sp=yrsplit.search(m).groups(0)
models['age'][m]=(int(sp[0])+(float(sp[1])/10000.))*10**int(sp[2])
maxage= float(max(models['age'].values))
minage= float(min(models['age'].values))
diffage=float(maxage-minage)
###print models['age']
modelcolors=['#%02x%02x%02x' % (int(255*(1-(i-minage)/diffage)),0,int(255 * ((i-minage)/diffage))) for i in models['age'].values]
models['col']=modelcolors
models['a']=0
match_df['x']=match_df['J']-match_df['K']
match_df['ex']=np.sqrt(match_df['e_J']**2+match_df['e_K']**2)
#plt.errorbar(match_df['x'],match_df['J']-6.2,fmt=None,ecolor='k',xerr=match_df['ex'],yerr=match_df['e_J'])
alphaarray=['']*len(models.index.values)
for j in range(len(alphaarray)):
i=models.index.values[j]
ff=it.testfitiso(match_df,d[i],6.2)
alphaarray[j]=1-2*ff
models['a']=alphaarray
#print 1-2*ff
#print models['a'][i]
print models['a']
for i in models.index.values:
plt.plot(d[i]['J']-d[i]['K'],d[i]['J'],color=models['col'][i],alpha=models['a'][i])
outfile.write('{0}\t{1}\ta={2}\n'.format(models['age'][i],ff,models['a'][i]))
plt.scatter(match_df['x'],match_df['J']-6.2,color=match_df['col'],marker='o')
xmin=float('inf')
xmax=-float('inf')
ymin=float('inf')
ymax=-float('inf')
for i in models.index.values:
color_of_model=d[i]['J']-d[i]['K']
import pandas as pd
import psycopg2 as p2
import matplotlib.pyplot as plt
import isotools as iso
df = pd.DataFrame
con = p2.connect("dbname='stars' host='localhost' user='******'")
cur = con.cursor()
cur.execute("select jmag, e_jmag, hmag, e_hmag, kmag, e_kmag from alphaperprevmembers where jmag-kmag<0.6")
ap = df(cur.fetchall(), columns=["J", "e_J", "H", "e_H", "K", "e_K"])
cur.close()
con.close()
con = p2.connect("dbname='parsec' host='localhost' user='******'")
cur = con.cursor()
cur.execute("select names from years_z021")
N = cur.fetchall()
d = {}
for n in N:
cur.execute("select j, h, k from {0}_z021;".format(n[0]))
d[n] = df(cur.fetchall(), columns=["J", "H", "K"])
d[n]["fit"] = iso.testfitiso(ap, d[n], 6.2)
outfile = open("bestiso.txt", "w")
for k in d.keys():
outfile.write("{0}\t{1}\n".format(k, d[k]["fit"][0]))
cur.close()
con.close()
outfile.close()
