def compare(zid,user_name,verbose=True):
their_answer = consensus.one_answer(zid,user_name)
expert_answer = consensus.checksum(zid)
nt = len(their_answer)
nx = len(expert_answer)
if nt == nx:
match_temp = True
for tk in their_answer.iterkeys():
if expert_answer.has_key(tk):
match_temp = True and expert_answer.has_key(tk)
if verbose:
if match_temp:
print "%s's answer for %s (%i sources) matches that of the experts." % (user_name,zid,nt)
else:
print "%s's answer for %s (%i sources) does not match that of the experts." % (user_name,zid,nt)
match_expert = match_temp
else:
if verbose:
print "%s's number of sources for %s does not match (%i vs %i)" % (user_name,zid,nt,nx)
match_expert = False
return match_expert
def compare(zid, user_name, verbose=True):
their_answer = consensus.one_answer(zid, user_name)
expert_answer = consensus.checksum(zid)
nt = len(their_answer)
nx = len(expert_answer)
if nt == nx:
match_temp = True
for tk in their_answer.iterkeys():
if expert_answer.has_key(tk):
match_temp = True and expert_answer.has_key(tk)
if verbose:
if match_temp:
print "%s's answer for %s (%i sources) matches that of the experts." % (
user_name, zid, nt)
else:
print "%s's answer for %s (%i sources) does not match that of the experts." % (
user_name, zid, nt)
match_expert = match_temp
else:
if verbose:
print "%s's number of sources for %s does not match (%i vs %i)" % (
user_name, zid, nt, nx)
match_expert = False
return match_expert
def mps_bending_angle(consensus_level = 0.50):
# Compute the bending and position angles for double-peaked, single-contour sources with optical counterparts
tstart = time.time()
# Load data
df = pd.read_csv('%s/bending_angles/multipeaked_singles_cc.csv' % rgz_dir,delimiter=',')
df2 = df[(df['ntotal'] == 2)]
with open('%s/bending_angles/angles_multipeaked_singles.csv' % rgz_dir,'w') as f:
print >> f,'zooniverse_id,bending_angle,position_angle'
# get optical counterpart for zooniverse_id (loop over eventually)
df2_pair1 = df2[::2]
df2_pair2 = df2[1::2]
_zid = np.array(df2_pair1['zooniverse_id'])
_x1 = np.array(df2_pair1['xc'])
_y1 = np.array(df2_pair1['yc'])
_x2 = np.array(df2_pair2['xc'])
_y2 = np.array(df2_pair2['yc'])
for zooniverse_id,x1,y1,x2,y2 in zip(_zid,_x1,_y1,_x2,_y2):
c = consensus.checksum(zooniverse_id)
try:
if (len(c['answer']) == 1) and (c['n_users']/float(c['n_total']) >= consensus_level):
if c['answer'][c['answer'].keys()[0]].has_key('ir_peak'):
peak_x,peak_y = c['answer'][c['answer'].keys()[0]]['ir_peak']
ir_x = peak_x * first_ir_scale_x
ir_y = peak_y * first_ir_scale_y
alpha = bending_angle(ir_x,ir_y,x1,y1,x2,y2)
alpha_deg = alpha * 180./np.pi
phi = position_angle(ir_x,ir_y,x1,y1,x2,y2)
phi_deg = phi * 180./np.pi
print >> f,'{0:s},{1:.4f},{2:.4f}'.format(zooniverse_id,alpha_deg,phi_deg)
else:
print "Had more than 1 IR sources and/or less than {0:.2f} percent consensus for {1:s}".format(consensus_level,zooniverse_id)
except TypeError:
print "No 'answer' key for %s" % zooniverse_id
# Timing the process
tend = time.time()
n = len(df2)/2
print '%.2f minutes for %i subjects' % ((tend - tstart)/60.,n)
print '%.2f subjects per second' % (n/(tend - tstart))
return None
def plot_gs_volunteers():
# Plot the results of the gold standard classifications by the users, removing the expert classifications
gs_gals = get_galaxies()
for gal in gs_gals:
print gal
c0 = consensus.checksum(gal,experts_only=False,excluded=experts)
consensus.plot_consensus(c0,savefig = True)
return None
def plot_gs_volunteers():
# Plot the results of the gold standard classifications by the users, removing the expert classifications
gs_gals = get_galaxies()
for gal in gs_gals:
print gal
c0 = consensus.checksum(gal, experts_only=False, excluded=experts)
consensus.plot_consensus(c0, savefig=True)
return None
def compare(zid, user_name, verbose=True, exclude_user=False):
# Compare the classification of a single galaxy by a single user to that of the expert science team
their_answer = consensus.one_answer(zid, user_name)
if exclude_user:
expert_answer = consensus.checksum(zid,
experts_only=True,
excluded=user_name)
else:
expert_answer = consensus.checksum(zid, experts_only=True)
# Total number of galaxies identified in each answer
nt = len(their_answer['answer'])
nx = len(expert_answer['answer'])
suff_x = '' if nx == 1 else 's'
suff_t = '' if nt == 1 else 's'
if nt == nx:
match_temp = True
for tk in their_answer['answer'].iterkeys():
match_temp = True and expert_answer['answer'].has_key(tk)
if verbose:
if match_temp:
print "Yes - %s's answer for %s (%i source%s) matches that of the experts." % (
user_name, zid, nt, suff_t)
else:
print "No - %s's answer for %s has the same number of source%s (%i) as the experts, but not the same arrangement." % (
user_name, zid, suff_t, nt)
match_expert = match_temp
else:
if verbose:
print "No - %s had %i source%s in %s, compared to %i source%s by the experts" % (
user_name, nt, suff_t, zid, nx, suff_x)
match_expert = False
return match_expert
import consensus
zids = ("hymor01_ARG0003o4o", "hymor01_ARG0003o4w", "hymor01_ARG0003o4t",
"hymor02_ARG0003hkm", "hymor02_ARG0003hlo", "hymor02_ARG0003hk3",
"hymor03_ARG0000gng", "hymor04_ARG0003czn", "hymor04_ARG0003cyx",
"hymor04_ARG0003cyr", "hymor04_ARG0003cyc", "hymor05_ARG0001arg",
"hymor05_ARG0001ark", "hymor05_ARG0001arv", "hymor05_ARG0001aqn",
"hymor06_ARG00021rf", "hymor07_ARG00024bn", "hymor07_ARG00024d2",
"hymor07_ARG0002495", "hymor07_ARG000248y", "hymor08_ARG0003j0n",
"hymor08_ARG0003j0a", "hymor08_ARG0003j0z", "hymor08_ARG0003j1d",
"hymor09_ARG00027o7", "hymor09_ARG00027p0", "hymor09_ARG00027mr",
"hymor10_ARG0003ph9", "hymor10_ARG0003pgv", "hymor10_ARG0003pgw",
"hymor10_ARG0003pht", "hymor11_ARG0002p17", "hymor11_ARG0002p1h",
"hymor11_ARG0002p1x", "hymor11_ARG0002ozg", "hymor11_ARG0002oza"
"hymor12_ARG00007vb", "hymor12_ARG00007us", "hymor12_ARG00007uw",
"hymor12_ARG00007up", "hymor12_ARG00007vp", "hymor13_ARG0001hz4",
"hymor13_ARG0001hyx", "hymor13_ARG0001hzl", "hymor13_ARG0001hys")
for zid in zids:
c = consensus.checksum(zid.split('_')[1])
if c != None:
consensus.plot_consensus(c, savefig=zid)
else:
print "Couldn't do %s" % zid
import consensus
zids=("hymor01_ARG0003o4o", "hymor01_ARG0003o4w", "hymor01_ARG0003o4t", "hymor02_ARG0003hkm", "hymor02_ARG0003hlo", "hymor02_ARG0003hk3", "hymor03_ARG0000gng", "hymor04_ARG0003czn", "hymor04_ARG0003cyx", "hymor04_ARG0003cyr", "hymor04_ARG0003cyc", "hymor05_ARG0001arg", "hymor05_ARG0001ark", "hymor05_ARG0001arv", "hymor05_ARG0001aqn", "hymor06_ARG00021rf", "hymor07_ARG00024bn", "hymor07_ARG00024d2", "hymor07_ARG0002495", "hymor07_ARG000248y", "hymor08_ARG0003j0n", "hymor08_ARG0003j0a", "hymor08_ARG0003j0z", "hymor08_ARG0003j1d", "hymor09_ARG00027o7", "hymor09_ARG00027p0", "hymor09_ARG00027mr", "hymor10_ARG0003ph9", "hymor10_ARG0003pgv", "hymor10_ARG0003pgw", "hymor10_ARG0003pht", "hymor11_ARG0002p17", "hymor11_ARG0002p1h", "hymor11_ARG0002p1x", "hymor11_ARG0002ozg", "hymor11_ARG0002oza" "hymor12_ARG00007vb", "hymor12_ARG00007us", "hymor12_ARG00007uw", "hymor12_ARG00007up", "hymor12_ARG00007vp", "hymor13_ARG0001hz4", "hymor13_ARG0001hyx", "hymor13_ARG0001hzl", "hymor13_ARG0001hys")
for zid in zids:
c = consensus.checksum(zid.split('_')[1])
if c != None:
consensus.plot_consensus(c,savefig=zid)
else:
print "Couldn't do %s" % zid
def plot_one_triple(zooniverse_id,pathdict,figno=1,savefig=False,anglepath=''):
# Make a four-panel plot of the consensus identification with marked bending angle and position angle for a triple source
cons = consensus.checksum(zooniverse_id)
subject = subjects.find_one({'zooniverse_id':zooniverse_id})
contours = get_contours(subject,pathdict)
radio_components = contours['contours']
# Plot image
answer = cons['answer']
# Download contour data
sf_x = 500./contours['width']
sf_y = 500./contours['height']
verts_all = []
codes_all = []
components = contours['contours']
for comp in components:
# Order of bounding box components is (xmax,ymax,xmin,ymin)
comp_xmax,comp_ymax,comp_xmin,comp_ymin = comp[0]['bbox']
# Only plot radio components identified by the users as the consensus;
# check on the xmax value to make sure
for v in answer.itervalues():
if comp_xmax in v['xmax']:
for idx,level in enumerate(comp):
verts = [((p['x'])*sf_x,(p['y']-1)*sf_y) for p in level['arr']]
codes = np.ones(len(verts),int) * Path.LINETO
codes[0] = Path.MOVETO
verts_all.extend(verts)
codes_all.extend(codes)
try:
path = Path(verts_all, codes_all)
patch_black = patches.PathPatch(path, facecolor = 'none', edgecolor='black', lw=1)
except AssertionError:
print 'Users found no components for consensus match of %s' % zooniverse_id
# Plot the infrared results
fig = plt.figure(figno,(15,4))
fig.clf()
ax3 = fig.add_subplot(143)
ax4 = fig.add_subplot(144)
colormaparr = [cm.hot_r,cm.Blues,cm.RdPu,cm.Greens,cm.PuBu,cm.YlGn,cm.Greys][::-1]
colorarr = ['r','b','m','g','c','y','k'][::-1]
if len(answer) > 0: # At least one galaxy was identified
for idx,ans in enumerate(answer.itervalues()):
if ans.has_key('peak_data'):
# Plot the KDE map
colormap = colormaparr.pop()
ax3.imshow(np.rot90(ans['peak_data']['Z']), cmap=colormap,extent=[xmin, xmax, ymin, ymax])
# Plot individual sources
color = colorarr.pop()
x_plot,y_plot = ans['ir_x'],ans['ir_y']
ax3.scatter(x_plot, y_plot, c=color, marker='o', s=10, alpha=1./len(x_plot))
ax4.plot([ans['ir_peak'][0]],[ans['ir_peak'][1]],color=color,marker='*',markersize=12)
elif ans.has_key('ir'):
color = colorarr.pop()
x_plot,y_plot = ans['ir']
ax3.plot([x_plot],[y_plot],color=color,marker='o',markersize=2)
ax4.plot([x_plot],[y_plot],color=color,marker='*',markersize=12)
else:
ax4.text(550,idx*25,'#%i - no IR host' % idx,fontsize=11)
ax3.set_xlim([0, 500])
ax3.set_ylim([500, 0])
ax3.set_title(zooniverse_id)
ax3.set_aspect('equal')
ax4.set_xlim([0, 500])
ax4.set_ylim([500, 0])
ax4.set_title('Consensus (%i/%i users)' % (cons['n_users'],cons['n_total']))
ax4.set_aspect('equal')
# Display IR and radio images
url_standard = subject['location']['standard']
im_standard = Image.open(cStringIO.StringIO(urllib.urlopen(url_standard).read()))
ax1 = fig.add_subplot(141)
ax1.imshow(im_standard,origin='upper')
ax1.set_title('WISE')
url_radio = subject['location']['radio']
im_radio = Image.open(cStringIO.StringIO(urllib.urlopen(url_radio).read()))
ax2 = fig.add_subplot(142)
ax2.imshow(im_radio,origin='upper')
ax2.set_title(subject['metadata']['source'])
ax2.get_yaxis().set_ticklabels([])
ax3.get_yaxis().set_ticklabels([])
# Plot contours identified as the consensus
if len(answer) > 0:
ax4.add_patch(patch_black)
# Add centers of bounding boxes
for comp in components:
bbox_radio = comp[0]['bbox']
bbox_ir = bbox_radio_to_ir(bbox_radio)
xrad = np.median((bbox_ir[0],bbox_ir[2]))
yrad = np.median((bbox_ir[1],bbox_ir[3]))
ax4.scatter(xrad,yrad, c='g', marker='s', s=15, alpha=1)
dbx = [bbox_ir[i] for i in (2,2,0,0,2)]
dby = [bbox_ir[i] for i in (3,1,1,3,3)]
ax4.plot(dbx,dby,color='g')
radiobeamsize = 5. # arcsec
imagesize = 3. # arcmin
imagescale = IMG_HEIGHT_NEW/imagesize / 60. # pixel / arcsec
radio_tol = radiobeamsize * imagescale
for ans in answer:
if answer[ans].has_key('ir_peak'):
# Optical counterpart position
xc,yc = answer[ans]['ir_peak']
# Measure all positions in radio pixel coordinates
radio_centroids = pix_radio(components)
maxdist = 0
for centroid in radio_centroids:
d = pix_dist(xc,yc,centroid[0],centroid[1])
maxdist = d if d > maxdist else maxdist
if d <= radio_tol:
middle_radio = centroid
radio_centroids.remove(middle_radio)
x1 = radio_centroids[0][0]
y1 = radio_centroids[0][1]
x2 = radio_centroids[1][0]
y2 = radio_centroids[1][1]
if len(radio_centroids) == 2:
m1 = (y1 - yc) / (x1 - xc)
b1 = yc - m1*xc
m2 = (y2 - yc) / (x2 - xc)
b2 = yc - m2*xc
xedge1 = 0 if x1 < xc else 500
yedge1 = y1 - (x1-xedge1)*(yc-y1)/(xc-x1)
xedge2 = 0 if x2 < xc else 500
yedge2 = y2 - (x2-xedge2)*(yc-y2)/(xc-x2)
# Draw and annotate the the bending angle
ax4.plot([xedge1,xc],[yedge1,yc],color='orange',linestyle='--')
ax4.plot([xedge2,xc],[yedge2,yc],color='orange',linestyle='--')
alpha_deg = bending_angle(xc,yc,x1,y1,x2,y2) * 180/np.pi
ax4.text(550,0,r'$\alpha$ = %.1f deg' % alpha_deg,fontsize=11)
else:
print "\tDidn't find match to optical ID for triple radio source %s" % zooniverse_id
else:
print "\tNo IR peak for %s" % zooniverse_id
ax4.yaxis.tick_right()
ax1.get_xaxis().set_ticks([0,100,200,300,400])
ax2.get_xaxis().set_ticks([0,100,200,300,400])
ax3.get_xaxis().set_ticks([0,100,200,300,400])
ax4.get_xaxis().set_ticks([0,100,200,300,400,500])
plt.subplots_adjust(wspace=0.02)
# Save hard copy of the figure
if savefig:
fig.savefig('{0:}/bending_angles/plots/individual/triples/{1:}ba_{2:}.pdf'.format(rgz_dir,anglepath,zooniverse_id))
plt.close()
else:
plt.show()
# Close figure after it's done; otherwise mpl complains about having thousands of stuff open
return None
def plot_one_double(zooniverse_id,pathdict,figno=1,savefig=False,anglepath='',dbltype='radio'):
# Make a four-panel plot of the consensus identification with marked bending angle and position angle for a double source
cons = consensus.checksum(zooniverse_id)
subject = subjects.find_one({'zooniverse_id':zooniverse_id})
contours = get_contours(subject,pathdict)
radio_components = contours['contours']
# Plot image
answer = cons['answer']
# Download contour data
sf_x = 500./contours['width']
sf_y = 500./contours['height']
verts_all = []
codes_all = []
components = contours['contours']
for comp in components:
# Order of bounding box components is (xmax,ymax,xmin,ymin)
comp_xmax,comp_ymax,comp_xmin,comp_ymin = comp[0]['bbox']
# Only plot radio components identified by the users as the consensus;
# check on the xmax value to make sure
for v in answer.itervalues():
if comp_xmax in v['xmax']:
for idx,level in enumerate(comp):
verts = [((p['x'])*sf_x,(p['y']-1)*sf_y) for p in level['arr']]
codes = np.ones(len(verts),int) * Path.LINETO
codes[0] = Path.MOVETO
verts_all.extend(verts)
codes_all.extend(codes)
try:
path = Path(verts_all, codes_all)
patch_black = patches.PathPatch(path, facecolor = 'none', edgecolor='black', lw=1)
except AssertionError:
print 'Users found no components for consensus match of %s' % zooniverse_id
# Plot the infrared results
fig = plt.figure(figno,(15,4))
fig.clf()
ax3 = fig.add_subplot(143)
ax4 = fig.add_subplot(144)
colormaparr = [cm.hot_r,cm.Blues,cm.RdPu,cm.Greens,cm.PuBu,cm.YlGn,cm.Greys][::-1]
colorarr = ['r','b','m','g','c','y','k'][::-1]
if len(answer) > 0: # At least one galaxy was identified
for idx,ans in enumerate(answer.itervalues()):
if ans.has_key('peak_data'):
# Plot the KDE map
colormap = colormaparr.pop()
ax3.imshow(np.rot90(ans['peak_data']['Z']), cmap=colormap,extent=[xmin, xmax, ymin, ymax])
# Plot individual sources
color = colorarr.pop()
'''
x_plot = [xt * 500./424 for xt in ans['ir_x'] if xt != -99.0]
y_plot = [yt * 500./424 for yt in ans['ir_y'] if yt != -99.0]
'''
x_plot,y_plot = ans['ir_x'],ans['ir_y']
ax3.scatter(x_plot, y_plot, c=color, marker='o', s=10, alpha=1./len(x_plot))
ax4.plot([ans['ir_peak'][0]],[ans['ir_peak'][1]],color=color,marker='*',markersize=12)
elif ans.has_key('ir'):
color = colorarr.pop()
x_plot,y_plot = ans['ir']
ax3.plot([x_plot],[y_plot],color=color,marker='o',markersize=2)
ax4.plot([x_plot],[y_plot],color=color,marker='*',markersize=12)
else:
ax4.text(550,idx*25,'#%i - no IR host' % idx,fontsize=11)
ax3.set_xlim([0, 500])
ax3.set_ylim([500, 0])
ax3.set_title(zooniverse_id)
ax3.set_aspect('equal')
ax4.set_xlim([0, 500])
ax4.set_ylim([500, 0])
ax4.set_title('Consensus (%i/%i users)' % (cons['n_users'],cons['n_total']))
ax4.set_aspect('equal')
# Display IR and radio images
url_standard = subject['location']['standard']
im_standard = Image.open(cStringIO.StringIO(urllib.urlopen(url_standard).read()))
ax1 = fig.add_subplot(141)
ax1.imshow(im_standard,origin='upper')
ax1.set_title('WISE')
url_radio = subject['location']['radio']
im_radio = Image.open(cStringIO.StringIO(urllib.urlopen(url_radio).read()))
ax2 = fig.add_subplot(142)
ax2.imshow(im_radio,origin='upper')
ax2.set_title(subject['metadata']['source'])
ax2.get_yaxis().set_ticklabels([])
ax3.get_yaxis().set_ticklabels([])
# Plot contours identified as the consensus
if len(answer) > 0:
ax4.add_patch(patch_black)
radio_centers = []
for component in components:
bbox = component[0]['bbox']
# Draw centers of bounding boxes
xradiocen = np.median((bbox[0],bbox[2])) / first_ir_scale_x
yradiocen = np.median((bbox[1],bbox[3])) / first_ir_scale_y
radio_centers.append((xradiocen,yradiocen))
ax4.scatter(xradiocen,yradiocen, c='g', marker='s', s=15, alpha=1)
# Draw edge of bounding box
xradiomin = bbox[2] / first_ir_scale_x
xradiomax = bbox[0] / first_ir_scale_x
yradiomin = bbox[3] / first_ir_scale_y
yradiomax = bbox[1] / first_ir_scale_y
ax4.plot([xradiomin,xradiomin,xradiomax,xradiomax,xradiomin],[yradiomin,yradiomax,yradiomax,yradiomin,yradiomin],color='g')
for ans in answer:
if answer[ans].has_key('ir_peak'):
# Optical counterpart position
xc,yc = answer[ans]['ir_peak']
# Position of radio sources for multi-peaked, single-component subjects
if dbltype == "mps":
local_maxima = mps_cc(subject,pathdict,plot=False,verbose=False)
suffix = '' if len(local_maxima) == 1 else 's'
assert len(local_maxima) >= 2, \
"%i peak%s in first radio component of %s; must have exactly 2 peaks to plot bending angle using mps method." % (len(local_maxima),suffix,zooniverse_id)
x1 = local_maxima[0][1][0] / first_ir_scale_x
y1 = local_maxima[0][1][1] / first_ir_scale_y
x2 = local_maxima[1][1][0] / first_ir_scale_x
y2 = local_maxima[1][1][1] / first_ir_scale_y
ax4.scatter(x1,y1, color='darkorange', marker='s', s=15, alpha=1)
ax4.scatter(x2,y2, color='darkorange', marker='s', s=15, alpha=1)
# Position of radio sources for double-lobed, two-component subjects
elif len(radio_centers) == 2:
x1,y1 = radio_centers[0]
x2,y2 = radio_centers[1]
else:
raise ValueError("Centers of radio boxes not defined.")
m1 = (y1 - yc) / (x1 - xc)
b1 = yc - m1*xc
m2 = (y2 - yc) / (x2 - xc)
b2 = yc - m2*xc
xedge1 = 0 if x1 < xc else 500
yedge1 = y1 - (x1-xedge1)*(yc-y1)/(xc-x1)
xedge2 = 0 if x2 < xc else 500
yedge2 = y2 - (x2-xedge2)*(yc-y2)/(xc-x2)
# Draw and annotate the the bending angle
ax4.plot([xedge1,xc],[yedge1,yc],color='orange',linestyle='--')
ax4.plot([xedge2,xc],[yedge2,yc],color='orange',linestyle='--')
alpha_deg = bending_angle(xc,yc,x1,y1,x2,y2) * 180/np.pi
ax4.text(550,0,r'$\alpha$ = %.1f deg' % alpha_deg,fontsize=11)
# Draw vector pointing north
# Draw the bisector vector
'''
yd = y_bisect(xc,yc,xedge1,yedge1,xedge2,yedge2)
ax4.arrow(xc,yc,-xc,yd-yc,head_width=20, head_length=40, fc='blue', ec='blue')
'''
# Compute the position angle with respect to north
phi_deg = position_angle(xc,500-yc,x1,500-y1,x2,500-y2) * 180/np.pi
ax4.text(550,50,r'$\phi$ = %.1f deg' % phi_deg,fontsize=11)
ax4.arrow(xc,yc,0,-yc,head_width=20, head_length=40, fc='grey', ec='grey',ls='dotted')
else:
print "No peak for %s" % zooniverse_id
ax4.yaxis.tick_right()
ax1.get_xaxis().set_ticks([0,100,200,300,400])
ax2.get_xaxis().set_ticks([0,100,200,300,400])
ax3.get_xaxis().set_ticks([0,100,200,300,400])
ax4.get_xaxis().set_ticks([0,100,200,300,400,500])
plt.subplots_adjust(wspace=0.02)
# Save hard copy of the figure
if savefig:
fig.savefig('%s/bending_angles/plots/individual/%sba_%s.pdf' % (rgz_dir,anglepath,zooniverse_id))
plt.close()
else:
plt.show()
# Close figure after it's done; otherwise mpl complains about having thousands of stuff open
return None
import consensus
# FIRST
first = consensus.checksum('ARG00029ls')
consensus.plot_consensus(first)
# ATLAS
atlas = consensus.checksum('ARG0003r18')
consensus.plot_consensus(atlas)
def plot_one_triple(zooniverse_id,
pathdict,
figno=1,
savefig=False,
anglepath=''):
# Make a four-panel plot of the consensus identification with marked bending angle and position angle for a triple source
cons = consensus.checksum(zooniverse_id)
subject = subjects.find_one({'zooniverse_id': zooniverse_id})
contours = get_contours(subject, pathdict)
radio_components = contours['contours']
# Plot image
answer = cons['answer']
# Download contour data
sf_x = 500. / contours['width']
sf_y = 500. / contours['height']
verts_all = []
codes_all = []
components = contours['contours']
for comp in components:
# Order of bounding box components is (xmax,ymax,xmin,ymin)
comp_xmax, comp_ymax, comp_xmin, comp_ymin = comp[0]['bbox']
# Only plot radio components identified by the users as the consensus;
# check on the xmax value to make sure
for v in answer.itervalues():
if comp_xmax in v['xmax']:
for idx, level in enumerate(comp):
verts = [((p['x']) * sf_x, (p['y'] - 1) * sf_y)
for p in level['arr']]
codes = np.ones(len(verts), int) * Path.LINETO
codes[0] = Path.MOVETO
verts_all.extend(verts)
codes_all.extend(codes)
try:
path = Path(verts_all, codes_all)
patch_black = patches.PathPatch(path,
facecolor='none',
edgecolor='black',
lw=1)
except AssertionError:
print 'Users found no components for consensus match of %s' % zooniverse_id
# Plot the infrared results
fig = plt.figure(figno, (15, 4))
fig.clf()
ax3 = fig.add_subplot(143)
ax4 = fig.add_subplot(144)
colormaparr = [
cm.hot_r, cm.Blues, cm.RdPu, cm.Greens, cm.PuBu, cm.YlGn, cm.Greys
][::-1]
colorarr = ['r', 'b', 'm', 'g', 'c', 'y', 'k'][::-1]
if len(answer) > 0: # At least one galaxy was identified
for idx, ans in enumerate(answer.itervalues()):
if ans.has_key('peak_data'):
# Plot the KDE map
colormap = colormaparr.pop()
ax3.imshow(np.rot90(ans['peak_data']['Z']),
cmap=colormap,
extent=[xmin, xmax, ymin, ymax])
# Plot individual sources
color = colorarr.pop()
x_plot, y_plot = ans['ir_x'], ans['ir_y']
ax3.scatter(x_plot,
y_plot,
c=color,
marker='o',
s=10,
alpha=1. / len(x_plot))
ax4.plot([ans['ir_peak'][0]], [ans['ir_peak'][1]],
color=color,
marker='*',
markersize=12)
elif ans.has_key('ir'):
color = colorarr.pop()
x_plot, y_plot = ans['ir']
ax3.plot([x_plot], [y_plot],
color=color,
marker='o',
markersize=2)
ax4.plot([x_plot], [y_plot],
color=color,
marker='*',
markersize=12)
else:
ax4.text(550, idx * 25, '#%i - no IR host' % idx, fontsize=11)
ax3.set_xlim([0, 500])
ax3.set_ylim([500, 0])
ax3.set_title(zooniverse_id)
ax3.set_aspect('equal')
ax4.set_xlim([0, 500])
ax4.set_ylim([500, 0])
ax4.set_title('Consensus (%i/%i users)' %
(cons['n_users'], cons['n_total']))
ax4.set_aspect('equal')
# Display IR and radio images
url_standard = subject['location']['standard']
im_standard = Image.open(
cStringIO.StringIO(urllib.urlopen(url_standard).read()))
ax1 = fig.add_subplot(141)
ax1.imshow(im_standard, origin='upper')
ax1.set_title('WISE')
url_radio = subject['location']['radio']
im_radio = Image.open(cStringIO.StringIO(urllib.urlopen(url_radio).read()))
ax2 = fig.add_subplot(142)
ax2.imshow(im_radio, origin='upper')
ax2.set_title(subject['metadata']['source'])
ax2.get_yaxis().set_ticklabels([])
ax3.get_yaxis().set_ticklabels([])
# Plot contours identified as the consensus
if len(answer) > 0:
ax4.add_patch(patch_black)
# Add centers of bounding boxes
for comp in components:
bbox_radio = comp[0]['bbox']
bbox_ir = bbox_radio_to_ir(bbox_radio)
xrad = np.median((bbox_ir[0], bbox_ir[2]))
yrad = np.median((bbox_ir[1], bbox_ir[3]))
ax4.scatter(xrad, yrad, c='g', marker='s', s=15, alpha=1)
dbx = [bbox_ir[i] for i in (2, 2, 0, 0, 2)]
dby = [bbox_ir[i] for i in (3, 1, 1, 3, 3)]
ax4.plot(dbx, dby, color='g')
radiobeamsize = 5. # arcsec
imagesize = 3. # arcmin
imagescale = IMG_HEIGHT_NEW / imagesize / 60. # pixel / arcsec
radio_tol = radiobeamsize * imagescale
for ans in answer:
if answer[ans].has_key('ir_peak'):
# Optical counterpart position
xc, yc = answer[ans]['ir_peak']
# Measure all positions in radio pixel coordinates
radio_centroids = pix_radio(components)
maxdist = 0
for centroid in radio_centroids:
d = pix_dist(xc, yc, centroid[0], centroid[1])
maxdist = d if d > maxdist else maxdist
if d <= radio_tol:
middle_radio = centroid
radio_centroids.remove(middle_radio)
x1 = radio_centroids[0][0]
y1 = radio_centroids[0][1]
x2 = radio_centroids[1][0]
y2 = radio_centroids[1][1]
if len(radio_centroids) == 2:
m1 = (y1 - yc) / (x1 - xc)
b1 = yc - m1 * xc
m2 = (y2 - yc) / (x2 - xc)
b2 = yc - m2 * xc
xedge1 = 0 if x1 < xc else 500
yedge1 = y1 - (x1 - xedge1) * (yc - y1) / (xc - x1)
xedge2 = 0 if x2 < xc else 500
yedge2 = y2 - (x2 - xedge2) * (yc - y2) / (xc - x2)
# Draw and annotate the the bending angle
ax4.plot([xedge1, xc], [yedge1, yc],
color='orange',
linestyle='--')
ax4.plot([xedge2, xc], [yedge2, yc],
color='orange',
linestyle='--')
alpha_deg = bending_angle(xc, yc, x1, y1, x2,
y2) * 180 / np.pi
ax4.text(550,
0,
r'$\alpha$ = %.1f deg' % alpha_deg,
fontsize=11)
else:
print "\tDidn't find match to optical ID for triple radio source %s" % zooniverse_id
else:
print "\tNo IR peak for %s" % zooniverse_id
ax4.yaxis.tick_right()
ax1.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax2.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax3.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax4.get_xaxis().set_ticks([0, 100, 200, 300, 400, 500])
plt.subplots_adjust(wspace=0.02)
# Save hard copy of the figure
if savefig:
fig.savefig(
'{0:}/bending_angles/plots/individual/triples/{1:}ba_{2:}.pdf'.
format(rgz_dir, anglepath, zooniverse_id))
plt.close()
else:
plt.show()
# Close figure after it's done; otherwise mpl complains about having thousands of stuff open
return None
def plot_one_double(zooniverse_id,
pathdict,
figno=1,
savefig=False,
anglepath='',
dbltype='radio'):
# Make a four-panel plot of the consensus identification with marked bending angle and position angle for a double source
cons = consensus.checksum(zooniverse_id)
subject = subjects.find_one({'zooniverse_id': zooniverse_id})
contours = get_contours(subject, pathdict)
radio_components = contours['contours']
# Plot image
answer = cons['answer']
# Download contour data
sf_x = 500. / contours['width']
sf_y = 500. / contours['height']
verts_all = []
codes_all = []
components = contours['contours']
for comp in components:
# Order of bounding box components is (xmax,ymax,xmin,ymin)
comp_xmax, comp_ymax, comp_xmin, comp_ymin = comp[0]['bbox']
# Only plot radio components identified by the users as the consensus;
# check on the xmax value to make sure
for v in answer.itervalues():
if comp_xmax in v['xmax']:
for idx, level in enumerate(comp):
verts = [((p['x']) * sf_x, (p['y'] - 1) * sf_y)
for p in level['arr']]
codes = np.ones(len(verts), int) * Path.LINETO
codes[0] = Path.MOVETO
verts_all.extend(verts)
codes_all.extend(codes)
try:
path = Path(verts_all, codes_all)
patch_black = patches.PathPatch(path,
facecolor='none',
edgecolor='black',
lw=1)
except AssertionError:
print 'Users found no components for consensus match of %s' % zooniverse_id
# Plot the infrared results
fig = plt.figure(figno, (15, 4))
fig.clf()
ax3 = fig.add_subplot(143)
ax4 = fig.add_subplot(144)
colormaparr = [
cm.hot_r, cm.Blues, cm.RdPu, cm.Greens, cm.PuBu, cm.YlGn, cm.Greys
][::-1]
colorarr = ['r', 'b', 'm', 'g', 'c', 'y', 'k'][::-1]
if len(answer) > 0: # At least one galaxy was identified
for idx, ans in enumerate(answer.itervalues()):
if ans.has_key('peak_data'):
# Plot the KDE map
colormap = colormaparr.pop()
ax3.imshow(np.rot90(ans['peak_data']['Z']),
cmap=colormap,
extent=[xmin, xmax, ymin, ymax])
# Plot individual sources
color = colorarr.pop()
'''
x_plot = [xt * 500./424 for xt in ans['ir_x'] if xt != -99.0]
y_plot = [yt * 500./424 for yt in ans['ir_y'] if yt != -99.0]
'''
x_plot, y_plot = ans['ir_x'], ans['ir_y']
ax3.scatter(x_plot,
y_plot,
c=color,
marker='o',
s=10,
alpha=1. / len(x_plot))
ax4.plot([ans['ir_peak'][0]], [ans['ir_peak'][1]],
color=color,
marker='*',
markersize=12)
elif ans.has_key('ir'):
color = colorarr.pop()
x_plot, y_plot = ans['ir']
ax3.plot([x_plot], [y_plot],
color=color,
marker='o',
markersize=2)
ax4.plot([x_plot], [y_plot],
color=color,
marker='*',
markersize=12)
else:
ax4.text(550, idx * 25, '#%i - no IR host' % idx, fontsize=11)
ax3.set_xlim([0, 500])
ax3.set_ylim([500, 0])
ax3.set_title(zooniverse_id)
ax3.set_aspect('equal')
ax4.set_xlim([0, 500])
ax4.set_ylim([500, 0])
ax4.set_title('Consensus (%i/%i users)' %
(cons['n_users'], cons['n_total']))
ax4.set_aspect('equal')
# Display IR and radio images
url_standard = subject['location']['standard']
im_standard = Image.open(
cStringIO.StringIO(urllib.urlopen(url_standard).read()))
ax1 = fig.add_subplot(141)
ax1.imshow(im_standard, origin='upper')
ax1.set_title('WISE')
url_radio = subject['location']['radio']
im_radio = Image.open(cStringIO.StringIO(urllib.urlopen(url_radio).read()))
ax2 = fig.add_subplot(142)
ax2.imshow(im_radio, origin='upper')
ax2.set_title(subject['metadata']['source'])
ax2.get_yaxis().set_ticklabels([])
ax3.get_yaxis().set_ticklabels([])
# Plot contours identified as the consensus
if len(answer) > 0:
ax4.add_patch(patch_black)
radio_centers = []
for component in components:
bbox = component[0]['bbox']
# Draw centers of bounding boxes
xradiocen = np.median((bbox[0], bbox[2])) / first_ir_scale_x
yradiocen = np.median((bbox[1], bbox[3])) / first_ir_scale_y
radio_centers.append((xradiocen, yradiocen))
ax4.scatter(xradiocen, yradiocen, c='g', marker='s', s=15, alpha=1)
# Draw edge of bounding box
xradiomin = bbox[2] / first_ir_scale_x
xradiomax = bbox[0] / first_ir_scale_x
yradiomin = bbox[3] / first_ir_scale_y
yradiomax = bbox[1] / first_ir_scale_y
ax4.plot([xradiomin, xradiomin, xradiomax, xradiomax, xradiomin],
[yradiomin, yradiomax, yradiomax, yradiomin, yradiomin],
color='g')
for ans in answer:
if answer[ans].has_key('ir_peak'):
# Optical counterpart position
xc, yc = answer[ans]['ir_peak']
# Position of radio sources for multi-peaked, single-component subjects
if dbltype == "mps":
local_maxima = mps_cc(subject,
pathdict,
plot=False,
verbose=False)
suffix = '' if len(local_maxima) == 1 else 's'
assert len(local_maxima) >= 2, \
"%i peak%s in first radio component of %s; must have exactly 2 peaks to plot bending angle using mps method." % (len(local_maxima),suffix,zooniverse_id)
x1 = local_maxima[0][1][0] / first_ir_scale_x
y1 = local_maxima[0][1][1] / first_ir_scale_y
x2 = local_maxima[1][1][0] / first_ir_scale_x
y2 = local_maxima[1][1][1] / first_ir_scale_y
ax4.scatter(x1,
y1,
color='darkorange',
marker='s',
s=15,
alpha=1)
ax4.scatter(x2,
y2,
color='darkorange',
marker='s',
s=15,
alpha=1)
# Position of radio sources for double-lobed, two-component subjects
elif len(radio_centers) == 2:
x1, y1 = radio_centers[0]
x2, y2 = radio_centers[1]
else:
raise ValueError("Centers of radio boxes not defined.")
m1 = (y1 - yc) / (x1 - xc)
b1 = yc - m1 * xc
m2 = (y2 - yc) / (x2 - xc)
b2 = yc - m2 * xc
xedge1 = 0 if x1 < xc else 500
yedge1 = y1 - (x1 - xedge1) * (yc - y1) / (xc - x1)
xedge2 = 0 if x2 < xc else 500
yedge2 = y2 - (x2 - xedge2) * (yc - y2) / (xc - x2)
# Draw and annotate the the bending angle
ax4.plot([xedge1, xc], [yedge1, yc],
color='orange',
linestyle='--')
ax4.plot([xedge2, xc], [yedge2, yc],
color='orange',
linestyle='--')
alpha_deg = bending_angle(xc, yc, x1, y1, x2, y2) * 180 / np.pi
ax4.text(550,
0,
r'$\alpha$ = %.1f deg' % alpha_deg,
fontsize=11)
# Draw vector pointing north
# Draw the bisector vector
'''
yd = y_bisect(xc,yc,xedge1,yedge1,xedge2,yedge2)
ax4.arrow(xc,yc,-xc,yd-yc,head_width=20, head_length=40, fc='blue', ec='blue')
'''
# Compute the position angle with respect to north
phi_deg = position_angle(xc, 500 - yc, x1, 500 - y1, x2,
500 - y2) * 180 / np.pi
ax4.text(550, 50, r'$\phi$ = %.1f deg' % phi_deg, fontsize=11)
ax4.arrow(xc,
yc,
0,
-yc,
head_width=20,
head_length=40,
fc='grey',
ec='grey',
ls='dotted')
else:
print "No peak for %s" % zooniverse_id
ax4.yaxis.tick_right()
ax1.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax2.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax3.get_xaxis().set_ticks([0, 100, 200, 300, 400])
ax4.get_xaxis().set_ticks([0, 100, 200, 300, 400, 500])
plt.subplots_adjust(wspace=0.02)
# Save hard copy of the figure
if savefig:
fig.savefig('%s/bending_angles/plots/individual/%sba_%s.pdf' %
(rgz_dir, anglepath, zooniverse_id))
plt.close()
else:
plt.show()
# Close figure after it's done; otherwise mpl complains about having thousands of stuff open
return None
def mps_bending_angle(consensus_level=0.50):
# Compute the bending and position angles for double-peaked, single-contour sources with optical counterparts
tstart = time.time()
# Load data
df = pd.read_csv('%s/bending_angles/multipeaked_singles_cc.csv' % rgz_dir,
delimiter=',')
df2 = df[(df['ntotal'] == 2)]
with open('%s/bending_angles/angles_multipeaked_singles.csv' % rgz_dir,
'w') as f:
print >> f, 'zooniverse_id,bending_angle,position_angle'
# get optical counterpart for zooniverse_id (loop over eventually)
df2_pair1 = df2[::2]
df2_pair2 = df2[1::2]
_zid = np.array(df2_pair1['zooniverse_id'])
_x1 = np.array(df2_pair1['xc'])
_y1 = np.array(df2_pair1['yc'])
_x2 = np.array(df2_pair2['xc'])
_y2 = np.array(df2_pair2['yc'])
for zooniverse_id, x1, y1, x2, y2 in zip(_zid, _x1, _y1, _x2, _y2):
c = consensus.checksum(zooniverse_id)
try:
if (len(c['answer']) == 1) and (
c['n_users'] / float(c['n_total']) >= consensus_level):
if c['answer'][c['answer'].keys()[0]].has_key('ir_peak'):
peak_x, peak_y = c['answer'][c['answer'].keys()
[0]]['ir_peak']
ir_x = peak_x * first_ir_scale_x
ir_y = peak_y * first_ir_scale_y
alpha = bending_angle(ir_x, ir_y, x1, y1, x2, y2)
alpha_deg = alpha * 180. / np.pi
phi = position_angle(ir_x, ir_y, x1, y1, x2, y2)
phi_deg = phi * 180. / np.pi
print >> f, '{0:s},{1:.4f},{2:.4f}'.format(
zooniverse_id, alpha_deg, phi_deg)
else:
print "Had more than 1 IR sources and/or less than {0:.2f} percent consensus for {1:s}".format(
consensus_level, zooniverse_id)
except TypeError:
print "No 'answer' key for %s" % zooniverse_id
# Timing the process
tend = time.time()
n = len(df2) / 2
print '%.2f minutes for %i subjects' % ((tend - tstart) / 60., n)
print '%.2f subjects per second' % (n / (tend - tstart))
return None