def GetSourceSize(self,kpc=False):
self.z=source_redshifts[self.name]
self.Da = astCalc.da(self.z)
self.scale = self.Da*1e3*np.pi/180./3600.
if len(self.srcs) == 1 or self.name == 'J0837':
self.Re_v = self.Ddic['Source 1 re']*0.05
self.Re_i = self.Re_v.copy()
self.Re_lower = self.Ldic['Source 1 re']*0.05
self.Re_upper = self.Udic['Source 1 re']*0.05
elif len(self.srcs) == 2 and self.name != 'J0837':
print 'test this out...!'
Xgrid = np.logspace(-4,5,1501)
Res = []
for i in range(len(self.imgs)):
#if self.name == 'J1605':
# source =
source = self.fits[i][-3]*self.srcs[0].eval(Xgrid) + self.fits[i][-2]*self.srcs[1].eval(Xgrid)
R = Xgrid.copy()
light = source*2.*np.pi*R
mod = splrep(R,light,t=np.logspace(-3.8,4.8,1301))
intlight = np.zeros(len(R))
for i in range(len(R)):
intlight[i] = splint(0,R[i],mod)
model = splrep(intlight[:-300],R[:-300])
if len(model[1][np.where(np.isnan(model[1])==True)]>0):
print "arrays need to be increasing monotonically! But don't worry about it"
model = splrep(intlight[:-450],R[:-450])
reff = splev(0.5*intlight[-1],model)
Res.append(reff*0.05)
self.Re_v,self.Re_i = Res
if kpc:
return [self.Re_v*self.scale, self.Re_i*self.scale]
return [self.Re_v, self.Re_i]
def GetSourceSize(self,z):
self.z=z
self.Da = astCalc.da(self.z)
self.scale = self.Da*np.pi/180./3600.
if len(self.srcs) == 1:
self.Re = self.Ddic['Source 1 re']*0.05
self.Re_lower = self.Ldic['Source 1 re']*0.05
self.Re_upper = self.Udic['Source 1 re']*0.05
self.Re_kpc = self.Re*self.scale
return self.Re
elif len(self.srcs) == 2:
print 'test this out...!'
Xgrid = np.logspace(-4,5,1501)
Ygrid = np.logspace(-4,5,1501)
Res = []
for i in range(len(self.imgs)):
source = self.fits[i][-3]*self.srcs[0].eval(Xgrid) + self.fits[i][-2]*self.srcs[1].eval(Xgrid)
R = Xgrid.copy()
light = source*2.*np.pi*R
mod = splrep(R,light,t=np.logspace(-3.8,4.8,1301))
intlight = np.zeros(len(R))
for i in range(len(R)):
intlight[i] = splint(0,R[i],mod)
model = splrep(intlight[:-300],R[:-300])
reff = splev(0.5*intlight[-1],model)
Res.append(reff*0.05)
self.Re_v,self.Re_i = Res
return self.Re_v, self.Re_i
def Arcsec2Kpc(self,z=None):
self.z=z
self.Da = astCalc.da(self.z)
self.scale = self.Da*np.pi/180./3600.
if len(self.srcs)==1:
self.Re_kpc = self.Re*scale
elif len(self.srcs)==2:
self.Re_v_kpc = self.Re_v*scale
self.Re_i_kpc = self.Re_i*scale
def findSeperationSpatial(data, center):
''' Finds the distance to all of the galaxies from the center of the
cluster in the spatial plane. Returns values in Mpc.
'''
# Add a new column to the dataframe
data['seperation'] = 0.0
for row in data.iterrows():
sepDeg = aco.calcAngSepDeg(center[0], center[1], row[1]['ra'],
row[1]['dec'])
sepMpc = sepDeg * aca.da(row[1]['redshift']) / 57.2957795131
data['seperation'][row[0]] = sepMpc
return data
def findseparationSpatial(data, center):
''' Finds the distance to all of the galaxies from the center of the
cluster in the spatial plane. Returns values in Mpc.
'''
# Add a new column to the dataframe
sepDeg = np.array(aco.calcAngSepDeg(center[0], center[1], data.ra.values,
data.dec.values))
da = np.array([aca.da(z) / 57.2957795131 for z in data.redshift.values])
sepMpc = da * sepDeg
data.loc[:, 'separation'] = sepMpc
return data
from astLib import astStats
from scipy.stats import scoreatpercentile
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies = filter(lambda galaxy: galaxy.ston_I > 10., galaxies)
#galaxies = filter(lambda galaxy: 10<galaxy.Mass <11 and galaxy.ston_I > 10. , galaxies)
#Upper and Lower limit arrow verts
arrowup_verts = [[0., 0.], [-1., -1], [0., 0.], [0., -2.], [0., 0.], [1, -1]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
f1 = pyl.figure(1, figsize=(6, 4))
f1s1 = f1.add_subplot(111)
for galaxy in galaxies:
re = 0.06 * galaxy.halflight * astCalc.da(galaxy.z) * 1000 / 206265.
if galaxy.ICD_IH * 100 < 50:
f1s1.scatter(re, galaxy.ICD_IH * 100, c='0.8', edgecolor='0.8', s=20)
else:
f1s1.scatter(re, 50, s=100, marker=None, verts=arrowup_verts)
x = [
round(0.06 * galaxy.halflight * astCalc.da(galaxy.z) * 1000 / 206265.)
for galaxy in galaxies
]
y = [galaxy.ICD_IH * 100. for galaxy in galaxies]
x_, y_median = zip(*sorted(
(xVal, pyl.median([yVal for a, yVal in zip(x, y) if xVal == a]))
for xVal in set(x)))
omegaMs = [1.0, 0.2, 0.05]
omegaLs = [0.0, 0.8, 0.0]
styles = ['r-', 'b--', 'g:']
z = numpy.arange(0, 6, 0.1)
# da
pylab.clf()
for m, l, s in zip(omegaMs, omegaLs, styles):
astCalc.OMEGA_M0 = m
astCalc.OMEGA_L = l
label = "$\Omega_{m0}$ = %.2f $\Omega_\Lambda$ = %.2f" % (m, l)
dH = astCalc.C_LIGHT/astCalc.H0
plotData = []
for i in z:
plotData.append(astCalc.da(i)/dH)
pylab.plot(z, plotData, s, label=label)
pylab.ylim(0, 0.5)
pylab.xlim(0, 5)
pylab.xlabel("$z$")
pylab.ylabel("$D_A/D_H$")
pylab.legend(loc='lower right')
# dV/dz
pylab.clf()
for m, l, s in zip(omegaMs, omegaLs, styles):
astCalc.OMEGA_M0 = m
astCalc.OMEGA_L = l
label = "$\Omega_{m0}$ = %.2f $\Omega_\Lambda$ = %.2f" % (m, l)
dH = astCalc.C_LIGHT/astCalc.H0
plotData = []
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies = filter(lambda galaxy: 0.06 * galaxy.halflight *\
astCalc.da(galaxy.z)*1000/206265. > 2, galaxies)
# Make figure
f1 = pyl.figure(1, figsize=(6, 4))
f1s1 = f1.add_subplot(121)
f1s2 = f1.add_subplot(122)
# f1s3 = f1.add_subplot(223)
# f1s4 = f1.add_subplot(224)
#Upper and Lower limit arrow verts
arrowup_verts = [[0., 0.], [-1., -1], [0., 0.], [0., -2.], [0., 0.],
[1, -1], [0, 0]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
# Add arrows first
if galaxy.ICD_IH > 0.5:
f1s1.scatter(galaxy.Mass,
0.5 * 100,
s=100,
marker=None,
verts=arrowup_verts)
else:
f1s1.scatter(galaxy.Mass,
galaxy.ICD_IH * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
f1s2.scatter(galaxy.Mass,
galaxy.ICD_IH * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
'''
if galaxy.ston_J > 30. and galaxy.ICD_JH != None:
# Add arrows first
if galaxy.ICD_JH > 0.12:
f1s3.scatter(galaxy.Mass, 12, s=100, marker=None,
verts=arrowup_verts)
else:
f1s3.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s4.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
'''
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul = 12
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x = pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x >= xmin, x < xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH * 100 for galaxy in grid[i]])
from boxplot_percentile_width import percentile_box_plot as pbp
#bp1 = f1s1.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s1, icd, indexer=list(index), width=width)
pbp(f1s2, icd, indexer=list(index), width=width)
'''
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_J > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
#bins_x =pyl.linspace(ll, ul, 7)
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_JH*100 for galaxy in grid[i]])
#bp2 = f1s2.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s3, icd, indexer=list(index), width=width)
pbp(f1s4, icd, indexer=list(index), width=width)
'''
# Finish Plot
# Tweak colors on the boxplot
#pyl.setp(bp1['boxes'], lw=2)
#pyl.setp(bp1['whiskers'], lw=2)
#pyl.setp(bp1['medians'], lw=2)
#pyl.setp(bp2['boxes'], lw=2)
#pyl.setp(bp2['whiskers'], lw=2)
#pyl.setp(bp2['medians'], lw=2)
#pyl.setp(bp['fliers'], color='#8CFF6F', marker='+')
#f1s1.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s1.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
f1s1.set_xlim(8, 12)
f1s2.set_xlim(8, 12)
# f1s3.set_xlim(8,12)
# f1s4.set_xlim(8,12)
f1s1.set_ylim(-10, 50)
f1s2.set_ylim(0, 15)
# f1s3.set_ylim(-5,12)
# f1s4.set_ylim(-1,3)
f1s1.set_xticks([8, 9, 10, 11, 12])
# f1s1.set_xticklabels([])
f1s2.set_xticks([8, 9, 10, 11, 12])
# f1s2.set_xticklabels([])
# f1s3.set_xticks([8,9,10,11,12])
# f1s4.set_xticks([8,9,10,11,12])
# f1s4.set_yticks([-1, 0, 1, 2, 3])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s1.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s2.set_xlabel(r"Log Mass ($M_{\odot})$")
# f1s3.set_ylabel(r"$\xi[J_{125},H_{160}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [],
marker='o',
mfc='0.8',
mec='0.8',
markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [],
marker='s',
mec='#348ABD',
mfc='None',
markersize=10,
linewidth=0,
markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles', 'Medians'),
'lower center',
prop=prop,
ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (12, 15)
xy2 = (8, 15)
con = ConnectionPatch(xyA=xy,
xyB=xy2,
coordsA='data',
coordsB='data',
axesA=f1s1,
axesB=f1s2)
xy = (12, 0)
xy2 = (8, 0)
con2 = ConnectionPatch(xyA=xy,
xyB=xy2,
coordsA='data',
coordsB='data',
axesA=f1s1,
axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (12, 3)
xy2 = (8, 3)
# con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
# axesA=f1s3, axesB=f1s4)
xy = (12, -1)
xy2 = (8, -1)
# con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
# axesA=f1s3, axesB=f1s4)
# f1s3.add_artist(con)
# f1s3.add_artist(con2)
pyl.draw()
pyl.show()
import matplotlib.ticker
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies = filter(lambda galaxy: galaxy.ston_I >30., galaxies)
f1 = pyl.figure(1, figsize=(6,4))
f1s1 = f1.add_subplot(111)
#x = [0.06 * galaxy.halflight * astCalc.da(galaxy.z) * 1000./206265
# for galaxy in galaxies]
galaxies = pyl.asarray(galaxies)
for galaxy in galaxies:
re = 0.06* galaxy.halflight * astCalc.da(galaxy.z)*1000/206265.
f1s1.scatter(re, galaxy.ICD_IH*100, c='0.8', edgecolor='0.8', s=20)
bins_x = pyl.arange(1, 20, 2)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
bunch = galaxies.compress(cond)
icds = [galaxy.ICD_IH*100 for galaxy in bunch]
if len(icds) >= 1:
med = pyl.median(icds)
f1s1.scatter((xmax-xmin)/2. + xmin, med, s=50, c='r')
def Arcsec2Kpc(self,z=None):
self.z=z
self.Da = astCalc.da(self.z)
self.scale = self.Da*1e3*np.pi/180./3600.
self.Re_v_kpc = self.Re_v*scale
self.Re_i_kpc = self.Re_i*scale
from scipy.stats import scoreatpercentile
galaxies = pickle.load(open("galaxies.pickle", "rb"))
galaxies = filter(lambda galaxy: galaxy.ston_I > 10.0, galaxies)
# galaxies = filter(lambda galaxy: 10<galaxy.Mass <11 and galaxy.ston_I > 10. , galaxies)
# Upper and Lower limit arrow verts
arrowup_verts = [[0.0, 0.0], [-1.0, -1], [0.0, 0.0], [0.0, -2.0], [0.0, 0.0], [1, -1]]
# arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
f1 = pyl.figure(1, figsize=(6, 4))
f1s1 = f1.add_subplot(111)
for galaxy in galaxies:
re = 0.06 * galaxy.halflight * astCalc.da(galaxy.z) * 1000 / 206265.0
if galaxy.ICD_IH * 100 < 50:
f1s1.scatter(re, galaxy.ICD_IH * 100, c="0.8", edgecolor="0.8", s=20)
else:
f1s1.scatter(re, 50, s=100, marker=None, verts=arrowup_verts)
x = [round(0.06 * galaxy.halflight * astCalc.da(galaxy.z) * 1000 / 206265.0) for galaxy in galaxies]
y = [galaxy.ICD_IH * 100.0 for galaxy in galaxies]
x_, y_median = zip(*sorted((xVal, pyl.median([yVal for a, yVal in zip(x, y) if xVal == a])) for xVal in set(x)))
x_, y_upper = zip(
*sorted((xVal, scoreatpercentile([yVal for a, yVal in zip(x, y) if xVal == a], 75)) for xVal in set(x))
)
x_, y_low = zip(*sorted((xVal, scoreatpercentile([yVal for a, yVal in zip(x, y) if xVal == a], 25)) for xVal in set(x)))
gc = aplpy.FITSFigure(fits)
try:
gc.show_rgb(png)
except FileNotFoundError:
gc.show_grayscale(stretch='arcsinh', pmin=1, pmax=98)
gc.set_theme('publication')
gc.set_tick_labels_format(xformat='hh:mm:ss', yformat='dd:mm')
gc.set_tick_labels_size('small')
###
# move things around and draw the labels
###
# recenter
window = 206265. / astCalc.da(confirmed.iloc[i]['z_cl_boada'])
gc.recenter(confirmed.iloc[i]['RA BCG'], confirmed.iloc[i]['DEC BCG'],
window / 3600)
# add the circles
gc.show_circles(confirmed.iloc[i]['RA'],
confirmed.iloc[i]['DEC'],
2 / 60,
linestyle='--',
edgecolor='#e24a33',
facecolor='none',
path_effects=[
pe.Stroke(linewidth=1.2, foreground='white'),
pe.Normal()
])
gc.show_circles(confirmed.iloc[i]['RA'],
dataframeIndex = list(LOSVsorted.index[list(indices[0])])
LOSVsorted = LOSVsorted.drop(dataframeIndex)
interlopers += dataframeIndex
else:
rejected = False
print 'interlopers',interlopers
return data.drop(interlopers)
def rejectInterlopers_group(data, sigmav=500)
deltaZmax = 2 * simgav / c
avgz = findClusterCenterRedshift(data)
deltaRmax = (c * deltaZmax)/(10*(1 + avgz)*aca.H0*aca.Ez(avgz)) # 1/Mpc
deltaThetamax = 206265 * deltaRmax * aca.da(avgz) # arcseconds
catalog = '/Users/steven/Projects/cluster/data/boada/may_2013/catalogs/XMMXCSJ124425.9+164758.0_complete.csv'
files = glob.glob('*.results')
center = 191.1050125, 16.7966666667
results = parseResults(files)
matched = matchToCatalog(results, catalog)
seperated = findSeperationSpatial(matched, center)
def testda(self):
""" astLib.da should give known result with known input """
for z, result in self.da:
answer = astCalc.da(z)
self.assertAlmostEqual(result, answer)
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies = filter(lambda galaxy: 0.06 * galaxy.halflight *\
astCalc.da(galaxy.z)*1000/206265. > 2, galaxies)
# Make figure
f1 = pyl.figure(1, figsize=(6,4))
f1s1 = f1.add_subplot(121)
f1s2 = f1.add_subplot(122)
# f1s3 = f1.add_subplot(223)
# f1s4 = f1.add_subplot(224)
#Upper and Lower limit arrow verts
arrowup_verts = [[0.,0.], [-1., -1], [0.,0.],
[0.,-2.], [0.,0.], [1,-1], [0,0]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
# Add arrows first
if galaxy.ICD_IH > 0.5:
f1s1.scatter(galaxy.Mass, 0.5*100, s=100, marker=None,
verts=arrowup_verts)
else:
f1s1.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s2.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
'''
if galaxy.ston_J > 30. and galaxy.ICD_JH != None:
# Add arrows first
if galaxy.ICD_JH > 0.12:
f1s3.scatter(galaxy.Mass, 12, s=100, marker=None,
verts=arrowup_verts)
else:
f1s3.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s4.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
'''
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile_width import percentile_box_plot as pbp
#bp1 = f1s1.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s1, icd, indexer=list(index), width=width)
pbp(f1s2, icd, indexer=list(index), width=width)
'''
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_J > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
#bins_x =pyl.linspace(ll, ul, 7)
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_JH*100 for galaxy in grid[i]])
#bp2 = f1s2.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s3, icd, indexer=list(index), width=width)
pbp(f1s4, icd, indexer=list(index), width=width)
'''
# Finish Plot
# Tweak colors on the boxplot
#pyl.setp(bp1['boxes'], lw=2)
#pyl.setp(bp1['whiskers'], lw=2)
#pyl.setp(bp1['medians'], lw=2)
#pyl.setp(bp2['boxes'], lw=2)
#pyl.setp(bp2['whiskers'], lw=2)
#pyl.setp(bp2['medians'], lw=2)
#pyl.setp(bp['fliers'], color='#8CFF6F', marker='+')
#f1s1.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s1.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
f1s1.set_xlim(8,12)
f1s2.set_xlim(8,12)
# f1s3.set_xlim(8,12)
# f1s4.set_xlim(8,12)
f1s1.set_ylim(-10,50)
f1s2.set_ylim(0,15)
# f1s3.set_ylim(-5,12)
# f1s4.set_ylim(-1,3)
f1s1.set_xticks([8,9,10,11,12])
# f1s1.set_xticklabels([])
f1s2.set_xticks([8,9,10,11,12])
# f1s2.set_xticklabels([])
# f1s3.set_xticks([8,9,10,11,12])
# f1s4.set_xticks([8,9,10,11,12])
# f1s4.set_yticks([-1, 0, 1, 2, 3])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s1.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s2.set_xlabel(r"Log Mass ($M_{\odot})$")
# f1s3.set_ylabel(r"$\xi[J_{125},H_{160}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='#348ABD', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), 'lower center', prop=prop, ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (12, 15)
xy2 = (8, 15)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
xy = (12, 0)
xy2 = (8, 0)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (12, 3)
xy2 = (8, 3)
# con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
# axesA=f1s3, axesB=f1s4)
xy = (12, -1)
xy2 = (8, -1)
# con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
# axesA=f1s3, axesB=f1s4)
# f1s3.add_artist(con)
# f1s3.add_artist(con2)
pyl.draw()
pyl.show()