#now we open the fit file of hihg membership stars from the Deacon&Hambly
dhf=fits.open("../DeaconHamblydata/DeaconHambly2004.HighProb.fit")
dhra=dhf[1].data.field('RAJ2000')
dhde=dhf[1].data.field('DEJ2000')
dhp=dhf[1].data.field('Prob')
mycoords=SkyCoord(kra,kde,unit='deg')
dhcoords=SkyCoord(dhra,dhde,unit='deg')
matches, dist = matchCatalog(dhcoords,mycoords,1.0,0)
print matches
pp=[]
ppm=[]
for i in matches:
nm, na = matchCatalog(SkyCoord(np.array(kra[i]),np.array(kde[i]),unit='deg'),dhcoords,1.0,0)
# print nm
if nm.size>0:
pp=pp+[(kp[i],dhp[nm])]
ppm=ppm+[(kpmp[i],dhp[nm])]
outfiel.write("kra{0}\tkde{1}\thgra{2}\tdhde{3}\tkp{4}\tdhp{5}\n".format(kra[i],kde[i],dhra[nm],dhde[nm],kp[i],dhp[nm]))
print len(pp)
kpp=[i[0] for i in ppm]
dhpp=[i[1] for i in ppm]
plt.plot(kpp, dhpp, 'm.')
plt.show()
dha=radecdist(dhra,dhde,np.mean(dhra),np.mean(dhde))
#print dha
#print dhpp
plt.plot(dha,dhp,'k.')
plt.show()
dhpp=dhpp+[dhp[nm]]
ppmra=ppmra+[kpmra[i]]
ppmde=ppmde+[kpmde[i]]
outfiel.write("kra{0}\tkde{1}\thgra{2}\tdhde{3}\tkp{4}\tdhp{5}\n".format(kra[i],kde[i],dhra[nm],dhde[nm],kp[i],dhp[nm]))
print len(pp)
plt.plot(pp, dhpp, 'k.')
plt.xlabel("p_mem as calculated by me")
plt.ylabel("P_mem as computed by Deacon and Hambly")
plt.title("my p_mem based off of spacial and proper motion variance")
plt.show()
plt.plot(ppm, dhpp, 'k.')
plt.xlabel("p_mem as computed by me")
plt.ylabel("p_mem as computed bu Deacon and Hambly")
plt.title("my p_mem based solely off PM as per DH")
plt.show()
dha=radecdist(dhra,dhde,np.mean(dhra),np.mean(dhde))
#print dha
#print dhpp
plt.plot(dha,dhp,'k.')
plt.xlabel("asngular separation of observed position from cluster center [deg]")
plt.ylabel("p_mem as computed by Deacon and Hambly")
plt.show()
ppmra=np.array(ppmra)/(3.6*10**6)
ppmde=np.array(ppmde)/(3.6*10**6)
pma=radecdist(ppmra,ppmde, np.mean(ppmra), np.mean(ppmde))*(3.6*10**6)
plt.plot(pma, dhpp, 'k.')
plt.xlabel("angular separation of preoper motion [mas/yr]")
plt.ylabel("p_mem as calculated by Deacon and Hambly")
plt.show()
plt.show()
import numpy as np
from radecdist import radecdist
import psycopg2
import matplotlib.pyplot as plt
con=psycopg2.connect("dbname='stars' user='******' host='localhost'")
cur=con.cursor()
cur.execute("select pmra, pmde, properdist from plotdata where knownmember=1")
data=cur.fetchall()
ra=np.array([datum[0] for datum in data])
de=np.array([datum[1] for datum in data])
dist=np.array([datum[2] for datum in data])
newdist=radecdist(ra,de,np.mean(ra),np.mean(de))
plt.plot(newdist,dist,'k.')
plt.show()
import numpy as np
from radecdist import radecdist
import psycopg2
import matplotlib.pyplot as plt
con=psycopg2.connect("dbname='stars' user='******' host='localhost'")
cur=con.cursor()
cur.execute("select raj2000, dej2000, pmra, pmde, spacialdist, properdist from plotdata where knownmember=1")
data=cur.fetchall()
ra=np.array([datum[0] for datum in data])
de=np.array([datum[1] for datum in data])
pmra=np.array([datum[2] for datum in data])/(3.6*10**6)
pmde=np.array([datum[3] for datum in data])/(3.6*10**6)
sdist=np.array([datum[4] for datum in data])
pdist=np.array([datum[5] for datum in data])
newsdist=radecdist(ra,de,np.mean(ra),np.mean(de))
newpdist=radecdist(pmra,pmde,np.mean(pmra),np.mean(pmde))*(3.6*10**6)
plt.plot(newsdist,sdist,'k.')
plt.xlabel("angular distance [deg]")
plt.ylabel("angular distance [deg] from poster")
plt.title("spatial distance")
plt.show()
plt.plot(newpdist,pdist,'k.')
plt.xlabel("angular distance proper motion space [mas/yr]")
plt.ylabel("angular distance proper motion space [mas/yr] from poster")
plt.title("proper motion distance")
plt.show()
