#inputs
print 'reading in data...'
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
cat_name = 'coma_supercluster_cal12.fits' #input name
cat = Table(pj(folder,cat_name))
#bands
bands = ['500', '350', '250'] #f250_1a
#find where sep is greater than 3.5"
w_sp = where(cat.Separation_mine_npg > 3.5)[0]
#loop through bands
for band in bands:
print 'starting ' + band
#extract fluxes
new_fluxes = cat['f' + band + '_1a']
w_sn = where((new_fluxes / cat['e' + band + '_1a']) < 3.0)[0]
#set all fluxes greater than 3.5 to 0
new_fluxes[w_sp] = 0.0
#set all fluxes to 0 that are less than s/n 3
new_fluxes[w_sn] = 0.0
#add new col to table with fluxes
cat.add_column('NGPFLUX' + band, new_fluxes, unit='Jy', dtype=float)
cat.write(pj(folder, 'test.fits'), overwrite=True)
print 'program complete'
cat['SMASS'] = 1.0
cat['SMASS'][w] = log_mass
#extract all galaxies without colours and K band magnites
if True:
for i in range(0, len(cat)):
#test to see if there is already a stellar mass there
if cat.SMASS[i] == 1.0: cat.SMASS[i] = BT2Stellar(cat.BTmag[i])
else:
print 'mass already present from K band and colour'
#load complete cols into mass and BT
mass = cat.SMASS
BT = cat.BTmag
cat.write(pj(folder,'stellar-mass-fornax_final.fits'),overwrite=True)
figM = plt.figure(figsize=(4.5, 4.5), facecolor='w', edgecolor='k')
subfi = plt.subplot(1,1,1)
#plot
subfi.plot(BT, BT2Stellar(BT), 'k')
#subfi.scatter(BT, mass, s=30, c='r', marker='+')
subfi.scatter(BT[w], mass[w], s=30, c='b', marker='+')
#subfi.xaxis_inverted()
subfi.set_ylabel('$\log_{10}$$(M_{star} / M_{\odot})$')
subfi.set_xlabel('$m_{BT}$')
subfi.set_xlim(9.8 ,15.8)
subfi.set_ylim(8.4 ,11.5)
plt.subplots_adjust(left =0.175, bottom= 0.125, hspace=0.0,wspace=0.0)
figM.savefig(pj('/Users/chrisfuller/Dropbox/phd/papers/fornax','BT2Stellarmass.pdf'))
plt.show()
#program to create dust-to-stars ratio
# Chris Fuller, April 2014
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
#Inputs
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12.fits' #input name
cat = Table(pj(folder,fname))
w1 = np.where(cat.D250==1)[0]
new_col = np.array([0.0]*len(cat))
new_col[w1] = cat.DMASS[w1] - cat.SMASS[w1]
cat.add_column('DUST_STARS', new_col)
cat.write(pj(folder,'test-dust-v2.fits'))
total = cat.D500 + cat.D350 + cat.D250 + cat.D160 + cat.D100
five_cluster = cat.where((total == 5) & (cat.RADIUS_VIR <= 1.0))
five_filament = cat.where((total == 5) & (cat.RADIUS_VIR > 1.0))
""" create new columum dmass and dmass_type """
dmass, dmass_type = np.array([0.0]*len(cat)) , np.array([0]*len(cat))
w_five = np.where(total == 5)[0]
w_250 = np.where((total != 5) & (total != 0))[0]
dmass[w_five] = cat.DMASS_SED[w_five]
dmass_type[w_five] = 2
dmass[w_250] = cat.DMASS_250[w_250]
dmass_type[w_250] = 1
cat.add_column('DMASS', dmass)
cat.add_column('DMASS_TYPE', dmass_type)
cat.write('/Users/chrisfuller/Dropbox/phd/herchel/coma/sed-fits/test.fits', overwrite=True)
cluster = cat.
#Inputs
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
ngp = Table(pj(folder, 'coma_supercluster_cal12.fits'),type='fits')
#create a kdtree
tree_npg = spatial.cKDTree(zip(ngp['GRA2000'], ngp['GDEC2000']))
def mod(x):
return np.sqrt(x**2)
#function to caculate veldis for nth nearst neigbours
def vel_dis_n(n):
#query kdtree
distance, ind = tree_npg.query(zip(ngp['GRA2000'], ngp['GDEC2000']), k=n)
#create empty list of velocity dispersions
vel_dis = []
#loop through cat
for i in range(len(ind)): vel_dis.append(np.mean(mod(ngp['velocity'][ind[i]]-7000.0)))
#add empty col to cat
ngp.add_column('dispersion_'+str(n), vel_dis, unit='km/s', null='', dtype=np.float)
#pool = Pool(4)
#pool.map(vel_dis_n, [5,10,20,30,40,50,100])
for n in [5,10,20,30,40,50,100]: vel_dis_n(n)
ngp.write(pj('/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/', 'test.fits'), overwrite=True)
for i in range(0,len(cat)): #cluster if (vel[i] > vmin) and (vel[i] < vmax) and (rad[i] < rmax): cat['TYPE'][i] = 1 #filament elif (vel[i] > vmin) and (vel[i] < vmax) and (rad[i] > rmax): cat['TYPE'][i] = 2 #bg elif (vel[i] < vmin) or (vel[i] > vmax): cat['TYPE'][i] = 3 else: raise 'error' cat.write(pj(folder, outname), overwrite=True) ##### functions ##### def catsplit(val, name): newcat = deepcopy(cat.where(cat['TYPE'] == val)) newcat.write(pj(folder,name), overwrite=True) def catnot(val, name): newcat = deepcopy(cat.where(cat['TYPE'] != val)) newcat.write(pj(folder,name), overwrite=True) # split catalouge into filmament and cluster if True: catsplit(1, 'coma_cluster-mgbg-130114-3.1arcsec.fits') catsplit(2, 'filament-mgbg-130114-3.1arcsec.fits') catnot(3,'cluster+filament-mybg-130114-3.1arcsec.fits')
#edit it table #100 cat.F100[rem_100] = 0.0 cat.SN100[rem_100] = 0.0 cat.R100[rem_100] = 0.0 cat.D100[rem_100] = 0 cat.DMASS_TYPE[rem_100] = 1 cat.DMASS[rem_100] = cat.DMASS_250[rem_100] cat.DUST_STARS[rem_100] = cat.DMASS[rem_100] - cat.SMASS[rem_100] #100 cat.F160[rem_160] = 0.0 cat.SN160[rem_160] = 0.0 cat.R160[rem_160] = 0.0 cat.D160[rem_160] = 0 cat.DMASS_TYPE[rem_160] = 1 cat.DMASS[rem_160] = cat.DMASS_250[rem_160] cat.DUST_STARS[rem_160] = cat.DMASS[rem_160] - cat.SMASS[rem_160] #change dmass_type #cat.DMASS_TYPE[sed_change_type] = 1 #cat.DMASS[sed_change_type] = cat.DMASS_250[sed_change_type] #cat.DUST_STARS[sed_change_type] = cat.DMASS[sed_change_type] - cat.SMASS[sed_change_type] cat.write(pj(folder,"coma_supercluster_cal12_pacscorrected.fits"), overwrite=True)
"""
ecols = ["SPIRE250_e","SPIRE350_e","SPIRE500_e"]
ecols2 = ["SPIRE250_error","SPIRE350_error","SPIRE500_error"]
flux = ["SPIRE250","SPIRE350","SPIRE500"]
for col in range(0,len(ecols)):
#cat.add_empty_column(ecols2[col],dtype=np.string_)
for i in range(0,len(cat)):
f = cat[flux[col]][i]
e = cat[ecols[col]][i]
if str(f).strip() == "-":
print f,e," vals"
fname = 'coma_supercluster_cal12.fits' #input name
cat = Table(pj(folder,fname))
gas1 = np.nan_to_num(cat.GMASS2)
gas2 = np.nan_to_num(cat.logMsun)
gasTot = np.array([0.0]*len(cat))
#loop through col and select gas data
g1 = 0
g2 = 0
for i in range(len(cat)):
if gas2[i] > 0.0:
gasTot[i] = gas2[i]
g2 += 1
elif gas1[i] > 0.0:
gasTot[i] = gas1[i]
g1 += 1
print g1
print g2
cat.add_column('HI_ALL2', gasTot)
cat.write(pj(folder,'test1.fits'))
