def main():
if len(sys.argv) > 1:
catalogue = sys.argv[1]
else:
catalogue = 'Mr19_age_distribution_matching_mock'
#open the ra,dec mock
filepath = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
filename = catalogue + '_radec_mock.dat'
mock_radec = ascii.read(filepath + filename,
delimiter='\s',
Reader=ascii.Basic,
data_start=1)
mock_radec = np.array(mock_radec)
for name in mock_radec.dtype.names:
print '\t', name
#open full mock
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
print 'opening mock catalogue:', catalogue + '.hdf5'
#open catalogue
f1 = h5py.File(filepath_mock + catalogue + '.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue)
mock = np.array(mock)
for name in mock.dtype.names:
print '\t', name
app_mag_lim = 17.77
Lbox = 250.0 #Mpc/h
cosmo = cosmology.core.FlatLambdaCDM(H0=100.0, Om0=0.27) #h=1
c = 299792.458 #speed of light in km/s
#determine abs mag limit as afunction of z to overplot
app_mag_r_lim = 17.77
z_bins = np.arange(1.0, 0.001, -0.001)
dm = dist_mod(z_bins, cosmo)
abs_mag_r_lim = app_mag_r_lim - dm
f_r = interp1d(abs_mag_r_lim,
z_bins,
kind='cubic',
bounds_error=False,
fill_value=max(z_bins))
z_lim = f_r(-19)
print z_lim
plt.figure()
plt.plot(mock_radec['z'], mock['M_r,0.1'][mock_radec['k']], '.', ms=2)
plt.plot(z_bins, abs_mag_r_lim, color='green')
plt.plot([0, z_lim], [-19, -19], color='red')
plt.plot([z_lim, z_lim], [-19, -24], color='red')
plt.plot([0.02, 0.02], [-19, -24], color='red')
plt.plot([0.068, 0.068], [-19, -24], '--', color='red')
plt.ylim([-18.5, -23])
plt.xlim([0, 0.1])
plt.show()
def get_gc_path(groupcat):
if groupcat=='berlind':
s = cu.get_output_path() + 'processed_data/berlind_groupcat/mock_runs/4th_run/custom_catalogues/'
if groupcat=='tinker':
s = cu.get_output_path() + 'processed_data/tinker_groupcat/mock_runs/4th_run/custom_catalogues/'
if groupcat=='yang':
s = cu.get_output_path() + 'processed_data/yang_groupcat/mock_runs/4th_run/version_2/custom_catalogues/'
return s
def get_mock(dm_sim):
"""
load dark matter simulation halo catalogue and make completeness cuts
"""
if dm_sim=='Bolshoi_250':
#open Bolshoi halo catalogue
box_size=250
filepath = cu.get_output_path() + 'processed_data/Multidark/Bolshoi/halo_catalogues/'
halo_catalogue = 'hlist_1.00030.list'
f = h5py.File(filepath+halo_catalogue+'.hdf5', 'r')
HC = f.get(halo_catalogue) #halo catalogue
#for name in HC.dtype.names: print(name)
elif dm_sim=='Chinchilla_125':
#open Chinchilla halo catalogue
box_size=125
filepath = cu.get_output_path() + 'processed_data/Chinchilla/halo_catalogues/Lb125/'
halo_catalogue = 'hlist_1.00000.list'
f = h5py.File(filepath+halo_catalogue+'.hdf5', 'r')
HC = f.get(halo_catalogue) #halo catalogue
#for name in HC.dtype.names: print(name)
elif dm_sim=='Chinchilla_250':
#open Chinchilla halo catalogue
box_size=125
filepath = cu.get_output_path() + 'processed_data/Chinchilla/halo_catalogues/Lb250/'
halo_catalogue = 'hlist_1.00000.list'
f = h5py.File(filepath+halo_catalogue+'.hdf5', 'r')
HC = f.get(halo_catalogue) #halo catalogue
#for name in HC.dtype.names: print(name)
else: print('simulation not available.')
if dm_sim=='Bolshoi_250':
mp_bolshoi = 1.35e8
mpeak_cut = mp_bolshoi*200.0
keep = (HC['Mpeak']>mpeak_cut)
HC = HC[keep]
#keep = (HC['vmax']>50.0)
#HC = HC[keep]
#print("number of (sub-)haloes in catalogue after cuts: {0}".format(len(HC)))
if dm_sim=='Chinchilla_125':
mp_bolshoi = 1.35e8
mp_chinchilla = mp_bolshoi/8.0
mpeak_cut = mp_chinchilla*200.0
keep = (HC['Mpeak']>mpeak_cut)
HC = HC[keep]
#keep = (HC['vmax']>50.0)
#HC = HC[keep]
#print("number of (sub-)haloes in catalogue after cuts: {0}".format(len(HC)))
if dm_sim=='Chinchilla_250':
mp_bolshoi = 1.35e8
mpeak_cut = mp_bolshoi*200.0
keep = (HC['Mpeak']>mpeak_cut)
HC = HC[keep]
#keep = (HC['vmax']>50.0)
#HC = HC[keep]
#print("number of (sub-)haloes in catalogue after cuts: {0}".format(len(HC)))
return HC, box_size
def main():
filepath = cu.get_output_path() + 'processed_data/CFHTLens/random_catalogues/'
savepath = cu.get_output_path() + 'processed_data/CFHTLens/random_catalogues/'
field1=sys.argv[1]
field=field1.lower()
print field1, field
filenames = os.listdir(filepath)
filenames = fnmatch.filter(filenames, field+'*.npy')
dtype = [('ra',float),('dec',float),('flag',int),('num_tile',int)]
randoms = np.recarray((0,), dtype = dtype)
#open up all the tile random catalogues and combine
for filename in filenames:
print filename
tile_randoms = np.load(filepath+filename)
randoms = np.hstack((randoms,tile_randoms))
print 'maximum number of tiles a random falls in:', max(randoms['num_tile'])
keep = np.where(np.logical_not((randoms['flag']==8192) & (randoms['num_tile']>1)))[0]
randoms = randoms[keep]
print len(randoms)
'''
#where tiles overlap, take a proportional number of randoms
#2 tiles overlap
result_2 = np.where(randoms['num_tile']==2)[0] #identify randoms
ran_int_2 = np.random.random_integers(1,2,len(result_2)) #roll a dice to keep it
keep_2 = np.where(ran_int_2==1)[0] #keep 1/(N tiles) of the points
keep_2 = result_2[keep_2]
#3 tiles overlap
result_3 = np.where(randoms['num_tile']==3)[0] #identify randoms
ran_int_3 = np.random.random_integers(1,3,len(result_3)) #roll a dice to keep it
keep_3 = np.where(ran_int_3==1)[0] #keep 1/(N tiles) of the points
keep_3 = result_3[keep_3]
#4 tiles overlap
result_4 = np.where(randoms['num_tile']==4)[0] #identify randoms
ran_int_4 = np.random.random_integers(1,4,len(result_4)) #roll a dice to keep it
keep_4 = np.where(ran_int_4==1)[0] #keep 1/(N tiles) of the points
keep_4 = result_4[keep_4]
#generate master list of randoms to keep
keep = np.where(randoms['num_tile']==1)[0] #all points in non-pverlap regions
keep = np.hstack((keep, keep_2, keep_3, keep_4)) #combine the "keep" lists
randoms = randoms[keep]
print len(randoms)
'''
np.save(savepath+field1+'_randoms', randoms)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directories
filepath_1 = cu.get_output_path() + 'processed_data/mpa_dr7/'
filepath_2 = cu.get_output_path() + 'processed_data/NYU_VAGC/'
#################################################################
catalogue_1 = 'mpa_dr7_unique'
catalogue_2 = 'nyu_vagc_dr7'
f_1 = h5py.File(filepath_1 + catalogue_1 + '.hdf5', 'r')
dset_1 = f_1.get(catalogue_1)
dset_1 = np.array(dset_1)
#print(dset_1.dtype.names)
f_2 = h5py.File(filepath_2 + catalogue_2 + '.hdf5', 'r')
dset_2 = f_2.get(catalogue_2)
dset_2 = np.array(dset_2)
#print(dset_2.dtype.names)
#open matching files. these are created with the 'match_to_NYU_VAGC.py' script.
filename = 'matches_into_nyu_vagc.npy'
match_1 = np.load(filepath_1 + 'matches/' + filename)
filename = 'nyu_vagc_matched_to_mpa.npy'
match_2 = np.load(filepath_1 + 'matches/' + filename)
N_col_1 = len(dset_1.dtype.descr)
N_col_2 = len(
dset_2.dtype.descr[8:]) #number of columns used from NYU VAGC
names_1 = dset_1.dtype.names
names_2 = dset_2.dtype.names[8:]
dtype = np.dtype(dset_1.dtype.descr + dset_2.dtype.descr[8:])
combined_data = np.recarray((len(dset_1), ), dtype=dtype)
combined_data.fill(-99)
for name in names_1:
print(name)
combined_data[name] = dset_1[name]
for name in names_2:
print(name)
combined_data[name][match_1] = dset_2[name][match_2]
filename = 'mpa_nyu_vagc_dr7'
savepath = filepath_1
f = h5py.File(savepath + filename + '.hdf5', 'w')
dset = f.create_dataset(filename, data=combined_data)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directories
filepath_2 = cu.get_output_path() + 'processed_data/mpa_dr7/'
filepath_1 = cu.get_output_path() + 'processed_data/NYU_VAGC/'
#################################################################
catalogue_2 = 'mpa_dr7_unique'
catalogue_1 = 'nyu_vagc_dr7'
f_1 = h5py.File(filepath_1+catalogue_1+'.hdf5', 'r')
dset_1 = f_1.get(catalogue_1)
dset_1 = np.array(dset_1)
print(dset_1.dtype.names)
f_2 = h5py.File(filepath_2+catalogue_2+'.hdf5', 'r')
dset_2 = f_2.get(catalogue_2)
dset_2 = np.array(dset_2)
print(dset_2.dtype.names)
#open matching files. these are created with the 'match_to_NYU_VAGC.py' script.
filename = 'matches_into_mpa_vagc.npy'
match_1 = np.load(filepath_1+'matches/'+filename)
filename = 'mpa_vagc_matched_to_nyu.npy'
match_2 = np.load(filepath_1+'matches/'+filename)
N_col_1 = len(dset_1.dtype.descr)
N_col_2 = len(dset_2.dtype.descr[25:]) #number of columns used from NYU VAGC
names_1 = dset_1.dtype.names
names_2 = dset_2.dtype.names[25:]
dtype = np.dtype(dset_1.dtype.descr + dset_2.dtype.descr[25:])
combined_data = np.recarray((len(dset_1),),dtype=dtype)
combined_data.fill(-99)
for name in names_1:
print(name)
combined_data[name] = dset_1[name]
for name in names_2:
print(name)
combined_data[name][match_1] = dset_2[name][match_2]
filename = 'nyu_mpa_vagc_dr7'
savepath = filepath_1
f = h5py.File(savepath+filename+'.hdf5', 'w')
dset = f.create_dataset(filename, data=combined_data)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath_cat1 = cu.get_output_path() + 'processed_data/yang_groupcat/'
filepath_cat2 = cu.get_output_path() + 'processed_data/mpa_dr7/'
#save data to directory...
savepath1 = filepath_cat1+'mpa_dr7_match/'
savepath2 = filepath_cat2+'yang_groupcat_match/'
#################################################################
catalogues_1=['sample1_L_petro','sample2_L_petro','sample3_L_petro',\
'sample1_M_petro','sample2_M_petro','sample3_M_petro',\
'sample1_L_model','sample2_L_model','sample3_L_model',\
'sample1_M_model','sample2_M_model','sample3_M_model']
catalogues_2=['gal_info_gal_totspecsfr_dr7_v5_2']
for catalogue in catalogues_1:
catalogue1 = catalogue
catalogue2 = catalogues_2[0]
print catalogue1,'match into', catalogue2
f1 = h5py.File(filepath_cat1+catalogue1+'.hdf5', 'r') #open catalogue file
GC = f1.get(catalogue1)
f2 = h5py.File(filepath_cat2+catalogue2+'.hdf5', 'r') #open catalogue file
W = f2.get(catalogue2)
da=2.0*1.0/3600.0 #matching length
result_1 = np.array(cu.spherematch(GC['RAgal'], GC['DECgal'], W['RA'], W['DEC'], tol=da, nnearest=1))
#result_2 = np.array(cu.spherematch(GC['RAgal'], GC['DECgal'], W['RA'], W['DEC'], tol=da, nnearest=2)) #not used
repeats = [item for item, count in Counter(result_1[1]).iteritems() if count > 1] #double matched objects
if len(repeats) > 0:
remove=np.zeros((0,),dtype=np.int) #which entries to remove
for repeat in repeats:
result_a = np.where(result_1[1]==repeat)[0] #find indices of the double matched object into catalogue2
result_b = np.where(result_1[2][result_a]>np.min(result_1[2][result_a]))[0] #find less good matches
result_c = result_a[result_b] #indices of less good matches into catalogue2
remove = np.hstack((remove,result_c)) #indices which should be removed
keep = np.arange(0,len(result_1[0]),1).astype(int)
keep = np.in1d(keep,remove)==False
result = result_1[:,keep]
#unique = np.setdiff1d(result_2[0], result_1[0]) #not used
else: result = result
filename1 = catalogue2+'_'+catalogue1+'_match'
filename2 = catalogue1+'_'+catalogue2+'_match'
np.save(savepath1+filename1, result[1].astype(int))
np.save(savepath2+filename2, result[0].astype(int))
def main():
"""
Make a random catalogue for each tile in a field in the CFHTLens survey.
example: python make_cfhtlens_random_cat.py W3
options: second user input specifies the mask file to use. A "?" argument returns the
list of mask files.
"""
global filepath
global savepath
filepath = cu.get_data_path() + 'CFHTLens/masks/'
savepath = cu.get_output_path() + 'processed_data/CFHTLens/random_catalogues/'
field = sys.argv[1]
print 'field:', field
if len(sys.argv)>2:
filenames = sys.argv[2]
else:
filenames = os.listdir(cu.get_data_path()+'CFHTLens/masks/')
filenames = fnmatch.filter(filenames, field+'*.fits')
# a "?" as the 2nd user input returns the lost of mask files and exits.
if filenames == '?':
filenames = os.listdir(cu.get_data_path()+'CFHTLens/masks/')
filenames = fnmatch.filter(filenames, field+'*.fits')
for filename in filenames: print(filename)
return 0
p = Pool(8)
p.map(do_work,filenames)
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/'
savepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
#################################################################
catalogues=['mr19_groups.fits', 'smthresh10.2.groups.dat', 'smthresh9.8.groups.dat']
filename = catalogues[0]
hdulist = fits.open(filepath+filename, memmap=True)
data = hdulist[1].data
print 'saving as:', savepath+filename[:-5]+'.hdf5'
f = h5py.File(savepath+filename[:-5]+'.hdf5', 'w')
dset = f.create_dataset(filename[:-5], data=data)
f.close()
gc.collect()
names = ['ra','dec','z','groupID','rank','Mstar','Mr','SSFR','Mgroup']
filename = catalogues[1]
data = ascii.read(filepath+filename, guess=True, Reader=ascii.Basic, names=names,data_start=0)
print 'saving as:', savepath+filename[:-4]+'.hdf5'
f = h5py.File(savepath+filename[:-4]+'.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
gc.collect()
filename = catalogues[2]
data = ascii.read(filepath+filename, guess=True, Reader=ascii.Basic, names=names,data_start=0)
print 'saving as:', savepath+filename[:-4]+'.hdf5'
f = h5py.File(savepath+filename[:-4]+'.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
gc.collect()
def main():
filepath = cu.get_data_path() + 'CFHTLens/masks/'
savepath = cu.get_output_path() + 'processed_data/CFHTLens/masks/'
field = 'W1'
filenames = os.listdir('/scratch/dac29/data/CFHTLens/masks/')
filenames = fnmatch.filter(filenames, field + '*.fits')
#for filename in filenames:
filename = filenames[0]
hdulist = fits.open(filepath + filename, memmap=True)
header = hdulist[0].header
print header
nxpix = header['NAXIS1']
nypix = header['NAXIS2']
tile = header['OBJECT']
w = wcs.WCS(hdulist[0].header)
corners = np.array(
[[0, 0], [0, nypix - 1], [nxpix - 1, nypix - 1], [nxpix - 1, 0]],
np.float_)
world = w.wcs_pix2world(corners, 1)
print world
data = hdulist[0].data
hdulist.close()
def main():
catalogue = 'sm_9.49_s0.2_sfr_c0.0_250'
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
#get slice in z
show = (mock['z']<10.0)
for i in range(284,360):
print(i)
fig = plt.figure(figsize=(10.0,10.0))
ax = fig.add_subplot(111, projection='3d')
ax.set_axis_bgcolor('black')
ax._axis3don = False
#fig.subplots_adjust(left=0.2, right=0.82, bottom=0.2, top=0.9)
r = [0, 250]
from itertools import product, combinations
for s, e in combinations(np.array(list(product(r,r,r))), 2):
if np.sum(np.abs(s-e)) == r[1]-r[0]:
ax.plot3D(*zip(s,e), color="white")
p = ax.scatter(mock['x'],mock['y'],mock['z'],c=mock['SSFR'],\
cmap='jet_r',vmax=-9.5, vmin=-12.5, marker='.',\
lw=0, s=3, rasterized=False)
ax.set_xlim([0,250])
ax.set_ylim([0,250])
ax.set_zlim([0,250])
ax.view_init(azim=i)
filepath = '/Users/duncan/Documents/projects/mpeak_vpeak_mock/movies/frames/movie_11/'
filename = str(i).zfill(3)+'_mock_3D.png'
plt.savefig(filepath+filename, dpi=400)
plt.clf()
def main():
catalogue = "sm_9.5_s0.2_sfr_c-1.0_250"
# catalogue = 'sm_9.5_s0.0_sfr_c-1.0_250_cen_shuffle'
# open mock
filepath = cu.get_output_path() + "processed_data/campbell_mocks/"
f = h5py.File(filepath + catalogue + ".hdf5", "r") # open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
print(mock.dtype.names)
hosts = mock["upid"] == -1
subs = mock["upid"] != -1
N = len(mock)
Ncen = np.sum(hosts)
Nsat = np.sum(subs)
print("N galaxies: ", N)
print("N centrals: ", Ncen)
print("N satellites: ", Nsat)
print("N ejected centrals: ", np.sum(mock["ejected"]))
print("N abandoned satellites: ", np.sum(mock["abandoned"]))
print("N ghosted satellites: ", np.sum(mock["ghost"]))
print("satellite fraction: ", Nsat / (1.0 * (Nsat + Ncen)))
def main():
sample='q'
sm_bin='10.0_10.5'
catalogue = 'sm_9.5_s0.2_sfr_c-0.75_250'
#load in fiducial mock
filepath = './'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'_cov.npy'
cov = np.matrix(np.load(filepath+filename))
diag = np.diagonal(cov)
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
filename = 'sm_9.5_s0.2_sfr_c-0.8_Chinchilla_250_wp_fiducial_'+sample+'_'+sm_bin+'.dat'
data = ascii.read(filepath+filename)
rbins = np.array(data['r'])
mu = np.array(data['wp'])
#load in comparison mock
plt.figure()
plt.errorbar(rbins, mu, yerr=np.sqrt(np.diagonal(cov)), color='black')
plt.plot(rbins, wp, color='red')
plt.xscale('log')
plt.yscale('log')
plt.show()
inv_cov = cov.I
Y = np.matrix((wp-mu))
X = Y*inv_cov*Y.T
print(X)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
#################################################################
catalogues=['mr19_groups', 'smthresh10.2.groups', 'smthresh9.8.groups']
filepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
for catalogue in catalogues:
print catalogue
f = h5py.File(filepath+catalogue+'.hdf5', 'r')
dset = f.get(catalogue)
dset = np.array(dset)
print 'length:', len(dset)
for name in dset.dtype.names: print '\t', name
def main():
"""
read in cfhtlens catalogue and check object mask values against fits mask.
"""
import h5py
if len(sys.argv)>1:
field = sys.argv[1]
else: field='W3'
if len(sys.argv)>2:
N_test = sys.argv[2]
else: N_test = 100
#import cfhtlens catalogues
filepath = cu.get_output_path()+'processed_data/CFHTLens/'
f = h5py.File(filepath+'catalogues/'+field+'.hdf5', 'r')
W = f.get(field)
for i in range(0,N_test):
ind = np.random.randint(0,len(W),1)[0]
field = W['field'][ind]
object_mask_value = W['MASK'][ind]
mask_filename = field+'_izrgu_finalmask_mosaic.fits'
filepath = cu.get_data_path()+'CFHTLens/masks/'
data, w = read_mask(filepath, mask_filename)
x,y = get_pixel((W['ALPHA_J2000'],W['DELTA_J2000']),w)
fits_mask_value = data[y,x] #row,column ---> y,x
print(i,object_mask_value,fits_mask_value)
assert object_mask_value==fits_mask_value, "fits mask does not match catalogue mask!"
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/mock_runs/2nd_run/'
savepath = cu.get_output_path(
) + 'processed_data/berlind_groupcat/mock_runs/2nd_run/'
#################################################################
catalogues=['Mr19_age_distribution_matching_mock_cen_shuffle_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_satsys_shuffle_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_cen_shuffle_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satrel_shuffle_radec_mock_groups.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satsys_shuffle_radec_mock_groups.dat']
names = ['IDgroup', 'IDgal', 'ra', 'dec', 'cz']
for i in range(0, len(catalogues)):
filename = catalogues[i]
data = ascii.read(filepath + filename, names=names)
print data
print savepath + filename[:-4] + '.hdf5'
f = h5py.File(savepath + filename[:-4] + '.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
def main():
filepath = cu.get_data_path() + 'Chinchilla/halo_catalogues/'
savepath = cu.get_output_path(
) + 'processed_data/Chinchilla/halo_catalogues/'
catalogue = 'hlist_1.00000.list'
name = ['scale', 'id', 'desc_scale', 'desc_id', 'num_prog', 'pid', 'upid',\
'desc_pid', 'phantom', 'sam_mvir', 'mvir', 'rvir', 'rs', 'vrms',\
'mmp?', 'scale_of_last_MM', 'vmax', 'x', 'y', 'z', 'vx', 'vy', 'vz',\
'Jx', 'Jy', 'Jz', 'Spin', 'Breadth_first_ID', 'Depth_first_ID',\
'Tree_root_ID', 'Orig_halo_ID', 'Snap_num', 'Next_coprogenitor_depthfirst_ID',\
'Last_progenitor_depthfirst_ID', 'Rs_Klypin', 'Mvir_all', 'M200b', 'M200c',\
'M500c', 'M2500c', 'Xoff', 'Voff', 'Spin_Bullock', 'b_to_a', 'c_to_a',\
'A[x]', 'A[y]', 'A[z]', 'T/|U|', 'Macc', 'Mpeak', 'Vacc', 'Vpeak',\
'Halfmass_Scale', 'Acc_Rate_Inst', 'Acc_Rate_100Myr', 'Acc_Rate_1*Tdyn',\
'Acc_Rate_2*Tdyn', 'Acc_Rate_Mpeak', 'Mpeak_Scale', 'Acc_Scale','M4%_Scale']
print 'reading in:', catalogue
filename = catalogue
data = ascii.read(filepath+filename, delimiter='\s', names=name, \
guess=False, Reader=ascii.Basic, data_start = 0)
f = h5py.File(savepath + catalogue + '.hdf5', 'w')
dset = f.create_dataset(catalogue, data=data)
f.close()
filename = catalogue
data_table = table.table.Table(data=data)
ascii.write(data_table, savepath + filename + '.dat')
def main():
catalogue = 'sm_9.49_s0.2_sfr_c0.0_250'
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
#get slice in z
show = (mock['z']<10.0)
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.82, bottom=0.2, top=0.9)
p = plt.scatter(mock['x'][show],mock['y'][show],c=mock['SSFR'][show],\
cmap='jet_r',vmax=-9.5, vmin=-12.5, marker='.',\
lw=0, s=6, rasterized=False)
plt.xlim([0,250])
plt.ylim([0,250])
plt.xlabel(r'$h^{-1} {\rm Mpc}$')
plt.ylabel(r'$h^{-1} {\rm Mpc}$')
cbar_ax = fig.add_axes([0.83, 0.2, 0.02, 0.7]) #xmin, ymin, +dx, +dy
cbar = fig.colorbar(p, cax=cbar_ax)
plt.show()
savepath = cu.get_plot_path()+'/analysis/central_quenching/'
filename = catalogue+'_slice'
fig.savefig(savepath+filename+'.png',dpi=400)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path() + 'processed_data/tinker_groupcat/'
#################################################################
catalogues=['group_dr7_m.star9.7','group_dr7_m.star10.1','group_dr7_m.star10.6','group_dr7_mag.r19']
filepath = cu.get_output_path() + 'processed_data/tinker_groupcat/'
for catalogue in catalogues:
print catalogue
f = h5py.File(filepath+catalogue+'.hdf5', 'r')
dset = f.get(catalogue)
dset = np.array(dset)
print 'length:', len(dset)
for name in dset.dtype.names: print '\t', name
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/mock_runs/3rd_run/'
savepath = cu.get_output_path() + 'processed_data/berlind_groupcat/mock_runs/3rd_run/'
#################################################################
catalogues=['Mr19_age_distribution_matching_mock_cen_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_satsys_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_cen_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satrel_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satsys_shuffle_radec_mock.dat']
#names = ['IDgroup','richness','ra_center','dec_center','z_center','sigma_v','rms_size','foo1','foo1b','IDgal',\
# 'ra','dec','z','foo2','foo3','foo4','foo5','R_proj']
names = ['IDgroup','IDgal','ra','dec','z']
N = len(catalogues)
for i in range(0,N):
filename = catalogues[i]
data = ascii.read(filepath+filename,names=names)
print data
print savepath+filename[:-4]+'.hdf5'
f = h5py.File(savepath+filename[:-4]+'.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
def main():
#set random seed to make mocks reproducible
np.random.seed(1)
#get parameters to make mock
if len(sys.argv)<5:
dm_sim = 'Bolshoi_250'
sfr_cor = -1.0
sm_lim = 9.5
sm_scat = 0.2
else:
sfr_cor = float(sys.argv[1])
sm_lim = float(sys.argv[2])
sm_scat = float(sys.argv[3])
dm_sim = sys.argv[4]
if dm_sim =='Bolshoi_250': box_size=250.0
if dm_sim == 'Chinchilla_250': box_size=250.0
elif dm_sim == 'Chinchilla_125': box_size=125.0
mock = return_mock(sm_lim = sm_lim, sigma = sm_scat, rho = sfr_cor, sim = dm_sim)
#save mock
savepath = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('saving hdf5 version of the extended catalogue...')
filename = 'sm_'+str(sm_lim)+'_s'+str(sm_scat)+'_sfr_c'+str(sfr_cor)+'_'+dm_sim
print(filename)
f = h5py.File(savepath+filename+'.hdf5', 'w')
dset = f.create_dataset(filename, data=mock)
f.close()
def main():
if len(sys.argv)>1:
catalogue = sys.argv[1]
else: catalogue = 'Mr19_age_distribution_matching_mock'
#open the ra,dec mock
filepath = cu.get_output_path()+'processed_data/hearin_mocks/custom_catalogues/'
filename = catalogue+'_radec_mock.dat'
mock_radec = ascii.read(filepath+filename, delimiter='\s', Reader=ascii.Basic, data_start=1)
mock_radec = np.array(mock_radec)
for name in mock_radec.dtype.names: print '\t', name
#open full mock
filepath_mock = cu.get_output_path() + 'processed_data/hearin_mocks/custom_catalogues/'
print 'opening mock catalogue:', catalogue+'.hdf5'
#open catalogue
f1 = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f1.get(catalogue)
mock = np.array(mock)
for name in mock.dtype.names: print '\t', name
app_mag_lim = 17.77
Lbox = 250.0 #Mpc/h
cosmo = cosmology.core.FlatLambdaCDM(H0=100.0,Om0=0.27) #h=1
c = 299792.458 #speed of light in km/s
#determine abs mag limit as afunction of z to overplot
app_mag_r_lim = 17.77
z_bins = np.arange(1.0,0.001,-0.001)
dm = dist_mod(z_bins,cosmo)
abs_mag_r_lim = app_mag_r_lim - dm
f_r = interp1d(abs_mag_r_lim, z_bins, kind='cubic', bounds_error=False, fill_value=max(z_bins))
z_lim = f_r(-19)
print z_lim
plt.figure()
plt.plot(mock_radec['z'],mock['M_r,0.1'][mock_radec['k']],'.',ms=2)
plt.plot(z_bins,abs_mag_r_lim,color='green')
plt.plot([0,z_lim],[-19,-19],color='red')
plt.plot([z_lim,z_lim],[-19,-24],color='red')
plt.plot([0.02,0.02],[-19,-24],color='red')
plt.plot([0.068,0.068],[-19,-24],'--',color='red')
plt.ylim([-18.5,-23])
plt.xlim([0,0.1])
plt.show()
def main():
if len(sys.argv)>1: catalogue = sys.argv[1]
else: catalogue = 'Mr19_age_distribution_matching_mock'
#open mock catalogue
filepath_mock = cu.get_output_path() + 'processed_data/hearin_mocks/custom_catalogues/'
print 'opening mock catalogue:', catalogue+'.hdf5'
f1 = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock1 = f1.get(catalogue)
#open mock catalogue
filepath_mock = cu.get_output_path() + 'processed_data/hearin_mocks/ideal_groups/'
catalogue = catalogue+'_groups'
print 'opening mock catalogue:', catalogue+'.hdf5'
f2 = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock2 = f2.get(catalogue)
ind = np.where(mock2['MGROUP']==-99.9)[0]
print len(ind)
#sys.exit()
plt.figure()
plt.plot(mock2['MGROUP'], mock2['HALO_M'],'.', ms=1, alpha=0.25)
plt.xlim([10,16])
plt.show()
print min(mock2['RANK']), max(mock2['RANK'])
print min(mock2['HALO_RANK']), max(mock2['HALO_RANK'])
b_sats = np.where(mock2['HALO_RANK']==1)[0]
print len(np.where(mock2['HALO_RANK']==-99)[0])
print len(np.where(mock2['HALO_RANK']==0)[0])
print len(np.where(mock2['HALO_RANK']==1)[0])
dM = mock2['M_r,0.1'][b_sats]-mock2['M_r,0.1'][mock2['CEN_IND'][b_sats]]
print dM
mag_bins = np.arange(-5,2,0.1)
mag_bin_centers = (mag_bins[:-1]+mag_bins[1:])/2.0
print mag_bin_centers
result = np.histogram(dM, bins=mag_bins)[0]
plt.figure()
plt.plot(mag_bin_centers,result)
plt.show()
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
#################################################################
catalogues = ['mr19_groups', 'smthresh10.2.groups', 'smthresh9.8.groups']
filepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
for catalogue in catalogues:
print catalogue
f = h5py.File(filepath + catalogue + '.hdf5', 'r')
dset = f.get(catalogue)
dset = np.array(dset)
print 'length:', len(dset)
for name in dset.dtype.names:
print '\t', name
def main():
sm_min=9.5
sm_max=10.0
#open mock
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250'
catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250_cen_shuffle'
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250_cen_sat_shuffle'
#catalogue = 'sm_8.5_s0.2_sfr_c-1.0_125'
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
Lbox = np.array([250.0, 250.0, 250.0])
#define centrals and satellites
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
#define quenched and star-forming
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
#define samples
red_host = host & red
blue_host = host & blue
#define sm threshold for central galaxies
sm_bin = (mock['Mstar']>sm_min) & (mock['Mstar']<sm_max)
red_host = red_host & sm_bin
blue_host = blue_host & sm_bin
#count pairs
rbins = np.linspace(-1,1.25,20)
rbins = 10**rbins
rbin_centers = (rbins[1:]+rbins[:-1])/2.0
data0 = np.vstack((mock['x'], mock['y'], mock['z'])).T
data1 = np.vstack((mock['x'], mock['y'], mock['z'])).T[red_host]
data2 = np.vstack((mock['x'], mock['y'], mock['z'])).T[red]
data3 = np.vstack((mock['x'], mock['y'], mock['z'])).T[blue]
weights = mock['SSFR']
result = clustering.marked_tpcf(data0, rbins, marks1=weights, wfunc=10, period=Lbox)
print(result)
plt.figure()
plt.plot(rbin_centers, result)
plt.xscale('log')
plt.show()
def main():
catalogues = ['sm_9.5_s0.2_sfr_c-1.0_250','sm_9.5_s0.2_sfr_c-0.75_250',\
'sm_9.5_s0.2_sfr_c-0.5_250','sm_9.5_s0.2_sfr_c-0.25_250',\
'sm_9.5_s0.2_sfr_c0.0_250']
rhos = [-1.0,-0.75,-0.5,-0.25,0.0]
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.85, bottom=0.2, top=0.9)
alphas = [1.0,0.8,0.6,0.4,0.2]
p1s =[]
p2s =[]
for i, catalogue in enumerate(catalogues):
#open mock
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('opening mock catalogue:', catalogue+'.hdf5')
#open catalogue
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
print(mock.dtype.names)
#define host haloes and subhaloes
hosts = (mock['upid']==-1)
subs = (mock['upid']!=-1)
#define SF and quenched subsamples
LHS = -11.0
blue = np.where(mock['SSFR']>LHS)[0]
red = np.where(mock['SSFR']<LHS)[0]
#calculate satellite fraction
bins = np.arange(9.5,12,0.1)
bin_centers = (bins[:-1]+bins[1:])/2.0
f_cen_red = cu.f_prop(mock['Mstar'],bins,red,blue,hosts)
f_sat_red = cu.f_prop(mock['Mstar'],bins,red,blue,subs)
p1, = plt.plot(bin_centers,f_sat_red, color='orange', alpha=alphas[i])
p2, = plt.plot(bin_centers,f_cen_red, color='green', alpha=alphas[i])
p1s.append(p1)
p2s.append(p2)
rhos = (r'$\rho=-1.0$',r'$\rho=-0.75$',r'$\rho=-0.5$',r'$\rho=-0.25$',r'$\rho=0.0$')
plt.legend(tuple(p1s),rhos, frameon=False, fontsize=10.0, loc=4, title='satellites',labelspacing=0.01)
plt.xlim([9.5,11.5])
plt.ylim([0,1])
plt.xlabel(r'$M_{\rm *} [h^{-2}M_{\odot}]$')
plt.ylabel(r'$f_{\rm q}$')
plt.show()
savepath = cu.get_plot_path()+'/analysis/central_quenching/'
filename = 'sat_quenching_rho_effect'
fig.savefig(savepath+filename+'.pdf')
def main():
catalogue = 'Mr19_age_distribution_matching_mock_radec_mock'
filepath = cu.get_output_path()+'processed_data/yang_groupcat/mock_runs/4th_run/'
f = h5py.File(filepath+catalogue+'.hdf5', 'r')
GC = f.get(catalogue)
print GC.dtype.names
print len(GC)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directories
filepath_1 = cu.get_output_path() + 'processed_data/mpa_dr7/'
filepath_2 = cu.get_output_path() + 'processed_data/NYU_VAGC/'
#################################################################
catalogue_1 = 'mpa_dr7_unique'
catalogue_2 = 'nyu_vagc_dr7'
f_1 = h5py.File(filepath_1 + catalogue_1 + '.hdf5', 'r')
dset_1 = f_1.get(catalogue_1)
dset_1 = np.array(dset_1)
#print(dset_1.dtype.names)
f_2 = h5py.File(filepath_2 + catalogue_2 + '.hdf5', 'r')
dset_2 = f_2.get(catalogue_2)
dset_2 = np.array(dset_2)
#print(dset_2.dtype.names)
print("number of objects in MPA-JHU catalogue: {0}".format(len(dset_1)))
print("number of objects in NYU catalogue: {0}".format(len(dset_2)))
da = 1 / 3600.0 * 2.0 #match length is 2"
ind1, ind2, ds = cu.spherematch(dset_1['RA'], dset_1['DEC'],\
dset_2['RA'], dset_2['DEC'],\
tol=da, nnearest=1)
print("minimum angular seperation: {0}''".format(np.min(ds) * 3600.0))
print("maximum angular seperation: {0}''".format(np.max(ds) * 3600.0))
print("number of matchs: {0}".format(len(ind1)))
print(dset_1[ind1]['Z'])
print(dset_2[ind2]['Z'])
#save matching file
filename = 'matches_into_nyu_vagc'
np.save(filepath_1 + 'matches/' + filename, ind1)
filename = 'nyu_vagc_matched_to_mpa'
np.save(filepath_1 + 'matches/' + filename, ind1)
def main():
catalogue = 'sm9.8_age_matching_SFR_mock'
sm_lim = 9.5
filepath_mock = cu.get_output_path() + 'processed_data/hearin_mocks/'
print('opening mock catalogue:', catalogue+'.hdf5')
#open catalogue
f1 = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f1.get(catalogue)
print(mock.dtype.names)
new_mock = get_mock_array(mock)
new_mock['id'] = mock['ID_halo']
new_mock['upid'] = mock['ID_host']
new_mock['x'] = mock['x']
new_mock['y'] = mock['y']
new_mock['z'] = mock['z']
new_mock['vx'] = mock['Vx']
new_mock['vy'] = mock['Vy']
new_mock['vz'] = mock['Vz']
new_mock['mvir'] = mock['M_vir']
new_mock['vz'] = mock['Vz']
new_mock['Mstar'] = mock['M_star']-(0.7**2)
new_mock['SSFR'] = mock['SSFR']
new_mock['Vpeak'] = mock['V_peak']
#make completeness cut
keep = (new_mock['Mstar']>sm_lim)
new_mock = new_mock[keep]
#save mock
savepath = cu.get_output_path() + 'processed_data/campbell_mocks/'
print('saving hdf5 version of the extended catalogue...')
filename = 'age_matching'
print(filename)
f = h5py.File(savepath+filename+'.hdf5', 'w')
dset = f.create_dataset(filename, data=new_mock)
f.close()
def main():
###make sure to change these when running in a new enviorment!###
#location of data directories
filepath_1 = cu.get_output_path() + 'processed_data/mpa_dr7/'
filepath_2 = cu.get_output_path() + 'processed_data/NYU_VAGC/'
#################################################################
catalogue_1 = 'mpa_dr7_unique'
catalogue_2 = 'nyu_vagc_dr7'
f_1 = h5py.File(filepath_1+catalogue_1+'.hdf5', 'r')
dset_1 = f_1.get(catalogue_1)
dset_1 = np.array(dset_1)
#print(dset_1.dtype.names)
f_2 = h5py.File(filepath_2+catalogue_2+'.hdf5', 'r')
dset_2 = f_2.get(catalogue_2)
dset_2 = np.array(dset_2)
#print(dset_2.dtype.names)
print("number of objects in MPA-JHU catalogue: {0}".format(len(dset_1)))
print("number of objects in NYU catalogue: {0}".format(len(dset_2)))
da = 1/3600.0 * 2.0 #match length is 2"
ind1, ind2, ds = cu.spherematch(dset_1['RA'], dset_1['DEC'],\
dset_2['RA'], dset_2['DEC'],\
tol=da, nnearest=1)
print("minimum angular seperation: {0}''".format(np.min(ds)*3600.0))
print("maximum angular seperation: {0}''".format(np.max(ds)*3600.0))
print("number of matchs: {0}".format(len(ind1)))
print(dset_1[ind1]['Z'])
print(dset_2[ind2]['Z'])
#save matching file
filename = 'matches_into_nyu_vagc'
np.save(filepath_1+'matches/'+filename,ind1)
filename = 'nyu_vagc_matched_to_mpa'
np.save(filepath_1+'matches/'+filename,ind1)
def main():
import numpy as np
import h5py
import matplotlib.pyplot as plt
import custom_utilities as cu
import sys
catalogue = sys.argv[1]
bins = np.arange(7.5, 11.5, 0.1)
bin_centers = (bins[:-1] + bins[1:]) / 2.0
S_r = 4.64
print 'opening mock catalogue:', catalogue + '.hdf5'
#open catalogue
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
f1 = h5py.File(filepath_mock + catalogue + '_extended.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue + '_extended')
mock = np.array(mock)
print 'length:', len(mock)
for name in mock.dtype.names:
print ' ', name
occupied = np.where(mock['M_r,0.1'] != -99)
empty = np.where(mock['M_r,0.1'] == -99)
host = np.where(mock['ID_host'] == -1)[0]
L = solar_lum(mock['M_r,0.1'], S_r)
color = mock['g-r']
LHS = 0.7 - 0.032 * (mock['M_r,0.1'] + 16.5) #Weinmann 2006
blue = np.array((color < LHS) & (mock['M_r,0.1'] != -99))
red = np.array((color > LHS) & (mock['M_r,0.1'] != -99))
host_ID = mock['ID_host']
host_ID[host] = mock['ID_halo'][host]
mass_bin = [12.9, 13.2]
phi = clf(host_ID, L, bins, mock['M_host'], mass_bin)
mask = red
phi_red = clf(host_ID, L, bins, mock['M_host'], mass_bin, mask)
mask = blue
phi_blue = clf(host_ID, L, bins, mock['M_host'], mass_bin, mask)
plt.figure()
plt.plot(bin_centers, phi, color='black')
plt.plot(bin_centers, phi_red, color='red')
plt.plot(bin_centers, phi_blue, color='blue')
plt.yscale('log')
plt.ylim([0.1, 100])
plt.ylabel(r'$\phi(L)dL/{\rm group}$')
plt.xlabel('L')
plt.show()
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path()+'processed_data/CFHTLens/catalogues/'
#################################################################
fields=['W1','W2','W3','W4']
for field in fields:
filename = field
print 'running for:', field
f = h5py.File(filepath+filename+'.hdf5', 'r')
dset = f.get(field)
dtype=[('id','S13'),('ALPHA_J2000','<f8'),('DELTA_J2000','<f8'), \
('x','<f8'),('y','<f8'),('z','<f8'), \
('Flag', '<i8'),('MASK', '<i8'), \
('MAG_u', '<f8'),('MAG_g', '<f8'),('MAG_r', '<f8'),('MAG_i', '<f8'),('MAG_y', '<f8'),('MAG_z', '<f8'), \
('PDZ', np.float64, (70,)),('Z_B', '<f8')]
dtype = np.dtype(dtype)
data = np.recarray((len(dset),), dtype=dtype)
data['id'] = dset['id']
data['ALPHA_J2000'] = dset['ALPHA_J2000']
data['DELTA_J2000'] = dset['DELTA_J2000']
data['Flag'] = dset['Flag']
data['MASK'] = dset['MASK']
data['MAG_u'] = dset['MAG_u']
data['MAG_g'] = dset['MAG_g']
data['MAG_r'] = dset['MAG_r']
data['MAG_i'] = dset['MAG_i']
data['MAG_y'] = dset['MAG_y']
data['MAG_z'] = dset['MAG_z']
data['Z_B'] = dset['Z_B']
#process pdz column
string = np.array(dset['PZ_full'])
for i in range(0,len(dset)):
print i
data['PDZ'][i] = np.array([float(x) for x in string[i].split(',')])
print 'converting ra,dec coordinates into vectors...'
x, y, z = spherical_to_cartesian(dset['ALPHA_J2000'],dset['DELTA_J2000'], threads=4)
data['x'] = x
data['y'] = y
data['z'] = z
print 'saving hdf5 version of the catalogue...'
filename = filename+'_abbreviated'
f = h5py.File(filepath+filename+'.hdf5', 'w')
dset = f.create_dataset(field, data=data)
f.close()
def main():
if len(sys.argv)==2:
rho = 'NA'
sigma = 'NA'
catalogue = sys.argv[1]
elif len(sys.argv)>2:
rho = sys.argv[1]
sigma = sys.argv[2]
catalogue = 'sm_9.5_s'+str(sigma)+'_sfr_c'+str(rho)+'_250'
else:
catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250_cen_shuffle'
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250'
#catalogue = 'age_matching'
#catalogue = 'sm_9.5_s0.2_sfr_c1.0_Halfmass_Scale_250'
rho = -1.0
sigma = 0.2
#open mock
filepath = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
print(catalogue)
print(mock.dtype.names)
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
#plot stellar mass halo mass relation for the mock
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.78, bottom=0.2, top=0.9)
p = plt.scatter(mock['mvir'][host],10.0**mock['Mstar'][host],c=mock['SSFR'][host],\
cmap='jet_r',vmax=-9.5, vmin=-12.5, marker='.',
lw=0, s=2, rasterized=True)
plt.text(10.0**10.0,5*10**11.0,r'$\rho_{\rm SSFR}=$'+str(rho))
plt.text(10.0**10.0,5*10**10.8,r'$\sigma_{\rm SMHM}=$'+str(sigma))
plt.xlabel(r'$M_{\rm vir}~[h^{-1}M_{\odot}]$')
plt.ylabel(r'$M_{*} ~[h^{-2}M_{\odot}]$')
plt.xlim([10.0**9.5,10.0**15.0])
plt.ylim([10.0**9.0,10.0**12.0])
plt.yscale('log')
plt.xscale('log')
plt.title("Mpeak-Vpeak mock")
cbar_ax = fig.add_axes([0.79, 0.2, 0.02, 0.7]) #xmin, ymin, +dx, +dy
cbar = fig.colorbar(p, cax=cbar_ax)
cbar.set_label(r'$\log({\rm SSFR}/{\rm yr}^{-1})$')
plt.show()
savepath = cu.get_plot_path()+'/analysis/central_quenching/'
filename = 'SMHM'
fig.savefig(savepath+filename+'.pdf')
def main():
catalogues_1 = ['sm_9.5_s0.2_sfr_c-1.0_250','sm_9.5_s0.2_sfr_c-1.0_250_cen_shuffle']
samples = ['all','q','sf']
sm_bins = ['9.0_9.5', '9.5_10.0', '10.0_10.5', '10.5_11.0', '11.0_11.5']
sm_bin = sm_bins[2]
#open correlation functions
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
names = ['r','wp']
filename = catalogues_1[0]+'_wp_'+samples[0]+'_'+sm_bin+'.dat'
result_1a = ascii.read(filepath+filename,names=names)
filename = catalogues_1[0]+'_wp_'+samples[1]+'_'+sm_bin+'.dat'
result_1b = ascii.read(filepath+filename,names=names)
filename = catalogues_1[0]+'_wp_'+samples[2]+'_'+sm_bin+'.dat'
result_1c = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[0]+'_'+sm_bin+'.dat'
result_2a = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[1]+'_'+sm_bin+'.dat'
result_2b = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[2]+'_'+sm_bin+'.dat'
result_2c = ascii.read(filepath+filename,names=names)
######################################################################################
#set up plot
######################################################################################
fig1, axes = plt.subplots(nrows=1,ncols=1,sharex=True,sharey=True,figsize=(3.3, 3.3))
fig1.subplots_adjust(hspace=0, wspace=0, left=0.2, right=0.9, bottom=0.2, top=0.9)
ax = axes
ax.set_xlim([0.1,20])
#ax.set_ylim([5,300])
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$(\omega_{\rm red}-\omega_{\rm blue})/\omega_{\rm all}$')
ax.set_xlabel(r'$r_p~[{\rm Mpc}~h^{-1}]$')
ax.set_title(r'10.0$<\log(M_{*}/M_{\odot}h^{-2})<10.5$')
#p1a, = ax.plot(result_1a['r'],result_1a['wp']*result_1a['r'],'-',color='black', alpha=1)
#p1b, = ax.plot(result_1b['r'],result_1b['wp']*result_1a['r'],'-',color='red', alpha=1)
#p1c, = ax.plot(result_1c['r'],result_1c['wp']*result_1a['r'],'-',color='blue', alpha=1)
#p2a, = ax.plot(result_2a['r'],result_2a['wp']*result_2a['r'],'--',color='black', alpha=1)
#p2b, = ax.plot(result_2b['r'],result_2b['wp']*result_2a['r'],'--',color='red', alpha=1)
#p2c, = ax.plot(result_2c['r'],result_2c['wp']*result_2a['r'],'--',color='blue', alpha=1)
p1a, = ax.plot(result_1a['r'],(result_1b['wp']-result_1c['wp'])/result_1a['wp'],'-',color='black', alpha=1)
p1b, = ax.plot(result_2a['r'],(result_2b['wp']-result_2c['wp'])/result_2a['wp'],'--',color='black', alpha=1)
plt.legend((p1a,p1b),('CAM mock','shuffled mock'),fontsize=10, frameon=False, loc=4)
plt.show()
fig1.savefig('/Users/duncan/Desktop/compare_wp.pdf')
def main():
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250_cen_shuffle'
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250_sat_shuffle'
#catalogue = 'sm_9.5_s0.2_sfr_c-1.0_250'
catalogue = 'sm_9.5_s0.2_sfr_c1.0_Halfmass_Scale_250'
#open mock
filepath = cu.get_output_path() + 'processed_data/campbell_mocks/'
f = h5py.File(filepath+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
mock = np.array(mock)
print(mock.dtype.names)
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
red = (mock['red']==1)
blue = (mock['red']==0)
halo_prop = 'mvir'
bins = np.arange(9.5,12.5,0.2)
bin_centers = (bins[:-1]+bins[1:])/2.0
mean_red_halo_mass = cu.statistics.binned_mean(mock[host&red], bins, 'Mstar', halo_prop, use_log=True)[1]
mean_blue_halo_mass = cu.statistics.binned_mean(mock[host&blue], bins, 'Mstar', halo_prop, use_log=True)[1]
std_red_halo_mass = cu.statistics.binned_std(mock[host&red], bins, 'Mstar', halo_prop, use_log=True)[1]
std_blue_halo_mass = cu.statistics.binned_std(mock[host&blue], bins, 'Mstar', halo_prop, use_log=True)[1]
print(std_red_halo_mass*10**mean_red_halo_mass)
#plot stellar mass halo mass relation for the mock
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
p = plt.scatter(10.0**mock['Mstar'][host],mock['mvir'][host],c=mock['SSFR'][host],\
cmap='jet_r',vmax=-9.5, vmin=-12.5, marker='.',
lw=0, s=2, rasterized=True)
#p = plt.plot(10.0**mock['Mstar'][host & red],mock['mvir'][host & red], '.', color='red', ms=2)
#p = plt.plot(10.0**mock['Mstar'][host & blue],mock['mvir'][host & blue], '.', color='blue', ms=2)
p1a = plt.errorbar(10**bin_centers, 10**mean_red_halo_mass, yerr=std_red_halo_mass*10**mean_red_halo_mass, color='red')
p1b = plt.errorbar(10**bin_centers, 10**mean_blue_halo_mass, yerr=std_blue_halo_mass*10**mean_blue_halo_mass, color='blue')
plt.title('age-matching')
plt.ylabel(r'$M_{\rm halo}~[h^{-1}M_{\odot}]$')
plt.xlabel(r'$M_{*} ~[h^{-2}M_{\odot}]$')
plt.xlim([10.0**9.5,10.0**12.5])
plt.ylim([10.0**11.0,10.0**15.0])
plt.yscale('log')
plt.xscale('log')
plt.show()
fig.savefig('/Users/duncan/Desktop/SMHM_age_matching.pdf')
def main():
import numpy as np
import h5py
import matplotlib.pyplot as plt
import custom_utilities as cu
import sys
group_cat = sys.argv[1] # tinker_mocks, berlind_mocks, yang_mocks
catalogue = sys.argv[
2] # Mr19_age_distribution_matching_mock, Mr19_age_distribution_matching_mock_sys_empty_shuffle_satrel_shuffle
filename = catalogue + '_HTP'
if group_cat == 'tinker_mocks':
filepath = cu.get_output_path(
) + 'processed_data/tinker_groupcat/mock_runs/4th_run/custom_catalogues/'
savepath = cu.get_output_path() + 'analysis/tinker_groupcat/'
plotpath = cu.get_plot_path() + 'analysis/tinker_groupcat/'
catalogue = catalogue + '_clf_groups_M19'
if group_cat == 'berlind_mocks':
filepath = cu.get_output_path(
) + 'processed_data/berlind_groupcat/mock_runs/4th_run/custom_catalogues/'
savepath = cu.get_output_path() + 'analysis/berlind_groupcat/'
plotpath = cu.get_plot_path() + 'analysis/berlind_groupcat/'
catalogue = catalogue + '_groups'
if group_cat == 'yang_mocks':
filepath = cu.get_output_path(
) + 'processed_data/yang_groupcat/mock_runs/4th_run/version_2/custom_catalogues/'
savepath = cu.get_output_path() + 'analysis/yang_groupcat/'
plotpath = cu.get_plot_path() + 'analysis/yang_groupcat/'
catalogue = catalogue + '_groups'
print 'opening group catalogue:', catalogue
#open catalogue
f = h5py.File(filepath + catalogue + '.hdf5', 'r') #open catalogue file
GC = f.get(catalogue)
#determine central/satellite designation
group_centrals = np.where(GC['RANK'] == 1)[0]
group_satellites = np.where(GC['RANK'] != 1)[0]
halo_centrals = np.where(GC['HALO_RANK'] == 1)[0]
halo_satellites = np.where(GC['HALO_RANK'] != 1)[0]
#define group/halo mass bins
mass_bins = np.arange(11, 15.2, 0.2)
bin_width = mass_bins[1] - mass_bins[0]
bin_centers = (mass_bins[:-1] - mass_bins[1:]) / 2.0
#calculate the HTP
H_cc, H_sc, H_ss, H_cs = htp(GC['MGROUP'], GC['HALO_M'], group_centrals,
group_satellites, halo_centrals,
halo_satellites, mass_bins)
#plot the results
fig = plot_htp(H_cc, H_sc, H_ss, H_cs, mass_bins)
plt.show()
print plotpath + filename + '.eps'
fig.savefig(plotpath + filename + '.eps')
def main():
filepath_cat = cu.get_output_path(
) + 'processed_data/yang_groupcat/custom_catalogues/'
savepath = filepath_cat
abs_mag_lim = float(sys.argv[1])
print abs_mag_lim
catalogues = ['sample1_L_model', 'sample2_L_model',
'sample3_L_model'] #groupcat catalogues
z = np.arange(0.01, 0.21, 0.01)
lim = np.zeros(len(z))
for i in range(0, len(z)):
print z[i]
lim[i] = abs_mag_lim_func(z[i])
print z
print lim
func = interpolate.interp1d(lim[::-1], z[::-1], kind='cubic')
for i in range(0, len(catalogues)):
catalogue = catalogues[i]
#open catalogue
f = h5py.File(filepath_cat + catalogue + '.hdf5',
'r') #open catalogue file
GC = f.get(catalogue)
print 'read in', catalogue + '.'
zmax = func(abs_mag_lim)
selection_1 = GC['ZGROUP'] < zmax
selection_2 = GC['M_r,0.1'] < abs_mag_lim
selection = selection_1 * selection_2
sample = GC[selection]
filename = catalogue + '.mr' + str(int(abs(abs_mag_lim)))
f = h5py.File(savepath + filename + '.hdf5', 'w')
dset = f.create_dataset(filename, data=sample)
f.close()
plt.plot(GC['ZGROUP'], GC['M_r,0.1'], '.', color='black', alpha=0.1)
plt.plot(sample['ZGROUP'], sample['M_r,0.1'], '.')
plt.plot(z, lim, color='red')
plt.ylim([-15, -25])
plt.xlabel('group z')
plt.ylabel('M_r,0.1')
plt.show(block=True)
def main():
catalogues_1 = ['sm_9.5_s0.2_sfr_c-1.0_250','sm_9.5_s0.2_sfr_c-1.0_250_cen_shuffle']
samples = ['all','q','sf']
sm_bins = ['12.0_12.5']
sm_bin = sm_bins[0]
#open correlation functions
filepath = cu.get_output_path() + 'analysis/central_quenching/observables/'
names = ['r','wp']
filename = catalogues_1[0]+'_wp_'+samples[0]+'_halo_'+sm_bin+'.dat'
result_1a = ascii.read(filepath+filename,names=names)
filename = catalogues_1[0]+'_wp_'+samples[1]+'_halo_'+sm_bin+'.dat'
result_1b = ascii.read(filepath+filename,names=names)
filename = catalogues_1[0]+'_wp_'+samples[2]+'_halo_'+sm_bin+'.dat'
result_1c = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[0]+'_halo_'+sm_bin+'.dat'
result_2a = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[1]+'_halo_'+sm_bin+'.dat'
result_2b = ascii.read(filepath+filename,names=names)
filename = catalogues_1[1]+'_wp_'+samples[2]+'_halo_'+sm_bin+'.dat'
result_2c = ascii.read(filepath+filename,names=names)
######################################################################################
#set up plot
######################################################################################
fig1, axes = plt.subplots(nrows=1,ncols=1,sharex=True,sharey=True,figsize=(3.3, 3.3))
fig1.subplots_adjust(hspace=0, wspace=0, left=0.2, right=0.9, bottom=0.2, top=0.9)
ax = axes
ax.set_xlim([0.1,20])
ax.set_ylim([5,300])
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylabel(r'$\omega_p(r_p)\times r_p$')
ax.set_xlabel(r'$r_p~[{\rm Mpc}~h^{-1}]$')
ax.set_title(r'$12.0<\log(M_{\rm vir}/M_{\odot}h^{-2})<12.5$')
p1a, = ax.plot(result_1a['r'],result_1a['wp']*result_1a['r'],'-',color='black', alpha=1)
p1b, = ax.plot(result_1b['r'],result_1b['wp']*result_1b['r'],'-',color='red', alpha=1)
p1c, = ax.plot(result_1c['r'],result_1c['wp']*result_1c['r'],'-',color='blue', alpha=1)
p2a, = ax.plot(result_2a['r'],result_2a['wp']*result_2a['r'],'--',color='black', alpha=1)
p2b, = ax.plot(result_2b['r'],result_2b['wp']*result_2b['r'],'--',color='red', alpha=1)
p2c, = ax.plot(result_2c['r'],result_2c['wp']*result_2c['r'],'--',color='blue', alpha=1)
plt.show()
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/'
savepath = cu.get_output_path() + 'processed_data/berlind_groupcat/'
#################################################################
catalogues = [
'mr19_groups.fits', 'smthresh10.2.groups.dat', 'smthresh9.8.groups.dat'
]
filename = catalogues[0]
hdulist = fits.open(filepath + filename, memmap=True)
data = hdulist[1].data
print 'saving as:', savepath + filename[:-5] + '.hdf5'
f = h5py.File(savepath + filename[:-5] + '.hdf5', 'w')
dset = f.create_dataset(filename[:-5], data=data)
f.close()
gc.collect()
names = [
'ra', 'dec', 'z', 'groupID', 'rank', 'Mstar', 'Mr', 'SSFR', 'Mgroup'
]
filename = catalogues[1]
data = ascii.read(filepath + filename,
guess=True,
Reader=ascii.Basic,
names=names,
data_start=0)
print 'saving as:', savepath + filename[:-4] + '.hdf5'
f = h5py.File(savepath + filename[:-4] + '.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
gc.collect()
filename = catalogues[2]
data = ascii.read(filepath + filename,
guess=True,
Reader=ascii.Basic,
names=names,
data_start=0)
print 'saving as:', savepath + filename[:-4] + '.hdf5'
f = h5py.File(savepath + filename[:-4] + '.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
gc.collect()
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/mock_runs/Berlind_run/'
savepath = cu.get_output_path() + 'processed_data/berlind_groupcat/mock_runs/Berlind_run/'
#################################################################
catalogue = 'Age_matching_group_tags.dat'
names = ['IDgal','IDgroup']
filename = catalogue
data = ascii.read(filepath+filename,names=names)
print data
print savepath+filename[:-4]+'.hdf5'
f = h5py.File(savepath+filename[:-4]+'.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path() + 'processed_data/mpa_dr7/'
#################################################################
catalogues = [
'gal_totspecsfr_dr7_v5_2', 'gal_info_dr7_v5_2', 'totlgm_dr7_v5_2'
]
for catalogue in catalogues:
print catalogue
f = h5py.File(filepath + catalogue + '.hdf5', 'r')
dset = f.get(catalogue)
dset = np.array(dset)
print 'length:', len(dset)
for name in dset.dtype.names:
print '\t', name
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/mock_runs/Berlind_run/'
savepath = cu.get_output_path(
) + 'processed_data/berlind_groupcat/mock_runs/Berlind_run/'
#################################################################
catalogue = 'Age_matching_group_tags.dat'
names = ['IDgal', 'IDgroup']
filename = catalogue
data = ascii.read(filepath + filename, names=names)
print data
print savepath + filename[:-4] + '.hdf5'
f = h5py.File(savepath + filename[:-4] + '.hdf5', 'w')
dset = f.create_dataset(filename[:-4], data=data)
f.close()
def main():
filepath_cat = cu.get_output_path() + 'processed_data/yang_groupcat/custom_catalogues/'
savepath = filepath_cat
mass_lim = float(sys.argv[1])
mag_lim = float(sys.argv[2])
catalogues=['sample1_M_model', 'sample2_M_model', 'sample3_M_model'] #groupcat catalogues
z = np.arange(0.01,0.21,0.01)
lim=np.zeros(len(z))
for i in range(0, len(z)):
print z[i]
lim[i] = abs_mag_lim_func(z[i])
print z
print lim
func = interpolate.interp1d(lim[::-1], z[::-1], kind='cubic')
for i in range(0,len(catalogues)):
catalogue = catalogues[i]
#open catalogue
f = h5py.File(filepath_cat+catalogue+'.hdf5', 'r') #open catalogue file
GC = f.get(catalogue)
print 'read in',catalogue+'.'
zmax = func(mag_lim)
selection_1 = GC['ZGROUP']<zmax
selection_2 = GC['MSTAR']>mass_lim
selection = selection_1*selection_2
sample = GC[selection]
filename = catalogue+'.sm'+str(abs(mass_lim))
f = h5py.File(savepath+filename+'.hdf5', 'w')
dset = f.create_dataset(filename, data=sample)
f.close()
plt.plot(GC['ZGROUP'],GC['MSTAR'],'.', color='black', alpha=0.1)
plt.plot(sample['ZGROUP'],sample['MSTAR'],'.')
plt.plot(z,lim, color='red')
plt.ylim([8,12])
plt.xlabel('group z')
plt.ylabel('$\log(M_{*})$')
plt.show(block=True)
def main():
filepath = cu.get_data_path() + 'tinker_groupcat/'
savepath = cu.get_output_path() + 'processed_data/tinker_groupcat/'
#################################################################
names=['ID','ra','dec','z','M_star','Mag_r','Mag_g','SSFR','p_sat','central_ind','M_halo','R_vir','d_proj']
catalogues=['group_dr7_m.star9.7','group_dr7_m.star10.1','group_dr7_m.star10.6','group_dr7_mag.r19']
for catalogue in catalogues:
print catalogue
filename = catalogue+'.txt'
print filepath+filename
data = ascii.read(filepath+filename, delimiter='\s', names=names, \
guess=False, Reader=ascii.Basic)
f = h5py.File(savepath+catalogue+'.hdf5', 'w')
dset = f.create_dataset(catalogue, data=data)
f.close()
gc.collect()
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path(
) + 'processed_data/yang_groupcat/catalogues/'
#################################################################
catalogues=['sample1_L_petro','sample2_L_petro','sample3_L_petro',\
'sample1_M_petro','sample2_M_petro','sample3_M_petro',\
'sample1_L_model','sample2_L_model','sample3_L_model',\
'sample1_M_model','sample2_M_model','sample3_M_model']
for catalogue in catalogues:
print 'reading in:', catalogue
f = h5py.File(filepath + catalogue + '.hdf5', 'r')
dset = f.get(catalogue)
dset = np.array(dset)
print 'length:', len(dset)
for name in dset.dtype.names:
print '\t', name
def main():
filepath = cu.get_data_path() + 'Berlind_groupcat/mock_runs/4th_run/'
savepath = cu.get_output_path(
) + 'processed_data/berlind_groupcat/mock_runs/4th_run/'
#################################################################
catalogues=['Mr19_age_distribution_matching_mock_cen_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_satsys_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_cen_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satrel_shuffle_radec_mock.dat',\
'Mr19_age_distribution_matching_mock_sys_empty_shuffle_satsys_shuffle_radec_mock.dat']
names = ['IDgroup', 'IDgal', 'ra', 'dec', 'cz']
for i in range(0, len(catalogues)):
filename = catalogues[i]
catalogue = filename[:-4]
data_1 = ascii.read(filepath + filename,
names=names,
format='no_header',
comment='#')
#create output file with galaxy ID and group ID
dtype = [('gal_ID', '>i8'), ('group_ID', '>i8')]
dtype = np.dtype(dtype)
data = np.recarray((len(data_1), ), dtype=dtype)
#fill in data structure.
data['gal_ID'] = data_1['IDgal']
data['group_ID'] = data_1['IDgroup']
#save
print 'saving:', savepath + catalogue + '.hdf5'
f = h5py.File(savepath + catalogue + '.hdf5', 'w')
dset = f.create_dataset(catalogue, data=data)
f.close()
print 'done.'
def get_sdss_sample():
from scipy import interpolate
filepath = cu.get_output_path() + 'processed_data/NYU_VAGC/'
galaxy_catalogue = 'nyu_lss_mpa_vagc_dr7'
f = h5py.File(filepath+galaxy_catalogue+'.hdf5', 'r')
GC = f.get(galaxy_catalogue) #halo catalogue
GC = np.array(GC)
#for name in GC.dtype.names: print(name)
#trim the catalogue a bit
zmin = 0.01
zmax = 0.2
selection = (GC['M']<=17.6) & (GC['Z']<zmax) & (GC['Z']>zmin) &\
(GC['ZTYPE']==1) & (GC['FGOTMAIN']>0)
GC = GC[selection]
sm_key = 'sm_MEDIAN'
GC[sm_key] = GC[sm_key]+np.log10(0.7**2.0)
#make cuts on data to get a clean and complete sample
cosmo = FlatLambdaCDM(H0=100, Om0=0.3)
z = np.linspace(0.001,1,1000)
dL = cosmo.luminosity_distance(z).value
#make cheater fit to dl(z) function
dL_z = interpolate.interp1d(z,dL,kind='linear')
Mstar_lim = (4.852 + 2.246*np.log10(dL) + 1.123*np.log10(1+z) - 1.186*z)/(1.0-0.067*z)
#dL = cosmo.luminosity_distance(GC['Z']).value
dL = dL_z(GC['Z'])
z = GC['Z']
LHS = (4.852 + 2.246*np.log10(dL) + 1.123*np.log10(1.0+z) - 1.186*z)/(1.0-0.067*z)
keep = (GC[sm_key]>LHS)
GC = GC[keep]
return GC
def main():
filepath = cu.get_data_path() + 'Multidark/Bolshoi/particle_catalogues/'
savepath = cu.get_output_path(
) + 'processed_data/Multidark/Bolshoi/particle_catalogues/'
catalogue = 'bolshoi_a1.0003_2e5_particles'
print 'reading in:', catalogue
filename = catalogue
hdulist = fits.open(filepath + catalogue + '.fits')
header = hdulist[1]
print header.data
x = np.array(header.data['POS'][:, 0])
y = np.array(header.data['POS'][:, 1])
z = np.array(header.data['POS'][:, 2])
data = np.recarray((len(x), ),
formats=['f4', 'f4', 'f4'],
names=['x', 'y', 'z'])
data['x'] = x
data['y'] = y
data['z'] = z
filename = catalogue
f = h5py.File(savepath + filename + '.hdf5', 'w')
dset = f.create_dataset(catalogue, data=data)
f.close()
filename = catalogue
data_table = table.table.Table(data=data)
ascii.write(data_table, savepath + filename + '.dat')
print data_table
def main():
if len(sys.argv) > 1: catalogue = sys.argv[1]
else: catalogue = 'Mr19_age_distribution_matching_mock'
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
plotpath = cu.get_plot_path() + 'analysis/hearin_mocks/'
print 'opening mock catalogue:', catalogue + '.hdf5'
#open catalogue
f1 = h5py.File(filepath_mock + catalogue + '.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue)
mock = np.array(mock)
print 'length:', len(mock)
for name in mock.dtype.names:
print ' ', name
#calculate the satellite fraction
N_sat = float(np.sum((mock['ID_host'] != -1)))
N_cen = float(np.sum((mock['ID_host'] == -1)))
fsat = N_sat / (N_sat + N_cen)
print 'satellite fraction:', fsat
#subsample the galaxies to make plotting easier
N = 10000
selection = np.random.permutation(len(mock))[0:N]
mock = mock[selection]
host = np.where(mock['ID_host'] == -1)[0]
sub = np.where(mock['ID_host'] != -1)[0]
color = mock['g-r']
LHS = 0.7 - 0.032 * (mock['M_r,0.1'] + 16.5) #Weinmann 2006
blue = np.where((color < LHS) & (mock['M_r,0.1'] != -99))[0]
red = np.where((color > LHS) & (mock['M_r,0.1'] != -99))[0]
fig = plt.figure(figsize=(3.3, 3.3))
ax = fig.add_subplot(1, 1, 1)
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
p1, = ax.plot(10.0**mock['M_host'][host],
mock['V_peak'][host],
'o',
ms=1,
alpha=1,
color='orange',
mew=0,
rasterized=True)
p2, = ax.plot(10.0**mock['M_host'][sub],
mock['V_peak'][sub],
'o',
ms=1,
alpha=1,
color='green',
mew=0,
rasterized=True)
ax.plot([10**10, 10**16], [100, 100], '--', color='black')
ax.plot([2 * 10**11, 2 * 10**11], [1, 800], '--', color='black')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim([50, 2000])
ax.set_xlim([10**10, 10**15])
ax.set_xlabel(r'$M_{\rm host}$ $[M_{\odot}/h]$')
ax.set_ylabel(r'$V_{\rm peak}$')
ax.legend((p1, p2), ('centrals', 'satellites'),
loc=2,
numpoints=1,
fontsize=10,
markerscale=2,
frameon=False)
plt.show()
filename1 = 'mock_props_censata_MhVpeak.pdf'
fig.savefig(plotpath + filename1, dpi=400)
fig = plt.figure(figsize=(3.3, 3.3))
ax = fig.add_subplot(1, 1, 1)
fig.subplots_adjust(left=0.2, right=0.9, bottom=0.2, top=0.9)
p1, = ax.plot(10.0**mock['M_host'][red],
mock['V_peak'][red],
'o',
ms=1,
alpha=1,
color='red',
mew=0,
rasterized=True)
p2, = ax.plot(10.0**mock['M_host'][blue],
mock['V_peak'][blue],
'o',
ms=1,
alpha=1,
color='blue',
mew=0,
rasterized=True)
ax.plot([10**10, 10**16], [100, 100], '--', color='black')
ax.plot([2 * 10**11, 2 * 10**11], [1, 800], '--', color='black')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_ylim([50, 2000])
ax.set_xlim([10**10, 10**15])
ax.set_xlabel(r'$M_{\rm host}$ $[M_{\odot}/h]$')
ax.set_ylabel(r'$V_{\rm peak}$')
ax.legend((p1, p2), ('red subsample', 'blue subsample'),
loc=2,
numpoints=1,
fontsize=10,
markerscale=2,
frameon=False)
plt.show()
filename2 = 'mock_props_color_MhVpeak.pdf'
fig.savefig(plotpath + filename2, dpi=400)
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath = cu.get_output_path() + 'processed_data/hearin_mocks/'
savepath = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
#################################################################
catalogues = [
'Mr19_age_distribution_matching_mock', 'sm9.8_age_matching_mock'
]
catalogue = sys.argv[1]
print 'runing satellite shuffle on:', catalogue
f = h5py.File(filepath + catalogue + '.hdf5', 'r')
GC = f.get(catalogue)
#remove all haloes not occupied in the original mock
#remove empty haloes
keep = np.where(GC_new['g-r'] != -99)[0]
GC = GC[keep]
print GC.dtype
#make new catalogue
dtype = GC.dtype.descr
dtype = np.dtype(dtype)
GC_new = np.recarray((len(GC), ), dtype=dtype)
GC_new.fill(0.0)
GC_new = np.array(GC, copy=True)
#identify centrals
centrals = np.where(GC['ID_host'] == -1)[0] #central galaxies
print 'number of centrals:', len(centrals)
#define mass bins, and which central are in each mass bin
mass_bins = np.arange(8.0, 16.0, 0.1) #log mass bins
mass_hist, bins = np.histogram(
GC['M_host'][centrals], bins=mass_bins) #group histogram by log(mass)
mass_bin_ind = np.digitize(
GC['M_host'][centrals],
bins=mass_bins) #indices of groups in log(mass) bins
#go through each mass bin
for i in range(0, len(mass_bins) - 1):
print 'mass bin:', i, mass_bins[i], mass_bins[i + 1]
ind = np.where(mass_bin_ind == i + 1)[0]
if len(ind) > 0: #if there are any haloes in the mass bin
print 'number of groups:', len(ind)
ids = GC['ID_halo'][centrals[ind]]
sat_galaxy_members = np.in1d(
GC['ID_host'], ids) #satellite galaxies in the mass bin
sat_galaxy_members = np.where(sat_galaxy_members)[
0] #indicies of galaxies
cen_galaxy_members = np.in1d(
GC['ID_halo'], ids) #central galaxies in the mass bin
cen_galaxy_members = np.where(cen_galaxy_members)[
0] #indicies of galaxies
galaxy_members = np.hstack(
(sat_galaxy_members, cen_galaxy_members))
print 'number of galaxies:', len(galaxy_members)
satellite_members = np.where(
GC['ID_host'][galaxy_members] != -1)[0] #satellites
satellite_members = galaxy_members[
satellite_members] #indices of satellites
central_members = np.where(
GC['ID_host'][galaxy_members] == -1)[0] #centrals
central_members = galaxy_members[
central_members] #indices of centrals
print 'number of centrals:', len(central_members)
print 'number of satellites:', len(satellite_members)
print 'check:', len(central_members) + len(
satellite_members) == len(galaxy_members)
#now shuffle satellites amongst groups in mass bin
shuffle = np.random.permutation(
np.arange(0, len(satellite_members), 1))
if len(satellite_members) > 0:
GC_new['M_r,0.1'][satellite_members] = GC['M_r,0.1'][
satellite_members[shuffle]]
GC_new['M_star'][satellite_members] = GC['M_star'][
satellite_members[shuffle]]
GC_new['g-r'][satellite_members] = GC['g-r'][
satellite_members[shuffle]]
print 'saving hdf5 version of the catalogue...'
filename = catalogue + '_sat_shuffle'
f = h5py.File(savepath + filename + '.hdf5', 'w')
dset = f.create_dataset(catalogue, data=GC_new)
f.close()
print 'saving ascii version of the catalogue...'
filename = catalogue + '_sat_shuffle'
data_table = table.table.Table(data=GC_new)
ascii.write(data_table, savepath + filename + '.dat')
print data_table
def main():
catalogue = 'Mr19_age_distribution_matching_mock'
if len(sys.argv) > 1: catalogue = sys.argv[1]
#open mock catalogue
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
print 'opening mock catalogue:', catalogue + '.hdf5'
f1 = h5py.File(filepath_mock + catalogue + '.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue)
centrals = np.array(mock['ID_host'] == -1) #central galaxies
satellites = np.array(mock['ID_host'] != -1) #satellite galaxies
LHS = 0.7 - 0.032 * (mock['M_r,0.1'] + 16.5) #Weinmann 2006 color cut
blue = np.array(mock['g-r'] < LHS)[0] #blue galaxies
red = np.array(mock['g-r'] > LHS)[0] #red galaxies
L = solar_lum(mock['M_r,0.1'], 4.64)
lumbins = np.arange(9.6, 11.0, 0.15)
lumbin_centers = (lumbins[:-1] + lumbins[1:]) / 2.0
luminds = np.digitize(solar_lum(mock['M_r,0.1'], 4.64), bins=lumbins)
massbins = np.arange(11, 15, 0.2)
massbin_centers = (massbins[:-1] + massbins[1:]) / 2.0
massinds = np.digitize(mock['M_host'], bins=massbins)
colorbins = np.arange(0, 1.5, 0.1)
colorbin_centers = (colorbins[:-1] + colorbins[1:]) / 2.0
colorinds = np.digitize(mock['g-r'], bins=colorbins)
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#ax.scatter(L[satellites],mock['g-r'][satellites],mock['M_host'][satellites],marker='.',alpha=0.1)
#ax.set_ylim([0,1.5])
#plt.show()
fig1, axes = plt.subplots(nrows=3,
ncols=3,
sharex=True,
sharey=True,
figsize=(10, 10))
fig1.subplots_adjust(hspace=0, wspace=0.0)
fig1.subplots_adjust(left=0.1, right=0.95, bottom=0.2, top=0.9)
axes = axes.flatten()
#satellites
ax = axes[0]
selection = np.array(luminds == 0)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[0])
ax = axes[1]
selection = np.array(luminds == 1)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[1])
ax = axes[2]
selection = np.array(luminds == 2)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[2])
ax = axes[3]
selection = np.array(luminds == 3)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[3])
ax = axes[4]
selection = np.array(luminds == 4)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[4])
ax = axes[5]
selection = np.array(luminds == 5)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[5])
ax = axes[6]
selection = np.array(luminds == 6)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[6])
ax = axes[7]
selection = np.array(luminds == 7)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[7])
ax = axes[8]
selection = np.array(luminds == 8)
selection = selection & satellites
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(11.5, 1.1, lumbin_centers[8])
#centrals
ax = axes[0]
selection = np.array(luminds == 0)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[1]
selection = np.array(luminds == 1)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[2]
selection = np.array(luminds == 2)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[3]
selection = np.array(luminds == 3)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[4]
selection = np.array(luminds == 4)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[5]
selection = np.array(luminds == 5)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[6]
selection = np.array(luminds == 6)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[7]
selection = np.array(luminds == 7)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[8]
selection = np.array(luminds == 8)
selection = selection & centrals
ax.plot(mock['M_host'][selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax.set_xlim([11, 15])
ax.set_ylim([0, 1.25])
plt.show()
fig2, axes = plt.subplots(nrows=3,
ncols=3,
sharex=True,
sharey=True,
figsize=(10, 10))
fig2.subplots_adjust(hspace=0, wspace=0.0)
fig2.subplots_adjust(left=0.1, right=0.95, bottom=0.2, top=0.9)
axes = axes.flatten()
#satellites
ax = axes[0]
selection = np.array(massinds == 5)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[5])
ax = axes[1]
selection = np.array(massinds == 6)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[6])
ax = axes[2]
selection = np.array(massinds == 7)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[7])
ax = axes[3]
selection = np.array(massinds == 8)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[8])
ax = axes[4]
selection = np.array(massinds == 9)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[9])
ax = axes[5]
selection = np.array(massinds == 10)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[10])
ax = axes[6]
selection = np.array(massinds == 11)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[11])
ax = axes[7]
selection = np.array(massinds == 12)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[12])
ax = axes[8]
selection = np.array(massinds == 13)
selection = selection & satellites
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='green')
ax.text(9.5, 1.1, massbin_centers[13])
#centrals
ax = axes[0]
selection = np.array(massinds == 5)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[1]
selection = np.array(massinds == 6)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[2]
selection = np.array(massinds == 7)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[3]
selection = np.array(massinds == 8)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[4]
selection = np.array(massinds == 9)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[5]
selection = np.array(massinds == 10)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[6]
selection = np.array(massinds == 11)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[7]
selection = np.array(massinds == 12)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax = axes[8]
selection = np.array(massinds == 13)
selection = selection & centrals
ax.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
ax.set_xlim([9.5, 11])
ax.set_ylim([0, 1.25])
plt.show()
plt.figure()
selection = np.array(massinds <= 8) & np.array(massinds > 6)
selection = selection & centrals
plt.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
plt.xlim([9.5, 11])
plt.ylim([0, 1.25])
plt.figure()
selection = np.array(massinds > 8)
selection = selection & centrals
plt.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
plt.xlim([9.5, 11])
plt.ylim([0, 1.25])
plt.figure()
selection = np.array(massinds < 5)
selection = selection & centrals
plt.plot(L[selection],
mock['g-r'][selection],
'.',
ms=1,
alpha=0.2,
color='orange')
plt.xlim([9.5, 11])
plt.ylim([0, 1.25])
plt.show()
def main():
filepath = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
savepath = filepath
filename = sys.argv[1]
catalogue = filename[0:-33]
#file format: ID x y z Vx Vy Vz Mag with headers
data = table.Table.read(filepath + filename, format='ascii')
data = np.array(data)
dtype = [('k', '>i8'), ('ID', '>i8'), ('ra', '>f8'), ('dec', '>f8'),
('z', '>f8'), ('app_mag', '>f8')]
dtype = np.dtype(dtype)
mock = np.recarray((len(data), ), dtype=dtype)
mock['ID'] = data['ID']
mock['k'] = np.arange(0, len(mock), 1, dtype=int)
app_mag_lim = 17.77
Lbox = 250.0 #Mpc/h
cosmo = cosmology.core.FlatLambdaCDM(H0=100.0, Om0=0.27) #h=1
c = 299792.458 #speed of light in km/s
#compute comoving distance from observer
r = np.sqrt(data['x']**2 + data['y']**2 + data['z']**2)
#compute radial velocity
ct = data['z'] / r
st = np.sqrt(1.0 - ct**2)
cp = data['x'] / np.sqrt(data['x']**2 + data['y']**2)
sp = data['y'] / np.sqrt(data['x']**2 + data['y']**2)
vr = data['Vx'] * st * cp + data['Vy'] * st * sp + data['Vz'] * ct
#compute cosmological redshift and add contribution from perculiar velocity
y = np.arange(0, 2.0, 0.01)
x = cosmology.funcs.comoving_distance(y, cosmo=cosmo)
f = interp1d(x, y, kind='cubic')
z_cos = f(r)
mock['z'] = z_cos + (vr / c) * (1.0 + z_cos)
#calculate spherical coordinates
theta = np.arccos(data['z'] / r)
phi = np.arccos(cp) #atan(y/x)
#convert spherical coordinates into ra,dec in radians
mock['ra'] = phi
mock['dec'] = (math.pi / 2.0) - theta
#remove galaxies beyond r=L_box
keep_1 = (r < Lbox)
#calculate the luminosity distance
dl = (1.0 + z_cos) * r #flat cosmology
#compute the apparant magnitude
mock['app_mag'] = data['Mag'] + 5.0 * np.log10(dl) + 25.0
#apply apparant magnitude limit
keep_2 = (mock['app_mag'] < app_mag_lim)
keep = (keep_2 & keep_1)
mock = mock[keep]
#determine abs mag limit as afunction of z to overplot
app_mag_r_lim = 17.77
z_bins = np.arange(1.0, 0.001, -0.001)
dm = dist_mod(z_bins, cosmo)
abs_mag_r_lim = app_mag_r_lim - dm
f_r = interp1d(abs_mag_r_lim,
z_bins,
kind='cubic',
bounds_error=False,
fill_value=max(z_bins))
print max(mock['z']), f(Lbox), (max(mock['z']) - f(Lbox)) * c
#convert ra,dec into degrees
mock['ra'] = mock['ra'] * 180.0 / math.pi
mock['dec'] = mock['dec'] * 180.0 / math.pi
#save output
print 'saving ascii version of the catalogue...'
filename = catalogue + '_radec_mock'
print filename
data_table = table.table.Table(data=mock)
data_table.write(savepath + filename + '.dat', format='ascii')
print data_table
def main():
###make sure to change these when running in a new enviorment!###
#location of data directory
filepath_cat1 = cu.get_output_path() + 'processed_data/yang_groupcat/'
filepath_cat2 = cu.get_output_path() + 'processed_data/mpa_dr7/'
#save data to directory...
savepath1 = filepath_cat1 + 'mpa_dr7_match/'
savepath2 = filepath_cat2 + 'yang_groupcat_match/'
#################################################################
catalogues_1=['sample1_L_petro','sample2_L_petro','sample3_L_petro',\
'sample1_M_petro','sample2_M_petro','sample3_M_petro',\
'sample1_L_model','sample2_L_model','sample3_L_model',\
'sample1_M_model','sample2_M_model','sample3_M_model']
catalogues_2 = ['gal_info_gal_totspecsfr_dr7_v5_2']
for catalogue in catalogues_1:
catalogue1 = catalogue
catalogue2 = catalogues_2[0]
print catalogue1, 'match into', catalogue2
f1 = h5py.File(filepath_cat1 + catalogue1 + '.hdf5',
'r') #open catalogue file
GC = f1.get(catalogue1)
f2 = h5py.File(filepath_cat2 + catalogue2 + '.hdf5',
'r') #open catalogue file
W = f2.get(catalogue2)
da = 2.0 * 1.0 / 3600.0 #matching length
result_1 = np.array(
cu.spherematch(GC['RAgal'],
GC['DECgal'],
W['RA'],
W['DEC'],
tol=da,
nnearest=1))
#result_2 = np.array(cu.spherematch(GC['RAgal'], GC['DECgal'], W['RA'], W['DEC'], tol=da, nnearest=2)) #not used
repeats = [
item for item, count in Counter(result_1[1]).iteritems()
if count > 1
] #double matched objects
if len(repeats) > 0:
remove = np.zeros((0, ), dtype=np.int) #which entries to remove
for repeat in repeats:
result_a = np.where(
result_1[1] == repeat
)[0] #find indices of the double matched object into catalogue2
result_b = np.where(
result_1[2][result_a] > np.min(result_1[2][result_a]))[
0] #find less good matches
result_c = result_a[
result_b] #indices of less good matches into catalogue2
remove = np.hstack(
(remove, result_c)) #indices which should be removed
keep = np.arange(0, len(result_1[0]), 1).astype(int)
keep = np.in1d(keep, remove) == False
result = result_1[:, keep]
#unique = np.setdiff1d(result_2[0], result_1[0]) #not used
else:
result = result
filename1 = catalogue2 + '_' + catalogue1 + '_match'
filename2 = catalogue1 + '_' + catalogue2 + '_match'
np.save(savepath1 + filename1, result[1].astype(int))
np.save(savepath2 + filename2, result[0].astype(int))
def main():
import numpy as np
import h5py
import matplotlib.pyplot as plt
import custom_utilities as cu
import sys
catalogue = sys.argv[1]
bins = np.arange(10, 15, 0.1)
bin_centers = (bins[:-1] + bins[1:]) / 2.0
print 'opening mock catalogue:', catalogue + '.hdf5'
#open catalogue
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
f1 = h5py.File(filepath_mock + catalogue + '_extended.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue + '_extended')
mock = np.array(mock)
print 'length:', len(mock)
for name in mock.dtype.names:
print ' ', name
occupied = np.where(mock['M_r,0.1'] != -99)
empty = np.where(mock['M_r,0.1'] == -99)
host = np.where(mock['ID_host'] == -1)[0]
color = mock['g-r']
LHS = 0.7 - 0.032 * (mock['M_r,0.1'] + 16.5) #Weinmann 2006
blue = np.where((color < LHS) & (mock['M_r,0.1'] != -99))[0]
red = np.where((color > LHS) & (mock['M_r,0.1'] != -99))[0]
print len(red), len(blue)
Ngal = np.zeros(len(mock))
Ngal[occupied] = 1
host_ID = mock['ID_host']
host_ID[host] = mock['ID_halo'][host]
mask = np.zeros(len(mock))
mask[:] = 1
avg_N = hod(host_ID, mock['M_host'], Ngal, bins, mask)
print sum(Ngal[np.where(mask == 0)[0]])
mask = np.zeros(len(mock))
mask[red] = 1
avg_N_red = hod(host_ID, mock['M_host'], Ngal, bins, mask)
print sum(Ngal[np.where(mask == 0)[0]])
mask = np.zeros(len(mock))
mask[blue] = 1
avg_N_blue = hod(host_ID, mock['M_host'], Ngal, bins, mask)
print sum(Ngal[np.where(mask == 0)[0]])
plt.figure()
plt.plot(bin_centers, avg_N, color='black')
plt.plot(bin_centers, avg_N_red, color='red')
plt.plot(bin_centers, avg_N_blue, color='blue')
plt.yscale('log')
plt.ylim([0.01, 500])
plt.ylabel('<N>')
plt.xlabel('M')
plt.show()
def main():
catalogue = 'Mr19_age_distribution_matching_mock'
if len(sys.argv) > 1: catalogue = sys.argv[1]
#open mock catalogue
filepath_mock = cu.get_output_path(
) + 'processed_data/hearin_mocks/custom_catalogues/'
print 'opening mock catalogue:', catalogue + '.hdf5'
f1 = h5py.File(filepath_mock + catalogue + '.hdf5',
'r') #open catalogue file
mock = f1.get(catalogue)
centrals = np.array(mock['ID_host'] == -1)
satellites = np.array(mock['ID_host'] != -1)
#galaxy color
color = mock['g-r']
LHS = 0.7 - 0.032 * (mock['M_r,0.1'] + 16.5) #Weinmann 2006
blue = np.array(color < LHS) #indices of blue galaxies
red = np.array(color > LHS) #indicies of red galaxies
massbins = np.arange(11, 15, 0.2)
massbin_centers = (massbins[:-1] + massbins[1:]) / 2.0
mass_inds = np.digitize(mock['M_host'], bins=massbins)
lumbins = np.arange(9.6, 11.8, 0.2)
lumbin_centers = (lumbins[:-1] + lumbins[1:]) / 2.0
lum_inds = np.digitize(solar_lum(mock['M_r,0.1'], 4.64), bins=lumbins)
colorbins = np.arange(0, 1.5, 0.05)
colorbin_centers = (colorbins[:-1] + colorbins[1:]) / 2.0
lum_selection = np.array(lum_inds == 5)
selection = lum_selection & centrals
result_cen = np.histogram(mock['M_host'][selection], bins=massbins)[0]
result_cen = result_cen / np.float(np.sum(result_cen))
selection = lum_selection & satellites
result_sat = np.histogram(mock['M_host'][selection], bins=massbins)[0]
result_sat = result_sat / np.float(np.sum(result_sat))
lower_cen = max(np.where(np.cumsum(result_cen) < 0.25)[0])
upper_cen = min(np.where(np.cumsum(result_cen) > .75)[0])
print lower_cen, upper_cen
'''
plt.figure()
plt.step(massbins[:-1],result_cen,where='pre')
plt.step(massbins[:-1],result_sat,where='pre')
plt.show()
'''
plt.figure()
for i in range(lower_cen, upper_cen):
mass_selection = np.array(mass_inds == i)
selection = mass_selection & lum_selection
selection = selection & centrals
colors = np.sort(mock['g-r'][selection])
N = np.cumsum(np.zeros(len(colors)) + 1.0)
N = N / np.float(len(N))
plt.plot(colors, N, color='black')
c = [(0.1,1,0,0.1),(0.1,1,0,0.2),(0.1,1,0,0.3),(0.1,1,0,0.4),(0.1,1,0,0.5),\
(0.1,1,0,0.6),(0.1,1,0,0.7),(0.1,1,0,0.8),(0.1,1,0,0.9),(0.1,1,0,1),(0.1,1,0,1),(0.1,1,0,1)]
print colors
for i in range(upper_cen, len(massbins) - 1):
print i
mass_selection = np.array(mass_inds == i)
selection = mass_selection & lum_selection
selection = selection & satellites
colors = np.sort(mock['g-r'][selection])
N = np.cumsum(np.zeros(len(colors)) + 1.0)
N = N / np.float(len(N))
print c[i - upper_cen]
plt.plot(colors, N, color=c[i - upper_cen])
plt.xlim([0, 1.5])
plt.ylim([0, 1])
plt.show()
