def get_test_runlist():
"""Get a runlist for the tests"""
from atpy import Table
table = Table()
table.add_column('Run', [18373, 20581])
table.add_column('Start_Time', ['2004-01-19 19:51:26', '2004-04-27 23:31:59'])
table.add_column('Duration', [1580.0, 1682.0])
table.add_column('GLON', [184.557, 359.346])
table.add_column('GLAT', [-5.784, 0.410])
return table
def get_test_runlist():
"""Get a runlist for the tests"""
from atpy import Table
table = Table()
table.add_column('Run', [18373, 20581])
table.add_column('Start_Time',
['2004-01-19 19:51:26', '2004-04-27 23:31:59'])
table.add_column('Duration', [1580.0, 1682.0])
table.add_column('GLON', [184.557, 359.346])
table.add_column('GLAT', [-5.784, 0.410])
return table
'figure.subplot.hspace': .5,
'figure.subplot.wspace': .15,
'figure.subplot.left': 0.05,
'figure.subplot.right': 0.95,
'figure.subplot.top': 0.95,
'figure.subplot.bottom': 0.05,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
def randomFraction(arr, f):
return (np.random.rand(len(arr)) < f)
tablename = sys.argv[1]
t = Table(tablename)
t.add_column('nii_ha', t.nii_6584_flux / t.halpha_flux)
sample_filter = (t.redshift < 0.17) & (t.redshift > 0.04)
sample_filter &= (t.m_r < 17.77)
# Completeness in redshift
bounds = [0.04, 0.17, -23.5, -18.5]
redshift = t.redshift[sample_filter]
Mr = t.r[sample_filter]
set_eps_output_1()
pylab.figure()
pylab.axis(bounds)
pylab.xlabel('Redshift')
# numbers of each sample
# Chris Fuller, July - 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))
cat.add_column('DUST_STARS_BELL', cat.DMASS - cat.SMASS_BELL)
cat.add_column('all', cat.g)
cat.all = 1
#caculated extra columns
cat.add_column('D2G', - cat.HI_ALL2 +cat.DMASS)
cat.add_column('G2S', cat.HI_ALL2 - cat.SMASS)
cat.add_column('SFR2G', cat.SRF - cat.HI_ALL2)
cat.add_column('SFR2D', cat.SRF - cat.DMASS)
cat.add_column('colour', cat.g - cat.r)
#select currentCat galaxies
firCat = cat.where(cat.DMASS_TYPE != 0)
gasCat = cat.where(np.nan_to_num(cat.HI_ALL2) > 0.0)
metalCat = cat.where(np.nan_to_num(cat.METAL) > 0.0)
gasfirCat = cat.where((cat.DMASS_TYPE != 0) & (np.nan_to_num(cat.HI_ALL2) > 0.0))
sedCat = cat.where(cat.DMASS_TYPE == 2)
hCat = cat.where(np.nan_to_num(cat.H) != 0)
jCat = cat.where(np.nan_to_num(cat.J) != 0)
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
import scipy
#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))
cat.add_column('DUST_STARS_BELL', cat.DMASS - cat.SMASS_BELL)
cat.add_column('all', cat.g)
cat.all = 1
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
#select detected galaxies
detected = cat.where(cat.DMASS_TYPE != 0)
#switchs
N_den = 7 #number of density bins
N = 60
bin_type = 'fixed'
types = ['late', 'inter', 'early']#, 'all']
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
from scipy.stats import pearsonr
np.seterr(all='ignore')
#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))
#caculated extra columns
cat.add_column('G2S', cat.GMASS - cat.SMASS)
cat.add_column('G2D', cat.GMASS - cat.DMASS)
cat.add_column('SFR2D', cat.SRF - cat.DMASS)
cat.add_column('colour', cat.g - cat.r)
#select detected galaxies
detected = cat.where((cat.DMASS_TYPE != 0) & (cat.bptclass ==1))
#find min dust
min_dust = min(detected.DMASS)
N = 10 #number of y bins
#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'
# Program to Name galaxies
#scaling relations plots
#Chris Fuller
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
#Inputs
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12_pacscorrected.fits' #input name
cat = Table(pj(folder,fname)
cat.add_column('OBJECT_NAME_PAPER', Mgas)
#cat.write(pj(folder,'test-dust-v2.fits'), overwrite=True)
# Chris Fuller, April 2014
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
import scipy
#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))
cat.add_column('all', cat.g)
cat.all = 1
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
#select detected galaxies
#cat = cat.where(np.nan_to_num(cat.K) != 0.0)
detected = cat.where(cat.DMASS_TYPE != 0)
undetected = cat.where(cat.DMASS_TYPE == 0)
cats = [detected, undetected]
du = ['FIR-detected', 'FIR-undetected']
lisy = ['-', '--']
mark = ['*', '+']
#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'))
xx1 = np.linspace(1.2,2.5,40)
yy1a = 0.5*xx1 - 1.11
xx2 = np.linspace(2.5,3.0,40)
yy2a = xx2 - 2.36
yy1b = yy1a - 0.130
yy2b = yy2a - 0.130
yy1c = yy1a - 0.202
yy2c = yy2a - 0.202
plt.plot(np.log10(cat.W50_1[w90]), np.log10(cat['Si(HI)'][w90]), 'ok')
plt.plot(np.log10(cat.W50_1[w50]), np.log10(cat['Si(HI)'][w50]), 'og')
plt.plot(np.log10(cat.W50_1[w25]), np.log10(cat['Si(HI)'][w25]), 'ob')
plt.plot(np.log10(cat.W50_1[wn90]), np.log10(cat['Si(HI)'][wn90]), 'or')
plt.plot(xx1,yy1a, '-k', xx2, yy2a, '-k', label='90$\%$ completeness')
plt.plot(xx1,yy1b, '--g', xx2, yy2b, '--g', label='50$\%$ completeness')
plt.plot(xx1,yy1c, '--b', xx2, yy2c, '--b', label='25$\%$ completeness')
#plt.legend()
plt.show()
cat.add_column('hiComp', hiComp)
cat.write(pj(folder,'test-v2.fits'))
# Chris Fuller, March 2014
#import
print 'importing modules...'
from atpy import Table
import numpy as np
from os.path import join as pj
#Inputs
print 'reading in cats'
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
cat_name = 'coma_supercluster_cal12.fits' #input name
t = Table(pj(folder,cat_name))
t.add_column('early', [0]*len(t), dtype=np.int16)
t.add_column('late', [0]*len(t), dtype=np.int16)
t.add_column('inter', [0]*len(t), dtype=np.int16)
t['early'][np.where(t.pE0 >= 0.8)[0]] = 1
t['late'][np.where(t.pS0 >= 0.8)[0]] = 1
t['inter'][np.where((t.pE0 < 0.8) & (t.pS0 < 0.8))[0]] = 1
for i in range(len(t)):
total = t.early[i] + t.late[i] + t.inter[i]
if total != 1: print 'error', i, t.early[i], t.late[i], t.inter[i], t.pE0[i], t.pS0[i], t.goldmine[i]
t.write(pj(folder, 'test.fits'), overwrite=True)
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
from scipy.stats import pearsonr
np.seterr(all='ignore')
#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))
#caculated extra columns
cat.add_column('G2D', cat.HI_ALL2 - cat.DMASS)
cat.add_column('G2S', cat.HI_ALL2 - cat.SMASS)
cat.add_column('SFR2D', cat.SRF - cat.DMASS)
cat.add_column('colour', cat.g - cat.r)
#select detected galaxies
detected = cat.where(cat.DMASS_TYPE != 0)
#find min dust
min_dust = min(detected.DMASS)
N = 10 #number of y bins
#columns to plot
plots = [ [['sSFR', 'G2S'], ['colour', 'G2S']]]
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12_pacscorrected.fits' #input name
cat = Table(pj(folder,fname))
virgo = Table('/Users/chrisfuller/Dropbox/phd/herchel/virgo/virgo-all-data-v2.fits')
fornax = Table('/Users/chrisfuller/Dropbox/phd/herchel/fornax/final_outputs/stellar-mass-fornax_final.fits')
cat = cat.where(cat.F250 >= 50.0E-3)
D_coma = 100.0*3.0857E22
D_virgo = 14.0*3.0857E22
D_fornax = 19.0*3.0857E22
#D_virgo = np.array(virgo.MPG*3.0857E22, dtype=np.float64)
#add columns coma
L250 = cat.F250 * (4.*np.pi*D_coma**2) * 10E-26
L250[np.nan_to_num(L250) > 0.0] = np.log10(L250[np.nan_to_num(L250) > 0.0])
cat.add_column('L250', L250)
#add columns virgo
L250_virgo = virgo.F250 * (4.*np.pi*D_virgo**2) * 10E-26
L250_virgo[np.nan_to_num(L250_virgo) > 0.0] = np.log10(L250_virgo[np.nan_to_num(L250_virgo) > 0.0])
virgo.add_column('L250',L250_virgo)
w_detected = np.where(np.nan_to_num(virgo.DMASS) > 6.6)[0]
d250_virgo = np.array([0]*len(virgo))
d250_virgo[w_detected] = 1
virgo.add_column('D250',d250_virgo)
#add columns fornax
L250_fornax = fornax.F250 * (4.*np.pi*D_fornax**2) * 10E-26
L250_fornax[np.nan_to_num(L250_fornax) > 0.0] = np.log10(L250_fornax[np.nan_to_num(L250_fornax) > 0.0])
fornax.add_column('L250',L250_fornax)
w_detected = np.where(np.nan_to_num(fornax.F250) > 0.0)[0]
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
import scipy
#Inputs
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12.fits' #input nametest.fits
#fname = 'test.fits'
cat = Table(pj(folder,fname))
cat.add_column('all', cat.g)
cat.all = 1
#cat = cat.where(cat.bptclass ==1)
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
cat.add_column('G2S', cat.HI_ALL2 - cat.SMASS)
cat.add_column('G2D', cat.HI_ALL2 - cat.DMASS)
#select detected galaxies
#cat = cat.where(np.nan_to_num(cat.K) != 0.0)
detected = cat.where((np.nan_to_num(cat.HI_ALL2) > 0.0) & (cat.D250 == 1))
undetected = cat.where((np.nan_to_num(cat.HI_ALL2) > 0.0) & (cat.D250 == 0))
fname = 'coma_supercluster_cal12.fits' #input name
cat = Table(pj(folder,fname))
virgo = Table('/Users/chrisfuller/Dropbox/phd/herchel/virgo/virgo-all-data-v2.fits')
fornax = Table('/Users/chrisfuller/Dropbox/phd/herchel/fornax/final_outputs/stellar-mass-fornax_final.fits')
cat = cat.where(cat.F250 >= 50.0E-3)
D_coma = 100.0*3.0857E22
D_virgo = 14.0*3.0857E22
D_fornax = 19.0*3.0857E22
#D_virgo = np.array(virgo.MPG*3.0857E22, dtype=np.float64)
#add columns coma
L250 = cat.F250 * (4.*np.pi*D_coma**2) * 10E-26
L250[np.nan_to_num(L250) > 0.0] = np.log10(L250[np.nan_to_num(L250) > 0.0])
cat.add_column('L250', L250)
cat.add_column('colour', cat.g - cat.r)
#cat.add_column('my_morph', cat.pS0 - cat.pE0)
#add columns virgo
L250_virgo = virgo.F250 * (4.*np.pi*D_virgo**2) * 10E-26
L250_virgo[np.nan_to_num(L250_virgo) > 0.0] = np.log10(L250_virgo[np.nan_to_num(L250_virgo) > 0.0])
virgo.add_column('L250',L250_virgo)
w_detected = np.where(np.nan_to_num(virgo.DMASS) > 6.6)[0]
d250_virgo = np.array([0]*len(virgo))
d250_virgo[w_detected] = 1
virgo.add_column('D250',d250_virgo)
#add columns fornax
L250_fornax = fornax.F250 * (4.*np.pi*D_fornax**2) * 10E-26
L250_fornax[np.nan_to_num(L250_fornax) > 0.0] = np.log10(L250_fornax[np.nan_to_num(L250_fornax) > 0.0])
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
#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))
#convert radius vir to log10
cat.add_column('G2S', cat.GMASS - cat.SMASS)
cat.add_column('G2D', cat.GMASS - cat.DMASS)
cat.add_column('SFR2D', cat.SRF - cat.DMASS)
w1 = np.where(cat.goldmine == 13)[0]
cat.goldmine[w1] = 9
w2 = np.where(cat.goldmine == 18)[0]
cat.goldmine[w2] = 8
#select detected galaxies
#cat = cat.where(np.nan_to_num(cat.K) != 0.0)
cat = cat.where((np.nan_to_num(cat.goldmine+10) != 0.0) & (cat.goldmine > -1000.0) & (cat.D250 == 1))
cols = ['SMASS', 'DUST_STARS', 'DMASS', 'sSFR','SRF', 'SFR2D']
labs = ['$\log_{10} (M_{stars} / $M$_{\odot}$)', '$\log_{10} (M_{dust} / M_{star}$)', '$\log_{10} (M_{dust} / $M$_{\odot}$)','$\log_{10}$(sSFR) (Yr$^{-1}$)','$\log_{10}$(SFR) (Yr$^{-1}$)', '$\log_{10}$($SFR/M_{Dust}$)']
# Chris Fuller, April 2014
#import mods
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
import scipy
#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))
cat.add_column('all', cat.g)
cat.all = 1
cat.add_column('colour', cat.g - cat.r)
#cat = cat.where(cat.bptclass ==1)
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
#select detected galaxies
#cat = cat.where(np.nan_to_num(cat.K) != 0.0)
detected = cat.where((cat.DMASS_TYPE != 0) & (np.nan_to_num(cat.METAL) > 0.0))
undetected = cat.where((cat.DMASS_TYPE == 0) & (np.nan_to_num(cat.METAL) > 0.0))
cats = [detected, undetected]
du = ['FIR-detected', 'FIR-undetected']
from atpy import Table
import matplotlib.pyplot as plt
import pylab as pl
cat = Table("/Users/chrisfuller/Dropbox/phd/herchel/coma/sed-fits/sed-all.fits")
bands = ["F500","F350","F250","F160","F100"]
""" Create detected column """
cat.remove_columns(['DETECTED'])
""" Loop through cat bands and add number to detected col """
for i in range(len(bands)):
band = bands[i]
cat.add_column('D' + band[-3:], [0]*len(cat))
flux = np.nan_to_num(cat[band])
w = np.where(flux != 0.0)[0]
cat['D' + band[-3:]][w] = 1
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 """
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
from matplotlib.ticker import MaxNLocator
from scipy.stats import pearsonr
np.seterr(all='ignore')
#Inputs
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12_pacscorrected.fits' #input name
cat = Table(pj(folder,fname))
#caculated extra columns
cat.add_column('D2G', - cat.HI_ALL2 +cat.DMASS)
cat.add_column('G2S', cat.HI_ALL2 - cat.SMASS)
cat.add_column('SFR2G', cat.SRF - cat.HI_ALL2)
cat.add_column('SFR2D', cat.SRF - cat.DMASS)
cat.add_column('colour', cat.g - cat.r)
#select currentCat galaxies
firCat = cat.where(cat.DMASS_TYPE != 0)
gasCat = cat.where(np.nan_to_num(cat.HI_ALL2) > 0.0)
metalCat = cat.where(np.nan_to_num(cat.METAL) > 0.0)
gasfirCat = cat.where((cat.DMASS_TYPE != 0) & (np.nan_to_num(cat.HI_ALL2) > 0.0))
sfCat = cat.where(cat.bptclass == 1.0)
sffirCat = cat.where((cat.DMASS_TYPE != 0) & (cat.bptclass == 1.0))
firsfmetalCat = cat.where((cat.DMASS_TYPE != 0) & (cat.bptclass == 1.0) & (np.nan_to_num(cat.METAL) > 0.0))
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'))
