In [1]:

    

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In [2]:

    

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import datapath
import fitsio
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z040_179_norm3020.fits')
clusters = fitsio.read('/Users/Benjamin/AstroData/Clusters/redmapper_dr8_public_v5.2_catalog.fits')
from astropy.coordinates import ICRS
from astropy import units as u
import astropy
from astropy.coordinates import SkyCoord
c1 = SkyCoord(ra=clusters['RA']*u.degree, dec=clusters['DEC']*u.degree)
c2 = SkyCoord(ra=clusters['RA']*u.degree, dec=clusters['DEC']*u.degree)
index,dist2d,dist3d = cat1.match_to_catalog_sky(cat2)
index,dist2d,dist3d = c1.match_to_catalog_sky(c2)
dist2d
dist3d
index,dist2d,dist3d = c1.match_to_catalog_sky(c2, nthneighbor=2)
index
dist2d
from astropy.cosmology import WMAP9 as cosmo
clusters.dtype.fields
plot(clusters[0]['PZ'])
plot(clusters[0]['PZBINS'], clusters[0]['PZ'])
clf()
plot(clusters[0]['PZBINS'], clusters[0]['PZ'])
xx = clusters['PZ']
xx.shape
xmaxindx = argmax(xx, axis=1)
xmaxindx.shape
distance = cosmo.comoving_distance(clusters['PZ'][xmaxindx]
)
zz = xx[:,xmaxindx]
zz.shape
xx.shape
xmaxindx.shape
zz = xx[np.arange(25325),xmaxindx]
zz/shape
zz.shape
distance = cosmo.comoving_distance(zz)
distance
zz[0]
xx.shape
xx.shape
xx[0,:]
xx[0,xmaxindx[0]]
xmaxindx[0]
zz = xx[np.arange(25325),xmaxindx]
yy = clusters['PZBINS']
zz = yy[np.arange(25325),xmaxindx]
distance = cosmo.comoving_distance(zz)
import cosmology as bgtcosmo
timeit distance = cosmo.comoving_distance(zz)
%timeit distance  = cosmo.comoving_distance(zz)
distance  = cosmo.comoving_distance(zz)
time distance  = cosmo.comoving_distance(zz)
CosPar={'Omega_M':0.3, 'Omega_L':0.7, 'Omega_b':0.045, 'Omega_nu':1e-5, 'n_degen_nu':3., 'h':0.7, 'sigma_8':0.8, 'ns':0.96}
time distance = bgtcosmo.comoving_distance(zz, CosPar)
time distance  = cosmo.comoving_distance([zz,zz])
zzz = c_[zz,zz]
zzz.shape
zzz = r_[zz,zz]
zzz.shape
zzz = r_[zzz,zzz]
zzz.shape
time distance  = cosmo.comoving_distance(zzz)
time distance = bgtcosmo.comoving_distance(zzz, CosPar)
time distance1  = cosmo.comoving_distance(zzz)
plot(distance, distance1)
plot([0,2500], [0,2500])
dist2d
0.3*3600.*5/1000.
dist2d.shape
index
c1 = SkyCoord(ra=clusters['RA']*u.degree, dec=clusters['DEC']*u.degree)
zz.shape
deltaz = absolute(zz - zz[index])
deltaz
iii = where(logical_and(deltaz<0.05, dist2d<0.3))
deltaz
dist2d
dist2d.value
iii = where(logical_and(deltaz<0.05, dist2d.value<0.3))
iii.shape
iii = where(logical_and(deltaz<0.05, dist2d.value<0.3))[0[
]]
iii = where(logical_and(deltaz<0.05, dist2d.value<0.3))[0]
iii.shape
iii = where(logical_and(deltaz<0.03, dist2d.value<0.3))[0]
iii.shape
iii = where(logical_and(deltaz<0.01, dist2d.value<0.3))[0]
iii.shape
iii = where(logical_and(deltaz<0.01, dist2d.value<0.2))[0]
iii.shape
cluster[iii[0:10]]
clusters[iii[0:10]]
clusters[iii[0:10]]['RA']
clusters[iii[0:10]]['DEC']
clusters[iii[0:10]]['LAMBDA']
%history
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ipython locate profile
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data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z040_179_norm3020.fits')
import fitsio
import datapath
clusters = fitsio.read('/Users/Benjamin/AstroData/Clusters/redmapper_dr8_public_v5.2_catalog.fits')
clusters.dtype
clusters.shape
hist(clusters['lambda']
)
clusters.dtype
hist(clusters['LAMBDA'])
hist(clusters['LAMBDA'], 10)
hist(clusters['LAMBDA'], 20)
clf()
hist(clusters['LAMBDA'], 20)
clusters[clusters['LAMBDA']>100]['RA']
clusters[clusters['LAMBDA']>200]['RA']
clusters[clusters['LAMBDA']>200]['RA', 'DEC']
clusters[clusters['LAMBDA']>200][('RA', 'DEC')]
clusters[clusters['LAMBDA']>200]['RA']
clusters[clusters['LAMBDA']>200]['DEC']
clf()
plot(clusters['RA'], clusters['DEC'], '.')
xlim(150, 200)
ylim(20, 40)
5.*60.
5.*3600./1000.
xlim(170, 180)

In [4]:

    

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import fitsio
import datapath
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z000_100_norm4150.fits')
data.dtype
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][3])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,3])
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z040_179_norm3020.fits')
clf()
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,3])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,4])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,6])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,10])
clf()
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,3])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,4])
ylim(0,4)
xlim(1400,6500)
savefig('eigen34.jpg')
clf()
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,6])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,10])
xlim(1400,6500); ylim(0,4)
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,10])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,6])
savefig('eigen610.jpg')
a.dtype
b.dtype
a = fitsio.read(datapath.qso_path()+'/QSO_decompose_NMF_106stats.fits')
b = fitsio.read(datapath.qso_path()+'/HW_dr7qso_newz_photo_more.fits')
c = fitsio.read(datapath.qso_path()+'/HW_dr7qso_newz_absorption_info.fits')
d = fitsio.read(datapath.qso_path()+'/HW_dr7qso_newz.fits')
e = fitsio.read(datapath.qso_path()+'/HW_dr7qso_newz_photo_lum.fits')
e.dtype
index = where(logical_and(logical_and(logical_and(d['Z']>0.4, d['Z']<1.8), c['MGII']==0), c['BAL_FLAG']==0))[0]
itest = (np.where(np.logical_and(np.logical_and(mi_z2[index]>-27, mi_z2[index]<-23.), eigen[index,4]>0.02)))[0]
mi_z2 = b['MI_Z2']
eigen = a['EIGEN_VALUES']
oiii = e['LOGL_OIII_5007']
mgii = e['LOGL_BROAD_MGII']
i=0; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,47], ybin=[0.1,0.4])
import quicklook
i=0; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,47], ybin=[0.1,0.4])
i=0; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=1; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=3; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=4; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=10; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=5; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=6; quicklook.plot_mean(oiii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
e.dtype
mgii = e['LOGL_BROAD_MGII']
i=0; quicklook.plot_mean(mgii[index], eigen[index,i], xbin=[41,44], ybin=[0.1,0.4])
i=0; quicklook.plot_mean(mgii[index], eigen[index,i], xbin=[42,45], ybin=[0.1,0.4])
i=0; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=1; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=2; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=3; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=4; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=5; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=6; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=7; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=8; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=9; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=10; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
i=11; quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.4])
quicklook.plot_mean(eigen[index,0]+eigen[index,8]-eigen[index,3]-eigen[index,4], mgii[index], ybin=[42,45], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,0]+eigen[index,8]-eigen[index,3]-eigen[index,9], mgii[index], ybin=[42,45], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,10]+eigen[index,8]-eigen[index,3]-eigen[index,9], mgii[index], ybin=[42,45], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,10]+eigen[index,8]-eigen[index,3]-eigen[index,9], oiii[index], ybin=[42,45], xbin=[-0.4,0.4])
i=11; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[42,45], xbin=[0.1,0.4])
i=11; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.4])
i=10; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.4])
i=9; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.4])
i=8; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
i=7; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
i=6; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
i=5; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
i=4; quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
for i in arange(4):
    quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,4]+eigen[index,8]-eigen[index,2]-eigen[index,3], oiii[index], ybin=[42,45], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,4]+eigen[index,8]-eigen[index,2]-eigen[index,3], oiii[index], ybin=[41,43], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,4]+eigen[index,8]-eigen[index,2]-eigen[index,3], oiii[index], ybin=[41,44], xbin=[-0.4,0.4])
import scipy.stats as stats
H_low, xedges, yedges, binnumber = stats.binned_statistic_2d(eigen[index_lowz,4]+eigen[index_lowz,3], eigen[index_lowz,6]+eigen[index_lowz,10], mi_z2[index_lowz], statistic='mean', bins=40, range=[[0.,1.], [0.,1]])
H, xedges, yedges, binnumber = stats.binned_statistic_2d(x, y, z, statistic='mean', bins=20, range=[[0.,1.], [0.,1]])
x = eigen[index,4]+eigen[index,8]
y = eigen[index,2]+eigen[index,3]
z = oiii[index]
H, xedges, yedges, binnumber = stats.binned_statistic_2d(x, y, z, statistic='mean', bins=20, range=[[0.,1.], [0.,1]])
index_lowz = where(logical_and(logical_and(logical_and(d['Z']>0.5, d['Z']<1.1), c['MGII']==0), c['BAL_FLAG']==0))[0]
index_highz = where(logical_and(logical_and(logical_and(d['Z']>1.1, d['Z']<1.8), c['MGII']==0), c['BAL_FLAG']==0))[0]
5007.*2.
5007.*1.8
5007.*2.8
index_highz = where(logical_and(logical_and(logical_and(d['Z']>1.1, d['Z']<1.8), c['MGII']==0), c['BAL_FLAG']==0))[0]
x = eigen[index,4]+eigen[index,3]
y = eigen[index,0]+eigen[index,8]
zmg = mgii[index]
H_low, xedges, yedges, binnumber = stats.binned_statistic_2d(eigen[index_lowz,4]+eigen[index_lowz,3], eigen[index_lowz,6]+eigen[index_lowz,10], mi_z2[index_lowz], statistic='mean', bins=40, range=[[0.,1.], [0.,1]])
H_low, xedges, yedges, binnumber = stats.binned_statistic_2d(eigen[index_lowz,4]+eigen[index_lowz,3], eigen[index_lowz,0]+eigen[index_lowz,8], mgii[index_lowz], statistic='mean', bins=20, range=[[0.,1.], [0.,1]])
H_high, xedges, yedges, binnumber = stats.binned_statistic_2d(eigen[index_highz,4]+eigen[index_highz,3], eigen[index_highz,0]+eigen[index_highz,8], mgii[index_highz], statistic='mean', bins=20, range=[[0.,1.], [0.,1]])
plot(H_high.ravel(), H_low.ravel(), '.')
xlim(41,45); ylim(41,45)
xlim(42,44); ylim(42,44)
quicklook.plot_mean(eigen[index,4]+eigen[index,8]-eigen[index,2]-eigen[index,3], oiii[index], ybin=[41,44], xbin=[-0.4,0.4])
9000./5007.
plot(mi_z2[index], oiii[index], '+'_
)
clf()
plot(mi_z2[index], oiii[index], '+')
xlim(-27,-23); ylim(42,44)
xlim(-27,-23); ylim(41,44)
xlim(-27,-22); ylim(41,44)
plot(mi_z2[index], mgii[index], '+')
xlim(-27,-22); ylim(41,46)
xlim(-27,-22); ylim(41,45)
eigen = a['EIGEN_VALUES']
index = where(logical_and(logical_and(logical_and(d['Z']>0., d['Z']<0.6), c['MGII']==0), c['BAL_FLAG']==0))[0]
1.6*5007.
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], oiii[index], ybin=[41,43], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,5]+eigen[index,8]-eigen[index,3]-eigen[index,7], oiii[index], ybin=[41,44], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,8]-eigen[index,3]-eigen[index,7], oiii[index], ybin=[41,44], xbin=[-0.4,0.4])
index = where(logical_and(logical_and(logical_and(d['Z']>0.4, d['Z']<0.6), c['MGII']==0), c['BAL_FLAG']==0))[0]
1.6*5007.
quicklook.plot_mean(eigen[index,5]+eigen[index,8]-eigen[index,3]-eigen[index,7], oiii[index], ybin=[41,44], xbin=[-0.4,0.4])
savefig('eigen58minus37_lowz.jpg')
2800.*1.6
index = where(logical_and(logical_and(logical_and(d['Z']>0.6, d['Z']<1.0), c['MGII']==0), c['BAL_FLAG']==0))[0]
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mi_z2[index], ybin=[-27,-23], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,1]+eigen[index,4]+eigen[index,11]-eigen[index,6]-eigen[index,9], mi_z2[index], ybin=[41,44], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,1]+eigen[index,4]+eigen[index,11]-eigen[index,6]-eigen[index,9], mi_z2[index], ybin=[-27,-23], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,1]+eigen[index,4]+eigen[index,11]-eigen[index,6], mi_z2[index], ybin=[-27,-23], xbin=[0.,0.7])
quicklook.plot_mean(eigen[index,4]-eigen[index,6], mi_z2[index], ybin=[-27,-23], xbin=[0.,0.7])
quicklook.plot_mean(eigen[index,4]-eigen[index,6], mi_z2[index], ybin=[-27,-23], xbin=[-0.3,0.3])
quicklook.plot_mean(eigen[index,4]-eigen[index,6], mi_z2[index], ybin=[-27,-23], xbin=[-0.1,0.3])
quicklook.plot_mean(eigen[index,4]+eigen[index,11]-eigen[index,6], mi_z2[index], ybin=[-27,-23], xbin=[-0.1,0.7])
savefig('eigen411minus6.jpg')
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[41,44], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,0]+eigen[index,7]-eigen[index,11]-eigen[index,2], mgii[index], ybin=[42,45], xbin=[-0.4,0.4])
savefig('eigen07minus211_medz.jpg')
index = where(logical_and(logical_and(logical_and(d['Z']>1.0, d['Z']<2.0), c['MGII']==0), c['BAL_FLAG']==0))[0]
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[41,44], xbin=[0.1,0.3])
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mgii[index], ybin=[42,45], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,0]-eigen[index,7], mgii[index], ybin=[42,45], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,0]-eigen[index,7], mgii[index], ybin=[42,45], xbin=[-0.3,0.3])
quicklook.plot_mean(eigen[index,4]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42,45], xbin=[-0.3,0.3])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42,45], xbin=[-0.3,0.3])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42,45], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[43,45], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42.5,44.5], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,4]-eigen[index,7], mgii[index], ybin=[42.5,45], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42.5,45], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42.5,45], xbin=[-0.2,0.4])
quicklook.plot_mean(eigen[index,5]+eigen[index,0]-eigen[index,9]-eigen[index,7], mgii[index], ybin=[42.5,45], xbin=[-0.2,0.4])
savefig('eigen05minus79_highz.jpg')
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mi_z2[index], ybin=[-26,-23], xbin=[0.1,0.3])
for i in arange(12):
    quicklook.plot_mean(eigen[index,i], mi_z2[index], ybin=[-27,-24], xbin=[0.1,0.3])
quicklook.plot_mean(eigen[index,2]+eigen[index,4]-eigen[index,0]-eigen[index,6], mi_z2[index], ybin=[-27,-24], xbin=[-0.4,0.4])
quicklook.plot_mean(eigen[index,2]+eigen[index,4]-eigen[index,0]-eigen[index,6], mi_z2[index], ybin=[-28,-23], xbin=[-0.5,0.4])
savefig('eigen24minus06_mi_highz.jpg')
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z000_1000_norm4150.fits')
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z000_100_norm4150')
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z000_100_norm4150.fits')
data.dtype
data.shape
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,5])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,8])
ylim(0,8)
ylim(0,6)
legend(['5', '8'])
savefig('basis1_58.jpg')
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,3])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,7])
ylim(0,3)
ylim(0,2)
legend(['3', '7'])
savefig('basis1_37.jpg')
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z040_179_norm3020.fits')
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,0])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,7])
ylim(0,2)
legend(['0', '7'])
savefig('basis2_07.jpg')
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,2])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,11])
ylim(0,10)
savefig('basis2_211.jpg')
legend(['2', '11'])
savefig('basis2_211.jpg')
data = fitsio.read(datapath.qso_path()+'/QSO_NMF_basis_z080_280_norm2150.fits')
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,0])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,5])
ylim(0,4)
legend(['0', '5'])
savefig('basis3_05.jpg')
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,7])
clf()
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,7])
plot(data[0]['WAVE'], data[0]['EIGEN_VECTORS'][:,9])
ylim(0,4)
legend(['7', '9'])
savefig('basis3_79.jpg')

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