def add_component(self, catalog_format, catalog_name,
object_name, plot_pivot=False, **ecpl_params):
""" Read necessary parameters from FITS file and plot butterfly
Parameters:
catalog_format = 'hess', 'fermi'
catalog_name = FITS file name
object_name = object name string in 'name' column
Note: Since every catalog has columns with different
names and units, a general SED plotting is not possible.
Instead for each catalog type a handler function that
deals converts to a standard format is called.
@todo: Possibly pass plotting parameters along here by
appending them to the ecpl_params dictionary
-> I don't think this works at the moment!!!"""
from atpy import Table
# Get the catalog from file and initialize some things
self.catalog_format = catalog_format
self.catalog_name = catalog_name
self.object_name = object_name
self.catalog = Table(catalog_name).data
# Build a dictionary of parameters needed for the plot
self.ecpl_params = ecpl_params
self.get_ecpl_params()
# Plot curve
self.plot_ecpl(plot_pivot=plot_pivot, **ecpl_params)
# Plot points if present
if self.plot_points is not None:
# Get the values needed for plotting
e = self.plot_points[0]
f = self.plot_points[1]
f_err = self.plot_points[2]
e_err = self.plot_points[3]
is_ul = self.plot_points[4]
for ii in range(e.size):
self.plot_point(e[ii], f[ii],
f_err=f_err[ii],
e_err=[[e_err[0][ii]], [e_err[1][ii]]],
ul=is_ul[ii])
# Remove so that it doesn't get plotted again.
self.plot_points = None
#program to genorate stellar masses from optical colours and 2mass magnitudes. For
# galaxies that only have a bt mag then we will fit a function to the
# other galaxies giving a fuction for btmag to stellar mass.
# Chris Fuller 15 July 2013
#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/fornax/final_outputs/"
cat = Table(pj(folder,"stellar-mass-fornax_v2.fits"))
###################### Functions ############################################################
# bell et al 2003
# log10 (M/L) - a + (b x color)
def stellar_mass(band, col, sun_band):
#constants used below
a = -0.206
b = 0.135
#distance modules of fornax
dist_mod = 31.24
#convernt to abs mag
Band = band - dist_mod
# making sed fit output for Coma Galaxies for thesis
# Chris Fuller, May 2013
import numpy as np
from atpy import Table
import matplotlib.pyplot as plt
folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder
fname = 'coma_supercluster_cal12_pacscorrected.fits' #input name
cat = Table(folder+fname,type='fits')
cat = cat.where(cat.DMASS_TYPE == 2)
bands = ['100', '160', '250', '350', '500']
#inverse_bands = np.array([500.,350.,250.,160.,100.], dtype=np.float)
#physical constants
h = 6.626e-34
c = 3.0e+8
k = 1.38e-23
########## functions ###############
#this gives a flux in jy for a wavelength, this uses a fixed beta
#wavelength = microns, distance = Mpc, mass = log10 solar mass
def modb(mass,temp,distance,wavelength):
m = (10**mass)*2E30 #converts mass into kg
d = distance*3.08567758E22 #converts Mpc to m
wav = wavelength*1E-6 #convert micron to m
k_l = kappa(wav)
class SED(list):
"""Class to plot GeV -- TeV SEDs
Internally the same units as in the Fermi catalog are used:
- Energies in MeV
- Flux densities in cm^-2 s^-2 MeV^-1
- Fluxes in cm^-2 s^-1
- Energy fluxes in erg cm^-2 s^-1"""
"""
def add_Fermi(self, name):
try:
self._fermi
self.append(self._fermi.sed_component(name))
except
component = catalog.sed_component(name)
self.append(component)
"""
def add(self, names, catalogs):
for name in names:
for catalog in catalogs:
try:
component = catalog.sed_component(name)
self.append(component)
logging.info('%s found in %s',
name, catalog.table.table_name)
except ValueError as e:
logging.warning(e)
logging.warning('%s not found in %s',
name, catalog.table.table_name)
pass
def plot(self, filename='sed.png', xlim=(8e-2, 2e5), ylim=(1e-14, 1e-8)):
import matplotlib.pyplot as plt
plt.figure()
plt.ylabel(r'E$^2$ dF/DE (erg cm$^{-2}$ s$^{-1}$)')
plt.xlabel('Energy (GeV)')
plt.loglog()
logging.info('Plotting {0} components in SED'.format(len(self)))
for component in self:
component.plot()
plt.xlim(xlim)
plt.ylim(ylim)
plt.legend()
logging.info('Writing {0}'.format(filename))
plt.savefig(filename)
def add_component(self, catalog_format, catalog_name,
object_name, plot_pivot=False, **ecpl_params):
""" Read necessary parameters from FITS file and plot butterfly
Parameters:
catalog_format = 'hess', 'fermi'
catalog_name = FITS file name
object_name = object name string in 'name' column
Note: Since every catalog has columns with different
names and units, a general SED plotting is not possible.
Instead for each catalog type a handler function that
deals converts to a standard format is called.
@todo: Possibly pass plotting parameters along here by
appending them to the ecpl_params dictionary
-> I don't think this works at the moment!!!"""
from atpy import Table
# Get the catalog from file and initialize some things
self.catalog_format = catalog_format
self.catalog_name = catalog_name
self.object_name = object_name
self.catalog = Table(catalog_name).data
# Build a dictionary of parameters needed for the plot
self.ecpl_params = ecpl_params
self.get_ecpl_params()
# Plot curve
self.plot_ecpl(plot_pivot=plot_pivot, **ecpl_params)
# Plot points if present
if self.plot_points is not None:
# Get the values needed for plotting
e = self.plot_points[0]
f = self.plot_points[1]
f_err = self.plot_points[2]
e_err = self.plot_points[3]
is_ul = self.plot_points[4]
for ii in range(e.size):
self.plot_point(e[ii], f[ii],
f_err=f_err[ii],
e_err=[[e_err[0][ii]], [e_err[1][ii]]],
ul=is_ul[ii])
# Remove so that it doesn't get plotted again.
self.plot_points = None
def get_ecpl_params(self):
"""Build self.ecpl_params dictionary
by parsing one of the supported catalogs"""
if self.catalog_format == 'hess':
self.get_ecpl_params_hess_cat()
elif self.catalog_format == 'fermi':
self.get_ecpl_params_fermi_cat()
# Change numpy types to regular types
# and replace nan values with 0
for key, value in self.ecpl_params.items():
if isinstance(value, np.float32):
value = float(value)
if isinstance(value, np.int16):
value = int(value)
def get_ecpl_params_fermi_cat(self):
""" Build self.ecpl_params dictionary from Fermi catalog fields """
i = self.find_object_index('source_name')
# Set all plot parameters:
self.ecpl_params['e_pivot'] = self.catalog.field('Pivot_Energy')[i]
self.ecpl_params['e_min'] = 1e2
self.ecpl_params['e_max'] = 1e5
self.ecpl_params['e_cut'] = 0.0
self.ecpl_params['e_cut_err'] = 0.0
self.ecpl_params['e_scale'] = 1
self.ecpl_params['norm'] = self.catalog.field('Flux_Density')[i]
self.ecpl_params['norm_err'] = self.catalog.field('Unc_Flux_Density')[i]
self.ecpl_params['norm_scale'] = 1
self.ecpl_params['index'] = self.catalog.field('Spectral_Index')[i]
self.ecpl_params['index_err'] = self.catalog.field('Unc_Spectral_Index')[i]
self.ecpl_params['color'] = 'green'
self.ecpl_params['butterfly'] = True
# Set flux point data
self.plot_points = self.get_flux_points_fermi(i)
# Add text label
fmt = '%s\n%s, %s\n' + \
r'S = %3.1f, C = %3.1f, $\Gamma = %1.2f \pm %1.2f$'
values = (self.object_name,
self.catalog.field('class1')[i],
self.catalog.field('assoc1')[i],
self.catalog.field('signif_avg')[i],
self.catalog.field('curvature_index')[i],
self.catalog.field('spectral_index')[i],
self.catalog.field('unc_spectral_index')[i]
)
self.ax.text(0.05, 0.95, fmt % values,
horizontalalignment='left',
verticalalignment='top',
transform=self.ax.transAxes)
# 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)
#program to create mass and parameter fuctions
# Chris Fuller, June 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
#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))
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
'font.family': 'serif',
'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)
#import
print 'importing modules...'
from atpy import Table
import numpy as np
from os.path import join as pj
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
#remove numpy runtime warings
#np.seterr(invalid='ignore')
#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
cat = Table(pj(folder,cat_name))
cluster = cat.where(cat['RADIUS_VIR'] <= 1.0)
filament = cat.where(cat['RADIUS_VIR'] > 1.0)
#User variables
params = ['SMASS','Mr'] #, 'g', 'r', 'i', 'z'] #parameters to check against detection rates
detCols = ['F250','NGPFLUX250']#, 'RERUNF250'] #detection rate columns
N = 4 #number of bins
morphological_types = ['early', 'late', 'inter'] #morphological types
morphological_colours = ['r', 'b', 'g'] #colous for each line
xlabs = ['$log_{10}(M_{star}/$M$_{\odot})$','$(M_{r})$']
texts = ['(This paper)', 'NGP H-ATLAS Catalogue', 'RERUN FLUX250']
### Functions ###
def plot_sub(sub, dcol, para, bins):
#program that reads in results from Matt's SED fitter and
# then creates a unfied dust mass cloumn
import numpy as np
from os.path import join as pj
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
# 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('DUST_STARS_BELL', cat.DMASS - cat.SMASS_BELL)
cat.add_column('all', cat.g)
cat.all = 1
#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)
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
#select currentCat galaxies
firCat = cat.where(cat.DMASS_TYPE != 0)
gasCat = cat.where(np.nan_to_num(cat.HI_ALL2) > 0.0)
# various parameters across the hubble sequence
# using the goldmine morphology
# 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
#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))
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
#switches
no_morph = 3 #number of morphology bins between 0 and 1
no_density = 4 #number of sigma and radial density bins between logmin and logmax
sfactor = 0.0
colours = ['r', 'g', 'b']
types = ['early', 'inter', 'late']
from lmfit import minimize, Parameters,report_fit
detect_col = 'F250' #'F250'
#Inputs
cat = Table("/Users/chrisfuller/Documents/phd/herchel/coma/final_outputs/coma_supercluster_cal12_pacscorrected.fits")
#cat = cat.where(cat.SMASS > 9.1)
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
###################### functions ######################
def er(a,b):
return np.sqrt(a) / b
def super_histogram(a, bins, sfactor):
#create temp array to hold histogram
hist = []
#loop through bins
for ii in range(0, len(bins)-1):
width = bins[ii+1] - bins[ii]
# 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 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))
#program to plot STELLAR MASS
# 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 = ['*', '+']
#into the folder and let it work its magic. This dose'nt inlcude that bg fit
# Chris Fuller March 2014
import os
import numpy as np
from os.path import join as pj
import aplpy as ap
from atpy import Table
import matplotlib.pyplot as plt
###################### inputs ######################
gal_name = 'CCC2099'
folder_fig = '/Users/chrisfuller/Dropbox/phd/herchel/coma/fig_gen/' + gal_name + '/'
folder_cat = '/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/'
print "reading in cat"
cat = Table(pj(folder_cat,"coma_supercluster_cal12.fits"))
#select galaxy
galaxy = cat.where(cat.OBJECT == gal_name)
#change dir to folder directory
os.chdir(folder_fig)
#################### functions ###################
#function to plot FIR contours
def plottabw(name,fname,texts,x,y):
print name
#find optical parameters
a,b,pa = galaxy.FULLMAJAX[0],galaxy.FULLMINAX[0], galaxy.PA[0]
#pa = pa + 90.
#program to plot FIR properties
# 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
#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))
selection = cat.where(cat.DMASS_TYPE == 2)
#selection = cat.where((cat.DMASS_TYPE == 2) & (cat.F160 > (43.8*10**-3)*3.0) & (cat.F100 > (24.0*10**-3)*3.0))
types = ['early', 'inter', 'late']
labels = ['early', 'uncertain', 'late']
colours = ['r', 'g', 'b']
ylims = [10, 39, 78]
#quanties on the yaxis
xlabs = ['$Log_{10} (M_{dust} / $M$_{\odot}$)', 'Dust Temp. (K)','$Log_{10} (M_{dust} / M_{stars}$)']
#create figure and subplots
fig, subs = plt.subplots(nrows=3, ncols=3, sharex=False, sharey=False, squeeze=False, figsize = (8., 4.), facecolor='w',edgecolor='w')
# Program to grab SDSS images for the HRS matched to size of SPIRE PLW
# written 29th May 2012
# import modules
import numpy as np
import os
import sys
from os.path import join as pj
from atpy import Table
print 'reading in cats'
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))
cat = cat.where((cat.goldmine < 2) & (cat.goldmine > -10) & (cat.late == 1))
npix=400
pixScale = 0.4
# outfolder
outFolder = '/Users/chrisfuller/Desktop/'
os.chdir(outFolder)
for i in range(len(cat)):
name = cat.OBJECT[i]
coord = cat.GRA2000[i], cat.GDEC2000[i]
outFile = name + "-SDSS.jpg"
from os.path import join as pj
#switches
no_morph = 3 #number of morphology bins between 0 and 1
no_density = 5 #number of sigma and radial density bins between logmin and logmax
sfactor = 1.0
colours = ['r', 'g', 'b']
types = ['Early', 'Inter', 'Late']
morph= 'pS0' #columb morphology data is taken from
detect_col = 'F250_ngp_test' #'F250'
#Inputs
#cat = Table("/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/coma_supercluster_cal12.fits")
cat = Table("/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/test.fits")
###################### functions ######################
def er(a,b):
return np.sqrt(a) / b
def super_histogram(a, bins, sfactor):
#create temp array to hold histogram
hist = []
#loop through bins
for ii in range(0, len(bins)-1):
width = bins[ii+1] - bins[ii]
lower = bins[ii] - width*sfactor
upper = bins[ii+1] + width*sfactor
#program to create mass and parameter fuctions
# Chris Fuller, June 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
#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))
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)
#volumes
volumes = [114.2, 2872.8]#, 62.4, 3.4] #volume Mpc using spere and cylender
#volumes = [114.2, 38782.8]
#volumes= [1.0, 1.0]
#cats
cats = [cat.where(cat.RADIUS_VIR <= 1.0), cat.where(cat.RADIUS_VIR > 1.0)]#, [-1.,3.6E7,0.006], [-0.9,5.7E7,0.7]]
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
prefix = "/Users/ncrighton/Projects/MPIA_QSO_LBG/Cloudy/J0004/comp1"
names = sorted(glob(prefix + "/uvb_k0[0246]/*tilted*tbl"))
fig1 = pl.figure(figsize=(5.1, 4.7))
fig1.subplots_adjust(left=0.2, bottom=0.15, top=0.96, right=0.96)
fig2 = pl.figure(figsize=(5.1, 4.7))
fig2.subplots_adjust(left=0.2, bottom=0.15, top=0.96, right=0.96)
ax1 = fig1.add_subplot(111)
ax2 = fig2.add_subplot(111)
for ax in (ax1, ax2):
N0 = -21
for i, n in enumerate(names):
T = Table(n)
ind = T.energy.searchsorted(0.99)
if i == len(names) - 1:
ref = T
ref.log10jnu = T.log10jnu - T.log10jnu[ind] + N0
color = '0.5' if i != 2 else 'k'
ax.semilogx(T.energy,
T.log10jnu - T.log10jnu[ind] + N0,
'-',
color=color)
puttext(0.04,
0.07,
'$\mathrm{Haardt\ & \ Madau}$\n$2012,\ z=2.5$',
ax=ax1,
ha='left')
#program to create mass and parameter fuctions
# Chris Fuller, June 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
#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))
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)
if len(y) > 3:#program to create functions
#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))
virgo = Table('/Users/chrisfuller/Dropbox/phd/herchel/virgo/virgo-all-data.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
#program to plot STELLAR MASS
# 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))
#convert radius vir to log10
cat.RADIUS_VIR = np.log10(cat.RADIUS_VIR)
#switchs
N_den = 7 #number of density bins
N = 60
bin_type = 'fixed'
coeffs_dict = {} #dic to hold coeffs
#quanties on x axcis
#program that takes 250um flux assumes a dust temp of 20K and then asigns dust mass based on this #Chris Fuller Feb 2014 import numpy as np from atpy import Table from astropy import units as u from os.path import join as pj #Inputs folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder input_cat = 'coma_supercluster_cal12.fits' #input name output_name= 'coma_supercluster_cal12-test.fits' #read in catalogue cat = Table(pj(folder,input_cat)) #column headers flux_col = 'F250' redshift = 0.023100 wav = 250.0 #um T_dust = 20 #K distance = 100.0 #Mpc #all my galaxies are at a fixed distance and thus readshift so the program will #create arrays of these, athought they could be fed into the fuction for each # individual galaxy # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #function that takes a flux at a single wavelenght, and returns a dust mass # # it requires a redshift, distance, flux, and wavelength_observed #
#program to plot STELLAR MASS
# 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]
# 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('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'
#code the reads in the main table and then sets ngp fluxes to 0 if sep gt 3.5 #Chris Fuller, March 2014 #import modules and functions print 'importing modules and functions...' from atpy import Table from numpy import where, float from os.path import join as pj #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
# 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 pylab as plt from atpy import Table prefix = "/Users/ncrighton/Projects/MPIA_QSO_LBG/Cloudy/J0004/comp1" name, = glob(prefix + "/uvb_k04/*tilted*tbl") fig1 = pl.figure(figsize=(7.1,4.7)) fig1.subplots_adjust(left=0.15, bottom=0.15, top=0.96,right=0.96) ax = fig1.add_subplot(111) N0 = -21 ref = Table(name) ind = ref.energy.searchsorted(0.99) ref.log10jnu = ref.log10jnu - ref.log10jnu[ind] + N0 ax.plot(np.log10(ref.energy), ref.log10jnu - ref.log10jnu[ind] + N0, '-', color='k') xvals = np.log10((IP * u.eV).to(u.Ry).value) y = np.interp(xvals, np.log10(ref.energy), ref.log10jnu) from barak.absorb import split_trans_name xdiff = np.diff(xvals) direction = ['up'] previousdown = False
#plot goldmine hist of early late and inter # Chris Fuller, March 2014 #import print 'importing modules...' from atpy import Table from numpy import histogram, arange, sqrt from os.path import join as pj from matplotlib.pyplot import subplots, show, subplots_adjust, legend from matplotlib.ticker import MaxNLocator #Inputs print 'reading in cats' folder = "/Users/chrisfuller/Dropbox/phd/herchel/coma/final_outputs/" # input/output folder cat_name = 'coma_supercluster_cal12_pacscorrected.fits' #input name cat = Table(pj(folder,cat_name)) early = cat.where(cat.early == 1) late = cat.where(cat.late == 1) inter = cat.where(cat.inter == 1) cats = [early, late, inter] cols = ['r', 'b', 'g'] morph = ['early', 'late', 'inter'] fig, subs = subplots(nrows=1, ncols=1, sharex=True, sharey=False, squeeze=True, figsize = (8.0,4.5), facecolor='w',edgecolor='w') sub1 = subs
#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
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))
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
