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In [1]:

    
#for comparing the full-depth and single-depth goods classifications

%pylab inline
from astropy.io import fits as pyfits
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from astropy.io import fits
from astropy.table import Table,Column
import scipy.stats.distributions as dist

import os
import warnings
import requests

from PIL import Image
import urllib, cStringIO

mpl.rcParams['text.usetex']=True
mpl.rcParams['axes.linewidth'] = 3

warnings.filterwarnings('ignore', category=RuntimeWarning, append=True)
warnings.filterwarnings('ignore', category=UserWarning, append=True);









    



Populating the interactive namespace from numpy and matplotlib



In [6]:

    
gdata=Table.read('goods_shallow_1_full_2_9_2016.fits')



In [7]:

    
#_1 refers to shallow depth and _2 refers to full depth:
print 'shallow: column _1 = %s, full: column_2 = %s' %(gdata['imaging_1'][0],gdata['imaging_2'][0])









    



shallow: column _1 = GOODS-N          , full: column_2 = GOODS-N-FULLDEPTH



In [18]:

    
p_features_shallow='t01_smooth_or_features_a02_features_or_disk_best_fraction_1'
p_features_full='t01_smooth_or_features_a02_features_or_disk_best_fraction_2'



In [87]:

    
def get_image_from_url(url):

    file = cStringIO.StringIO(urllib.urlopen(url).read())
    img = Image.open(file)
    return img

def df(data):
    df = np.nanmean(data[p_features_full]-data[p_features_shallow])
    return round(df,2)

def dz(data):
    z_in_range = (data['Z_BEST_2'] > 0.0) & (data['Z_BEST_2'] < 2.0)
    dz = np.nanmean(data[z_in_range]['Z_BEST_2'])
    return round(dz,2)



In [10]:

    
def compare_shallow_and_full_images(gal):
    f=plt.figure(figsize=(12,8))
    gs=gridspec.GridSpec(1,2)
    fs = 30
    #left: show shallow image
    ax1=plt.subplot(gs[0,0])
    plt.imshow(get_image_from_url(gal['shallow_url']))
    plt.tick_params(labelbottom='off',labelleft='off')

#        ax1.annotate('z: %s\n$\mu: %s$'%(round(gal['Z_BEST_1'],2),round(gal['MU_HI_1'],2)),fontsize=fs,xy=(0,1),
#            xycoords='axes fraction',verticalalignment='top',color='white')
#        ax1.annotate('%s'%gal['OBJNO_1'],fontsize=fs,xy=(1,1),
#            xycoords='axes fraction',verticalalignment='top',horizontalalignment='right',color='white')
#        ax1.annotate('$\mathrm{p_{features}: %s}$\n$\mathrm{p_{features,best}: %s}$'%(round(gal[p_features_shallow_weighted],2),round(gal[p_features_shallow_best],2)),fontsize=fs,xy=(0,0),
#            xycoords='axes fraction',color='white')

    plt.title('shallow depth',fontsize=25)
    #right: full image
    ax2=plt.subplot(gs[0,1])
    plt.imshow(get_image_from_url(gal['full_url']))
    plt.tick_params(labelbottom='off',labelleft='off')
 
#        ax2.annotate('z: %s\n$\mu: %s$'%(round(gal['Z_BEST_2'],2),round(gal['MU_HI_2'],2)),fontsize=fs,xy=(0,1),
#            xycoords='axes fraction',verticalalignment='top',color='white')
#    ax2.annotate('%s'%gal['OBJNO_2'],fontsize=fs,xy=(1,1),
#            xycoords='axes fraction',verticalalignment='top',horizontalalignment='right',color='white')
#        ax2.annotate('$\mathrm{p_{features}: %s}$\n$\mathrm{p_{features,best}: %s}$'%(round(gal[p_features_full_weighted],2),round(gal[p_features_full_best],2)),fontsize=fs,xy=(0,0),
#            xycoords='axes fraction',color='white')

    plt.title('full depth',fontsize=25)



In [11]:

    
compare_shallow_and_full_images(gdata[2261])



In [19]:

    
features_hist,xedges,yedges=np.histogram2d(gdata[p_features_shallow],gdata[p_features_full],range=((0,1),(0,1)))
extent=[xedges[0],xedges[-1],yedges[0],yedges[-1]]


mpl.rcParams['xtick.labelsize'] = 20
mpl.rcParams['ytick.labelsize'] = 20 

f=plt.figure(figsize=(10,7))
cmap=cm.CMRmap

plt.imshow(features_hist.T,origin='lower',extent=extent,interpolation='nearest',cmap=cmap)
plt.xlabel('$\mathrm{p_{features}~shallow~depth}$',fontsize=30)
plt.ylabel(r'$\mathrm{p_{features}~full~depth}$',fontsize=30)
f.text(.86,.5,'$\mathrm{Count}$',fontsize=30,rotation=270)
plt.colorbar()

plt.savefig('../../writeup/figures/full_shallow_p_plot.pdf')



In [5]:

    
## Figures for appendix

Define 'clean featured' sample as $p_{features}$ > 0.8, 'clean smooth' sample as $p_{features}$ < 0.2, and intermediate as anything in between.



In [20]:

    
disk_cut = 0.8
smooth_cut = 0.2



In [21]:

    
smooth_to_smooth = (gdata[p_features_shallow]<smooth_cut) & (gdata[p_features_full]<smooth_cut)
smooth_to_int = (gdata[p_features_shallow]<smooth_cut) & (gdata[p_features_full]>smooth_cut) & (gdata[p_features_full] < disk_cut)
smooth_to_featured = (gdata[p_features_shallow]<smooth_cut) & (gdata[p_features_full]>disk_cut)

int_to_smooth = (gdata[p_features_shallow]>smooth_cut) & (gdata[p_features_shallow] < disk_cut) & (gdata[p_features_full]<smooth_cut)
int_to_int = (gdata[p_features_shallow]>smooth_cut) & (gdata[p_features_shallow] < disk_cut) & (gdata[p_features_full]>smooth_cut) & (gdata[p_features_full] < disk_cut)
int_to_featured = (gdata[p_features_shallow]>smooth_cut) & (gdata[p_features_shallow] < disk_cut) & (gdata[p_features_full]>disk_cut)

featured_to_smooth = (gdata[p_features_shallow]>disk_cut) & (gdata[p_features_full]<smooth_cut)
featured_to_int =  (gdata[p_features_shallow]>disk_cut) & (gdata[p_features_full]>smooth_cut) & (gdata[p_features_full] < disk_cut)
featured_to_featured = (gdata[p_features_shallow]>disk_cut) & (gdata[p_features_full]>disk_cut)



In [22]:

    
def plot_shallow_corrections(data,int_list,i,ax):
    gal = data[int_list[i]]
    try: 
        plt.imshow(get_image_from_url(gal['shallow_url']))
        plt.tick_params(labelbottom='off',labelleft='off')
        ax.annotate('$\mathrm{f_{features}: %s}$'%round(gal[p_features_shallow],2),fontsize=35,xy=(0.02,.02),
            xycoords='axes fraction',color='white')
    except IOError:
        pass

def plot_full_corrections(data,int_list,i,ax):
    gal = data[int_list[i]]
    try:
        plt.imshow(get_image_from_url(gal['full_url']))
        plt.tick_params(labelbottom='off',labelleft='off')
        ax.annotate('$\mathrm{f_{features}: %s}$'%round(gal[p_features_full],2),fontsize=35,xy=(0.02,.02),
            xycoords='axes fraction',color='white')
    except IOError:
        pass
    
def int_list(table_data):
    ints = np.linspace(0,len(table_data),len(table_data)+1)
    ints = [int(s) for s in ints]
    random.shuffle(ints)
    return ints



In [23]:

    
def plot_goods_comparisons(type1_to_type2,type1,type2):
    gs=gridspec.GridSpec(2,5)
    gs.update(wspace=0.01)
    gs.update(hspace=0.05)
 

    s_s = gdata[type1_to_type2]
    s_s_ints = int_list(s_s)



    ls = 25

    f=plt.figure(figsize=(25,10))
    for i in range(0,5):
        ax_s = plt.subplot(gs[0,i])
        try:
            plot_shallow_corrections(s_s,s_s_ints,i,ax_s)
        except IndexError:
            pass
        if i==0:
            plt.ylabel('shallow~depth~image',fontsize=ls)
        if i==4:
            ax_s.yaxis.set_label_position("right")
            plt.ylabel(type1,fontsize=ls,rotation=270,labelpad=25)

        ax_f = plt.subplot(gs[1,i])
        try:
            plot_full_corrections(s_s,s_s_ints,i,ax_f)
        except IndexError:
            pass
        if i==0:
            plt.ylabel('full~depth~image',fontsize=ls)
        if i==4:
            ax_f.yaxis.set_label_position("right")
            plt.ylabel(type2,fontsize=ls,rotation=270,labelpad=25)



In [25]:

    
plot_goods_comparisons(smooth_to_smooth,'smooth','smooth')
plt.savefig('../../writeup/figures/smooth_to_smooth.pdf')



In [27]:

    
plot_goods_comparisons(smooth_to_int,'smooth','intermediate')
plt.savefig('../../writeup/figures/smooth_to_intermediate.pdf')



In [29]:

    
plot_goods_comparisons(smooth_to_featured,'smooth','featured')
plt.savefig('../../writeup/figures/smooth_to_featured.pdf')



In [30]:

    
plot_goods_comparisons(int_to_smooth,'intermediate','smooth')
plt.savefig('../../writeup/figures/intermediate_to_smooth.pdf')



In [31]:

    
plot_goods_comparisons(int_to_int,'intermediate','intermediate')
plt.savefig('../../writeup/figures/intermediate_to_intermediate.pdf')



In [32]:

    
plot_goods_comparisons(int_to_featured,'intermediate','featured')
plt.savefig('../../writeup/figures/intermediate_to_featured.pdf')



In [33]:

    
plot_goods_comparisons(featured_to_smooth,'featured','smooth')
plt.savefig('../../writeup/figures/featured_to_smooth.pdf')



In [34]:

    
plot_goods_comparisons(featured_to_int,'featured','intermediate')
plt.savefig('../../writeup/figures/featured_to_intermediate.pdf')



In [35]:

    
plot_goods_comparisons(featured_to_featured,'featured','featured')
plt.savefig('../../writeup/figures/featured_to_featured.pdf')

Calculate how many galaxies changed (or did not change) morphology



In [96]:

    
cats = [smooth_to_smooth,smooth_to_int,smooth_to_featured,int_to_smooth,int_to_int,int_to_featured,featured_to_smooth,featured_to_int,featured_to_featured]
strs = ['smooth_to_smooth','smooth_to_int','smooth_to_featured','int_to_smooth','int_to_int','int_to_featured','featured_to_smooth','featured_to_int','featured_to_featured']

for i,c in enumerate(cats):
    print '%s: %s, %s %%, average change in f = %s, average z = %s \n' %(strs[i],len(gdata[c]), round(len(gdata[c])/float(len(gdata)),2),df(gdata[c]),dz(gdata[c]))









    



smooth_to_smooth: 708, 0.16 %, average change in f = 0.0, average z = 0.72 

smooth_to_int: 346, 0.08 %, average change in f = 0.21, average z = 0.7 

smooth_to_featured: 6, 0.0 %, average change in f = 0.8, average z = 0.45 

int_to_smooth: 266, 0.06 %, average change in f = -0.24, average z = 0.66 

int_to_int: 2363, 0.53 %, average change in f = 0.0, average z = 0.75 

int_to_featured: 121, 0.03 %, average change in f = 0.17, average z = 0.82 

featured_to_smooth: 3, 0.0 %, average change in f = -0.73, average z = 0.71 

featured_to_int: 370, 0.08 %, average change in f = -0.15, average z = 0.69 

featured_to_featured: 277, 0.06 %, average change in f = -0.05, average z = 0.71



In [98]:

    
print 'total change in f = %s, deviation = %s' %(round(nanmean(gdata[p_features_full]-gdata[p_features_shallow]),2),round(np.std(gdata[p_features_full]-gdata[p_features_shallow]),2))









    



total change in f = -0.01, deviation = 0.18



In [100]:

    
print 'fraction of galaxies with no morphological change = %s' %(float((len(gdata[smooth_to_smooth]) + len(gdata[int_to_int]) + len(gdata[featured_to_featured])))/len(gdata))









    



fraction of galaxies with no morphological change = 0.75067264574



In [81]:

    
print 'fraction of galaxies with large morphological change = %s' %(float((len(gdata[smooth_to_featured]) + len(gdata[int_]) + len(gdata[featured_to_featured])))/len(gdata))









    Out[81]:




<Table masked=True length=327>

zooniverse_id_1 survey_id_1 Table_1 RA_1 DEC_1 imaging_1 Z_BEST_1 GZ_MU_I_1 correction_type_1 total_count_1 total_weight_1 t01_smooth_or_features_a01_smooth_fraction_1 t01_smooth_or_features_a01_smooth_weighted_fraction_1 t01_smooth_or_features_a01_smooth_debiased_fraction_1 t01_smooth_or_features_a01_smooth_lower_limit_1 t01_smooth_or_features_a01_smooth_upper_limit_1 t01_smooth_or_features_a01_smooth_best_fraction_1 t01_smooth_or_features_a01_smooth_flag_1 t01_smooth_or_features_a02_features_or_disk_fraction_1 t01_smooth_or_features_a02_features_or_disk_weighted_fraction_1 t01_smooth_or_features_a02_features_or_disk_debiased_fraction_1 t01_smooth_or_features_a02_features_or_disk_lower_limit_1 t01_smooth_or_features_a02_features_or_disk_upper_limit_1 t01_smooth_or_features_a02_features_or_disk_best_fraction_1 t01_smooth_or_features_a02_features_or_disk_flag_1 t01_smooth_or_features_a03_star_or_artifact_fraction_1 t01_smooth_or_features_a03_star_or_artifact_weighted_fraction_1 t01_smooth_or_features_a03_star_or_artifact_flag_1 t01_smooth_or_features_total_count_1 t01_smooth_or_features_total_weight_1 t02_edgeon_a01_yes_fraction_1 t02_edgeon_a01_yes_weighted_fraction_1 t02_edgeon_a02_no_fraction_1 t02_edgeon_a02_no_weighted_fraction_1 t02_edgeon_total_count_1 t02_edgeon_total_weight_1 t03_bar_a01_bar_fraction_1 t03_bar_a01_bar_weighted_fraction_1 t03_bar_a02_no_bar_fraction_1 t03_bar_a02_no_bar_weighted_fraction_1 t03_bar_total_count_1 t03_bar_total_weight_1 t04_spiral_a01_spiral_fraction_1 t04_spiral_a01_spiral_weighted_fraction_1 t04_spiral_a02_no_spiral_fraction_1 t04_spiral_a02_no_spiral_weighted_fraction_1 t04_spiral_total_count_1 t04_spiral_total_weight_1 t05_bulge_prominence_a01_no_bulge_fraction_1 t05_bulge_prominence_a01_no_bulge_weighted_fraction_1 t05_bulge_prominence_a02_just_noticeable_fraction_1 t05_bulge_prominence_a02_just_noticeable_weighted_fraction_1 t05_bulge_prominence_a03_obvious_fraction_1 t05_bulge_prominence_a03_obvious_weighted_fraction_1 t05_bulge_prominence_a04_dominant_fraction_1 t05_bulge_prominence_a04_dominant_weighted_fraction_1 t05_bulge_prominence_total_count_1 t05_bulge_prominence_total_weight_1 t06_odd_a01_yes_fraction_1 t06_odd_a01_yes_weighted_fraction_1 t06_odd_a02_no_fraction_1 t06_odd_a02_no_weighted_fraction_1 t06_odd_total_count_1 t06_odd_total_weight_1 t07_rounded_a01_completely_round_fraction_1 t07_rounded_a01_completely_round_weighted_fraction_1 t07_rounded_a02_in_between_fraction_1 t07_rounded_a02_in_between_weighted_fraction_1 t07_rounded_a03_cigar_shaped_fraction_1 t07_rounded_a03_cigar_shaped_weighted_fraction_1 t07_rounded_total_count_1 t07_rounded_total_weight_1 t08_odd_feature_a01_ring_fraction_1 t08_odd_feature_a01_ring_weighted_fraction_1 t08_odd_feature_a02_lens_or_arc_fraction_1 t08_odd_feature_a02_lens_or_arc_weighted_fraction_1 t08_odd_feature_a03_disturbed_fraction_1 t08_odd_feature_a03_disturbed_weighted_fraction_1 t08_odd_feature_a04_irregular_fraction_1 t08_odd_feature_a04_irregular_weighted_fraction_1 t08_odd_feature_a05_other_fraction_1 t08_odd_feature_a05_other_weighted_fraction_1 t08_odd_feature_a06_merger_fraction_1 t08_odd_feature_a06_merger_weighted_fraction_1 t08_odd_feature_a07_dust_lane_fraction_1 t08_odd_feature_a07_dust_lane_weighted_fraction_1 t08_odd_feature_total_count_1 t08_odd_feature_total_weight_1 t09_bulge_shape_a01_rounded_fraction_1 t09_bulge_shape_a01_rounded_weighted_fraction_1 t09_bulge_shape_a02_boxy_fraction_1 t09_bulge_shape_a02_boxy_weighted_fraction_1 t09_bulge_shape_a03_no_bulge_fraction_1 t09_bulge_shape_a03_no_bulge_weighted_fraction_1 t09_bulge_shape_total_count_1 t09_bulge_shape_total_weight_1 t10_arms_winding_a01_tight_fraction_1 t10_arms_winding_a01_tight_weighted_fraction_1 t10_arms_winding_a02_medium_fraction_1 t10_arms_winding_a02_medium_weighted_fraction_1 t10_arms_winding_a03_loose_fraction_1 t10_arms_winding_a03_loose_weighted_fraction_1 t10_arms_winding_total_count_1 t10_arms_winding_total_weight_1 t11_arms_number_a01_1_fraction_1 t11_arms_number_a01_1_weighted_fraction_1 t11_arms_number_a02_2_fraction_1 t11_arms_number_a02_2_weighted_fraction_1 t11_arms_number_a03_3_fraction_1 t11_arms_number_a03_3_weighted_fraction_1 t11_arms_number_a04_4_fraction_1 t11_arms_number_a04_4_weighted_fraction_1 t11_arms_number_a05_more_than_4_fraction_1 t11_arms_number_a05_more_than_4_weighted_fraction_1 t11_arms_number_a06_cant_tell_fraction_1 t11_arms_number_a06_cant_tell_weighted_fraction_1 t11_arms_number_total_count_1 t11_arms_number_total_weight_1 t12_clumpy_a01_yes_fraction_1 t12_clumpy_a01_yes_weighted_fraction_1 t12_clumpy_a02_no_fraction_1 t12_clumpy_a02_no_weighted_fraction_1 t12_clumpy_total_count_1 t12_clumpy_total_weight_1 t13_bright_clump_a01_yes_fraction_1 t13_bright_clump_a01_yes_weighted_fraction_1 t13_bright_clump_a02_no_fraction_1 t13_bright_clump_a02_no_weighted_fraction_1 t13_bright_clump_total_count_1 t13_bright_clump_total_weight_1 t14_bright_clump_central_a01_yes_fraction_1 t14_bright_clump_central_a01_yes_weighted_fraction_1 t14_bright_clump_central_a02_no_fraction_1 t14_bright_clump_central_a02_no_weighted_fraction_1 t14_bright_clump_central_total_count_1 t14_bright_clump_central_total_weight_1 t15_clumps_arrangement_a01_line_fraction_1 t15_clumps_arrangement_a01_line_weighted_fraction_1 t15_clumps_arrangement_a02_chain_fraction_1 t15_clumps_arrangement_a02_chain_weighted_fraction_1 t15_clumps_arrangement_a03_cluster_fraction_1 t15_clumps_arrangement_a03_cluster_weighted_fraction_1 t15_clumps_arrangement_a04_spiral_fraction_1 t15_clumps_arrangement_a04_spiral_weighted_fraction_1 t15_clumps_arrangement_total_count_1 t15_clumps_arrangement_total_weight_1 t16_clumps_count_a02_2_fraction_1 t16_clumps_count_a02_2_weighted_fraction_1 t16_clumps_count_a03_3_fraction_1 t16_clumps_count_a03_3_weighted_fraction_1 t16_clumps_count_a04_4_fraction_1 t16_clumps_count_a04_4_weighted_fraction_1 t16_clumps_count_a05_more_than_4_fraction_1 t16_clumps_count_a05_more_than_4_weighted_fraction_1 t16_clumps_count_a06_cant_tell_fraction_1 t16_clumps_count_a06_cant_tell_weighted_fraction_1 t16_clumps_count_a01_1_fraction_1 t16_clumps_count_a01_1_weighted_fraction_1 t16_clumps_count_total_count_1 t16_clumps_count_total_weight_1 t17_clumps_symmetrical_a01_yes_fraction_1 t17_clumps_symmetrical_a01_yes_weighted_fraction_1 t17_clumps_symmetrical_a02_no_fraction_1 t17_clumps_symmetrical_a02_no_weighted_fraction_1 t17_clumps_symmetrical_total_count_1 t17_clumps_symmetrical_total_weight_1 t18_clumps_embedded_a01_yes_fraction_1 t18_clumps_embedded_a01_yes_weighted_fraction_1 t18_clumps_embedded_a02_no_fraction_1 t18_clumps_embedded_a02_no_weighted_fraction_1 t18_clumps_embedded_total_count_1 t18_clumps_embedded_total_weight_1 zooniverse_id_2 survey_id_2 Table_2 RA_2_1 DEC_2_1 Z_BEST_2 GZ_MU_I_2 imaging_2 correction_type_2 total_count_2 total_weight_2 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In [61]:

    
np.ma.mean(gdata[smooth_to_int]['Z_BEST_2'],mask=(gdata[(gdata[smooth_to_int]['Z_BEST_2']>3)]))









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-61-191e9548f806> in <module>()
----> 1 np.ma.mean(gdata[smooth_to_int]['Z_BEST_2'],mask=(gdata[(gdata[smooth_to_int]['Z_BEST_2']>3)]))

/usr/local/lib/python2.7/dist-packages/numpy/ma/core.pyc in __call__(self, a, *args, **params)
   6285         method = getattr(a, method_name, None)
   6286         if method is not None:
-> 6287             return method(*args, **params)
   6288         # Still here ? Then a is not a MaskedArray
   6289         method = getattr(MaskedArray, method_name, None)

TypeError: mean() got an unexpected keyword argument 'mask'



In [ ]: