdE3hBN0G9CCC/tSip7Vo1ozSBH3+nDPUxUviSsmYQSDgicDF3/kf7fS1z/aUtGWDoO99w581arPw7mQ+qRNA0P0liKAPnckySeHZ2b/5i67lihD0ltENfSGCbgQ2JugrVuieid2/CR0+8xF08yCWrVytCZmg//qk6Zq8G4+4m0NveMT9yYWL9cTsmQh6GdAZAwLDmMDF//0L7fT18O3O7s+Wxx+vp3/609q/7339dRo1Lmwp+aROAEH3lyCCPnQmz2S/f46gG81dXhLXPthh/5K45ct1z6Tu34RG0NufT3l6WLpitQ4+rfsE/dcnzdDk3bbMcxlt2iRQ/0vAJx5/Uk/OOaLWWxm/g84JepvBcTkEEiVw8Vm/0E7fGETQr/2TRo0fn+idUXYjAQTd33xA0BH0js5KBL19/MNe0J97TvdMntID8rzJ79PGm+9d+9884t7+/Bqoh6XLV+vgz3UL+m9OnqFJL0DQbUj37rUu6Isee0JPHTkLQS8DOmNAYBgT6Cfob3mLnv7Zz2pEXvSna7TRlqz9VZgeCLq/FBH0oTMJv4sefgDySX/RtVwRj7i3jG7oC3nE3QhspNt1fQV90vu08TgE3TKNJctX6ZDPXVgb4v9OnqGJCLol7p6+62vMwkee0OIX1wX9aB17dvevgw62Bg30F6FfGb+ip99Tl/x/9q4DKqqjjV460hXpHSkqINgVGyhi7zGaYjTGGGMsf0zTRI29d4ktxiT2FhWxIGBF7L1gAcSCIIpK7+U/M4+3BRZY2OKiM+fo2eXNfDNz5+17c+dr5SMxdz71neD6Ef8g6JYoZ7tQ2aGaUkBmnTAEGAICBCLWbofNytmC73U//QRvt+/gCHrUWWiamjK03gMEGEFXvUVUzhtX9eb9IY+IEXQFrT4j6AoCtgqxRZlZeNhCqEE/2mwidBhBV+hivM3KR9PZjKArFGQJwvlnzItnL/C2qz+tEd06EIMYQVf2UrD+GAIfBALH12yD9ao5grmaDB2C1J27OIIeeQaaZmZywaGkoABJU6dCr3UbmAwcIBeZTIj0CDCCLj1WyqrJCLo40toA+gMg+WssSy+9AHAOQDCAfGUtjAL7YQRdQeAygq4gYKsk6Jl42KKloFZoswnQNnKn35mJu2LW5E1WPpqVEvQD37WDjx3zQ1QM0uJSBQT9aRLeBnaWiaBv0UrAJw/CsdfVH0OLHMU6KkEJupwaJ/gb06ArY3VZHwwB1UMgYs1W2KyaKxiY8eDBSNuzh353OX0aWhbmchl0+tGjeP79JCqr0f17cpHJhEiPACPo0mOlrJqMoAuRdgFwDIA1gIsAkksvWQBoDSABQA8AscpaHAX1wwi6goBlBF1BwFZF0DMy8LBlKxGCPh7aRg0ZQVfgcrzOzEPzORG0h+Dv2sG7AoKekpmHLeefYHALW9jW1VPgiD4M0fwzJulJIlK7daGTvtsqEB9trr6Ju9uVGbDNfIU0bX1c9V0kBmBZgn7YPwh1mIn7h3GTsVkyBEQQKEvQX7YPhPlZLv6Iy6mT0LLkdVmywZZ+5AieT/qBCml45zbUNDVlE8haVwsBRtCrBZdSKjOCLoSZ2GtmAfgCAEmvJloIqd0MgDjqdVPKyiiuE0bQFYQtI+gKArYKsUXp6XjYipyhceVo8/HQMWQEXZGrQYh3i1KCfnBcOzSxlaxBH/bXRUTGpMDGpA6iJnMaX1ZqjgD/jEmMT0RaD56gd8VHm1dRodXxQRf1MT/h94fYoBhBr/kasZYMgfcJgYigLbAJmieY0o3G7eATHUW/u5w4Di1rotOSvWSdP4+nX47k5J45DS1z+WjmZR/ZhyGBEXTVW2dG0IVrkg2AqOHuVLBMXqWa9dquBmIEXUG/Q0bQFQRsFWLLEvTQZkyDruiVeJWRh5ZzOQ16yLj28LI1ltily69HUFhcQq89XtBL0cN67+Xzz5jnjxKQ3rMrnW90ywAM2rKafpYXQS9GCQJETNyZBv29v7XYBBkCEhEIX70Ztn/MF1y76+ELj7vE6xNoEBEBbVsbuSAnRtCPR0DLRj5y5TK4D0AII+iqt8iMoAvXJBHAaACHKlimPgDWl5rAq95KSj8iRtClx6paNRlBrxZccqtclJaGh63bCOSFNhsHbaNG9DvzQZcbzGKCXmbkotXc4/RvlWnQ3aceRV5hMa3HCLrsa8E/YxLiniGjVyAVGN0iAIO2KpagH/IPgh4zcZd9AZkEhkAtQyB81WbYrhES9AeevnC/U0rQw8OgbWcnlxllno3Cs1GjOOIvR7lyGdwHIIQRdNVbZEbQhWsyCwCJikPySZCdp6gPOrElnAqA7IJmqN4yVmtEjKBXCy7pKzOCLj1W8qxZlJqKh21IXEeuMIIuT3Qly3qZnotW86om6I2nhyI7v4gRdDktCf+MeRb7FJm9OW+r6OZdMGhbEP2sKA06I+hyWkAmhiFQyxAIX/UvbNcsEIw6zrMtGtw5zxHpY6HQdnCQy4wyIyPx7GuiI5OvXLkM7gMQwgi66i0yI+jia/ILgImlEdw5u0yAYEQiua8AIB5JR/XWU5oRMYIuDUo1qMMIeg1Ak0OTwrdvEdPWVyCJEXQ5gFqFiOT0XLSWgqB7zTiGjNxCKo1p0GVfF/4Z8/ThE2T17U4FRjfvjEHbOB9yeRH0IpSgq4iJOyPosq8dk8AQqI0IhK38B3ZrFwqG/sSzDRzuXKDfnY8egY6Tk1ymlXHqFBLGfMvJPXIEOs7ykSuXwX0AQhhBV71FZgRd8pqQJ4NomrV41Vu6Go+IEfQaQ1d5Q0bQFQRsFWLLE/TvoG3UmLZiJu6KWZMXabloM79qDXrTWWF4m13ACLqcloF/xjx58BjZ/UhSESC6mT8GbV/DCLqcMGZiGAIMASECYSv+ht06oW7qqUdr2N8liY4IkT4MHWdnucCVceIkEsaO5eQeCoGOC0msxIqyEGAEXVlIS98PI+jSY6WMmlMADCRZJgDklOZfJ1r9ByKdkzWbCeBrACR0MgmnSY4dY6QcICPoUgJV3WqMoFcXMfnUL0fQm34HbWNG0OWDrmQpSWk5aDv/BL1YmQ9656n74Xv7JMIcWuLSqmGKHNIHIVtA0O/HI7t/Tzrn6Kb+GLRDsQQ9xD8I+swH/YO4x9gkGQKiCJQl6M8at4Bd9BW5E+mMiAgkjBtP5TodDIaumxtbCCUiwAi6EsGWsitG0KUECgCJhEGIMZcHQjElFMBOAJcBkCSQJLeFJwDCNkgKOFIIYSdEfjgAotknPvMkwjypkyvFsBhBlwKkmlRhBL0mqMnepvDNG8T4thMICm06FtrGHvQ706DLjq8kCYmpOfBdwBH0yvKg7/XvD4+kB0iuUxd+17nAQqzUHAH+GfP4bhxyBvWmgqKb+mHQjrX0s7xM3AtRgkARE3dG0Gu+ZqwlQ6A2IxC2fBPs1i8WTCGhUXPY3rtKv8uTSKcfC8PzicTDFHA6sB+6DblUqawoBwFG0JWDc3V6YQRderS8AVwDoCF9E5lrmgF4CaATgDOl/vAk2vxSAEtKpZP8RiSg3YhScl9Vp4ygV4VQDa8zgl5D4GRsVvj6NWLatRdIYQRdRkClaP48NQftpCDodxp5QKOEi+Le6P49KSSzKpUhwD9j4u/GIncQSSwCRPt0wqCd6+hnRtDZ/cMQYAjIE4GyBP25e1PYPLhOu3AKPgBdd3e5dPfm8FEk/zCJykpevAH3DK0xppMz1NQYTZELwFUIYQRdGShXrw925wvx6lsFdMTRhhBjZRJ04oRDTNeJhpzkZydjiAPQFMANkfGeLv3OHT+KFx0A5B9fDAEkpKWlwciIcPV3W8iGsrJSmQZUmpErkzQrsy/RufMYyoqVNHiqYp3ClBTEtO8gGFpo02+hbUwMT5gGXVHrlfA2G+0XnqTiK9Og32rsBa1iLkgcI+iyrwb/jHl0OwZ5g7lXVrR3JwzapViCftA/CAbMxF32BWQSGAK1DIGwZX/BbgOvDwKeu3rDJuYmnYXT/n3QbcSlNJW1hK7aDIfSdG4TOk1ETF077BzdBm2cTWUVzdpLgQAj6FKApOQqjKALASdqHhK5vTJMyHVlEXR14t5Z6mfOqwdJqGric24NIEnkXtldOvYhEu4fkhbu97J/ZwRd/r80RtDlj6k0EgtevUJsh46CqoygS4OabHWevclGh0VVE/QbHk2gU8QFiWMEXTbMSWv+GRN36yHyP+5HBUZ7d8CgXRvoZ3lp0AtQgm4iJu6MoMu+dkwCQ6A2IhC2dCPs/iS6Ka4kunjBOvY2/ey4dy/qeHLuZLKWA0s2wX0jZ0q/1qsfXFMT4DjlZ/Tp3ERW0ay9FAgwgi4FSEquwgi6EPDnAEgIyeAK1sAHAHG8URZBJ06FJEwvIecJpWOqCUFnGvR1nZXys2IEXSkwl+uk4OVLxHYkXiBcYQRd8esgLUG/7ukD3cI8OiBG0GVfFwFBv3kf+UMGUIHRTTpg0G5G0GVHl0lgCDAEyiJwbMmfsN+4TPDnJGcPWD26S7/b7doFA2/5EOiQxRvh8pfwIIDIz2jeFq22bWKLogQEGEFXAsjV7IIRdCFgRFtNzManV4Ah8UEnjjdEs63oEgSAqEeIWlA0xVtNTNzLjpX5oCto9RhBVxCwVYjNT36JuE4iBN1nDLRNiFcIM3FX1Io8fZ2Njour1qBf9WoGvQKSkIIRdHmsBf+Mib1xHwVDSwm6V3sM2vMnFc806PJAmclgCDAEeATKEvQXTo1hGR9NL9ts3wGjZkR3JXs5vPhPOP8lPAggEnNtHND0OImdzIqiEWAEXdEIV18+I+hCzIgTqz5RwFUAI7nWAgDx91ZUIeuxGgDZeflJSJ1GrpMgccQhiD9qJISbBJJjQeIkrIoySbMy+xKd6ofug573IhmP/MjPhSvHfMZAixF0RT2jqNwnr7PQafEp+vnAd+3gY0cyPpYvl7xbwDCPS0DBNOiyLwn/jIm5dg+Fn5KMnEC0VzsM2rORfpYbQS8pRrfTXMojUpiJu+xrxyQwBGojAscW/wl7EeKc7NAQFk/u06lYbdkKk5bN5TKto4s2wHHTcjFZOY4uaBYaIhf5TEjlCDCCrnp3CCPoqrUmJJntp6Xac9Hc52mledHJaEmatcll0qwRGyOWZk3CWiqTNCuzL9GpMoJelqB/Ay0TzuyOpVlTzAPucUoW/JZUTdAv+LSCcW4GI+hyWgYBQb96F4WffUSl3vNsh4F75UvQ80uK0P30BMGog/2DYMiCxMlpFZkYhkDtQSB00QY4iBDnl3auMH9GYhcDFv9uRr3WLeUymWML18H+75VisrJdGqH5oX1ykc+EMIJe2+4BRtBVa8VIEDpJ5UsA/5ReIGtG8rGPLg0gd7bUd/6hlFNhJu5SAlXdaoygVxcx+dTPTUxCfGdhnIFQn2+gzQi6fMCtQEp8Shb8GUFXKMaShPPPmIdX7qDo88EcQffwxcD//qKf5aVBZwRd6UvLOmQIqCQCoQvXwUGEOL+0dYF5Qiwdq9mmv1Hft41cxh2xcC1s/l4lJiuzYRO0PLBLLvKZEEbQa9s9wAh6bVsx2cfLCLrsGEqUwAi6goCtQmzO80Q87tJFUOuYD9OgK3olHr3KROelnLdPZSbuTIMu35XgnzEPLt9B8TCeoLfFwP+4QEryIugFxYXodkaYtTPYbzUMlRJ+pXKrF/miyaQxBBgCVSFQlqC/snGG2fNHtJnpn5tg3qFtVSKkun58wRpY/0M8PIUlw6s5Wu3ZKlV7Vkk2BJiJu2z4KaI1I+iKQFW1ZTKCrqD1YQRdQcBWITY74TmeBAQICbr3aGjVJTEdmYm7olYk7lUmujCCrih4K5TLP2PuX7yNkuEf03r3GrfFwH2KJegH/FbCCJpKmW9lbilKGQDrhCHAEBAgELpgHRz+EZqep1g5on7SY3q97ro/YenHZwGWDbSTC4Jg+c8fYkLSvVuh9a5/ZRPMWkuFACPoUsGk1EqMoCsVbpXojBF0BS0DI+gKArYqgv7sOZ50ZQRdmejHvsxEwDJOg75/rC+a2teV2D3ToMt3VfhnzL0LN4ARn1Dh9xu1wYD9f9PPNdWgH/cLghqE24H84gJ0P/M/weCDO62AoZqWfCdTgTRG0JUCM+uEISAVAqEL1sLhH6Hp+WtLB5i+eELbGq9ZD+vOJNmQ7OX0vNUw30zCMAlLmncrtGEEXXZwpZDACLoUICm5CiPoSgZcBbpjBF1Bi8AIuoKArUJs5tMEPAvsKqh1zPtraNXlUr+wIHGKWZPYlxkIWHaGCmcEXTEYS5IqIOjnbgAjeYLeGgP2cyFKakrQI/yCoC5C0AuK89HtzPeCITCCrrw1Zj0xBFQJgaPz18DxX6Hp+WtzO5i+fEaHaBi0FrYBwgwqsow7cu5K1N+yTpyge7VAmz1bZBHL2kqJACPoUgKlxGqMoCsRbBXpihF0BS0EI+gKArYKsRlPniGhWyAj6EqEPyY5A12XM4KuRMjFCHh01HWofUUSfgD3G7bCgAOcGWhNCXq4XxA0RAl6UT66RYoS9OUwVNNWynSZBl0pMLNOGAJSIXB03ho4bhYS9Df1rVEvhWT7BQxW/gG7bsIArVIJrKBS5JwVqL91vThB92yONnuZD7osuErblhF0aZFSXj1G0JWHtar0xAi6glaCEXQFAVuF2PT4p3jeo5ugVliTUdCs15R+Zxp0xazJw+QMBDKCrhhwK5HKP2Punr0K9VGfcwTdvSUGBG+WiaCH+QVBU4yg56Fb5CTBSII7LYOhmo5S5ssIulJgZp0wBKRCIHTeH3DYHCSo+7aeJeq+eUG/6y1bDYeeQvcyqQRWUOnMzKUw28Gli+RLmkcztPlvmyxiWVspEWAEXUqglFiNEXQlgq0iXTGCrqCFYARdQcBWITbt0WMk9uwhqHWsyShoMYKu0MV48CID3VYwDbpCQZYgnH/G3DlzBRqjh9Ea8iDox/yCoCXmg56H7mcYQVf2+rL+GAKqhsDRuUFw3CIM3pZa1xwmb1/SYeouXgGnPsLDcVnGfvr3xTDfxQW7FBD0xk3RZt92WcSytlIiwAi6lEApsRoj6EoEW0W6YgRdQQvBCLqCgK2KoMc9RmIvRtCVif79F+noviKSdjm5R0OM6dRAYvcsSJx8V0UiQXdrgQEHOT/Nmpq4h/oFQVvMBz0X3c78IBh8cKelMFTTle9kKpDGNOhKgZl1whCQCoEjs1fDaZsweFuqiRlMUl/RtjoLl8O5X3ep5FRV6fTUhTDfy8XS4EtaIx+02b+jqqbsuhwQYARdDiDKWQQj6HIGtBaIYwRdQYvECLqCgK1CbGpcPJJ69RTUCmvyFTTrNaPfmYm7YtbkXlI6eqzkCDopjxf0ktjRRZ+WMMrNpNca3b+nmMF8QFL5Z8ztU5ehOeYLOvMHrs3RP4Tz06wpQT/qFwQdMRP3XHSLFCXoS2CoVkcpSDOCrhSYWScMAakQODxrFZy3rxXUTTMyhXH6a/pda/4SuAyQ/OyXSrhIpVO/zYfFf5yrDl/SGnqjzYGd1RXF6tcAAUbQawCagpswgq5ggFVQPCPoCloURtAVBGwVYt/EPEJyH+EmgRF0xa9DdGI6eq5iBF3xSIv3wD9jbp28CK1vR9CLD1yaof8hzk+zpgT9iH8QdEtE0qwV5aK7KEHvuASG6oygK3u9WX8MgXeNwKGZK9FghzC6erphPRhlvKHD0py7CK6D+shliKcmz4HFAXF/8zT3JmgTvEsu8pmQyhFgBF317hBG0FVvTRQ9IkbQFYQwI+gKArYKsa8fxOFlv96CWoygK34d7iamodeqs4KOmAZd8ZiLEvBbJy5CayxP0Jui/yHOT7OmBP2wfxDqiBH0HHSP/FEwqYMdF8NAXU8pk2QadKXAzDphCEiFwKHfV6DBLmF09Qx9ExhmpdK26rMWwP3jflLJqarSyZ9mwTJE3Jw9zc0LbQ7urqopuy4HBBhBlwOIchbBCLqcAa0F4hhBV9AiMYKuIGCrIuj34/CyvyhBHwnNes1pK2birpg1ufM8Db1XM4KuGHQrlso/Y24evwDt776kFR82aIp+h2Uj6If8g6AnRtCz0T3yJ8FAgjsugqG6vgNtA/4AACAASURBVFKmywi6UmBmnTAEpELg0LSlaLBHGF09U88IBtnptK3ajHloOHSAVHKqqnTixxmwOiSuLU9z9UCbkL1VNWXX5YAAI+hyAFHOIhhBlzOgtUAcI+gKWiRG0BUEbBViU+7H4FX/voJaYU2+hGa9FoygK3A5pCXol7xbwjCP+aDLayn4Z8yN8CjojB9VStB90O8wp3mqqQY9xD8I+iIEPa8oGz0YQZfXsjE5DIFai0DIb4vh8p8wunpWHQPo53DP9JJpc9D4s0FymduJ76fB6qg4GU9r0BhtDv8nF/lMSOUIMIKuencII+iqtyaKHhEj6ApCmBF0BQFbhdhX0Q+RMlBoZvehEfTQO0loZGUEB1PlaDjJctxOSEOfoKo16Iygy/c3wT9jrodFQXdCKUF39ka/I1wgpZoS9IP+QTAQIei5hVnoefZnweAPdlgIAw0D+U6mAmlMg64UmFknDAGpEAiZshAu+4XR1bN19KCXl03bFv82Cx7DBkslp6pKxydOhfUxcTKe1qAR2hzeV1VTdl0OCDCCLgcQ5SyCEXQ5A1oLxDGCrqBFYgRdQcBWIfbl3Yd4PejDJOg3n7zBnc9HIKWOCSaGiUfAVeRq3EpIRd+gKEEXFfmgM4Iu31UQEPRjZ6E78Wsq/KGTN/odlY2gB/sHwVBUg16YhR5iBH0BDDQM5TsZRtCVgifrhCEgCwKHJs9HgwPCd0uuli50C3KpyMIpM+A1fIgs4gVtj4+fAuvwA2Ky0pzc0eao+N/k0hkTUg4BRtBV76ZgBF311kTRI2IEXUEIM4KuIGCrEPvizn28/UjoBxfuNQIapi1pq/fdB/3grgi4/j6ezlWZacxuPktFvz8YQVf2HS8g6KFnoPu/b0oJehP0O8r5btZUg37AfzWMStQF08ktzETPs78Ivod0WAB9RtCVvdysP4bAO0cg5Ke5cClN40gGk6epA53CPDqugp+no8nIT+QyxuPf/QLr4wcZQZcLmtUXwgh69TFTdAtG0BWNsOrJZwRdQWvCCLqCgK1CbNLt+0gdLCToYV4joClngp6uVowSAMalJEZVzHAP7wyD84yJFCH36LtQVxeSLEWuxo1nqejPCLoiIZYom3/GXDtyBnUmcQQ9xtELfUO5SMc1Jej7/VcL7m0iJ7cgHT2jpgjGcLD9fBhokleH4ouq/LYUP1PWA0NA9REI+WE2XEqDUJLR5mtoQbuogA4878ep8Bn1mVwmETHmJ9icOiQmK83RDW1Cg+UinwmpHAFG0FXvDmEEXfXWRNEjYgRdQQgzgq4gYKsQm3grGmkfCwPVyJugE2p+uPg+NIqL0VWzMbSgVqlmXpkoHNkZBqdSgu5y4wa0dHWU0v31p28xYM05QV8Vm7i3gGFeFq2nTA2/UkB4B53wz5irR05Db9IYOoIYR0/0Dd1DP9eUoO/rvBomxaIa9FT0PPubYIYH28+DgaaxUmbMCLpSYGadMASqRKAg+SXCZy2H03GhmXmhuiY0iws5gj7pV/iMHlalHGkqRIz+ATZnjohVTbd3QeuwEGmaszoyIsAIuowAKqA5I+gKAFXFRb43BH3bxSdwMtWHr0v9CiFXJmlWZl+iEyb9kvKhbmwTb9xF2tCPBJCEew2HhmmrKjHhcavq91pQXIiAyO+hUVKMo22mI1XfDOkdTPFDoDvs6iknN3RFYzyy4xicZv6PXna4dBl6RsoJ5HXt6VsMZAS9qltH7tcFBP3QKej9+C2VH+Pgib7HZCPo/3VejbqiBL3gLXpGTRWMP6T9POgzgi739WQCGQKqikDBy5eI7dip3PCK1NTpu5CUnImT0ezb4XKZQsTX38MmMlScoNs1QOtwca26XDpjQsohwAi66t0UjKCr3pooekTvBUG/+uQtBq3lNHgVae/INWWSZmX2xQi6EIHn1+8g/RNhJNlwz+HQqC8/gl5UlIuukT/QDk/6jEOkdia+vhOCfb2+QdBs+WgPavqjP7rzGBxncATdNuo8DE1NaiqqWu1Ef3+V/QYveTMNerWAraIy/4y5EnIC+j99R2vH2nugTxiXnqimGvS9nVejnihBz3+NnuemC0YT0n4u9DWVc299qAeN8rxPmCyGgDQInHn4Cn+djcf8gV6wNqkjTo7Dw/F8/IRKxWSP+wnNx42Upqsq60SMnAibc2Fi9TJsHNHq+NEq28qzQnFxCdTUADXy3wdUGEFXvcX+sO5A1cP/XYzovSDoB28mYsKO6xQ/RtA/bA16wuWbyBg2VPBbkjdBLyzMQmBpROuIpt8h4PoftK80HQO0uXn5XfyGBX2G7gyFw4zv6XfLk2dQ18pMKeO5+uQNBq09L+iLmbgrBXYBAb988AQMfuYJemP0CePSE9WUoO/pvBqmogQ9LwU9z/8uJOjt5kBfq65SJskIulJgZp0wBOA4+TBFoaObGTaP5A61+ZIZFYVnX3GpHCsqWWN/QIvSdI+ywhn+5XjYno8QE5NhaYdWp8RJO18h7O4LXIx/g197NoKGunyoDCHnvVefhb6OBnZ/0/aDIumMoMt6B8u/vXzuavmPi0lUHALvBUEPuZmI8YygCzbl5MOHurF9dvEaMocLA9VEeA6Dev02FBt5RHEvKMhAt6jJVF64z7foemMt/Zyvrgnv6NuK+6VKITl0x1E4zJxEa9YPOwEzeyspWsle5crjN/hoHSPosiNZPQk8Ab8UfByGv4yjjWPtGqNPuGwEfXfn1agvRtBfouf5mYLBhTCCXr2FYrUZArUAAZ6gN7Q0ROj/OoqNOOvSJTz9onLz9Ywx36PV/0bLZabhX4yF7aWTYrIyza3R8sxxifL5sS/72BsDm9nKZQxPXmeh0+JTVNb92d2hq6UhF7m1QQgj6Kq3Soygq96aKHpEjKArCGFm4q4gYKsQ+/TcFWSNFJqaH/f4DGpmvrSVXAh6fiq6neMCZoX7fIOuN9bTz4Vq6vC6d1fqSefkF1HTOXm+9I9uDoHjvJ/pGExCQmHl6iD1eGSpePnxGwyWgqBf9m4Og7xs2hULEicL4lxbAUE/EA7DyZz5aaxtI/SJ2Cd2vWxPkuItdD7FaeBJ2dV5NcxECXruC/S8MFtwPaTdbOhr1ZN9AlJI+FAPGqWAhlVhCMgVAZ7kNrIywtGJHcRkS6NBTx89Ea1Lg1XKOrCwz8fA7sppMTFZphZoEcUR5rKFH/vkHg0xplMDWbun7Z++zkbHxdwhASPocoGUCZEBAUbQZQCvljZ9Lwj6oVuJGLedmbiTe/BDDxL35OxFZI8aIfg5nmw8FCXm3GZDLgQ9LwXdSs19I5qMQsCtjYK+pCWd+YXFaDQ9FNoa6rg7sxvU5WSSd2TTfjgt+pWOx3DfIdg2ls9Gpapn26X4N/h4fdUa9MveLWDAorhXBafU1wUEfV8YDH/l0uvF2jZEn4j99LO0Ju4lKEGXU5wGnpSdnVfDXJSg5ySi58W5gushvrOgr20q9ThlqcgIuizosbYMAekRqIygZ5w6hYQxXCDKikraqPFo8+NY6TuspGbYp6Nhdy1SrEZ23fpofl78b3wFRtDlArtACNOgyxdPeUhjBF0eKNYuGe8FQT98Kwnfbb9GkWc+6B+2D/rj0+eR840wUM2pRh+j2IKLPisPgp6f9xLdS819I7y+RMDtvwW/eGkJ+rM32eiwiDuZj57VDXramnJ5ahxZvxtOyzlfYb1d++Hg3VAucqsScvHRawzZcEFQraLf4LvUoJeUlCApLbdc8KOq5qbK13kCfmFvKIyncrEHYm3c0ac0DZLUBL2kGF1OjxdMdUeX1bAoEkmzlv0cPS/NE1w/6DsLBoygq/KtwcbGEKg2AjzJbWxlhCNlNOjpYWF4PoE7BKyopH75HdqWutpUu/MyDY4NGQX7m1Fif80xqotml4TpPEUvKoKgK+o9LSs2ymjPCLoyUK5eH4ygVw+v96H2e0HQj9xOwthtjKCTG/JD16A/PhGFnLHCYDanG36EIkt/+luVB0EvyElCt4tzqLzjnsPQ5c4WwXNAWoJOfNt+mRSEIjUN/L3+fzDQkQ9BP/zHNjiv5samu3U3nFp4KeUZdeHRawxVcYI+/8g9rD/zCDP6NMaIdk5KwUXRnfAE/NyuI6j7O5dZIMbaDX1PBNPP0hL04pIiBJwWRmje0XkVLIqF/pZ52c/Q49ICwXSC282EoVbF6SzlOW+mQZcnmkwWQ6BiBCoj6GmHDyPxhx8rhe/t8G/hO6XySO/S4n9s8EjY3xZaZZF2ufpGaHr1okQR/Nin9GiIb+Rk4i5K0G/PCIShrpa0w6/19RhBV70lZARd9dZE0SN6Lwj60dtJ+JYRdMGmvCoyquib6l3Kj4+IRO44YaCaSPcBKLAKoEOSC0HPTkC3S/OpvOONP0GX6B2C6UpL0B89SkJez860neXFK6hrrC8XyA6t+BcN1nFESuvv7XBp21QucqsSci4uBZ/+Kdw4qZoGvbCoGC6/cel5iDfBo/m9qppSrbjOE/CoHSGoN5OLPRBr5YY+J6tJ0IsLEHCGS89HyrbOK2FVLDw0ys9+gu6XFgmuB/vOgKG2cjIEMIJeK25FNsj3AIHKCPq1DVtRZ5nQzUXSdN8OGw3f3zhLHlnLsUHDYX/3kpiYvDr68Ll+RaJofuy/9myI0R3l49qV8DYb7Rdylm43fw+EcR1G0GVdV9a+5ggwgl5z7Gpry/eCoIfeScKYrdJr0DNyCwSnoQVFxXjyOhsNzPSrlUYjPbcAd5+no7VTPYk+xCxI3Lv5STwKO4W8CUJfuSjXfsizCaSDkQdBL0mPRZdry6m8442GoMu9XYKJSkvQY24+ROGQfrRdvVNRsLCUT8CtkCV/wWXjEipX88/NcO3QUimLEBnzCsP+Em6mKiLoV5o0h34+FySu4b3oav3eZJmItFHmZenjXbTlnzFntxyA6dwpdAgxVq7oe/Ig/Sy1Br0oDwGRXPR/Urb5r4BViXAzmpf1GD0uLxZc3+/7O4y1zZUyZUbQlQIz64QhIEiz5mFthMMTxIPEhS9aD9tNKypF6c0no9Cu1JJHVjiPDfgC9vfE05bm69SB901un1e28AT9t56N8HVHZ1m7p+1FCfqN6V1hoqctF7m1QQjToKveKjGCrnprougRvRcEfeuFJ5h64A7FqomtMeYP9IKHtTFikjOwMPQB/hfgCpI6pM9PYciw0EbC2xys+qQp+npbo/H0UGTnF4Gk5+jmYYmdl58hsLEF7OrpVYp9z5WRiE5Kx9wBnvistQNSs/PFHuCMoCv61pUsP/bocRR8Lwx4dc6lD3Jtu9PK8iDomm9uouOtDVTeyYaD4H+fS2lFirQE/d61+8CnA2gb47CTsLa3rDZYe648w7aLT7FhWHOYG+nS9gcXrIfrP9wmSmPtJrj5t6223Jo0OPngJb78W7iZkoagu929Cw0NoZ9zTfqVto20Gn5p5alKPf4ZE/nPPtRfwGUWiLV0QZ9TIfSz1AS9MBcBZzkTeVK2+S+FVQl3T5GSn/kI3a8sFXzf5zsdJtoWSoGBEXSlwMw6YQhUStBD56yGw9Y1laL0evBwtJ/NpSCVtYT2+xwOD66KiSnU0obX7ZsSRctC0MnebUVEDD5qbgtPG2OB/OepOWi3gIvpc21aV9TTZwRd1nVl7WuOACPoNceutrZ8Lwg6/3AWXYR7s7rDb8lJJKfnQUdTHQsHNcH/dt0QWyeSOqPhtFD6N9u6deDnboatF57CSFcTt2Z0q3RN+T597EwQ0MgcS8IeUpLP5+BUVYJe+OoVSgoKoGVtXVvv2UrHHXsoHAU/Cv3gzjfohRy7nrSNPAi60fPjaBHDpbE65dYffg8PCMYjLUG/ef42tL/8mLbTOxgKB7fqp0Pj77/+PtZYMZQzZT845w+4bg2in9VWbUDDQHEtiKIWPCI6GaM2C00PpSHoLrduQUtbOSaD0mr4FYWPouTyz5gzm/bCbNE02k2MRQP0PX2IfpaeoGcj4OxPgmFu81sCK9QRfM/PjEP3K8sE3/e1nQYTneofKtUEB0bQa4Iaa8MQqBqBuFeZsK+nB63Sg1L+neJpY4RD48XfHUemL4XTbmHGEknSXw/8HO3ncQeFspZjfT6BfYz4fq1IQwOedzlFTNnCj31qr0YY1aF6GvSJO68j+EYiFSn67hLVoF/+LQBmhjqyTqvWtGcadNVbKkbQVW9NFD2i95agm+pr43VWvgA/EhxqRki0GJ6HxrdH79Vn6d8MdTRR31AH8SlZ5R7Ulb0QiGb+/osMWkVTXQ2x8zgyWNHmmJCFOloaaOEoH7PmsmPjg8QNXuRLrQGGtrQTaFhJJOv7jRrTJm5XrkDDQD6+z4q+SasjP+bgMRT+LPSnveDcHdn2fagIeRB00ych8I7nDnVOu/ZGpxiODJEiLUE/ceQcrCZ9Rdvo7DkIZy/X6kyR1uU3JB1c62PLV63p34JnrITbznWcrGV/oFGpn3u1hVezQeidFxizVajtkIagO1+/AZ06ytnwSKvhr+a033l1/hlz+s9dMF86g44n1twZfc4cpp+lJugFmQiI+kUwn+2dFsFSTfhsyM94iO5XVwqu7/WdhnrajKC/8xugFg2AvHtWHLgGR11gQI/mtWjk7+dQg288x8SdN9C5oTk2jeBcoSoj6EenLIDj/n8rBeN136Fov4jLIiJrCe09FA6x4tryEjU1NL4nvofj+5GFoAcuP42HyZlUlOi7SzRI3KVfuwj2UbLOrTa0ZwRd9VaJEXTVWxNFj+i9JehlgSORsjPzCivEU09bA+pqaoI6laVrE32ZOdfXx6NSUi/6gJe0OX6TlY9ms8PpGB7N6ym3/Neik+IJ+lEPLdx5no5WTvWw+xvO1LmksBD3PbnI3s6HQqDj4qLo+0vp8h/uP4KiKUJz3YvOgciy5/y9JRH0gqQkqGlqYv2021KN1Tx+HzyfHKd1zzTojo5xHFknxeXkCWQZmWJD5CMMaGoDVwtDiTJDdh+Hy3TODF9z2x64NveUqm/RSvyGpJVjPewew61v8G9L4PbfX/Rz0cKV8OzH+d5LKuS38CItBy7mksdYnQGJpjkk7aQh6I5Xr6GOvlBLW53+qltXWg1/deW+6/r8M+bUuh2wWDGLDieuviN6n+UC4klP0NMREMX5sJOyvdNCWKoZCL4XpN9Ht2urBd/3+E6FqbaVUqbPNOhKgVnhndxNTEN2oB8MCnPR4PRpaFsoJ4aBwidWSzvos/osbj9Po6Pnn9f8O8XLxhgh49uLzezoj7PheGh7pbNN6TUYHZZyzyFJpai4BC8zcmFlXPVz/1jPwbB/VF5b3jD6LtTUy7tGyULQuy47jZiX5Qn609fZ6LiYCxJ3YUoXWBoL3X5q6bJLPWxG0KWGSmkVGUFXGtQq09EHQ9Crizj/0nrwIgP/nn9M/djNDYUPaP6FIImgn7ifjHOrbmPqGi56OF8evcpE56Wn6ddbMwJhpIC0HTxBX2ySI+iXn0txbi4e+HDm0M4hB6HjWn3NrTQ4notNoUH4vGyF/lzStJNHnYd7DqJomlAbeMkpAJkOnL932c1+cVYWHjRvQa+d6BQEqKkBJSXQz0pEtp4FStTLpz+zjNuFxs/OcGto3QJNEsWjyr6ycsIXrb+j1ysiqoe2HEaDuaUpazZtRyNf6aKtk3vxQXIG+jSxgtOUI7QPbzsTBH/Xjn4O/mUB3II5LUfR3GXwHNSjQkiJbx3xsds31hfN7OsK6hGtAfHZJq4avOljVevCa2P4ehUT9GbQz+fuS4dLl6FnJCSBVfUhy3VpNfyy9PEu2vIE/MSarbBaxUVYjje1Q8+oMPpZaoKen4aAc78KprC903xYqpFXA1cK0u6h23XOdYKUPW1/g6mOclxkGEF/F3eW/Pu8HpsM3d5+VLD+8tWw7yH+bpR/j5IlEk3+wZuJIIHQKjuczCsswhd/XaJBYCcFuit8ePmPH0Ojfn1oGCjnmVhtgj5xGhyP7a0Uh5RuA9Bh5TyxOiRA54yQu5jZ1wOrjsfi9MNX2P51a/g2qDxN47HuH8H+8d1y/TW8dRNq2uV9wfn92LTejfFVe2EaTXIQfT7uNYilma6WMHWkqOCKCPrjlCz4LTlFq56b3BnWJlUfLCj8RlFSB4ygKwnoanTDCHo1wHpPqjKCXsFCnv7JDw6m+gKzL1JNlHjwLwSn+voCs3hSJ+jTphi3/TqVuvGLFvj94F0sH+JDNdkzQ+7i76jH9FrU5M6wKX3gk02DGiGHImX7xacg5vArhvpAR1Pyi0XS0MsSdOJ/f3hCe/pystIqwcMWHCF1Cg6GrrtbORFEG0pKryY105AlpubAtzSwSkVEjZyiR8WmoKeXVbXmJs1v7v7WPSiZM11Q9bJDZ2Q4DaLfyWY/Ly4OCRMmov6YMdBxdUF8f468n+qwApqF2fC8vQYmmQmIN/NGvMdoqBUXoURdiL9FzFZ4PBfPz1p2XH959MJeV3+x+yW3oAi7Lj+jJoU39hyCy3LOFLBo7T/QsLHGxlMx+LZ/y0o3jfw9t3lkK/zvj3A0SHuO/OatETy+I5UV/OMcuB3aRj8XzFyEJkM4035JhZf1TUdnTOnZSFCl4bSjyC0oxuQeDTGmNJ9scXEJdRepyAdv1fEYLAt/KJAhDUG3u3ARBiZCEijN2ta0TlkNP/G7HOHriJEiG7mayn6X7QQEPWgzrIK41H9P6tqi+3nOSkdagl6S9xpdzgt/M9s6zoOVuvBwrTDtDgKvrxVMdVfb32DGCPq7XPpa1/fFs7dgNGoIHXfx0j/g0YtLM6nscuzuC3yzhXPHqcxKbv/1BHy/izOxrsqaTtY5ZJyNQsKoUdDv0gX2fwgPwmSVW1n73qsjqYWd6Pwq06AfHjsFzieE8VYkyU7p0gcd/hCmYyR1JMUH6uZhgfXDuH1IRSUscADsnt4vd9n9+jWo1xEnymT/xB9YlyXow/66iMiYFPq8n9HXQ2J3ActOI1aCBp24O/qXEvTIn/2rDBysyPVStmxG0JWNeNX9MYJeNUbvWw1G0PkVLSmBRfYbJOvV4zSppS9m0ReMJIJuZayLpLTcSu8LbU11LCoTpO7MT/6wN9XDT3tu4vqzVBB/eNETXr7fWf088EVbRzH5V5+8xYHrz/FjN/dyuTl5gr7UKBMfx5zEbXNX3K3LBSELH+WDwt6c5sIp+ACe17OBoa4mtQx4mZ6LpWEPsevKM3r97sxu0Ncpr0Gu6gdwKf4NPl7PEdiKNjZ+i0/i8etsSgAJEZRniVy1CfXXCFNCPTZ1R7plR6SY+eDLUUZ48vkwid1FNxyGxve3iF1LqeeJuqkPcNNrLIrVteD4+BCMU2OgVVJU5ZDPWXliZMROqGlooCD5JVbfeINVJ+Nou9Xmr+CyYSH9nLd8HYp+mgi9wjz88OUKHP45ELHzFiMjIxs+v/+C4sxMZJw4iezLl/FxTmM8NzTHwKY26LVoHKyzXuNImwGYtHEWNdMP/t8MuIVyad/yp81F46H98dW/V2gKwY6uZvB1MRUciFRE0EU3aYTI9m5ihbDoZOy//hxbv2qN9q7imo81p2KxKPSBGB5x83pCgyQcL1OuNBFq0G2izsPI1KRKHKWpEJ2YjvDoZHza2h5H7yTRg5/6BkL/9rIafl6mojfe0oxdljo8AT++8m9Yr+U2xk9NrNHtAueCIS1BR85LdL44UzCUbR3mwEpDaFVR8uY6utwSBoja1fZXmOnYyDJ0qdsyDbrUUKl0xTOHImH242jumTdzIXyG9FXIeHPyi7Ax8hECPSzhblnefWf+kXtYf+YR7buy3z85IP91P+f2pOjnxLl+Q1D3wS3al7RxTGQFr9eqSNxNlEzQSSacg+PETdwPf/0DnCM5q62KSopfT3RYJ8z2QOrVlKBHdO4Dm8TYcl25XbksZmVADr6JNQBvoj6qvROm9ubi7Ij2TxQVD+ZItiiriKCTIHpdSi0e+f2arLjXlvaMoKveSjGCrnprougRvTcEXauoEAUa5QmlWkkxfrq6A08MLbHLvUuFeH59+yAGxp3BOq9+CG7ARTCd3d8T00rTt5V9UUt68VRnsU7+6AeifeflrBzqA29bEzjW54Iz8X//PsANEwNc8TYrH1qa6iC+9Py1z1rbY+4ALxy9nYRDt5NopPp/J3Lm19fTz+CHaxxZW9p0COKNrZBSxxg7j3IbcaOtO9FubwL9LBrNnp/D1akBMBUhOdLO7eKj1xiy4QKtHj+/p8Rc1xUdekjTB/UZ19KCZv3yJnK7Lj/FpQWrMequMHAbL/OebXs0SuACAla3FKprQ7NYGHBQ2vY5BiZwnPAtkudx2s0kvXqY3nYUepW8QP8Tm8uJia7niMZvOAuLisp4v//BJTUBE28IzQ3rfvopLKdPQ/C4qXCL4NK+nRkyAbfcWuPw7SS4vX2KIjV1ePm1oubxHWz14TaPu086upnh89b2dENLfAQb/FrxJkxXSx33Z4tvciT9Dm5OD4SxnjBCO9nshkW/wHfLxsCggDNxtzpzFibmpmLTfJicgYzcQjR3EJJDSTgQ0/xVETH4sr0jGloaldsEkijEIePaC+69fdcSMGl3+fQ8ohtvYiWgXnqoQDQyIbeSaGpGtwriCPDjys4vpGkbST1yOKWvo0FTPCqj8AQ8YtlG2GzgNsYJxpboepHzm5SaoGcnovMlzkSelB0dZsNCQxjEUi3lIvzvCO/XPa1/gmkd8UNDRc2XEXQhssvDH9LUiuuHNUNzB8UEGVXUOoZvPwLbWVxskIwfpqPV158opKt5R+5hQyUEfFZINDZFxdO+KyPeoqlbFU3Qd7fvDa8U7vBWWQSdTxMrigP/LJdI0EdMgPMFzjKnopLSIRAd/hQGkyT1JL0fSBrbDV9UrkE/0akHrJLLvwvdLpyHhonwYPfMw1f4YtMlsSFJUqSQIMC3Z4pn5knLLsDAtVGIe8UFBi57TxCtOiHvpJz6e8fS/gAAIABJREFU0U+wN1PIjatiQhlBV7EFIZl5VG9IbEQKRqDWE/SM3AJ8+/Vi/HZ5C5b7DEaYIxfRmi/kxbfoLGeeObjnLPR4fAG94s/j13ajkWhgBt3CPORqaONosDDN0Eqfj/DQxA5dn17BQed2SDLgyODAZjbYd+05NnzijdE7JOfjlHa9yEuqr4+1wByeb7fms2ZUA8i/2EjKN+LPTUgJKe4WhtQPmS8P5nSH+1QuUBnRdr669AoXdQsx9EEEht8TBjAj17c0DMSw+5x/6hnrJpjf6osKh0ui3o9oJ/TlKluxpKiIaof5kpSWQzXxlx+/wdBSgh47twfuJKZj9fEYakbtYs7514m+tE/80AnOZtL53RVnZ+NBMy4CsKRgMR3nhmH9lonSLsF7VU8v6gpOfD8dbS5xBHtR809w0q45dArzcOAQl/qmb58F+PrOQfR6fB7f+X2Px8acH7F2UQHWvz6OVXDGdXM3/O/ablhnvcKUdmNQJGLeT+rym5+lYQ+wMTIeOQXlrQl6eFpi5dCmKCwuhp628EBp9+FpMCwl6JanIlHXkvtdpecW4Oc9txB69wX9fum3LmLxHsjf/jobj8zcQpBgjnOP3OPGramOh3N6SNwE1jfQxs7RbajLADm4+eW/8kEAidni6C1XYWGkg1MPXlHfRWIieerBS4wozet+8/dAkE1gQCMLXHnyBnMP38OSwd6CfLm8/yIxofznHLehrOhgSt43HE/AwxdvgO1fy6n4REMzdLnMHb5IS9CLs54i4DJn0UHK9vYzYakpPADTSI5Ep3s7BdcPNf0GesZN5D0difIYQRfCIggMKRL4s7qLQA56676DfM5H1u+GU6lbz9tvJsH3+6+rO3Sp6g9YE4XrT1NpXUnE+vfgO/j3/JMKr/OdbDn/GNOCOR9oRRP0/Z36oGEypy1WBYLubWuM4DIa9COfjYHTVY6sVlRSfLugwyZxE31JBL1rYwv8WQVBP9W+KyxSOAWCaHGNOgtNU+HBbtkUmmXXi++f5DAnucyT03OpH3xfb2usOx1H85+LFtG1jknOQNfl3LO0OvsUqW5UFa/ECLrqLRAj6Kq3JooeUa0n6CQyenJzbzGcljf9GPfqOWDMrWDEG1liUBz3kM3QqiMgCOQ7ueaU/gJXzd3Q/KXQh1ZU2BsdQ3zWg/MX1ivIhU5RPtYfX4yz1k2wqulghawPeZHw0d6r6uBbvwZYe4o7fR934z8Y5WdhXsthGBR7CqPucumWKio9+i+hl8yz3qBl8j3Uy8vATrcuKNDgtJ985NKitDRkxsRizNV8NLE3wZj023i5aBFy5izFD9El+FjrFd6GhiF5yEgMbueC9TPWI97ICqErhqPx9GNUlrOZPqb0aAQShGzWIWGqFBLgjAQ640v+M87EXtvODoWvXyP98GEY9+2LrHPnkHwkDIURnDzXyDPQIC9qNTWqKc1PSEBcQNeq4Hpvr4/p/AO6P76E/o8i6RyXNf0YkTbecHv7DAujuNRrI7pOwT/hnDaflK8CfkHXp5cx9OEJibh86z9JQOL5CsFjfdHgWTTGrDuNszbivztRIXyaQ+JveOxuMr2059BUGsWZlOPzt+CzQI7krT4RK9Bqke984Dpirjp+xzU0ta+LxcfEzej5vsiGqsWEbfB6/QinbX1QrFY+wm9Fi04OEo7e4Q4F+EL8+0U1MiS40NmHLzGpbir+TNZBuo4+OrmZ4d+RrWgTSRvQ5UO80dndQmBFQDTyZI7EneTLMode5BrR+DW0MqJyJRWCQx1t4WEY2WSaG+pgzbcnaVyFsIXrYPc3p7lK0jeF/5VI+puQlqAXZsYj8Ar3LCBlR7sZsNASjkUr6Tg6PNgnuB7u8Sk0zLjAhIoushL0v6PiQd4RPygh0JeiseDvNR87ExwoDQwp2uerjDzMOHgXxLLK16W8hdEfJ2Pp72jhIC8MaWlPm5ID7sKiEoWT9uDFf8HtL+4eS/n8G3SYKkyFWVPc0nIKQEyXRV3DRIOfkUNwcm3VJ8IgnMRsnVj0kDKlR0MQsijpgPjfc49p/BhSyIEbIXXEjWZSV7caWZZVNscDfn3h/oIjiu63bkJdQhA0aTAi5t4kdzeJkk4OMsvGtSEyyPWQm0lYGCr07y4bxZ0Q9I3DW2JZ+AN80soeTWxNcOTjUXC6FSUYRpq2PozzhZpnuq6tOqHD5tI0n6U1a0rQT7fxg3kq994QLS6nT0NLJAMAWZOvN4sHapWkQSeHsBd/DQAfHJW41xUUFdODX9HCtyWWgCcevMT605w7xP6xvvQ9VFkhY1l87D6WfewjOMDl6994lorN5x7jlx4NYWGk+tHgGUGX5hen3DqMoCsXb1XordYTdGKmlNhMsdqcBS0+g2ZxEX68thPpWnowKsima8cT3HILWVIC+4xkJBrUR2FpJHCN4iLUzctASh3JfrdWmSkYf/M/7HbrjBtmwujqjmmJWHtyGTZ49sF+l06CrjSLC1ECNTR99RDENJpYARw+yEUvH9XlZ7RKvofRd0Iqvcf48R89UBpRvLT2JYuG2N+gI2bGhsDp11+QNH8BipNfYGbrEbhg5Qm+fnKduoiy9qKuAaTscfFDTwc96B/ntLgJ+06Ve3mWHZAoQc/MyMazlpyG3OHwYST/OgW5N2/BIKALMiM4v1q+GH80COmHDqMkNxeGgYFQNzRA2n9CEqHIH9dBJ1/0jT+nyC5qJPuIYxv0fMy5F5Q9jCJ/K4IaNFBSbdlHHVojyGcQgg9OhmZJsaD9yIDJMM1Nw4O6DihRA7VOOW/pCe+UWKTp6OOKhTD4HGm099BU6JcS9E+7T8dbXS5IHDFpJ3EV+LLu8+bwtjNG2/mSDw5EJ0CCMhp/MRD1c9PoodAEv4mC31xVE9XSUINxxlu0S7qNk7bNKPmWVLo+uYRJ13fjqYE5vgn4mVqCREzqBJKt4NONFyvsht/sXXv6FgPXcPcLSa/4Ij0XvVefxch2jpRI8deIxp9s8vr72AgyIITeScLYbdeoKwvZLPP+9ETbX+dwEnQ+sofFqcPw2rOeyn+hVxc+kaew5+ozrA15gNBpncttCPk4FfzACzJi0e0qp4EnZUe732GuZQZiG6EJNeg8P4p2MUK3kc2N+8LUIhDaJYBGDQzvClCCcL0CuBZo0H+VFZ6gP3mdhQPXEzGinWO5uBsVtSduC86lLhu8S1FV94QqX6/KNei7bdeoSwspkrS+ZduLBte6N6u72CFQdXAgrjHkH7Foqajsm7EKjXZylmzJAz6H33zOqqeqQuJLkPRWRAMqWojVTZMZnDXY2V/8YVtXj37usTIS95I432q+kLlpaqjhbXY+lh4Txlkh10m4mfj5vcoNgxzszAzhDpLHd3ahB2xUvqcl1n4u3zzuB/36wPUFJ9/1XBQ069XMfUF0/ctqqUkk85sJqZSQkoMc0VKOoNuZwNpYV3B4Sa4fGTgcTtFCU/I4I2s0SE8Uk5PSvD06bPtT7G+SCDqxRto4vHIT98iW7VE/43W5dbmy4G/A0grD2nBxdf67moAf9ohbM0oi6LZ16+DsL50FB6pEYeDbwBRbL3CHNXwpiwX/d749+U4sAgm5Lxvdn5+rXb06iPxZPAgif030cLfc5FToD4ygq9BilA6FEXTVWxNFj6jWE/T0nHw8b1qxJk+RAA7pMRMZ2lxE0TZJ0VR79UbXiG74J5T6CO929ccO9wDsLzUzztbUwezWIygJVy8uwvSL/yDOxAY+r2LQ+A1nenfaxgeJ+qbY3LiHgAzTzUGpxntQzEkx7fjdeo7Y4+qPGRf/pu0XN/sEP13bUeXUR3adjCT9+mJ9VNUoR0MbdYqk88cWPcAgWnpC5Pc36IBkfaGJGjnpz84vAsm9+vRRgsBHvqpxlL2u2607co8JTfqJCX/HRC7wjjwL0So/M7TAnxGLYJVdfgMhz75qoyzR+6NXv0VUo93yxT36u1gc+Yfg3hke+Cs0SorR/vkt6kaSp6kDg/xsZGpzG21STHPS8FHMSYTbt8QjExuQOBOkFKhrQKeoAHma2iAHX4dKD6bItW3uXbHLrQvIARY5HFt5egW1kjns2BYbPXsjV5MLHmecl4FszTr49fJmtHkRjZO2TbHSZzC6PbmIa+buSDAU5mmeG7UezV5xGi7i/hLm0Ap7upnj+OINyNPQwt8evSRq7knQOrIJ5LM68HN6TQ4mSgNRWhrpUsJetlz8tQvScwoEJpbkuqa6GgqLyx+w9Hl0FmNvcRGWX9YxgV1YuCCdIxkD0RSSTBA8gVo95jhy1AB9cqoCwORxMJo95sgOKbM6/ozz9cxRpxj4OlUDZk+C0eIJl26IlCeGFhjT5Sc0KFBHnyxtEIqtDjXkqpVAp4T4yolvJeI0i7DPgHtmTEjVxQ2dQpypw61l7ywtOBdoQKe0TRFKkK0GGJaOjSfon4xbA9vEOGDI51g8pHxaQhLQae/VBIzu4CzQBpMUS56/cxY3Ryd2QCOr6mUNKMrMhLq+vkRNZLkFU8IfJBH0BUfvUy3y913dKsw4Qoa2f+8pTLnwht7/JNTCo/m9QDSuDadxz8yISR0rzSBR0fQIySeHTQRrcmhVUWrGPT/Og+chLgjni24D4b9SGPOAl737yjOqqSaxVEislTvP06hsUnjixGc8ufDotcCVKqCROdX4khK4/DQeJnM5rflCUpoO++sSbj5LpenV+OBo/HVe9sqIGKRk5oEEZt0U9RizRSy9+LqOpno49ZO/XFf7WLtA2L/mLMcaHAuFtgNHPiUVkpt715WnGOHrVC6rRlkyzM9L9HcgSWZZUkosNMgByKNS32xy/VivIbCPE75PSRBU3yTxPOUp3m3QYRe3BxHgNbm8FR/5HZLfI18kZbGJatYW9bI5VwXRwu9ZSNwcYh0hepAiup7E1Y5YGok+e8lBTvuFXHyOisqu0W3Q2tlUomVU9KxuIL+3zaUuEkQeSeHp525OD21F8SeuUcZ1hHFYKiPvcr2Z5CSMEXQ5ASlHMYygyxHMWiKq1hP0rLfpeNpW3O9cmdiTwF9W2W8q7VKS5rJX34UCjXdFjXv2W4QjwT8LLhMN4dxWX2BjhNBfVJlzrW5fogR9zYmlcEpPwlsdA3za/XcBQSGHFCSCOSFmhJBtPTa7ut1IrP9Zt2nYVgNZj4yssKzZUASdEmoU+Q4K1dTRpx8XLdsoLwu7jnKuD/Iu290C8OnDCKrx/s/Vj0bjL1vmthxGD1ckjVPe46mpvI0evXHGxhubw7jNeIGahiAC/jedf8T6E5zJ61krL7RP4nzESZv/XDrh++u7EPhUaLpINO6bwudDt6hA6uGQ9RLV+BNLgBDndpTcLzq7BlrFFUfjJ9Yiv1/8R2JfD0zs4JqaAPVSawRyKPFV18l4q2MI3aJ8+CbeoQdkp228ccmiMXo+Po8g70FwS32G76/vxh5XP2zy6E1l+ybeRudnV7Gs2RBK8oklDE/eyXWdwny0So6mMv9t3IMGCLxr6iSwPiB1+sVFYsztYCqPkP/Ief8IIlUTUp5fyFk9zGhpAs/E+5gaZ477uuoYka6DVnc3wSmZSwvJl38bdcdOdy7bw9oTS+GYzmllRYuoBYRGCeCbrYZL2nnI09KGe74G+mRrCYj6YhMufgYploVqeK1RggKR3YZzKdEvUgOCjLnDis8zdGBVpE5N+Em515Czxoiu74Q9X0yjVgErhvgIyDOfHpD4lo7u6ExNebPyigTaNUIIGpgZ4PqTN6ijqYYdC/+G/9Mr8P9rJW7lamHbhacgEaCd054j/PRtPDpzAYHXQ2Ey+CNYzS7/TMrK4+IhFDxPhJa5mcTczGS8JJ+2uppahcS1HLClfyAWJYPWclYXhBiQ9JUByzhLJVJIpoSUjBy0mxtOD6L2jmmLj9YJ0z8SApNXUAwdLXUUXb2MpyO+BDnI/bHjOJphgbQnpv+8K5WohYFYwMQysUbKjpf4tDedzQUPIzEdSHBIMvb2LvXF5rxn7FR4nuACWCZ16I7Of4o/X0UDVJKAqf18bCCaIeKLtg40PgTBRF9bE8N9HTBm6zUqr7GVEY5M7ABCXjsuLv+s5A+BK8KaEFDRiN0bhjXHvaQMLI8o7/YmSTsqKrewqJhq3ls61aN+ztKUM606wiz9Fa2auHAduvQTWsqVbd9iTgQ9ROjuYYl1w8Q1+ZIIuughR2XzJ9dEXShI8Ev+oIPE+TjTYwBsngnxIAftA+I4dyq+vHLxRN11GxGdlAa7unoghyirSi0PyvZNrIWsjesgK7+QWj2U1fhf8G4J4zzxgxYig1gHkkwmN6Z3hYmeNsihStl1IhloPEoP5qTBv2ydxR81wU97yx/uf9zCFruvCP3ih7a0w87L3MEKuYfK4k9cK/h7gL/mYKqH02UOeIgVgF09PZqKV1UKI+iqshLCcTCCrnprougR1X6CnpiEp53fTU5VWRZnSrtvMD+KM0utqETYNUfAMy5na20tN+o3QIyJHQbHCjVw290DcMyhFQ3WJ4l8ymOuRHt7WORwQ5LMBAMz2GZyGyO+JNcxwYhuU9Ez/hzG3xQ3me/Td4HAfJpocw+GTK5wqNfM3HDZoiG+uXNQqulMa/sVplzeik0evXDYyRcoIapI7pFMNMt5Gtpi/VXkniBVZ0qsdNvUCV6vxf38SPdh9i0R+PSyxJEct22GLgnc5vt9LuctPdD2BefnSkzTLbM5M39iPjqp03i4v30qCHBZFodZrUYgS0sXt+o3wOCYkxgZzbmV0AOwHjPKw1ZSIgiESSL6x5jYYKNnXyyJ/EMixISAk+wXTUstB8pWIpkhyCFFmENLxJnYYv3xRfTQ6otuU2kMi9Hxd1AvMwk3nHsgVJ/TlpNSpyAX427uw0s9E/zbuGeFy9soXwO9s7UpQU9Jz8GrVs0Edfl7f0bvRvDYtgoGJ49xBzuuftSiglgjkUMM3lqCNAx2TcPWR7nwOHsIrZO5IIN8Ia4axPppWcw+NIoWklz+eoqBKWBigvoJcXju0w7f2vWhGUPaJN0RHOLEDfwSgaM/Bskyod+2LW2a8+Qp+m6/h5i0QhrMkLdgKHz7FicT8/Dox1/QtvgVPP7bQ3/qb7Ztpy47ORpaSA9ajb8b98B/Ln6YP6gJft9zjc7rjqkTsrXq4M7Mbnj8v0nQOBmOX31H44aZC0xz0pGiJ3Sh8n92DQ0L36LvzaNi2DmkJ2GnWQIKvvgKoxYdwvDoo2ju5QidlGQcbD0AN67F4KexfVDv+EGkBR9Etp0zXhSqw3fXPzDW10VJQQFS1q6DppUlnhlZou/JdNinv8Ci+3ux3tqXuov80NUN47u44tCtRHpAkbV0ETwvcNr6xBYd0WXrenp4QVzUzI10QQ48REkVITyi/uKk3e5v2gpSeIquH7EIIAcOfC7ssjdV3dx0engmevAlWof0JZp9pMKbEoCNSR1ETe4MSVpf0k40fzpxQ/m5u7sgtWVFci/5tIRhLkdGf2k3BnvXj5foLiB6iKGvrYE/h7egrgUkEGdTOxOBOwffjyTSKGkMJGYB8ZHmLQuIBj2vsFjgKkDSdE49uACWyZyFHyl/evTG16XZUsg70yInFVkGJvgoYGpl8FV6jaSa9bQxBjnkuO7dXBCvRLQROdh9amSJHV+TAKAGNP4OH5W/xh3XsCGJA/Ky1F2A3BPEv71sIVYlotp15/r6OPGjn6CaqqZyYwS9hjeFApsxgq5AcFVUdK0n6ORhPmXwT4INqorizIZVBoF7dR3Q6K3whS9vgMgmvqxvfdk+iFmzf4K49vC2qTN+7jAW7m+eYMWZ1WJNxvZahngtThtJNIF/H+LSBokWYgI9IO4MprUZhRYv7wtIfr8+85GvoYXujy+IpUgjbYnGuMOy2RheJl1MWdlrTy+H49vn0HZ3R9y8dTA10MaSaX9iypUtlWqDy8ohZvornXOh/dcaibBnaurSQ4Jdbp2x8vRK2JU5xJD3WjF5NUdgepuRlETz2qw0bT0M7TkLDVITMPThcTRPfkDdCsLtWqDrM/FgSpX1mquhJbW1whTf0Zh/bgMVN7vVcJjkZQju+38a9aDk0S/hOo3LwbsKkLo/txuD22YuCHh6maaEJO4/mxr3RJR1E6TqGqJHthYWLfJH/zkhCNor9Fkmh2/EbYJ36eHnsd6zL41FMCiWizZ9v649wu1bIEdTFz9f3V5zkCW0HOf3PwSdWiFRZppxfZQ09oLJeU6bSw7fzNxd8NXVvdC/zR24VucQquxabPTohZFxx6GeW941gsgm2UxItgRJ6Saf69eHTVaKYNx56prQKRYeoFQG0k2/gbjbeSA+nf55pVgS66VcQxNEjvRE8w2cZczs2zvRIo67/5I8WsB/72aqOb3/IoOmsCIBFJvPiaAWI8TC5eQwd0xZvI8ecjqlJVEXmbHNzfBzbPlUqkTmxC6uWHlcGJG7fk4qmiU/oPcDyVyy1b0rtjXqhvrZqdSSi8jlCTshsifuJ2PkP1X/PkiwsQWDmuDLvy9TP+0JXVyx5cITbBrRklp1kMjgxAyaL1N7NaI+9MTUmhwkEO0p0ZbyhRD92x5NoFW6BsRyZ+2a7yUG7SMBVlePmoqmLx9iVusvkaMlDDa2YKAXJu8Tz1Rx+bcAtJwbUe37nhB0QvxvP08TtN1yejHqv+WCthErnS0Nu+F/N/bQ76KHrSRTiKR0txUNwuN1PIY8iMA+Vz8EDuuNbzo2wMSd1/HV3BESnz9j/SchvjT7SLUnpsAGJNAtcaEoW34MdAPxSloWzlkflM3HLhqfZJy/C37s5q7AUUovmhF06bFSVk1G0JWFtOr0U+sJOnnBjf1kusDnW3WgZSN5VwjMb/E5ztj6wCorBZvCFwiGsdarP769zfnqkkJiCIy9tZ9uePjAf6MCfsFzAzMaH4APukfqrvYeCEtTf8RpFVGfWeNiNWjE/U03j6m6BjQLwIzWX+KilYdAvkfKIyw5y5FgXutHUpoFh0wRg2ZUt18RtXIY1Rzcep4mCBpWFr+Y8T54s3kL6g4bBm1bG3qZmM6RAIObIoTzFG1HDgaIJq1t0h2B5pCM5cYUf5zs3h8N33Imeocd2yDI56NyS0Y2uluOzQEhfqMCJnM+4iLaWNEGogEU39Xas36B6sSJEMXrtnUreCWK5xQWvf5vox4Yfk+ojVUU1lFWnpjTegQVT6xHrLNSsPL0KkV1x+RWAwFpXLpExRG3ltv1G6DZK6F5NLGqmtL+W65aSQnmeGjBSF8XP1x4i3nnNsBTgsWNqExiWUAsmDom3sTC5p/BIeMFGqQl0gwkJFDlZ/fDqjx0EA30uaf9UMRYuCA+NZ86rRATapKeklgtldW6k/SNKZnlY7D097HGiqFNqUZXNEJ6WWh7eVnhj8+agUTUJ37Sa/u54bEvZ3FBypJmQzEj6CcBiSeR6r/46yK6NLJAOxs96PXnXE9InJkT9kITdxLwsqBIPD7FnP6emHpA3E+c74e830jsDWJpkqZTdZrTHWGzYZKdhoNO7ei7lVglkNS2pKxp0h9f3wmhh8QktshLPelNtVecWgn31Gf0/UIOFvlyKPhnGqOkbJnQaSJi6tpV445Vvaok/aymBhdQ8VxcCj79kws0SkzoF330buIplUWJEXTVu28YQVe9NVH0iGo9QScAtZqwBf+GzRNgRdJJEZ9M8gJi5cNCgKQOI/nt+UI2EttDuRc/MR/kU47xEbnJ33s9isK4W/tpHVG/ed6E9bGhBb7zn4Tv0/VpMCy+iPrWipqkk1z1nA9sITo+v4mnhhaY8b++gtzagx+eELP4KDh8Ek0aWIotFEl/RExi+Rz35GJlkZkbv46HQ/oLuuEivt0kivvQnjMFwctIbvNuTznyVRhxAZ42RnCafBiN3zzGYyMrZItoY6S5Y7SKCugG6qu7h9A7/jyIJveJkSU+vR8h6EcaOYuafyp37aZovyQjwYw2I8ViOfDXCXlY26Q/JtzkfGOvmbnCPuMl1brx5aGJLX7sMI76xBeoa8Lz9SPkq2vihpmbIKWdNPOsDXU2+S9B67t/w+MlZ3ZftoS3m4+uUeKHS4qaFzHbJodZZbXk8uqPWMqQ1HysvBsESAwWEnOBWDaUtWJ6NyMS75W4gZDnG7HqIHFfbDJfIcy+FQ1MSQpxE+j++CLNukIOC4jFx6bR7fH5XxVndSDtSATzXk0s8f0uLvL4gtxr8A4VWneEOPnCZPIULDp8F3+cXAbiXvF7y+HUtYrP6MKPlLdAkYQXOdiyyH5D3z0kxgtPwomLyYjoozAoyEHnhGsgz7eJfpJT3qmVFKNETZ2Scf4dOixwKnWjWOdeAIeF3LNgZtuvMObGPljkvMX3Hcfjfr2Kg9yVHauolRv/7jXMz8LuI1x8F5LmlqR/Jc9c7eJCaqFzztpLFW4RmcbQ1N4ESwd708j6/L3Q0c0MJM2nKhRG0FVhFcTHwAi66q2Jokf0XhB0okWcF7WemnoSv+KvA7h0Y5vC5gsibZPI6J2e35CI55aGgdQMjpXaj4Ck1Hdub5/CMf0FNcXrHX+Omv4SAkB82Ugh2oQuz67iTn1nZNSpj0yRbEHEp7VETQ2BOTrwyhc3r9xmkIdEzVKT99Jo3CSyK4nMTaJtX4p/I8ipfXhCe5rSZcelp1g0qAleXLyCLkG/YUnzT7D2n1/FcvmKrgIfFIj8rTKC3sa5Hi484oIVkk1YjqYORrZ3hoGuJlo51oNPXjKeffQRTVlnFxRE65Hxfby+vM9tZXcB8aF7/DqLmu1JKq7mBhjkY4VOY/tVeTMRi4aDDdpT32USdX32+Y1wFglI9keTAbhq4S5mBSEqdIXPYOo7vHn2EETtCkXIxTjqt52oXx+3XFsipkQPxepcGq/fLv2L9omcCWjRZ1/i41RnqiEja/vL5a30cGF7w0B6nQRim3bxH/zTuAdO2okHYiKBqvjozvGzumLpyXixlHEPAAAgAElEQVSsO34fIQe5eARa9vYw//5/0G/XDr/9eRLD/5Qcp0DH1RV5sbFw2PwvNC0s8OSrUSh8xlkzvItCrEkG5NVDgzexaHmDy6kuWkIa9oO+ZSA6n/ruXQyvWn3ym/rKGpHnxOf3juGzB1yAs7KFmOqSDAImeZk08B8fL4GYhCcYmAtSTB1yaksPqKQtxL2EkFKSWUC0XLBsTH+z5OCAkKdQx9YYHh0Ks5y3aJUsNJkm7jC8CT9xH/BLuEZdZ9Y0GUADIorGFCAHi44ZnFnyLtfOuGHuii5Pr1B3g30undAp4QYlVaQQM3q1khJE2njTrCNkLHUK87DuxBKp3GdIKlLdwnz6jq0obkGoQyt0f8IdEhIcrLNeY9qlf6WFTiXqfRH4G02H2lJkTcjAyEGgZnExtRQg7xZitZSsVxcv9erSrBCv6xgLxk/eR4Q4E1P+nUclxIuQMFNykJmjqV0ucCUh9CRrBZFvk/GSxqwhrh2SDrYIuSXj4gNy8t0QV6+z1k0o8SVj++XKNro2pJB7jMQ9IJZgxHpiZOCv9O/hgSYoHDuKfp7c9zd8cXYrzUJD+iCBLsn9c8/UUTAT9ZJiNHkVSw+QSYwKvogSdBKvhBx4kDL7/F90Lze53Rh6H466EyKwACPxN85be9J6JLBsy+R7OE6f02rweBNP73GCAzlwvW7uhoPO7elvROpSGv+FjNkwPxv1ctPpb+ZBXftyhwPkEPH/7F0HVFRHG72CSK+ComIFFRV7R+y9915ijSX2llhijyV2k9iNLWpssffee++KFSsoiiAgiv7nzvqWx7ILu7Do4v++c3KIu/PmzXwz7+3cr9yPVUVoyMjz6oF4ZyzIXw8vrR31LvcpjYu/nXv66yYI1Hv8RmioAHQjKNHIXSgA3cgKTQHdfTcAnYd8siXvzVxMTZRD4hqJKbpx7XGw+vge9e8dxSVXL3S8tk19yCJJEH9InSND0eXqZnFAkudKsqwQDyCDz63C/sxF0Ola3NIhxlprvuzjY5c25D7LvKuj3U1VmSFJCGoyvHuF5ncSrjFtyL2M3XbD8AU48CQCL5+9xOCzK1Do5V31LdpWHy4Ik/iD2fXKJhFeLokE0ItGmuOclQ6W7i8/wGT/vS6rmesanQqtQy3xwvwT/rWPHcZY850FfD7EBuis5+xvEY2fR/vFyiuUxvImPAqFxqgAAAF6HncHPHkTIdquPvMIP69XAUZtwFvqI/+oXQiNVOWIxgfQS7M0jKstTt1/JQh00pibxcljJDmVuaMjUpnFWCDkzLNkcmau3JZLsevb8t5LOhTHm/AP8MvpClc7S8GYTIbokuNj16eXDhkLBk9F5AVV3jE9zyQz47NHhnDv148wN3+DOHXHD3YtjPFjlqLu9X2w/RCBQdUH49Xn1JBHHNSsP1k857/1qAGbMr6CzMjBykIYPoZ8ycGkLlZ2KakmjWKpsxUdiyMi6CVs3NMLXV549BoHbgWBhg0pxJCfy1l2R22+hiXHH8Ta2rv6lhP/dra1QDp7K0F0tePKc2S5fxVpN/+LDGNGI02WLKLNy9BInOoxAO/TWKG/WyU1J0LGqVPgWDtu7WVe8+FFIMb8OFaUYRxzYqHIw3Rq3hznfGtj5vlgdCuWHnl7tYzzuHn89Sc2jJ+L4k+0h7Tygrf2LrB3tEOqx7Fr/5KUrmel/ugZYgXrz6ngEHwdXnf+xT7vxrAJvoSVuasip5kr/CIt4Hh/PYo+3A9WGrjvmEEd5sr+CVb9vZohKviMKD9X9dGZZI1iuuCWE8cy5hfvARpU0kWockBnFWoSK+Xpx8qDxGGfwDZP8EPxPT2K9HwOOL9KAKiPGTxw/pM92tzcjcD0WbAmg6pslyQ+L2n8CRAlAQkwsoc8xYpq7vA9ZQZ6Gm0/RIqDOkEKQ/Kb394Ht4gQAVgI1EgISvCxLmdFYUCbeGwu7jlmxOwCjRLM2ZVAzJl0uTHCtwuKvbghDv/cI5rS4tZe1L13DEzVkXKUdYVrc9xSlZAmtcYK0kHNkG7qKBU+C04OhpRLxggCtzW5KqJSwHkBup7LSmdyTOybBJzz900WQ+TvJ43kOzYNEv9miLZXyONYTOCn0ucRUQ30+CZFFuWrLbgX9mYpJtaclTle2LggyMZJ7JVZB2cIYwb38AtbF1FZIbmFEUZjTv4d6zYM9Zf/riX3GBLqv2Gd3zDp6BzkehPDVM5rJK4WqQoAP7vcIRee1K8nuuzYZhY67lsQB/jTyMWqCHzmJLJUf8dMIiIh1MIG71NbqA2b2sbGEH7uE0r3S/+h3n1VVQMKoyTLPL2iJqqjk4VGgR+vbonTFQ3zrKLB3Hju42Fnlolnk2ld+zMXBfPgSbTJ99wTO1dR+lZe/UPeYYuao9TRCJ5vnmDGoZlgtMVTO9dYRjd+Rj6aC265xG/g9bTZhJGhSOAd8Z6k0YGfc4/SecDz5znP4oL80RREAeimsAqxx6AAdNNbk+Qe0XcD0LUpSp7vK9Vklrej59T2YyRC09jGuZwHsrJPLmNxvlqx2IDZUBvR13WXrBhfvB3+PDANTlHvYvUnlZc6krEAHKLeoaAMbGreePnA2cJj07NJadQfsQ6ZQwMN9jIwv25OwUbqrhnuVur5NWElJ7OxbVQEFu8ZL4iETFEOVG6FHn/9imEbrmDFKRWYWNSuKLouOYWPqcxxalgVNTBs4H9Y/eN/Nl1u/OrbRbTv/NYSpyw/4oqldpAukbV4Dd2uri094I2VCGH/jM+Y7RCJcJknXRtAl3QnlYLS1CVL1eQdoTKQbOnph/weMV4U5psvPHpf1Mku4BHDvKzZR8clZ7D/ZiDS2qbBuV+rxlku5jLOPuCPdd19RZ1nXezCuta57aJTOHLnJRoX8cDUZqr8Nwm0dyvvibwZHUTtYJaoim9+0ncSQB+09hLWnlMd9Fg2qc+/qugVhtazhu+6cwEo4+kqDCS8D0l2KJoGiWZzT+DcvSD0O78abbo3xsncvsjiYiPGJRcaFXqtUhH+SSV4JN2s7eYbp738WtbLZRmrP/f7Y0gtb3U9aPn+k9pLrLyGPjfUKb1Tk71ToUCfH2MZSTT7kvTPWtBXRlWLU4f7tm8ZRAeroiX6lO8Nq/z5sbmnHwICQzDvr41ovTqGj8D78iUBup5fuo70RfIDUVFY8uMGRNikBz5/gtMbf5x3dsd7Cxvk0TBAsX8pjaNEZGqUj7RAKpame/cQjx08sNJR9Wz1emmGu1af4fjZAh7R5rgUdB2hqT4hOn02jN6nima66t0eHo92wyk8tvHnua0b1nqVFaBS8vbq0i0P5fXuHUPZlzfxW8HmuCULpbW2MEejy9uFZ3Nr219Q48BK5Lt6DJo5qwwx3ntD5VWm0YyEXowwIeP40Tsv8SwkAk2Keoi9cNT/JU7dj1tCk8zcrUtmFXWOpedt+5VncLFNAycbCyw9/hDrTtwD+Rue2bmKe2VwsMSztwkDTx7Qmd7C6BbpmWnof0gA/snV++KcuSq/lwzSqc1S4WmIdqI4ffenW/hrQVBWuUoxQXamTXpX8lKXy6IhnGke9PhL0Snx3cs3qwPCA57iYrSt2IddL29Eg3tHsS1babiHvxLGVQLl5XlrqLshuKd3ecNWFTEgQ7kZWs4yo8L7+vmzMIik/hwtDDMsX7jApy625PDTa9r0ujJaQGK8J0DrdXEdsoa+wGNbV5HuE2ZhEytvXq+OE9mIhp2pRVrgl7MrEtnD17nsuHs+jC3VQdyMxuJuLX4Vho/IgsWQ8+j2WPwuxhjRo3K1caJOByw98QCNbh9A52R0jOg7XqaOMUKTnvImX8go9b02oXY0OOafOhH5S+RNqGmyf68A9GRXscE3UAC6wSpL8ReYNEDfd2kNCucoDxd7lderx5yKOHjwIPz9/ZEhQwZUr14dFhYWcepPyleF+VPMdXsWcB1Rz27DKnsRWHnkRcTDS3h7+j/xY2+Tyxf2hWIOCNL1n6M/IPz2Sbx/ehPR71ShgOa2zrDMlAc2XiXw67mVKPPsKnZlKYEZRZqJ7wmGeTArGnhL/YNCr5+QVKlE7trc/VPVQyQj786spcRhheWK5F5Sqa6rJhv5tMLN0F+y+nvlxFgXX5x2z4PBZ1cibWQIRpbqpCLz+iI2n8IRHOAPi3TZYW5tj+jwEERc3ImW13egur09PC0tTWYj87AW1PQHNPcIxdR1h3D6xWdYZy+CB7/Xw9xD9wRYZK6WxH5KD89PN7agfcfaKHIqtTrnmgDd+ZOZGmBwgovbF0eHJTGlvahrz6HbBWMtZdAba/E36uN7nPXfh83Bl/Ax7LUASK7hEaiUpz6K56ocR1e6ADpBuNcwFanWhh6+KJzF2WA9s+bt30fvo3nxzMiaNq4hiR3yPhLpjKE3IAnRwVuBog4ty/VQCBC535va3sS0iePi7ZJAgiKFyksAff7huxi/XRWaKy/3Q/AzpaluIhx68Gftu4NK3ukEM/K9oDDBgPxTRS/ULpBB51hY67vLsrMomtVZMCtLkhTdPHj5DpWnHULToh4iiuFNRBSWdywJM9IxGyisCcz+WpRQedfjExoaaHD4uYY3ulfwjNM08tYtvFq0CC3Dcos0jbzprdE+00scP34cL168wOfoaLilSYOPAW6YuWcSQkJCsG/fPlSrVg0uLi6Y9MMGnLi1HR+jP4j3q7uz7pxRCaAXfW+OShGq/FvKG7NPmPM5AEEbx+Pji3vw9iiKDlV+xYJdI3H7qcpQYp3GDlNKt8P5Jxew/MEFfPoUjWI5/NAwV36sz1EUZpauaBOmYqK+bB6ODscnIcI+GxyCLsHiUxQC3PIh4xt/HHn9Ehvd3fEgNBzvPqdBiwa10KJxA9yKTodRW1Q8I/sHlEelqSr2dkZPlM7ugiOXH2HCoQC0KpkFv34hy9o3oDxo+HgVFhWv0UaaJ+t8E3QTsE/dc1tEptz+rWZCSygIwLZdfoYNF56Az1ibUlnQq1JOnLj7Suzjn9dfxn/nn4CluJ68jsDOa89B7opd/VQRGpRq0w+pa1GPq59PRHzsuxmo0teoaqLOub4s3XwG2y8+jYO3YspKMg92VovC6uifun8cFczdJBdrUyqr4NDYduUZqud1F5EiJWTRMvISU9J4i2RxwvlHsZmsh9byhk9GR/RcdUEYFVzOHxcGZ+Y+u0SGCr6H/zqOgk81P7VBQj3/B6cE4/r8/PVECDGFNcqP+b/E0hOJqwCytltpNJXVi9e1kMyDJnA/TUNEKjOQc2Pp7t/grKU2d4KbIYEGJLdjZNGxMmZYMWc96t4/jmikwmU3L+EVDrR2EkYNCgnZ3qaxFVUMmOsdntoKg86vivcO6z3LCQOHd/DDWJGBho57rVcF/O1TR1zG/XT2QTAWHLmHBoUyofs/59TREYb2q6u9Y9OmyDh2jDBWzRoxN1a0jrHuYYr9eF+/Fq8B92uMWQHoX0PLht3D8FOHYf0rrU1PAyYN0HvOqwyzVGbImbEQinpWxJ2Iwzh79ixcXV3x6tUr5MqVC4cPH0bxqbqZh6ny0Is7ELxnLtKky44PwU/hUq07gnfPhq13WcDMDO+uHoBT+XZwKBaTN/vh9VMErhmB6LBgpMmQSwBzCoELgb65fVrkqjsQVd+9wIHMReIwoTKnttXNPTiTPk+sXCz2US67E/ofmIfLz0IFgRUPAJJoC2MOWvQ3Xk5WgXyyiW/P7ivKxTDP6cGkOloNFBIoev/0Ft5uGI2Id6GwtXeEXcNReLlxAmBmjgxhQQj6+BH/ZMmKvFYxJVuksdwuWwe5jmyNtWsZBvdbMQeU/ByMsInj8fl9jFeI5X1IuGXj5Yn3d2JK3rCDx1Xqw2Pvplh9MWQuX/ADdH0cgCkZMsLe3Bw/NJuAj9vH4+L5s3B2SYvg4GA4umfBnUun4eYWQ/7GjiRPoz09jaOrx9JD/zdWMEcqzHOIxFszFQC/P6GWqFVKrxM9uVt7lY0D0INCnuCPrYPwIfo9UpunwZuwIGTPWgL3HqrIf+ytndCodA8UzlEOqb/ktekC6PIxru9eGkWz6s9u+y1fFSznc/z0ObSqXQHR0TrSBDQGKK1FrvR22N2vPAiYCbTL53ZD8Wwu6rUhMU7joh46p/ch+hNO3QtGkaxOaoPBt9RF5IdoURqHRpqvJQRDN5+FIn8mx3iNAdQ531PBa0cgKkQF3Giw9PT0FO/IE8dOIn2GdHjz5g3CwsLg7OyMPXv2oHrFujAzMxPe35DwV+hfbwYyu+XSOr1NNu9xO80ndHprCZdPspASABP2jURURAga5W+OM7f34k34S7wJCxQGroLZy+LMnb2ws3bC58+fUK9EJ3z6/AlbTi+Cn09DZCzRBF4fzGHzWabXL6knf27ogdpZi8DVuxFWXhiNK1evinnZ29sjknXCI1SRP2XKlMH4v5agVL7ssDA3E97ve0HvUMZL5bWWy4GbgQgKfY9mxRPHAP3p02fhTc+XyUGkVOgrjMzYde05mhXLDFvLmBQZ6j70/Ud1X/SaW6Y2j1X/Wg7Q+T7vtOSMGqBLvxMN/jomalhT6NGnMUCbsL1/YKgApx3LZIdnOjuUzO6CtHYxxlnWJCdplZ+Xa5x99zbyAwqMUvG0TGiUHy1LZMHiY/dF1IFkNBhS01vomJFBkkjvPc73UXA4Og77R5Sn+2BhCYsPqt+OXKdOitQbebqNtjn8WC4HhtbKg5GbriYaoG/uWQb1/jym7/LFaUeyuJA0tqL6R8Egf3StPAhhFtboVsELC4/cQ/ZXj9D95nY8SeOAPVmKI1vxAsh1/SQ+37gmSpORTK7E8+vwfXZVTY7HFIypAxugRHYX+E7Yp46KYPk5RnHU8HEHuUgoPSp4YvbBmHQv8cxHfxDpQ6xnz/QNjovg/oZL1i/h1zG/m06RoQixtBVh530vrBF8CkHWjrHCx3UpZ7BfdzTpWE+kUpXPFdMnDdwsERpx7CjGnVio9fKLrl5wjHonDC6awkojjJog38OSPRPUX+fYvg2WOXLg3MNgtPrjIDZ+iaiQX2+VNy8ir8cmA2bKjudbVaQOSf4MTZkI8cwLx7vXhV4YCm9MoQc+vshFpnm0P/SfMW+ZqL4UgJ4otSXrRV/vBJKs01A6N0ADJg/Qm/j+JDwyVx6cgJm5Gdq2bYsBAwbAyckJDRo0QPHixbHDUWXV1SVPF/aAffH6sC9YHZEPLyNw3ShU6zgQNxxLIuzKPkRd24N3z+8hTYaYGpRRgfdgldkHrrX7w8wyxhvNe3x6H46X26bh84f3SN98rN7q/hQVifcBV9CnlAsC34Rh2RcvwODquWFeoBaaF8uCLGlj34ud83Bz/fgF1N30WIQVMse2RYnMyOFqJ8Km6/95FJcex/4h4YGMHvh6tWugmE8uTJ8+HX/88QemzZgJn8LFMXr0KLxu0RR/vHwp0g5HbD+GR1NmoGiDKvD+oQU+R0biY1AQ7laPHVkQtPmw8GCLcX38iJBNm/Fs2DA4DxuOIR9zoqZ3WtSxeI2Hbdqq9WLu6oodo/5GtZ6qnDWK2U990Cc6L/pU8kLtQh64PHAQcvfriz5jx+LQoUPYunUrsmfPjut37qNdy6ZinefMmRNL14XH7Mbr8A9Y3KE4KuZOhzsvQkVd3btzbsDiC9v6XIdIhH4B6NTJySu3MebPZchmFQEr88+CbX3fjUBkjkyFLqV+wuztQ+Bslw4tyvYVoGzPxVW48/QybgScQd0SnbDj/HLhebSxtENxryrwzVMLv63ppHMPMESdgHd7n7ICRJiKXL58Od6h3Lx5Ey1btkw0QNfsnDViGfXQ3jfbVwW7pqLv5BgHAc2Lf4cjVdgLZHK2xYQJEwQYHzduHHx8fOCXtiOm7egGc3NzPH/+HJMnT8bMmTORxa4AWpUfIIb0z8HJCH8fih+rx5Q4ko+V6R5kY7CUVS+Qvv9laWP0rD0JHq5eiHgfhsFLGsDBxgUtyvXDx+go7L+8DvdfXBcRUDaW9uIy3svSwgbDmmo/yLPNwMX1MLjRbGw+tRD3Xl1Cw4YNsWTJEvWwfv31V2zbtg3W1tbImDEj1q5V1WP+3qTqtEO4ExgmpsV3l5TuIv2bfyWvN///3vhaGLrhiiCcvBDwJlZdZgnQG5oCI+mUBrdcw1XRQOMb5hdRCZLw2T50O0hEe0zZdQvzDsew458aWlnUB5fk+aOneF0tJgrJzM4Ouc+qoprkAJ11vTdfeorjd1VkZZT+VXOJ6Bht3BD6rv2OPmVF/XVdwveTJu+EtraTGufHkHWX1MZ1RnBkcrbG3cB32HTpCeYdUumAel97NgCD1qnet6ydPrR2HhEVVBOBuByqKuv2Xw9fFMnijFP3XqH5/JNoVzorxtRXEaFRGAnEQK8T914JMKwpJCd9HhKJJhrRATSaTJDVZdc171X2d3Dm4DlszeEL14gQTLJ5iLCDB3E0Y37ccM6KfVmKIsTSPl6+FK4fiep8MthhrHUAAhYvE7wMcwo0xCGPwuLWTK+r8OQCmL/NZLLt2Uojf6VSwnB24dEbTNl9C9HRqhSHW1MaimvOPXyNxnOOC+6GOfunwjEqDLMKNUXB9s3E+WFlo864DnuRGljq2TUMK/MjxqQNxs9v3IWBgg4Qm4+RmLtvCh44uGNX1hI4m94brW7tQYnnNwR3xd7MRcU4q2Sywqz+tUGDXI6h21Hy2TW4RbxBYZ+s8F05Q60+GmkYLRlpaQMbKwukClEZyTbmKIuSz68LcuI3Lu5IiyiRjvRnwUY4ny4XntmqjIfdL21AvfsxhiLmpZNkkQSNx/9UlZj8lqIA9G+pfe33VgC66a1Jco/I5AH6sKaLkMElG0avaotaDavi1KlTuH37NooWLQo/Pz/8999/QIu/4tXTo6mNkbf3IoRZqPJcH05ugMkrdmDM5BkCoGfJVwwPL5+AfZEYoB96fivydJ+NcDtVvWlNiQp6gOfLBiDLAFWJJrmMrJsXo7+EXvJzhtU98b+OTq0a431EBCIjwkWoadDLl0hjaY1MGdLj3r34S/4Evo1UhxhWzO2GxR1iynEwlPKnledjjUE6kPE+W7ZsEYf37du3q3MmhUfw0ydR9zVd6tR4+vYtzKxVId5y+RAYCP9yMcyieW7eiNMmOiwM5nYxOcrh5y/gYatWop3n3j1I7eqKVZdeIGfnhsJ67FC7NjJOnIBUFipPFL15BBDp0qWDt7c3fv/9d9SrFwPmGZ7bpUuXODqi5Z75wxmdYo/7r24xBHir7N7j8Rem9UVVrUS/OXLkAAEoQcyDBw+ETtLbZkfvulPRf1Ft/NJkHtI5qry8BOMD/q4jwnPHt1srAMfao3+gnE8DnLy1C4Ehj1GsWFExvhYtWggvn1zYNx2DiQmLjndTJ/FL6px7gOPTFOlz/jXUg64ZppvEYSqXx6MBHoj5brOwscOuTetQoUIF0frly5eoXbs23j79hKdvbwuv88ePH8VfKysr9K//B7Kl8xZtA4JuY96uXzGuzWqDdT3w77r4pcl8uDpkEN7xvgtrwAxmqFa4JfZdXosC2coIL3rhHBUQ/v4tHgXdQlGvyjh7Zy+mdIxL5iQNgM/g4EZzMHlDD9jaW2P//v0oWDAmLeLu3bsoVKiQSHfinENDQw0ee0q4QBOgS8SSrBJxcqgK5NaaeURNdKkZfSUHvPERUeqjC74nCo7ejbeRH7Gic0mtUQrsZ/qe25i5LyZ66u74WjCXpYSwn1uFCqsjrywyZ4bXHpVnXj5e1opmNEDRL55jfk8Sy+yutokG6IOq50a9ghlR9vcDsabctlRWVM/nLjzYLG3JqICzD4MxY69qHtpqn1OftWcdwbWnb0WbQ4MqqFOQ9lx/IdJtKGwXERWN5vNPIFtaW8HHQbn8OARe6exQYcpBEXVA7gGmXlD4W08iTm2/GfK62fJJ8D43nr2NZXzY9FMZOFhboOKUg/Eu8U8VPTGouncs/Uv7Rb4m23uXjTcthECadeCH184jvOya45EPYkSdvPj72H0MrJYbDQrHnLGYCsR0FBonSnumFZfQg954jqpSAol+o1OZC8K3G2NqwDqNOeRRJNI9aBhiegk5JiRhVCPBujxiUVMxlb3TYVF7FUFko9nH1CkbXLfqlm8RvHQZDuX0xcib0QJQVy2UGVPalEDkjZso8fe1WHxG1CGdGPSALD7xCGO2xnj67a1So33xjKg/YwCs8uVDhTQV1KzvSX1W9XmeE2qjAPSENPT1v1cA+tfXuTHuyLo3pEZlzSgW1+zFqh96dpwCAPrfyOCSFUOWNcaJM0eRL18+HDlyBIsWLRKek/DwcGT9OXYYtjR3vmwZfvd4dnvs3rwOHXeG4WPoKzyZ3R6/zFiM34f0hmvtfljYyQ/dOrdHmeFrxaFgfruiKJ0/J+bPn4+qNWoKsL3yC1mZ1He4/ykE7/oLHj8tEx9lcrIWDN0UEjs1G7oPN9JEC+/0/Qm1xUGSIflz586Fo6MjLl26JMI227Rpgz59+qBRoxhSN11r13npWUFwJD8MSG3ppa089RCiolVlv6SXvJ2dHSpXrixSAmbMmCE80UuXLhWgeOzo0fghNAxjAl8gIppj1f4KuOGdRz0kbQBdc7zvTp/Go3Y/iI+9r11FKnNzEfI8ed0Z+Dl/RvnqpWJdwrEwd5Yh7OnTpxcHcq6zJA8fPkTu3LkFwNBH5AA9JNUn7Lf+gOLvU2PfmWGoWbMmRo8eLYA014BGgdatW8M6ODvK5quHYcuboVuNceqQ3/D3Yfh5SQNxWwL09x8iMW5NB8zovFN85v/sMl47X8S6devEvxlKnBKEIdA0hHBvaJNr166hbt26CkA34cUsOGg7rs1tC7wPxZ1bt0TEiSQErQVzlsLDoJsi2oiedRpbuO8H1p0DV4eMomlw6AuMXd0e0zurvKOGyJQNPUXueZ3iHXDi5k5sPr0Q7z9EwI4hsyU6g+ki284uQf/6M4oSCAUAACAASURBVJHD3QdbzyzGy7dPcfXRKUzpsFnnrWZtGYi8mYtjz6V/kc0zMwYPHox27dqp269fvx79+/cX77EmTZoIg8T3KJoAnV49hjz7ZHIUhHSUNgtPic8oyQnQ2T8jlJ6/jRQh8Lp+KwjQJu1UgSJ6q0lcqSm3y5ZFdFDMmkm/KXIwyHQk/hZLVTDo2R5VT/WbIPegezhb4/Fr7WSnOdxsMbdNUZHb3rdKTgHCGTHlNyk2QO/slx3D68Ql5iLJJH9XOWd57j7tDfcmxHhZOaZro6urUxhohGDufr6MjsKgEJ8whYb/OdnE8DvE157pDASkklTLmx7Fsjnjx3KeuP0iFNWmH1Z/R/0zmkIbQGckAnkRDt0KQt2CGUU7bQYd6TPbNOa4NiYuT09Cz51m2oKFeSrB+XJlVIy+EuqDfA7aeAOk/d5w9jHhfZfLueFVRPoG0zDkzpKE7kV9zm9XTDRrMuc4zj5UcQ9xP0p7ft6hu+qohPO/VlU/iz1WnMP2K6ryiYcHVYwVDckICAJ0KWpyTdfSwiAkiTGNaQnNUZ/vFYCuj5a+bhsFoH9dfRvjbs0BECF2A8Ak2b4AmgJgrLYqKTF+MXmAnjtTEVhZ2ODOs0tYvW4l6tSJ8XITyDHM3aVHbJIU/vCQLOpSQIgIVwveMwfuobcR5F4KBNYWaTPDLeIRHj15BqeKHZHp8QEBXAn6JRkxYgT+/PNPMJySIIbAccSmq9hy8gYiH15CyPHVsC9aB05+rcUlJ4dURqkJqpJTl0ZUw8IBh/GpQSY0LJxJsELzkEzvP4Em///EiRPIkyeP+OyHH34QHt2kCkMQCYQp0o8X70Hwu3PnTpQoUQI2NjY4efIkChQogM2bN2PYwIF4HRqKx8/i5oZJ47mRzwf4kousD0D//OEDArr3APPD0vXvl+C0CNAJnC0tLYVXbNmyZbHWmTqqX7++8LLrI3KALm//y4r6uHjxosjRZT7u0aMqgw+BeiW/6hjTeiWWH5iEV6EvRIg7c8wJOuglf/LqHsa3XYPnrx9h2YGJGNs6Zs8xB50/aKtXrxae9JQgJFgsW7Yshg8frnW41EnhwoXx6ZNqPyUk0gFD8aAnpCnjff979304F70F6xb9gY4dO6JXr17iPUXhM/9rlz/E/uVeJ1EcATpDw29vMEOa1Krc4wcvbmDhntEY1+Zfgwd2PeAMFuwaoY7K+anWJPy1/RfB38Cw96ev7iFNaiv0rT8DHmk9xXM0cV1XpHfKgp8bx05Xkd/83vNrmLNjCNwcPRAWHYQPHz6gW7duwmt+69YtEabPSBgaavl7wNSd71Ekb2mfyjnRr6p2joCHr96h/5pL6FouB6rlo40+RjosPi2I5bzd7bHzS3nA5NbTkmP31cR9ujyBcoNv+mHD4NK2jRhWlWmH4C8L6ZfnvdP7TaJIihygywkoNeembd7kBPCdGLvEKHPyR9TVzZwtN4Iw95rglucLSmBopCDrY+nMryE00ngN2y7C3Sly4EiOgSrTYgD63v7lRPWNrsvP4eGrcNx6oYo0kcLpNccbH0Df2bcsvN3jGlsSmrMmQGcVjAyOVrH4GBLqQwr712wn7S+Gv9N7LwlTLUiwSVl6/AFGbr6m8xaMmJDOTGxU08cdc9qwpjrACiKnH6hIUOV7WQ7Q5Z/T8LD72nMUyeocK61DujlJQhkxQWF0g1S5hP9WAHpCu0D5XgHoKW8PEJQzgavnl6EzyTWAXGIsParHdEweoBfJUQEWXw6T/cd2RLNmKrZ0Cj0rzKW9WYhBAzHCOtAMj5LIZJj7XSNsF5Zs2C0Y2F2qdkWv9P74+edfROhy+fLlsWbNGuFNlcukSZPEYZDAUBX2q8oHN7dzhn2xenAs2QSj6uYVeeCFMjuj/OQDIpzvwIAKmNPjAOTkYfQOk2U5Z86cwpPOQyVBEoE5w/XfvYtdmk2PtYvTRBtAp7d44sSJuHHjBrJly4asWbNi5cqVgmDp/v37Yiw87NIrpUvenTyFJ/37w33kSDhUr5aYocV7TYcOqtItkhCsa1tnGhn0EV0AfezGVjhw4IAwjOTNm1fohXMnGC1RrBSmddqG0IjXmLfzVzwMvClC05xt3dCl+mj8vr678KDTYx4SHowKPqr8OEp8JHH6jPdbtNmwYYPYc4zg0CavX78WBhwaj/QRBaDroyXjtuE+597js3z+/Hm8f/9e7eXhe8re2gVFSxZAQECASA+R0hXkz8fm04vw4k0AulQblajBvQp9jkdBt5HFLRfS2rvj3vOr+Gv7EBTI5ovSuWtgw8n5KJOnFvzy1sWNgLNYsHsk2lQYhCKeqnB8XUKQvv74X3gYdCtOE76LGX3UqVMnwa1Bw973KgyPZhhvYoQM9KvPBgiWbXfHuCSgiekzoWuC30UJIs6yOV3VnkjNa56NHIU3q1UpFd43rqv3LCPQlp94iB98syKDozXkZSrjA+irTj/CyE3XMKdNERE6LoXY/1A6K0bL8rh5P+ZpS4Z0aVxz2xRBDR/dFSIYqk2vddfyniIP/lsL8/2lPHQ5QGTFC6mSAcfIvPgcX8pjXnsagtqzVEzwmt5daT7aQOLVJyHCCFHJW2X4M1Q0AfqtcTUEGaIhwsoHLRecjHOJNHe5p5uN5DpZfuIBft2kG6DTk00v+46rKuM/Kyz81aqI+P/m806oyyzqA9ATmhP3N58NiqbBQwHoCWlP+V4B6ClrDzAmKpyROOSmkA19KQAmW8dQkuuel0kDdM1hawIhAgwSIHmPUnmuJVn9YymUzJEWx/1fotXCU5By9jRfggRnZIFnnjY9qTz0yUXkt9NKff++AOn/nn6EdTfCYOEU46mQai6zHcOYKCx5JR2epf5Y4qh9+/Zo1aqV8LLSsNC7d28sX74cBEP0EidVtAF09snoABJH0SDAOdODz5z0WbNmCcMEjQQJHXITSy6U1DnxemmdmT+rj+gC6HuezxK5udT/wIEDsWnTJrEmXOd3L4Bedb6Uw6NnJOSxyD+nt8/cLP4DRUoE6Pro0ZA20rP1Nb11hozve2wrvWP4/nj69KmIjpGiTNzd3bF90n0B4BnuTgBPQyRF/nxEfYgUJX0szPULsdVHjysOTYGzbTrUKtYOh65uxMZT85EjfT4B5Atl90PrCszI0k+ajc2PK1eu4Pr16yKag4ZOzo1GTQcHwz16+t1VaZUUDbAKAcvR6QqD528JibPMnZxE+pMuYei3968qo2x8AJ3fs/qDRMJ5NygMO68+F4SUcuZ8tpNzuYytnw/OtmlQI597gmUq5f0nRTfGuPbInSC0XaTKYpQDR7mHlt/JU+Hk+eCM8HO0iVuJQJ5zbaw8aOl3gXn+g2vkhr0BFRAkXbGsXuuFcc9H0hibzj2OMw9iPOjysf9z8iGGfymxqE33bMsKCvm/VCiQe9Bbzj8pSPk09azLg57Q2sr3ntx4wuuWnXiAEZuuqavLJNRXcn+vhLgnt4YN718B6Ibr7FtewSTCJwB8AagYNFTyOwCexEpqGRxdDXJ3AxmtHjP80RQOO7qAlTQPXUBI00orAXReR8txFhcb8cOgCdA1Pbea+lq8eHGsj+jNYH5dngz2+Hn9FfEd69FqK7ujCdBZHo4H5YoVKyIwMFDkVEoedYbWM3wzqVJs3B68DCPncuwf7n/++UeQRBGMnjt3DjVq1BDly9KkSSPYkZs3Z6bE9yO69lHNwdlEjjjD+wn6WQ1AWoNCVk0E23RiRAHoMSF6CkBPzA5K3DWa7xjNXnR9n9B7NnGjiblq6+nFyJelFLKmzy3KZJ66vRtn/ffDwdoZdYp1gLN97Eil+O6nPFtJXY2Uez1Bcc5hKm4EgrseFVQh7ixnNm6biqzUUCBJQjapdjzLmJHMLKWJHOjJ5898eTkB3pHBFdWh91ceh6DunyoPuiZxnzR/OdmaoXrVpUPpzCWVyEuMrkna12aRboDebN4JUStdEvnYGV0x5D/VWU2bsK18n1XJkx4Lf1DloLdeeBLH/I0H0BnRUnjsHtG3fG34b6aztFt0WpDwbe7plxg1GfUaBaAbVZ1G6UwB6EZR41frJDEAnXGMIzVHaMoAPSzyLc77H4CTnRvm7YydL0viI4a59x45GXX+UP34UKQQd+nf9B4xD7PiPBWLZtjV/cj9+iQePXokQr579uwpGLjlwnxOhlkzT1dTmPdXfrIql+j6mOpa6zUndHhOjl1y+fEbdJx5HOUjLDD1z6riFtLcJQIpeuxnz54togI49379+sWZe3KMLaE+me9/+vRp1KpVS4yH46SXn14zEuiNGTMGqVPH1BGOrz9dACS+w76ua0h6df7uIQS9fSK4EJhfy3z0t+GqAwHLSrXpUV8YPzTrtCc0Z1P+nmHRI0eOxN9//63XMBUPul5qMmoj6R1DkjSuE3kt5B70z8/c0Ou35oIYjukh5NXgczVq0CQEhwUKg1S5fPVRzKtSosYVERU3Lefpq/uYvrkPUiGViD7pVHUE/t43Dq/ePhMeVXOz1OhRa6KaRT6hG7f4raCINiKLOyOdOFeSXdKw9tdff4l0FUW+Tw1Ipa44O7kHnTnD47ffQIXcbqiQW39jD/t5FfZezQyvK9Q7JWiThH10OshTFzQJ8CSWc86H0X0NZx9HRicrzGurAqCaIi/XamyArouITx9dc71rzTqCJ68jEPEhWn2JNEZ5KDq/lI9dqnyg7T7yMUm/X+QYWNpRVSGn7aJTOHInLgFjYj3ock6FY79UEsTCknzLCEVtulEAuj478+u2UQD619V3Uu+WmBD3FOVBJwnR3B1DRQkfCtnPSSImeZuvXr2K/Pnzi7xwuXd8ZZeS8PVU1Zuk8HA3depUdN77HqGXduHt/gXo0e1Hcbgj6dCCBQsEwRLDn0niRqAllaEioRhzHZmLy9BKihyg3xxbQzCgaoomQK9UqZIIpWZ4uVz4IiTRHQnvjCES0JTAqDT3KlWqYOHChSKsniHe0tz5GfPsSTL1rYTh92QUZxrAsWPH0LdvX1GzmcYDrgPzTLt37y7Y1/URXWB76u7OOHPmDNKmVZVvkYSGHq8seTG61T8Yt7oD+tWfCVsrB7wOC8T0TX0RERUGR5u0eP7mkQAeBbL7we0LCzZz1p+EXxfVBHbt2oVixbQffvQZtym1YV5+kSJFDGZxVzzoX28Vuc9LdLIXqSsMb+czLieJ27RuO0Ijg4URk3wa8+bNE89/Sc8aSO+cFYFvAnD85nY08f0Jpb1rGjzwXvNVRkC5fP7yrpZ/ltUtt8hBB1KJ+5GgTp5OouvGDwJvYsH+oYKAke/NPXv2oGnTpmJP0rjKOugkeuQ+VeT71ID0uy7VQE/qLOPzYia17299/bOQCJSeoDpHTGlaEE2KqkqFSpIQCKz7x1FceRIimhsboCdExJeQ7qSxT9tzG7O+lPHTB6CvORuAwV9q0Gveg+XgOpfNIT6W9lkZr7RY0VlVZabd36dx+HZQHH0kFqDLUzZ0pRkkpIev9b0C0L+WpvW/jwLQ9deVqbRk3A+TkSSWNJLEPQLw5/dAEjd0eVPhQe1XbwbevAvC6jOTRWg288ZLly4NnQC9c0n4esUAdB5eSZK2aqI/Fhzsi5/79cKPP/4owpwJzFmuh/cRHh5zcxF+zvB2HghZQ3zFihWCBZnkZQS35SpXQ4HRqlAl1mtlzrmmaAJ0ea1veVuGu2fKlEkwFRtDNAE65878TQLTHj16IEuWLMJzLgk9pQSXLKv1rcTLy0sAdHrKCQyZX8o1YfkzCgnNCDLu3ImprxvfWHUB9F7zq6jrrcuvJ+O1R8bMmNFlJ3rNqyLI4OytnbF033iQBKt7zfGCndrdOasoS2Vv44wOlYepu+gxp6Jgmaanj17MlCAkgItPSCrGFABD66ArAP3rrT73+fKLQ+Hh4SGqTWjm/K4acwqTN3ZH5syZBaClsY6GrqhznupBnrmzD7surMDwZvpFSshnN/yf5siU1hOVCzRFqlSqd+DcncNEqTXmtH+IVqXbWKa2htkXHgcCeHPz1Jj0wwadinry6q74btXh6Sjil0fwRbCMId/DJMRjSk7Lli2F0ZT593w/KPJ9akACTlKt7qTOMiT8AwqOUdVd1wwzTmrf3/r6F28jUXK8io9nfXdfNdO8vuOS13U3NkCXl8nTdzza2hGok9AtfyZHdfh+fB70deceY+BaVh+OK6zF3tFPVZpS2mclsrlgTbfS4rP2i0+ry+sZgySOfW66+ESUmWtUJLbxJCk6SY5rFYCeHFpNWp8KQE+a/r7F1UweJilc1y9AnWXWSHPOGhMv9BiQSZPEESx1rDoChXOUE1PpPruC8JjT6713716wlrPkQf9l/WX8e4YE9sA/nUrCL2cMQGc7ejdPLgjBmA0tsXv3bnFY7dq1q+hn2LBh+Omnn0RpInpk6GW6e/eu+DeZ3QmeeQhkGCnb00tVt0lLNG3VFpVLFtSqZgmgE7RR6PWnl5xhmpIQ/DD0lJ6tBw8e6LFcCTfRBOice9WqVcX4+eNGb5vcg8wX8Y4dOwRI/1ZCIwKJ6mg8oDA3/sKFC+pa6KyDTtZ1fZnuNQH65QfHRb8Ldo8QwJ+RGPI12LdvH9b+swkjWiyNBdBHrWqD5mX7Io9HMfRbWBM/N56H8PehWLxvXJwyaxw/y5JFRGivyfutdKvrvlKECPeELiHgUwC6qa1czHi4zwcura1mb4+zlqw6AdX6cr35LuC77+gcVV4lJSjkKcav64zpnbYbPNF3kW9BQrjIqHdoV+kXONm6YdDi+iLipHjOKmhWphd+XtoQgxr+hcxuKvZrlimctqk3fm8v5zWNfWu+91lBQfLGqypoqOYhGSH4l0ZHkl4+fvzY4LErF6QMDUjAqUvZ7BhWW3cpNH1nIw8z/t4Aujy/XrOMlz76qTnzCEgmRzE2QJenKOgzFkPatJh/Aifvac9B33DhMfqt1g7QR9bNiw5lYgP0QpmdsPGnMuL2HZecwf6bqmrFxgLohszrW7ZVAPq31L72eysA3fTWRJ8RscQaaXEZf30RQO8vNdH1udakAXrPeZXRrcY4+GRVWTSlsG2SfNErzFxEesB5eON/2YeoDpnLO5VA2Zxu6vm3bdtWsJQXTt0Kh4LnijD2sWPHigMrPTP0eq5atUp4QCksxcWQdAmgyxVJTxSBOsnV6H3WBWAkgC4BIfahDQwxTJMl14wVYq4J0Dl3MrUToHPM0tylOTHPWz53fTaNsdvkyJFD5MWTvI5ecm9vb/z7778inJWyfft2YUBh3rw+ognQxYGfh3uzmIO+1A+Z+1l+rmKOdsiftfQXgL4O9tZOGLa8GX6qPQkZXbJj5MrWqFXsB+TMUBBjV7fH9M4q8iJpX7J2O3N8jWVo0WeeSWnDqA3qnPXltQnrxTOSwVCAnieDA3b0icvbkJSxKtdq1wD3+ZRdnUReNtNUKleuHKvhsqHHsenWVBEdExUVhcaNG4vn3z2oorrdrgsrcc5/P4Y2XZhoNR+5thk7z/+DhqW74cTNHbj15DzaVRqCV2+f4vTtvcjh7oO2FQeL/lcfmYmnwffRr/4MnfcjqG9Q8kesPfYH9h3YKyIAKKy0QWMiyTbr1q0r3gecT0oxiiVawf/HF0oAXVvJtMSoRc7Y/b0BdHl+/ZaefqL8qyFSY8Zh3HyuqpVuLIC+8+oz7Lr2AuMb5k90ucCE5hCfB50e6z7/8lgcV7QBdJ9MDtjaS/X71XnpGey9oQB0ACqrjSLfVAMKQP+m6v8mNzdpgN5/UW3hiWlZrp9QjpzkiyCdIe4SOOf30o85ST5I9iEJSxAxNDL1ewfUa1kFc+bMEeCDwJxeTwJzAlgSlFF4oPXx8dEK0KU+eV960+md1iYSQKf3l20JQkmCJicSo6eYHnqG1RtLNAE6586Qdnrwy5Urp567lIN+8uTJWHM31jgM6YfhuYwiIFikN5shrKzV/ssvvwjdMEe9SZMmmDZtml7d6gpxJ5ih142GGU2RriGYz+CSTYTkBoU8QZsKg0UEB8tFbTg5Fz5ZSuHO04voXnOC6OJtxGtY5Q4SPAZTpkwRaQQpQeh55J4g+Z42YaoBnw2mfugj0rOnAHR9tGWcNmLP5r8hjJQlS5bEkCFDYuWgTx+2BCf9dwjjJFN0+vfvL57/jI6eSO+UFYEhAXjw4ga6VB+NfFm0Ff3Qf5zPXj/Akn3jRWrIzcdnRb65nZUj+tSdKrzswWEvBH9DeFQYulUfh9weuvPG/9z2M7wy5AfD71euXyyMpZRt27aJ/799+7bYm3x/871AQ6ki36cGpPdKyxKZMaFRgSRPMuz9R/iM3CX6OfpzRXg42yS5T1Pp4E14FAqNUaXebe3lB59MhgH0atMP4faLMHG9sQD619BNfAB986Wn6L3qghjG740LiDrn+b6s/6i6edFew4MuT9Hqsuws9lxXBaLK9XHu4Ws0nqOKyktJejJkLRQPuiHa+jptFYD+dfRsSncxaYDOg9rjl3cw8QdVPXJNFm6CaIJpyTMt/Zgv6VA8DrsricCaVeyOJ1GXwfxaAg96twmQCQYJ4Cn0xjC3kbnnDJ3UJBTTd/G+BYs7x6YJ0PkZPaVkSafQ68S5Z8iQQcyZRGzfmtiM45k4caI4cPv6+gpgvnr1apF3ztB7ess4fltb/Uri6MPiHhkZCXlddema7WeXxVribOnzIG/m4uKzc/4HsP7EbLwNfw3pZcka0sWLFxPgh6z/KUWOHDkiUgYYtaBN+B1LA0q1sxOal/Ts5c3ggO2KBz0hdRnle+kdQ66ATZs2gcZAKeKBhi0Pl5yY9McolChRQkSgMLqD776PUdGi+gA92xULNAZJ3JIqTCP59OmjKKkWEOSPj5+iUNq7FrK45RLvZ/9nl/Ex+gM8XD0Fc3x8cun+UZHH/vLtM7QdWCNOlQnmnZNDgUCdqSXr169P6vCV601UAxUmH8CDV+FY1rEEysmM7okdrhygazJpJ7ZPU7kuJOIDCo5W5ddv6+2HfBkNA+iVpx7E3SBVZYaUBDzjC3HffuUZeqw4L+Y0s0Uh1C+USe3IGV0vH37wzSa+k36/crjZYv+ACuKzrsvPCu+/Nn2cuPsKWdLaxGJiN5V9YIxxKADdGFo0bh8KQDeuPlNCbyYN0DUVmFBNXOklu7h9cVT0jlt+RRM00wPPnOz379+LnHQKPYcEbsxZZ0hlYoX3ylxLZY3WR+jRNIZoA+g0OjRs2FAwpDOcm2HdcmEptu9JdAF0chj89ttvmDt3roiO4AGfkQ304N87EgFfb1UERUISHf0RYZEqtlt6CXsvqJbQJd/99wpA//pLrPk+I1cGw90pjBKZ3+tIHKMmv9P1fCRlBlIaSYJ9pEqFP35Uefn0kfje+TTe0RDBCAFFvk8NEFA/ehWOPBns45AgJmbGcoB+/JdKyCgrdZWY/kzpGvncmGbEaCZDRFfdb0P6+BZt4wPox/1fotVCVQ31P1oWRt2CGdVgfEz9fGhXOjZAz+xijSODVRE7P604j21Xnon/T0kGC2OsgQLQjaFF4/ahAHTj6jMl9PZdAvTd/cohV3r7OPrX5tWmp5AedHpiKAz9Jns4c8OTIrwXWcP1EUPIuBLqTxtAp2eXefUMByXBnSbbM+tdm6J8/PhR79rn8vHrAiCvMh4VJHEM6yYbPw00BOj01v/SezQGNlRFGRgqCRmODO3vW7ZnhAVzfvWtOS+NVQHoX3/VEorS0fV9cgB0afY0Xr0Kew57KydYW9qJj7V9pq+2vqdnS985K+2STwNk0PYcquKq0VUiNfnunrw9h0d9RN4RqvD9nX3LwtvdMIDOOurDN15FZ78csUh2k3fUSe89PoBO0juS31H+alVEhLhLv1Vj6+dDWw2AnsHRCieGqLg8AoLD0WjOcXQokw09KnglfaApqAcFoJveYikA3fTWJLlHZNIAfcjSxqLmdN0SnWBn5aDVGyRX0NkHwXgWEimspHKhh5xM7RtX7EaXvq3x888/i7xmhlVT6L2mV9XBIfYPGr3prJ9OZvdnz56JkHgCOtYtHzRoUJz28nsmdHhOroXVBtAZEcCyRCSMYw4ny9SRHI564WcdOnRIruHo1S+Z7ElaRkZ+hrtLXu7nz5+LUPyePXuKNdM0LOjqXBcAmb73R5HrTjIte3t7ES3B9aRxpkjB4pjcYZPo8vGru9h/aS3uPr+Kt+GvRAmptA4Z4Jk+H6I/RyMg6A7eRqhYYx2sXVC3WVUxRjLNp3QhLwL1QkOVIaIAdEO0ZZy20juG5SDJoWFnZyfIJlm6ke+7K+dvIH+RPCJFhIZIpivw+WfZyt0XV4qQ8+I5q6K0t/Y0h4RGuefivyjv01DUNf/0KRobTy3AwSvr8enzZ/GuLO5VBTaWdiCJHJ8bPr8lclZFy7L9RKk1XbLm6B8o4lke5+8ewrg5/VG2rEI6mNBaKN/rrwHWQuceTWv3fUVeyOts63JS6K+llNMyPoD+PCQSpSaoSs/Nbl0EtfLLAHoDH7QtpSo5K/1+udpZ4uzwGMdKQrXjU46WDBupAtAN09fXaK0A9K+hZdO6h0kDdLK4S8Ia1JNnjUObNm0M1iDzg8lknjutL24HnxBAkGRhBKc8SNKDXqRIEVEbXRKGfbM2N4nj6E3/77//xOGXedDMeeSLm8Dd3Z3k+XElPoCumf9s8ITiuUAToP/zzz9ingRcJFMiYzzzzulNJxjm96wvzH9/KyFrO0nWeBCn4YBGEQIMiciOn3HMBOn6iC6AzpJUBOMkzZMDdFYEKFywKKZ12obrAWewYPdI5MtcAhapLcF82FK5ayA0IhgX7h1GanMLlMvXAG4OKiMQSeJCLP1x7tw5kQfMlImUIKw5r004B4I46ofCfa+PSAecfBkdsK23Aqj00VlS23CfO/o9FZwZElkm01doZGEuuqWZLULCVSXVcubMKYyMfP57dO0liA9ZxoxE3eBEwwAAIABJREFUbD9UHoLCOcobPJxe86tifNs1ghhu76U12H1hJSLeh6FPvWm4/ug09lxaLe7BNJD0zlmQI30+nLy1E5UKNEXVQi103k9eZo2gPnv27OLdzHnqet8aPHjlAkUD35kGoj5+Qq7hquoie/qVQ04tUYTf2ZTFdOID6O/ef1STws1oXggNCsfkoI9r4IM2XwC6lH/fonhmTGycdDLClK5nBaCb3goqAN301iS5R2TyAP3H6mPFoe56wGnh8aE3uE6dOgI88+Cmj7C+Nkuj3VpnJsLOtVlFNT/jv8l43K1bN9UP3p49oj76jRs3RF30mjVrilDgxYsXax2CJkDngXn8+PFa85+ZF96pUyd9ppJgG02ATrZjkkSRQIpAnaHd9FAT8FIIhul9o7HhWwnXlPngXCcaT1iuTCqxxjGRvblv376iBJs+ogug/31mkJg3jTxygM6Q92Vz1qJf/ZmYuK4ryuStg7J564pb3Xh8FuuO/YXUZqnhk7UUHgTeFPWepbJRbMMw3FGjRgkwK5Xq02ec37INDVNk9dd8hlgujhElTk5OYni69rfm2BWA/vVXk/t84akBIAEm03IqVqwo3ktMT+A+z/iqMsZvaSu853wH0MBFb3rNQu1RqYDKILfv0hpcenAM/evPNHgCBNLj260VAH3i+q7wy1MXq4/MEBFPW88uFs+MWSpz1CneAfcDb+DivcMo6lkJAS9vY1izRTrvx3571v4df2xTRSmFhYWJOdGgyPkRrNNwyj2siKIBRQMqDXyM/gSvYSqA/r2VkItvjeMD6PKUhkmN86N58Sxqb/muvuWQ211liA4MjRSM7Q0KZYKtpe7onv+XvaYAdNNbaQWgm96aJPeITB6gD2u6SJS9otiUfCiAuZQvTk8oSwyRxTg+sbGxEddsGe8vwBQ9TBcuXFCTwJHdmIRwPMhKQq85wTjBM4WAnWREBLoMuyYLNusKM5xUm2gCdIJAXfnPrOdOBnNjiCZAZ9grD7IMZ2dONw+5BKdSjjENB/S6SeRSxhiDoX1kzJhRgNtSpUoJDxlrHdOwIAmBOUn8SAqlj+gC6B41Q4UXjuWouB6jR4/GrVu3QFDapepY5PEohn4La2J488VIa6+KjOC6911YQ4TnDmmyAKERr7Fw92hM/CGGOZp7iv2wbFlKqcnMOvNM06Ae5CkO3AsMcTc0XF8C6PI6svqsldIm8RrgPv/5n3pib/J95uXlpSaAZEnH4/NeY/DyuoJIje9Apozw3Tew/l/IlNZT3Pj560eYvrkPJv2wweCBqAD6OthbO4G1y/vUnYYJa7vA3sYZ1Qu3xtYzi/EhOgozOqtAA0sV7jy3HO8/RopoFV0iAf+hy5oi4HGAILdcuHChKGvJ8bOmO6tv0KjJvct5K6JoQNEAMHPvHbyL+oihtQxLUUrJums5/yRO3FNFClE0Cd2k3yaJtf1l2Hu8eBtpMMt9StaRoWNXALqhGkv+9gpAT34dm9odUhRAlwiDCCZZimvt2rXgi0Qqs6ZLuQyhJhh7ecANJTrZw8/PT3iyJWBCkjAyedOLKwkPfX/99Zc6ZNnf3x/shyCRh0SSaRHU6wKNmgCd/enKfy5dujRYOsgYognQnZ2dheeMRgUKIwIIynjApQQFBYEkcXLjhDHGYUgfLAH16NEjbNy4UdQRpxFh/vz56pxzRi6w5Nfx46raowmJLoDO/UPDCudPEErPHFMb6LG/85/Kaj5qVVs08+utLq3GWuhjV7eHq0MmlMlbGwWz+eG3tZ0wvZOKaIjCflmjnWOWjEcJjdEUvqdhitEEJA4kAOJeUQC6KayMfmPgPh++urEwvF28eBGenp7C+Mb3ISM5dvz+AENXNQRTamiko/GI3w+sOweuX1I0WMps/NrO8QJmXaMhkKZ33NLCGrsv/ouOlYdj5pb+MDMzFyHsR69vEQB9asetoovAkMcYv7aLyFn/vf1GnZOUA/TnL56r31V8RzASinv1yZMnIlWFaTtSaTn9tKa0UjSgaOB70oC+AH1oLW/8WE5lmFQkfg0oAN30dogC0E1vTZJ7RCkSoMuVwnJoCeX90kNNr2nWtHnxKPiGIIibPHmy8BLy4MrwbubdyvshcdmVK1dELjQ95wRgzOOUcnIZFj58+HBRh12baAJ0euR15T8z/JRg0RiiCdCLFy8uxlm/vqr2MF+8PKRLhGv0ShEg0wP8rSQkJARVqlQRobo0VtDwQtCYK1cu0DASHBwsyt6VLFlSryHGB9B1dSBds+PcMhy7sR3Vi7RGajML7L+yDukcMqGoVyUs2f8bPNJ64XVYIJr4/iS6okc9yjUA3C/kMmBURUoSGkNovGIoO3kAGAJNsKd40E1/Fblnl5z7WUS/MB2HNe35fDPKh/wN83oexqwD3fHq1SsRKs4a6Px+2aAz6uf/5uNzWHN0Fka0WGrwhEesaBWLuLGCTyMcuLJelCAkVwPF0sIGY1uvFP/PPPWzd/aJ7+KrmKALoEsDpAGC7y2+M/i3atWqBo9duUDRgKKB70MD+gL0/lVzoXflnN/HpJN5FgpAT2YFJ6J7BaAnQmkp/BKTBuiDFtfDL43nI62DKtw4KSV3CJ7mjV+LG4HHROg6wTcPtDzsMfxTyrmVryfLk5FEjeHhBO8zZ84U9YUpDCGlZ4p5vNpEE6AXLVpUZ/4z89vp2TWGaAJ0GhLSpk0rxslwfP5HUCbJokWLxOF97Nixxrh9ovtgXj/HsmXLFgEkOEZ6/cuUKYPu3bvDw8ND774TAuj0xnMPUAhEuTbSNdGforHl9CKcubMXHz99QB6P4gKM21k74t7za9h2dimeBt8TZFgUhvNWq10Rffr0EcaFlCo0UrVr106AOz4bCkA3/ZXkns1YPURtYKxdu3asQfP7k+8Wi/B3po/Q80yRPx+7L6xC1MdI4Qk3luw8/4/IbSexJw1ajrZpcf/FdfH8kJ3dysIWznZu4nYV8sclKxy5sjUGNZqNyf/1wK37V8X7SxFFA4oGFA1o04C+AP2nip4YVN1bUaIeGlAAuh5K+spNFID+lRVuArczaYCuqZ+kAHTpYJrUPvRdM02ATnZsXfnPW7duNZoXSFuZNTKM894EpZrpAMaswa6vbpK7nS6AXn94LlFujjmtkkGGXntfX1/UyNZLDRoMHd/X2lOGjsvQ9oziuHv3rkjlYNSIISLl+eXP5IgtvfwMuVRpm0gNxFcpIr73na7nI5HDiHMZAbZ+kgqjW/0Tb9Pv5dnSTx9KK0UDigYM1YAcoKdKBdyfENtQWeK3vQgMfY/13X1RNKuzod3/X7ZXALrpLbsC0E1vTZJ7RApAl2mYuYz0HjK3kfm4SRFth2dd+c/VqlVLyq1iXasNoJNgjfmpzEVn6DhBOUnpSMRGBnXO1xTl4MGDIqyd6QGGiC4AsuXB7yKMnmR9uXPnFl0ytJ9cBG8CPuKn2hN13ub204vIli6PyJ/VFAVExNSRVQC6ITs1aW11AXRG/Dx+/Bgbx9/EgEWxD6u844wuO/Em7CWc7FxhYZ4m0YNgfrkZzNQ1zYNCnuLYja14/S4IrvYZUNq7pjAInri1Q6SFuNilF5+5Oqj4MPQR+bPFXHuSW0p/9bleaaNoQNHA962BhAB6aOQHPAuJRK7/k7JzxlhtBaAbQ4vG7UMB6MbVZ0rozeQB+rL9E3H3xVXkzFAQJ27uEKHEJHhjCDQ9n4cOHdJLzwzvXDB5Dbr90lIAMhLDMXSaDMckJiNRGMPACVwZ8k6vNvOfCaorVKigvobltHgAZsky5u7qkoS8W3oNOhGNtAF05pxLLM9SlyS6I1Eac1VNVRI7Rl0AnXXQSTQnZ4jn3BlhULpUmVjEb5o66T2/GoY2XSDCdimPX/qL/PSXIU9R2M8bPXv2FPskpQhTHSSiQI6ZeefTp08Xe56pBYbOR/KgF/BwxOaeigf9a+wD7nPbUo8EwSTzscmRwXfZkiVL1JEyfIYYPUOCSr63yDexZPESfAaEoa507ppoUuanRAH1GZv7o7xPfVFD/e7zq/hj60BRVi1T2hyCHI4M8ayD7u6URdRBJ0lc4JvHooRaDvd8OlXEGuqOtq4IefcSP06oic2bN4sce7LQc8wE/az8wD1Ko6PEp/E1dK7cQ9GAogHT0kCrBSdx/K6KxV2bB920RpsyRqMAdNNbJwWgm96aJPeITBqgz981ApcfHIOjTVq8jQhG5cqVsW/fPvGX3u4DBw6I+r/ME49PSBJHojSvdEUQGOkvDqkEI8x7btGiBVhyiv0wDJ19Ll++XPRJhnPmnhO0S9ewvjDvzfrhJJpjTV5tYioAnSzlzOl2cXGJlWdPQMZQZnrQKefPn0/uvaazf45RmyR2jLoA+sz93cS6kpRPLuQTqF21IUa1XC7qOWsTAnI3h0ywTGON9x8iEBz6AtnT50O2dLmRKt1rsW+4N3VxEnwz5eq4MQ1Tz549EyCdRgsaF2jwom6od0PnowD0r7/C3OdTd3cWxkq+swhkaYjk+4lA/fKep9h1cYVg5hdVLF6+xLp161A1VwekdyJgDsDGk/NRIJsvGpTSvu/jm9XAxfUwuNFspHP0wLB/msPeygnPXj9AMa9KsLG0x/Gb2wWYLpMnxovPPHRz89Tx1l1n1YSW5fpj1eFpqNe0hiDmZCSNRObJyBpW4jhz5ozg9SBIV0TRgKKB/08NyD3o5XO5YWnH2L/v/59aSdqsFYCeNP0lx9UKQE8OrZp2nyYN0PssqA5f79poXrY3zvrvw5J944U3iB4iSvv27QXjd0IlwuglZhm114fd4dvVWYAQeuEJuukxJEM4y7YRpBPMs4Qay6Lx0NuqVSvhfZauYe1dCgnN6NUh4ZgugM4SSPp6d8hUbgzR9KDzcE7QyHJEmTJlEoCMzPVr1qwRxgfmYjPcm6XWvpVwjPQAkshKEh7sSVzHsnCSp1ffMeoC6KyDPn78eFE+r1ixYuJWXL9evXohv0NNFMzuh94LqsE7UxFkS5c3Ziz4DLK7l8xVXdRHP317N3JmLIjWFQaJNgzD7du3r0iPIEhPCcI9QI8kdcsUi8yZM4s9LYmh81EA+tdfde7zAUtqiRuzRCTZzFkj/Pfff0fTpk0FGVz/xTVhZ2cnDHRccxJd3t0YE9bu//Qylh6YgLGtVxk8gf6LamNwozlwd84CvqvpOQ946Y8srjmRxsJKeNVpPMiZsYC67w8fowSIl0qvabtp34U1MKL5UoxZ/QNyeGYXVTdYGpFRTpzXtm3bxPPGdzX/3rlzx+CxKxcoGlA08H1ooNOSM9h3M1BM5vyvVeFim/i0ne9DI0mfhQLQk65DY/egAHRja9T0+zNpgN5zXmX0rz9LHQ7Jf7NWtlQyjGCIh1I5K7k2lTNknSXOtoz3F2CKXmOGNdeqVUuUlmIONkE8GcTJhMzSaT4+PoLROkuWLKJL6RrWPqcQ2LOEma765Twc25UOUA+HpY540CQolti+6ZmncYDGA3qCjCGaAJ2EaE2aNBHjZIirZDCQyOJ4aP/WdYQ5RhpeaCAhCOeYKImtyS0H6IMW11fP2dImtTDmyHNYpf9PnUpVm5mgYvmBSSjuVRk1i7WDWSrVWLj3+tabDq8MBTB0eTN0qTYK2dOrQDz3FPcMvdCMukgJIgfoDBeml1JuIDF0PgpA//qrzn0+eWdHUaKRKTl8zlnrnKXHmMbB78dvaSs854yYIEjn+/PUwrfqwT5//RC//9cjUXXQZ20ZiLyZi6NKoeaYtrE3yuStg1O3dqs/G/Nve4RFvolV8/zCvcP478SceA0C0vO1YPcomFt+EkavmjVrYseOHWJeBOTk1WCKDv+Gh4d/feUrd1Q0oGjAJDQQEByODkvOoEvZ7GheXHVeUyRpGlAAetL0lxxXKwA9ObRq2n2aOECvgm41xsInq6p8VZ+F1UTOOUtvUXhwo+dVk5lcU+UsjcZc8v0znwkwRW8hwyVZ+5leGJbxodech1uyV//999/o2rUrLl++rM7Rlq6RCNUI0HlYDA0N1brCmiHurI9dsWJFkTcplz///FMcqHlwNoZoy0GnkYEGBuai03NOwrjEgl9jjFFbH6yFTm8512HFihVJGqMcoJ+8tUt9uyrtY7zimmPYu+Q6SuWuLj5mCbV/j8zAq9Dn+KHSULg5ZhQAvUetCSKsfeK6ruhcbSQyu6pqqnJPkf28QIECCUZzJJf+DO2XAJ1Ax83NDYUKFcL69etj5eYbOh8JoBf0cMQmJQfd0OVIVHvu86dp94nIB64hwSrTdfieEe+2zjuw4MRAsTf5HVODhOHFrSssLawR9SES/xycjPCoMPSsPcngMTBcfc6OIaiQvzHsrBxFlEm+LKVw/t5BZHXNjYdBN5EqlRkqFWiiCql/E4CDVzegWuGWqFqohc77rT4yU5DKOdm54VHYJfGOp4GB7/n58+cLHhJGvjAKhn+ZoqGIogFFA4oGFA0YRwMKQDeOHo3ZiwLQjanNlNGXSQP0AX/XFaCpaRkVqNVky2bII8M5WY88PmG+IsOYXx5wi9MHczJ37twpADu97BSyfE+aNEl4c5s3b661a3qshgwZIsKatYkmQGeYKXN7GTovFwJ9Hq7pBTOG6CKJ470PHz6MoUOHinxUGgr4maH1ro0xxvj6ILBI6hh1hbjHx7Yuv4a1of3y1sWl+0ex5cwi1Cr6A/49Mv2LJz4V8PkzWpTrp86tZb/M/x0wYECKCbclQJdHU5BErEuXLuqlMXQ+CkBP7icjbv/cs11m+aFBgwbYvXs3SAjHKBlGFJHtPDr6kyBN4lozioh53DRUfvoIUZs85N0r2Fo5CNK29E6ZEzUBgnR6xB8Gqt6dlM/4LMjiPn2OjtUnGePdHDNhaFNVPXZdQgPZH9sG413kWyBNFJj+w/HTgCpF1zA1g9EwjEDivBRRNKBoQNGAogHjaEAB6MbRozF7UQC6MbWZMvoyaYB++Npm2Frao6hXRaFNTYBFgjeGZzMPPT5hCLWtrS2OzQ2O0wevmz17tjjUyr3b0jUEz9pE2zXydpoAnZ733r17CxAnF5LNzZo1S4TTG0O0AXSGj5ctWxadO3cWAJKh5PQ8Xb161eQAOnWQ1DEmFaAP/LsufmkyD64OGQXz9NJ94/Ew6BbaVBgsctApDjYualDDfcncXub/Dhqkyks3ddGsfkDm9ly5cqmHbeh8FID+9Vdc/o6hwZCklTdu3BAh3/Q4W6VyQLsuLYTHmc86U3hIGPng6ks42rggh7uPIHSjNz2pEhrxBq/ePsOnz5/wKOg2NpycKypv3Hl6EVnTeSMk/BXC34eiQDY/tK04OMHbRUd/xPFbOxBie1NwgNCASQ86I4G4TytVqiSqcHh4eCTYl9JA0YCiAUUDigb014AC0PXX1ddqqQD0r6Vp07mPSQN0TTUltd60NmZ1hsmT0Z0HWwpz0Uk8xLDKpIjmvUhsR4DMXErJ43Pq1CnhvWcePAnvjCHaAPpvv/0mQvmZX58/f35xeKenjYddelFpODA1ocGE6QMODg56E+1Jc0gqQB/wdx0MaTJfAHQKQcf7qHBYpbHVOpak7ktT031ixqMA9MRoLWnXJFQpQtf3up6PpI0m5urxazujTJ46KO/TANKzlNY+A1Ydni4MA7WL6/+uU54tY62K0o+iAUUDigb004AC0PXT09dspQD0r6lt07jX/zVApxec3iWSK0nEbSdPnhSliAjayfKeWNF2OCYgp7dcbgwgODZmiKY2gJ49e3ad0yBAp1ftexJjA/SEdKOACEAN0DM7YdNPKo4IRZJXA6YK0MnuPqzZIhFtMnhJA/SpO00wvJOQbtbWgRjfNv6IJ7nWlGcrefeQ0ruiAUUDigY0NaAAdNPbEwpAN701Se4R/V8DdIZHsryaJnEby3CxHNeTJ08Srf+EDs+6Op44caIgS2P5s8SINoCemH5S8jUKQP/6q6cA9K+v84TeMd/Kgz78n+boXnOCAOX0plcr3EqE0jNfffaOIZjSYbPeylIAut6qUhoqGlA0oGjAKBpQALpR1GjUThSAblR1pojO/q8Bui7iNuZAk6E9KcRtCR2ede0OhnSTvC1HjhyJ2kAKQIcoL6VN9CWJ0wxxT2ghFBAR40EvlNkJGxUPekJbxijfJ/SO+VYAffG+35DFLRcqF2iKHeeW49DVjSiQzRc3H58TlQ+6VB+t9/yVZ0tvVSkNFQ0oGlA0YBQNKADdKGo0aicKQDeqOlNEZ//XAL1Vq1YCiGsSe02ZMkWQqLFkUWIlocOzrn5ZCo31fY0N0B8/fiyYxh89eiTIzOQybdq0xE7TJK9TAPrXXxbJg64A9K+n+4TeMd8KoJN9/UN0FJxsXQV/w96Lq3HvxTWkc8yEGkXawMbSXm8lKQBdb1UpDRUNKBpQNGAUDSgA3ShqNGonCkA3qjpTRGf/1wB93LhxIBhnXXV5DjoZ3Mm2Tm+2JIYSqSV0eP6aAJ1EePXq1ROgn6XkfHx88ODBA8GKXKRIEezfr93jnCJ2sJZBagL0iKh3otWPM8rrnNL8vodgncZWfD97+xC0Lj9QlKLSRxQQoXjQ9dknxm6T0DsmOQH6k1d340wn8M3jeKeY1iEjzM3M1G2O39yBGkXawt7aUed1yrNl7F2j9KdoQNGAooH4NaAAdNPbIQpAN701Se4RmSxAH7a8WZy52zimiVcfe/bsQb58+WK1adSokfrf9y4EIUdhN/W/WUNXmxC4SjWipe+rV68e773nzp0L1uaVJKHDs67OjOlBl+Z+4MABuLu7C4Z6etErV64MS0tLnDlzBunTp4/lrdecR3JvQPbfv39/vW6jr6dfE6D3mlcFLAjNmtC61htIhQJZfbV+/yBQxfCvTTxcPZGraKY4X30LPeqlxC+NjK1zxYNuiPaN07ZQ9rIIM3+ms7OAh09gYWke63s3NzfYfkqP1GYWWq9rUa4v7K2dExyg9Ezh82d1W9Y/j19SQf4Isn3vulORK2NMKcv1x2eLLu48vST+pssaYyTV1TdJNlmRYvjw4XBxcUlw7EoDRQOKBhQNKBrQrQEFoJve7lAAuumtSXKPyGQBes95leFs5wZz2UHSKZ2NTn34+/uDQLRChQqx2piZmaFZs2awtrbGjePPkMc3Q7w6XblyJT58+KC+Rp8F4DVkZpeHpZsCQJfmvn79etSvXx8E/xxrrVq1BAldcHAw6F1v2rSpmKa2eegz/6S2qVhRVec+PqHBRF9PvyZAlw77DQcUEVEDnP/ChQuRKVMMsN4w9TxmbRmAwp7lYWFuGWsop27vgou9O8xSxXj/2IB1nwtm90Oh8p6x2n8rPSakQ/n3xta5BNALZ3HChh4Ki7sha5HYttyPBLk0DPJZ15Tnz5+rSynyu4iICGF4LJjND5YWcd+l5/z3Y3jzv9XlBeMbFwH6wEZ/wd4qhsxy5MrW+LH6WNhaxQ5h5xj/3Pozfqo9ES526UW3/IztNQE6+82ePq8IibeysIGVbfxG2ZCQEJQoUQLnzp3DrVu3Ep0alNg1UK5TNKBoQNHA96YBBaCb3ooqAN301iS5R2TSAH1Y00XI4JJNrYP4wh158NQF0HlQ5SFWF2hmTvb9+/fh6ekJZ2dnvHv3DtI1+iyANq+3qQB0zqNAgQJCN/Sg582bF2SKZ8g7c90Z3i+R4SXVe6+Prr5Gm4Ry0HWtF8HB+HZr43gQdX0ukcmNXNkm1rS+Fz0aslYKQDdEW8ZpKwxGqaDzXcXvn79Qvfso3Jc0UA2qN1crCDeEHHHmlv7oUm0MbCzt1JPR9pn0pba0kd7zq6Jf/ZkCkEsiPWtDlzUVz+IvSxvHqyzpWStYsGCSuDuMsyJKL4oGFA0oGkj5GlAAuumtoQLQTW9NkntEJgvQD1xejzJ56yJN6hgPSnwAnTXN27RpEytvnMo7dOiQAKGpU6eOA9DDw8PRq1cvLF26VOj59u3bePr0qchLL168OIYNG6aX/o8ePSraM2xcEgmg80Wnj0j57vTuLlq0CBkyxO/p19WnnMVdmjvrvNeuXRtdunTBwIEDsWnTJrRv3x7//fefMEjs3btXdKdtHvqM3dTaJBag09Oew90H5maxw4J1fX732RVkSZcbfRfUiKWC70WPhqyrBNCLZHHCf4oH3RDVJbpt33rTUaCBI1q3bh3r3SN1yO+n/NdLvPuk5/vu3bt4ddgVlhbWce4r7WcL8/i91okesMaF2gwCJ2/tQlGvijjnf0D81Xy2NO/NaBVGB/Gdxr+2tioeCUUUDSgaUDSgaCBxGlAAeuL0lpxXKQA9ObVrmn2bLEDXpq6kEgZperX79OkDEsLNmDEDNWrUwOXLl0WIJA97o0aNwoULFxK9atK9GHqqmc8u71TKd4+Ojk70veQXaiuzdu/ePeElpyed0QEkwDt+/Dhy5swJ5nVnzZrVKPdObCfGzodOLEBP7PiTui8Te9+kXGdsnSsAPSmrkbhrE4rSSU6SuMSNOPZV+njsU+KzZQzdKH0oGlA0oGjgW2lAAejfSvO676sAdNNbk+QekUkD9NdhQbHm336i9txWDw+PePUkebHJ1C1n8iab+eLFi4X3m/nIUnkz5rOz/NqTJ08S1L+c6V0TKPNwSS+2JLryn/l9+fK6GcYTHISsgSZAN9SDb8i9jNXW2PnQ+gB0GmOyZ8+unoJ0jcT4rjm3yKjwWB9ZpYnJ4ZXvKV37wVi6MlY/xta5AtCNtTL698M92/b3Yjov4PuuxZjC4nv5vuTnmiJVMND/7oBE6Ca/RuJ7kH+WM2PBON02/l975wEuRXW+8RdQUWzYAEHFil1R7BpRY+8aNZoYu1hiL8H6t7fYNbGgsfcae4k9sfdGNEERe40iVhT4P++wc12WvXdmd2f2nt39nefBK3tnzvnm954d5p3znXNW2lMY9EpocywEIACB+hDAoNeHcyWtYNArodUcxwZt0L1QXJpi49tRiUcfq/CAAAAgAElEQVSxfVjxSt7jx49vG912HU7/9Ai6jfrAgQPLLrwUt5M08t3e6FXe85NLDXq9R/DT6JX3MaUG/aL7joqajFfwv+OOO7TGGmtMkg7rFf53XecYta1OXRLkhAnjJ/mkS9GCce5TSf0h72vu7Pox6PVXwP1872FrRn0vTYkXkpvk8MJN8dwh/0hTxSTHeM55cRk3fpw+/N/IyerpN/O8k3zmKSReHK697QwPvnST6L783Y9jOsw+8kKXLl7skgIBCEAAAtkQwKBnwzHLWjDoWdJsjLqCNuieh15cfvXbAWWpOlW9oxKPYnulbq/kHRfvbe5V370d2XrrrafXX389GlX1vPSnn35ap556aqKK7Y18h2LQfe0bbrhh2evwQ/BUU00VZQ/st99+2nHHHROvN4QDPBr40ksvtbtic6lBv/LhP0dhd7SCv1f4/8Pqf2rb3qn0Ot//fMQkH80x6/xtfy/uU1llQoTAuTiGJOaxQR/UfybdvEf57epCu6ZGj8f9fLFtuunee+8teynP3ztKsy0y8Z/1ueaaK5rG4+L7YGkpN8pdKZ/2Xm611ZPyZYDnobu89fFrWnSVvu2Gscoqq0S/23777SsNleMhAAEIQKAdAhj08LoGBj08TfKOKGiDXnrxtc5HLDXNXszLxtyLy1122WXabbfdNHz48Gh+to3toEGDquYfikH3BXiO/QknnBA9oHtLIpdnnnkmerDff//9oxXsr7zySp177rnRQnKhl6QshKQU93LX1945aVjU2i/TtNHZxyQxx6DXX6Fq5qB7B4duby4yyerrxZHf/+K1WmWRjdr9fUdXWZze7m3Uzr/nMP1u1QPVc9pZJzmtkpcBrfDdqn/PoUUIQAAC7RPAoIfXOzDo4WmSd0RBG/S856AbrhdQO/PMM/Xaa69F+wQvvfTSGjp0aOqF05LmoJcKaKNTOv85S5HLzUH3CNOqq66qnXfeeZKmLrnkkmhv8auuuipayX7YsGF69dVXswwnl7qSzGItBr1V5qBXKkwS89igL9N/Jt3ECHqleKs6vhqD7vvVgRue1+5e5wddspEO2eLCVHuhJ81B/+CLt9R7prm08ByTz5P3HPQ0BYOehhLHQAACEMiOAAY9O5ZZ1YRBz4pk49QTtEHPew56LFO5+cPFc7fj3xfLmjTnOH54dvp8cSk3/9m/95ZnWZRyc9Adazz/tLQNz8P377zFXLzKexZx5FlHklmsxaAzB728cknMMeh59vjydcf3GG+VWG6niB+/+1nde0zcYs3Fc7Wt40Ebnd+uAU+zcFtcX+kcdH9ePBXEL7umnrKH5pxtgUkuoIu6RHPQ2yvMQa9/X6JFCEAAAjEBDHp4fQGDHp4meUcUtEHPew56Kdzi+cPFq687Dd57k/ftO/l8yKQ56GnndXs1+SxKqUH3dWy55ZbRn6222mqSJm644QbdeOON0R8/5K+99tr6+OOPswgj1zqSzGItBr3cKtSlxsN/b7U56EnMMei5dvmylccG3dkv5coDlw3Xmjss0vYrZ9JkadCTrrgSs19cF3PQk8jyewhAAAL5EcCg58e22pox6NWSa9zzgjbopViT0h09x/rCCy9sNz09KSW0PRmTzEm586ptq9auVG4f9Isuukh77LGH1l9//bY56M8++6zuvvtuXXDBBVHq++mnnx7NS7/++utrDSH385MWLKvFoFcTfFK/rKbO0M5JYo5Br79iSfeYcr+vv0G/SLPOMHsEp1z6fFJKfZrv1rXXXquNN95Yt99+e/Rz2mmnrb8YtAgBCECgSQhg0MMTEoMeniZ5R9RUBt1png8//HC0MntxaW8f9FK4xfPJi/cP98j5448/Psm+2fG5lc5Bz1vQ9vZBf+qpp6I55v/973+jEBZYYIFoUbzll18++nuj7N/tWJNemNRi0JmDXr6HJjGPDfqyc8+kG3dnFfe8v+euPzSDHm9nGF/7q+8+qQF9l1L3KaaOPnp11BMa0G9p7bXBKW14kkbZ0xj0+OWRt8bsaHeHemhCGxCAAAQanQAGPTwFMejhaZJ3RC1h0NvbBz2GmzQHPZ6nXSxG2jnoeQtYWn8t+6B7hefdd99d8f7C9Y49bm/s2LHRyvLzzTefppjilzm08e+9+v6yyy6r7t27lw2xFoPeqnPQa2WOQa//t6Wz56CXXnG8nWF7JJ797wNaYu6VtMvaR2dq0OOXR0suuaRefvnldrdfrL9CtAgBCECg8Qhg0MPTDIMeniZ5R9QSBr29fdBL4eYxBz1vAZMMevFc+o5i8bUnpTHnfS3fffddtAd9PKfWC9fNO++80Wfeq/2QQw5JFUItBr3V5qBnxTw26MvNPbNu2H3FVDpxUG0EqpmD7mkuq8yyo2acdpayjZ9396H6/eCD2v19LRGXGy3PYgQdg16LKpwLAQhAYFICGPTwegQGPTxN8o6oJQx6DDEpJTQ+rnQkOSm91+eVnpO2rawFLjcHPW0bvk7PVT/ssMM6ZRR93333jaYSeN92ryfg7ehs0G+77TYdffTRevHFF1NdSi0GPVUDRQd53+jL7zt1Ml6hZCMkXU9WzDHoSaSz/33xPeaDDz7QzTffHO3G4LLgggvq62d66fArfxv9vfj3D972lBaaYxktM//q6jntbNkH1k6NGPS6oaYhCEAAAlUTwKBXjS63EzHouaENtuKWNOjF88vLKePRWqdRO2XSxaNOXsV99tknLnZUrpSOPjeqQfe1dVaaaP/+/aNF6lZYYYVJ5pmPGDEi2p8+SbdYl3oadC9y9fobr06WVtvZ2Qhp7zhZMcegpyWe3XHxPea8887TAQccIE9TiNeS8HelW9cpdPY5Z0UNFv9+9OjRkrpoim5TaPMV99Cqi26SXVAd1JTXInGMoNdFPhqBAARahAAGPTyhMejhaZJ3RC1p0Iv3OC8HOJ5zPm7cuNT8S0fZG9Wge259PHKd+uIzOrBHjx567bXXIrNbzNMvDFZddVVNNBbJpZ4G3aOCw9+YGHNxSZN1kXwl+R+RFXMMev5albbgfj73Rt9rk0020X777acDDzyw7SXiRx99pN+vs7ceff3W6LT999+/7fdTT9VDe23wZ73w1iN69PW/a7d1jtOic01cLDLPwgh6nnSpGwIQgEA2BDDo2XDMshYMepY0G6OupjLovXr10v333y+v5luuxKa5eF62DalHyC+++OJonrNLvO/5Nttsk1pFDHpqVO0eaBPu/do959w8/aJgnnnmif7u1efvvffeVI1g0FNhig7KinmbQZ9nZt2wG3PQ0ytQ/ZHu5ze+caxWWWUVHX/88ZNV5N//+Z4do89HjRrV9nsb9EO3GKZZZ+irO565RG99/Jr22/iM6gNJeeZbH72quXotqCm7TdV2RrnPiqtLs4r7YostpnvuuSe6b/vnnHPOmTIiDoMABCAAgVICGPTw+gQGPTxN8o6oIQz6u5+9qftevEbjp5s4gjpgwIBowbBlllmmIj7tjWqXmutqRj8x6BVJUfZgTyvwQ/a2226ryy67LNoGbvjw4XriiSfklyqDBg1K1QgGPRWm6KCsmGPQ0zPP6kj380Ov2VTPPvtsNOe8tPj3Q6/aOPr4m2++KWvQP/nqPZ16y546bac7Kg7rnuevmOyccossLtB34lSh4rLeoO1StRcb9B9++CGa/vLVV1/J/+/77VprrRVtF0mBAAQgAIHsCGDQs2OZVU0Y9KxINk49wRv0Sx44LkrFLFd++9vf6rrrrktNu9UMeto52/G8VWcSPPbYY52W4m4h33rrrWjBPae121R47vnQoUO1+OKLV6RzuYM7Go1rz9QnNdroKe5ZMcegJ/WU7H/vPvunKzfSq69OvgaCW/PvD75iQzlL6Pvvvy9r0D//+kOdeOOuOmPnuyoO8OSbdpvsnE++erfts5/H/aQJmqA5Zpl/4mddpE+/el8/jRurc4f8o932bn7iPI0bP07dunbTooNn1+mnn67ll18+mv7i6Uf+M91000XX9Y9//EMrrkjGRsXicQIEIACBdghg0MPrGhj08DTJO6KgDfpdz16me164UnPOOkC/W/VAnXzzkIjHSy+9pJ133lkvvPBCtLr3UUcdlYpTqxn0pLn25fZyryZ7IBX8Oh7ECHodYReawqDXn7n7+eUvHCJPxfEc89Li359y9w7Rx++++4txLk5xf/CVG/XCiId18ObnZXYB738+Qrc9fbH+8+GLWmHBdbXNqvur3GftNXjCDTtro+V21h3P/E0XXHa2vvjii2j+/KGHHqqTTjpJv/71r/Xtt9+qd+/e+vTTT3XXXZW/XMjsYqkIAhCAQJMRwKCHJygGPTxN8o4oaIN+0KUbq+9Mc+uATc+JOJSOgHrupUdVnPaYpnRk0OP5zq4nzartpe2VnhPCInFJc+3jayje/72aa0/DvqNjKh3pT2qvngbd+0Y/8PTfJ1vhvzM4JnEp/n3WzGODvvw8M+t65qBXIkXVx7qfT7fie9HWiKeddpqGDBmiKaaYIqrv559/1u9WO0B/f+YCdenSRWeeeWbb7/vOPI92W/d4vf7u0/r708P021X2iYx0reXzrz/SXc9equfffkQD5/6VNlxuR3Xt0m2yz3rNOEeHTXm196G/uUCn3Ly7Xh3+so488sgopX3MmDHRzz333DO6R999993Rzw8//LDW0DkfAhCAAAQKBDDo4XUFDHp4muQdUdAGfa8Lf6091jtJi861XFmD7gWB/IDmkeA0xQ+06x7cXxtssEE0ArPEEkuoe/fuuv3226MUSo/IFJdbbrkl+qvTri+99NLo59lnny0vRue255prLi266KJlmw7BoBcH5n2QvdK4pwU4pd1zVjfffPO2hfHS8MvrmGpG+juKJQuD/tW3n+mlt/+pT0e/HzXVq+ecGjjPKtH/l35+1vWHBMGxEn2yZo5Br4R+NsfG95iDDjpIZ5xxRmRe55tvvuh+6HvVmDHfaPXVV4sWWvT9K/79Z++O0edjPtKPP32v1RbbTL9Zac+aAvrm+9G6+/kr9MS/79K8fRbTJsvvolmmn32yz/r3WihVOwdfunE0ou+58S+/9qLWXnvtyKQfd9xx0c811lhDCy+8sP79739HP4vT91M1wEEQgAAEINAuAQx6eJ0Dgx6eJnlHFLxB32ej0zWg78RV2UtH0B955BGtvvrqqQ36fhufqWEPHK6ePXvqk08+0WabbRY9tHoO5+effx7VVVz8UOtRaC9ctvLKK0fzs/1QaKPredLPPfecbrrppuANerxP8o8//hhdr82Zb8BTTTVV9GDvEanOLNWM9Odp0B97/Tbd8uT5GjfuZzkd2OWHsd9FI5GeSDthwvhJPp+qexgcK9Ewa+axQV9h3pl13RDmBFeiRbXHxgbde5y/99576tu3b7Tbgedoe/rPF5//T9PPMF00mu4RdGcb+fejXvtCHsUeNP/qmqf3ItU2Hxn8B1++UQ+9epNmm6GvNl5uF83bZ9HJPlt4zsoW8zzt1r201HyD9eJbj2rL7TeM5qDHo+UjRoyI0vW33357XX755dHPd955p+pr4EQIQAACEJiUAAY9vB6BQQ9Pk7wjCtqgH/C3DbRgv6W127rHlTXo3v/3wQcfnGSF4o6A+WF076G7yA+0xXOtn3nmmWg0+f33J46WFhcvQOStvyo5x+eHMoLu+ZnxPsnnn39+24JS3if51FNP1bnnnqvbbrstykQIpdQ6D76WEfTXRj2lYfcdqdUW/41+vcSWmnHaWSIsT7/5D131yClyrsZ2qw/VcgPWij4f/e0X+ny2J4PkWImetTLHoFdCO5tj43uMtxk78cQTtfHGE1ds94tFz9nef72/6oirttZOO+00yVztahdELI360Cu2iEz64MU21aD51/AacDrnjoM09ucfor8vMuey6qIu6tVz8pT2frPM1y6El0f+S5c+cLxmm3EOffTlSM0yyyxR6v6yyy6rO+64I1o0cuTIkVFmgH/ecMMN2QClFghAAAIQiAZwZpxxRpPwf74GSecTwKB3vgaOYG5JR0paQ1IfSZ5gd5WkEySNLQpxLknnS/Kwr/fQuVzSoZ5+WMFlBG3Qr370dD35xt1aer7VtO1qQ7X/xRPnSXqbne2220433nhjtFic9zBPU7pPOY3e+M/w6MGu2JB4BGahhRaK6i0tXi3YI+yVnOM6QjHoq622Wts+ycV7i8fXecQRR0RbbTkbIZRSq1msxaCfdfsBmq/PYtpouZ0mwRF/7g9L9412ZkeIHCvRs1bmGPRKaGdz7GFbXqTjr985ygjyiPn8809cLd2LxlnPWb9cNvq919fwCziveO6pLRfu9VjZAD763zvRVA6vnp6m7H3hmr8c5uySCV6zffLpRjbpUSkc458dreLuQ998/wW99u5Tmm/5maJpSN7bfO+991aPHj10zDHHyOtmOAvEP32Po0AAAhCAQDYEMOjZcMyyFgx6ljSrr8su9LeSrpU0QtJiki6SdKWkgwrV+gnqJUkfSzpY0uySvCmtjzusgqaDNui+jlNu3kPvff6f6JKmmWaa6Gc853CppZaKHkzTlpmmm0133X+bVlpppUkM+q233irP4/S8zdIyxxxzRCM0lZzjOkIw6M4K8IiTH2D9wO7/9/zNaaedtu0yvfDS008/Hb0xDaXUahZrMeheoMrzX3v3nHMSHPHn/rB032gb9DfffDMa4QuJYyV61socg14J7WyO7dq1mz755ONoL3DvhR7vCe6XiZ6rvcsuu0a/93obnqs95ZRTRjtg3PPn8inh7uOHbHGhZp2hb6oA/zfmk8mO89oNpaXntLNN9tnM00+63kd7DXa0NWKqIDkIAhCAAAQqIoBBrwhXXQ7GoNcFc1WN2ITvIWnewtnrSbpTkp+k4qek3e1nJflpqHikvaMGgzfoDv7xf98lzwse23WiiezXr5/++Mc/apdddqkI5q+X3EpjZ/goGnkfMGBAZO49F92j8f5Tbrs2G3cb2ErOCcWg77jjjrrqqquiFHcbsHLFBt0L3vkhPpRSbqS/kthqMeieVuGRyVKTEn/uOEr3jbaJePvtt6O92kPiWAmzWpnHBn3FeWfRtUNWqKRpjq2SQNcuXTVktyHRIpc254MGDYrW0vB33t/9Sy65JOqTzgwaNWqUxo0bF83ZHvns6LIt/nP47Tpiq0tSG/Qqw+7wtA++eEuzzzyPPvrfyOjnJKP0Zc58/fXXo6wAvyDzz3gV+zxio04IQAACrUAAgx6eyhj08DSJIzpekkfW49V2jpXkCYcTV0+bWOaR9LakpSW9mPJSGsKgx9dS62jK2bvep9d0ky677LLoYdUPc/75u9/9LvqsW7fJUzvHjh0bvQyo5BzHG8IIuuNYbrnl2t0n2b/3InHXXXedPA+/s4pH+otLuZF+/z5eVT8pzloMukfHPX92jSW2mKSZ+HOn8JbuG+1+GQLHJC7Fv8+aOQa9EvrZHLtA34GaY8GZ9Nlnn2n48OHRnEG/IPIuDTbmTz/xrLr3mDLKOPLCcc6cWWSRRfTZO+2/jNthjcPb1l3oKMrYSPslQVzKfRb/rlz6fLnP9h62lk78w4067Moto5+HXD7pvaE0Jl+rswIGDhwY/fQCnhQIQAACEKieAAa9enZ5nYlBz4tsbfV6YuHzhfR2p7C7DJPUX9I6RVV7uWk/eXm1r3vaabK7JP+Ji4dV3x89enT0UNfZpdhYeQXfxedZWVN0nbivr0tHBt0Lna2zzjqaeuqp272M2DR7FWCvaPzNN9/IafJxamhH11/pOaEYdK903N4+yRdeeKEOPvhgeZX3HXbYodPk92hfmuLFr9KUWgz6U2/ep+v/dbY2W2E3rbzwhm3zcZ984x5d98+zohm2W6+yr1ZaeOKieuPGj1O3Jf8TBMc0bOJjsmYeG/SV5ptF1+zKCHolWlR7bPE9xotl3nnnnerTp0/bXO349/Gc7XiudhaLxMVGevpperaFX+6z+Jfl0ufLfeYR85UW3kCP//tOeb/2PnPP1CGel19+OcoKuPrqq6OXFBj0ansT50EAAhCYSACDHl5PwKDnq8nJkoYmNLGwpDeKjukn6VFJXsGrOJ+7WoN+tKSjSmMI0aB7D/TDt7pEs8/k9xATS0cG3VtgPfzwwx0uGFRP01zPtor1jB++i1mV2yfZKdl+QbHPPvtEWzA1U6nFoJvDLU9eoIdfuUndp+qhWWeYXXbl3jf6h7ETRx6nnmraST4f+/P3Tcmxkj6BQa+EVjbHJt1j2vt9Jga9YKSnmuKX970Pv3pzZKq7dpk8E+mD/72t5QesrWmm+mX9i3Ip9V6M0WvJvf+5l19RlAGQVJwV4C0jr7nmGs0+u5djoUAAAhCAQLUEMOjVksvvPAx6fmxds+eGT9yzqf3iFPV4/rjnl9uYPyXJw5vji06rNsW9YUbQbdB795xLU3abqu2ye/Vvf5Tf88nbM+jHHmtc0jN3vK3lNmo/BfL//u//2tqKz0nqEsXnFB+b9PCcVG+1vy9n0F3XU089pWuvvTbaB9nFc/C33nprrbBC84121mrQzWfkJ8P1/IiH9OnoDyJe8b7R/v/Sz0+66OCm5FhJH8SgV0Irm2Pdz3+156zyNmtdu/6Sah7XXu4e5DnbD579YeqV2tuLNDbSxb+PTXV75/SZaW5N0e2XjCgfl5RSX+u0pmxIUwsEIACB1iGAQQ9Pawx6OJp45PzhQmr7ts6kLQktXiTOwwWfFn43xAtM20tI+jHlpQQ7B/2gSzeON+dpu5Sk0RQbdM9FLC1OY3f5/L1vNOuc003ya4+8e4EhL6Tk+ehxic8px7G9c4qPDc2gp+wPTXFYFga9EhCYCAmDXkmPyeZY9/N9LlpLH3/8sWabbfKV0svdgzyV6cANz+vUheAquXq+W5XQ4lgIQAACtRPAoNfOMOsaMOhZE62uPptzj5yPkrR9iTn3tmou8TZr3iP9T4X90r0NmzcEb6pt1ooR1vqwVvrA6kWFDjnkED300EPaaaeddMEFFyQqlvacEAy69z9ub3St3IXGKyI3+krI1Rr09ha5au/zeJGrfYatNQnOZuGY+GUoOiA26CvPP4uu3qX5sjIqYVGvY93P9x62poYMGRLtD15aXnrgXQ1cc65JPvZ6E4dufjEGvV4i0Q4EIACBBiOAQQ9PMAx6GJo4nb291bCKNfLk7PMlrVZYHO5ySYdI+rmCywh2BL3cNWRl0EeOHBntE3z99dfLq1kff/zxiQvFVXpOCAbdq9K3N7pWjm+8InKjL7RUrUFvb5Gr9j6PF7k66honufxSmoVjBfeRthF0DHol1Go71v38xjeOlTN6ypUP/vOl+g2YfJG1tebYM9VK7bVFl83Ztd7zs4mCWiAAAQi0DgEMenhaY9DD0yTviFrKoJ+y/a16f4aHNGzYMK2yyio6+eSTteyyy3bI2PsKexXkSs5xhSEYdM9LbW90rdxFe3StGVZCrtqgl1n4ypzaW/wqXvhqxfW9tuMvpVk4VnLziUfQV5l/Vl21y/KVnMqxVRJIusfkuUhclSFXfBoGvWJknAABCECgJgIY9Jrw5XIyBj0XrEFX2hIG3XsDn3baaTr5xFO1yGIL6qSTTtLaa6/doTDxOd7fev755091TnGFSQ/PefWK4kXivK1Se6Nr7bXfDCshV2vQyy18ZU4dLX7lha/6Lzz5/N9m4FhJH8WgV0Irm2OT7jEY9Gw4UwsEIACBViKAQQ9PbQx6eJrkHVFLGHTvDTxmzBitPGBjnXrZIe2a1iWWWKKNd3zO3nvvrW222SbVOaEZ9Lw7T6j1V2vQq70eRvl+WSSOEfRqe1Hl52HQK2fGGRCAAAQg0DEBDHp4PQSDHp4meUfUEga9bQuiCVKXrl00YcKENq4eYfbf/bN4FffibYviY+KT2jsHg553d01XPwY9Hacsj2IEPUua6erCoKfjxFEQgAAEIJCeAAY9Pat6HYlBrxfpcNppCYM+apQXxJeuOOwJbXfiSu3S79/f6+5NLPE5SVIVn4NBT6JVn99j0OvDubiV2KD/aoFZdeXOzEGvhwIY9HpQpg0IQAACrUUAgx6e3hj08DTJO6KWMOgxxKQH2ixh17Ot0hcD/nsrp11j0LPsyenqwqCn45TlUUn3GOagZ0mbuiAAAQi0BgEMeng6Y9DD0yTviJreoBfvBZ70QGvY3sP6xx9/1MCBA1Wc5t6REOX2vU7TVh7iFi8Sl0f9jVAnBr3+KmHQ68886R6DQa+/JrQIAQhAoNEJYNDDUxCDHp4meUfU9Aa9eC/wpAdaw/Ye1l7Bvdb9w9O0lYe4GPSJW9yVKx1lFbR3ThqNWjlbIeaDQU/TU7I9Jukeg0HPlje1QQACEGgFAhj08FTGoIenSd4RNb1BL94L/KUH3tXANefqkKn3sB47dmzN+4cnPTznJSwGHYOeV9/qqF4Mev2pJ91jMOj114QWIQABCDQ6AQx6eApi0MPTJO+Imt6gF+8F/sF/vlS/ATMlMnWKe/fu3ROPKz6gdN/rpIfniiqv4GAMOga9gu6S2aEY9MxQpq4o6R6DQU+NkgMhAAEIQKBAAIMeXlfAoIenSd4RNb1BLwaY9ECbJex6tlV6jf57K6ddk+KeZU9OVxcGPR2nLI9Kusdg0LOkTV0QgAAEWoMABj08nTHo4WmSd0QY9JwIJz0859Rs2/xrDPrkhJmDnlevk2KDvuqA2XTFTsvl1xA1txFIusdg0OksEIAABCBQKQEMeqXE8j8eg54/49BawKDnpEjSw3NOzWLQRYp7Xn2ro3ox6PWnnnSPwaDXXxNahAAEINDoBDDo4SmIQQ9Pk7wjwqDnRDjp4TmnZjHoGPS8ulaH9WLQ64896R6DQa+/JrQIAQhAoNEJYNDDUxCDHp4meUeEQc+JcNLDc07NYtAx6Hl1LQx6p5Btv9GkewwGPTDBCAcCEIBAAxDAoIcnEgY9PE3yjgiDnubyn6EAACAASURBVBPhpIfnnJrFoGPQ8+paqQz64AGz6XLmoNdFg6R7DAa9LjLQCAQgAIGmIoBBD09ODHp4muQdEQY9J8JJD885NYtBx6Dn1bUw6J1ClhH0wLATDgQgAIGmJoBBD09eDHp4muQdEQY9J8IY9JzApqiWbdZSQMr4kHgO+moLzqbLdmQV94zxlq0u6R7DCHo9VKANCEAAAs1FAIMenp4Y9PA0yTsiDHpOhJMennNqlhF0RtDz6lqpRtAx6PXDn3SPwaDXTwtaggAEINAsBDDo4SmJQQ9Pk7wjwqDnRDjp4TmnZjHoGPS8uhYGvVPItt9o0j0Ggx6YYIQDAQhAoAEIYNDDEwmDHp4meUeEQc+JcNLDc07NYtAx6Hl1LQx6p5DFoAeGnXAgAAEINDUBDHp48mLQw9Mk74gw6DkRxqDnBDZFtcxBTwEp40PiOeirLzibLmUOesZ0y1eXdI9hBL0uMtAIBCAAgaYigEEPT04Menia5B0RBj0nwkkPzzk1ywg6I+h5da1UI+gY9PrhT7rHYNDrpwUtQQACEGgWAhj08JTEoIenSd4RYdBzIpz08JxTsxh0DHpeXSuVQV9joV66ZIdlOyWGVms06R6DQW+1HsH1thKB8ePHa+zYsa10yVxrRgSmnHJKdevWrd3aMOgZgc6wGgx6hjAbpCoMek5CJT0859QsBh2DnlfXwqB3Ctn2G026x2DQAxOMcCCQEQEb85EjR8omnQKBagj07NlTffr0UZcuk1s/DHo1RPM9B4OeL98Qa8eg56RK0sNzTs1i0DHoeXUtDHqnkMWgB4adcCDQqQQmTJigd999Vz/99JP69u2rrl27dmo8NN5YBNx/vvvuO3366aeySZ999tknuwAMeniaYtDD0yTviDDoORHGoOcENkW1LBKXAlLGh8SLxJHinjHYDqpLuscwgl4/LWgJAvUiYGM+YsSIyJzPOOOM9WqWdpqMwBdffBGZ9AEDBkyW7o5BD09sDHp4muQdEQY9J8JJD885NcsIOiPoeXWtDuuNDfqvF+qlvzEHvS4aJN1jMOh1kYFGIFBXAj/88EOU3j733HNrmmmmqWvbNNY8BL7//nu98847mmeeeTT11FNPcmEY9PB0xqCHp0neEWHQcyKc9PCcU7MYdAx6Xl0Lg94pZNtvNOkeg0EPTDDCgUAGBGKDXs5YZVA9VbQIgY76EQY9vE6AQQ9Pk7wjwqDnRDjp4TmnZjHoGPS8uhYGvVPIYtADw044EOhUAhj0TsXfNI1j0BtLSgx6Y+mVRbQY9CwolqkDg54T2BTVMgc9BaSMDyHFPWOgKapLuscwgp4CIodAoMEINLJB9wJlu+22m2666SZ9+eWX0Rz6HXbYQWeddVaDqdD44WLQG0tDDHpj6ZVFtBj0LChi0HOiWF21GPTquNVyVmzQ11y4ly7enn3Qa2GZ9lwMelpSHAeB5iHQyAb9nnvu0SabbKJHHnlE8847r7bYYgsts8wyGPRO6J4Y9E6AXkOTGPQa4DXoqRj0nIRLenjOqVlS3Elxz6trdVgvBr3+2JPuMYyg118TWoRA3gQa2aD/5S9/0amnnqpRo0ZFmFZbbTUNHDgQg553pylTPwa9E6DX0CQGvQZ4DXoqBj0n4ZIennNqFoOOQc+ra6U06L118fbLdEoMrdZo0j0Gg95qPYLrbQUCjWrQncp++eWXt0nUv3//aCX6YoPepUsX3Xrrrdp0003bjvNe3U6B9/lXXHGF9txzT7344otaYIEFomP894ceekgvvPCCevTo0QpdIJNrxKBngrFulWDQ64Y6mIYw6DlJkfTwnFOzGHQMel5dC4PeKWTbbzTpHoNBD0wwwoFABgRKjZXndX//07gMaq68immm7Cab6jRl9OjROuecczRs2DA9++yz0d7bW265ZUUG3e1stdVW0fZgTzzxhO677z5tttlmevLJJzVo0KA0YXBMgQAGvbG6QrpvWWNdE9F2TACDnlMPSXp4zqlZDDoGPa+uhUHvFLIY9MCwEw4EOpVAqbH6buzPWuT/7uuUmIYfu456TDVF6rY9Eu4/NtgupSnuSSPoPseLyy2xxBLaaKONdMstt2ifffbRYYcdljoGDpxIAIPeWD0Bg95YemURLQY9C4pl6sCg5wQ2RbUsEpcCUsaH/DIHnRT3jNG2W13SPYYR9HopQTsQqB+BVjfoJn3//fdrnXXW0UorraR//vOf6tq1a/0EaJKWMOiNJSQGvbH0yiJaDHoWFDHoOVGsrloMenXcajkrNuhrLdJbF23HHPRaWKY9F4OelhTHQaB5CDRqirsVSBpBt9G++eabo7T1uEw77bT661//Gs1Bj8sRRxyhk08+WXPNNZdefvllTT/99M0jcJ2uBINeJ9AZNYNBzwhkA1WDQc9JrKSH55yaJcWdFPe8ulaH9WLQ64896R7DCHr9NaFFCORNoFEXiUtj0Hv37q2jjjoqWvjN5b///a8GDBigSy+9tM2ge+754MGDdfvtt2vo0KFaaqmlJll8Lm/+zVI/Br2xlMSgN5ZeWUSLQc+CYpk6kh6ec2oWg45Bz6trpTLoay/SW8MYQa+LBkn3GAx6XWSgEQjUlUAzG/RtttkmGhG/+uqrNW7cuMiAO4XdC8t5BH3MmDHRonJe5f3000/Xq6++qmWXXVZXXXVVtKc6JT0BDHp6ViEciUEPQYX6xoBBz4l30sNzTs1i0DHoeXUtDHqnkG2/0aR7DAY9MMEIBwIZEGhmg/7hhx9qxx131OOPP66+ffvq7LPPlk17vM3aTjvtpOeeey5aBb579+4RzTPOOEMnnHCCXnnlFfXr1y8Dwq1RBQa9sXTGoDeWXllEi0HPgmKZOpIennNqFoOOQc+ra2HQO4UsBj0w7IQDgU4l0MgGvVPB0fgkBDDojdUhMOiNpVcW0WLQs6CIQc+JYnXVskhcddxqOSueg06Key0UKzs36SUgI+iV8eRoCDQCAQx6I6gUfowY9PA1Ko4Qg95YemURLQY9C4oY9JwoVlctBr06brWcFRv0dRbtrQv/wCrutbBMey4GPS0pjoNA8xDAoDePlp15JRj0zqRfedsY9MqZNfoZGPScFEx6eM6pWVLcSXHPq2t1WC8Gvf7Yk+4xjKDXXxNahEDeBDDoeRNujfox6I2lMwa9sfTKIloMehYUGUHPiWJ11TKCXh23Ws6KDfq6i/bRBX8YVEtVnJuSAAY9JSgOg0ATEcCgN5GYnXgpGPROhF9F0xj0KqA1+CkY9JwETHp4zqlZRtAZQc+ra3VYLwa9/tiT7jGMoNdfE1qEQN4EMOh5E26N+jHojaUzBr2x9MoiWgx6FhTL1JH08JxTsxh0DHpeXQuD3ilk22806R6DQQ9MMMKBQAYEMOgZQKQKYdAbqxNg0BtLryyixaBnQRGDnhPF6qolxb06brWcxQh6LfSqOxeDXh03zoJAIxPAoDeyeuHEjkEPR4s0kWDQ01BqrmMw6DnpmfTwnFOzjKAzgp5X1+qw3tigr7dYH52/LXPQ6yFC0j2GEfR6qEAbEKgvAQx6fXk3a2sY9MZSFoPeWHplES0GPQuKjKDnRLG6ahlBr45bLWdh0GuhV925GPTquHEWBBqZQKMa9NVWW00DBw7UWWed1en4//73v+uggw7SyJEjtffee0dx7bfffvrqq686Pbb2Athhhx2i+Bx7mvLII49o9dVX15dffqmePXtOdgoGPQ3FcI7BoIejRb0iwaDnRDrp4TmnZhlBZwQ9r67VYb2xQV9/8T467/eMoNdDhKR7DCPo9VCBNiBQXwIY9PK8kwxp8Vm9e/fWjjvuqH322UfTTz+9pphiCo0ZM0a9evWKDjv66KMjI/zSSy/VV9wOWsOgByNFpwSCQe8U7J3aKAY9J/xJD885NYtBx6Dn1bU6rHeN0x/R2599qwu2HaR1F+vTKTG0WqNJ9xgMeqv1CK63FQhg0Gsz6N98801kyh966KFohLlcwaB/rRlnnNFo/J+vW+F7Ffo1YtDDU6i7pKclLSlpKUnFr/PmknS+JN9hvpF0uaRDJf1cwWVg0CuAVcmhSQ/PldRVybFxevcfL1ijktOa6lhS3Osv57c//qyRn3+rRfvOoC5d+KekHgok3WMw6PVQgTYgUF8CjWzQF1tssQjWlVdeqSmnnFJ77LGHjj322LZ/M3788Ucdfvjhuvbaa6N0bh9/yimnyOnxLqNGjdJee+2lf/3rXxo7dqzmnntunXrqqVpkkUU0zzzzTCLE9ttvr8suu2ySz+JR9uIPH374Yb3zzjttKe4+x6PrxeXSSy+VR7BLSzyqvdxyy+nss8+W4z/ggAN02GGH6dBDD9Xf/vY39ejRQ8cdd9wkdb766qvad9999eSTT0a//81vfqMzzjhD0003XdTEuHHjdPDBB+uSSy5Rt27dtPPOO+uTTz7R6NGj21Lcx48fH7EZNmyYPv74Yw0YMEBHHnmktthii6iOpIwCUtzr+72ttTWeqmolmP35Z0taQNJ6JQa9W8GsfyzpYEmzS7pC0kWSDqsgDAx6BbAqOTTp4bmSuio5FoOutiyCUm4dvbRoz9SnYd/KL0PS8OGYfAgk3WMw6Plwp1YIdCaBRjbozz//fGQ2bcyfe+45DRkyJJqTvuuuu0ZI/XP48OE6+eST1bdvX91666064ogjZEO7wAILaMMNN4yM+emnn65pp502OnaGGWbQyiuvrNtuuy0yum+++Wb02TTTTBOPArfJ5XNtxhdccEHdfPPNWmmllTTzzDPrmmuuaTPo33//fWR07733Xj3wwAPRuR5Ndn3lDPott9yi7bbbLprL/vjjj0fXt84662jVVVfVlltuqeuvvz56CfH2229rjjnm0Lfffhtdy4orrqhjjjlGn376qXbZZZfo+PiFwp///OeIwcUXX6yFF144ut4bbrhBa6yxRptBP+GEE3TVVVdF/FzfY489pt1331333XefBg8ejEHvzC9pDm1j0HOAWkOVNuVnSPqNpNdLDLp/d6ekvpI+KbSxu6RTJM0maWzKdjHoKUFVeljSw3Ol9aU9HoOOQU/bVziusQkk3WMw6I2tL9FDoByBUoM+YcIETfj++06B1WWaaVJnTHkU3Gb09ddfbzvnkEMO0e233x4Z7XfffVfzzjtv9NPmPC5rrrmmPEJ94oknaokllohM+FFHHTXZ9SaNGMcneGR+pplmkkfO45F5G+PiReLSprh7BN3t2nx37do1amKhhRaK5rLbMLt4NNwG32Z766231kUXXaShQ4fqvffei14yuNx9993aaKON9OGHH8rz4339+++/fzSK7vLzzz9HGQKDBg2KDLpH6v1iwS8QbPTjYqP/3XffRS8ckngwgt4pX5mqG8WgV40u8xN7S3pe0qaSPpc0ssSgHytpY0kDi1p2fs/bkpaW9GI7ETll3n/iMr2k95024zeOnV2SRjFrHalMeqDN8vrr2VZx3Bh0DHqW/Zi6wiWQdI/BoIerHZFBoFoCpcZq/Hff6c2lO2dhzgVfeF5de/RIdSk2wzbgTtuOi0e9nZLta/KItUfIY9MaH2Mzuvnmm0cj0Ta5Hn23Ybdxt1m3aXdJMqRxfVkb9M8++0x33XVX2zV59Nqp+X/961/bPuvfv78OPPDAaFE6p8C/+OKL0QuCuPgZ3CutP/roo1pyySXb/t+j6nHZbLPN5JcxNuh+yeE2Slk5Q2CppZbS008/ncgDg56q2wZzEAY9DCmsw92SHpd0vKS5yxj0YZL6S1qnKGTfJb+VtL6ke9q5lKMlTfbqEYOevfBJD8/ZtzixRgw6Bj2vvkW9YRFIusdg0MPSi2ggkAWBZjXoN910k37/+99H5tPzrouL52b36TNx8VGPPNsQ33///brzzjuj9G+nl3eWQS/d+qzcdnKeK+8Rev/JwqDbgK+wwgrRNffr128SVt27d9ecc86ZyAODnsW3sX51YNDzZX2ypKEJTSwsaW1JW0ka7OyYjA06I+h1Wjwt6eE5r66GQceg59W3qDcsAkn3GAx6WHoRDQSyINDIKe4ebbYBj4sXUvMoulPc//Of/0Rzw50a/qtf/SoVKp9vs/7KK6/oiSeeiOaif/7555plllnaPT/NCLrT6b1Qnee+d1TKbX2WZNCrTXF39sHSSy8djaB7S7jZZpstSpf/wx/+UDbEpBcWGPRUXSyYgzDo+UrhueHt3zUmtu0U9RskbSRpQlE4fp1os361pO0lVZviXnqFzEHPSfOkh+ecmmUEnW3W8upa1BsYgaR7DAY9MMEIBwIZEGj0ReK8ENxuu+2mF154IVoUziPg/rvLtttuGy205s+cqm1D/+CDD0Zp7BtssEE0Ar3eeutFK5Z/+eWX2nPPPeX0cae/f/DBB9HIsVdcX3/99aNF3eJV0YuxpzHonsPtBey8WrwXdvO2bB6ZLi3VGHTPEZ9//vmjBeo8193X6LnjfikRLxLn1dm9UJxXgfecdq/wft11102ySJwXz7vgggsiVqusskq0wrvZebqqV7DHoGfwZQuoCgx6GGJ4+7TiCeFeLeM+Sd47wVuuvV9Y1d2LxHn19k8LYQ+RdKqkXpJ+THkpGPSUoCo9LOnhudL60h7PCDoj6Gn7Csc1NoGkewwGvbH1JXoIlCPQyAZ90UUXlbcHswF2Grvnkx9//PFti8b99NNP0d+vuOKKyHDPOuusUSq3VztffPHFo1T2e+65R++//35kRNddd12deeaZbSPm3s7svPPOi7Yk88rqpdusmWcag+55706398sBH5+0zZpHteOSNILu45K2WfOicAcddFDUrhef22mnnaLMgOJt1jwf/ZxzztH5558fLVLnOeweYfcWb567jkFvrvsHBj1MPcvNQY+3WftQ0p8keXLOlZIuZpu19kVMeqDNUv56tlUcNwYdg55lP6aucAkk3WMw6OFqR2QQqJZAoxr0aq+X8/IhQIp7PlzzqhWDnhfZ2uotZ9BdoxeJO1/SaoXF4S6XdIh3ZKigOUbQK4BVyaFJD8+V1FXJsRh0DHol/YVjG5dA0j0Gg9642hI5BNojgEGnb2RBAIOeBcX61YFBrx/rUFrCoOekRNLDc07NMgedOeh5dS3qDYxA0j0Ggx6YYIQDgQwIYNAzgEgV0dZ2I0eOjPZXn3rqqSch8vXXX0d7t0vyf74GV+cTwKB3vgb1jgCDnhPxpIfnnJrFoGPQ8+pa1BsYgaR7DAY9MMEIBwIZEMCgZwCRKjDoDdYHMOgNJlgG4WLQM4BYroqkh+ecmsWgY9Dz6lrUGxiBpHsMBj0wwQgHAhkQwKBnAJEqMOgN1gcw6A0mWAbhYtAzgIhBzwlildXG8/BLT//jBWu0W2N756QJoaN605zPMRCohgAGvRpqnAOBxiaAQW9s/UKJnhT3UJRIFwcGPR2nZjoKg56TmkkPzzk1ywg6I+h5dS3qDYxA0j2GEfTABCMcCGRAIDZWc889d7TXNwUC1RD4/vvv9c477zAHvRp4nXAOBr0ToHdykxj0nARIenjOqVkMOgY9r65FvYERSLrHYNADE4xwIJABAe8VPmLECPXt2zdeyCuDWqmi1Qh88cUX+vTTTzVgwIBoT/riwiJx4fUGDHp4muQdEQY9J8JJD885NYtBx6Dn1bWoNzACSfcYDHpgghEOBDIgMGHCBL377ruyUbdJ79q1awa1UkWrEHD/+e677yJz3rNnT80+++yTXToGPbzegEEPT5O8I8Kg50Q46eE5p2Yx6Bj0vLoW9QZGIOkeg0EPTDDCgUBGBMaOHRttkTV+/PiMaqSaViNgc96nTx916TK59cOgh9cbMOjhaZJ3RBj0nAgnPTzn1CwGHYOeV9ei3sAIJN1jMOiBCUY4EMiQgM25jToFApUSmHLKKSdLay+uA4NeKdH8j8eg5884tBYw6DkpkvTwnFOzGHQMel5di3oDI5B0j8GgByYY4UAAAhBoAAIY9PBEwqCHp0neEWHQcyKc9PCcU7MYdAx6Xl2LegMjkHSPwaAHJhjhQAACEGgAAhj08ETCoIenSd4RYdBzIpz08JxTsxh0DHpeXYt6AyOQdI/BoAcmGOFAAAIQaAACGPTwRMKgh6dJ3hFh0HMinPTwnFOzGHQMel5di3oDI5B0j8GgByYY4UAAAhBoAAIY9PBEwqCHp0neEUUG/b333tMMM/h/O7cM2+/RDgMYctbgmgJ0/bXWkTaAerZVHFPMsF7XmZZHPY9rrx91xCSp73UUfyuzrqeutDUpgaR7THu/r6Wv11sDvlv1Jk57EIBAqxOwQZ9zzjmNYUZJX7c6jxCuH4Meggr1jaGfpPfr2yStQQACEIAABCAAAQhAAAIBE5hD0gcBx9cyoWHQW0bqtgu15n0ljWm9S099xdMXXmL4RgWn1Nia5kD0bxopq7oQ9K8KW9OchP5NI2VVF4L+VWFripNaXXtf/4eSJjSFmg1+ERj0BheQ8HMhEE0DINUnF7aNUCn6N4JK+cWI/vmxbYSa0b8RVMovRvTPj23oNaN96Aq1UHwY9BYSm0tNTYCbdGpUTXkg+jelrKkvCv1To2rKA9G/KWVNfVHonxpV0x2I9k0naeNeEAa9cbUj8vwIcJPOj20j1Iz+jaBSfjGif35sG6Fm9G8ElfKLEf3zYxt6zWgfukItFB8GvYXE5lJTE+gu6VBJJ0n6MfVZHNgsBNC/WZSs7jrQvzpuzXIW+jeLktVdB/pXx60ZzkL7ZlCxSa4Bg94kQnIZEIAABCAAAQhAAAIQgAAEINDYBDDoja0f0UMAAhCAAAQgAAEIQAACEIBAkxDAoDeJkFwGBCAAAQhAAAIQgAAEIAABCDQ2AQx6Y+tH9BCAAAQgAAEIQAACEIAABCDQJAQw6E0iJJcBAQhAAAIQgAAEIAABCEAAAo1NAIPe2PoRfToChxRWZD9b0n6FU9z3j5G0q6Sekh6XtIek/xZVObWk0yVtLcmre94naU9JnxQdM7OkcyVtJGm8pJsl7Svpm3ShcVROBI6WdFRJ3W9KWgj9cyIeVrX9JJ0iaT1JPSSNkLSjpOfQPyyhcojmHUn9y9R7nqQ/SuLenwP0gKrsJsn3/20l9ZH0oaTLJB0vaQLf/4CUyieU6SUdJ2kzSb0kvVh4JnsW7fMBTq35EMCg58OVWsMhsKykGyR9LenhIoM+tLCV2vaSRhZu6ItLWkTSD4Xwz5e0gaQdJI2W9JeCCV+56PLukTS7pN0kTSnpUkn+h+B34SBoyUj8gLaFpDWLrv5nSZ8X/o7+zdstZio8lPn77u/wZ5IWkPRW4Y+vHP2bV//ZJNmkxWUxSf+QtLqkR9C+eYUvXNlhkg6Q5H/bX5e0TOHf5cMlncP9v+n1v16Sv/MecPHLGb+o2b/wbPcB3/+m179pLhCD3jRSciFlCEwn6YXCqPcRkl4qGHT3e9+4PTp+WuG8GQsj4zbj10ny3/1gb6N9U+EYj77+W9KKkp6StLCk4ZL8EiAemVtX0t2S5ii0gTCdQ8AGfVNJA8s0j/6do0m9Wj1Zkl+i/aqdBtG/XkqE0c5ZkjYsvKRxRNz7w9AlryjuLPxbvnNRA85s+75g1vj+50W+8+udRtIYSZtIuqsonOcleTDlSL7/nS8SEaQjgEFPx4mjGpPA5ZL+V3h76pGT2KDPWxhJW6rwWXx1jxb+7hT1NSQ9KMmjcV8VXf4oSX7gO1PSTgWT72PiMkVhBH5LSbc2JramiNoG/eBC5oMzIp4sZEy8Kwn9m0Lidi/CL808HcUvyQZL8qiJ05svKpyB/s2tf/HVTVV4ID9D0ol891tCeI+gD5G0tqT/SFpS0v2FUfWr6QNN3Qec3u5sSWfO+fktLv+S5Aw6P7M5k4pnv6buBs1xcRj05tCRq5icgOeNO6XNo9s2aMUGfaXCnPO+kj4qOtWp8J6j9tvCyLnT1T33vLg8U0iVd4qsHwScRrdgyTGfFuY/O72W0jkEPPfYGRSed+4pCJ6P7nnJTn3zVAavOYD+naNN3q3GU1Rsym4s3AO8/sTukvzSju9/3gqEU/9Wkq6RNFfBqKN9ONrkFUnXwsuYP0kaV5ju4GeBkwoN0gfyIh9GvU9IGlt4hvN6QdsU7vvxOiT82x+GTkSRQACDThdpRgJzFlLO15L0SuECMejNqHT6a/JCgM5+8NxET1PgH+n07BrtSD+cecqJH8Tj4rmnflnn6Sk8oDeaotXH60wK9wcv4umC9tWzbJQz/XL+1EIGleege5qTs9587+cFXaOoWH2c80m6RNKqhRc0nuboTIpBkjztgX/7q2fLmXUkgEGvI2yaqhsBzz12ernfnsfFiwZ5dNwrrXvE229TSXOqmyRBNOTF+x4opDqT5haEJLkE4RcxXhRsl6LavWCQ16FwFgUp7rlgD65Sr+T+tqTNJd1WiA7tg5Mp84DeK+zg4EVd4+LvvhcL8zoy9IHMkQdZ4bSSZihkSXrhOGfU7U2Ke5BaEVQZAhh0ukUzEvA8pNJtdpyu/kbhH26/VfdCQV4gzgvFufhG7tT00kXinB7lBWZcbOxdR+kicV4l1ouQuHje270sEhdct/I/zn5wc6q7t8VD/+AkyiwgpzQ7i6Z4kTivGbF8YQQ1XiSK739myIOsyOtQeHcN9wXPP3VB+yClyjSoLwrT2y4oqvXQwjaLA+gDmbJuhMq8RpB36vGUB69Dwr/9jaAaMUb/WFEg0AoEilPcfb2eQ+790Yu3WVuizDZr6xdMuxcesbFzKU6d9cqgvQvzW+Nt1pxeyzZrndurbL7uKKS1e66597x3qqO30fPq/Ojfufrk2bpT2T0P0S9jvK7EcoUHMy8c5UWi+P7nST+Muj0P2Q/l1xbu88VR8d0PQ6O8ovCe514kzC9n/DLemXLDCmnP1p7vf17kw6h3ncJLGK8/M39huoPXJfEL25/4tz8MkYgimQAGPZkRRzQHgVKD7r5v0+aHds9P9iqfexbmKsVXOvz++QAAIABJREFUPHVhhN2j6F4szvMZfczHRUhmLuyP7jmOTp/3aPs+kr5pDmwNexXeKs9z0GYpGHLr64WCnNrugv4NK22qwL2tlheF8v7nNmpeMC5exR39UyFs6IOcyeT7tbOePP+0uPDdb2hpE4N3Bt1xkjaT1KswYuoXNccW1iPg+5+IsKEP8MKQvvd7Fw/v4uNnMv/bP5p/+xta15YLHoPecpJzwRCAAAQgAAEIQAACEIAABCAQIgEMeoiqEBMEIAABCEAAAhCAAAQgAAEItBwBDHrLSc4FQwACEIAABCAAAQhAAAIQgECIBDDoIapCTBCAAAQgAAEIQAACEIAABCDQcgQw6C0nORcMAQhAAAIQgAAEIAABCEAAAiESwKCHqAoxQQACEIAABCAAAQhAAAIQgEDLEcCgt5zkXDAEIAABCECgrgS8J/WRkrw3sbet/LSurdMYBCAAAQhAoIEIYNAbSCxChQAEIAABCDQYAe9LbWO+uaStJf0oaWiDXQPhQgACEIAABOpGAINeN9Q0BAEIQAACEJiEwCOSXpK0X45cqm3jHUlnFf7UEt5UhWtcVdKOkrpKOqWWCjkXAhCAAAQg0MwEMOjNrC7XBgEIQAACIROo1jxXck3VtlGtQR8s6SpJcxYFebCkkyX9T9IchVH0Sq6BYyEAAQhAAAItQwCD3jJSc6EQgAAEIBAYgWrNcyWXUW0b1Rr0MyRNLWnPoiBvlTRK0hBJy0t6tZIL4FgIQAACEIBAKxHAoLeS2lwrBCAAAQiERKDYPP9B0r6SFpT0raSHCqnv8YJqq0l6WNK6hdHohSQ9WZjXPUiSjXE/SXdK2kXSd4ULdRuvFf7fbfwk6XxJ/ydpQuHzXpL+JmlNSR9LOkLSCSUp7gcUUtTnLYyE3yHpT5K+KQE6QtJeku4tfD6bpA8kLS7pGEkfSnJdFAhAAAIQgAAEyhDAoNMtIAABCEAAAp1DoNig7yTpo8KCajbMNtxfSVq/EFps0J+SdFDBgN9QML9eeO0QSdNJ8mj1qUXzvN2GDbwNuI35MpKGFcz/RYW675bUV9LuBQN/jqSlJB1WNAfd8+RfljRSkk36eYWXCMUj5YsWXhrMKmlsoe79Jf1O0rKSNizE4RcJP3cOclqFAAQgAAEIhE0Agx62PkQHAQhAAALNS6Cj9HMb6WcleRV0j1LHBt2j3A8WkNiUnyRpPklvFz67QNLchZF2f+Q2bPhtnuMRc88H31jSIpIGFF4KLFdoz+d4dP7fkmyuvVBcubKFJLdlMx4XG3ob+y2LPnulYMrPljRF4SWER/hva15ZuTIIQAACEIBA9QQw6NWz40wIQAACEIBALQSKDbpHuY+WtKSkmQqrnfcoGOvhRQbdZvuzQqNeFf0vkqYtCsJp5BtJWrrwmduwefcIfVw2kXRTYa64R7X9/90ljS865stCSnps0P1i4NCCeZ+hYLY919xtx+n0TxfiubJQj6/Jn3l0Pk7Vd7xeKG7TWsBxLgQgAAEIQKBZCWDQm1VZrgsCEIAABEInEBv0wwuLqN1XGJW2AZ9Lkv/uEWlvxRaPoNu8O/XdZYfCCHfPogu1ybf5HZihQfeI/BuFFPnrC3PQVymMjMfxzF64hj6F37t5m/E/ShpXFJ+fO/x3p7nHLxpC14n4IAABCEAAAnUjgEGvG2oaggAEIAABCExCIDboHnF+rmDK3yscsa0kf56FQfdCbU5xj4vT4j2K7hR3L0pn812c4h5/Fqe4/0bStYUR93iU3QvJHVcY7fcLA6/Q7rnmfpHg4hF5z6k/XtL9JbrfXHgRcSb9AQIQgAAEIACBSQlg0OkREIAABCAAgc4hEBt0r5j+viTP0/a87sUKC715fngWBt2p5l4Q7sJC6rv//8DC333l90jqLWmPwuJtTmv3OfEicU679yi+F4rz6u0rF+a+exQ8HkH36vFeed6L27lsVXjB4JT80SV4fb1Ow1+ic7DTKgQgAAEIQCBcAhj0cLUhMghAAAIQaG4CxXPQt5F0oiSnir9QMMC3Z2TQXy/MafcIt9PLvZq7R8DjReOcln5xYZu1Twq/8+i4jXo8B92j6QdLcjr9Y5KulnRFwaB767bPC1upeZu12PR7tH2DMhJ6fvzzhRXl/ZMCAQhAAAIQgECBAAadrgABCEAAAhCAQC0ENi+ksjtlngIBCEAAAhCAQA0EMOg1wONUCEAAAhCAAAS0dmHOudPfKRCAAAQgAAEI1EAAg14DPE6FAAQgAAEIQAACEIAABCAAAQhkRQCDnhVJ6oEABCAAAQhAAAIQgAAEIAABCNRAAINeAzxOhQAEIAABCEAAAhCAAAQgAAEIZEUAg54VSeqBAAQgAAEIQAACEIAABCAAAQjUQACDXgM8ToUABCAAAQhAAAIQgAAEIAABCGRFAIOeFUnqgQAEIAABCEAAAhCAAAQgAAEI1EAAg14DPE6FAAQgAAEIQAACEIAABCAAAQhkRQCDnhVJ6oEABCAAAQhAAAIQgAAEIAABCNRAAINeAzxOhQAEIAABCEAAAhCAAAQgAAEIZEUAg54VSeqBAAQgAAEIQAACEIAABCAAAQjUQACDXgM8ToUABCAAAQhAAAIQgAAEIAABCGRFAIOeFUnqgQAEIAABCEAAAhCAAAQgAAEI1EAAg14DPE6FAAQgAAEIQAACEIAABCAAAQhkRQCDnhVJ6oEABCAAAQhAAAIQgAAEIAABCNRAAINeAzxOhQAEIAABCEAAAhCAAAQgAAEIZEUAg54VSeqBAAQgAAEIQAACEIAABCAAAQjUQACDXgM8ToUABCAAAQhAAAIQgAAEIAABCGRFAIOeFcls6rEefSWNyaY6asmAwPySRmRQD1VkQwA9suGYVS3okRXJbOpBj2w4ZlXL9JI+lDQhqwqpBwIQgAAEmp8ABj0sjftJej+skIgGAhCAAAQgAIEqCcwh6YMqz+U0CEAAAhBoQQIY9LBEn0HS6EcffVTTTTddWJG1YDQff/yxNthgA6FHGOKjRxg6xFGgB3qERSCsaL755hsNHjzYQc0o6euwoiMaCEAAAhAImQAGPSx1IoP+/PPPY9AD0MUGxA9Y6BGAGJLQIwwdig06349wNOH7EY4WjsQGfdCgQRj0sGQhGghAAAINQQCDHpZMGPSA9OCBNyAxMOhhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1MKgB6QHBj0gMTCEYYmBHugRHIGwAsKgh6UH0UAAAhBoJAIY9LDUwqAHpAcGPSAxMIRhiYEe6BEcgbACwqCHpQfRQAACEGgkAhj0sNTCoAekBwY9IDEwhGGJgR7oERyBsALCoIelB9FAAAIQaCQCGPSw1ArWoE+YMEH/93//p/vuu0+jR4/W9NNPr80220yHH354WAQzjCYUg37IIYfo66+/1nnnnVfV1T399NPabrvt9Oyzz2qGGWbQLbfcohNPPFHPPfdcVfV11kl56vH+++/r17/+tf7+979r4YUX7qxLrKjdc889Vw888IBuu+22is7L6uA89cgqxrieP/zhD1pooYVqvl/V+l3M+rqK62skParlUKmOnXmvw6BXqzLnQQACEIAABj2sPhCsQX/00Uf1xz/+UVdccYXmnHNO7bPPPlpsscVqfuANC/+k0YTywFurKSg16D/88IO+/fZbzTLLLCHjnyy2PPUI3aAvuOCC+utf/6o111yzjYs1HDt2rGaaaaZO0TFPPbK+oK+++kpTTDGFpptuupqqHjNmjPyy0i+6QiuNpEe17DDo1ZLjPAhAAAIQaCQCGPSw1ArWoF911VX629/+pocffjgiVumDUliY00UTygNv1gY93dWHd1SeenSGQbe5nmqqqVKBLmfQU52Y40F56pFj2E1bdSvoUem/O4ygN21358IgAAEINDUBDHpY8gZp0G0Qb7311jZS/fr1k/8Up4yWMxDLLLOMDjvsMG2++eZR6vAxxxwT1TP33HNHdR199NF66qmnos+mmWaasJTohDm29957bzRKOmrUqIiHU62d1n7sscdOkuL+yiuvaMiQIdppp520/vrrR6OqN954oxZffPE2hpdddpkuv/xyPfjgg1Fqe0cp7nGq9I477qhzzjknmsKw6qqr6rjjjmsbcRw/frwuuugiXX/99fr8888jDffcc0+tu+66ddOtVgPy2GOP6fzzz9d///tfdevWTQMHDowyQOaaay7FBv2MM87QlVdeqddff139+/ePpnUst9xy0TWai7V4/PHH9d1336lPnz7abbfd9Jvf/Cb6/ZtvvqkTTjhBL730UqTf2muvLX93pp122uj38YsW63T11VdH5vyhhx7SGmusEdXx1ltvRX/39JHdd99dv//976Pz/PsPPvhgku+fjytNcbdG7i833HCD/ve//2m++ebTgQceGGnpEl+jz/M1uh/5Gv29XGqppSrWsVY92muwo74WZ4NcfPHFOv300/X2229HOp555pl67bXXdPLJJ+uTTz7R6quvruOPP77tvlJq7Mzf34+PPvoo4u17lfu+S3vfwx49erRpGE838UuWP//5z7rrrrvklGZnFR166KFaYoklorrieP19PPXUUyON/b32FJN55523YuYdnZCXHmmCNN8BAwaoa9eu0b1+yimn1H777acNN9wwuo+Y6ayzzqojjjhCgwcPjqp85plnInZvvPGGevbsqU033TQ6x5kOLv6O+d+If/zjH9F3yPc7vyAu/nfH/K39nXfeKWc3LLDAAjrooIO0/PLLR3Vg0NOoxzEQgAAEIBAaAQx6WIoEadD94OPUdj/433TTTZG52XfffSsy6Mbsc2w0rrvuOv3rX//SXnvtFf2/H2pDLPV84P30008jU3HwwQdHhtvpy54j7odWP+DGc9CffPJJ7b333tFxv/3tbyNsfnC10TrqqKPaMG688cZRPZ6KkDQH3Ybtkksu0SqrrBLV7bb8oGzTuP/++0d12tjefvvt0QsXm3ObfrfnrIrYwOatYa16eP2ELl26yC+T/PB/9tlnR/3Rc7g//PDDaA66Tbevcf7559ell16qu+++O3rJ4TRym/MXXngh0sN/f/fdd+XpAjbQrs+G3EbXDL/44ovIjCy77LKRaXSxQXcMa621lnbdddfoMxsKn+8UbJty/87fjZNOOil6IbLyyitHZnvFFVeMPvvVr34Vff9mnnnmyQy6TaC1dJw2gTfffHNkQm1erFls0G0Mhw4dGvWZ2Njef//9bcYorY616tFeOx31NaeX+2WTTfmf/vSnyIC7r/bu3Tt64eEXEtbC03F23nnn6EWWS7FBf/XVV6Pvjs2h9fKLF3/XXG9H30ObxNJsFr8EsKb+6ZeWfnHglyfmadMZf/eWXHLJyDhaN39vxo0bF937six56ZEmRvP1S61ddtklemno781f/vKXqP+6T/se4f55zz336JFHHonuMeuss060jsm2226rkSNHRt8Xv5Ty98fF5txTq/wyw9zcV23qfV+K1z7xOSNGjIjY9urVKzLzZ511lu64446oz2PQ06jHMRCAAAQgEBoBDHpYigRp0I3ID1c26X74LH3g9d+TRtB9jB+EbRxtRP0g5Yc6m5JQSz0feP1w60wD8/WDfnGJTYEfZm1KPErrh+C4+GHYD7M2djYprssPsV5AbI455khl0G20fX48R9fmxabFL2U8SuUHbBvW4pFWPyTboHoksx4laz1i4+uHeY+O2qDb4MWm7ueff44+s4GwoXZftTG3US4t5nTaaadF5sN1udhc+Jx//vOf0eihdfT/exSwOLXdBt2m2eYuLn4x4hFZm/T2vl+lI+g27zY4xd+pLbbYIsqssCmMDbrN5JZbbhnVa3OzwQYbRIbKI+6VlKz1cNtJfW2rrbaKjLTvR35p4TJs2LCoD7q/e30MF2c++OWL+3Xp/crm2aPc1qd0TnpH30PXU2zQ/SLA3wv3h4022ihq56effopeuGy//faRWS0eQY/jdbvuY85g6N69eyXIOzw2Dz3SBud7uV86XHPNNdEp/n9nJdic+17i8tlnn0UvAZ2F4++AX2zYsPulmYuzGvwdev755/X9999Ho+DOOlhvvfWi3/sllkff3Qd87/FLNb+EdF1+QROXHXbYIcpgOOCAAzDoaQXkOAhAAAIQCIoABj0oOdTUBt2obQI9smWj54c5p0SGWur5wOsHWnPxQ7sfYv3HI0wzzjhjZArMzYbSabjFC4WZnU2N05iPPPLIyGzZgDmN26OnLmlG0J2C6jTduNgAOQ3ao8euy6mqsfGMj7EZ8Uit0+vrUWrV45133on4vfzyy/ryyy+jxb5ssmzwbE5txr3Wgke94+KRWC8IZhNmY+WMBI/MeWTQOiy99NLRof798OHDI2ZxceaJTUpcp3V0+rVfdBQXGzq/nHFGSVysnf/EL8TKvQArNujxitFuvzijwaOPTiH2y7XYoFuvOAXbL818fOl1p9GzVj3KtZHU1zxSaoPuTBKPqro4U8BZDZ5aEBfrbOMWT80pHkE3q2222SYyjH6p4T82kh6N7+h76LqLDbq5brLJJpO9VCvuM/F3rzhe9xO/bHN8ffv2TYM61TF56JGq4UKGgrNOirN4/CLWL4z8osLF3zenp3t6gHXx1ILil10xT3PxCLvZljJyRpG/nzbofhnmKSal9yXfD62nR9IZQU+rIMdBAAIQgEBIBDDoIamhxjXofvCyYfCDUVychuqRLJuPuDhN0aOCs88+e5RaXOuqynnKV+8HXj/AOoXac5ydYeC53h6Zjeel2/B5RNycPcezuDiN2ubG6cE2HH6AdbaCSxqDXrpdV3HGhA2tR61s/opHqly3R4KtZT1KrXp4vryzE2wYnA7ruc5+8WC+7r9JBt3X6JckNurWyCOxNiBOF09r0Mttl1dvg168lZzjseGxgY/n7abVslY9yrWT1Nc8raB4PQXXUc6ElWYXlM5Bd3aE06X94ss6+kWhp+/4ZUx730OPzldr0OMtDh3vv//972jqil9++fucVclDj7SxlVu8zf3aWnlEOy7xiyb3wVoNurM+/MLGUzg87aO42LTPNttsGPS0AnIcBCAAAQgERQCDHpQcjWvQV1pppWjeZ7ywlUcrPQJs4xIbdJtPP8h5BMWpjIsssohOOeWUsBQoiqYzH3g9kucRKC/c5sXHbKQ8Smh+88wzTzQ6VGzSvfiUzabNoud+2nhMPfXUmRh0jzg6Pdft21h0VqlFD4+Yr7DCClEarUe1XZzC7/5abND9wB/PD7eJ8yi5j4k/K752zyF2+q77ddoU9/YMukfw43R2t+H0XL+QiT/zOg1O4/Z3Ki5pU9w9Wu4XZeVWqg/NoCf1tdKXTdUa9GIdnUXhlxR+eeh1BIpL8ffQ38U0Ke5+0eMUd2fElIsXg/5XeR2Acinu7uP+XpZLcXe2h1PcPT3DLyA9b90v3Yq/06X3JkbQO+tuTbsQgAAEIFALAQx6LfSyP7dhU9xtKJyiaOPth9p4LqEXrLJB94O3zZ0Njx9ybTo9P9ZzDOu5EnglktViCCtpx8d65NBpsE6d9v7k/rsXgrN59DzN2Ng5LdejUl5czCuOxyseu46tt946Wsna88+9Mndcah1Bdz02Lzak1m7QoEGRebQxdQaE03XrUWrRw6PlfsngqQBOJff8VZsBG4Vig+6UYy8S5znhTjH3/HSPdDqd2ovKLbroohF7p9H6fC8G55RxGwpnjzjl3fV7pN0LWPllQPEice0ZdJsPzx339+OJJ56I1hm48MILo2wIFxtzx++XYM5a8NSHUoMeLxLnFynOCLA58Weli8SFPIKe1NesT60j6E6bfu+99yJT7hFzZ0SYmRdBtFlv73toc1i6SJx18vQQ/3Rs8SJxzoCxRhj08iPoXhfBfdr/NvgFWLlF4pwu7zUbzNb3RN+DvOuH/92IF4nzCzXfh6yLp9v4RZz180j9aqutxgh6PW7MtAEBCEAAApkTwKBnjrSmChvWoHturY2NH5acPuz/94Jb8TZrXpTJ5tHzReMFsjwX94ILLogejEtTp2uimNHJtRjCSkPwCLjnC3t+ql9m+GHfo+VeoKzUFHilaf/OD6Q2iXF6p1N0/eBaPMfYcWRh0J326zToa6+9NhqJdXqqMyBsKovnbFd63ZUcX6seNr6en29z5iwEG2hzLDbo5mlj7lFOr3Luef0eeXdx5ofNrhcfc3aCX1S4X8cLk6XdZi3eoiu+9jjF3VMU4oXLvIiYjWhcPBfdRt9t+7tSyzZroRv0jvqaX7TUatA9QuuXLdbrxx9/jHR2P/bCix19D61F6XfR5/slo/uFd15ob5s1Utwn9uTitRSStlkzz+Jt1pzB4O9H8TZrXgfD03rcp31f9Mr5nlrlleDdFiPoldxhORYCEIAABEIhgEEPRYmJcQRr0MPCVJ9oajWE9Ynyl1ZsND2a51HfZiyNpkdaDcrN1U17bmce16x6dCbTWtpGj1roZX9uvHCjpBklfZ19C9QIAQhAAALNSgCDHpayGPSA9GiUB16PNHlk1YsxeU9oL+jWjKVR9KiUPQa9UmIcX45As34/GlVtDHqjKkfcEIAABDqfAAa98zUojgCDHpAejfLA67Rbp9h6/nJxyntAKDMJpVH0qPRiMeiVEuN4DHr4fQCDHr5GRAgBCEAgVAIY9LCUwaAHpEezGsKAEFcUCnpUhCv3g9Ejd8QVNYAeFeHK/WAMeu6IaQACEIBA0xLAoIclLQY9ID144A1IDEnogR5hEQgrGr4fYemBQQ9LD6KBAAQg0EgEMOhhqYVBD0gPHngDEgODHpYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0stDHpAemDQAxIDQxiWGOiBHsERCCsgDHpYehANBCAAgUYigEEPSy0MekB6YNADEgNDGJYY6IEewREIKyAMelh6EA0EIACBRiKAQQ9LLQx6QHpg0AMSA0MYlhjogR7BEQgrIAx6WHoQDQQgAIFGIoBBD0utyKA/+uijmm666cKKrAWjsUHfYIMNhB5hiI8eYegQR4Ee6BEWgbCisUEfPHiwg5pR0tdhRUc0EIAABCAQMgEMeljq9JP0flghEQ0EIAABCEAAAlUSmEPSB1Wey2kQgAAEINCCBDDoYYluPfpKGhNWWC0dzfySRrQ0gbAuHj3QIywCYUXD9yMsPaaX9KGkCWGFRTQQgAAEIBAyAQx6yOoQGwQgAAEIQAACEIAABCAAAQi0DAEMestIzYVCAAIQgAAEIAABCEAAAhCAQMgEMOghq0NsEIAABCAAAQhAAAIQgAAEINAyBDDoLSM1FwoBCEAAAhCAAAQgAAEIQAACIRPAoIeScDXcAAADWklEQVSsDrFBAAIQgAAEIAABCEAAAhCAQMsQwKC3jNRcKAQgAAEIQAACEIAABCAAAQiETACDHrI6xAYBCEAAAhCAAAQgAAEIQAACLUMAg94yUnOhEIAABCAAAQhAAAIQgAAEIBAyAQx6yOoQGwQgAAEIQAACEIAABCAAAQi0DAEMestIzYVCAAIQgAAEIAABCEAAAhCAQMgEMOghq0NsEIAABCAAAQhAAAIQgAAEINAyBDDoLSM1FwoBCEAAAhCAAAQgAAEIQAACIRPAoIesDrFBAAIQgAAEIAABCEAAAhCAQMsQwKC3jNRcKAQgAAEIQAACEIAABCAAAQiETACDHrI6xAYBCEAAAhCAAAQgAAEIQAACLUMAg94yUnOhEIAABCAAAQhAAAIQgAAEIBAyAQx6yOoQGwQgAAEIQAACEIAABCAAAQi0DAEMestIzYVCAAIQgAAEIAABCEAAAhCAQMgEMOghq0NsEIAABCAAAQhAAAIQgAAEINAyBDDoLSM1FwoBCEAAAhCAAAQgAAEIQAACIRPAoIesDrFBAAIQgAAEIAABCEAAAhCAQMsQwKC3jNRcKAQgAAEIQAACEIAABCAAAQiETACDHrI6xAYBCEAAAhCAAAQgAAEIQAACLUMAg94yUnOhEIAABCAAAQhAAAIQgAAEIBAyAQx6yOoQGwQgAAEIQAACEIAABCAAAQi0DAEMestIzYVCAAIQgAAEIAABCEAAAhCAQMgEMOghq0NsEIAABCAAAQhAAAIQgAAEINAyBDDoLSM1FwoBCEAAAhCAAAQgAAEIQAACIRPAoIesDrFBAAIQgAAEIAABCEAAAhCAQMsQwKC3jNRcKAQgAAEIQAACEIAABCAAAQiETACDHrI6xAYBCEAAAhCAAAQgAAEIQAACLUMAg94yUnOhEIAABCAAAQhAAAIQgAAEIBAyAQx6yOoQGwQgAAEIQAACEIAABCAAAQi0DAEMestIzYVCAAIQgAAEIAABCEAAAhCAQMgEMOghq0NsEIAABCAAAQhAAAIQgAAEINAyBDDoLSM1FwoBCEAAAhCAAAQgAAEIQAACIRPAoIesDrFBAAIQgAAEIAABCEAAAhCAQMsQwKC3jNRcKAQgAAEIQAACEIAABCAAAQiETOD/AT+B9qyKOonJAAAAAElFTkSuQmCC\" width=\"1000\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'SpectrumViewer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-e43e430e57d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpecUtil_inline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSDSSDriver\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib notebook'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfileName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'example_lite.fits'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Users\\wzuo\\Developer\\SciScript-Python\\SpectrumViewer\\SDSSDriver.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#from py3.SciServer import CasJobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0murlparse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCoaddObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mCoadd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mZObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# def getSpecObjID(ra, dec, width, height, context=None, URL=True):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'SpectrumViewer'"
+     ]
     }
    ],
    "source": [
diff --git a/inline viewer demo 2.ipynb b/inline viewer demo 2.ipynb
index 6ff334f..e1d2dfa 100644
--- a/inline viewer demo 2.ipynb	
+++ b/inline viewer demo 2.ipynb	
@@ -2,9 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -787,7 +787,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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\" width=\"1000\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -805,6 +805,133 @@
     "inline.view(fileName, fileSource)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import SpectrumViewer.InlineViewer as inline\n",
+    "%matplotlib notebook\n",
+    "fileName = 'SpectrumViewer/example_lite.fits'\n",
+    "fileSource = 'SDSS'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astropy.io import fits\n",
+    "coaddData =1\n",
+    "zData =3\n",
+    "hdulist = fits.open(fileName)\n",
+    "c=hdulist[coaddData].data\n",
+    "z=hdulist[zData].data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(z['LINEZ'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(z['LINENAME'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name = z['LINENAME']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "range(len(name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z['LINEWAVE']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = z['LINEZ']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z['LINEZ_ERR']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tup = zip(name,z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(name)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 64b952fbfdf3633b8d79d09eaec812acd3960c4f Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Thu, 18 Jan 2018 14:27:12 -0500
Subject: [PATCH 06/10] a/e merged

---
 SpectrumViewer/SpecUtil_inline.py             |  12 +++++++-----
 .../SpecUtil_inline.cpython-36.pyc            | Bin 7068 -> 6948 bytes
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
index faa6a94..12c8a81 100644
--- a/SpectrumViewer/SpecUtil_inline.py
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -33,12 +33,13 @@ def __init__(self,filename,coaddObj,zObj):
         self.skylinebutton.on_clicked(self.skylineClicked)
 
         self.axabsorbbutton = plt.axes([0.3, 0.05, 0.1, 0.075])
-        self.absorbbutton = Button(self.axabsorbbutton, 'absorption')
+        self.absorbbutton = Button(self.axabsorbbutton, 'a/e line')
         self.absorbbutton.on_clicked(self.absorbClicked)
 
-        self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
-        self.emissionbutton = Button(self.axemission, 'emission')
-        self.emissionbutton.on_clicked(self.emissionClicked)
+        #
+        # self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
+        # self.emissionbutton = Button(self.axemission, 'emission')
+        # self.emissionbutton.on_clicked(self.emissionClicked)
 
         self.axmodelbutton = plt.axes([0.5, 0.05, 0.1, 0.075])
         self.modelbutton = Button(self.axmodelbutton, 'model')
@@ -148,7 +149,8 @@ def plotLines(self,axes,actionIndex):
         elif actionIndex==2:
             #plot emission line
             #self.plotSegment(axes,actionIndex)
-            self.plotVLine(axes, actionIndex)
+            #self.plotVLine(axes, actionIndex)
+            pass
         elif actionIndex ==3:
             #plot absorption line
             #self.plotSegment(axes,actionIndex)
diff --git a/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc b/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc
index 3e30e49771d155f22700101f8f8458346b15ad89..6d9091ac23f0e8d55077c9a1ffd17803d5f40d20 100644
GIT binary patch
delta 1316
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zcDrr;_u##WO>2~p4wxjh#FKbS@=0cZ>*2%`@Pl8rJrss~QfVY4OR$#FNW8F}@g07e
zMo0zpnfu9E_+YLh7T7iS7MF1xM=SPCC3>1g4Jwl(q0w`R{>%%cgdti+Sej1aXf-BU
zV9T;t%{-B(I%$?BBsrGMo<iap1cjN(|5)n7Xfj!hv2GBovuVi^B>UjKb-pQ$rj{#3
zL#i55<jzRQAC>|G&~1C1R{tdGOYp}wmzFF=G7QUE!3w<-qE8~}!8FTIAaFJEVCp`I
z*#pMNjs|U1iYgNlV`H$9J)Dw7(Vp-$d)HIQdf=A*dToj(?1uK<l^J-H>w#^1m*<!(
z(4ONavv4P8)OQSlPp-KP(JOmHzJblm!*0%4%0{s9%fqTdhZ+6^8<FZ$lyDd&4sJXL
zrz2>*n4_mCV1b)TK#DBFies=Qg?5&qt-~*&%GpX*VZd2M;_%E_F5Zpv0U^xO+j19r
zR`h~#Zp<7faejd(oZO008%AHij<cM+0p9hRl);KCUp%~4^fZp@6&}@#h9d$gGLDId
zFRour`?Sbwd_$vVl82&F(l_+i))on=F~1TWzqAg-O>v`RZqa?P#cY9T2bvAobbrD|
z7V_%znY6e!B^|hkfwjcPyd{E!-{W)d7pHo@4f9)xcb*-Ve_GI9bU3eNjYpJNk2D&^
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aJZb}Th)@ULyp{Mrvlsdu$vn&z7X1U%FB4n<

delta 1420
zcmaiz&rcIU6vubAZMtn~OZjO_`6U)8B0t2SC<<yMnkXT`5W)dy*`>6!yUpzKORJ(y
zj1UrxFB*T~#c1LI6AkfVqJM!G<H;VpsZkG_a5El!v*ni!luhR4&3m7DZ)SGBPg|D`
zTUzXPYvR}Nz{OXV7$Fp<NF8ynJRwD-RWn2cu~@W=Iie$EOp*jQ<k74NF5(gQBzc^b
zpkETnLF-Kt!iiij=l8%n{&D$$H4s!48rQ;#9D4YGeV;l81Su9)RSbb-Xd<O>&+x-%
z4NR{`2OR5Oa6;1*IptiR!?aN*KKN!FBuAmkRBz@Bh=VwIP)&WMRUF4r--fA1FJ)H8
zl&R5h=+Men(=k%Ukd$Fun#tg3Ehbu_!#rQZJU>r$(j-kvvX;s2MdB>{HedE_VQCXa
zGs#kn^}uc6>aJuNlKs$PxxOn|f#e{3vE0~|EJsp=1?%jtWF?XjXts^*(@#Y7(`Ote
zWe$j9S9D@J4sUHk>1)~^v{EUiOiGMZ;dD+gD~m;Y$7iBpS(<|PIl;Ot8?dMAuHl!U
z-!9c>!JSz8%-TCUkM*y>cYCNY%YwXg{jF<e;hH0obx+vjBzxgO)*=b{PN{f@7O?0n
zBsbxib1eNow!b_93tmt}DLMxSbBAkpsD=M4WlL{YhZo>Y?h*15R&%S#E%3Oi%EbgP
z3gbK-L{XX&B*7<n!pYqjwO~|(9#<i`52LOsvJCUCN1eKOyn5Zh2vh4hS(K(T-T<vf
zU=fGvLBkP&c+<n+04Z;^c|&}DKE*dQrHpbQCS}z4V{MOyRc%Cx#ZN55Ts}oT|IPOo
zY^s+Dm!3lN0@?~bkSeeh9w=g^{cBgI1IID&e5Jc^i6HT_sIg$9Io0#+n18*}Q2d$Y
z?e0$R|M`?H9#ynnDHKD6e}u4G_SfJF>Su=CI%P7aOu>|ilU6XpBt<Llcv{=oO;EN6
n${Nu=W;Q$Mp1utuCIlR619OP59~6&|xZ$p6BsY@>p~U+ShIBBL


From cc54962f1edbdb008038529a5598d2697113525f Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Tue, 23 Jan 2018 13:35:21 -0500
Subject: [PATCH 07/10] the coloring of a/e/other lines are finished

---
 SpectrumViewer/.idea/SpectrumViewer.iml       |  11 ++
 SpectrumViewer/.idea/misc.xml                 |   4 +
 SpectrumViewer/.idea/modules.xml              |   8 +
 SpectrumViewer/.idea/workspace.xml            | 181 ++++++++++++++++++
 SpectrumViewer/SpecUtil_inline.py             |  65 +++++--
 SpectrumViewer/Untitled.ipynb                 | 161 ++++++++++++++--
 SpectrumViewer/ZObj.py                        |  23 +++
 .../__pycache__/CoaddObj.cpython-35.pyc       | Bin 0 -> 701 bytes
 .../__pycache__/InlineViewer.cpython-35.pyc   | Bin 0 -> 525 bytes
 .../__pycache__/SDSSDriver.cpython-35.pyc     | Bin 0 -> 1060 bytes
 .../SpecUtil_inline.cpython-35.pyc            | Bin 0 -> 8561 bytes
 .../__pycache__/ZObj.cpython-35.pyc           | Bin 0 -> 1047 bytes
 .../__pycache__/__init__.cpython-35.pyc       | Bin 0 -> 148 bytes
 SpectrumViewer/demo 1.ipynb                   |   2 +-
 SpectrumViewer/line dictionary                |  40 ++++
 inline viewer demo 2.ipynb                    |  58 ++++--
 16 files changed, 502 insertions(+), 51 deletions(-)
 create mode 100644 SpectrumViewer/.idea/SpectrumViewer.iml
 create mode 100644 SpectrumViewer/.idea/misc.xml
 create mode 100644 SpectrumViewer/.idea/modules.xml
 create mode 100644 SpectrumViewer/.idea/workspace.xml
 create mode 100644 SpectrumViewer/__pycache__/CoaddObj.cpython-35.pyc
 create mode 100644 SpectrumViewer/__pycache__/InlineViewer.cpython-35.pyc
 create mode 100644 SpectrumViewer/__pycache__/SDSSDriver.cpython-35.pyc
 create mode 100644 SpectrumViewer/__pycache__/SpecUtil_inline.cpython-35.pyc
 create mode 100644 SpectrumViewer/__pycache__/ZObj.cpython-35.pyc
 create mode 100644 SpectrumViewer/__pycache__/__init__.cpython-35.pyc
 create mode 100644 SpectrumViewer/line dictionary

diff --git a/SpectrumViewer/.idea/SpectrumViewer.iml b/SpectrumViewer/.idea/SpectrumViewer.iml
new file mode 100644
index 0000000..6711606
--- /dev/null
+++ b/SpectrumViewer/.idea/SpectrumViewer.iml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/SpectrumViewer/.idea/misc.xml b/SpectrumViewer/.idea/misc.xml
new file mode 100644
index 0000000..6c69f03
--- /dev/null
+++ b/SpectrumViewer/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5.4 (~/anaconda2/envs/python3/bin/python)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/SpectrumViewer/.idea/modules.xml b/SpectrumViewer/.idea/modules.xml
new file mode 100644
index 0000000..2728bec
--- /dev/null
+++ b/SpectrumViewer/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/SpectrumViewer.iml" filepath="$PROJECT_DIR$/.idea/SpectrumViewer.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/SpectrumViewer/.idea/workspace.xml b/SpectrumViewer/.idea/workspace.xml
new file mode 100644
index 0000000..1401a08
--- /dev/null
+++ b/SpectrumViewer/.idea/workspace.xml
@@ -0,0 +1,181 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
+    <list default="true" id="01c9e434-c2ef-46ab-930d-b0b36f6e808a" name="Default" comment="" />
+    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
+    <option name="TRACKING_ENABLED" value="true" />
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileEditorManager">
+    <leaf>
+      <file leaf-file-name="SpecUtil_inline.py" pinned="false" current-in-tab="false">
+        <entry file="file://$PROJECT_DIR$/SpecUtil_inline.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="54">
+              <caret line="3" column="11" lean-forward="false" selection-start-line="3" selection-start-column="11" selection-end-line="3" selection-end-column="11" />
+              <folding />
+            </state>
+          </provider>
+        </entry>
+      </file>
+      <file leaf-file-name="SDSSDriver.py" pinned="false" current-in-tab="true">
+        <entry file="file://$PROJECT_DIR$/SDSSDriver.py">
+          <provider selected="true" editor-type-id="text-editor">
+            <state relative-caret-position="18">
+              <caret line="1" column="0" lean-forward="false" selection-start-line="1" selection-start-column="0" selection-end-line="1" selection-end-column="0" />
+              <folding>
+                <element signature="e#32#59#0" expanded="true" />
+              </folding>
+            </state>
+          </provider>
+        </entry>
+      </file>
+    </leaf>
+  </component>
+  <component name="JsBuildToolGruntFileManager" detection-done="true" sorting="DEFINITION_ORDER" />
+  <component name="JsBuildToolPackageJson" detection-done="true" sorting="DEFINITION_ORDER" />
+  <component name="JsGulpfileManager">
+    <detection-done>true</detection-done>
+    <sorting>DEFINITION_ORDER</sorting>
+  </component>
+  <component name="ProjectFrameBounds" extendedState="6">
+    <option name="x" value="85" />
+    <option name="y" value="-8" />
+    <option name="width" value="1378" />
+    <option name="height" value="780" />
+  </component>
+  <component name="ProjectView">
+    <navigator currentView="ProjectPane" proportions="" version="1">
+      <flattenPackages />
+      <showMembers />
+      <showModules />
+      <showLibraryContents />
+      <hideEmptyPackages />
+      <abbreviatePackageNames />
+      <autoscrollToSource />
+      <autoscrollFromSource />
+      <sortByType />
+      <manualOrder />
+      <foldersAlwaysOnTop value="true" />
+    </navigator>
+    <panes>
+      <pane id="Scope" />
+      <pane id="Scratches" />
+      <pane id="ProjectPane">
+        <subPane>
+          <expand>
+            <path>
+              <item name="SpectrumViewer" type="b2602c69:ProjectViewProjectNode" />
+              <item name="SpectrumViewer" type="462c0819:PsiDirectoryNode" />
+            </path>
+          </expand>
+          <select />
+        </subPane>
+      </pane>
+    </panes>
+  </component>
+  <component name="PropertiesComponent">
+    <property name="WebServerToolWindowFactoryState" value="false" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/.." />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
+  </component>
+  <component name="RunDashboard">
+    <option name="ruleStates">
+      <list>
+        <RuleState>
+          <option name="name" value="ConfigurationTypeDashboardGroupingRule" />
+        </RuleState>
+        <RuleState>
+          <option name="name" value="StatusDashboardGroupingRule" />
+        </RuleState>
+      </list>
+    </option>
+  </component>
+  <component name="ShelveChangesManager" show_recycled="false">
+    <option name="remove_strategy" value="false" />
+  </component>
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="Default task">
+      <changelist id="01c9e434-c2ef-46ab-930d-b0b36f6e808a" name="Default" comment="" />
+      <created>1516654383285</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1516654383285</updated>
+    </task>
+    <servers />
+  </component>
+  <component name="ToolWindowManager">
+    <frame x="85" y="-8" width="1920" height="1173" extended-state="6" />
+    <editor active="true" />
+    <layout>
+      <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
+      <window_info id="TODO" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
+      <window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
+      <window_info id="Database" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
+      <window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
+      <window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="false" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
+      <window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
+      <window_info id="Structure" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+      <window_info id="Terminal" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
+      <window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
+      <window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
+      <window_info id="Data View" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
+      <window_info id="Cvs" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
+      <window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
+      <window_info id="Message" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
+      <window_info id="Commander" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
+      <window_info id="Find" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+      <window_info id="Inspection" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
+      <window_info id="Ant Build" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
+    </layout>
+  </component>
+  <component name="TypeScriptGeneratedFilesManager">
+    <option name="version" value="1" />
+  </component>
+  <component name="VcsContentAnnotationSettings">
+    <option name="myLimit" value="2678400000" />
+  </component>
+  <component name="XDebuggerManager">
+    <breakpoint-manager />
+    <watches-manager />
+  </component>
+  <component name="editorHistoryManager">
+    <entry file="file://$PROJECT_DIR$/SpecUtil.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="0">
+          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
+          <folding />
+        </state>
+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/WindowViewer.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="0">
+          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
+          <folding />
+        </state>
+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/SpecUtil_inline.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="54">
+          <caret line="3" column="11" lean-forward="false" selection-start-line="3" selection-start-column="11" selection-end-line="3" selection-end-column="11" />
+          <folding />
+        </state>
+      </provider>
+    </entry>
+    <entry file="file://$PROJECT_DIR$/SDSSDriver.py">
+      <provider selected="true" editor-type-id="text-editor">
+        <state relative-caret-position="18">
+          <caret line="1" column="0" lean-forward="false" selection-start-line="1" selection-start-column="0" selection-end-line="1" selection-end-column="0" />
+          <folding>
+            <element signature="e#32#59#0" expanded="true" />
+          </folding>
+        </state>
+      </provider>
+    </entry>
+  </component>
+</project>
\ No newline at end of file
diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
index 12c8a81..2e74d8e 100644
--- a/SpectrumViewer/SpecUtil_inline.py
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -14,9 +14,10 @@ def __init__(self,filename,coaddObj,zObj):
                          2: True,
                          3: True,
                          4: True,
-                         5: True}
+                         5: True,
+                         6: True}
         #0 flux 1 skyline 2 emission 3 absorb 4 model 5 redshift table
-
+        #6 other line
         #self.updateFig()
         self.figure = plt.figure(filename,figsize=(10, 8), dpi=100)
         #self.figure1 = plt.figure(filename, figsize=(10, 8), dpi=100)
@@ -33,19 +34,24 @@ def __init__(self,filename,coaddObj,zObj):
         self.skylinebutton.on_clicked(self.skylineClicked)
 
         self.axabsorbbutton = plt.axes([0.3, 0.05, 0.1, 0.075])
-        self.absorbbutton = Button(self.axabsorbbutton, 'a/e line')
+        self.absorbbutton = Button(self.axabsorbbutton, 'absorption')
         self.absorbbutton.on_clicked(self.absorbClicked)
 
-        #
-        # self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
-        # self.emissionbutton = Button(self.axemission, 'emission')
-        # self.emissionbutton.on_clicked(self.emissionClicked)
 
-        self.axmodelbutton = plt.axes([0.5, 0.05, 0.1, 0.075])
+        self.axemission = plt.axes([0.4, 0.05, 0.1, 0.075])
+        self.emissionbutton = Button(self.axemission, 'emission')
+        self.emissionbutton.on_clicked(self.emissionClicked)
+
+
+        self.axother = plt.axes([0.5, 0.05, 0.1, 0.075])
+        self.otherbutton = Button(self.axother, 'other')
+        self.otherbutton.on_clicked(self.otherClicked)
+
+        self.axmodelbutton = plt.axes([0.6, 0.05, 0.1, 0.075])
         self.modelbutton = Button(self.axmodelbutton, 'model')
         self.modelbutton.on_clicked(self.modelClicked)
 
-        self.axzbutton = plt.axes([0.6, 0.05, 0.1, 0.075])
+        self.axzbutton = plt.axes([0.7, 0.05, 0.1, 0.075])
         self.zbutton = Button(self.axzbutton, 'redshift')
         self.zbutton.on_clicked(self.zClicked)
 
@@ -104,6 +110,13 @@ def absorbClicked(self, event):
         else:
             self.plotDict[3] = True
         self.updateFig()
+
+    def otherClicked(self, event):
+        if self.plotDict[6]:
+            self.plotDict[6] = False
+        else:
+            self.plotDict[6] = True
+        self.updateFig()
     def modelClicked(self, event):
         if self.plotDict[4]:
             self.plotDict[4] = False
@@ -149,8 +162,8 @@ def plotLines(self,axes,actionIndex):
         elif actionIndex==2:
             #plot emission line
             #self.plotSegment(axes,actionIndex)
-            #self.plotVLine(axes, actionIndex)
-            pass
+            self.plotVLine(axes, actionIndex)
+            #pass
         elif actionIndex ==3:
             #plot absorption line
             #self.plotSegment(axes,actionIndex)
@@ -163,7 +176,9 @@ def plotLines(self,axes,actionIndex):
         elif actionIndex==5:
             #plot the redshift table
             self.plotZTable(axes)
-
+        elif actionIndex ==6:
+            #plot other line
+            self.plotVLine(axes,actionIndex)
         else:
             pass
 
@@ -180,13 +195,15 @@ def plotZTable(self,axes):
                    colLabels=col_labels,
                    loc='right',bbox=[1.1, 0, 0.3, 0.04*len(self.zObj.LINENAME)])
         axes.text(12, 3.4, 'redshift and error', size=8)
+
     def plotVLine(self,axes,actionIndex):
         # read e/a line from zobj and plot it on axes with function from axhspan
         if actionIndex==2:#emissiaon line
             for lineIndex in range(0,len(self.zObj.LINENAME)):
 
                 width = self.zObj.LINEEW[lineIndex]
-                if width >0:
+                if self.zObj.LINEZ_type[lineIndex]=='e':
+                    #print(self.zObj.LINEZ_type[lineIndex])
                     #print(width)
                     position = self.zObj.LINEWAVE[lineIndex]
                     # startPostition = position-width/2
@@ -199,17 +216,31 @@ def plotVLine(self,axes,actionIndex):
         elif actionIndex==3:#absorption line
             for lineIndex in range(0,len(self.zObj.LINENAME)):
                 width = self.zObj.LINEEW[lineIndex]
-                if width >0:
+                if self.zObj.LINEZ_type[lineIndex]=='a':
+                    #print(self.zObj.LINEZ_type[lineIndex])
                     #print(width)
                     position = self.zObj.LINEWAVE[lineIndex]
                     #startPostition = position-width/2
                     #endPostition = position + width / 2
                     name= self.zObj.LINENAME[lineIndex]
                     #print(name)
-                    axes.axvline(position,c='b')
+                    axes.axvline(position,c='r')
                     #axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
-                    axes.text(position,0,name , rotation=90)
-
+                    axes.text(position-5,5,name , rotation=90)
+        elif actionIndex==6:#other lines
+            for lineIndex in range(0,len(self.zObj.LINENAME)):
+                width = self.zObj.LINEEW[lineIndex]
+                if self.zObj.LINEZ_type[lineIndex]=='ae' or self.zObj.LINEZ_type[lineIndex]=='other':
+                    #print(self.zObj.LINEZ_type[lineIndex])
+                    #print(width)
+                    position = self.zObj.LINEWAVE[lineIndex]
+                    #startPostition = position-width/2
+                    #endPostition = position + width / 2
+                    name= self.zObj.LINENAME[lineIndex]
+                    #print(name)
+                    axes.axvline(position,c='g')
+                    #axes.axvspan(xmin=startPostition,xmax=endPostition, facecolor='C4')
+                    axes.text(position-5,5,name , rotation=90)
     def plotSegment(self,axes,actionIndex):
 
         # read e/a line from zobj and plot it on axes with function from axhspan
diff --git a/SpectrumViewer/Untitled.ipynb b/SpectrumViewer/Untitled.ipynb
index 7db7bb1..14a9a26 100644
--- a/SpectrumViewer/Untitled.ipynb
+++ b/SpectrumViewer/Untitled.ipynb
@@ -2,38 +2,157 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = open('line dictionary','r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lineList = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'SpectrumViewer'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-1-ded0ca7b2600>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpecUtil_inline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSDSSDriver\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdriver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib notebook'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mfileName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'example_lite.fits'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mC:\\Users\\wzuo\\Developer\\SciScript-Python\\SpectrumViewer\\SDSSDriver.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#from py3.SciServer import CasJobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0murlparse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCoaddObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mCoadd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpectrumViewer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mZObj\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# def getSpecObjID(ra, dec, width, height, context=None, URL=True):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'SpectrumViewer'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ae', 'Ly_alpha', '1215.67', '1215.08']\n",
+      "['a', 'NV', '1240.81', '1240.22']\n",
+      "['a', 'OI', '1305.53', '1304.95']\n",
+      "['a', 'CII', '1335.31', '1334.73']\n",
+      "['ae', 'SiIV+OIV', '1399.80', '1399.22']\n",
+      "['ae', 'CIV', '1549.48', '1548.90']\n",
+      "['ae', 'HeII', '1640.40', '1639.81']\n",
+      "['e', 'OIII', '1665.85', '1665.26']\n",
+      "['a', 'AlIII', '1857.40', '1856.78']\n",
+      "['e', 'CIII', '1908.73', '1908.10']\n",
+      "['a', 'CII', '2326.00', '2325.29']\n",
+      "['e', 'NeIV', '2439.50', '2438.76']\n",
+      "['ae', 'MgII', '2799.12', '2798.29']\n",
+      "['e', 'NeV', '3346.79', '3345.83']\n",
+      "['e', 'NeV', '3426.85', '3425.87']\n",
+      "['e', '[OII]', '3727.09', '3726.03']\n",
+      "['e', '[OII]', '3729.88', '3728.81']\n",
+      "['ea', 'Htheta', '3798.98', '3797.90']\n",
+      "['ea', 'Heta', '3836.47', '3835.38']\n",
+      "['a', 'CaII-K', '3934.78', '3933.66']\n",
+      "['a', 'CaII-H', '3969.59', '3968.46']\n",
+      "['ae', 'Hdelta', '4102.89', '4101.73']\n",
+      "['a', 'G-band', '4305.61', '4304.40']\n",
+      "['ae', 'Hgamma', '4341.68', '4340.46']\n",
+      "['e', '[OIII]', '4364.44', '4363.21']\n",
+      "['ae', 'Hbeta', '4862.68', '4861.32']\n",
+      "['e', '[OIII]', '4960.29', '4958.91']\n",
+      "['e', '[OIII]', '5008.24', '5006.84']\n",
+      "['a', 'Mgb', '5176.70', '5175.26']\n",
+      "['a', 'Na', '5895.60', '5893.96']\n",
+      "['e', '[OI]', '6302.05', '6300.30']\n",
+      "['e', '[NII]', '6549.86', '6548.05']\n",
+      "['ae', 'Halpha', '6564.61', '6562.79']\n",
+      "['e', '[NII]', '6585.27', '6583.45']\n",
+      "['a', 'Li', '6707.89', '6706.04']\n",
+      "['e', '[SII]', '6718.29', '6716.43']\n",
+      "['e', '[SII]', '6732.67', '6730.81']\n",
+      "['a', 'CaII', '8500.36', '8498.02']\n",
+      "['a', 'CaII', '8544.44', '8542.09']\n",
+      "['a', 'CaII', '8664.52', '8662.13']\n"
      ]
     }
    ],
    "source": [
-    "import SpecUtil_inline as inline\n",
-    "\n",
-    "import SDSSDriver as driver\n",
-    "%matplotlib notebook\n",
-    "fileName = 'example_lite.fits'\n",
-    "fileSource = 'SDSS'\n",
-    "coaddObj, zObj = driver.loadFITS(fileName, fileSource)"
+    "lineDict = {}\n",
+    "for item in lineList:\n",
+    "    listTemp =item.split()\n",
+    "    print(listTemp)\n",
+    "    lineDict[listTemp[1]]=listTemp[0]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(lineDict.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AlIII': 'a',\n",
+       " 'CII': 'a',\n",
+       " 'CIII': 'e',\n",
+       " 'CIV': 'ae',\n",
+       " 'CaII': 'a',\n",
+       " 'CaII-H': 'a',\n",
+       " 'CaII-K': 'a',\n",
+       " 'G-band': 'a',\n",
+       " 'Halpha': 'ae',\n",
+       " 'Hbeta': 'ae',\n",
+       " 'Hdelta': 'ae',\n",
+       " 'HeII': 'ae',\n",
+       " 'Heta': 'ea',\n",
+       " 'Hgamma': 'ae',\n",
+       " 'Htheta': 'ea',\n",
+       " 'Li': 'a',\n",
+       " 'Ly_alpha': 'ae',\n",
+       " 'MgII': 'ae',\n",
+       " 'Mgb': 'a',\n",
+       " 'NV': 'a',\n",
+       " 'Na': 'a',\n",
+       " 'NeIV': 'e',\n",
+       " 'NeV': 'e',\n",
+       " 'OI': 'a',\n",
+       " 'OIII': 'e',\n",
+       " 'SiIV+OIV': 'ae',\n",
+       " '[NII]': 'e',\n",
+       " '[OIII]': 'e',\n",
+       " '[OII]': 'e',\n",
+       " '[OI]': 'e',\n",
+       " '[SII]': 'e'}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lineDict"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -54,7 +173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/SpectrumViewer/ZObj.py b/SpectrumViewer/ZObj.py
index 7409f03..974b25b 100644
--- a/SpectrumViewer/ZObj.py
+++ b/SpectrumViewer/ZObj.py
@@ -8,3 +8,26 @@ def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW
         self.LINEEW = LINEEW
         self.LINEER_ERR = LINEEW_ERR
 
+        f = open('SpectrumViewer/line dictionary', 'r')
+        lineList = f.readlines()
+        self.lineDict = {}
+
+        for item in lineList:
+            listTemp = item.split()
+            #print(listTemp)
+            self.lineDict[listTemp[1]] = listTemp[0]
+        self.LINEZ_type =[]
+        for i in range(len(LINENAME)):
+            if LINENAME[i] in self.lineDict.keys():
+                #e or a
+                if self.lineDict[LINENAME[i]]=='a':
+                    self.LINEZ_type.append('a')
+                elif self.lineDict[LINENAME[i]]=='e':
+                    self.LINEZ_type.append('e')
+                else:
+                    self.LINEZ_type.append('ae')
+
+            else:
+                #other
+                self.LINEZ_type.append('other')
+
diff --git a/SpectrumViewer/__pycache__/CoaddObj.cpython-35.pyc b/SpectrumViewer/__pycache__/CoaddObj.cpython-35.pyc
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diff --git a/SpectrumViewer/__pycache__/ZObj.cpython-35.pyc b/SpectrumViewer/__pycache__/ZObj.cpython-35.pyc
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GIT binary patch
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diff --git a/SpectrumViewer/__pycache__/__init__.cpython-35.pyc b/SpectrumViewer/__pycache__/__init__.cpython-35.pyc
new file mode 100644
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diff --git a/SpectrumViewer/demo 1.ipynb b/SpectrumViewer/demo 1.ipynb
index 68979a6..f539559 100644
--- a/SpectrumViewer/demo 1.ipynb	
+++ b/SpectrumViewer/demo 1.ipynb	
@@ -58,7 +58,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/SpectrumViewer/line dictionary b/SpectrumViewer/line dictionary
new file mode 100644
index 0000000..6999336
--- /dev/null
+++ b/SpectrumViewer/line dictionary	
@@ -0,0 +1,40 @@
+ae  Ly_alpha   1215.67  1215.08
+a        NV    1240.81  1240.22
+a        OI    1305.53  1304.95
+a       CII    1335.31  1334.73
+ae  SiIV+OIV   1399.80  1399.22
+ae      CIV    1549.48  1548.90
+ae     HeII    1640.40  1639.81
+e      OIII    1665.85  1665.26
+a     AlIII    1857.40  1856.78
+e      CIII    1908.73  1908.10
+a       CII    2326.00  2325.29
+e      NeIV    2439.50  2438.76
+ae     MgII    2799.12  2798.29
+e       NeV    3346.79  3345.83
+e       NeV    3426.85  3425.87
+e      [OII]    3727.09  3726.03
+e      [OII]    3729.88  3728.81
+ea    Htheta    3798.98  3797.90
+ea      Heta    3836.47  3835.38
+a     CaII-K    3934.78  3933.66
+a     CaII-H    3969.59  3968.46
+ae    Hdelta    4102.89  4101.73
+a     G-band    4305.61  4304.40
+ae    Hgamma    4341.68  4340.46
+e     [OIII]    4364.44  4363.21
+ae     Hbeta    4862.68  4861.32
+e     [OIII]    4960.29  4958.91
+e     [OIII]    5008.24  5006.84
+a       Mgb    5176.70  5175.26
+a        Na    5895.60  5893.96
+e      [OI]    6302.05  6300.30
+e     [NII]    6549.86  6548.05
+ae   Halpha    6564.61  6562.79
+e     [NII]    6585.27  6583.45
+a       Li    6707.89  6706.04
+e    [SII]    6718.29  6716.43
+e    [SII]    6732.67  6730.81
+a     CaII    8500.36  8498.02
+a     CaII    8544.44  8542.09
+a     CaII    8664.52  8662.13
diff --git a/inline viewer demo 2.ipynb b/inline viewer demo 2.ipynb
index e1d2dfa..916d22f 100644
--- a/inline viewer demo 2.ipynb	
+++ b/inline viewer demo 2.ipynb	
@@ -89,7 +89,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
+       "        fig.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -736,9 +736,12 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -787,7 +790,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"999.9999783255842\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -795,6 +798,41 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ae\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n",
+      "other\n"
+     ]
     }
    ],
    "source": [
@@ -808,9 +846,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import SpectrumViewer.InlineViewer as inline\n",
@@ -935,9 +971,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -958,7 +992,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,

From d1be4d5ff571b09b0328714d2d86ed08e6a6d2ac Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Tue, 23 Jan 2018 13:39:32 -0500
Subject: [PATCH 08/10] color conflict solved

---
 SpectrumViewer/SpecUtil_inline.py             |   6 +++---
 .../SpecUtil_inline.cpython-35.pyc            | Bin 8561 -> 8487 bytes
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
index 2e74d8e..8565601 100644
--- a/SpectrumViewer/SpecUtil_inline.py
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -156,9 +156,9 @@ def removeModel(self):
         self.updateFig()
     def plotLines(self,axes,actionIndex):
         if actionIndex==0:
-            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'C0',label='flux')
+            axes.plot(self.coaddObj.lam, self.coaddObj.flux,'k',label='flux')
         elif actionIndex==1:
-            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'C1',label='skyline')
+            axes.plot(self.coaddObj.lam, self.coaddObj.sky,'y',label='skyline')
         elif actionIndex==2:
             #plot emission line
             #self.plotSegment(axes,actionIndex)
@@ -172,7 +172,7 @@ def plotLines(self,axes,actionIndex):
         elif actionIndex ==4:
 
             #plot best fit model
-            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'C3', label='best fit model')
+            axes.plot(self.coaddObj.lam, self.coaddObj.model, 'c', label='best fit model')
         elif actionIndex==5:
             #plot the redshift table
             self.plotZTable(axes)
diff --git a/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-35.pyc b/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-35.pyc
index a69abe640349d67501efca52e93b8aeb45f5dee6..353aac25396d8e4a8dd50c0021f74013771210db 100644
GIT binary patch
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Bj1B+*


From 24eb1f795f1ce2081dc79ab1ce90420b3e5a78c9 Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Wed, 24 Jan 2018 20:45:59 -0500
Subject: [PATCH 09/10] table title removed

---
 SpectrumViewer/SpecUtil_inline.py             |   2 +-
 .../SpecUtil_inline.cpython-36.pyc            | Bin 6948 -> 7537 bytes
 .../__pycache__/ZObj.cpython-36.pyc           | Bin 545 -> 942 bytes
 inline viewer demo 2.ipynb                    | 106 ++++++++----------
 4 files changed, 49 insertions(+), 59 deletions(-)

diff --git a/SpectrumViewer/SpecUtil_inline.py b/SpectrumViewer/SpecUtil_inline.py
index 8565601..2b6ed36 100644
--- a/SpectrumViewer/SpecUtil_inline.py
+++ b/SpectrumViewer/SpecUtil_inline.py
@@ -194,7 +194,7 @@ def plotZTable(self,axes):
                    rowLabels=row_labels,
                    colLabels=col_labels,
                    loc='right',bbox=[1.1, 0, 0.3, 0.04*len(self.zObj.LINENAME)])
-        axes.text(12, 3.4, 'redshift and error', size=8)
+        #axes.text(12, 3.4, 'redshift and error', size=8)
 
     def plotVLine(self,axes,actionIndex):
         # read e/a line from zobj and plot it on axes with function from axhspan
diff --git a/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc b/SpectrumViewer/__pycache__/SpecUtil_inline.cpython-36.pyc
index 6d9091ac23f0e8d55077c9a1ffd17803d5f40d20..30c88cee980c37eea1189709f83ee7995ebc38c7 100644
GIT binary patch
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diff --git a/SpectrumViewer/__pycache__/ZObj.cpython-36.pyc b/SpectrumViewer/__pycache__/ZObj.cpython-36.pyc
index d198d030990fecbd1518420d4793d81c5b196423..8b8dbb55d2bdf780e7a53deef55a321e24fe41a0 100644
GIT binary patch
literal 942
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diff --git a/inline viewer demo 2.ipynb b/inline viewer demo 2.ipynb
index 916d22f..b9ff09e 100644
--- a/inline viewer demo 2.ipynb	
+++ b/inline viewer demo 2.ipynb	
@@ -89,7 +89,7 @@
        "        };\n",
        "\n",
        "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
+       "        this.ws.close();\n",
        "    }\n",
        "\n",
        "    this.ws.onmessage = this._make_on_message_function(this);\n",
@@ -736,12 +736,9 @@
        "    // Check for shift+enter\n",
        "    if (event.shiftKey && event.which == 13) {\n",
        "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
        "    }\n",
        "}\n",
        "\n",
@@ -790,7 +787,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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\" width=\"1000\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -798,41 +795,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ae\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n",
-      "other\n"
-     ]
     }
    ],
    "source": [
@@ -846,7 +808,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import SpectrumViewer.InlineViewer as inline\n",
@@ -858,7 +822,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "from astropy.io import fits\n",
@@ -872,7 +838,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "c"
@@ -881,7 +849,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "type(z)"
@@ -890,7 +860,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "len(z['LINEZ'])"
@@ -899,7 +871,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "len(z['LINENAME'])"
@@ -908,7 +882,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "name = z['LINENAME']"
@@ -917,7 +893,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "range(len(name))"
@@ -926,7 +904,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "z['LINEWAVE']"
@@ -935,7 +915,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "z = z['LINEZ']"
@@ -944,7 +926,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "z['LINEZ_ERR']"
@@ -953,7 +937,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "tup = zip(name,z)"
@@ -962,7 +948,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "type(name)"
@@ -971,7 +959,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
@@ -992,7 +982,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

From 4a06d65eb44e0d137dfca3694a029c1cfaf22fea Mon Sep 17 00:00:00 2001
From: pryo <2012213057@bupt.edu.cn>
Date: Fri, 26 Jan 2018 10:21:58 -0500
Subject: [PATCH 10/10] line naming covention fixed

---
 SpectrumViewer/SDSSDriver.py                  |   2 +-
 SpectrumViewer/ZObj.py                        |  12 ++++---
 .../__pycache__/SDSSDriver.cpython-36.pyc     | Bin 1021 -> 1223 bytes
 .../__pycache__/ZObj.cpython-36.pyc           | Bin 942 -> 981 bytes
 SpectrumViewer/line dictionary                |   2 +-
 inline viewer demo 2.ipynb                    |  30 +++++++++++++++++-
 6 files changed, 38 insertions(+), 8 deletions(-)

diff --git a/SpectrumViewer/SDSSDriver.py b/SpectrumViewer/SDSSDriver.py
index 2a55464..bae0ad9 100644
--- a/SpectrumViewer/SDSSDriver.py
+++ b/SpectrumViewer/SDSSDriver.py
@@ -43,7 +43,7 @@ def loadFITS(filename,fileSource):
             LINENAME=z['LINENAME'],
             LINEWAVE=z['LINEWAVE'],
             LINEZ=z['LINEZ'],
-
+            LINENameFormatted = [x.split()[0].replace('_','') for x in z['LINENAME']],
             LINEEW=z['LINEEW'],
             LINEZ_ERR=z['LINEZ_ERR'],
             LINEEW_ERR=z['LINEEW_ERR'])
diff --git a/SpectrumViewer/ZObj.py b/SpectrumViewer/ZObj.py
index 974b25b..2cb7021 100644
--- a/SpectrumViewer/ZObj.py
+++ b/SpectrumViewer/ZObj.py
@@ -1,7 +1,7 @@
 class ZObj:
-    def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW=None,LINEEW_ERR=None):
+    def __init__(self,LINENAME=None,LINENameFormatted=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW=None,LINEEW_ERR=None):
         self.LINENAME = LINENAME
-
+        self.LINENameFormatted = LINENameFormatted
         self.LINEWAVE = LINEWAVE
         self.LINEZ = LINEZ
         self.LINEZ_ERR = LINEZ_ERR
@@ -18,13 +18,15 @@ def __init__(self,LINENAME=None,LINEWAVE=None,LINEZ =None,LINEZ_ERR= None,LINEEW
             self.lineDict[listTemp[1]] = listTemp[0]
         self.LINEZ_type =[]
         for i in range(len(LINENAME)):
-            if LINENAME[i] in self.lineDict.keys():
+            #name = LINENAME[i].split()[0]
+            if self.LINENameFormatted[i] in self.lineDict.keys():
                 #e or a
-                if self.lineDict[LINENAME[i]]=='a':
+                if self.lineDict[self.LINENameFormatted[i]]=='a':
                     self.LINEZ_type.append('a')
-                elif self.lineDict[LINENAME[i]]=='e':
+                elif self.lineDict[self.LINENameFormatted[i]]=='e':
                     self.LINEZ_type.append('e')
                 else:
+
                     self.LINEZ_type.append('ae')
 
             else:
diff --git a/SpectrumViewer/__pycache__/SDSSDriver.cpython-36.pyc b/SpectrumViewer/__pycache__/SDSSDriver.cpython-36.pyc
index d2b354aae9b330b4cf818b2385a637ba60b3f430..8c8edb9146b33f93eeb1f1513a80619271e0f1e7 100644
GIT binary patch
delta 438
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zaPbScIE!Dv(NW0a_Yef{D&igQkLT`v-uL;I-=@_yO;hXJUAw4^)w|Ry0ECF3E@sc;
z$4ir>N#+*9B++!(gc0IuRu}K=gmXHO+`T+hLOE1Uu?2xl@*{+RfCa{Uw1FO|i}+4)
zFs}qJi<e*FID-%l3qus54vhGS4jmp%@Cc9b`4nFuF)<sX;}{GaC;Wle4q}<nf#<lC
z|KRFMjIN)6<)U}Iv;L*lytW^>{ocT=iV%^Qgqxz*cSxhTZ&hnv-*r6S<l@g5eW1#+
z%u?LSG=UioJG0wtZMB>pZS+~s2?9!3K{R!Ng(!<($}A`J1bMDv<u65e{ME>imNb+`
zNq$pEk(d!3Np-U%h88*OtTmu+z%F_RZF)sn)!sYlu!?Ax1q^}zh~J#(Q!4!c1Pf#e

delta 215
zcmX@k`Inu`n3tEUN+~?*82d!7Hl9@s3=B1lDU8`nMe`=kDbuTAOA(M{0Fr`WQb>{^
zMYxx(mZOHfh9iwBn4yF{MWmULks*a4m_bu?ascD>$)}kVWY~&$fCd%uf(Skk!4Dz?
zK!o6ADdrt4D;bIuCi^gX3Ebky$xlpi^9%`=1j-g0O#aBM#m&UV$i~RS$iZBsG+Bei
fnN1X=QDkx<i=@0bh$#jllt6?6Dxo-eC5s#YfAcHw

diff --git a/SpectrumViewer/__pycache__/ZObj.cpython-36.pyc b/SpectrumViewer/__pycache__/ZObj.cpython-36.pyc
index 8b8dbb55d2bdf780e7a53deef55a321e24fe41a0..9f6b5ef37d98058e0f99df19ad9b754e5efefce8 100644
GIT binary patch
delta 461
zcmZ3-ewCfen3tF9id%LR6YE4STSoSY9y($i3=qHzq*#I28HkJjOzcvxuVJrYh-WKd
zPhm`9YG$hCsNu-sXlAJ8%wzzHb5^L;aJn$W%GGk!aAk1;#kp&^Q<x=za#@@}7K<c<
z4MU+vI72!}HFrf@2~P=c4P!G90(F(u@&MH_*6={oXYn;NWV02$0PBT{O9nHfux2wA
z?EngLXYr@7HZw9Z6n;Tb!jQrSRB;Qcf(@<$q@EqjtAomMKzNhO89n3#ZgKc{`nmc!
z`nui{1hW!zQ{D25auZ8RQd1_MVysl+y~R?Tnv+(<2@H%PE)c;DB6vUqD4>e?CMPf%
zG0IGy%oNO}0~BCjWCOy<@0cXznM9aG82K21n3<2IiZ{wXDN8RtJ~J<~BtBk~-EXog
nv#zKd$P5`&LUwXGv!WWhAW+RM4x8Nkl+v73J4PU{7>F4FDR5<w

delta 424
zcmcc0zK)&Cn3tDpno(v{BFjWBTSm5t9y(&|3=qHrq*#I28HkHNOzcvxFJVhzOkrwf
zs%5WX&th+8sO89Hs9}ibs8FlnaAAm*tL3cW%;E%!bJcLAFiQgEvN(V&7D)yhhC-)s
zhIEi>u8M{d?h>9F#%3S{>ME$^u3@a<hN#ZsZDz=3E4l;L2^N)PNMX%pDq05=ap%h7
zOJQwhWMn9Ofuev7sNft>1SHP}mk06K!MqBn90!CqxtY;}oBtMvkEfrjpQEqq<d=+<
zvOKp~ic@paia3G3E#d+Z+#muJ2t~Y;TbPU(r6+G^3bxk*3NSFTF|jd1ArluP2apdW
znZ$r%LX1L8LX3PsnvaQ(rHVJoKPgKuK0Y%qvm`!VlWnpOv#zKt$WUoiLS}Lwv!Vco
I;C^N?00Wm+<^TWy

diff --git a/SpectrumViewer/line dictionary b/SpectrumViewer/line dictionary
index 6999336..5cd2d74 100644
--- a/SpectrumViewer/line dictionary	
+++ b/SpectrumViewer/line dictionary	
@@ -1,4 +1,4 @@
-ae  Ly_alpha   1215.67  1215.08
+ae  Lyalpha   1215.67  1215.08
 a        NV    1240.81  1240.22
 a        OI    1305.53  1304.95
 a       CII    1335.31  1334.73
diff --git a/inline viewer demo 2.ipynb b/inline viewer demo 2.ipynb
index b9ff09e..9f1ccd1 100644
--- a/inline viewer demo 2.ipynb	
+++ b/inline viewer demo 2.ipynb	
@@ -787,7 +787,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"1000\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -956,6 +956,34 @@
     "type(name)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for n in name:\n",
+    "    print (n.split()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name[0].replace('_','')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,