

In [1]:

    
%pylab inline

from __future__ import division

import os
import copy
import argparse
import fnmatch
import numpy as np

import scipy
from scipy.interpolate import interp1d

# Astropy
from astropy.io import fits
from astropy    import units as u
from astropy.stats import sigma_clip
# AstroML
from astroML.plotting import hist

# Matplotlib related
# Matplotlib default settings
rcdef = plt.rcParams.copy()
pylab.rcParams['figure.figsize'] = 12, 10
pylab.rcParams['xtick.major.size'] = 8.0
pylab.rcParams['xtick.major.width'] = 2.5
pylab.rcParams['xtick.minor.size'] = 4.0
pylab.rcParams['xtick.minor.width'] = 2.5
pylab.rcParams['ytick.major.size'] = 8.0
pylab.rcParams['ytick.major.width'] = 2.5
pylab.rcParams['ytick.minor.size'] = 4.0
pylab.rcParams['ytick.minor.width'] = 2.5

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from matplotlib.ticker import NullFormatter
from matplotlib.ticker import MaxNLocator

# Personal
import hscUtils as hUtil
import galSBP

import cPickle as pickle

amag_sun_g = 5.33
amag_sun_r = 4.67
amag_sun_i = 4.48
amag_sun_z = 4.42









    



Populating the interactive namespace from numpy and matplotlib



In [ ]:

    
# Location of the cutouts 

""" On Astro3 / For MacOS"""
baseDir = '/Users/songhuang/astro3/hscs' 
redBCG = os.path.join(baseDir, 'redmapper')
nonBCG = os.path.join(baseDir, 'nonbcg')

A Few Useful Functions



In [2]:

    
def normProfile(rad, sbp, minRad, maxRad): 
    """
    Normalize the profile 
    """
    offset = np.nanmedian(sbp[(rad >= minRad) & (rad <= maxRad)])
    
    return (sbp-offset)



In [3]:

    
def logAdd(para1, para2):    
    """
    Doc
    """
    return np.log10((10.0 ** np.asarray(para1)) + (10.0 ** np.asarray(para2)))



In [4]:

    
def loadGalfitOutput(pklFile):     
    """
    Load a .pkl file for GALFIT model 
    Return the GalfitParser object 
    """
    if not os.path.isfile(pklFile): 
        raise Exception("XXX Can not find the .pkl file : %s") % pklFile 
    else: 
        return pickle.load(open(pklFile, 'rb'))



In [5]:

    
def findProfile(pattern, loc, verbose=False):
    """
    Find the prefix of the ellipse profiles 
    """
    result = []
    for root, dirs, files in os.walk(loc):
        for name in files:
            if fnmatch.fnmatch(name, pattern):
                result.append(os.path.join(root, name))
                
    if verbose: 
        print "### %d files found !" % len(result)
    
    return result



In [6]:

    
def readProfile(ellFile):
    """
    Load the pickle format 1-D profile
    """
    if os.path.isfile(ellFile):
        return pickle.load(open(ellFile, 'rb'))
    else:
        raise Exception("XXX Can not find the Ellipse file")



In [7]:

    
def getEllipProfile(objid, loc, stage='3', verbose=False):
    
    pattern = '*' + str(objid) + '*_img_ellip_' + stage + '.pkl'
    
    result = findProfile(pattern, loc)
    
    if len(result) == 0: 
        #raise Exception('XXX Can not find the Ellipse profile ! %s' % pattern)
        if verbose:
            print 'XXX Can not find the Ellipse profile ! %s' % pattern
            return None
    elif len(result) == 1: 
        ellProf = readProfile(result[0])
        return ellProf
    else: 
        if verbose:
            print "XXX Warning: More than 1 profiles found! Use the first one : %s" % result[0]
        ellProf = readProfile(result[0])
        return ellProf



In [1]:

    
def pixKpc(redshift, pix=0.168, show=True, npix=1.0):
    """
    The corresponding Kpc size of a pixel 
    """
    
    pixKpc = pix * npix * hUtil.cosmoScale(redshift)
    
    if show:
        print "# %d pixel(s) = %6.3f Kpc" % (npix, pixKpc)
        
    return pixKpc



In [9]:

    
def correctProf(ellProf, redshift, verbose=False, extinction=0.0, zp=27.0, 
                amag_sun=None, corCurve=False):
    
    """
    DOC
    """
    
    scale = hUtil.cosmoScale(redshift)
    distmod = hUtil.cosmoDistMod(redshift)
    
    if verbose: 
        print "### REDSHIFT : %6.4f" % redshift
        print "### SCALE : %7.4f kpc/arcsec" % scale 
        print "### DISTMOD : %7.4f mag" % distmod
    
    sma_kpc = ellProf['sma_asec'] * scale
    if not corCurve:
        abs_mag = -2.5 * np.log10(ellProf['growth_ori']) + zp - extinction - distmod
    else:
        abs_mag = -2.5 * np.log10(ellProf['growth_cor']) + zp - extinction - distmod

    if amag_sun is not None: 
        abs_sbp = (amag_sun + 21.572 - (ellProf['sbp'] - extinction))/2.5 + 6.0
    else:
        abs_sbp = ellProf['sbp'] - extinction

    return sma_kpc, abs_sbp, abs_mag



In [10]:

    
def photoCompare(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
                 pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
                 magStr='cModelAbsMag_i', stage='3', plot=True, color='b', 
                 radius=None, corCurve=False, idStr='objid_dr12', 
                 massStr='logMass', zStr='z', extCorrect=True, ax=None, 
                 marker='o'):
    
    """
    DOC
    """
    
    """ Read in the sample catalog """
    catFile = prefix + '_' + sample + '.fits' 
    catData = fits.open(catFile)[1].data
    
    """ Useful columns """
    gal_id   = catData[idStr]
    gal_z    = catData[zStr]
    
    if extCorrect:
        gal_alam = catData[aStr]
    else: 
        gal_alam = gal_z * 0.0
        
    gal_mag  = catData[magStr]
    gal_logm = catData[massStr]

    """ The "total" magnitude from SBP """
    mag_sbp = [] 
    mag_sdss = []
    
    for ii in range(len(catData)): 
        
        """ Get the Ellipse profile """
        ellProf = getEllipProfile(gal_id[ii], sample, stage=stage)
        
        if ellProf is not None:
            
            """ Get the radius in Kpc, luminosity profile, and growth curve for absolute magnitude """
            sma_kpc, abs_sbp, abs_cog = correctProf(ellProf, gal_z[ii], extinction=gal_alam[ii], 
                                                    zp=27.0, amag_sun=amag_sun, corCurve=corCurve)
            
            """ Distance module """
            distmod = hUtil.cosmoDistMod(gal_z[ii])
            
            if radius is None: 
                mag_use = ellProf['mag_tot'][0] - gal_alam[ii]
                if absMag:
                    mag_use = mag_use - distmod
            else: 
                rsma_use = radius ** 0.25
                rsma_common = np.arange(0, 4.5, 0.1)
                
                indexUse = np.isfinite(abs_cog)
                intrpFunc = interp1d(sma_kpc[indexUse]**0.25, abs_cog[indexUse], kind='linear')
                try:
                    mag_use = intrpFunc(rsma_use) - gal_alam[ii]
                    if not absMag:
                        mag_use = mag_use + distmod
                except Exception:
                    print "### Interpolation range is bad : %d" % gal_id[ii]
                    mag_use = ellProf['mag_tot'][0] - gal_alam[ii]
                    if absMag:
                        mag_use = mag_use - distmod
                
            mag_sbp.append(mag_use) 
            mag_sdss.append(gal_mag[ii])
            
            if verbose:
                print "### %22d -- %6.3f -- %6.3f -- %6.3f" % (gal_id[ii], gal_mag[ii], 
                                                               ellProf['mag_tot'][0], distmod)
    mag_sbp = np.asarray(mag_sbp)
    mag_sdss = np.asarray(mag_sdss)
    
    if plot:
        if ax is not None: 
            ax.scatter(mag_sbp, mag_sdss, marker=marker, color=color)
        else:
            scatter(mag_sbp, mag_sdss, marker=marker, color=color)
        
    return mag_sbp



In [33]:

    
def getStackProfile(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
                    pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
                    magStr='cModelAbsMag_i', stage='3', plot=True, plotAvg=True, 
                    color='b', width=0.9, singleColor='k', 
                    radius=None, corCurve=False, useMedian=True, alpha=0.1,
                    minRsma=0.3, maxRsma=3.5, extCorrect=True, idStr='objid_dr12', 
                    zStr='z', massStr='logMass', rsmaStep=0.1, ax=None, root=None, 
                    idSuffix=None, dirSuffix=None, catFile=None):
    
    """
    DOC
    """
    
    """ Read in the sample catalog """
    if catFile is None:
        catFile = prefix + '_' + sample + '.fits' 
        
    if root is not None: 
        catFile = os.path.join(root, catFile)
        
    if not os.path.isfile(catFile):
        raise Exception('XXX Can not find the input catalog : %s', catFile)
    else:
        catData = fits.open(catFile)[1].data
    
    """ Useful columns """
    gal_id   = catData[idStr]
    gal_z    = catData[zStr]
    print "### Median Redshift : %5.3f" % np.nanmedian(gal_z)
    
    if extCorrect:
        try:
            gal_alam = catData[aStr]
        except Exception: 
            print "### Can not find the column : %s" % aStr 
            gal_alam = gal_z * 0.0
    else:
        gal_alam = gal_z * 0.0
    
    if magStr is not None:
        try:
            gal_mag = catData[magStr]
            print " ## Median Magnitude : %5.2f" % np.nanmedian(gal_mag)
        except Exception:
            print "### Can not find the column : %s" % magStr 
            gal_mag = gal_z * 0.0
    else: 
        gal_mag = gal_z * 0.0 
        
    if massStr is not None:
        try:
            gal_mass = catData[massStr]
            print " ## Median Log Stellar Mass : %5.2f" % np.nanmedian(gal_mass)
        except Exception: 
            print "### Can not find the column : %s" % massStr 
            gal_mass = gal_z * 0.0            
    else: 
        gal_mass = gal_z * 0.0
    
    """ Plot """
    for ii in range(len(catData)): 
        
        if root is not None: 
            profDir = os.path.join(root, sample)
        else: 
            profDir = sample 
            
        if idSuffix is None: 
            pattern = str(gal_id[ii])
        else: 
            pattern = str(gal_id[ii]) + '*' + idSuffix 
            
        if dirSuffix is None: 
            location = profDir
        else: 
            location = profDir + '_' + dirSuffix
            
        ellProf = getEllipProfile(pattern, location, stage=stage)
    
        if ellProf is not None:
            sma_kpc, abs_sbp, abs_cog = correctProf(ellProf, gal_z[ii], 
                                                    extinction=gal_alam[ii], 
                                                    zp=27.0, verbose=False, 
                                                    amag_sun=amag_sun, 
                                                    corCurve=corCurve)
                    
            if plot:
                if ax is not None:
                    ax.plot(sma_kpc**0.25, abs_sbp, singleColor, alpha=alpha, linewidth=width)
                else: 
                    plot(sma_kpc**0.25, abs_sbp, singleColor, alpha=alpha, linewidth=width)  
                        
            intrpFunc = interp1d(sma_kpc**0.25, abs_sbp, 
                                 kind='linear')
            try:
                rsma_common = np.arange(minRsma, maxRsma, rsmaStep)
                asbp_intrp = intrpFunc(rsma_common)
                
                try: 
                    asbp_stack = np.vstack((asbp_stack, asbp_intrp))
                except NameError: 
                    asbp_stack = asbp_intrp
                        
            except Exception as errMsg:
                print errMsg
                print "### Bad interpolation range: %d " % gal_id[ii]
                print "    %5.2f -- %5.2f" % (np.nanmin(sma_kpc**0.25), 
                                              np.nanmax(sma_kpc**0.25))
    
    if useMedian:
        asbp_avg = np.nanmedian(asbp_stack, axis=0) 
    else:
        asbp_avg = np.nanmean(asbp_stack, axis=0) 

    if plotAvg:
        if ax is not None:
            print len(rsma_common)
            print len(asbp_avg)
            ax.plot(rsma_common, asbp_avg, color, linewidth=3.5)
        else:
            plot(rsma_common, asbp_avg, color, linewidth=3.5)
    
    return rsma_common, asbp_avg, asbp_stack

Playground



In [38]:

    
#def photoCompare(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
#                 pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
#                 magStr='cModelAbsMag_i', stage='3', plot=True, color='b', 
#                 radius=None, corCurve=False, idStr='objid_dr12', 
#                 massStr='logMass', zStr='z', extCorrect=True, ax=None, 
#                 marker='o'):

fig = plt.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

maxR = 100.0

print "### Z1"
mag_sbp_z1 = photoCompare('z1', color='k', radius=maxR, ax=ax1)
print "### Z2"
mag_sbp_z2 = photoCompare('z2', color='r', radius=maxR, ax=ax1)
#mag_sbp_z2_b = photoCompare('z2', color='b', radius=100.0)
print "### Z3"
mag_sbp_z3 = photoCompare('z3', color='g', radius=maxR, ax=ax1)
print "### Z4"
mag_sbp_z4 = photoCompare('z4', color='b', radius=maxR, ax=ax1)

ax1.set_xlim(-21.5, -24.5)
ax1.set_ylim(-21.5, -24.5)
ax1.set_xlabel('Absolute Magnitude (HSC)',  size=16.0)
ax1.set_ylabel('Absolute Magnitude (SDSS)', size=16.0)

magRange = np.arange(-25.0, -20.0, 0.2)
ax1.plot(magRange, magRange, 'k')









    



### Z1
### Z2
### Z3





    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: RuntimeWarning: divide by zero encountered in log10
/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: RuntimeWarning: invalid value encountered in log10






    



### Z4






    Out[38]:





[<matplotlib.lines.Line2D at 0x112375910>]



In [43]:

    
fig = plt.figure(figsize=(10, 6))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

hist(mag_sbp_z4, bins='knuth', histtype='stepfilled', alpha=0.5, 
     normed=True, color='b')
hist(mag_sbp_z3, bins='knuth', histtype='stepfilled', alpha=0.3, 
     normed=True, color='g')
hist(mag_sbp_z2, bins='knuth', histtype='step', alpha=0.8, 
     normed=True, color='r', linewidth=3.0)
hist(mag_sbp_z1, bins='knuth', histtype='step', alpha=0.8, 
     normed=True, color='k', linewidth=3.0)

ax1.set_xlabel('Absolute Magnitude', size=19.0)
ax1.set_ylabel('Fraction', size=19.0)
ax1.set_xlim(-21.5, -24.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(19) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(19)









    Out[43]:





(-21.5, -24.5)



In [544]:

    
#def photoCompare(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
#                 pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
#                 magStr='cModelAbsMag_i', stage='3', plot=True, color='b', 
#                 radius=None, corCurve=False, idStr='objid_dr12', 
#                 massStr='logMass', zStr='z', extCorrect=True, ax=None, 
#                 marker='o'):


R1 = 20.0
R2 = 50.0 
R3 = 100.0
R4 = 120.0

sample = 'z2'

print "### R1"
mag_sbp_r1 = photoCompare(sample, radius=R1, plot=None)
print "### R2"
mag_sbp_r2 = photoCompare(sample, radius=R2, plot=None)
print "### R3"
mag_sbp_r3 = photoCompare(sample, radius=R3, plot=None)
#print "### R4"
#mag_sbp_r4 = photoCompare(sample, radius=R4, plot=None)









    



### R1
### R2
### R3






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [555]:

    
fig = plt.figure(figsize=(10, 10))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

magRange = np.arange(-25.0, -20.0, 0.2)
ax1.plot(magRange, magRange, 'k', linestyle='--', linewidth=3.0, alpha=0.8)

ax1.scatter(mag_sbp_r1, mag_sbp_r2, marker='o', color='g', s=75, 
           alpha=0.7)
ax1.scatter(mag_sbp_r1, mag_sbp_r3, marker='o', color='r', s=75,
           alpha=0.5)
#ax1.scatter(mag_sbp_r1, mag_sbp_r4, marker='o', color='b')

ax1.set_xlim(-22.2, -24.3)
ax1.set_ylim(-22.2, -24.3)

ax1.set_xlabel('Absolute Magnitude (within 20 Kpc)',  size=25.0)
ax1.set_ylabel('Absolute Magnitude (within larger aperture)', size=25.0)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(20) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(20) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)

ax1.text(-23.45, -23.0, '$0.3<z<0.4$', size=32.0)
ax1.text(-23.25, -22.8, 'log $(M_{s}/M_{\odot})>11.6$', size=32.0)

ax1.text(-23.35, -22.55, 'within 50 Kpc', color='g', size=32.0)
ax1.text(-23.30, -22.35, 'within 100 Kpc', color='r', size=32.0)









    Out[555]:





<matplotlib.text.Text at 0x114d52e50>



In [559]:

    
fig = plt.figure(figsize=(12, 7.5))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

hist(mag_sbp_r1, bins='knuth', histtype='stepfilled', alpha=0.5, 
     normed=True, color='b')
hist(mag_sbp_r2, bins='knuth', histtype='stepfilled', alpha=0.3, 
     normed=True, color='g')
hist(mag_sbp_r3, bins='knuth', histtype='stepfilled', alpha=0.6, 
     normed=True, color='r', linewidth=3.0)
#hist(mag_sbp_r4, bins='knuth', histtype='step', alpha=0.8, 
#     normed=True, color='k', linewidth=3.0)

ax1.set_xlabel('Magnitude', size=30.0)
ax1.set_ylabel('Fraction', size=30.0)
ax1.set_xlim(-22.01, -24.4)
ax1.set_ylim(0.01, 1.45)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(28) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(28) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)

ax1.text(-22.1, 1.2, '$<20$ Kpc', size=32, color='b')
ax1.text(-22.1, 1.0, '$<50$ Kpc', size=32, color='g')
ax1.text(-22.1, 0.8, '$<100$ Kpc', size=32, color='r')









    Out[559]:





<matplotlib.text.Text at 0x1191abf90>

Average Profile



In [36]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_z1, asbp_avg_z1, asbp_stack_z1 = getStackProfile('z1', minRsma=0.1, maxRsma=3.8,
                                                             ax=ax1, alpha=0.1)

rsma_common_z1m, asbp_avg_z1m, asbp_stack_z1m = getStackProfile('z1', prefix='hsc_xmm_mem', minRsma=0.1, 
                                                                maxRsma=3.8, ax=ax1, alpha=0.7, plotAvg=False,
                                                                width=3.0, singleColor='g')
rsma_common_z1b, asbp_avg_z1b, asbp_stack_z1b = getStackProfile('z1', prefix='hsc_xmm_bcg', minRsma=0.1, 
                                                                maxRsma=3.8, ax=ax1, alpha=0.95, plotAvg=False, 
                                                                width=3.0, singleColor='r')

print asbp_stack_z1.shape

std_stack_z1 = np.nanstd(asbp_stack_z1, axis=0)

#ax1.fill_between(rsma_common_z1, asbp_avg_z1+std_stack_z1, asbp_avg_z1-std_stack_z1,
#                 facecolor='b', alpha=0.2)

ax1.text(2.4, 8.6, '$0.15<z<0.30$', size=42)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=32)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=32)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)









    



### Median Redshift : 0.283
 ## Median Magnitude : -22.30
 ## Median Log Stellar Mass : 11.71
37
37
### Median Redshift : 0.290
 ## Median Magnitude : -22.62
 ## Median Log Stellar Mass : 11.75
### Median Redshift : 0.281
 ## Median Magnitude : -22.85
 ## Median Log Stellar Mass : 11.81
(68, 37)






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [30]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_z2, asbp_avg_z2, asbp_stack_z2 = getStackProfile('z2', minRsma=0.1, maxRsma=3.8,
                                                             ax=ax1, alpha=0.1)
rsma_common_z2m, asbp_avg_z2m, asbp_stack_z2m = getStackProfile('z2', prefix='hsc_xmm_mem', minRsma=0.1, 
                                                                maxRsma=3.8, ax=ax1, alpha=0.5, plotAvg=False,
                                                                width=3.0, singleColor='g')
rsma_common_z2b, asbp_avg_z2b, asbp_stack_z2b = getStackProfile('z2', prefix='hsc_xmm_bcg', minRsma=0.1, 
                                                                maxRsma=3.8, ax=ax1, alpha=0.9, plotAvg=False,
                                                                width=3.0, singleColor='r')

print asbp_stack_z2.shape

std_stack_z2 = np.nanstd(asbp_stack_z2, axis=0)
#ax1.fill_between(rsma_common_z2, asbp_avg_z2+std_stack_z2, asbp_avg_z2-std_stack_z2,
#                 facecolor='b', alpha=0.2)

ax1.text(2.4, 8.6, '$0.30<z<0.40$', size=42)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=32)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=32)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 

ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)









    



### Median Redshift : 0.334
 ## Median Magnitude : -22.37
 ## Median Log Stellar Mass : 11.73
37
37
### Median Redshift : 0.333
 ## Median Magnitude : -22.35
 ## Median Log Stellar Mass : 11.72
### Median Redshift : 0.382
 ## Median Magnitude : -22.49
 ## Median Log Stellar Mass : 11.74
(89, 37)






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [34]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_z3, asbp_avg_z3, asbp_stack_z3 = getStackProfile('z3', minRsma=0.1, maxRsma=3.8,
                                                             ax=ax1, alpha=0.03)
rsma_common_z3m, asbp_avg_z3m, asbp_stack_z3m = getStackProfile('z3', prefix='hsc_xmm_mem', minRsma=0.1, 
                                                                maxRsma=3.6, ax=ax1, alpha=0.5, plotAvg=False,
                                                                width=3.0, singleColor='g')
rsma_common_z3b, asbp_avg_z3b, asbp_stack_z3b = getStackProfile('z3', prefix='hsc_xmm_bcg', minRsma=0.1, 
                                                                maxRsma=3.6, ax=ax1, alpha=0.9, plotAvg=False,
                                                                width=3.0, singleColor='r')

print asbp_stack_z3.shape

std_stack_z3 = np.nanstd(asbp_stack_z3, axis=0)
#ax1.fill_between(rsma_common_z3, asbp_avg_z3+std_stack_z3, asbp_avg_z3-std_stack_z3,
#                 facecolor='b', alpha=0.2)

ax1.text(2.4, 8.6, '$0.40<z<0.50$', size=42)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=32)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=32)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)









    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10
/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: invalid value encountered in log10






    



### Median Redshift : 0.458
 ## Median Magnitude : -22.50
 ## Median Log Stellar Mass : 11.76
37
37
### Median Redshift : 0.447
 ## Median Magnitude : -23.01
 ## Median Log Stellar Mass : 11.93
### Median Redshift : 0.446
 ## Median Magnitude : -23.09
 ## Median Log Stellar Mass : 11.97
(176, 37)



In [528]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_z4, asbp_avg_z4, asbp_stack_z4 = getStackProfile('z4', minRsma=0.1, maxRsma=3.8,
                                                             ax=ax1, alpha=0.05)

print asbp_stack_z4.shape

std_stack_z4 = np.nanstd(asbp_stack_z4, axis=0)
ax1.fill_between(rsma_common_z4, asbp_avg_z4+std_stack_z4, asbp_avg_z4-std_stack_z4,
                 facecolor='b', alpha=0.2)

ax1.text(2.4, 8.6, '$0.50<z<0.60$', size=42)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=32)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=32)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)









    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10
/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: invalid value encountered in log10






    



### Median Redshift : 0.555
 ## Median Magnitude : -22.66
 ## Median Log Stellar Mass : 11.75
(234, 37)



In [193]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot(rsma_common_z1, asbp_avg_z1, 'k', linewidth=3.5)
ax1.fill_between(rsma_common_z1, asbp_avg_z1+std_stack_z1, asbp_avg_z1-std_stack_z1,
                 facecolor='k', alpha=0.08)

ax1.plot(rsma_common_z2, asbp_avg_z2, 'r', linewidth=3.5)
ax1.fill_between(rsma_common_z2, asbp_avg_z2+std_stack_z2, asbp_avg_z2-std_stack_z2,
                 facecolor='r', alpha=0.08)

ax1.plot(rsma_common_z3, asbp_avg_z3, 'g', linewidth=3.5)
ax1.fill_between(rsma_common_z3, asbp_avg_z3+std_stack_z3, asbp_avg_z3-std_stack_z3,
                 facecolor='g', alpha=0.06)

ax1.plot(rsma_common_z4, asbp_avg_z4, 'b', linewidth=3.5)
ax1.fill_between(rsma_common_z4, asbp_avg_z4+std_stack_z4, asbp_avg_z4-std_stack_z4,
                 facecolor='b', alpha=0.05)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)



In [537]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

asbp_avg_z1norm = normProfile(rsma_common_z1, asbp_avg_z1, 2.1, 2.2)
asbp_avg_z2norm = normProfile(rsma_common_z2, asbp_avg_z2, 2.1, 2.2) 
asbp_avg_z3norm = normProfile(rsma_common_z3, asbp_avg_z3, 2.1, 2.2) 
asbp_avg_z4norm = normProfile(rsma_common_z4, asbp_avg_z4, 2.1, 2.2) 

ax1.plot(rsma_common_z1, asbp_avg_z1norm, 'k', linewidth=3.5)
ax1.fill_between(rsma_common_z1, asbp_avg_z1norm+std_stack_z1, asbp_avg_z1norm-std_stack_z1,
                 facecolor='k', alpha=0.08)

ax1.plot(rsma_common_z2, asbp_avg_z2norm, 'r', linewidth=3.5)
ax1.fill_between(rsma_common_z2, asbp_avg_z2norm+std_stack_z2, asbp_avg_z2norm-std_stack_z2,
                 facecolor='r', alpha=0.08)

ax1.plot(rsma_common_z3, asbp_avg_z3norm, 'g', linewidth=3.5)
ax1.fill_between(rsma_common_z3, asbp_avg_z3norm+std_stack_z3, asbp_avg_z3norm-std_stack_z3,
                 facecolor='g', alpha=0.06)

ax1.plot(rsma_common_z4, asbp_avg_z4norm, 'b', linewidth=3.5)
ax1.fill_between(rsma_common_z4, asbp_avg_z4norm+std_stack_z4, asbp_avg_z4norm-std_stack_z4,
                 facecolor='b', alpha=0.05)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=34)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=34)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(-3.9, 2.9)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(34) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(34) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)

ax1.text(0.3, -1.2, '$0.15<z<0.30$', size=42, color='k')
ax1.text(0.3, -1.7, '$0.30<z<0.40$', size=42, color='r')
ax1.text(0.3, -2.2, '$0.40<z<0.50$', size=42, color='g')
ax1.text(0.3, -2.7, '$0.50<z<0.60$', size=42, color='b')

ax1.text(0.3, -3.3, 'Normalized at 20 Kpc', size=32, color='k')

ax1.text(2.15, 2.0, 'log $(M_{s}/M_{\odot}) > 11.6$)', size=35, color='k')









    Out[537]:





<matplotlib.text.Text at 0x1151e36d0>



In [65]:

    
for ii in range(len(catData)): 
    
    ellProf = getEllipProfile(gal_id[ii], loc, stage='3')
    
    if ellProf is not None:
        sma_kpc, asbp_i, amag_i = correctProf(ellProf, gal_z[ii], extinction=gal_ai[ii], zp=27.0, 
                                              verbose=False, amag_sun=amag_sun_i)
    
        plot(sma_kpc**0.25, amag_i, color='k', alpha=0.3)









    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: RuntimeWarning: divide by zero encountered in log10

XMM Good BCGs



In [561]:

    
#def getStackProfile(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
#                    pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
#                    magStr='cModelAbsMag_i', stage='3', plot=True, color='b', 
#                    radius=None, corCurve=False, useMedian=True, 
#                    minRsma=0.3, maxRsma=3.5, extCorrect=True, idStr='objid_dr12', 
#                    zStr='z', massStr='logMass', rsmaStep=0.1, ax=None, root=None, 
#                    idSuffix=None, dirSuffix=None, catFile=None)

clusterRoot = '/Users/songhuang/Downloads/hsc_cutout/xmm_cluster'
clusterCat = 'xmm_cluster_good.fits'
bandUse = 'r'

fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_bcg_r, asbp_avg_bcg_r, asbp_stack_bcg_r = getStackProfile('good', root=clusterRoot, catFile=clusterCat,
                                                                      dirSuffix=bandUse, minRsma=0.1, maxRsma=4.0,
                                                                      ax=ax1, idStr='ID', zStr='Z_LAMBDA', 
                                                                      magStr='IMAG')

print asbp_stack_bcg_r.shape

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)









    



### Median Redshift : 0.335
### Can not find the column : extinction_i
 ## Median Magnitude : 17.56
### Can not find the column : logMass
(40, 39)






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [196]:

    
#def getStackProfile(sample, zp=27.0, prefix='hsc_xmm_log11.6', amag_sun=amag_sun_i, 
#                    pix=0.168, absMag=True, aStr='extinction_i', verbose=False,
#                    magStr='cModelAbsMag_i', stage='3', plot=True, color='b', 
#                    radius=None, corCurve=False, useMedian=True, 
#                    minRsma=0.3, maxRsma=3.5, extCorrect=True, idStr='objid_dr12', 
#                    zStr='z', massStr='logMass', rsmaStep=0.1, ax=None, root=None, 
#                    idSuffix=None, dirSuffix=None, catFile=None)

clusterRoot = '/Users/songhuang/Downloads/hsc_cutout/xmm_cluster'
clusterCat = 'xmm_cluster_good.fits'
bandUse = 'z'


fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_bcg_z, asbp_avg_bcg_z, asbp_stack_bcg_z = getStackProfile('good', root=clusterRoot, catFile=clusterCat,
                                                                      dirSuffix=bandUse, minRsma=0.1, maxRsma=4.0,
                                                                      ax=ax1, idStr='ID', zStr='Z_LAMBDA', 
                                                                      magStr='IMAG')

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)









    



### Median Redshift : 0.335
### Can not find the column : extinction_i
 ## Median Magnitude : 17.56
### Can not find the column : logMass






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [590]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot(rsma_common_z1, asbp_avg_z1, 'k', linewidth=4.0, alpha=0.85)
ax1.fill_between(rsma_common_z1, asbp_avg_z1+std_stack_z1, asbp_avg_z1-std_stack_z1,
                 facecolor='k', alpha=0.1)

ax1.plot(rsma_common_bcg_z, asbp_avg_bcg_z, color='b', linewidth=4.5)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=35)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=35)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)

ax1.text(1.90, 8.6, '40 redMapper BCGs', size=32, color='b')
ax1.text(2.40, 8.2, '$0.10<z<0.40$', size=30, color='b')
ax1.text(2.26, 7.8, '${\t{No\ Mass\ Limit}}$', size=30, color='b')

ax1.text(0.30, 5.7, '68 Massive Galaxies', size=32, color='k')
ax1.text(0.65, 5.3, '$0.15<z<0.30$', size=30, color='k')
ax1.text(0.45, 4.9, 'log $(M_{s}/M_{\odot})>11.6$', size=30, color='k')

ax1.text(0.46, 3.8, 'WIDE/XMM-LSS', size=40, color='k')









    Out[590]:





<matplotlib.text.Text at 0x12160a350>



In [ ]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

asbp_avg_z1norm = normProfile(rsma_common_z1, asbp_avg_z1, 2.2, 2.3)
asbp_avg_z2norm = normProfile(rsma_common_z2, asbp_avg_z2, 2.2, 2.3) 
asbp_avg_z3norm = normProfile(rsma_common_z3, asbp_avg_z3, 2.2, 2.3) 
asbp_avg_z4norm = normProfile(rsma_common_z4, asbp_avg_z4, 2.2, 2.3) 
asbp_avg_bcgNorm_z = normProfile(rsma_common_bcg_z, asbp_avg_bcg_z, 2.2, 2.3)


ax1.plot(rsma_common_z1, asbp_avg_z1norm, 'k', linewidth=3.5)
ax1.fill_between(rsma_common_z1, asbp_avg_z1norm+std_stack_z1, asbp_avg_z1norm-std_stack_z1,
                 facecolor='k', alpha=0.05)

ax1.plot(rsma_common_z2, asbp_avg_z2norm, 'r', linewidth=3.5)
ax1.fill_between(rsma_common_z2, asbp_avg_z2norm+std_stack_z2, asbp_avg_z2norm-std_stack_z2,
                 facecolor='r', alpha=0.05)

ax1.plot(rsma_common_bcg_z, asbp_avg_bcgNorm_z, color='b', linewidth=4.0)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
#ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)

COSMOS Massive



In [175]:

    
cosmosRoot = '/Users/songhuang/Downloads/hsc_cutout/cos_massive'
cosmosCat = 'cos_massive_sample.fits'

fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_cos, asbp_avg_cos, asbp_stack_cos = getStackProfile('prof', root=cosmosRoot, catFile=cosmosCat,
                                                                 minRsma=0.1, maxRsma=3.8, ax=ax1, 
                                                                 idStr='CATAID', zStr='z', 
                                                                 magStr='F814W_08', massStr='logm')

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)









    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10
/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: invalid value encountered in log10






    



### Median Redshift : 0.529
### Can not find the column : extinction_i
 ## Median Magnitude : 20.98
 ## Median Log Stellar Mass : 10.70






    



/usr/local/lib/python2.7/site-packages/scipy/interpolate/interpolate.py:469: RuntimeWarning: invalid value encountered in add
  y_new = slope*(x_new - x_lo)[:, None] + y_lo



In [180]:

    
cosmosRoot = '/Users/songhuang/Downloads/hsc_cutout/cos_massive'
cosmosCat = 'cos_massive_csample1.fits'

fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

rsma_common_compact, asbp_avg_compact, asbp_stack_cospact = getStackProfile('prof', root=cosmosRoot, catFile=cosmosCat,
                                                                 minRsma=0.1, maxRsma=3.8, ax=ax1, 
                                                                 idStr='CATAID', zStr='z', 
                                                                 magStr='F814W_08', massStr='logm')

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)









    



### Median Redshift : 0.701
### Can not find the column : extinction_i
 ## Median Magnitude : 20.85
 ## Median Log Stellar Mass : 11.50






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10



In [591]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

cosmosRoot = '/Users/songhuang/Downloads/hsc_cutout/cos_massive'
cosmosCat = 'cos_massive_sample3.fits'

rsma_common_cos1, asbp_avg_cos1, asbp_stack_cos1 = getStackProfile('prof', root=cosmosRoot, catFile=cosmosCat,
                                                                 minRsma=0.1, maxRsma=3.8, ax=ax1, 
                                                                 idStr='CATAID', zStr='z', singleColor='b',
                                                                 magStr='F814W_08', massStr='logm')

cosmosCat = 'cos_massive_csample1.fits'

rsma_common_compact, asbp_avg_compact, asbp_stack_cospact = getStackProfile('prof', root=cosmosRoot, catFile=cosmosCat,
                                                                 minRsma=0.1, maxRsma=3.8, ax=ax1, color='r',
                                                                 idStr='CATAID', zStr='z', singleColor='r',
                                                                 magStr='F814W_08', massStr='logm')

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=35)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=35)

ax1.set_xlim(0.0, 4.2)
ax1.set_ylim(3.1, 8.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(30) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)

ax1.text(2.20, 7.6, '30 "Normal" ETGs', size=32, color='b')
ax1.text(2.50, 7.2, '$0.60<z<0.80$', size=30, color='b')
ax1.text(2.36, 6.8, 'log $(M_{s}/M_{\odot})>11.3$', size=30, color='b')

ax1.text(0.30, 5.4, '12 "Compact" ETGs', size=32, color='r')
ax1.text(0.65, 5.0, '$0.60<z<0.80$', size=30, color='r')
ax1.text(0.45, 4.6, 'log $(M_{s}/M_{\odot})>11.3$', size=30, color='r')

ax1.text(0.32, 3.6, 'UDeep/COSMOS', size=40, color='k')


#ax1.plot(rsma_common_compact, asbp_avg_compact, color='r', linewidth=3.5)









    



### Median Redshift : 0.692
### Can not find the column : extinction_i
 ## Median Magnitude : 20.78
 ## Median Log Stellar Mass : 11.36
### Median Redshift : 0.701
### Can not find the column : extinction_i
 ## Median Magnitude : 20.85
 ## Median Log Stellar Mass : 11.50






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: RuntimeWarning: divide by zero encountered in log10






    Out[591]:





<matplotlib.text.Text at 0x121ef4910>



In [184]:

    
fig = plt.figure(figsize=(12, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

asbp_avg_cos_norm_1 = normProfile(rsma_common_cos1, asbp_avg_cos1, 2.3, 2.5)
asbp_avg_cos_norm_2 = normProfile(rsma_common_cos2, asbp_avg_cos2, 2.3, 2.5) 
asbp_avg_cos_norm_c = normProfile(rsma_common_compact, asbp_avg_compact, 2.3, 2.5) 

ax1.plot(rsma_common_cos1, asbp_avg_cos_norm_1, 'k', linewidth=3.5)

ax1.plot(rsma_common_cos2, asbp_avg_cos_norm_2, 'b', linewidth=3.5)

ax1.plot(rsma_common_compact, asbp_avg_cos_norm_c, 'r', linewidth=3.5)

ax1.set_xlabel('$R^{1/4}$ (Kpc)', size=24)
ax1.set_ylabel('log $(\mu/[L_{\odot} Kpc^{-2}])$', size=24)

ax1.set_xlim(0.0, 4.2)
#ax1.set_ylim(3.2, 9.5)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(22) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(22)

Test of .pkl file



In [208]:

    
loc = '/Users/songhuang/Downloads/hsc_cutout/cos_massive/galfit'
pklFile = 'cosmos_6006354_HSC-I_full_1ser.pkl'

obj1Ser = loadGalfitOutput(os.path.join(loc, pklFile))



In [211]:

    
print obj1Ser.reduced_chisq



In [212]:

    
obj1Ser.component_1.re * obj1Ser.component_1.ar









    Out[212]:





1.7477975300000002



In [274]:

    
obj1Ser.component_1.n









    Out[274]:





6.9677



In [216]:

    
import coaddCutoutGalfitSimple as gSimple 

aic, bic, hq = gSimple.galfitAIC(obj1Ser)
print aic, bic, hq









    



132882.56314 132980.054414 132887.199341



In [228]:

    
cosmosDir = '/Users/songhuang/Downloads/hsc_cutout/cos_massive'
cosmosPklDir = os.path.join(cosmosDir, 'galfit')

cosmosCat = os.path.join(cosmosDir, 'cos_massive_sample.fits')

cosmosData = fits.open(cosmosCat)[1].data



In [301]:

    
pix = 0.168
chisqThr = 4.0

galZ   = []
galMs  = []
galRe  = []
galEll = []
galMag = []
galNs  = []
gal814 = []

galReHSC  = []
galEllHSC = []
galMagHSC = []
galNsHSC = []

chi1Ser = []
aic1Ser = []
bic1Ser = []

for galaxy in cosmosData: 
    
    galId = galaxy['CATAID']
    pattern1Ser = '*' + str(galId) + '*1ser.pkl'
    
    pklFound = findProfile(pattern1Ser, cosmosPklDir)
    
    if len(pklFound) == 1:
        
        pklFile = pklFound[0]
        obj1Ser = loadGalfitOutput(pklFile)
        
        nser = obj1Ser.component_1.n
        chisq = obj1Ser.reduced_chisq
        
        if (chisq <= chisqThr) and (nser < 8.0):
    
            scale = galaxy['scale']
            distmod = hUtil.cosmoDistMod(galaxy['z'])
            
            galZ.append(galaxy['z'])
            galMs.append(galaxy['logm'])
            galRe.append(galaxy['re'])
            galEll.append(galaxy['ell'])
            galMag.append(galaxy['Imag3'] - distmod)
            gal814.append(-2.5 * np.log10(galaxy['FLUX_GIM2D']) + 27.0 - distmod)
            galNs.append(galaxy['SERSIC_N_GIM2D'])
        
            aic, bic, hq = gSimple.galfitAIC(obj1Ser)
        
            chi1Ser.append(chisq)
            aic1Ser.append(aic)
            bic1Ser.append(bic)
        
            galReHSC.append(obj1Ser.component_1.re * 0.168 * scale)
            galEllHSC.append(1.0 - obj1Ser.component_1.ar)
            galMagHSC.append(obj1Ser.component_1.mag - 0.03 - distmod)
            galNsHSC.append(obj1Ser.component_1.n)

            
print "### Number of models : %d" % len(galRe)
        
galZ   = np.asarray(galZ)
galMs  = np.asarray(galMs)
galRe  = np.asarray(galRe)
galEll = np.asarray(galEll)

galReHSC  = np.asarray(galReHSC)
galEllHSC = np.asarray(galEllHSC)
galNsHSC = np.asarray(galNsHSC)

chi1Ser = np.asarray(chi1Ser)
aic1Ser = np.asarray(aic1Ser)
bic1Ser = np.asarray(bic1Ser)









    



### Number of models : 1030



In [594]:

    
fig = plt.figure(figsize=(13, 13))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([0.0,0.5,1.1], [0.0,0.5,1.1], 'k--', alpha=0.6,
        linewidth=3.5)

sca = ax1.scatter(galEll, galEllHSC, alpha=0.5, c=galZ, s=(galMs-9.8)*80.0)

ax1.set_xlabel('Ellipticity (HST/ACS F814W)', size=32)
ax1.set_ylabel('Ellipticity (HSC/$i$-band)', size=32)

ax1.set_xlim(0.01, 0.899)
ax1.set_ylim(0.01, 0.899)

cbar = fig.colorbar(sca)
cbar.set_label('Redshift', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)



In [597]:

    
fig = plt.figure(figsize=(13, 13))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([-3.0,105.1], [-3.0, 105.1], 'k--', alpha=0.6, linewidth=3.5)

sca = ax1.scatter(np.log10(galRe), np.log10(galReHSC), alpha=0.5, 
                  c=galZ, s=(galMs-9.8)*80.0)

ax1.set_xlabel('log ($R_e$/Kpc) (HST/ACS F814W)', size=32)
ax1.set_ylabel('log ($R_e$/Kpc) (HSC/$i$-band)', size=32)

ax1.set_xlim(-0.49, 1.89)
ax1.set_ylim(-0.49, 1.89)

cbar = fig.colorbar(sca)
cbar.set_label('Redshift', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)



In [596]:

    
fig = plt.figure(figsize=(13, 13))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([-3.0,105.1], [-3.0, 105.1], 'k--', alpha=0.6, linewidth=3.5)

sca = ax1.scatter(np.log10(galRe), np.log10(galReHSC), alpha=0.5, 
                  c=galNsHSC, s=(galMs-9.8)*80.0)

ax1.set_xlabel('log ($R_e$/Kpc) (HST/ACS F814W)', size=32)
ax1.set_ylabel('log ($R_e$/Kpc) (HSC/$i$-band)', size=32)

ax1.set_xlim(-0.49, 1.89)
ax1.set_ylim(-0.49, 1.89)

cbar = fig.colorbar(sca)
cbar.set_label('Sersic Index', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)



In [302]:

    
fig = plt.figure(figsize=(13.5, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([-25.0,-10.1], [-25.0, -10.1], 'k--', alpha=0.6, 
         linewidth=3.5)

sca = ax1.scatter(galMag, galMagHSC, c=galZ, alpha=0.5, 
                  s=(galMs-9.8)*80.0)

ax1.set_xlabel('Magnitude (Suprime-Cam i-band)', size=24)
ax1.set_ylabel('Magnitude (HSC/$i$-band)', size=24)

ax1.set_xlim(-19.0, -25.5)
ax1.set_ylim(-19.0, -25.5)

cbar = fig.colorbar(sca)
cbar.set_label('Redshift', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(24) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(24)



In [303]:

    
fig = plt.figure(figsize=(13.5, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([-25.0,-10.1], [-25.0, -10.1], 'k--', alpha=0.6, 
         linewidth=3.5)

sca = ax1.scatter(galMag, galMagHSC, c=galNsHSC, alpha=0.5, 
                  s=(galMs-9.8)*80.0)

ax1.set_xlabel('Magnitude (Suprime-Cam i-band)', size=24)
ax1.set_ylabel('Magnitude (HSC/$i$-band)', size=24)

ax1.set_xlim(-19.0, -25.5)
ax1.set_ylim(-19.0, -25.5)

cbar = fig.colorbar(sca)
cbar.set_label('Sersic Index', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(24) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(24)



In [595]:

    
fig = plt.figure(figsize=(13, 13))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([-25.9,-10.1], [-25.9, -10.1], 'k--', alpha=0.6, 
         linewidth=3.5)

sca = ax1.scatter(np.asarray(gal814)+7.2, galMagHSC, c=galNsHSC, alpha=0.5, 
                  s=(galMs-9.8)*80.0)

ax1.set_xlabel('Magnitude (HST/ACS F814W)', size=32)
ax1.set_ylabel('Magnitude (HSC/$i$-band)', size=32)

ax1.set_xlim(-19.0, -25.5)
ax1.set_ylim(-19.0, -25.5)

cbar = fig.colorbar(sca)
cbar.set_label('Sersic Index', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(26) 
    
ax1.spines['top'].set_linewidth(3.5)
ax1.spines['right'].set_linewidth(3.5)
ax1.spines['bottom'].set_linewidth(3.5)
ax1.spines['left'].set_linewidth(3.5)



In [322]:

    
fig = plt.figure(figsize=(13.5, 12))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

ax1.plot([0.0, 8.5], [0.0, 8.5], 'k--', alpha=0.6, 
         linewidth=3.5)

sca = ax1.scatter(galNs, galNsHSC, c=galZ, alpha=0.5, 
                  s=(galMs-9.8)*80.0)

ax1.set_xlabel('Sersic Index (GIM2D - HST/F814W)', size=24)
ax1.set_ylabel('Sersic Index (GALFIT - HSC/$i$-band)', size=24)

ax1.set_xlim(0.1, 8.4)
ax1.set_ylim(0.1, 8.4)

cbar = fig.colorbar(sca)
cbar.set_label('Redshift', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(24) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(24)

XMM_ETG & XMM_Massive



In [412]:

    
xmmEtgDir = '/Users/songhuang/Downloads/hsc_cutout/xmm_etg/'

xmmEtg2PklDir = os.path.join(xmmEtgDir, 'xmm_ell_2_galfit')
xmmEtg3PklDir = os.path.join(xmmEtgDir, 'xmm_ell_3_galfit')

xmmEtg2Cat = os.path.join(xmmEtgDir, 'hsc_sdss_xmm_nearby_ell_z0.05_0.13.fits')
xmmEtg3Cat = os.path.join(xmmEtgDir, 'hsc_sdss_xmm_nearby_ell_z0.16_0.24.fits')

xmmEtg2Data = fits.open(xmmEtg2Cat)[1].data
xmmEtg3Data = fits.open(xmmEtg3Cat)[1].data



In [413]:

    
xmassDir = '/Users/songhuang/Downloads/hsc_cutout/xmm_massive/'

xmassZ1PklDir = os.path.join(xmassDir, 'galfit/z1_i')
xmassZ2PklDir = os.path.join(xmassDir, 'galfit/z2_i')
xmassZ3PklDir = os.path.join(xmassDir, 'galfit/z3_i')
xmassZ4PklDir = os.path.join(xmassDir, 'galfit/z4_i')

xmassZ1Cat = os.path.join(xmassDir, 'hsc_xmm_log11.6_z1.fits')
xmassZ2Cat = os.path.join(xmassDir, 'hsc_xmm_log11.6_z2.fits')
xmassZ3Cat = os.path.join(xmassDir, 'hsc_xmm_log11.6_z3.fits')
xmassZ4Cat = os.path.join(xmassDir, 'hsc_xmm_log11.6_z4.fits')

xmassZ1Data = fits.open(xmassZ1Cat)[1].data
xmassZ2Data = fits.open(xmassZ2Cat)[1].data
xmassZ3Data = fits.open(xmassZ3Cat)[1].data
xmassZ4Data = fits.open(xmassZ4Cat)[1].data



In [414]:

    
def get1SerSize(cat, pklDir, zStr='z', idStr='objid_dr12', pix=0.168, 
                chisqThr=4.0, nSerThr=8.0, extinctionStr='extinction_i'):
    
    galUse = np.empty(len(cat), dtype=bool)
    galUse[:] = False
    
    galRe1Ser = []
    galAr1Ser = []
    galMag1Ser = []
    galNs1Ser = []

    chi1Ser = []
    aic1Ser = []
    bic1Ser = []

    for ii, galaxy in enumerate(cat): 
                
        galId = galaxy[idStr]
        pattern1Ser = '*' + str(galId) + '*1ser.pkl'
        pattern2Ser = '*' + str(galId) + '*2ser.pkl'
        
        pkl1SerFound = findProfile(pattern1Ser, pklDir)
        pkl1SerFound = findProfile(pattern1Ser, pklDir)

        if len(pkl1SerFound) == 1:
            
            pkl1SerFile = pkl1SerFound[0]
            obj1Ser = loadGalfitOutput(pkl1SerFile)
            
            if type(obj1Ser) is not numpy.core.records.recarray:
            
                nser1Ser  = obj1Ser.component_1.n
                chisq1Ser = obj1Ser.reduced_chisq
            
                try: 
                    ai = galaxy[extinctionStr]
                except Exception:
                    ai = 0.05
            
                if (chisq1Ser <= chisqThr) and (nser1Ser < nSerThr):
                
                    scale = pixKpc(galaxy[zStr], show=False)
                    distmod = hUtil.cosmoDistMod(galaxy[zStr])
                
                    aic1, bic1, hq1 = gSimple.galfitAIC(obj1Ser)
                
                    chi1Ser.append(chisq1Ser)
                    aic1Ser.append(aic1)
                    bic1Ser.append(bic1)
        
                    galRe1Ser.append(obj1Ser.component_1.re * scale)
                    galAr1Ser.append(obj1Ser.component_1.ar)
                    galMag1Ser.append(obj1Ser.component_1.mag - ai - distmod)
                    galNs1Ser.append(obj1Ser.component_1.n)
                    
                    galUse[ii] = True
                    
    print "### Number of models : %d" % len(galRe1Ser)
    
    return galUse, galRe1Ser, galAr1Ser, galMag1Ser, galNs1Ser, chi1Ser, bic1Ser



In [415]:

    
xEtg2Use, xEtg2Re, xEtg2Ar, xEtg2Mag, xEtg2Ns, xEtg2Chi, xEtg2Bic = get1SerSize(xmmEtg2Data, xmmEtg2PklDir, 
                                                                                zStr='z', idStr='id', pix=0.168, 
                                                                                chisqThr=10.0, nSerThr=8.0, 
                                                                                extinctionStr='extinction_i')
xEtg3Use, xEtg3Re, xEtg3Ar, xEtg3Mag, xEtg3Ns, xEtg3Chi, xEtg3Bic = get1SerSize(xmmEtg3Data, xmmEtg3PklDir, 
                                                                                zStr='z', idStr='id', pix=0.168, 
                                                                                chisqThr=10.0, nSerThr=8.0, 
                                                                                extinctionStr='extinction_i')









    



### Number of models : 19
### Number of models : 156



In [418]:

    
xMass1Use, xMass1Re, xMass1Ar, xMass1Mag, xMass1Ns, xMass1Chi, xMass1Bic = get1SerSize(xmassZ1Data, xmassZ1PklDir, 
                                                                                       zStr='z', idStr='objid_dr12', 
                                                                                       pix=0.168, chisqThr=10.0, 
                                                                                       nSerThr=8.0, 
                                                                                       extinctionStr='extinction_i')
xMass2Use, xMass2Re, xMass2Ar, xMass2Mag, xMass2Ns, xMass2Chi, xMass2Bic = get1SerSize(xmassZ2Data, xmassZ2PklDir, 
                                                                                       zStr='z', idStr='objid_dr12', 
                                                                                       pix=0.168, chisqThr=10.0, 
                                                                                       nSerThr=8.0, 
                                                                                       extinctionStr='extinction_i')
xMass3Use, xMass3Re, xMass3Ar, xMass3Mag, xMass3Ns, xMass3Chi, xMass3Bic = get1SerSize(xmassZ3Data, xmassZ3PklDir, 
                                                                                       zStr='z', idStr='objid_dr12', 
                                                                                       pix=0.168, chisqThr=10.0, 
                                                                                       nSerThr=8.0, 
                                                                                       extinctionStr='extinction_i')
xMass4Use, xMass4Re, xMass4Ar, xMass4Mag, xMass4Ns, xMass4Chi, xMass4Bic = get1SerSize(xmassZ4Data, xmassZ4PklDir, 
                                                                                       zStr='z', idStr='objid_dr12', 
                                                                                       pix=0.168, chisqThr=10.0, 
                                                                                       nSerThr=8.0, 
                                                                                       extinctionStr='extinction_i')









    



### Number of models : 32
### Number of models : 72
### Number of models : 144
### Number of models : 189



In [467]:

    
xEtg2logm = xmmEtg2Data[xEtg2Use]['logms_pca']
xEtg3logm = xmmEtg3Data[xEtg3Use]['logms_pca']
scatter(xEtg2logm, np.log10(np.asarray(xEtg2Re)*np.asarray(xEtg2Ar)))
scatter(xEtg3logm, np.log10(np.asarray(xEtg3Re)*np.asarray(xEtg3Ar)))

xMass1logm = xmassZ1Data[xMass1Use]['logms_pca_bc03']
xMass2logm = xmassZ2Data[xMass2Use]['logms_pca_bc03']
xMass3logm = xmassZ3Data[xMass3Use]['logms_pca_bc03']
xMass4logm = xmassZ4Data[xMass4Use]['logms_pca_bc03']

scatter(xMass1logm, np.log10(np.asarray(xMass1Re)*np.asarray(xMass1Ar)))
scatter(xMass2logm, np.log10(np.asarray(xMass2Re)*np.asarray(xMass2Ar)))
#scatter(xMass3logm, np.log10(np.asarray(xMass3Re)*np.asarray(xMass3Ar)))
#scatter(xMass4logm, np.log10(np.asarray(xMass4Re)*np.asarray(xMass4Ar)))









    Out[467]:





<matplotlib.collections.PathCollection at 0x1155a6190>



In [508]:

    
def get2SerSize(cat, pklDir, zStr='z', idStr='objid_dr12', pix=0.168, 
                chisqThr=4.0, nSerThr=8.0, extinctionStr='extinction_i', 
                factor=0.95, massStr='logms_pca'):
    
    galUse = np.empty(len(cat), dtype=bool)
    galUse[:] = False
    
    galRe1Ser = []
    galAr1Ser = []
    galMag1Ser = []
    galNs1Ser = []

    chi1Ser = []
    aic1Ser = []
    bic1Ser = []
    
    galRe2SerI = []
    galAr2SerI = []
    galMag2SerI = []
    galNs2SerI = []
    
    galRe2SerO = []
    galAr2SerO = []
    galMag2SerO = []
    galNs2SerO = []
    
    innerFrac = []
    
    chi2Ser = []
    aic2Ser = []
    bic2Ser = []
    
    logmI = []
    logmO = []
    
    for ii, galaxy in enumerate(cat): 
                
        galId = galaxy[idStr]
        pattern1Ser = '*' + str(galId) + '*1ser.pkl'
        pattern2Ser = '*' + str(galId) + '*2ser.pkl'
        
        pkl1SerFound = findProfile(pattern1Ser, pklDir)
        pkl2SerFound = findProfile(pattern2Ser, pklDir)

        if (len(pkl1SerFound) == 1) and (len(pkl2SerFound) == 1):
            
            pkl1SerFile = pkl1SerFound[0]
            obj1Ser = loadGalfitOutput(pkl1SerFile)
            
            pkl2SerFile = pkl2SerFound[0]
            obj2Ser = loadGalfitOutput(pkl2SerFile)
            
            if (type(obj1Ser) is not numpy.core.records.recarray) and (
                type(obj2Ser) is not numpy.core.records.recarray):
            
                nser2A = obj1Ser.component_1.n
                nser2B = obj2Ser.component_2.n
                
                chisq1 = obj1Ser.reduced_chisq
                aic1, bic1, hq1 = gSimple.galfitAIC(obj1Ser)
                
                chisq2 = obj1Ser.reduced_chisq
                aic2, bic2, hq2 = gSimple.galfitAIC(obj2Ser)
            
                try: 
                    ai = galaxy[extinctionStr]
                except Exception:
                    ai = 0.05
            
                logmT = galaxy[massStr]

                if (logmT >= 11.2) and (bic2 < bic1) and (aic2 < aic1) and (
                    nser2A <= nSerThr) and (nser2B <= nSerThr):
                
                    scale = pixKpc(galaxy[zStr], show=False)
                    distmod = hUtil.cosmoDistMod(galaxy[zStr])
                
                    chi1Ser.append(chisq1)
                    aic1Ser.append(aic1)
                    bic1Ser.append(bic1)
        
                    galRe1Ser.append(obj1Ser.component_1.re * np.sqrt(obj1Ser.component_1.ar) * scale)
                    galAr1Ser.append(obj1Ser.component_1.ar)
                    galMag1Ser.append(obj1Ser.component_1.mag - ai - distmod)
                    galNs1Ser.append(obj1Ser.component_1.n)
                    
                    """ 2 Sersic part """
                    chi2Ser.append(chisq2)
                    aic2Ser.append(aic2)
                    bic2Ser.append(bic2)
                    
                    if (obj2Ser.component_1.re <= obj2Ser.component_2.re): 
                        compInner = obj2Ser.component_1
                        compOuter = obj2Ser.component_2
                    else: 
                        compInner = obj2Ser.component_2 
                        compOuter = obj2Ser.component_1

                    galRe2SerI.append(compInner.re * scale * np.sqrt(compInner.ar))
                    galAr2SerI.append(compInner.ar)
                    galMag2SerI.append(compInner.mag)
                    galNs2SerI.append(compInner.n)

                    galRe2SerO.append(compOuter.re * scale * np.sqrt(compOuter.ar))
                    galAr2SerO.append(compOuter.ar)
                    galMag2SerO.append(compOuter.mag)
                    galNs2SerO.append(compOuter.n)  
                                        
                    fluxI = 10.0 ** ((27.0 - compInner.mag) / 2.5)
                    fluxO = 10.0 ** ((27.0 - compOuter.mag) / 2.5)
                    innerFrac.append(fluxI / (fluxI + fluxO))
                    
                    logmI.append(logmT + np.log10(fluxI / (fluxI + fluxO)))
                    logmO.append(logmT + np.log10(fluxO / (fluxI + fluxO)))

                    galUse[ii] = True
                    
    print "### Number of models : %d" % len(galRe1Ser)
    
    return galRe2SerI, galRe2SerO, logmI, logmO



In [509]:

    
xEtg3ReI, xEtg3ReO, xEtg3logmI, xEtg3logmO = get2SerSize(xmmEtg3Data, xmmEtg3PklDir, zStr='z', idStr='id', 
                                                         chisqThr=5.0, nSerThr=6.0, massStr='logms_pca')

xEtg2ReI, xEtg2ReO, xEtg2logmI, xEtg2logmO = get2SerSize(xmmEtg2Data, xmmEtg2PklDir, zStr='z', idStr='id', 
                                                         chisqThr=5.0, nSerThr=6.0, massStr='logms_pca')









    



### Number of models : 89
### Number of models : 6



In [510]:

    
xMass1ReI, xMass1ReO, xMass1logmI, xMass1logmO = get2SerSize(xmassZ1Data, xmassZ1PklDir, zStr='z', 
                                                             idStr='objid_dr12', chisqThr=5.0, nSerThr=6.0, 
                                                             massStr='logms_pca_bc03')
xMass2ReI, xMass2ReO, xMass2logmI, xMass2logmO = get2SerSize(xmassZ2Data, xmassZ2PklDir, zStr='z', 
                                                             idStr='objid_dr12', chisqThr=5.0, nSerThr=6.0, 
                                                             massStr='logms_pca_bc03')
xMass3ReI, xMass3ReO, xMass3logmI, xMass3logmO = get2SerSize(xmassZ3Data, xmassZ3PklDir, zStr='z', 
                                                             idStr='objid_dr12', chisqThr=5.0, nSerThr=6.0, 
                                                             massStr='logms_pca_bc03')
xMass4ReI, xMass4ReO, xMass4logmI, xMass4logmO = get2SerSize(xmassZ4Data, xmassZ4PklDir, zStr='z', 
                                                             idStr='objid_dr12', chisqThr=5.0, nSerThr=6.0, 
                                                             massStr='logms_pca_bc03')









    



### Number of models : 24
### Number of models : 47
### Number of models : 77
### Number of models : 91



In [505]:

    
cgsInner = fits.open('cgs_inner.fits')[1].data
cgsOuter = fits.open('cgs_outer.fits')[1].data
cgsMass = fits.open('cgs_massive.fits')[1].data



In [611]:

    
fig = plt.figure(figsize=(11.0, 11.0))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

#ax1.plot([-25.9,-10.1], [-25.9, -10.1], 'k--', alpha=0.6, 
#         linewidth=3.5)

""" CGS """

ax1.scatter(cgsMass['logm_inner']+0.15, np.log10(cgsMass['re_inner']),
            s=90, color='r', linewidth=3.0,
            facecolor='none', alpha=0.2)

ax1.scatter(cgsMass['logm_outer']+0.15, np.log10(cgsMass['re_outer']), 
            s=90, color='b', linewidth=3.0,
            facecolor='none', alpha=0.2)

""" 1 Sersic """
#xEtg2logm = xmmEtg2Data[xEtg2Use]['logms_pca']
#xEtg3logm = xmmEtg3Data[xEtg3Use]['logms_pca']

#ax1.scatter(xEtg2logm, np.log10(np.asarray(xEtg2Re)*np.asarray(xEtg2Ar)), 
#            color='k', alpha=0.6)
#ax1.scatter(xEtg3logm, np.log10(np.asarray(xEtg3Re)*np.asarray(xEtg3Ar)), 
#            color='k', alpha=0.6)

#xMass1logm = xmassZ1Data[xMass1Use]['logms_pca_bc03']
#xMass2logm = xmassZ2Data[xMass2Use]['logms_pca_bc03']
#xMass3logm = xmassZ3Data[xMass3Use]['logms_pca_bc03']
#xMass4logm = xmassZ4Data[xMass4Use]['logms_pca_bc03']

#ax1.scatter(xMass1logm, np.log10(np.asarray(xMass1Re)*np.asarray(xMass1Ar)), 
#            color='k', alpha=0.6)
#ax1.scatter(xMass2logm, np.log10(np.asarray(xMass2Re)*np.asarray(xMass2Ar)), 
#            color='k', alpha=0.6)
#ax1.scatter(xMass3logm, np.log10(np.asarray(xMass3Re)*np.asarray(xMass3Ar)), 
#            color='k', alpha=0.6)
#ax1.scatter(xMass4logm, np.log10(np.asarray(xMass4Re)*np.asarray(xMass4Ar)), 
#            color='k', alpha=0.6)

""" 2 Sersic """

xEtg3logmT = logAdd(xEtg3logmI, xEtg3logmO)
ax1.scatter(xEtg3logmI, np.log10(np.asarray(xEtg3ReI)), s=(xEtg3logmT-10.5)*80, 
            c='r', alpha=0.7, marker='s')
ax1.scatter(xEtg3logmO, np.log10(np.asarray(xEtg3ReO)), s=(xEtg3logmT-10.5)*80, 
            c='b', alpha=0.6)

xEtg2logmT = logAdd(xEtg2logmI, xEtg2logmO)
ax1.scatter(xEtg2logmI, np.log10(np.asarray(xEtg2ReI)), s=(xEtg2logmT-10.5)*80, 
            c='r', alpha=0.7, marker='s')
ax1.scatter(xEtg2logmO, np.log10(np.asarray(xEtg2ReO)), s=(xEtg2logmT-10.5)*80, 
            c='b', alpha=0.6)

xMass1logmT = logAdd(xMass1logmI, xMass1logmO)
ax1.scatter(xMass1logmI, np.log10(np.asarray(xMass1ReI)), s=(xMass1logmT-10.5)*80, 
            c='r', alpha=0.7, marker='s')
ax1.scatter(xMass1logmO, np.log10(np.asarray(xMass1ReO)), s=(xMass1logmT-10.5)*80, 
            c='b', alpha=0.6)

xMass2logmT = logAdd(xMass2logmI, xMass2logmO)
ax1.scatter(xMass2logmI, np.log10(np.asarray(xMass2ReI)), s=(xMass2logmT-10.5)*80, 
            c='r', alpha=0.7, marker='s')
ax1.scatter(xMass2logmO, np.log10(np.asarray(xMass2ReO)), s=(xMass2logmT-10.5)*80, 
            c='b', alpha=0.6)

xMass3logmT = logAdd(xMass3logmI, xMass3logmO)
#ax1.scatter(xMass3logmI, np.log10(np.asarray(xMass3ReI)), s=(xMass3logmT-10.5)*80, 
#            c='r', alpha=0.7, marker='s')
#ax1.scatter(xMass3logmO, np.log10(np.asarray(xMass3ReO)), s=(xMass3logmT-10.5)*80, 
#            c='b', alpha=0.6)

xMass4logmT = logAdd(xMass4logmI, xMass4logmO)
#ax1.scatter(xMass4logmI, np.log10(np.asarray(xMass4ReI)), s=(xMass4logmT-10.0)*80, 
#            c='r', alpha=0.9, marker='s')
#ax1.scatter(xMass4logmO, np.log10(np.asarray(xMass4ReO)), s=(xMass4logmT-10.0)*80, 
#            c='b', alpha=0.8)

ax1.set_xlabel('log ($M_{s}/M_{\odot}$)', size=33)
ax1.set_ylabel('log ($R_{e}$/Kpc)', size=33)

ax1.set_xlim(10.0, 12.2)
ax1.set_ylim(-0.9, 2.4)

#cbar = fig.colorbar(sca)
#cbar.set_label('Sersic Index', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(28) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(28)
    
ax1.spines['top'].set_linewidth(4.0)
ax1.spines['right'].set_linewidth(4.0)
ax1.spines['bottom'].set_linewidth(4.0)
ax1.spines['left'].set_linewidth(4.0)

ax1.text(10.2, 2.0, '~200 Ellipticals in HSC/XMM', size=36)
ax1.text(10.2, 1.7, 'log $(M_{s}/M_{\odot})>11.2$', size=26)
ax1.text(10.2, 1.3, '$0.1<z<0.3$', size=26)

ax1.text(11.52, -0.2, 'Inner', color='r', size=36)
ax1.text(11.5, -0.5, 'Outer', color='b', size=36)









    Out[611]:





<matplotlib.text.Text at 0x1159df250>



In [609]:

    
fig = plt.figure(figsize=(11.0, 11.0))
fig.subplots_adjust(left=0.1, right=0.95, bottom=0.15)
ax1 = fig.add_subplot(111)

#ax1.plot([-25.9,-10.1], [-25.9, -10.1], 'k--', alpha=0.6, 
#         linewidth=3.5)

""" CGS """

ax1.scatter(cgsMass['logm_inner']+0.15, np.log10(cgsMass['re_inner']),
            s=90, color='r', linewidth=3.0, marker='s',
            facecolor='none', alpha=0.5)

ax1.scatter(cgsMass['logm_outer']+0.15, np.log10(cgsMass['re_outer']), 
            s=98, color='b', linewidth=3.0, marker='o',
            facecolor='none', alpha=0.5)

ax1.set_xlabel('log ($M_{s}/M_{\odot}$)', size=33)
ax1.set_ylabel('log ($R_{e}$/Kpc)', size=33)

ax1.set_xlim(10.0, 12.2)
ax1.set_ylim(-0.9, 2.4)

#cbar = fig.colorbar(sca)
#cbar.set_label('Sersic Index', size=20)

for tick in ax1.xaxis.get_major_ticks():
    tick.label.set_fontsize(28) 
for tick in ax1.yaxis.get_major_ticks():
    tick.label.set_fontsize(28)
    
ax1.text(10.2, 2.0, '40 Ellipticals in CGS', size=36)
ax1.text(10.2, 1.7, 'log $(M_{s}/M_{\odot})>11.2$', size=26)

ax1.text(11.52, -0.2, 'Inner', color='r', size=36)
ax1.text(11.5, -0.5, 'Outer', color='b', size=36)
    
ax1.spines['top'].set_linewidth(4.0)
ax1.spines['right'].set_linewidth(4.0)
ax1.spines['bottom'].set_linewidth(4.0)
ax1.spines['left'].set_linewidth(4.0)

Test using z1



In [26]:

    
loc = 'z2'
zp = 27.0

amag_sun_g = 5.33
amag_sun_r = 4.67
amag_sun_i = 4.48
amag_sun_z = 4.42



In [27]:

    
catFile = 'hsc_xmm_log11.6_' + loc + '.fits' 
catData = fits.open(catFile)[1].data

gal_id = catData['objid_dr12']
gal_z  = catData['z']
gal_ai = catData['extinction_i']
gal_amagi = catData['cModelAbsMag_i']
gal_logm = catData['logMass']

ii = 2

ellProf = getEllipProfile(gal_id[ii], loc, stage='3')
sma_kpc, asbp_i, amag_i = correctProf(ellProf, gal_z[ii], extinction=gal_ai[ii], 
                                      zp=27.0, amag_sun=amag_sun_i, corCurve=True)
print gal_id[ii]

plot(sma_kpc**0.25, amag_i)









    



1237679253597913345






    



/usr/local/lib/python2.7/site-packages/IPython/kernel/__main__.py:19: RuntimeWarning: divide by zero encountered in log10






    Out[27]:





[<matplotlib.lines.Line2D at 0x1116c9210>]



In [29]:

    
catData.columns









    Out[29]:





ColDefs(
    name = 'objid_dr12'; format = 'K'; null = -9223372036854775808
    name = 'specobjid_dr12'; format = 'K'; null = -9223372036854775808
    name = 'bestobjid'; format = 'K'; null = -9223372036854775808
    name = 'fluxobjid'; format = 'K'; null = -9223372036854775808
    name = 'plateid'; format = 'K'; null = -9223372036854775808
    name = 'firstRelease'; format = '32A'
    name = 'plate'; format = 'I'; null = -32768
    name = 'mjd'; format = 'J'; null = -2147483648
    name = 'fiberid'; format = 'I'; null = -32768
    name = 'ra_fib'; format = 'D'
    name = 'dec_fib'; format = 'D'
    name = 'ra_pho'; format = 'D'
    name = 'dec_pho'; format = 'D'
    name = 'raerr'; format = 'D'
    name = 'decerr'; format = 'D'
    name = 'z'; format = 'E'
    name = 'zerr'; format = 'E'
    name = 'vdisp_sdss'; format = 'E'
    name = 'vdisperr_sdss'; format = 'E'
    name = 'nchild'; format = 'I'; null = -32768
    name = 'photo_type'; format = 'I'; null = -32768
    name = 'extinction_u'; format = 'E'
    name = 'extinction_g'; format = 'E'
    name = 'extinction_r'; format = 'E'
    name = 'extinction_i'; format = 'E'
    name = 'extinction_z'; format = 'E'
    name = 'logms_pca_bc03'; format = 'D'
    name = 'logms_err_pca_bc03'; format = 'D'
    name = 'vdisp_pca_bc03'; format = 'D'
    name = 'vdisperr_pca_bc03'; format = 'D'
    name = 'cModelAbsMag_u'; format = 'D'
    name = 'cModelAbsMag_g'; format = 'D'
    name = 'cModelAbsMag_r'; format = 'D'
    name = 'cModelAbsMag_i'; format = 'D'
    name = 'cModelAbsMag_z'; format = 'D'
    name = 'logMass'; format = 'D'
    name = 'logMass_median'; format = 'D'
    name = 'logMass_err'; format = 'D'
    name = 'tau'; format = 'D'
    name = 'tau_err'; format = 'D'
    name = 'tau_mean'; format = 'D'
    name = 'age'; format = 'D'
    name = 'age_mean'; format = 'D'
    name = 'age_min'; format = 'D'
    name = 'age_max'; format = 'D'
    name = 'ssfr'; format = 'D'
    name = 'ssfr_mean'; format = 'D'
    name = 'ssfr_min'; format = 'D'
    name = 'ssfr_max'; format = 'D'
)



In [123]:

    
ellProf









    Out[123]:




<Table masked=False length=76>

sma intens int_err pix_var rms ell ell_err pa pa_err x0 x0_err y0 y0_err grad grad_err grad_r_err rsma mag mag_lerr mag_uerr tflux_e tflux_c tmag_e tmag_c npix_e npix_c a3 a3_err b3 b3_err a4 a4_err b4 b4_err ndata nflag niter stop a_big sarea a1 a1_err b1 b1_err a2 a2_err b2 b2_err pa_norm sbp sbp_low sbp_upp sma_asec rsma_asec avg_x0 avg_y0 avg_q avg_pa growth_ori avg_bkg growth_cor rad_outer mag_tot
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
0.0 45.5986121708 nan nan nan nan nan nan nan 502.0 nan 500.0 nan -5.822931 nan nan 0.0 49.85254 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan 1 0 nan nan nan nan nan nan nan nan nan nan nan nan nan 18.9790831117 nan nan 0.0 0.0 502.0 500.0 0.7778232 18.48034 0.0 0.00353782925 0.0 183.933787056 18.2148556008
0.5498202 42.3970521708 0.2931644 1.494851 1.057019 0.2221768 0.06044755 18.48034 9.068295 502.0 0.01763681 500.0 0.02099757 -5.677571 7.906383 1.392564 0.8611031 49.93157 0.00753322 0.007480868 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.01139915 0.03854505 0.02189545 0.04549393 -0.101749 0.1421053 0.02474631 0.03597587 13 0 1.0 0.0 nan 2.0 0.0003243068 0.03301069 -0.01815842 0.03190967 0.0003942768 0.03252446 -0.01780623 0.03138227 18.48034 19.0581166591 19.05063554 19.0655977782 0.0923697936 0.551293019301 502.0 500.0 0.7778232 18.48034 0.0 0.00353782925 0.0 183.933787056 18.2148556008
0.6048022 42.0848821707 0.3209774 1.63667 1.1573 0.2221768 0.05986534 18.48034 8.980952 502.0 0.01921364 500.0 0.02287486 -5.650878 7.867163 1.392202 0.8818675 49.93959 0.008310868 0.008249613 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.01063999 0.03785248 0.02126034 0.044691 -0.1008079 0.1407597 0.02466124 0.03584518 13 0 1.0 0.0 nan 2.0 0.000340561 0.03604436 -0.01967556 0.03484216 0.000404695 0.03552199 -0.01929759 0.03427454 18.48034 19.066139852 19.0578911547 19.0743885494 0.1016067696 0.564586723095 502.0 500.0 0.7778232 18.48034 6.5291087166 0.00353782925 6.52855989885 183.933787056 18.2148556008
0.6652824 41.7431221707 0.3512357 1.790958 1.266398 0.2221768 0.0592742 18.48034 8.892269 502.0 0.0209263 500.0 0.02491388 -5.619387 7.823971 1.392318 0.9031326 49.94845 0.009172891 0.009097167 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.009864141 0.03717054 0.02055876 0.04383769 -0.09984129 0.1394274 0.02461119 0.0357738 13 0 1.0 0.0 nan 2.0 0.0003561079 0.0393402 -0.02128926 0.03802807 0.0004120665 0.03878044 -0.02088478 0.03741855 18.48034 19.0749920715 19.0658954392 19.0840887037 0.1117674432 0.578200987452 502.0 500.0 0.7778232 18.48034 14.365179837 0.00353782925 14.3639669498 183.933787056 18.2148556008
0.7318107 41.3692721708 0.3841477 1.958776 1.385064 0.2221768 0.05858501 18.48034 8.788877 502.0 0.02275128 500.0 0.02708662 -5.586484 7.776958 1.392102 0.9249104 49.95821 0.01012873 0.01003403 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.008959543 0.03639773 0.01976314 0.04286409 -0.09870614 0.1378278 0.02454486 0.0356706 13 0 1.0 0.0 nan 2.0 0.0003688922 0.0428923 -0.02298687 0.0414617 0.0004126597 0.04229444 -0.02255543 0.04080916 18.48034 19.0847588618 19.0747242846 19.0947934389 0.1229441976 0.592143558429 502.0 500.0 0.7778232 18.48034 23.7619273539 0.00353782925 23.7599109417 183.933787056 18.2148556008
0.8049918 40.9604521708 0.4199227 2.141194 1.514053 0.2221768 0.05784043 18.48034 8.677176 502.0 0.02470835 500.0 0.02941661 -5.549232 7.725887 1.392244 0.9472135 49.96899 0.01118767 0.01107287 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.00795107 0.03558345 0.01889768 0.04182085 -0.09747347 0.1361281 0.02445681 0.03554674 13 0 1.0 0.0 nan 2.0 0.0003777329 0.04672648 -0.02476463 0.045168 0.0004039323 0.04609037 -0.02430657 0.04447178 18.48034 19.09554078 19.0844675355 19.1066140245 0.1352386224 0.60642232974 502.0 500.0 0.7778232 18.48034 35.0196360915 0.00353782925 35.0166474144 183.933787056 18.2148556008
0.885491 40.5137421707 0.4587579 2.339215 1.654075 0.2221768 0.05700747 18.48034 8.552217 502.0 0.02678777 500.0 0.03189227 -5.508815 7.670327 1.392373 0.9700543 49.9809 0.01236301 0.01222467 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.006832937 0.03472181 0.01791837 0.04065501 -0.09607911 0.1342019 0.02436516 0.03541612 13 0 1.0 0.0 nan 2.0 0.000381933 0.0508447 -0.02660799 0.04914885 0.0003843021 0.05017089 -0.02612405 0.048409 18.48034 19.1074457019 19.0952215016 19.1196699023 0.148762488 0.62104541276 502.0 500.0 0.7778232 18.48034 48.4929171276 0.00353782925 48.48875201 183.933787056 18.2148556008
0.9740401 40.0259421707 0.5008539 2.553864 1.805854 0.2221768 0.05220751 18.48034 7.832129 502.0 0.0269855 500.0 0.03212769 -5.872277 7.598301 1.293927 0.9934459 49.99405 0.01366984 0.01350135 45.60215 45.60215 49.85254 49.85254 1.0 1.0 0.005201421 0.031372 0.01566387 0.03575424 -0.08798704 0.1142764 0.02257451 0.03069148 13 0 1.0 0.0 nan 2.0 0.0003790492 0.05526703 -0.02850911 0.05342369 0.0003493789 0.05455668 -0.02800098 0.05264077 18.48034 19.1205965375 19.1070959546 19.1340971203 0.1636387368 0.63602110875 502.0 500.0 0.7778232 18.48034 64.5993108559 0.00353782925 64.5937222457 183.933787056 18.2148556008
1.071444 39.4539521708 0.5448861 2.778384 1.964615 0.2221768 0.03840595 18.48034 5.76163 502.0 0.02183679 500.0 0.02599786 -6.844525 7.487941 1.094004 1.017402 50.00968 0.01509825 0.01489057 126.5437 206.6235 48.7444 48.21205 3.0 5.0 0.00634128 0.0234846 0.0124204 0.02549096 -0.0668695 0.07337838 0.01322502 0.01546109 13 0 1.0 0.0 nan 2.0 0.0001810941 0.04072266 -0.02568183 0.03936443 -2.360399e-05 0.039914 -0.02527088 0.03851232 18.48034 19.1362227708 19.1213319368 19.1511136049 0.180002592 0.6513579073 502.0 500.0 0.7778232 18.48034 83.8095457201 0.00353782925 83.8022346857 183.933787056 18.2148556008
1.178589 38.7206021707 0.588872 3.00267 2.123208 0.2221768 0.02763417 18.48034 4.145657 502.0 0.01728341 500.0 0.02057682 -7.455918 7.363017 0.98754 1.041935 50.03005 0.0166365 0.01638724 126.5437 206.6235 48.7444 48.21205 3.0 5.0 0.004050368 0.01688386 0.002099611 0.01590358 -0.04922763 0.04889908 -0.005737036 0.007574377 13 0 1.0 0.0 nan 2.0 -0.001501648 0.04584903 -0.009143668 0.04431981 -0.00329138 0.04577971 -0.009486661 0.04417202 18.48034 19.1565919547 19.1402056113 19.1729782981 0.198002952 0.667064634257 502.0 500.0 0.7778232 18.48034 106.622069987 0.00353782925 106.612674806 183.933787056 18.2148556008
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
269.6161 -0.00229407225 0.0009707853 0.1643595 0.01004188 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -0.0001068721 nan nan 4.052159 61.26316 1.646546 0.6263705 9313.354 11929.65 44.07723 43.80843 177629.0 228373.0 -0.05720736 0.05885445 -0.01420104 0.03990409 -0.1045356 0.09089732 -0.07855418 0.07218049 107 19 1.0 4.0 nan 267.8917 0.002524938 0.001019482 0.001306565 0.0009568091 -0.0008248038 0.001007203 0.0004010799 0.0009609584 18.48034 30.3897075636 29.7633364582 31.016078669 45.2955048 2.59426165195 502.0 500.0 0.7778232 18.48034 3700.35590509 0.00353782925 3071.92509047 183.933787056 18.2148556008
296.5777 0.00170286175 0.001222235 0.2318424 0.01287704 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -5.769885e-05 5.321355e-05 0.9222637 4.149871 59.70153 0.2883214 0.2276033 11131.92 12876.51 43.88357 43.7255 214939.0 276349.0 -0.008158027 0.07248045 -0.08885738 0.1082324 -0.1390571 0.1456371 -0.05441003 0.08679032 111 15 1.0 4.0 nan 324.1557 0.004888365 0.001124922 0.002064241 0.001065518 0.001460899 0.001073107 -0.001815665 0.001065996 18.48034 28.8280750243 28.6004734503 29.0556765983 49.8250536 2.65681884843 502.0 500.0 0.7778232 18.48034 3895.84840022 0.00353782925 3135.446617 183.933787056 18.2148556008
326.2354 -8.35725e-06 0.0009984201 0.2045359 0.01032774 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -5.719997e-05 3.568318e-05 0.6238321 4.24994 60.13073 0.3610216 0.270466 14163.31 15534.17 43.62209 43.52178 260063.0 334401.0 0.03561273 0.06120889 -0.1271876 0.09903508 -0.08366753 0.07576704 0.1640129 0.1163901 107 19 1.0 4.0 nan 392.2193 0.003771417 0.0009755543 -0.001492485 0.0009277947 -0.0005711308 0.0009239655 -0.001917657 0.0009315877 18.48034 29.2572720564 28.986806236 29.5277378767 54.8075472 2.72088440614 502.0 500.0 0.7778232 18.48034 4055.15571332 0.00353782925 3135.06940164 183.933787056 18.2148556008
358.859 -0.00187442325 0.0005985932 0.1342594 0.006162894 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -4.575998e-05 nan nan 4.352422 60.94751 0.4843109 0.333737 15206.32 16220.75 43.54494 43.47482 314693.0 404549.0 0.06340237 0.06776278 -0.04541507 0.05803023 0.02006976 0.04752551 0.05456956 0.06248949 106 20 1.0 4.0 nan 474.5916 -0.000285856 0.0007420002 -0.0008940522 0.0007162092 0.0003658538 0.0007375089 2.451674e-05 0.000718032 18.48034 30.0740507494 29.7403152911 30.4077862077 60.288312 2.78649508322 502.0 500.0 0.7778232 18.48034 4146.00286806 0.00353782925 3032.69750984 183.933787056 18.2148556008
394.7449 -0.00084903525 0.0006861875 0.1700941 0.007097978 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -3.660798e-05 nan nan 4.457375 60.42611 0.3199055 0.246784 15885.9 17874.99 43.49747 43.36938 380737.0 489565.0 0.03302658 0.06377282 0.03340059 0.06606904 -0.008403908 0.05801927 0.09690192 0.09768435 107 19 1.0 4.0 nan 574.2605 -0.0002746453 0.0008420969 0.0001409187 0.0008461322 0.0008916375 0.0008211868 0.0006369927 0.0008534189 18.48034 29.5526525828 29.3058679172 29.7994372484 66.3171432 2.85368775929 502.0 500.0 0.7778232 18.48034 4323.68971284 0.00353782925 2976.58968058 183.933787056 18.2148556008
434.2194 -0.00013574325 0.0008375644 0.2326067 0.008824289 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -2.928639e-05 nan nan 4.564858 60.17064 0.3068489 0.2389604 17213.15 19675.54 43.41035 43.26518 460741.0 592333.0 0.1808012 0.1601208 0.1135316 0.1158382 -0.03136584 0.0732329 0.1110628 0.1131265 111 15 1.0 4.0 nan 694.8404 -0.002631187 0.0008202018 0.00152878 0.0008409711 0.0002940207 0.0008161382 -0.000399818 0.0008406116 18.48034 29.2971831892 29.0582212929 29.5361450855 72.9488592 2.92250071549 502.0 500.0 0.7778232 18.48034 4595.72705219 0.00353782925 2965.73538925 183.933787056 18.2148556008
477.6413 0.00185173875 0.0008567441 0.2581685 0.008903546 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -4.143658e-05 2.426579e-05 0.5856128 4.674934 59.67112 0.187962 0.1601751 23346.63 33068.11 43.07944 42.70148 557479.0 716761.0 0.1766528 0.1126375 0.06628869 0.05886539 0.006703264 0.04318574 0.02286248 0.0477745 108 18 1.0 4.0 nan 840.7764 -0.000490203 0.0008519073 -0.0007277534 0.0008980471 0.001883432 0.0008216612 0.001208368 0.0008708136 18.48034 28.7976615192 28.6374872082 28.9578358302 80.2437384 2.99297292643 502.0 500.0 0.7778232 18.48034 5117.18830359 0.00353782925 3144.89817295 183.933787056 18.2148556008
525.4055 -0.00012744425 0.0007806092 0.2240816 0.007025483 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -3.314927e-05 nan nan 4.787664 60.16799 0.2822114 0.2237843 26819.06 36173.31 42.92889 42.60403 670805.0 845889.0 0.09002178 0.08783728 0.08645318 0.08478167 0.08799858 0.08588289 0.0230934 0.05160373 81 45 1.0 4.0 nan 1017.326 -0.00172911 0.0007081591 -0.001265921 0.0009413427 0.001450604 0.0007136199 0.001636292 0.0008232179 18.48034 29.2945378849 29.0707537591 29.5183220108 88.268124 3.06514464711 502.0 500.0 0.7778232 18.48034 5516.45021329 0.00353782925 3129.9779705 183.933787056 18.2148556008
577.946 0.00315543675 0.001597466 0.4886099 0.01392638 0.2221768 nan 18.48034 nan 502.0 nan 500.0 nan -5.998454e-05 3.480823e-05 0.5802867 4.903112 59.43591 0.2960636 0.2323877 35200.79 46664.14 42.63362 42.32754 778019.0 928285.0 0.1986922 0.1245509 0.02329751 0.04477692 0.01755663 0.04431974 -0.07151687 0.06103832 76 50 1.0 4.0 nan 1230.971 -0.0003014806 0.001290697 -0.001799561 0.001966291 0.004261598 0.001269987 0.003575082 0.001455952 18.48034 28.5624511965 28.3300642169 28.7948381762 97.094928 3.13905652423 502.0 500.0 0.7778232 18.48034 6464.59997742 0.00353782925 3576.96851447 183.933787056 18.2148556008
635.7407 -0.00031134625 0.001222764 0.2669522 0.006917 0.2221768 0.1154221 18.48034 15.87183 502.0 21.76365 500.0 29.5061 -4.798763e-05 nan nan 5.021344 60.22818 0.5172321 0.3488925 43552.96 48425.63 42.40246 42.28731 884124.0 980361.0 0.09733404 0.08495402 0.03880866 0.04471033 -0.06172758 0.05675741 -0.01730118 0.03450554 32 94 1.0 1.0 nan 1489.469 -0.001798938 0.0007341676 -0.00355072 0.001329896 0.001461613 0.001065375 -0.0007253814 0.001042586 18.48034 29.3547229556 29.0058301179 29.7036157933 106.8044376 3.2147508837 502.0 500.0 0.7778232 18.48034 7017.637697 0.00353782925 3523.6019788 183.933787056 18.2148556008



In [217]:

    
index = np.isfinite(amag_i)
intrpFunc = interp1d(sma_kpc[index]**0.25, amag_i[index], kind='linear')

sma_common = np.arange(0.9, 4.0, 0.1)

sbp_intrp = intrpFunc(sma_common)

plot(sma_kpc**0.25, amag_i, 'r', linewidth=2.0, alpha=0.8)
plot(sma_common, sbp_intrp)

print pixKpc(gal_z[ii])**0.25
print np.min(sma_kpc**0.25)

temp = intrpFunc(1.5)









    



# 1 pixel(s) =  0.764 Kpc
0.934972199355
0.0

sma	intens	int_err	pix_var	rms	ell	ell_err	pa	pa_err	x0	x0_err	y0	y0_err	grad	grad_err	grad_r_err	rsma	mag	mag_lerr	mag_uerr	tflux_e	tflux_c	tmag_e	tmag_c	npix_e	npix_c	a3	a3_err	b3	b3_err	a4	a4_err	b4	b4_err	ndata	nflag	niter	stop	a_big	sarea	a1	a1_err	b1	b1_err	a2	a2_err	b2	b2_err	pa_norm	sbp	sbp_low	sbp_upp	sma_asec	rsma_asec	avg_x0	avg_y0	avg_q	avg_pa	growth_ori	avg_bkg	growth_cor	rad_outer	mag_tot
float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int64	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
0.0	45.5986121708	nan	nan	nan	nan	nan	nan	nan	502.0	nan	500.0	nan	-5.822931	nan	nan	0.0	49.85254	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1	0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	18.9790831117	nan	nan	0.0	0.0	502.0	500.0	0.7778232	18.48034	0.0	0.00353782925	0.0	183.933787056	18.2148556008
0.5498202	42.3970521708	0.2931644	1.494851	1.057019	0.2221768	0.06044755	18.48034	9.068295	502.0	0.01763681	500.0	0.02099757	-5.677571	7.906383	1.392564	0.8611031	49.93157	0.00753322	0.007480868	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.01139915	0.03854505	0.02189545	0.04549393	-0.101749	0.1421053	0.02474631	0.03597587	13	0	1.0	0.0	nan	2.0	0.0003243068	0.03301069	-0.01815842	0.03190967	0.0003942768	0.03252446	-0.01780623	0.03138227	18.48034	19.0581166591	19.05063554	19.0655977782	0.0923697936	0.551293019301	502.0	500.0	0.7778232	18.48034	0.0	0.00353782925	0.0	183.933787056	18.2148556008
0.6048022	42.0848821707	0.3209774	1.63667	1.1573	0.2221768	0.05986534	18.48034	8.980952	502.0	0.01921364	500.0	0.02287486	-5.650878	7.867163	1.392202	0.8818675	49.93959	0.008310868	0.008249613	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.01063999	0.03785248	0.02126034	0.044691	-0.1008079	0.1407597	0.02466124	0.03584518	13	0	1.0	0.0	nan	2.0	0.000340561	0.03604436	-0.01967556	0.03484216	0.000404695	0.03552199	-0.01929759	0.03427454	18.48034	19.066139852	19.0578911547	19.0743885494	0.1016067696	0.564586723095	502.0	500.0	0.7778232	18.48034	6.5291087166	0.00353782925	6.52855989885	183.933787056	18.2148556008
0.6652824	41.7431221707	0.3512357	1.790958	1.266398	0.2221768	0.0592742	18.48034	8.892269	502.0	0.0209263	500.0	0.02491388	-5.619387	7.823971	1.392318	0.9031326	49.94845	0.009172891	0.009097167	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.009864141	0.03717054	0.02055876	0.04383769	-0.09984129	0.1394274	0.02461119	0.0357738	13	0	1.0	0.0	nan	2.0	0.0003561079	0.0393402	-0.02128926	0.03802807	0.0004120665	0.03878044	-0.02088478	0.03741855	18.48034	19.0749920715	19.0658954392	19.0840887037	0.1117674432	0.578200987452	502.0	500.0	0.7778232	18.48034	14.365179837	0.00353782925	14.3639669498	183.933787056	18.2148556008
0.7318107	41.3692721708	0.3841477	1.958776	1.385064	0.2221768	0.05858501	18.48034	8.788877	502.0	0.02275128	500.0	0.02708662	-5.586484	7.776958	1.392102	0.9249104	49.95821	0.01012873	0.01003403	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.008959543	0.03639773	0.01976314	0.04286409	-0.09870614	0.1378278	0.02454486	0.0356706	13	0	1.0	0.0	nan	2.0	0.0003688922	0.0428923	-0.02298687	0.0414617	0.0004126597	0.04229444	-0.02255543	0.04080916	18.48034	19.0847588618	19.0747242846	19.0947934389	0.1229441976	0.592143558429	502.0	500.0	0.7778232	18.48034	23.7619273539	0.00353782925	23.7599109417	183.933787056	18.2148556008
0.8049918	40.9604521708	0.4199227	2.141194	1.514053	0.2221768	0.05784043	18.48034	8.677176	502.0	0.02470835	500.0	0.02941661	-5.549232	7.725887	1.392244	0.9472135	49.96899	0.01118767	0.01107287	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.00795107	0.03558345	0.01889768	0.04182085	-0.09747347	0.1361281	0.02445681	0.03554674	13	0	1.0	0.0	nan	2.0	0.0003777329	0.04672648	-0.02476463	0.045168	0.0004039323	0.04609037	-0.02430657	0.04447178	18.48034	19.09554078	19.0844675355	19.1066140245	0.1352386224	0.60642232974	502.0	500.0	0.7778232	18.48034	35.0196360915	0.00353782925	35.0166474144	183.933787056	18.2148556008
0.885491	40.5137421707	0.4587579	2.339215	1.654075	0.2221768	0.05700747	18.48034	8.552217	502.0	0.02678777	500.0	0.03189227	-5.508815	7.670327	1.392373	0.9700543	49.9809	0.01236301	0.01222467	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.006832937	0.03472181	0.01791837	0.04065501	-0.09607911	0.1342019	0.02436516	0.03541612	13	0	1.0	0.0	nan	2.0	0.000381933	0.0508447	-0.02660799	0.04914885	0.0003843021	0.05017089	-0.02612405	0.048409	18.48034	19.1074457019	19.0952215016	19.1196699023	0.148762488	0.62104541276	502.0	500.0	0.7778232	18.48034	48.4929171276	0.00353782925	48.48875201	183.933787056	18.2148556008
0.9740401	40.0259421707	0.5008539	2.553864	1.805854	0.2221768	0.05220751	18.48034	7.832129	502.0	0.0269855	500.0	0.03212769	-5.872277	7.598301	1.293927	0.9934459	49.99405	0.01366984	0.01350135	45.60215	45.60215	49.85254	49.85254	1.0	1.0	0.005201421	0.031372	0.01566387	0.03575424	-0.08798704	0.1142764	0.02257451	0.03069148	13	0	1.0	0.0	nan	2.0	0.0003790492	0.05526703	-0.02850911	0.05342369	0.0003493789	0.05455668	-0.02800098	0.05264077	18.48034	19.1205965375	19.1070959546	19.1340971203	0.1636387368	0.63602110875	502.0	500.0	0.7778232	18.48034	64.5993108559	0.00353782925	64.5937222457	183.933787056	18.2148556008
1.071444	39.4539521708	0.5448861	2.778384	1.964615	0.2221768	0.03840595	18.48034	5.76163	502.0	0.02183679	500.0	0.02599786	-6.844525	7.487941	1.094004	1.017402	50.00968	0.01509825	0.01489057	126.5437	206.6235	48.7444	48.21205	3.0	5.0	0.00634128	0.0234846	0.0124204	0.02549096	-0.0668695	0.07337838	0.01322502	0.01546109	13	0	1.0	0.0	nan	2.0	0.0001810941	0.04072266	-0.02568183	0.03936443	-2.360399e-05	0.039914	-0.02527088	0.03851232	18.48034	19.1362227708	19.1213319368	19.1511136049	0.180002592	0.6513579073	502.0	500.0	0.7778232	18.48034	83.8095457201	0.00353782925	83.8022346857	183.933787056	18.2148556008
1.178589	38.7206021707	0.588872	3.00267	2.123208	0.2221768	0.02763417	18.48034	4.145657	502.0	0.01728341	500.0	0.02057682	-7.455918	7.363017	0.98754	1.041935	50.03005	0.0166365	0.01638724	126.5437	206.6235	48.7444	48.21205	3.0	5.0	0.004050368	0.01688386	0.002099611	0.01590358	-0.04922763	0.04889908	-0.005737036	0.007574377	13	0	1.0	0.0	nan	2.0	-0.001501648	0.04584903	-0.009143668	0.04431981	-0.00329138	0.04577971	-0.009486661	0.04417202	18.48034	19.1565919547	19.1402056113	19.1729782981	0.198002952	0.667064634257	502.0	500.0	0.7778232	18.48034	106.622069987	0.00353782925	106.612674806	183.933787056	18.2148556008
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
269.6161	-0.00229407225	0.0009707853	0.1643595	0.01004188	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-0.0001068721	nan	nan	4.052159	61.26316	1.646546	0.6263705	9313.354	11929.65	44.07723	43.80843	177629.0	228373.0	-0.05720736	0.05885445	-0.01420104	0.03990409	-0.1045356	0.09089732	-0.07855418	0.07218049	107	19	1.0	4.0	nan	267.8917	0.002524938	0.001019482	0.001306565	0.0009568091	-0.0008248038	0.001007203	0.0004010799	0.0009609584	18.48034	30.3897075636	29.7633364582	31.016078669	45.2955048	2.59426165195	502.0	500.0	0.7778232	18.48034	3700.35590509	0.00353782925	3071.92509047	183.933787056	18.2148556008
296.5777	0.00170286175	0.001222235	0.2318424	0.01287704	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-5.769885e-05	5.321355e-05	0.9222637	4.149871	59.70153	0.2883214	0.2276033	11131.92	12876.51	43.88357	43.7255	214939.0	276349.0	-0.008158027	0.07248045	-0.08885738	0.1082324	-0.1390571	0.1456371	-0.05441003	0.08679032	111	15	1.0	4.0	nan	324.1557	0.004888365	0.001124922	0.002064241	0.001065518	0.001460899	0.001073107	-0.001815665	0.001065996	18.48034	28.8280750243	28.6004734503	29.0556765983	49.8250536	2.65681884843	502.0	500.0	0.7778232	18.48034	3895.84840022	0.00353782925	3135.446617	183.933787056	18.2148556008
326.2354	-8.35725e-06	0.0009984201	0.2045359	0.01032774	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-5.719997e-05	3.568318e-05	0.6238321	4.24994	60.13073	0.3610216	0.270466	14163.31	15534.17	43.62209	43.52178	260063.0	334401.0	0.03561273	0.06120889	-0.1271876	0.09903508	-0.08366753	0.07576704	0.1640129	0.1163901	107	19	1.0	4.0	nan	392.2193	0.003771417	0.0009755543	-0.001492485	0.0009277947	-0.0005711308	0.0009239655	-0.001917657	0.0009315877	18.48034	29.2572720564	28.986806236	29.5277378767	54.8075472	2.72088440614	502.0	500.0	0.7778232	18.48034	4055.15571332	0.00353782925	3135.06940164	183.933787056	18.2148556008
358.859	-0.00187442325	0.0005985932	0.1342594	0.006162894	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-4.575998e-05	nan	nan	4.352422	60.94751	0.4843109	0.333737	15206.32	16220.75	43.54494	43.47482	314693.0	404549.0	0.06340237	0.06776278	-0.04541507	0.05803023	0.02006976	0.04752551	0.05456956	0.06248949	106	20	1.0	4.0	nan	474.5916	-0.000285856	0.0007420002	-0.0008940522	0.0007162092	0.0003658538	0.0007375089	2.451674e-05	0.000718032	18.48034	30.0740507494	29.7403152911	30.4077862077	60.288312	2.78649508322	502.0	500.0	0.7778232	18.48034	4146.00286806	0.00353782925	3032.69750984	183.933787056	18.2148556008
394.7449	-0.00084903525	0.0006861875	0.1700941	0.007097978	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-3.660798e-05	nan	nan	4.457375	60.42611	0.3199055	0.246784	15885.9	17874.99	43.49747	43.36938	380737.0	489565.0	0.03302658	0.06377282	0.03340059	0.06606904	-0.008403908	0.05801927	0.09690192	0.09768435	107	19	1.0	4.0	nan	574.2605	-0.0002746453	0.0008420969	0.0001409187	0.0008461322	0.0008916375	0.0008211868	0.0006369927	0.0008534189	18.48034	29.5526525828	29.3058679172	29.7994372484	66.3171432	2.85368775929	502.0	500.0	0.7778232	18.48034	4323.68971284	0.00353782925	2976.58968058	183.933787056	18.2148556008
434.2194	-0.00013574325	0.0008375644	0.2326067	0.008824289	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-2.928639e-05	nan	nan	4.564858	60.17064	0.3068489	0.2389604	17213.15	19675.54	43.41035	43.26518	460741.0	592333.0	0.1808012	0.1601208	0.1135316	0.1158382	-0.03136584	0.0732329	0.1110628	0.1131265	111	15	1.0	4.0	nan	694.8404	-0.002631187	0.0008202018	0.00152878	0.0008409711	0.0002940207	0.0008161382	-0.000399818	0.0008406116	18.48034	29.2971831892	29.0582212929	29.5361450855	72.9488592	2.92250071549	502.0	500.0	0.7778232	18.48034	4595.72705219	0.00353782925	2965.73538925	183.933787056	18.2148556008
477.6413	0.00185173875	0.0008567441	0.2581685	0.008903546	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-4.143658e-05	2.426579e-05	0.5856128	4.674934	59.67112	0.187962	0.1601751	23346.63	33068.11	43.07944	42.70148	557479.0	716761.0	0.1766528	0.1126375	0.06628869	0.05886539	0.006703264	0.04318574	0.02286248	0.0477745	108	18	1.0	4.0	nan	840.7764	-0.000490203	0.0008519073	-0.0007277534	0.0008980471	0.001883432	0.0008216612	0.001208368	0.0008708136	18.48034	28.7976615192	28.6374872082	28.9578358302	80.2437384	2.99297292643	502.0	500.0	0.7778232	18.48034	5117.18830359	0.00353782925	3144.89817295	183.933787056	18.2148556008
525.4055	-0.00012744425	0.0007806092	0.2240816	0.007025483	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-3.314927e-05	nan	nan	4.787664	60.16799	0.2822114	0.2237843	26819.06	36173.31	42.92889	42.60403	670805.0	845889.0	0.09002178	0.08783728	0.08645318	0.08478167	0.08799858	0.08588289	0.0230934	0.05160373	81	45	1.0	4.0	nan	1017.326	-0.00172911	0.0007081591	-0.001265921	0.0009413427	0.001450604	0.0007136199	0.001636292	0.0008232179	18.48034	29.2945378849	29.0707537591	29.5183220108	88.268124	3.06514464711	502.0	500.0	0.7778232	18.48034	5516.45021329	0.00353782925	3129.9779705	183.933787056	18.2148556008
577.946	0.00315543675	0.001597466	0.4886099	0.01392638	0.2221768	nan	18.48034	nan	502.0	nan	500.0	nan	-5.998454e-05	3.480823e-05	0.5802867	4.903112	59.43591	0.2960636	0.2323877	35200.79	46664.14	42.63362	42.32754	778019.0	928285.0	0.1986922	0.1245509	0.02329751	0.04477692	0.01755663	0.04431974	-0.07151687	0.06103832	76	50	1.0	4.0	nan	1230.971	-0.0003014806	0.001290697	-0.001799561	0.001966291	0.004261598	0.001269987	0.003575082	0.001455952	18.48034	28.5624511965	28.3300642169	28.7948381762	97.094928	3.13905652423	502.0	500.0	0.7778232	18.48034	6464.59997742	0.00353782925	3576.96851447	183.933787056	18.2148556008
635.7407	-0.00031134625	0.001222764	0.2669522	0.006917	0.2221768	0.1154221	18.48034	15.87183	502.0	21.76365	500.0	29.5061	-4.798763e-05	nan	nan	5.021344	60.22818	0.5172321	0.3488925	43552.96	48425.63	42.40246	42.28731	884124.0	980361.0	0.09733404	0.08495402	0.03880866	0.04471033	-0.06172758	0.05675741	-0.01730118	0.03450554	32	94	1.0	1.0	nan	1489.469	-0.001798938	0.0007341676	-0.00355072	0.001329896	0.001461613	0.001065375	-0.0007253814	0.001042586	18.48034	29.3547229556	29.0058301179	29.7036157933	106.8044376	3.2147508837	502.0	500.0	0.7778232	18.48034	7017.637697	0.00353782925	3523.6019788	183.933787056	18.2148556008