"""

Utility function to retrieve x,y vectors from an ASCII data file.

It returns the first column as x, the second as y.

Usage:

x,y = get_xy_data("mydata.txt")

"""

import numpy as np

data = np.loadtxt(filename, usecols=(0,1))

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

if file is None:

raise ArgumentError("please give file name to plot")

x, y = get_xy_data(file)

plt.plot(x,y)

xlbl = r"$R_g (\rm{kpc})$"

plt.xlabel(xlbl)

ylbl = r"$n_{HI} \ (\rm{cm^{-3}})$"

plt.ylabel(ylbl)

plt.ylim(-0.05,4)

plt.xlim(0,25)

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

if velsfile is None:

raise ArgumentError("Please give a file name to plot")

l, v = get_xy_data(velsfile)

plt.plot(l,v,'k+',label=plot_label,alpha=1)

# xlim(340.,288.)

plt.legend()

plt.xlabel(r'$Galactic\, Longitude\, (deg)$')

plt.ylabel(r'$V_t\, (km\, s^{-1})$')

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

import numpy as np

from scipy.signal import medfilt

if velsfile1 is None:

raise ArgumentError("Please give input file")

l1,v1 = get_xy_data(velsfile1)

l2,v2 = get_xy_data(velsfile2)

sinl1 = abs(np.sin(l1*np.pi/180))

sinl2 = abs(np.sin(l2*np.pi/180))

v2_med = medfilt(v2,kernel_size=5)

v2 = abs(v2_med)

plt.plot(sinl1,v1,'b+',label="QI")

plt.plot(sinl2,v2,'r+',label="QIV")

plt.legend()

plt.xlabel(r'$sin(l)$')

plt.ylabel(r'$|V_t|\, (km\, s^{-1})$')

# Fit the arrays

x = sinl1

y = v1

pars = np.polyfit(x,y,1)

print pars

yfit = np.polyval(pars,x)

plt.plot(sinl1,yfit,'b--')

x = sinl2

y = v2

pars = np.polyfit(x,y,1)

print pars

yfit = np.polyval(pars,x)

plt.plot(sinl2,yfit,'r--')

plt.ylim(15.,140.)

import matplotlib.pyplot as plt

from matplotlib.ticker import MultipleLocator

plt.ion()

plt.clf()

import numpy as np

from scipy.signal import medfilt

if velsfile1 is None:

raise ArgumentError("Please give a file name to plot")

# Put the minor tick marks on the plot

xminorLocator=MultipleLocator(2)

yminorLocator=MultipleLocator(5)

ax1=plt.subplot(111)

l1,v1 = get_xy_data(velsfile1)

l2,v2 = get_xy_data(velsfile2)

v2 = medfilt(v2,kernel_size=3)

v1 = medfilt(v1,kernel_size=3)

# l2=abs(360.0-l2)

v2 = -v2

plt.plot(l1,v1,'r+',label="QI",alpha=1)

ax1.xaxis.tick_bottom()

plt.xlabel(r'$Galactic\, Longitude\, (deg)$')

plt.ylabel(r'$|V_t|\, (km\, s^{-1})$')

plt.xlim(15.,70.)

from matplotlib import rc

rc('mathtext', default='regular')

ax1.xaxis.set_minor_locator(xminorLocator)

ax1.yaxis.set_minor_locator(yminorLocator)

plt.minorticks_on()

# Fit the array (currently unweighted so dominated by dense sampling at high R)

x = abs(np.sin(l1*np.pi/180))

y = v1

pars = np.polyfit(x,y,1)

print pars

yfit = np.polyval(pars,x)

plt.plot(l1,yfit,'r--')

ax2= plt.twiny()

ax2.xaxis.tick_top()

plt.plot(l2,v2,'b+',label="QIV",alpha=1)

plt.xlim(345.,290.)

ax2.xaxis.set_minor_locator(xminorLocator)

ax2.yaxis.set_minor_locator(yminorLocator)

# Fit the array (currently unweighted so dominated by dense sampling at high R)

x = abs(np.sin(l2*np.pi/180))

y = v2

pars = np.polyfit(x,y,1)

print pars

yfit = np.polyval(pars,x)

plt.plot(l2,yfit,'b--')

plt.show()

plt.figure(2)

x1 = abs(np.sin(l1*np.pi/180))

resid1 = np.polyval(pars,x1) - v1

x2 = abs(np.sin(l2*np.pi/180))

l2a = abs(l2-360.)

resid2 = np.polyval(pars,x2) - v2

plt.plot(l1,resid1,'r+')

plt.plot(l2a,resid2,'b+')

plt.xlabel(r"$|l|\, (deg)$")

plt.ylabel(r"$\Delta V_t\, (km\,s^{-1})$")

import numpy as np

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

if velsfile1 is None:

raise ArgumentError("Please give input file")

from scipy.signal import medfilt

r0 = 8.5

theta0 = 220.0

# r0=8.34 # Reid et al (2014) values

# theta0=240.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1 = medfilt(vel1,kernel_size=5)

v2 = medfilt(vel2,kernel_size=5) # Do the median filter that was done in McG07

sinl1 = abs(np.sin(l1*np.pi/180))

sinl2 = abs(np.sin(l2*np.pi/180))

rot1 = abs(vel1) + theta0*sinl1

rg1 = r0*sinl1

rot2 = abs(v2) + theta0*sinl2

rg2 = r0*sinl2

plt.plot(rg1,rot1,'r+',label="QI")

plt.plot(rg2,rot2,'b+',label="QIV")

plt.minorticks_on()

plt.legend()

plt.xlabel(r"$R\, (kpc)$")

plt.ylabel(r"$\Theta\, (km\,s^{-1})$")

print 'Number of 1st quad points: ',len(rot1)

print 'Number of 1st quad points: ',len(rot2)

plt.xlim(3.0,8.0)

plt.ylim(190.,260.)

import numpy as np

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

import numpy as np

if velsfile1 is None:

raise ArgumentError("Please give input file")

from scipy.signal import medfilt

r0 = 8.5

theta0 = 220.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1 = new_vLSR(l1,vel1) # Apply the VLSR correction

v2_temp = new_vLSR(l2,vel2)

#v1 = medfilt(vel1,kernel_size=5)

v2 = medfilt(v2_temp,kernel_size=5) # Do the median filter that was done in McG07

#v1 = medfilt(vel1,kernel_size=5)

#v1 = vel1

#v2 = medfilt(vel2,kernel_size=5)# Do the median filter that was done in McG07

ndat1=len(v1)

ndat2 = len(v2)

sinl1 = abs(np.sin(l1*np.pi/180))

wt1= array(sinl1)

sinl2 = abs(np.sin(l2*np.pi/180))

wt2=array(sinl2)

rot1 = array(abs(v1) + theta0*sinl1)

rg1 = array(r0*sinl1)

rot2 = array(abs(v2) + theta0*sinl2)

rg2 = array(r0*sinl2)

#

# Create arrays with both datasets

print "Creating array of length",ndat1+ndat2;

rg = append(rg1,rg2)

rot = append(rot1,rot2)

wt = append(wt1,wt2)

x = rg/r0

y = rot/theta0

weights = wt/mean(y)

# Fit the array (currently unweighted so dominated by dense sampling at high R)

pars = polyfit(x,y,1)

print pars

yfit = polyval(pars,x)

rotfit = yfit * theta0

#

# Create arrays of the differences from fit

fit1 = polyval(pars,rg1/r0)

fit2 = polyval(pars,rg2/r0)

diff1 = rot1 - fit1*theta0

diff2 = rot2 - fit2*theta0

#

# Show the B&B93 fit

p=[1.00767, 0.0394, 0.000712]

bbr = arange(3.0,8.0,0.01)

bbx = bbr/r0

bbfit = (p[0]*bbx**p[1] + p[2])*theta0

#

# Clemens 1985 curve

cra=arange(3.825,13.6,0.01)

crb=arange(0.765,3.825,0.01)

crota=np.zeros(len(cra),float)

pa=[-2342.65,2507.60,-1024.06,224.5627,-28.40800,2.0697,-0.080508,0.00129]

pa.reverse()

crota=polyval(pa,cra)

# polya=poly1d(pa)

# crota=polya(cra)

print max(crota),min(crota)

pb=[325.09,-248.14,231.87, -110.73,25.07,-2.11]

pb.reverse()

crotb=polyval(pb,crb)

crot = append(crotb,crota)

cr = append(crb,cra)

crot = crot *226.0/220.0

#

# Show the Reid et al (2014) curve

rrg = arange(3.,8.0,0.1)

rr = theta0 + 0.2 * (rrg - r0)

# Plot the points and fits

plt.plot(rg1,rot1,"r+",label="QI")

plt.plot(rg2,rot2,"b+",label="QIV")

plt.plot(rg,rotfit,label="Joint fit",marker="None",color="black",linestyle="-")

plt.plot(bbr,bbfit,linestyle=":",color="black",label="BB93 fit")

#plot(cr,crot,linestyle="-.",color="black",label="Clemens (1985)")

plt.plot(rrg,rr,linestyle="-.",color="black",label="Reid14")

plt.legend(loc=2)

plt.minorticks_on()

plt.xlabel(r"$R\, (kpc)}$")

plt.ylabel(r"$\Theta\, (km\, s^{-1})$")

print 'Number of 1st quad points: ',len(rot1)

print 'Number of 4th quad points: ',len(rot2)

plt.xlim(3.0,8.0)

plt.ylim(180.0,267.0)

figure(2)

plt.plot(rg1,diff1,'r+',label="QI")

plt.plot(rg2,diff2,'b+',label="QIV")

plt.xlabel(r"$R \,(kpc)$")

plt.ylabel(r"$\Delta\Theta (km\, s^{-1})$")

plt.xlim(3.0,8.0)

plt.ylim(-15.,15.)

print mean(diff1),mean(diff2)

print median(diff1),median(diff2)

print std(diff1),std(diff2)

legend(loc=2)

plt.show()

import numpy as np

Uo_IAU = 10.27 # km/s precessed to J2000

Vo_IAU = 15.32

Wo_IAU = 7.74

# Modern Uo, Vo, Wo values from Reid et al (2014)

Uo = 10.00 # km/s

Vo = 5.25

Wo = 7.17

#

# Assume b=0, so

cos_b = 1.0

sin_b = 0.0

v_newlsr=np.zeros(len(v_lsr))

for i in range(len(v_lsr)):

cos_l = np.cos(l[i]*np.pi/180.)

sin_l = np.sin(l[i]*np.pi/180.)

v_helio = v_lsr[i] - (Vo_IAU*sin_l + Uo_IAU*cos_l)*cos_b - Wo_IAU*sin_b

v_newlsr[i] = v_helio + (Vo*sin_l + Uo*cos_l)*cos_b + Wo*sin_b

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

import numpy as np

if velsfile1 is None:

raise ArgumentError("Please give input file")

from scipy.signal import medfilt

r0 = 8.5

theta0 = 220.0

# r0=8.34 # Reid et al (2014) values

# theta0=240.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1_temp = new_vLSR(l1,vel1) # Apply the VLSR correction

v2_temp = new_vLSR(l2,vel2)

v1 = medfilt(v1_temp,kernel_size=5)

v2 = medfilt(v2_temp,kernel_size=5) # Do the median filter that was done in McG07

sinl1 = abs(np.sin(l1*np.pi/180))

sinl2 = abs(np.sin(l2*np.pi/180))

rot1 = np.abs(v1) + theta0*sinl1

rg1 = r0*sinl1

rot2 = np.abs(v2) + theta0*sinl2

rg2 = r0*sinl2

plt.plot(rg1,rot1,'r+',label="QI")

plt.plot(rg2,rot2,'b+',label="QIV")

plt.minorticks_on()

plt.legend()

plt.xlabel(r"$R\, (kpc)$")

plt.ylabel(r"$\Theta\, (km\,s^{-1})$")

print 'Number of 1st quad points: ',len(rot1)

print 'Number of 1st quad points: ',len(rot2)

plt.xlim(3.0,8.0)

plt.ylim(190.,260.)

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

from scipy.signal import medfilt

if velsfile1 is None:

raise ArgumentError("Please give a file name to plot")

# Put the minor tick marks on the plot

xminorLocator=plt.MultipleLocator(2)

yminorLocator=plt.MultipleLocator(5)

ax1=plt.subplot(111)

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

#

v1 = new_vLSR(l1,vel1) # Apply the VLSR correction

v2_temp = new_vLSR(l2,vel2)

v2 = medfilt(v2_temp,kernel_size=3)

# l2=abs(360.0-l2)

v2 = -v2

vel2 = -vel2

l2 = abs(l2-360.)

plt.plot(l1,v1,'r+',label="QI",alpha=1)

plt.plot(l2,v2,'b+',label="QIV",alpha=1)

plt.plot(l1,vel1,'ro',label="QI",alpha=1)

import numpy as np

import matplotlib.pyplot as plt

plt.ion()

plt.clf()

if velsfile1 is None:

raise ArgumentError("Please give input file")

from scipy.signal import medfilt

r0 = 8.5

theta0 = 220.0

# r0=8.34 # Reid et al (2014) values

# theta0=240.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1 = medfilt(vel1,kernel_size=5)

v2 = medfilt(vel2,kernel_size=5) # Do the median filter that was done in McG07

sinl1 = abs(np.sin(l1*np.pi/180))

sinl2 = abs(np.sin(l2*np.pi/180))

xl1 = abs(l1*np.pi/180)

xl2 = abs((l2-360.)*np.pi/180)

rot1 = abs(vel1) + theta0*sinl1

rg1 = r0*sinl1

xg1 = r0*xl1

rot2 = abs(v2) + theta0*sinl2

rg2 = r0*sinl2

xg2 = r0*xl2

# old version plt.plot(rg1,rot1,'r+',label="QI")

# old version plt.plot(rg2,rot2,'b+',label="QIV")

plt.plot(xg1,rot1,'r+',label="QI")

plt.plot(xg2,rot2,'b+',label="QIV")

plt.minorticks_on()

plt.legend(loc=4)

# old version plt.xlabel(r"$R\, (kpc)$")

plt.xlabel(r"$x\, (kpc)$")

plt.ylabel(r"$\Theta\, (km\,s^{-1})$")

print 'Number of 1st quad points: ',len(rot1)

print 'Number of 1st quad points: ',len(rot2)

# plt.xlim(3.0,8.0)

# plt.ylim(190.,260.)

import numpy as np

if (len(x) != len(y)):

print "error unequal input lengths"

if (len(x) < 2 ):

print "error too short input lengths"

n=len(x)-1

xret=[]

xnorm=[]

x1=x

y1=y

for i in range(len(x)):

z=x1*y1

xret.insert(i,sum(z))

xnorm.insert(i,len(x)-i)

x2=np.roll(x1,1)

x2[0]=0

x1=x2

xr1=np.divide(xret,xnorm)

print xnorm[0:10]

print xret[0:10]

x1=x

xret=[]

xnorm=[]

for i in range(1,len(x)):

x2=np.roll(x1,-1)

x2[n]=0

x1=x2

z=x1*y1

xret.insert(i,sum(z))

xnorm.insert(i,len(x)-i)

xr2=np.divide(xret,xnorm)

print xnorm[0:10]

print xret[0:10]

xr0=xr2[::-1]

xret=np.concatenate((xr0,xr1))

#

# this version uses x as the spatial variable (LSCP length)

#

import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import medfilt,correlate

plt.ion()

plt.clf()

if velsfile1 is None:

raise ArgumentError("Please give input file")

r0 = 8.5

theta0 = 220.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1_temp = new_vLSR(l1,vel1) # Apply the VLSR correction

v2_temp = new_vLSR(l2,vel2)

v1 = medfilt(v1_temp,kernel_size=5)

v2 = medfilt(v2_temp,kernel_size=5) # Do the median filter that was done in McG07

ndat1=len(v1)

ndat2 = len(v2)

sinl1 = abs(np.sin(l1*np.pi/180))

wt1= np.array(sinl1)

sinl2 = abs(np.sin(l2*np.pi/180))

wt2=np.array(sinl2)

rot1 = np.array(abs(v1) + theta0*sinl1)

rg1 = np.array(r0*sinl1)

rot2 = np.array(abs(v2) + theta0*sinl2)

rg2 = np.array(r0*sinl2)

#

# Create arrays with both datasets

# print "Creating array of length",ndat1+ndat2;

rg = np.append(rg1,rg2)

rot = np.append(rot1,rot2)

wt = np.append(wt1,wt2)

x = rg/r0

y = rot/theta0

weights = wt/np.mean(y)

# Fit the array (currently unweighted so dominated by dense sampling at high R)

pars = np.polyfit(x,y,1)

# print pars

yfit = np.polyval(pars,x)

rotfit = yfit * theta0

#

# Create arrays of the differences from fit

fit1 = np.polyval(pars,rg1/r0)

fit2 = np.polyval(pars,rg2/r0)

diff1 = rot1 - fit1*theta0

diff2 = rot2 - fit2*theta0

#

# diff1 and diff2 are the residual velocities after subtracting the best fits

#

# now go back to longitude for equal step in x = linear dist along magic circle

# we'll just take the range x=3 to x=9.5 kpc (scaled by r0) defined by x01,x02

#

x0=abs(l1*r0*np.pi/180.)

# the 1st quadrant data are in the opposite order now, flip both arrays

diff0 = diff1

x1=x0[::-1]

diff1=diff0[::-1]

x2=abs((360.-l2)*r0*np.pi/180.)

x01=np.linspace(3.,9.5,1300)

x02=np.linspace(3.,9.5,1300)

y01=np.interp(x01,x1,diff1)

y02=np.interp(x02,x2,diff2)

#

# note the new step size is Delta-x = 6500/1300 pc = 5 pc

#

plt.plot(x1,diff1,'r+',label="QI")

plt.plot(x2,diff2,'b+',label="QIV")

plt.plot(x01,y01,'r-',label="QI interp")

plt.plot(x02,y02,'b-',label="QIV interp")

# plt.plot(x01,y01,'ro',label="QI interp")

# plt.plot(x02,y02,'bo',label="QIV interp")

plt.xlabel(r"$s \,(kpc)$")

plt.ylabel(r"$\Delta \Theta \, (km\, s^{-1})$")

# plt.legend(loc=2)

boxx=[3.,3.,9.5,9.5,3.]

boxy=[-14.5,+14.5,+14.5,-14.5,-14.5]

plt.plot(boxx,boxy,'k-')

plt.show()

#

z=correlate(y01,y02)/(len(y01)*np.std(y01)*np.std(y02))

z1=correlate(y01,y01)/(len(y01)*np.var(y01))

z2=correlate(y02,y02)/(len(y01)*np.var(y02))

# just for a test:

## z=jdccf_0pad(y02,y01)/(np.std(y01)*np.std(y02))

## z1=jdccf_0pad(y01,y01)/(np.var(y01))

## z2=jdccf_0pad(y02,y02)/(np.var(y02))

# test done

# print len(z)

zx1=np.linspace(1,len(z),len(z))

zx2=(zx1-(len(z)/2.))*.005

plt.figure(2)

plt.plot(zx2,z,'g-',label="ccf")

plt.plot(zx2,z1,'r-',label="acf Q1")

plt.plot(zx2,z2,'b-',label="acf Q4")

plt.legend(loc=1)

## plt.xlabel(r"$R \,(kpc)$")

## plt.ylabel(r"$\Delta\Theta (km\, s^{-1})$")

plt.xlabel(r"$\Delta x \, \, \, (kpc)$")

plt.ylabel("Normalised Correlation Coefficient")

# plt.xlim(-3.5,3.5)

#

# the y axis is normalized to 1 for 100% correlation

# the x axis is in lag steps of 7.2 pc

#

# keep this for later, in case plots of the raw residuals vs. R are needed

# plt.plot(rg1,diff1,'r+',label="QI")

# plt.plot(rg2,diff2,'b+',label="QIV")

# plt.xlabel(r"$R \,(kpc)$")

# plt.ylabel(r"$\Delta\Theta (km\, s^{-1})$")

# plt.xlim(3.0,8.0)

# plt.ylim(-15.,15.)

# print np.mean(diff1),np.mean(diff2)

# print np.median(diff1),np.median(diff2)

# print np.std(diff1),np.std(diff2)

# plt.legend(loc=2)

# plt.show()

#

# this version uses r as the spatial variable, not x (LSCP length)

#

import numpy as np

import matplotlib.pyplot as plt

from scipy.signal import medfilt,correlate

rc('mathtext', default='regular')

plt.ion()

plt.clf()

if velsfile1 is None:

raise ArgumentError("Please give input file")

r0 = 8.5

theta0 = 220.0

l1,vel1 = get_xy_data(velsfile1)

l2,vel2 = get_xy_data(velsfile2)

v1_temp = new_vLSR(l1,vel1) # Apply the VLSR correction

v2_temp = new_vLSR(l2,vel2)

v1 = medfilt(v1_temp,kernel_size=5)

v2 = medfilt(v2_temp,kernel_size=5) # Do the median filter that was done in McG07

ndat1=len(v1)

ndat2 = len(v2)

sinl1 = abs(np.sin(l1*np.pi/180))

wt1= np.array(sinl1)

sinl2 = abs(np.sin(l2*np.pi/180))

wt2=np.array(sinl2)

rot1 = np.array(abs(v1) + theta0*sinl1)

rg1 = np.array(r0*sinl1)

rot2 = np.array(abs(v2) + theta0*sinl2)

rg2 = np.array(r0*sinl2)

#

# Create arrays with both datasets

# print "Creating array of length",ndat1+ndat2;

rg = np.append(rg1,rg2)

rot = np.append(rot1,rot2)

wt = np.append(wt1,wt2)

x = rg/r0

y = rot/theta0

weights = wt/np.mean(y)

# Fit the array (currently unweighted so dominated by dense sampling at high R)

pars = np.polyfit(x,y,1)

# print pars

yfit = np.polyval(pars,x)

rotfit = yfit * theta0

#

# Create arrays of the differences from fit

fit1 = np.polyval(pars,rg1/r0)

fit2 = np.polyval(pars,rg2/r0)

diff1 = rot1 - fit1*theta0

diff2 = rot2 - fit2*theta0

#

# diff1 and diff2 are the residual velocities after subtracting the best fits

#

# now go back to longitude for equal step in x = linear dist along magic circle

# we'll just take the range x=3 to x=9.5 kpc (scaled by r0) defined by x01,x02

#

### x0=abs(l1*r0*np.pi/180.)

x0=sinl1*r0

# the 1st quadrant data are in the opposite order now, flip both arrays

diff0 = diff1

x1=x0[::-1]

diff1=diff0[::-1]

### x2=abs((360.-l2)*r0*np.pi/180.)

x2=sinl2*r0

### x01=np.linspace(3.,9.5,1300)

### x02=np.linspace(3.,9.5,1300)

x01=np.linspace(3.,7.65,930)

x02=np.linspace(3.,7.65,930)

y01=np.interp(x01,x1,diff1)

y02=np.interp(x02,x2,diff2)

#

# note the new step size is Delta-x = 6500/1300 pc = 5 pc

# Delta-x = 4800/960 pc = 5 pc

#

plt.plot(x1,diff1,'r+',label="QI")

plt.plot(x2,diff2,'b+',label="QIV")

plt.plot(x01,y01,'r-',label="QI interp")

plt.plot(x02,y02,'b-',label="QIV interp")

# plt.plot(x01,y01,'ro',label="QI interp")

# plt.plot(x02,y02,'bo',label="QIV interp")

plt.xlabel(r"$r_G \,(kpc)$")

### plt.xlabel(r"$x \,(kpc)$")

plt.ylabel(r"$\Delta \Theta \, (km\, s^{-1})$")

# plt.legend(loc=2)

### boxx=[3.,3.,9.5,9.5,3.]

boxx=[3.,3.,7.65,7.65,3.]

boxy=[-14.5,+14.5,+14.5,-14.5,-14.5]

plt.plot(boxx,boxy,'k-')

plt.show()

#

z=correlate(y01,y02)/(len(y01)*np.std(y01)*np.std(y02))

z1=correlate(y01,y01)/(len(y01)*np.var(y01))

z2=correlate(y02,y02)/(len(y01)*np.var(y02))

# just for a test:

# z=jdccf_0pad(y01,y02)/(np.std(y01)*np.std(y02))

# z1=jdccf_0pad(y01,y01)/(np.var(y01))

# z2=jdccf_0pad(y02,y02)/(np.var(y02))

# test done

# print len(z)

zx1=np.linspace(1,len(z),len(z))

zx2=(zx1-(len(z)/2.))*.005

plt.figure(2)

plt.plot(zx2,z,'g-',label="ccf")

plt.plot(zx2,z1,'r-',label="acf Q1")

plt.plot(zx2,z2,'b-',label="acf Q4")

plt.legend(loc=1)

## plt.xlabel(r"$R \,(kpc)$")

## plt.ylabel(r"$\Delta\Theta (km\, s^{-1})$")

### plt.xlabel(r"$\Delta x \, \, \, (kpc)$")

plt.xlabel(r"$\Delta r_G \, (kpc)$")

plt.ylabel("Normalised Correlation Coefficient")

plt.minorticks_on()

vlines(0.,-0.5,1.5,linestyles='dotted',linewidth=1.2)

#hlines(0.,-3.,3.,linestyles='dotted',linewidth=1.2)

ylim(-0.5,1.2)

xlim(-3,3)

## plt.xlim(-3.5,3.5)

#

# the y axis is normalized to 1 for 100% correlation

# the x axis is in lag steps of 7.2 pc

#

# keep this for later, in case plots of the raw residuals vs. R are needed

# plt.plot(rg1,diff1,'r+',label="QI")

# plt.plot(rg2,diff2,'b+',label="QIV")

# plt.xlabel(r"$R \,(kpc)$")

# plt.ylabel(r"$\Delta\Theta (km\, s^{-1})$")

# plt.xlim(3.0,8.0)

# plt.ylim(-15.,15.)

# print np.mean(diff1),np.mean(diff2)

# print np.median(diff1),np.median(diff2)

# print np.std(diff1),np.std(diff2)

# plt.legend(loc=2)

# plt.show()