def add_physical_sizes(sname='final'):
p = pyfits.open(data_path + 'gz2sample_%s_kcorrect.fits' % sname)
d = p[1].data
r50_arcsec = d.field('petroR50_r')
redshift = d.field('redshift')
r50_kpc = redshift * 0.0
zmask = notNaN(redshift)
# H0=70 angular scale
angscale = cosmology.ang_scale_flat(redshift[zmask]).astype(N.float32)
r50_kpc[zmask] = (r50_arcsec[zmask] * angscale)
oldcols = p[1].columns
cols = []
for c in oldcols:
name = c.name
cols.append(
pyfits.Column(name=c.name, format=c.format, array=d.field(c.name)))
cols.append(pyfits.Column(name='PETROR50_R_KPC', format='E',
array=r50_kpc))
tbhdu = pyfits.new_table(cols)
tbhdu.name = 'data'
outfile = data_path + 'gz2sample_%s_abs.fits' % sname
file_exists = os.path.isfile(outfile)
if file_exists:
os.remove(outfile)
tbhdu.writeto(outfile)
p.close()
def add_physical_sizes(sname='final'):
p = pyfits.open(data_path+'gz2sample_%s_kcorrect.fits'%sname)
d = p[1].data
r50_arcsec = d.field('petroR50_r')
redshift = d.field('redshift')
r50_kpc = redshift * 0.0
zmask = notNaN(redshift)
# H0=70 angular scale
angscale = cosmology.ang_scale_flat(redshift[zmask]).astype(N.float32)
r50_kpc[zmask] = (r50_arcsec[zmask] * angscale)
oldcols = p[1].columns
cols = []
for c in oldcols:
name = c.name
cols.append(pyfits.Column(name=c.name, format=c.format,
array=d.field(c.name)))
cols.append(pyfits.Column(name='PETROR50_R_KPC', format='E',
array=r50_kpc))
tbhdu=pyfits.new_table(cols)
tbhdu.name = 'data'
outfile = data_path+'gz2sample_%s_abs.fits'%sname
file_exists = os.path.isfile(outfile)
if file_exists:
os.remove(outfile)
tbhdu.writeto(outfile)
p.close()
def create_binned_data(min_class=20,min_prob=0.8,
zmin = 0.01, zmax = 0.16, zstep = 0.01,
magmin = -24, magmax = -16, mag_numbins = 20,
sizemin = 0, sizemax = 15, size_numbins = 20,
taskno = '01',
plot=False
):
p = pyfits.open(gz2both_data_file)
gzdata = p[1].data
d1 = {'total_weight':'t01_smooth_or_features_total_weight',
'taskno': 1,
'nresponses': 2,
'responses':('t01_smooth_or_features_a01_smooth_weighted_fraction','t01_smooth_or_features_a02_features_or_disk_weighted_fraction'),
'names':('sp','el')
}
d4 = {'total_weight':'t03_bar_total_weight',
'taskno': 3,
'nresponses': 2,
'responses':('t03_bar_a06_bar_weighted_fraction','t03_bar_a07_no_bar_weighted_fraction'),
'names':('bar','nobar')
}
taskdict = {'01':d1, '04':d4}
# Task-specific commands
taskcount = gzdata[taskdict[taskno]['total_weight']] > min_class
probarr =
datalist=[]
namelist = taskdict[taskno]['names']
for responses in taskdict[taskno]['responses']:
sp_prob = gzdata['t01_smooth_or_features_a02_features_or_disk_weighted_fraction'] > min_prob
sp = gzdata[sp_prob & taskcount]
datalist.append(var)
#taskcount = gzdata['t01_smooth_or_features_total_weight'] > min_class
#sp_prob = gzdata['t01_smooth_or_features_a02_features_or_disk_weighted_fraction'] > min_prob
#el_prob = gzdata['t01_smooth_or_features_a01_smooth_weighted_fraction'] > min_prob
#sp = gzdata[sp_prob & taskcount]
#el = gzdata[el_prob & taskcount]
#datalist = (sp,el)
#namelist = ('sp','el')
if plot:
fig = plt.figure(1,(14,8))
fig.clf()
for index, (mdata, mname) in enumerate(zip(datalist,namelist)):
redshift = mdata['REDSHIFT']
mr = mdata['PETROMAG_MR']
r50_kpc = mdata['PETROR50_R_KPC']
r90 = mdata['PETROR90_R']
# Mask the data where no values exist
redshift_mask = np.isfinite(redshift)
# Add masks for magnitude, size, and surface brightness limits
dmod = cosmology.dmod_flat(redshift[redshift_mask])
ang_scale = cosmology.ang_scale_flat(redshift[redshift_mask])
absmag_lim = 17.77 - dmod
surfacebrightness_app_lim = 23.0
r90_kpc = r90[redshift_mask] * ang_scale
magnitude_mask = mr[redshift_mask] > (absmag_lim - 1.0)
size_mask = r90_kpc < (3.0 * ang_scale)
surfacebrightness_mask = (mr[redshift_mask] + dmod + 2.5*np.log10(6.283185*(r50_kpc[redshift_mask]/ang_scale)**2)) > (surfacebrightness_app_lim - 1.0)
totalmask = magnitude_mask | size_mask | surfacebrightness_mask
print '%s galaxies removed from %s sample due to magnitude cutoff'%(np.sum(magnitude_mask.astype(int)),mname)
print '%s galaxies removed from %s sample due to angular size cutoff'%(np.sum(size_mask.astype(int)),mname)
print '%s galaxies removed from %s sample due to surface brightness cutoff'%(np.sum(surfacebrightness_mask.astype(int)),mname)
print '%s percent of the total'%(np.sum(totalmask.astype(float))/len(redshift_mask) * 100)
print ' '
z_masked = redshift[redshift_mask]
m_masked = mr[redshift_mask]
r_masked = r50_kpc[redshift_mask]
zbins = np.arange(zmin, zmax, zstep)
magbins = np.linspace(magmin, magmax, mag_numbins)
sizebins = np.linspace(sizemin, sizemax, size_numbins)
h, edges = np.histogramdd((z_masked, m_masked, r_masked),bins=(zbins,magbins,sizebins))
if plot:
ax = fig.add_subplot(1,2,index)
imarr = np.squeeze(h[3:4,:,:]) # Pick an example redshift range to slice on
im = ax.imshow(imarr, extent=(-24,-16,0,15), interpolation="nearest", origin='lower')
ax.set_title(mname)
ax.set_xlabel(r'$M_R [mag]$')
ax.set_ylabel(r'$R_{50} [kpc]$')
ax.set_aspect('auto')
cb = plt.colorbar(im)
ax.set_xlim(min(magbins),max(magbins))
ax.set_ylim(min(sizebins),max(sizebins))
edges_redshift = edges[0]
edges_mag = edges[1]
edges_size = edges[2]
centers_redshift = edges_redshift[:-1] + (edges_redshift[1]-edges_redshift[0])/2.
centers_mag = edges_mag[:-1] + (edges_mag[1]-edges_mag[0])/2.
centers_size = edges_size[:-1] + (edges_size[1]-edges_size[0])/2.
col1_edge = pyfits.Column(name = 'edges', format='E', array=edges_redshift)
col2_edge = pyfits.Column(name = 'edges', format='E', array=edges_mag)
col3_edge = pyfits.Column(name = 'edges', format='E', array=edges_size)
col1_centers = pyfits.Column(name = 'centers', format='E', array=centers_redshift)
col2_centers = pyfits.Column(name = 'centers', format='E', array=centers_mag)
col3_centers = pyfits.Column(name = 'centers', format='E', array=centers_size)
primary_hdu = pyfits.PrimaryHDU(h)
hdulist = pyfits.HDUList([primary_hdu])
tb1_hdu = pyfits.new_table(pyfits.ColDefs([col1_edge]))
tb1_hdu.name = 'REDSHIFT_BIN_EDGES'
tb2_hdu = pyfits.new_table(pyfits.ColDefs([col2_edge]))
tb2_hdu.name = 'MR_BIN_EDGES'
tb3_hdu = pyfits.new_table(pyfits.ColDefs([col3_edge]))
tb3_hdu.name = 'R50_KPC_BIN_EDGES'
tb4_hdu = pyfits.new_table(pyfits.ColDefs([col1_centers]))
tb4_hdu.name = 'REDSHIFT_BIN_CENTERS'
tb5_hdu = pyfits.new_table(pyfits.ColDefs([col2_centers]))
tb5_hdu.name = 'MR_BIN_CENTERS'
tb6_hdu = pyfits.new_table(pyfits.ColDefs([col3_centers]))
tb6_hdu.name = 'R50_KPC_BIN_CENTERS'
hdulist.append(tb1_hdu)
hdulist.append(tb2_hdu)
hdulist.append(tb3_hdu)
hdulist.append(tb4_hdu)
hdulist.append(tb5_hdu)
hdulist.append(tb6_hdu)
hdulist.writeto(fits_path+'task%s_%s'%(taskno,mname)+'binned_counts.fits',clobber=True)
p.close()
return None
def plot_ratio_baseline_fit(fitbins=19, kernel_size = 3, plot_contour=False):
ratio_baseline_fit = pyfits.getdata(fits_path+'local_ratio_baseline_fit.fits')
f_counts = fits_path+'el_binned_counts.fits'
p_counts = pyfits.open(f_counts)
zbins = p_counts['REDSHIFT_BIN_CENTERS'].data['centers']
magbins = p_counts['MR_BIN_CENTERS'].data['centers']
sizebins = p_counts['R50_KPC_BIN_CENTERS'].data['centers']
zedges = p_counts['REDSHIFT_BIN_EDGES'].data['edges']
magedges = p_counts['MR_BIN_EDGES'].data['edges']
sizeedges = p_counts['R50_KPC_BIN_EDGES'].data['edges']
ratio_baseline_masked = pickle.load(open(gz_path+'local_ratio_baseline_masked.pkl','rb'))
ratio_baseline_fit_masked = ma.array(ratio_baseline_fit,mask=ratio_baseline_masked.mask)
kernel = np.ones((kernel_size,kernel_size),dtype=int)
convarr = np.logical_not(ratio_baseline_masked.mask)
bw = convolve2d(np.logical_not(ratio_baseline_masked.mask).astype(int),
kernel,mode='same').astype(bool).astype(int) * -1
fig = plt.figure(4)
fig.clf()
ax = fig.add_subplot(111)
# Plot the best fit function as a semitransparent layer
pfit, pfit_err = pickle.load(file(fits_path+'ratio_baseline_fit.pickle', 'r'))
magbinsplot = np.linspace(magbins[0],magbins[-1],fitbins)
sizebinsplot = np.linspace(sizebins[0],sizebins[-1],fitbins)
fitarray_fn = ratio_function(pfit, magbinsplot, sizebinsplot)
fit_extent=(magedges[0],magedges[-1],sizeedges[0],sizeedges[-1])
imf = ax.imshow(fitarray_fn,
alpha=1.0,
extent = fit_extent,
vmin = -2., vmax = 2.,
interpolation='nearest', origin='lower')
cb = plt.colorbar(imf)
cb.set_label(r'log$_{10}(n_{el}/n_{sp})$',fontsize=20)
# Plot the local masked baseline relation as opaque layer
masked_extent = fit_extent
cmap = cm.jet
cmap.set_bad(alpha=0.0)
im = ax.imshow(ratio_baseline_masked,
alpha=0.5,
extent = masked_extent,
vmin = -2., vmax=2.,
interpolation='nearest',origin='lower')
# Plot the outline of the local region.
# This plots the contour, but not in the right region (or quite the right size)
if plot_contour:
cont = plt.contour(magbins, sizebins, bw,
levels = [-1],
color='w',
linewidths=3, linestyles='dashed')
zc = cont.collections[0]
zc.set_color('w')
SBapp = 23.0
appmag_lim = 17.0
SBlim_size = np.arange(200) / 10.0
SBlim_mag = (SBapp - cosmology.dmod_flat(np.mean(zbins))- 2.5*np.log10(6.283185*(SBlim_size/cosmology.ang_scale_flat(np.mean(zbins)))**2))
absmag_lim = appmag_lim - cosmology.dmod_flat(np.mean(zbins))
r50_lim = cosmology.ang_scale_flat(np.mean(zbins))
ax.autoscale(False)
ax.plot(SBlim_mag, SBlim_size,'w--')
ax.plot(np.zeros(2)+absmag_lim, np.array([sizeedges[0],sizeedges[-1]]),'w--')
ax.plot(np.array([magedges[0],magedges[-1]]), np.zeros(2)+r50_lim,'w--')
# Set general axes properties
ax.set_xlabel(r'$M_R [mag]$',fontsize=22)
ax.set_ylabel(r'$R_{50} [kpc]$',fontsize=22)
ax.set_aspect('auto')
rc(('xtick','ytick'), labelsize=12)
plt.show()
return None
def plot_ratio_baseline(plot_mag_lims=False):
f_ratio = fits_path+'local_ratio_baseline.fits'
f_counts = fits_path+'el_binned_counts.fits'
p_ratio = pyfits.open(f_ratio)
p_counts = pyfits.open(f_counts)
zbins = p_counts['REDSHIFT_BIN_CENTERS'].data['centers']
magbins = p_counts['MR_BIN_CENTERS'].data['centers']
sizebins = p_counts['R50_KPC_BIN_CENTERS'].data['centers']
zedges = p_counts['REDSHIFT_BIN_EDGES'].data['edges']
magedges = p_counts['MR_BIN_EDGES'].data['edges']
sizeedges = p_counts['R50_KPC_BIN_EDGES'].data['edges']
ratio_baseline_masked = pickle.load(open(gz_path+'local_ratio_baseline_masked.pkl','rb'))
counts_baseline_masked = pickle.load(open(gz_path+'local_counts_baseline_masked.pkl','rb'))
titlenames = ('counts','ratio')
labelnames = (r'$N_{el} + N_{sp}$',r'$log_{10}(N_{el}/N_{sp})$')
fig = plt.figure(1,(14,8))
fig.clf()
for index, data in enumerate((np.sum(counts_baseline_masked, axis=2), ratio_baseline_masked)):
ax = fig.add_subplot(1,2,index+1,aspect=1)
cmap = cm.jet
cmap.set_bad('k')
#imextent=(magbins[0],magbins[-1],sizebins[0],sizebins[-1])
imextent=(magedges[0],magedges[-1],sizeedges[0],sizeedges[-1])
im = ax.imshow(data,
vmin = -2., vmax=2.,
extent=imextent,
interpolation='nearest',origin='lower')
ax.set_title('sp/el '+titlenames[index])
ax.set_xlabel(r'$M_R [mag]$',fontsize=22)
ax.set_ylabel(r'$R_{50} [kpc]$',fontsize=22)
ax.set_aspect('auto')
rc(('xtick','ytick'), labelsize=12)
cb = plt.colorbar(im,orientation='vertical')
cb.set_label(labelnames[index],fontsize=16)
SBapp = 23.0
appmag_lim = 17.0
SBlim_size = np.arange(200) / 10.0
SBlim_mag = (SBapp - cosmology.dmod_flat(np.mean(zbins))- 2.5*np.log10(6.283185*(SBlim_size/cosmology.ang_scale_flat(np.mean(zbins)))**2))
absmag_lim = appmag_lim - cosmology.dmod_flat(np.mean(zbins))
absmag_lim_loz = appmag_lim - cosmology.dmod_flat(0.0005)
absmag_lim_hiz = appmag_lim - cosmology.dmod_flat(0.25)
r50_lim = cosmology.ang_scale_flat(np.mean(zbins))
ax.autoscale(False)
ax.plot(SBlim_mag, SBlim_size,'w--')
ax.plot(np.zeros(2)+absmag_lim, np.array([sizeedges[0],sizeedges[-1]]),'w--')
if plot_mag_lims:
ax.plot(np.zeros(2)+absmag_lim_loz, np.array([sizeedges[0],sizeedges[-1]]),'g--')
ax.plot(np.zeros(2)+absmag_lim_hiz, np.array([sizeedges[0],sizeedges[-1]]),'g--')
ax.plot(np.array([magedges[0],magedges[-1]]), np.zeros(2)+r50_lim,'w--')
plt.show()
#ax.set_xlim(magedges[0],magedges[-1])
#ax.set_ylim(sizeedges[0],sizeedges[-1])
p_ratio.close()
p_counts.close()
return None
def create_binned_data(min_classifications=20,
#min_prob=0.8,
zmin = 0.01, zmax = 0.16, zstep = 0.01,
magmin = -24, magmax = -16, mag_numbins = 20,
sizemin = 0, sizemax = 15, size_numbins = 20,
plot=False
):
p = pyfits.open(gz2both_data_file)
gzdata = p[1].data
# Find galaxies with sufficient classifications on bulge prominence question
taskcount = gzdata['t05_bulge_prominence_total_weight'] > min_classifications
bulge = gzdata[taskcount]
print ''
print '%s bulge galaxies above threshold'%len(bulge)
print ''
bulgestat = compute_wbp(bulge)
lobulge = bulge[bulgestat < 0.5]
hibulge = bulge[bulgestat >= 0.5]
datalist = [lobulge, hibulge]
namelist = ['lobulge','hibulge']
if plot:
fig = plt.figure(1,(14,8))
fig.clf()
for index, (mdata, mname) in enumerate(zip(datalist,namelist)):
redshift = mdata['REDSHIFT']
mr = mdata['PETROMAG_MR']
r50_kpc = mdata['PETROR50_R_KPC']
r90 = mdata['PETROR90_R']
# Mask the data where no values exist
redshift_mask = np.isfinite(redshift)
# Add masks for magnitude, size, and surface brightness limits
dmod = cosmology.dmod_flat(redshift[redshift_mask])
ang_scale = cosmology.ang_scale_flat(redshift[redshift_mask])
absmag_lim = 17.77 - dmod
surfacebrightness_app_lim = 23.0
r90_kpc = r90[redshift_mask] * ang_scale
magnitude_mask = mr[redshift_mask] > (absmag_lim - 1.0)
size_mask = r90_kpc < (3.0 * ang_scale)
surfacebrightness_mask = (mr[redshift_mask] + dmod + 2.5*np.log10(6.283185*(r50_kpc[redshift_mask]/ang_scale)**2)) > (surfacebrightness_app_lim - 1.0)
totalmask = magnitude_mask | size_mask | surfacebrightness_mask
print '%s galaxies removed from %s sample due to magnitude cutoff'%(np.sum(magnitude_mask.astype(int)),mname)
print '%s galaxies removed from %s sample due to angular size cutoff'%(np.sum(size_mask.astype(int)),mname)
print '%s galaxies removed from %s sample due to surface brightness cutoff'%(np.sum(surfacebrightness_mask.astype(int)),mname)
print '%s percent of the total'%(np.sum(totalmask.astype(float))/len(redshift_mask) * 100)
print ' '
z_masked = redshift[redshift_mask]
m_masked = mr[redshift_mask]
r_masked = r50_kpc[redshift_mask]
zbins = np.arange(zmin, zmax, zstep)
magbins = np.linspace(magmin, magmax, mag_numbins)
sizebins = np.linspace(sizemin, sizemax, size_numbins)
h, edges = np.histogramdd((z_masked, m_masked, r_masked),bins=(zbins,magbins,sizebins))
if plot:
ax = fig.add_subplot(1,2,index)
imarr = np.squeeze(h[3:4,:,:]) # Pick an example redshift range to slice on
im = ax.imshow(imarr, extent=(magmin,magmax,sizemin,sizemax), interpolation="nearest", origin='lower')
ax.set_title(mname)
ax.set_xlabel(r'$M_R [mag]$')
ax.set_ylabel(r'$R_{50} [kpc]$')
ax.set_aspect('auto')
cb = plt.colorbar(im)
ax.set_xlim(min(magbins),max(magbins))
ax.set_ylim(min(sizebins),max(sizebins))
edges_redshift = edges[0]
edges_mag = edges[1]
edges_size = edges[2]
centers_redshift = edges_redshift[:-1] + (edges_redshift[1]-edges_redshift[0])/2.
centers_mag = edges_mag[:-1] + (edges_mag[1]-edges_mag[0])/2.
centers_size = edges_size[:-1] + (edges_size[1]-edges_size[0])/2.
col1_edge = pyfits.Column(name = 'edges', format='E', array=edges_redshift)
col2_edge = pyfits.Column(name = 'edges', format='E', array=edges_mag)
col3_edge = pyfits.Column(name = 'edges', format='E', array=edges_size)
col1_centers = pyfits.Column(name = 'centers', format='E', array=centers_redshift)
col2_centers = pyfits.Column(name = 'centers', format='E', array=centers_mag)
col3_centers = pyfits.Column(name = 'centers', format='E', array=centers_size)
primary_hdu = pyfits.PrimaryHDU(h)
hdulist = pyfits.HDUList([primary_hdu])
tb1_hdu = pyfits.new_table(pyfits.ColDefs([col1_edge]))
tb1_hdu.name = 'REDSHIFT_BIN_EDGES'
tb2_hdu = pyfits.new_table(pyfits.ColDefs([col2_edge]))
tb2_hdu.name = 'MR_BIN_EDGES'
tb3_hdu = pyfits.new_table(pyfits.ColDefs([col3_edge]))
tb3_hdu.name = 'R50_KPC_BIN_EDGES'
tb4_hdu = pyfits.new_table(pyfits.ColDefs([col1_centers]))
tb4_hdu.name = 'REDSHIFT_BIN_CENTERS'
tb5_hdu = pyfits.new_table(pyfits.ColDefs([col2_centers]))
tb5_hdu.name = 'MR_BIN_CENTERS'
tb6_hdu = pyfits.new_table(pyfits.ColDefs([col3_centers]))
tb6_hdu.name = 'R50_KPC_BIN_CENTERS'
hdulist.append(tb1_hdu)
hdulist.append(tb2_hdu)
hdulist.append(tb3_hdu)
hdulist.append(tb4_hdu)
hdulist.append(tb5_hdu)
hdulist.append(tb6_hdu)
hdulist.writeto(fits_path+'%s_'%mname+'binned_counts.fits',clobber=True)
p.close()
return None
