Python atpy.Table类代码示例

    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 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.

#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

#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]

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

#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)

# 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"

#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]]

# 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)

#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

# 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)) 

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