def analytic_smf_evol(source='li-drory-march'):
'''
Evolution of the analytic SMF evolution
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(np.arange(0.0, 1.5, 0.25)):
smf = SMF()
analytic_mass, analytic_phi = smf.analytic(z, source=source)
sub.plot(analytic_mass,
analytic_phi,
lw=4,
ls='--',
c=pretty_colors[i_z],
label=r"$ z = " + str(round(z, 2)) + "$")
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([8.0, 12.0])
sub.legend(loc='upper right')
fig.savefig(''.join(['figure/'
'analytic_smf_', source, '.png']),
bbox_inches='tight')
plt.close()
def analytic_smf_evol(source='li-drory-march'):
'''
Evolution of the analytic SMF evolution
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(np.arange(0.0, 1.5, 0.25)):
smf = SMF()
analytic_mass, analytic_phi = smf.analytic(z, source=source)
sub.plot(analytic_mass, analytic_phi, lw=4, ls='--', c=pretty_colors[i_z],
label=r"$ z = "+str(round(z,2))+"$")
sub.set_yscale('log')
sub.set_ylim([10**-5, 10**-1])
sub.set_xlim([8.0, 12.0])
sub.legend(loc='upper right')
fig.savefig(
''.join([
'figure/'
'analytic_smf_', source, '.png'
]),
bbox_inches='tight'
)
plt.close()
def plot_avg_Pkfast_test(**kwargs):
'''
Quick test to compare average P_l(k) for Ngrid=960 in order to make sure it is consistent with P_l(k) for Ngrid=720.
'''
prettyplot()
pretty_colors = prettycolors()
for i_l, l_i in enumerate([0, 2, 4]):
# P(k) fast
k_fast_i, plk_fast_i = pk_extrap.average_Pk(l_i, 10, Ngrid=960)
k_i, plk_i = pk_extrap.average_Pk(l_i, 10, Ngrid=720)
l_i_str = str(l_i)
try:
l_i_str = str(l_i)
except TypeError:
l_i_str = l_i
# plot P(k) data
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
sub.scatter(
k_fast_i,
np.abs(plk_fast_i),
c=pretty_colors[i_l*2+1]
)
sub.plot(
k_i,
np.abs(plk_i),
label=r"$l = "+l_i_str+"$",
c='k',
lw=2,
ls='--'
)
if 'xrange' in kwargs.keys():
sub.set_xlim(kwargs['xrange'])
file_str = '_xranged'
else:
sub.set_xlim([1e-3, 1.0])
file_str = ''
sub.set_ylabel(r"$\mathtt{|P_l(k)|}$", fontsize=40)
sub.set_xlabel(r"$\mathtt{k}$", fontsize=40)
sub.set_xscale('log')
sub.set_yscale('log')
sub.legend(loc='upper right')
fig_file = ''.join([
'qaplot_avgP', str(l_i), 'k_fast_test', file_str, '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def test_fllp_k(k, l, rc=0.43):
'''
plot f_l_l' for l = 0, 2 and l' = 0, 2, 4, 6, 8, 10 in order to determine how it behaves
Parameter
---------
- l : ell value
Notes
-----
'''
q_arr = np.logspace(-3, 3, num=100)
n_mock = 7
krc = k * rc
k_fit = 4.
k_fixed = 4.34
data_dir = '/mount/riachuelo1/hahn/power/Nseries/Box/'
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(14,8))
sub = fig.add_subplot(111)
maxmax, minmin = 0., 0.
for i_lp, ellp in enumerate(range(20)):
lp = 2 * ellp
true_pk_file = ''.join([data_dir, 'power3600z_BoxN1.dat'])
tr_k = np.loadtxt(true_pk_file, unpack=True, usecols =[0])
fllp = []
for q in q_arr:
fllp.append(fourier_corr.f_l_lp(q*rc, krc, l, lp))
int_label = "$ l' = "+str(lp)+ "$"
sub.plot(q_arr, np.array(fllp), c=pretty_colors[i_lp+1], lw=4, ls='-', label=int_label)
maxmax = np.max([np.max(fllp), maxmax])
minmin = np.min([np.min(fllp), minmin])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=25)
sub.set_ylabel(r"$\mathtt{f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
sub.vlines(tr_k[-1], minmin, maxmax, color='k', linestyles='--', linewidth=2)
sub.text(2.*10**-3, 1.01*maxmax, r"k = "+str(round(k,2)), fontsize=20)
sub.legend(loc='upper right')
fig.savefig(''.join([
'figure/',
'fllp.l', str(l), '.k', str(round(k,2)), '.png'
]), bbox_inches='tight')
plt.close()
def plot_avg_P4k_comp(n_mocks, Ngrid=360, yscale='log', **kwargs):
'''
Plot average P_l(k)
'''
prettyplot()
pretty_colors = prettycolors()
# plot P(k) data
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_l, l_i in enumerate([4, '4fast']):
k_i, plk_i = pk_extrap.average_Pk(l_i, n_mocks, Ngrid=Ngrid)
l_i_str = str(l_i)
try:
l_i_str = str(l_i)
except TypeError:
l_i_str = l_i
sub.plot(
k_i,
np.abs(plk_i),
label=r"$l = "+l_i_str+"$",
c=pretty_colors[i_l*2+1],
lw=4
)
if 'xrange' in kwargs.keys():
sub.set_xlim(kwargs['xrange'])
else:
sub.set_xlim([1e-3, 1.0])
if 'yrange' in kwargs.keys():
sub.set_ylim(kwargs['yrange'])
else:
pass
sub.set_ylabel(r"$\mathtt{|P_4(k)|}$", fontsize=40)
sub.set_xlabel(r"$\mathtt{k}$", fontsize=40)
sub.set_xscale('log')
if yscale == 'log':
sub.set_yscale('log')
else:
pass
sub.legend(loc='upper right')
fig_file = ''.join([
'qaplot_avgP4_4fast_k_comparison_',
'Ngrid', str(Ngrid), '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
def delPcorr_0_ktrust_comp(ktrust, ell):
'''
Comparison between Del P^corr |q=0 to q=k_trust of
Nseries vs Nseries Box.
RESULTS:
As expected, there is negligible difference between Nseries and Nseries Box
Del P^corr |_0^k_trust.
'''
# Nseries (Geomtry)
geo_corrdelPk_pickle_file = ''.join([
'/mount/riachuelo1/hahn/power/Nseries/',
'corrdelP', str(ell), 'k',
'_qmax', str(round(ktrust,2)),'_',
'AVG_POWER_Q_CutskyN.fidcosmo.dat.grid960.P020000.box3600'
'.p'
])
geo_k, geo_DelPk = pickle.load(open(geo_corrdelPk_pickle_file, 'rb'))
# Nseries Box
box_corrdelPk_pickle_file = ''.join([
'/mount/riachuelo1/hahn/power/Nseries/Box/',
'corrdelP', str(ell), 'k',
'_qmax', str(round(ktrust,2)),'_',
'AVG_power3600z_BoxN.dat',
'.p'
])
box_k, box_DelPk = pickle.load(open(box_corrdelPk_pickle_file, 'rb'))
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1)
sub = fig.add_subplot(111)
sub.plot(geo_k, geo_DelPk, c = 'k', lw = 4, ls='-', label='Geometry')
sub.plot(box_k, box_DelPk, c = pretty_colors[2], lw = 4, ls='--', label='Box')
sub.set_xscale('log')
sub.set_xlim([10**-3,10**0])
sub.set_xlabel(r"$\mathtt{k}\;\;(\mathtt{Mpc}/h)$", fontsize=30)
sub.set_ylabel(r"$\mathtt{\Delta P_{"+str(ell)+r"}^{corr}(k)\bigg|_{q = 0}^{q=k_{trust}}}$",
fontsize=30)
sub.legend(loc='upper left', scatterpoints = 1)
fig.savefig(
''.join([
'figure/',
'qaplot_delP_'+str(ell)+'corr_0_ktrust', str(ktrust), '_geoboxcomp.png'
]),
bbox_inches="tight")
codif.notif(subject='delPcorr_0_ktrust figure finished')
plt.close()
def __init__(self, **kwargs):
""" Class that describes dLOS distribution plots
"""
self.kwargs = kwargs
# pretty figure up
prettyplot()
pretty_colors = prettycolors()
self.fig = plt.figure(1)
self.sub = self.fig.add_subplot(111)
self.hist_max = 0.0
def plot_sfms_wetzel_vs_lee():
'''
Plot Wetzel parameterized SFMS and Lee et al. (2015) parameterized SFMS.
'''
# wetzel
wetzel_sfr_mstar_z, wetzel_sig_sfr_mstar_z = get_param_sfr_mstar_z()
# Lee et al. (2015)
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
logM_lim = np.array([8.50, 9.00, 9.00, 9.30, 9.30, 9.30])
masses = np.arange(7.5, 12.0, 0.1)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(z_mid):
sub.scatter(
masses,
lee_et_al_2015(masses, z),
c = pretty_colors[i_z],
label = r"$\mathtt{z = "+str(round(z, 2))+"}$"
)
sub.plot(
masses,
wetzel_sfr_mstar_z(masses, z),
c = pretty_colors[i_z],
ls = '--',
lw = 3
)
#sub.vlines(logM_lim[i_z], -5.0, 2.0, color='k', linestyle='--', lw=2)
sub.set_xlim([7.0, 12.0])
sub.set_ylim([-3.0, 2.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=30)
sub.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=30)
sub.legend(loc='lower right', scatterpoints=1)
fig_file = ''.join([
'figure/',
'qaplot_sfms_wetzel_vs_lee.png'
])
fig.savefig(fig_file, bbox_inches='tight')
plt.show()
def test_photoz(catalog_name):
""" Test that the assigned photometric redshifts in photoz correction
method reproduce the CMASS photometric redshifts.
"""
catdict = {'name': catalog_name, 'n_mock': 20}
corrdict = {'name': 'photoz'}
cat_corr = {'catalog': catdict, 'correction': corrdict}
dataclass = Data('data', cat_corr)
dataclass.read()
# only consider galaxies with photometric redshifts
hasphotoz = np.where(
dataclass.photoz > -999.
)
deltaz_z = (dataclass.z[hasphotoz] - dataclass.photoz[hasphotoz]) / (1.0 + dataclass.z[hasphotoz])
prettyplot()
pretty_colors = prettycolors()
plt.figure(figsize=(8,8))
bovy.scatterplot(
dataclass.z[hasphotoz],
deltaz_z,
scatter = True,
levels = [0.68, 0.95, 0.997],
color = pretty_colors[1],
s = 3,
xrange = [0.43, 0.7],
yrange = [-0.3, 0.3],
xlabel = '\mathtt{z_{spec}}',
ylabel = r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1 + z_{spec}}}'
)
plt.show()
# save figure to file
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
catalog_name, '_deltaz_zspec_zphoto.png'
])
plt.savefig(fig_file, bbox_inches='tight')
plt.close()
return None
def plot_fllp(l, k_value=0.3, rc=0.4):
'''
plot f_l,l'
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for lp in [0,2,4]:
q = np.arange( 0.002, 1.0, 0.005 )
start_time = time.time()
fllp_integrated = [tophat.f_l_lp(q_i*rc, k_value*rc, l, lp, first_order=True) for q_i in q]
print 'Integrated f_l,lp takes ', time.time() - start_time, ' seconds'
start_time = time.time()
fllp_estimated = [tophat.f_l_lp_est(q_i*rc, k_value*rc, l, lp) for q_i in q]
print 'Estimated f_l,lp takes ', time.time() - start_time, ' seconds'
sub.scatter(
q,
fllp_integrated,
c=pretty_colors[lp],
s=10,
label = r"$\mathtt{l' = "+str(lp)+"}$ First order"
)
sub.plot(
q,
fllp_estimated,
c=pretty_colors[lp],
lw = 4,
ls = '--',
label = r"theoretical estimate"
)
sub.set_xlim([10**-3,10**0])
sub.set_xscale("log")
sub.set_xlabel(r"$\mathtt{q}\;\;(\mathtt{Mpc}/h)$", fontsize=30)
sub.set_ylabel(r"$\mathtt{f_{l,l'} (q r_{fc}, "+str(round(k_value,1))+"r_{fc}})$", fontsize=30)
sub.legend(loc='upper left', scatterpoints=1)
fig_file = ''.join(['qaplot_fl', str(l), 'lp024_k', str(k_value), '.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.close()
def plot_corrected_Pk(l, fs=1.0, rc=0.4):
'''
'''
if l == 0:
l_cols = [0, 1]
else:
l_cols = [0, 2]
# true P_l(k)
k, Pk = np.loadtxt(
'POWER_Q_CutskyN1.fidcosmo.dat.grid360.P020000.box3600',
unpack = True,
usecols = l_cols
)
# Upweighted P_l(k)
k, Pk_upw= np.loadtxt(
'POWER_Q_CutskyN1.fidcosmo.fibcoll.dat.grid360.P020000.box3600',
unpack = True,
usecols = l_cols
)
corrdelP = pickle.load(open('delP'+str(l)+'k_corr.p', 'rb'))
uncorrdelP = tophat.delP_uncorr(k, l, fs=fs, rc=rc)
# FC P_l(k)
fc_Pk = Pk + uncorrdelP + corrdelP
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
sub.plot(k, fc_Pk, color=pretty_colors[3], label='FC TopHat')
sub.plot(k, Pk_upw, color=pretty_colors[5], label='Upweighted (data)')
sub.plot(k, Pk, color=pretty_colors[1], label='True (data)')
sub.set_xlim([10**-3,10**0])
sub.set_xscale("log")
sub.set_xlabel(r"$\mathtt{k}\;\;(\mathtt{Mpc}/h)$", fontsize=30)
sub.set_yscale('log')
sub.set_ylabel(r"$\mathtt{P_{"+str(l)+"}(k)}$", fontsize=30)
sub.legend(loc='upper left')
fig.savefig('qaplot_P_'+str(l)+'_tophat.png', bbox_inches="tight")
plt.close()
def plot_avg_Plk(l, n_mocks, Ngrid=360, yscale='linear', **kwargs):
'''
Plot average P_l(k)
'''
if not isinstance(l, list):
ls = [l]
else:
ls = l
prettyplot()
pretty_colors = prettycolors()
# plot P(k) data
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for l_i in ls:
k_i, plk_i = pk_extrap.average_Pk(l_i, n_mocks, Ngrid=Ngrid)
sub.plot(k_i, plk_i, label=r"$l = "+str(l_i)+"$", c=pretty_colors[l_i+1], lw=4)
if 'xrange' in kwargs.keys():
sub.set_xlim(kwargs['xrange'])
else:
sub.set_xlim([1e-3, 1.0])
if 'yrange' in kwargs.keys():
sub.set_ylim(kwargs['yrange'])
else:
pass
sub.set_ylabel(r"$\mathtt{P_l(k)}$", fontsize=40)
sub.set_xlabel(r"$\mathtt{k}$", fontsize=40)
sub.set_xscale('log')
if yscale == 'log':
sub.set_yscale('log')
else:
pass
sub.legend(loc='upper right')
fig_file = ''.join([
'qaplot_avgP', ''.join([str(l_i) for l_i in l]), 'k_',
'Ngrid', str(Ngrid), '.png'
])
fig.savefig(fig_file, bbox_inches='tight')
def plot_sfms_wetzel_vs_lee():
'''
Plot Wetzel parameterized SFMS and Lee et al. (2015) parameterized SFMS.
'''
# wetzel
wetzel_sfr_mstar_z, wetzel_sig_sfr_mstar_z = get_param_sfr_mstar_z()
# Lee et al. (2015)
z_mid = np.array([0.36, 0.55, 0.70, 0.85, 0.99, 1.19])
logM_lim = np.array([8.50, 9.00, 9.00, 9.30, 9.30, 9.30])
masses = np.arange(7.5, 12.0, 0.1)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(10, 10))
sub = fig.add_subplot(111)
for i_z, z in enumerate(z_mid):
sub.scatter(masses,
lee_et_al_2015(masses, z),
c=pretty_colors[i_z],
label=r"$\mathtt{z = " + str(round(z, 2)) + "}$")
sub.plot(masses,
wetzel_sfr_mstar_z(masses, z),
c=pretty_colors[i_z],
ls='--',
lw=3)
#sub.vlines(logM_lim[i_z], -5.0, 2.0, color='k', linestyle='--', lw=2)
sub.set_xlim([7.0, 12.0])
sub.set_ylim([-3.0, 2.0])
sub.set_xlabel(r'$\mathtt{log\;M_*}$', fontsize=30)
sub.set_ylabel(r'$\mathtt{log\;SFR}$', fontsize=30)
sub.legend(loc='lower right', scatterpoints=1)
fig_file = ''.join(['figure/', 'qaplot_sfms_wetzel_vs_lee.png'])
fig.savefig(fig_file, bbox_inches='tight')
plt.show()
def plot_2pcf(catalog):
'''
'''
prettyplot()
pretty_colors = prettycolors()
data_dir = '/mount/riachuelo1/hahn/data/Nseries/'
if catalog == 'true':
pickle_name = ''.join([
data_dir,
'2pcf_CutskyN1.fidcosmo.p'
])
elif catalog == 'fced':
pickle_name = ''.join([
data_dir,
'2pcf_CutskyN1.fidcosmo.fced.p'
])
elif catalog == 'nocoll':
pickle_name = ''.join([
data_dir,
'2pcf_CutskyN1.fidcosmo.nocoll.p'
])
pickle_data = pickle.load(open(pickle_name, "rb"))
rp_bins = pickle_data[0]
pi_bins = pickle_data[1]
twopcf = pickle_data[2]
#print twopcf
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
r_p, pi = np.meshgrid(rp_bins, pi_bins)
sub.pcolormesh(r_p, pi, twopcf.T, cmap=plt.cm.afmhot)
sub.set_xscale('log')
sub.set_xlabel('$\mathtt{r_{p}}$', fontsize=40)
sub.set_ylabel('$\pi$', fontsize=40)
sub.set_xlim([0.0, 20.0])
sub.set_ylim([0.0, 20.0])
sub.set_title('N-series '+catalog.upper() + ' Catalog')
plt.show()
"""
def plot_avg_Plk_negative(l, n_mocks, Ngrid=360):
'''
Plot the negative portion of the average P_l(k). The y-axis is linear not logarithmic
'''
if not isinstance(l, list):
ls = [l]
else:
ls = l
prettyplot()
pretty_colors = prettycolors()
# plot P(k) data
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(111)
for l_i in ls:
k_i, plk_i = pk_extrap.average_Pk(l_i, n_mocks, Ngrid=Ngrid)
negative = np.where(plk_i < 0.)
if l_i < 4:
sub.plot(
k_i[negative],
plk_i[negative],
label=r"$P_"+str(l_i)+"(k)$", c=pretty_colors[l_i+1], lw=4)
else:
sub.scatter(
k_i[negative],
plk_i[negative],
label=r"$P_"+str(l_i)+"(k)$", c=pretty_colors[l_i+1])
sub.set_xlim([1e-3, 1.0])
sub.set_ylabel(r"$\mathtt{P_l(k)}$", fontsize=40)
sub.set_xlabel(r"$\mathtt{k}$", fontsize=40)
sub.set_xscale('log')
sub.legend(loc='upper right')
plt.show()
def test_quenching_fraction(
tau_prop = {'name': 'instant'},
n_snap0 = 13,
**kwargs
):
''' Plot evolution of the quenching population in the
CenQue SSFR distribution
Parameters
----------
Mrcut : Absolute magnitude cut that specifies the group catalog
nsnaps : List of snapshot #s to plot
'''
if tau_prop['name'] in ('instant', 'constant', 'satellite', 'long'):
tau_str = ''.join(['_', tau_prop['name'], 'tau'])
elif tau_prop['name'] in ('line'):
tau_str = ''.join([
'_', tau_prop['name'], 'tau',
'_Mfid', str(tau_prop['fid_mass']),
'_slope', str(round(tau_prop['slope'], 4)),
'_yint', str(round(tau_prop['yint'],4))
])
prettyplot()
pretty_color = prettycolors()
fig1 = plt.figure(1, figsize=(15, 7))
sub1 = fig1.add_subplot(111)
fig2 = plt.figure(2, figsize=(12, 7))
sub2 = fig2.add_subplot(111)
# Overplot CenQue of specified Snapshots
snaps = []
for i_nsnap in reversed(xrange(1, n_snap0)):
fqing_file = ''.join([
'/data1/hahn/f_quenching/',
'quenching_fraction',
tau_str,
'_nsnap',
str(i_nsnap),
'.dat'
])
mass_bin, fqing = np.loadtxt(
fqing_file,
skiprows=1,
unpack=True,
usecols=[0,1]
)
#print i_nsnap, 'slope = ', (fqing[-1] - fqing[0])/(mass_bin[-1] - mass_bin[0])
#print i_nsnap, 'slope = ', (fqing[-2] - fqing[0])/(mass_bin[-2] - mass_bin[0])
#print i_nsnap, 'slope = ', (fqing[-3] - fqing[0])/(mass_bin[-3] - mass_bin[0])
#print ((fqing[-1] - fqing[0])/(mass_bin[-1] - mass_bin[0]) + (fqing[-2] - fqing[0])/(mass_bin[-2] - mass_bin[0]) + (fqing[-3] - fqing[0])/(mass_bin[-3] - mass_bin[0]))/3.0
sub1.plot(mass_bin, fqing, c=pretty_color[i_nsnap+1], lw='4', label='Snapshot'+str(i_nsnap))
#sub1.plot(mass_bin, (0.03 * (np.array(mass_bin) - 9.5))*(1.8 - get_z_nsnap(i_nsnap))**5.0, ls='--', lw='3', c=pretty_color[i_nsnap+1])
if i_nsnap == n_snap0-1:
fqing_m = []
for i_m, mass in enumerate(mass_bin):
fqing_m.append([])
for i_m, mass in enumerate(mass_bin):
fqing_m[i_m].append(fqing[i_m])
snaps.append(get_z_nsnap(i_nsnap))
#print fqing_m
for i_m, mass in enumerate(mass_bin):
#print mass, np.around(fqing_m[i_m],4)
#print 'slope = ', (fqing_m[i_m][-1] - fqing_m[i_m][0])/(snaps[0] - snaps[-1])
#print 'slope = ', (fqing_m[i_m][-2] - fqing_m[i_m][0])/(snaps[0] - snaps[-2])
#print 'slope = ', (np.log10(fqing_m[i_m][-1]) - np.log10(fqing_m[i_m][1]))/(snaps[1] - snaps[-1])
fqing_massbin = np.array(fqing_m[i_m])
fqing_massbin[np.where(fqing_massbin == 0.)] = 10.**-10
#print snaps, fqing_massbin
sub2.scatter(snaps, fqing_massbin, c=pretty_color[i_m+1])
sub2.plot(snaps, fqing_massbin, lw='3', c=pretty_color[i_m+1], label=r'$\mathtt{M_* =\;}$'+str(mass))
#sub2.plot(snaps, (0.03 * (mass - 9.5)) * (1.8 - np.array(snaps))**2.0, ls='--', lw='3', c=pretty_color[i_m+1])
del snaps
del fqing_m
sub1.legend(loc='lower right')
sub1.set_yscale('log')
sub1.set_ylim([0.0, 1.0])
sub1.set_xlim([9.5, 14.0])
sub1.set_xlabel('Stellar Mass ($M_*$)')
sub1.set_ylabel('Predicted Quenching Fraction')
sub2.legend(loc='lower right')
sub2.set_yscale('log')
sub2.set_xlim([0.9, -0.3])
sub2.set_ylim([0.0001, 1.0])
sub2.set_xlabel('Redshift ($\mathtt{z}$)')
sub2.set_ylabel('Predicted Quenching Fraction')
fig1.savefig(
''.join([
'/home/users/hahn/research/pro/tinker/central_quenching/figure/',
'f_quenching',
tau_str,
'.png']),
bbox_inches='tight'
)
fig2.savefig(
''.join([
'/home/users/hahn/research/pro/tinker/central_quenching/figure/',
'f_quenching_evol',
tau_str,
'.png']),
bbox_inches='tight'
)
fig1.clear()
fig2.clear()
plt.close()
def test_delPk_corr_scatter(ell, n_mocks, Ngrid=960, k_fit=0.25, k_fixed=0.6, fs=1.0, rc=0.43, **kwargs):
'''
test the scatter in del P(k)^corr from extrapolation of each individual mock catalog realizations
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(14,8))
sub = fig.add_subplot(111)
for i_mock in range(1, n_mocks+1):
# default cat-corr for Nseries
cat_corr = {
'catalog': {'name': 'nseries', 'n_mock': i_mock},
'correction': {'name': 'true'}
}
spec_i = Spec('pk', cat_corr, ell=4, Ngrid=Ngrid)
spec_i.read()
specs_i = [getattr(spec_i, 'p0k'), getattr(spec_i, 'p2k'), getattr(spec_i, 'p4k')]
# extrapolation parameters for P0, P2, P4
extrap_pars = []
for i_ell, ellp in enumerate([0,2,4]):
extrap_pars.append(
pk_extrap.pk_powerlaw_bestfit(spec_i.k, specs_i[i_ell], k_fit=k_fit, k_fixed=k_fixed)
)
print extrap_pars
# calculate delP
corrdelP = fourier_corr.delP_corr(spec_i.k, specs_i, ell, fs=fs, rc=rc, extrap_params=extrap_pars, k_fixed=k_fixed)
if i_mock == 1:
delpk_label = r"$\mathtt{\Delta P^{corr}}$ (Extrap.)"
else:
delpk_label = None
sub.plot(spec_i.k, corrdelP, lw=2, c=pretty_colors[i_mock % 20], label=delpk_label)
# upw P_l(k) - true P_l(k0
true_cat_corr = {
'catalog': {'name': 'nseries', 'n_mock': 1},
'correction': {'name': 'true'},
}
true_avg_spec = AvgSpec(n_mocks, 'pk', true_cat_corr, ell=ell, Ngrid=Ngrid)
true_avg_spec.read()
upweight_cat_corr = true_cat_corr.copy()
upweight_cat_corr['correction']['name'] = 'upweight'
upweight_avg_spec = AvgSpec(n_mocks, 'pk', upweight_cat_corr, ell=ell, Ngrid=Ngrid)
upweight_avg_spec.read()
sub.plot(
true_avg_spec.k,
getattr(upweight_avg_spec, 'p'+str(ell)+'k') - getattr(true_avg_spec, 'p'+str(ell)+'k'),
c = 'k',
lw = 4,
ls = '--',
label = r"$\mathtt{P^{upw}(k) - P^{true}(k)}$"
)
# x-axis
sub.set_xlabel(r'$\mathtt{k}$', fontsize=30)
sub.set_xscale('log')
sub.set_xlim([10**-3, 10**0.])
# y-axis
sub.set_ylabel(r"$\mathtt{\Delta P_{"+str(ell)+"}(k)}$", fontsize=30)
sub.set_ylim([-50., 300.])
sub.legend(loc='upper left')
fig_file = ''.join([
'figure/',
'qaplot_delP_corr_extrap_scatter.',
str(n_mocks), 'mocks.',
'kfit', str(round(k_fit,2)), '.',
'kfixed', str(round(k_fixed,2)),
'.png'
])
fig.savefig(fig_file, bbox_inches="tight")
plt.close()
def plot_cute2pcf_residual(n_mocks, n_rp, n_pi, corrections=['true', 'upweighted', 'collrm'], scale='large', **kwargs):
'''
Plot xi(r_p, pi) from CUTE 2PCF code
'''
prettyplot()
pretty_colors = prettycolors()
if scale == 'large':
contour_range = np.arange(-0.05, 0.05, 0.005)
elif scale == 'small':
contour_range = np.arange(-0.1, 0.11, 0.01)
elif scale == 'smaller':
contour_range = np.arange(-0.5, 0.5, 0.05)
elif scale == 'verysmall':
contour_range = 20
for corr in corrections: # for each correction
for i_mock in n_mocks: # for each mocks
corr_file = ''.join([
'/mount/riachuelo1/hahn/2pcf/corr/',
'corr_2pcf_CutskyN',
str(i_mock), '.',
corr,
'.cute2pcf.',
scale,
'scale.dat'
])
print corr_file
tpcf = np.loadtxt(corr_file, unpack=True)
if i_mock == n_mocks[0]:
rp_bins = tpcf[0].reshape(n_rp, n_pi)[0]
pi_bins = tpcf[1].reshape(n_rp, n_pi)[:,0]
r_p, pi = np.meshgrid(rp_bins, pi_bins)
twop_corr = tpcf[2].reshape(n_rp, n_pi)
else:
twop_corr += tpcf[2].reshape(n_rp, n_pi)
twop_corr /= np.float(len(n_mocks)) # average 2PCF
if corr == 'true':
true_corr = twop_corr.T
continue
print (twop_corr.T).shape
fig = plt.figure(figsize=(15,10))
sub = fig.add_subplot(111)
# contour of 1 - (1 + xi^fc)/(1+xi^true)
residual_2pcf = 1.0 - (1.0 + twop_corr.T)/(1.0 + true_corr)
print np.max(residual_2pcf)
cont = sub.contourf(
r_p, pi,
residual_2pcf,
contour_range,
cmap=plt.cm.afmhot
)
plt.colorbar(cont)
sub.vlines(0.4, 0.0, np.max(r_p), lw=4, linestyle='--', color='red')
sub.set_xlabel('$\mathtt{r_{p}}$', fontsize=40)
sub.set_ylabel('$\pi$', fontsize=40)
sub.set_xlim([np.min(rp_bins), np.max(rp_bins)])
sub.set_ylim([np.min(pi_bins), np.max(pi_bins)])
sub.set_title(r"$1 - (1 + \xi^\mathtt{"+corr.upper()+r"})/(1+ \xi^\mathtt{TRUE})$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'2pcf_Nseries_', corr, '_', str(len(n_mocks)), 'mocks.', scale, '.tophat_comparison.png'
])
fig.savefig(fig_name, bbox_inches="tight")
plt.close()
def cosmic_variance_kbin(ell):
'''
Tests the correlation of the cosmic variance by calculating it
with different k_bins
'''
specdict = {
'P0': 20000,
'Lbox': 3600,
'Ngrid': 960,
'ell': ell
}
true_cat_corr = {
'catalog': {'name': 'nseries', 'n_mock': 1},
'correction': {'name': 'true'},
'spec': specdict
}
true_spec = AvgSpec(20, 'pk', true_cat_corr)
orig_stddev = len(true_spec.stddev())
orig_k = true_spec.k
orig_avg_spec = true_spec.avg_spec
rebin_stddev = len(true_spec._stddev_kbin('double'))
rebin_k = true_spec.k
rebin_avg_spec = true_spec.avg_spec
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=[14,8])
sub = fig.add_subplot(111)
orig_spec_lower = orig_avg_spec - orig_stddev
orig_spec_upper = orig_avg_spec + orig_stddev
if orig_spec_lower.min() < 0.:
orig_spec_lower[np.where(orig_spec_lower < 0.)] = 10**-3
orig_spec_upper[np.where(orig_spec_upper < 0.)] = 10**-3
rebin_spec_lower = rebin_avg_spec - rebin_stddev
rebin_spec_upper = rebin_avg_spec + rebin_stddev
if rebin_spec_lower.min() < 0.:
rebin_spec_lower[np.where(rebin_spec_lower < 0.)] = 10**-3
rebin_spec_upper[np.where(rebin_spec_upper < 0.)] = 10**-3
sub.fill_between(orig_k,
orig_spec_lower,
orig_spec_upper,
color = pretty_colors[0],
label = 'Original k binning')
sub.fill_between(rebin_k,
rebin_spec_lower,
rebin_spec_upper,
color = pretty_colors[2],
label = 'Rebinned k binning')
sub.set_xscale('log')
sub.set_yscale('log')
sub.set_xlim([10**-3, 10**0])
sub.set_ylim([10**2, 10**5])
sub.set_xlabel(r"$\mathtt{k}\;\;(\mathtt{Mpc}/h)$", fontsize=30)
sub.set_ylabel(r"$\mathtt{P_{"+str(ell)+"}(k)}$", fontsize=30)
fig.savefig(
''.join(['figure/'
'P', str(ell), 'k.',
'cosmic_variance.kbinning_test.png']),
bbox_inches='tight')
plt.close()
def test_pk_extrap_scatter(ell, n_mocks, Ngrid=960, k_fit=0.25, k_fixed=0.6, **kwargs):
'''
test the scatter in pk extrapolation
'''
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure()
sub = fig.add_subplot(111)
alphas, ns = [], []
for i_mock in range(1, n_mocks+1):
# default cat-corr for Nseries
cat_corr = {
'catalog': {'name': 'nseriesbox', 'n_mock': i_mock},
'correction': {'name': 'true'}
}
spec_i = CorrSpec('pk', cat_corr, ell=ell, Ngrid=Ngrid)
print spec_i.file_name
spec_i.read()
spec_i_spec = getattr(spec_i, 'p'+str(ell)+'k')
alpha_i, n_i = pk_extrap.pk_powerlaw_bestfit(spec_i.k, spec_i_spec, k_fit=k_fit, k_fixed=k_fixed)
if i_mock == 1:
pk_label = 'Data'
pk_extrap_label = 'Extrap.'
else:
pk_label = None
pk_extrap_label = None
sub.plot(spec_i.k, np.abs(spec_i_spec), lw=2, c=pretty_colors[0], label=pk_label)
sub.plot(
np.arange(k_fit, 10, 0.01),
np.abs(pk_extrap.pk_powerlaw(np.arange(k_fit, 10, 0.01), [alpha_i, n_i], k_fixed=k_fixed)),
ls='--', lw=2, c=pretty_colors[1], label = pk_extrap_label
)
alphas.append(alpha_i)
ns.append(n_i)
alphas = np.array(alphas)
ns = np.array(ns)
# x-axis
sub.set_xlabel(r'$\mathtt{k}$', fontsize=30)
sub.set_xscale('log')
sub.set_xlim([0.1, 10.])
# y-axis
sub.set_ylabel(r'$\mathtt{P_'+str(ell)+'(k)}$', fontsize=30)
sub.set_yscale('log')
sub.set_ylim([10**0, 10**5])
sum_stat = ''.join([
r"$\bar{\alpha}, \sigma_{\alpha} = ",
str(round(np.mean(alphas), 1)), ",\;", str(round(np.std(alphas),1)), "$",
'\n',
r"$\bar{n}, \sigma_{n} = ",
str(round(np.mean(ns),2)), ",\;", str(round(np.std(ns),2)), "$"
])
sub.text(1.0, 10**4.25, sum_stat, fontsize=20)
sub.legend(loc='lower right')
plt.show()
def test_qPqfllp_k(k, l, rc=0.43, noextrap=''):
'''
Test the polynomial estimates of the f_l_lp integrals.
Parameter
---------
- l :
- lp :
Notes
-----
'''
q_arr = np.logspace(-3, 3, num=100)
n_mock = 7
krc = k * rc
k_fit = 4.
k_fixed = 4.34
data_dir = '/mount/riachuelo1/hahn/power/Nseries/Box/'
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(14,8))
sub = fig.add_subplot(111)
maxmax, minmin = 0., 0.
for i_lp, ellp in enumerate(range(6)):
lp = 2 * ellp
for i_mock in xrange(1, 8):
true_pk_file = ''.join([data_dir, 'power3600z_BoxN', str(i_mock), '.dat'])
if lp == 0:
l_index = -1
else:
l_index = 1 + int(lp/2)
tr_k, tr_pk_i = np.loadtxt(
true_pk_file,
unpack = True,
usecols =[0,l_index]
)
if i_mock == 1:
tr_pk = tr_pk_i
else:
tr_pk += tr_pk_i
tr_pk /= 7.
tr_specs = (2.0*np.pi)**3 * tr_pk
# interpolation function
Pk_interp = interp1d(tr_k, tr_specs, kind='cubic')
# extrapolation parameter
tr_extrap_par = pk_extrap.pk_powerlaw_bestfit(tr_k, tr_specs, k_fit=4., k_fixed=4.34)
qPqfllp = []
Pqs = []
for q in q_arr:
if not noextrap:
Pq = pq(q, Pk_interp, tr_extrap_par, k_min=tr_k[0], k_max=tr_k[-1], k_fixed=4.34)
else:
Pq = pq_noextrap(q, Pk_interp, k_min=tr_k[0], k_max=tr_k[-1], k_fixed=4.34)
fllp = fourier_corr.f_l_lp(q*rc, krc, l, lp)
qPqfllp.append(
q * Pq * fllp
)
Pqs.append(Pq)
int_label = "$ l' = "+str(lp)+ "$"
sub.plot(q_arr, np.array(qPqfllp), c=pretty_colors[i_lp+1], lw=4, ls='-', label=int_label)
maxmax = np.max([np.max(qPqfllp), maxmax])
minmin = np.min([np.min(qPqfllp), minmin])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=25)
sub.set_ylabel(r"$\mathtt{q P(q) f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
sub.vlines(tr_k[-1], minmin, maxmax, color='k', linestyles='--', linewidth=2)
sub.text(2.*10**-3, 1.01*maxmax, r"k = "+str(round(k,2)), fontsize=20)
sub.legend(loc='upper right')
fig.savefig(''.join([
'figure/',
'qPqfllp', noextrap, '.l', str(l), '.k', str(round(k,2)), '.png'
]), bbox_inches='tight')
plt.close()
def plot_bao_cute2pcf(n_mocks, n_rp, n_pi, corrections=['true'], f_down=0.2, xiform='asinh', cmap='hot', **kwargs):
'''
Plot xi(r_p, pi) from CUTE 2PCF code
'''
prettyplot()
mpl.rcParams['font.size']=24
pretty_colors = prettycolors()
contour_range = 10
for corr in corrections: # for each correction
fig = plt.figure(figsize=(14,10))
sub = fig.add_subplot(111)
for i_mock in n_mocks: # for each mocks
corr_file = ''.join([
'/mount/riachuelo1/hahn/2pcf/corr/',
'corr_2pcf_CutskyN',
str(i_mock), '.',
corr,
'.cute2pcf.', str(round(f_down,2)), 'down.BAOscale.dat'
])
print corr_file
tpcf = np.loadtxt(corr_file, unpack=True)
if i_mock == n_mocks[0]:
rp_bins = tpcf[0].reshape(n_rp, n_pi)[0]
pi_bins = tpcf[1].reshape(n_rp, n_pi)[:,0]
twop_corr = tpcf[2].reshape(n_rp, n_pi)
else:
twop_corr += tpcf[2].reshape(n_rp, n_pi)
twop_corr /= np.float(len(n_mocks)) # average 2PCF
quad_rp_bins = np.hstack([-1.0 * rp_bins[::-1], rp_bins])
quad_pi_bins = np.hstack([-1.0 * pi_bins[::-1], pi_bins])
quad_tpcf_0, quad_tpcf_1, quad_tpcf_2 = [], [], []
for i_pi, pibin in enumerate(quad_pi_bins):
for i_rp, rpbin in enumerate(quad_rp_bins):
quad_tpcf_0.append(rpbin)
quad_tpcf_1.append(pibin)
if i_pi < n_pi:
ipi = n_pi - i_pi - 1
else:
ipi = i_pi % n_pi
if i_rp < n_rp:
irp = n_rp - i_rp - 1
else:
irp = i_rp % n_rp
quad_tpcf_2.append(twop_corr[irp, ipi])
#if (np.abs(rpbin) < 10.) and (np.abs(pibin) >95.):
# print twop_corr[irp, ipi]
quad_tpcf_0 = np.array(quad_tpcf_0)
quad_tpcf_1 = np.array(quad_tpcf_1)
quad_tpcf_2 = np.array(quad_tpcf_2)
quad_rp_bins = quad_tpcf_0.reshape(2*n_rp, 2*n_pi)[0]
quad_pi_bins = quad_tpcf_1.reshape(2*n_rp, 2*n_pi)[:,0]
quad_twop_corr = quad_tpcf_2.reshape(2*n_rp, 2*n_pi)
quad_rp, quad_pi = np.meshgrid(quad_rp_bins, quad_pi_bins)
if cmap == 'hot':
colormap = plt.cm.afmhot
else:
colormap = plt.cm.Paired
# contour of log(xi(r_p, pi))
if xiform == 'log':
print np.min(quad_twop_corr), np.max(quad_twop_corr)
norm = mpl.colors.SymLogNorm(0.001, vmin=-0.01, vmax=5.0, clip=True)
cont = sub.pcolormesh(quad_rp, quad_pi, quad_twop_corr, norm=norm, cmap=colormap)
#cont = sub.contourf(quad_rp, quad_pi, np.log10(quad_twop_corr), contour_range, cmap=plt.cm.afmhot)
ticker = mpl.ticker.FixedLocator([-0.01, 0.0, 0.5, 0.1, 1.0, 3.0])
elif xiform == 'asinh':
contour_range = np.arange(-0.025, 0.105, 0.005)
#cont = sub.contourf(quad_rp, quad_pi, np.arcsinh(10. * quad_twop_corr), contour_range, cmap=plt.cm.afmhot)
norm = mpl.colors.Normalize(-0.1, 0.1, clip=True)
cont = sub.pcolormesh(quad_rp, quad_pi, np.arcsinh(10. * quad_twop_corr), norm=norm, cmap=colormap)
#cont.set_interpolation('none')
elif xiform == 'none':
contour_range = np.arange(-0.5, 5.0, 0.05)
cont = sub.contourf(quad_rp, quad_pi, quad_twop_corr, contour_range, cmap=colormap)
#cont = sub.contourf(r_p, pi, twop_corr.T, contour_range, cmap=plt.cm.afmhot)
else:
raise ValueError
#sub.contour(r_p, pi, np.log10(twop_corr.T), contour_range, linewidths=4, cmap=plt.cm.afmhot)
if xiform == 'log':
plt.colorbar(cont, ticks=ticker)
else:
plt.colorbar(cont)
sub.set_xlabel('$\mathtt{r_{\perp}} \; (\mathtt{Mpc}/h)$', fontsize=50)
sub.set_ylabel('$\mathtt{r_{||}} \; (\mathtt{Mpc}/h)$', fontsize=50)
sub.set_xlim([np.min(quad_rp_bins), np.max(quad_rp_bins)])
sub.set_ylim([np.min(quad_pi_bins), np.max(quad_pi_bins)])
if cmap == 'hot':
colormap_str = 'hot'
else:
colormap_str = 'jet'
if xiform == 'log':
#sub.set_title(r'$\mathtt{'+corr.upper()+r"}\;log\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'log2pcf_Nseries_', corr, '_', str(len(n_mocks)), 'mocks.', str(round(f_down,2)), 'down.BAO.', colormap_str ,'.png'
])
elif xiform == 'asinh':
sub.set_title(r'$\mathtt{'+corr.upper()+r"}\;arcsinh\:10\times\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'arcsinh2pcf_Nseries_', corr, '_', str(len(n_mocks)), 'mocks.', str(round(f_down,2)), 'down.BAO.', colormap_str, '.png'
])
elif xiform == 'none':
sub.set_title(r'$\mathtt{'+corr.upper()+r"}\;\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'2pcf_Nseries_', corr, '_', str(len(n_mocks)), 'mocks.', str(round(f_down,2)), 'down.BAO.', colormap_str, '.png'
])
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
def test_quenching_fraction(tau_prop={'name': 'instant'},
n_snap0=13,
**kwargs):
''' Plot evolution of the quenching population in the
CenQue SSFR distribution
Parameters
----------
Mrcut : Absolute magnitude cut that specifies the group catalog
nsnaps : List of snapshot #s to plot
'''
if tau_prop['name'] in ('instant', 'constant', 'satellite', 'long'):
tau_str = ''.join(['_', tau_prop['name'], 'tau'])
elif tau_prop['name'] in ('line'):
tau_str = ''.join([
'_', tau_prop['name'], 'tau', '_Mfid',
str(tau_prop['fid_mass']), '_slope',
str(round(tau_prop['slope'], 4)), '_yint',
str(round(tau_prop['yint'], 4))
])
prettyplot()
pretty_color = prettycolors()
fig1 = plt.figure(1, figsize=(15, 7))
sub1 = fig1.add_subplot(111)
fig2 = plt.figure(2, figsize=(12, 7))
sub2 = fig2.add_subplot(111)
# Overplot CenQue of specified Snapshots
snaps = []
for i_nsnap in reversed(xrange(1, n_snap0)):
fqing_file = ''.join([
'/data1/hahn/f_quenching/', 'quenching_fraction', tau_str,
'_nsnap',
str(i_nsnap), '.dat'
])
mass_bin, fqing = np.loadtxt(fqing_file,
skiprows=1,
unpack=True,
usecols=[0, 1])
#print i_nsnap, 'slope = ', (fqing[-1] - fqing[0])/(mass_bin[-1] - mass_bin[0])
#print i_nsnap, 'slope = ', (fqing[-2] - fqing[0])/(mass_bin[-2] - mass_bin[0])
#print i_nsnap, 'slope = ', (fqing[-3] - fqing[0])/(mass_bin[-3] - mass_bin[0])
#print ((fqing[-1] - fqing[0])/(mass_bin[-1] - mass_bin[0]) + (fqing[-2] - fqing[0])/(mass_bin[-2] - mass_bin[0]) + (fqing[-3] - fqing[0])/(mass_bin[-3] - mass_bin[0]))/3.0
sub1.plot(mass_bin,
fqing,
c=pretty_color[i_nsnap + 1],
lw='4',
label='Snapshot' + str(i_nsnap))
#sub1.plot(mass_bin, (0.03 * (np.array(mass_bin) - 9.5))*(1.8 - get_z_nsnap(i_nsnap))**5.0, ls='--', lw='3', c=pretty_color[i_nsnap+1])
if i_nsnap == n_snap0 - 1:
fqing_m = []
for i_m, mass in enumerate(mass_bin):
fqing_m.append([])
for i_m, mass in enumerate(mass_bin):
fqing_m[i_m].append(fqing[i_m])
snaps.append(get_z_nsnap(i_nsnap))
#print fqing_m
for i_m, mass in enumerate(mass_bin):
#print mass, np.around(fqing_m[i_m],4)
#print 'slope = ', (fqing_m[i_m][-1] - fqing_m[i_m][0])/(snaps[0] - snaps[-1])
#print 'slope = ', (fqing_m[i_m][-2] - fqing_m[i_m][0])/(snaps[0] - snaps[-2])
#print 'slope = ', (np.log10(fqing_m[i_m][-1]) - np.log10(fqing_m[i_m][1]))/(snaps[1] - snaps[-1])
fqing_massbin = np.array(fqing_m[i_m])
fqing_massbin[np.where(fqing_massbin == 0.)] = 10.**-10
#print snaps, fqing_massbin
sub2.scatter(snaps, fqing_massbin, c=pretty_color[i_m + 1])
sub2.plot(snaps,
fqing_massbin,
lw='3',
c=pretty_color[i_m + 1],
label=r'$\mathtt{M_* =\;}$' + str(mass))
#sub2.plot(snaps, (0.03 * (mass - 9.5)) * (1.8 - np.array(snaps))**2.0, ls='--', lw='3', c=pretty_color[i_m+1])
del snaps
del fqing_m
sub1.legend(loc='lower right')
sub1.set_yscale('log')
sub1.set_ylim([0.0, 1.0])
sub1.set_xlim([9.5, 14.0])
sub1.set_xlabel('Stellar Mass ($M_*$)')
sub1.set_ylabel('Predicted Quenching Fraction')
sub2.legend(loc='lower right')
sub2.set_yscale('log')
sub2.set_xlim([0.9, -0.3])
sub2.set_ylim([0.0001, 1.0])
sub2.set_xlabel('Redshift ($\mathtt{z}$)')
sub2.set_ylabel('Predicted Quenching Fraction')
fig1.savefig(''.join([
'/home/users/hahn/research/pro/tinker/central_quenching/figure/',
'f_quenching', tau_str, '.png'
]),
bbox_inches='tight')
fig2.savefig(''.join([
'/home/users/hahn/research/pro/tinker/central_quenching/figure/',
'f_quenching_evol', tau_str, '.png'
]),
bbox_inches='tight')
fig1.clear()
fig2.clear()
plt.close()
def cmass_zspec_zphoto():
""" Compare matching spectroscopic redshifts and photometric redshifts
of the CMASS catalog.
"""
prettyplot()
pretty_colors = prettycolors()
# matching spectroscopic and photometric redshifts
z_spec, z_photo = match_zspec_zphoto_cmass()
# scatter plot of z_spec versus z_photo
bovy.scatterplot(
z_spec,
z_photo,
scatter=True,
color=pretty_colors[1],
s=3,
xrange=[0.0, 1.0],
yrange=[0.0, 1.0],
xlabel='\mathtt{z_{spec}}',
ylabel='\mathtt{z_{photo}}'
)
plt.plot(np.arange(0.0, 2.0, 0.1), np.arange(0.0, 2.0, 0.1), c='k', lw=4) # y = x
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
'cmass_zspec_zphoto.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
plt.clf()
# scatter plot of delta z/(1+z) vs z
delta_z = (z_spec - z_photo) / (1.0 + z_spec)
bovy.scatterplot(
z_spec,
delta_z,
scatter=True,
levels=[0.68, 0.95, 0.997],
color=pretty_colors[1],
s=3,
xrange=[0.43, 0.7],
yrange=[-0.3, 0.3],
xlabel='\mathtt{z_{spec}}',
ylabel=r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1\;+\;z_{spec}}}'
)
fig_file = ''.join([
fig_dir,
'cmass_delta_zphoto_zspec.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
return None
def test_qPqfllp(l, lp, rc=0.43):
'''
Test the polynomial estimates of the f_l_lp integrals by comparing
q P(q) f_l,l'(q, k) with the integrals evaluated versus polynomial
estimates.
Parameter
---------
- l :
- lp :
Notes
-----
- Comparison assumes that there is No extrapolation
'''
q_arr = np.logspace(-3, 3, num=100)
n_mock = 7
k_fit = 4.
k_fixed = 4.34
data_dir = '/mount/riachuelo1/hahn/power/Nseries/Box/'
for i_mock in xrange(1, 8):
true_pk_file = ''.join([data_dir, 'power3600z_BoxN', str(i_mock), '.dat'])
if lp == 0:
l_index = -1
else:
l_index = 1 + int(lp/2)
tr_k, tr_pk_i = np.loadtxt(
true_pk_file,
unpack = True,
usecols =[0,l_index]
)
if i_mock == 1:
tr_pk = tr_pk_i
else:
tr_pk += tr_pk_i
tr_pk /= 7.
tr_specs = (2.0*np.pi)**3 * tr_pk
# interpolation function
Pk_interp = interp1d(tr_k, tr_pk, kind='cubic')
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(14,8))
sub = fig.add_subplot(111)
maxmax, minmin = 0., 0.
for ik, k in enumerate([0.01, 0.05, 0.1, 0.5]):
krc = k * rc
qPqfllp = []
qPqfllp_est = []
for q in q_arr:
Pq = pq_noextrap(q, Pk_interp, k_min=tr_k[0], k_max=tr_k[-1])
fllp = fourier_corr.f_l_lp(q*rc, krc, l, lp)
fllp_est = fourier_corr.f_l_lp_est(q*rc, krc, l, lp)
qPqfllp.append(q * Pq * fllp)
qPqfllp_est.append(q * Pq * fllp_est)
int_label = '$ k = '+str(round(k, 2))+ '$'
est_label = '$ k = '+str(round(k, 2))+ '$ estimate'
sub.plot(q_arr, np.array(qPqfllp),
c=pretty_colors[ik+1], lw=4, ls='-', label=int_label)
sub.plot(q_arr, np.array(qPqfllp_est),
c='k', lw=2, ls='--', label=est_label)
maxmax = np.max([np.max(qPqfllp), maxmax])
minmin = np.min([np.min(qPqfllp), minmin])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=25)
sub.set_ylabel(r"$\mathtt{q P(q) f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
if l == 0:
sub.vlines(tr_k[-1], -20., 100., color='k', linestyles='--', linewidth=2)
sub.set_ylim([-20, 20])
elif l == 2:
sub.vlines(tr_k[-1], -25., 5., color='k', linestyles='--', linewidth=2)
sub.set_ylim([-5, 5])
sub.text(2.*10**-3, 1.01*maxmax, r"l = "+str(l)+", l' = "+str(lp), fontsize=20)
sub.legend(loc='upper right')
#plt.show()
fig.savefig(''.join([
'figure/',
'qPqfllp.l', str(l), '.lp', str(lp), '.noextrap.png'
]), bbox_inches='tight')
plt.close()
def plot_pk_comp(cat_corrs, n_mock, ell=0, type='ratio', **kwargs):
''' Plot comparison of average power spectrum monopole or quadrupole (avg(P(k)))
for multiple a list of catalog and correction specifications. Main use is to
compare the effects of fiber collisions correction method. However, it can be
used to compare any power spectra as long as cat_corr dictionary is specified.
--------------------------------------------------------------------------
Paramters
--------------------------------------------------------------------------
cat_corrs : list of catalog correction dictionary
n_mock : number of mocks
ell : ell-th component of multipole decomposition
type : 'regular' compares the actual P2(k) values, 'ratio' compares the ratio
with the true P2(k), 'residual' compares the difference with the true P2(k)
--------------------------------------------------------------------------
Notes
--------------------------------------------------------------------------
* Long ass code with a lot of idiosyncracies.
* Make sure k values agree with each other.
--------------------------------------------------------------------------
Example
--------------------------------------------------------------------------
cat_corrs = [
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'true'}},
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'upweight'}},
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'dlospeak', 'fit': 'gauss', 'sigma': 3.9, 'fpeak': 0.68}}
]
plot_pk_comp(cat_corrs, 84, quad=False, type='Pk')
plot_pk_comp(cat_corrs, 84, quad=False, type='ratio')
'''
if 'Ngrid' in kwargs.keys():
Ngrid = kwargs['Ngrid']
else:
Ngrid = 360
if isinstance(n_mock, int):
n_mock_list = [ n_mock for i in xrange(len(cat_corrs)) ]
else:
if len(n_mock) != len(cat_corrs):
raise ValueError()
else:
n_mock_list = n_mock
corr_str = ''
prettyplot() # set up plot
pretty_colors = prettycolors()
if 'figsize' in kwargs.keys():
fig = plt.figure(1, kwargs['figsize'])
else:
fig = plt.figure(1, figsize=(7, 8)) # set up figure
sub = fig.add_subplot(111)
for i_corr, cat_corr in enumerate(cat_corrs):
catdict = cat_corr['catalog']
corrdict = cat_corr['correction']
specdict = {
'P0': 20000,
'Lbox': 3600,
'Ngrid': Ngrid,
'ell': ell
}
cat_corr_i = {
'catalog': {'name': catdict['name'], 'n_mock': 1},
'correction': corrdict,
'spec': specdict
}
avg_spec = AvgSpec(n_mock_list[i_corr], 'pk', cat_corr_i)
avg_spec.read()
spec_type = 'pk'
spec_key = ''.join(['p', str(ell), 'k'])
k_arr = avg_spec.k
avg_pk = getattr(avg_spec, spec_key)
if type == 'Pk': # Compare P(k) to each other
sub.plot(
k_arr, np.abs(avg_pk),
color = pretty_colors[i_corr + 1],
label = plot_label(cat_corr),
lw = 4
)
elif type == 'Pk_err': # Compare P(k) with sample variance error bar
pk_err = avg_spec.stddev()
pk_err[np.abs(pk_err) > np.abs(avg_pk)] = avg_pk[np.abs(pk_err) > np.abs(avg_pk)] * 0.999999
sub.fill_between(k_arr, np.abs(avg_pk - pk_err), np.abs(avg_pk + pk_err),
color = pretty_colors[i_corr + 1], alpha=0.9)
sub.plot(k_arr, np.abs(avg_pk), lw=4, color = pretty_colors[i_corr + 1], label = plot_label(cat_corr))
#sub.errorbar(
# k_arr, avg_pk,
# yerr = [pk_err, pk_err],
# color = pretty_colors[i_corr + 1],
# label = plot_label(cat_corr),
# fmt='--o'
# )
elif type == 'Pk_all':
if isinstance(n_mock_list[i_corr], int):
n_mock_list_i = range(1, n_mock_list[i_corr]+1)
else:
n_mock_list_i = n_mock_list[i_corr]
for i_mock in n_mock_list_i:
k_i, spec_i_spec = avg_spec.spec_i(i_mock)
sub.plot(
k_i, spec_i_spec,
color = '0.25',
lw = 1
)
sub.plot(
k_arr, avg_pk,
color = pretty_colors[i_corr + 1],
label = plot_label(cat_corr),
lw = 4
)
elif type == 'kPk': # Compare k^1.5 * P(k) with each other. Enhances the
# BAO signature? (Upon Paco's request).
kPk = k_arr**1.5 * avg_pk
sub.scatter(
k_arr, kPk,
color = pretty_colors[i_corr+1],
label = plot_label(cat_corr)
)
elif type == 'ratio': # Compare the ratio of the power spectra (P/P_denom)
if i_corr == 0 :
avg_pk_denom = avg_pk
#denom_cat = catdict['name']
denom_cat = corrdict['name']
else:
sub.scatter(
k_arr,
avg_pk/avg_pk_denom,
color = pretty_colors[i_corr+1],
label = plot_label(cat_corr)
)
print plot_label(cat_corr)
largescale = np.where(k_arr < 0.2)
smallscale = np.where(k_arr > 0.2)
print np.sum( np.abs((avg_pk/avg_pk_denom) - 1.0 ) )
print 'Large scale k < 0.2'
print np.sum( np.abs((avg_pk[largescale]/avg_pk_denom[largescale]) - 1.0 ) )
print 'Small scale k > 0.2'
print np.sum( np.abs((avg_pk[smallscale]/avg_pk_denom[smallscale]) - 1.0 ) )
if corrdict['name'] == 'true':
pk_err = avg_spec.stddev()
sub.plot(
k_arr, 1.0 + pk_err/np.abs(avg_pk),
color = 'k',
lw = 2,
ls = '-.',
label = r"$\mathtt{1 + \Delta P^{true} (k) / P^{true}}$"
)
sub.plot(
k_arr, 1.0 + -1.0 * pk_err/np.abs(avg_pk),
color = 'k',
lw = 2,
ls = '-.'
)
elif type == 'l1_norm':
if i_corr == 0 :
avg_pk_denom = avg_pk
denom_cat = catdict['name']
else:
sub.scatter(
k_arr,
avg_pk - avg_pk_denom,
color = pretty_colors[i_corr+1],
label = plot_label(cat_corr)
)
print plot_label(cat_corr)
print (avg_pk-avg_pk_denom)[-10:]
del avg_pk
# Specify corrections for figure file name
if 'dlospeak' in corrdict['name']:
try:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name'], '_',
corrdict['fit'], '_',
'_sigma', str(corrdict['sigma']),
'fpeak', str(corrdict['fpeak'])
])
except KeyError:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name'], '_',
'_sigma', str(corrdict['sigma'])
])
elif corrdict['name'] == 'fourier_tophat':
corr_str += ''.join([
catdict['name'], '_',
corrdict['name'],
'.fs', str(round(corrdict['fs'], 1)),
'.rc', str(round(corrdict['rc'], 2)),
'.kfit', str(round(corrdict['k_fit'], 2)),
'.kfixed', str(round(corrdict['k_fixed'], 2))
])
else:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name']
])
# Dictate the x-range and y-range of the plotting
# based on type of comparison
if (type == 'Pk') or (type == 'Pk_err') or (type == 'Pk_all'):
if 'yrange' in kwargs.keys():
ylimit = kwargs['yrange']
yytext = 10**.5 * min(ylimit)
else:
ylimit = [10**2,10**5.5]
yytext = 10**2.5
if 'ylabel' in kwargs.keys():
ylabel = kwargs['ylabel']
else:
ylabel = r'$\mathtt{|P_'+str(ell)+'(k)|}$'
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'yscale' in kwargs.keys():
sub.set_yscale(kwargs['yscale'])
else:
sub.set_yscale('log')
if type == 'Pk':
resid_str = ''
elif type == 'Pk_err':
resid_str = '_err'
elif type == 'Pk_all':
resid_str = '_all'
elif type == 'kPk':
if 'yrange' in kwargs.keys():
ylimit = kwargs['yrange']
yytext = 10**.5*min(ylimit)
else:
ylimit = [10**0,10**2.0]
yytext = 10**0.1
if 'ylabel' in kwargs.keys():
ylabel = kwargs['ylabel']
else:
ylabel = r'$\mathtt{k^{1.5} P_0(k)}$'
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'yscale' in kwargs.keys():
sub.set_yscale(kwargs['yscale'])
else:
sub.set_yscale('log')
resid_str = '_kPk'
elif type == 'ratio':
if 'yrange' in kwargs.keys():
ylimit = kwargs['yrange']
yytext = 0.05 + min(ylimit)
else:
ylimit = [0.5, 1.5]
yytext = 0.55
ylabel = ''.join([
r"$\mathtt{\overline{P_", str(ell), "(k)}/\overline{P_", str(ell), r"(k)_{\rm{", denom_cat, "}}}}$"
])
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'yscale' in kwargs.keys():
sub.set_yscale(kwargs['yscale'])
else:
pass
resid_str = '_ratio'
elif type == 'residual':
if 'yrange' in kwargs.keys():
ylimit = kwargs['yrange']
yytext = np.mean(ylimit)
else:
ylimit = [0.0, 5.0]
yytext = 2.5
ylabel = ''.join([
r"$\mathtt{|\overline{P_", str(ell), "(k)} - \overline{P_", str(ell), r"(k)_{\rm{True}}}|/\Delta P_", str(ell), "}$"
])
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'yscale' in kwargs.keys():
sub.set_yscale(kwargs['yscale'])
if (kwargs['yscale'] == 'log') and \
('yrange' not in kwargs.keys()):
ylimit = [10**-3, 10**1]
else:
pass
resid_str = '_residual'
elif type == 'l1_norm':
if 'yrange' in kwargs.keys():
ylimit = kwargs['yrange']
yytext = 0.05 + min(ylimit)
else:
ylimit = None
yytext = 0.55
ylabel = ''.join([
r"$\mathtt{\overline{P_", str(ell), "(k)} - \overline{P_", str(ell), r"(k)_{\rm{", denom_cat, "}}}}$"
])
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'yscale' in kwargs.keys():
sub.set_yscale(kwargs['yscale'])
else:
pass
resid_str = '_l1norm'
else:
raise NotImplementedError('asdfasdfasdf')
if 'xrange' in kwargs.keys(): # specify x-range
sub.set_xlim(kwargs['xrange'])
yxtext = 1.5*min(kwargs['xrange'])
else:
sub.set_xlim([10**-3,10**0])
yxtext = 1.5*10**-3
if type == 'ratio':
sub.axhline(y = 1.0, lw=2, ls='--', c='k')
sub.set_ylim(ylimit)
sub.set_xlabel('k (h/Mpc)', fontsize=20)
sub.set_ylabel(ylabel, fontsize=20)
# Display the number of mocks for given catalog so that
# I know how many mocks the P(k) is averaged over.
n_mock_text = '\n'.join([
' '.join([
str(n_mock_list[ii]),
((cat_corrs[ii])['catalog'])['name'].upper()
])
for ii in xrange(len(n_mock_list))
])
sub.text(yxtext, yytext, n_mock_text)
sub.legend(scatterpoints=1, loc='upper left', prop={'size':14})
try:
n_mock_str = '_'.join([str(nm) for nm in n_mock])
except TypeError:
n_mock_str = str(n_mock)
fig_name = ''.join([
spec_key, '_',
n_mock_str,
'mock_fibcoll_',
corr_str,
resid_str,
'_comparison_Ngrid',
str(Ngrid),
'.png'
])
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
print fig_name
fig.savefig(
''.join([fig_dir, fig_name]),
bbox_inches="tight"
)
#plt.show()
plt.close()
return None
def plot_delpoverp_comp(cat_corrs, n_mock, ell=0, **kwargs):
'''
Plot comparison of delta P/ avg(P) for multiple lists of catalog and correction
specifications.
--------------------------------------------------------------------------
Paramters
--------------------------------------------------------------------------
cat_corrs : list of catalog correction dictionary
n_mock : number of mocks
ell : ell-th component of multipole decomposition
--------------------------------------------------------------------------
Notes
--------------------------------------------------------------------------
* Long ass code with a lot of idiosyncracies.
* Make sure k values agree with each other.
--------------------------------------------------------------------------
Example
--------------------------------------------------------------------------
cat_corrs = [
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'true'}},
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'upweight'}},
{'catalog': {'name': 'nseries'}, 'correction': {'name': 'dlospeak', 'fit': 'gauss', 'sigma': 3.9, 'fpeak': 0.68}}
]
'''
if 'Ngrid' in kwargs.keys():
Ngrid = kwargs['Ngrid']
else:
Ngrid = 360
if isinstance(n_mock, int):
n_mock_list = [ n_mock for i in xrange(len(cat_corrs)) ]
else:
if len(n_mock) != len(cat_corrs):
raise ValueError()
else:
n_mock_list = n_mock
corr_str = ''
prettyplot() # set up plot
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(14, 8)) # set up figure
sub = fig.add_subplot(111)
for i_corr, cat_corr in enumerate(cat_corrs):
catdict = cat_corr['catalog']
corrdict = cat_corr['correction']
specdict = {
'P0': 20000,
'Lbox': 3600,
'Ngrid': Ngrid,
'ell': ell
}
cat_corr_i = {
'catalog': {'name': catdict['name'], 'n_mock': 1},
'correction': corrdict,
'spec': specdict
}
avg_spec = AvgSpec(n_mock_list[i_corr], 'pk', cat_corr_i)
avg_spec.read()
spec_type = 'pk'
spec_key = ''.join(['p', str(ell), 'k'])
k_arr = avg_spec.k
avg_pk = getattr(avg_spec, spec_key)
pk_err = avg_spec.stddev()
sub.plot(
k_arr, pk_err/np.abs(avg_pk),
color = pretty_colors[i_corr + 1],
label = plot_label(cat_corr),
lw = 4
)
# Specify corrections for figure file name
if 'dlospeak' in corrdict['name']:
try:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name'], '_',
corrdict['fit'], '_',
'_sigma', str(corrdict['sigma']),
'fpeak', str(corrdict['fpeak'])
])
except KeyError:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name'], '_',
'_sigma', str(corrdict['sigma'])
])
else:
corr_str += ''.join([
catdict['name'], '_',
corrdict['name']
])
# x-axis
if 'xscale' in kwargs.keys():
sub.set_xscale(kwargs['xscale'])
else:
sub.set_xscale('log')
if 'xrange' in kwargs.keys(): # specify x-range
sub.set_xlim(kwargs['xrange'])
yxtext = 1.5*min(kwargs['xrange'])
else:
sub.set_xlim([10**-3,10**0])
yxtext = 1.5*10**-3
sub.set_xlabel('k (h/Mpc)', fontsize=20)
# y-axis
if 'ylabel' in kwargs.keys():
ylabel = kwargs['ylabel']
else:
ylabel = r'$\mathtt{\Delta P_'+str(ell)+'(k)/|\overline{P_'+str(ell)+'}|}$'
if 'yrange' in kwargs.keys(): # specify x-range
sub.set_ylim(kwargs['yrange'])
else:
sub.set_ylim([-1.,1.0])
sub.set_xlabel('k (h/Mpc)', fontsize=20)
sub.set_ylabel(ylabel, fontsize=20)
# Display the number of mocks for given catalog so that
# I know how many mocks the P(k) is averaged over.
n_mock_text = '\n'.join([
' '.join([
str(n_mock_list[ii]),
((cat_corrs[ii])['catalog'])['name'].upper()
])
for ii in xrange(len(n_mock_list))
])
sub.text(yxtext, 0.05, n_mock_text)
sub.legend(scatterpoints=1, loc='upper left', prop={'size':14})
try:
n_mock_str = '_'.join([str(nm) for nm in n_mock])
except TypeError:
n_mock_str = str(n_mock)
plt.figtext(0, 0, "testing \n testing")
fig_name = ''.join([
'del', spec_key, 'over', spec_key, '_',
n_mock_str,
'mock_fibcoll_',
corr_str,
'_comparison_Ngrid',
str(Ngrid),
'.png'
])
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig.savefig(
''.join([fig_dir, fig_name]),
bbox_inches="tight"
)
#plt.show()
plt.close()
return None
def plot_lpsum_qPqfllp(ell, rc=0.43):
'''
Compare \Sum_l' q P(q) f_l,l'(q*rc, k*rc) of Nseries vs Nseries Mock
'''
q_arr = np.logspace(-3, 3, num=100)
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(10,7))
sub = fig.add_subplot(111)
for i_k, k in enumerate([0.01, 0.05, 0.1, 0.3, 0.5, 0.8]):
print k
krc = k * rc
for mock in ['nseries', 'nseriesbox']:
if mock == 'nseries': # Nseries
n_mock = 20
filename = lambda s: ''.join([
'/mount/riachuelo1/hahn/power/Nseries/',
'POWER_Q_CutskyN', str(s), '.fidcosmo.dat.grid960.P020000.box3600'])
ell_range = 3
lstyle = '--'
else: # Nseries box
n_mock = 7
filename = lambda s: ''.join([
'/mount/riachuelo1/hahn/power/Nseries/Box/',
'power3600z_BoxN', str(s), '.dat'])
ell_range = 6
lstyle = '-'
qPqfllp = np.zeros(len(q_arr))
for i_lp, ellp in enumerate(range(ell_range)):
lp = 2 * ellp
for i_mock in xrange(1, n_mock+1):
true_pk_file = filename(i_mock)
if lp == 0:
if mock == 'nseriesbox':
l_index = -1
else:
l_index = 1
else:
l_index = 1 + int(lp/2)
tr_k, tr_pk_i = np.loadtxt(true_pk_file, unpack = True, usecols =[0,l_index])
if i_mock == 1:
tr_pk = tr_pk_i
else:
tr_pk += tr_pk_i
if mock == 'nseriesbox':
tr_specs = (2.0*np.pi)**3 * tr_pk / np.float(n_mock)
else:
tr_specs = tr_pk/ np.float(n_mock)
# interpolation function
Pk_interp = interp1d(tr_k, tr_specs, kind='cubic')
qPqfllp_lp = np.zeros(len(q_arr))
for i_q, q in enumerate(q_arr):
Pq = pq_noextrap(q, Pk_interp, k_min=tr_k[0], k_max=tr_k[-1])
fllp = fourier_corr.f_l_lp(q*rc, krc, ell, lp)
qPqfllp_lp[i_q] = q * Pq * fllp
qPqfllp += qPqfllp_lp
label_str = None
if mock == 'nseriesbox':
if i_k == 0:
label_str = mock+ '\n k = '+str(round(k, 2))
else:
label_str = 'k = '+str(round(k, 2))
else:
if i_k == 0:
label_str = mock
sub.plot(q_arr, qPqfllp, c=pretty_colors[i_k], lw=2, ls=lstyle, label=label_str)
sub.vlines(tr_k[-1], sub.axis()[2], sub.axis()[3], color='k', linestyles='--',
linewidth=2)
# x axis
sub.set_xlim([10.**-3, 10.**2])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=26)
# y axis
if ell == 0:
sub.set_ylim([0.0, 2000.0])
elif ell == 2:
sub.set_ylim([-2000.0, 1000.0])
sub.set_ylabel(r"$\mathtt{\sum\limits_{l'} q P(q) f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
sub.legend(loc='upper right')
fig.savefig(''.join(['figure/', 'SUMqPqf', str(ell), 'lp.nseries_nseriesbox..png']),
bbox_inches='tight', dpi=150)
plt.close()
def plot_bispec_fibcolcorr_comparison(BorQ='B', x_axis='triangles', triangle='all',
catalog='PTHalo', filespec={'version':'v11p0', 'n_mock':range(1,11), 'Nrandom':100},
correction_method=['true', 'delta', 'peak'], corrspec={'sigma':[0, 0, 6.3], 'fpeak':[1.0, 1.0, 1.0]}, resid='False'):
'''
Comparison of B(k)avg for different fibercollision correciton methods
Make sure 'true' is first in the correction method list
'''
prettyplot()
fig = plt.figure(1, figsize=(7, 8))
sub = fig.add_subplot(111)
corr_color = ['grey', 'blue', 'red']
for i_corr, correction in enumerate(correction_method): # loop through correction methods
for i_mock in filespec['n_mock']: # compute average[B(k)] for each correction method
i_filespec = filespec.copy()
i_filespec['n_mock'] = i_mock
i_corrspec = corrspec.copy()
for key in corrspec.keys():
i_corrspec[key] = corrspec[key][i_corr]
bispec_i = spec(spectrum='bispec', catalog=catalog, filespec=i_filespec, fibcolcorr=correction, corrspec=i_corrspec)
bispec_i.readfile()
if i_mock == filespec['n_mock'][0]:
if x_axis == 'triangles': # specify x-axis (triangle index, avgk, or max k)
avg_k = bispec_i.i_triangle # x-axis is triangles
elif x_axis == 'avg_k':
avg_k = bispec_i.avgk # x-axis is avg(k1,k2,k3)
elif x_axis == 'max_k':
avg_k = bispec_i.kmax # x-axis is max(k1,k2,k3)
avg_k1 = bispec_i.k1
avg_k2 = bispec_i.k2
avg_k3 = bispec_i.k3
if BorQ == 'B':
sum_Bk = bispec_i.Bk
elif BorQ == 'Q':
sum_Bk = bispec_i.Q
else:
if BorQ == 'B':
sum_Bk = sum_Bk + bispec_i.Bk
elif BorQ == 'Q':
sum_Bk = sum_Bk + bispec_i.Q
avg_Bk = [ sum_Bk[i]/float(len(filespec['n_mock'])) for i in range(len(sum_Bk))] # average B(k)
if triangle == 'all':
tri_index = np.array([True for i in range(len(avg_Bk))], dtype=bool)
else:
tri_index = classify_triangles(avg_k1, avg_k2, avg_k3, triangle=triangle)
print tri_index
if resid == 'False': # B(k) Comparison
if correction == 'true':
sub.scatter(avg_k[tri_index], avg_Bk[tri_index],
color=corr_color[i_corr], label=r"$\mathtt{\overline{"+BorQ+"(k)_{"+correction+"}}}$")
else:
sub.scatter(avg_k[tri_index], avg_Bk[tri_index],
color=corr_color[i_corr], label=r"$\mathtt{\overline{"+BorQ+"(k)_{"+correction+"}}}$")
else: # B(k) residual comparison
if correction == 'true':
avg_Bk_true = avg_Bk
else:
sub.scatter(np.array(avg_k)[tri_index], residual(np.array(avg_Bk)[tri_index], np.array(avg_Bk_true)[tri_index]),
color=corr_color[i_corr],
label=''.join([r"$\mathtt{", "\overline{", BorQ, "(k)_{",correction, "}}/\overline{", BorQ, "(k)_{True}}}$"]))
if correction == 'peak':
sigma_flag = ''.join(['_sigma', str(corrspec['sigma'][i_corr]), '_fpeak', str(corrspec['fpeak'][i_corr])])
if resid=='False':
if catalog.lower() == 'tilingmock':
ylimit = [10**2,10**5.5]
ytext = 10**5.2
elif catalog.lower() == 'pthalo':
ylimit = [10**2,10**5.5]
ytext = 10**5.2
ylabel = '$\mathtt{'+BorQ+'(k)}$'
sub.set_yscale('log')
fig_name = ''.join(['bispec_', BorQ, 'k_', x_axis, '_', catalog.lower(),
'_fibcoll_', '_'.join(correction_method), sigma_flag, '_', triangle, 'triangles_comparison.png'])
else:
ylimit = [0.9,1.2]
ytext = 1.15
ylabel = r"$\mathtt{\overline{"+BorQ+"(k)}/\overline{"+BorQ+"(k)_{\rm{True}}}}$"
fig_name = ''.join(['bispec_', BorQ, 'k_', x_axis, '_', catalog.lower(),
'_fibcoll_', '_'.join(correction_method), sigma_flag, '_', triangle, 'triangles_residual_comparison.png'])
sub.set_ylim(ylimit)
sub.text(1.5*10**-3, ytext,
''.join([str(len(filespec['n_mock'])), ' ', catalog.upper()])) # number of mocks + Catalog name
if x_axis == 'triangles':
sub.set_xlabel('Triangles', fontsize=20)
sub.set_xlim([0, 7500])
elif x_axis == 'avg_k':
sub.set_xlabel('avg(k1, k2, k3)', fontsize=20)
sub.set_xscale('log')
sub.set_xlim([10**-3,10**0])
elif x_axis == 'max_k':
sub.set_xlabel('max(k1, k2, k3)', fontsize=20)
sub.set_xscale('log')
sub.set_xlim([10**-3,10**0])
sub.set_ylabel(ylabel, fontsize=20)
sub.legend(loc='lower left', prop={'size':14}, scatterpoints=1)
sub.grid(True)
fig.savefig(''.join(['/home/users/hahn/research/figures/boss/fiber_collision/', fig_name]))
fig.clear()
def plot_bao_cmass_cute2pcf(n_rp, n_pi, xiform='asinh', cmap='hot', **kwargs):
'''
Plot xi(r_p, pi) from CUTE 2PCF code specifically for CMASS
'''
prettyplot()
mpl.rcParams['font.size']=24
pretty_colors = prettycolors()
contour_range = 10
fig = plt.figure(figsize=(14,10))
sub = fig.add_subplot(111)
corr_file = ''.join([
'/mount/riachuelo1/hahn/2pcf/corr/',
'corr_2pcf_cmass-dr12v4-N-Reid.cute2pcf.BAOscale.dat'
])
print corr_file
tpcf = np.loadtxt(corr_file, unpack=True)
rp_bins = tpcf[0].reshape(n_rp, n_pi)[0]
pi_bins = tpcf[1].reshape(n_rp, n_pi)[:,0]
twop_corr = tpcf[2].reshape(n_rp, n_pi)
# annoying stuff to reflect the quadrants
quad_rp_bins = np.hstack([-1.0 * rp_bins[::-1], rp_bins])
quad_pi_bins = np.hstack([-1.0 * pi_bins[::-1], pi_bins])
quad_tpcf_0, quad_tpcf_1, quad_tpcf_2 = [], [], []
for i_pi, pibin in enumerate(quad_pi_bins):
for i_rp, rpbin in enumerate(quad_rp_bins):
quad_tpcf_0.append(rpbin)
quad_tpcf_1.append(pibin)
if i_pi < n_pi:
ipi = n_pi - i_pi - 1
else:
ipi = i_pi % n_pi
if i_rp < n_rp:
irp = n_rp - i_rp - 1
else:
irp = i_rp % n_rp
quad_tpcf_2.append(twop_corr[irp, ipi])
#if (np.abs(rpbin) < 10.) and (np.abs(pibin) >95.):
# print twop_corr[irp, ipi]
quad_tpcf_0 = np.array(quad_tpcf_0)
quad_tpcf_1 = np.array(quad_tpcf_1)
quad_tpcf_2 = np.array(quad_tpcf_2)
quad_rp_bins = quad_tpcf_0.reshape(2*n_rp, 2*n_pi)[0]
quad_pi_bins = quad_tpcf_1.reshape(2*n_rp, 2*n_pi)[:,0]
quad_twop_corr = quad_tpcf_2.reshape(2*n_rp, 2*n_pi)
quad_rp, quad_pi = np.meshgrid(quad_rp_bins, quad_pi_bins)
if cmap == 'hot':
colormap = plt.cm.afmhot
else:
colormap = plt.cm.Paired
# contour of log(xi(r_p, pi))
if xiform == 'log':
print np.min(quad_twop_corr), np.max(quad_twop_corr)
norm = mpl.colors.SymLogNorm(0.005, vmin=-0.02, vmax=4.0, clip=True)
cont = sub.pcolormesh(quad_rp, quad_pi, quad_twop_corr, norm=norm, cmap=colormap)
ticker = mpl.ticker.FixedLocator([-0.01, 0.0, 0.5, 0.1, 1.0, 3.0])
elif xiform == 'asinh':
contour_range = np.arange(-0.025, 0.105, 0.005)
#cont = sub.contourf(quad_rp, quad_pi, np.arcsinh(10. * quad_twop_corr), contour_range, cmap=plt.cm.afmhot)
norm = mpl.colors.Normalize(-0.1, 0.1, clip=True)
cont = sub.pcolormesh(quad_rp, quad_pi, np.arcsinh(10. * quad_twop_corr), norm=norm, cmap=colormap)
#cont.set_interpolation('none')
elif xiform == 'none':
contour_range = np.arange(-0.5, 5.0, 0.05)
cont = sub.contourf(quad_rp, quad_pi, quad_twop_corr, contour_range, cmap=colormap)
#cont = sub.contourf(r_p, pi, twop_corr.T, contour_range, cmap=plt.cm.afmhot)
else:
raise ValueError
if xiform == 'log':
plt.colorbar(cont, ticks=ticker)
else:
plt.colorbar(cont)
sub.set_xlabel('$\mathtt{r_{\perp}} \; (\mathtt{Mpc}/h)$', fontsize=50)
sub.set_ylabel('$\mathtt{r_{||}} \; (\mathtt{Mpc}/h)$', fontsize=50)
sub.set_xlim([np.min(quad_rp_bins), np.max(quad_rp_bins)])
sub.set_ylim([np.min(quad_pi_bins), np.max(quad_pi_bins)])
if cmap == 'hot':
colormap_str = 'hot'
else:
colormap_str = 'jet'
if xiform == 'log':
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'log2pcf_cmass-dr12v4-N-Reid.BAO.', colormap_str ,'.png'
])
elif xiform == 'asinh':
sub.set_title(r'$\mathtt{'+corr.upper()+r"}\;arcsinh\:10\times\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'arcsinh2pcf_cmass-dr12v4-N-Reid.BAO.', colormap_str ,'.png'
])
elif xiform == 'none':
sub.set_title(r'$\mathtt{'+corr.upper()+r"}\;\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'2pcf_cmass-dr12v4-N-Reid.BAO.', colormap_str ,'.png'
])
plt.gca().set_aspect('equal', adjustable='box')
fig.savefig(fig_name, bbox_inches="tight")
plt.close()
def cmass_deltaz_zspec_zphoto_test(overplot=True):
''' Spherematch photometric and spectroscopic catalogs to
determine standard deviation redshift errors of photmetric redshifts
wrt spectroscopic redshifts
Parameters
----------
Notes
-----
* Using pyspherematch
* Details on CMASS redshifts in: https://trac.sdss3.org/wiki/BOSS/clustering/WGCatalogCode
* Assuming delta_z/z distribution is gaussian
'''
prettyplot() # set up plot
pretty_colors = prettycolors()
# matching spectroscopic and photometric redshifts
z_spec, z_photo = match_zspec_zphoto_cmass()
delta_z = (z_spec - z_photo) / (1.0 + z_spec)
if overplot:
bovy.scatterplot(
z_spec,
delta_z,
scatter=True,
levels=[0.68, 0.95, 0.997],
color=pretty_colors[1],
s=3,
xrange=[0.43, 0.7],
yrange=[-0.3, 0.3],
xlabel='\mathtt{z_{spec}}',
ylabel=r'\mathtt{\frac{|z_{spec} - z_{photo}|}{1\;+\;z_{spec}}}'
)
# summary statistics (mu and sigma) of delta z / (1+z)
z_mid, z_low, z_high, mu_deltaz, sigma_deltaz = cmass_deltaz_zspec_zphoto()
plt.errorbar(
z_mid,
mu_deltaz,
yerr=sigma_deltaz,
c=pretty_colors[3]
)
plt.scatter(
z_mid,
mu_deltaz,
c=pretty_colors[3]
)
fig_dir = '/home/users/hahn/powercode/FiberCollisions/figure/'
fig_file = ''.join([
fig_dir,
'cmass_deltaz_zspec_zphoto.png'
])
plt.savefig(
fig_file,
bbox_inches='tight'
)
plt.close()
def qPqfllp_comp(k, ell, rc=0.43):
'''
Compare \Sum_l' q P(q) f_l,l'(q*rc, k*rc) of Nseries vs Nseries Mock
'''
q_arr = np.logspace(-3, 3, num=100)
krc = k * rc
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(1, figsize=(14,8))
sub = fig.add_subplot(111)
maxmax, minmin = 0., 0.
for mock in ['nseries', 'nseriesbox']:
qPqfllp = np.zeros(len(q_arr))
if mock == 'nseries': # Nseries
n_mock = 20
data_dir = '/mount/riachuelo1/hahn/power/Nseries/'
filename = lambda s: ''.join([data_dir, 'POWER_Q_CutskyN', str(s), '.fidcosmo.dat.grid960.P020000.box3600'])
ell_range = 3
i_col = 1
lstyle = '-.'
else: # Nseries box
n_mock = 7
data_dir = '/mount/riachuelo1/hahn/power/Nseries/Box/'
filename = lambda s: ''.join([data_dir, 'power3600z_BoxN', str(s), '.dat'])
ell_range = 6
i_col = 3
lstyle = '--'
for i_lp, ellp in enumerate(range(ell_range)):
lp = 2 * ellp
for i_mock in xrange(1, n_mock+1):
true_pk_file = filename(i_mock)
if lp == 0:
if mock == 'nseriesbox':
l_index = -1
else:
l_index = 1
else:
l_index = 1 + int(lp/2)
tr_k, tr_pk_i = np.loadtxt(
true_pk_file,
unpack = True,
usecols =[0,l_index]
)
if i_mock == 1:
tr_pk = tr_pk_i
else:
tr_pk += tr_pk_i
if mock == 'nseriesbox':
tr_specs = (2.0*np.pi)**3 * tr_pk / np.float(n_mock)
else:
tr_specs = tr_pk/ np.float(n_mock)
# interpolation function
Pk_interp = interp1d(tr_k, tr_specs, kind='cubic')
qPqfllp_lp = np.zeros(len(q_arr))
for i_q, q in enumerate(q_arr):
Pq = pq_noextrap(q, Pk_interp, k_min=tr_k[0], k_max=tr_k[-1])
fllp = fourier_corr.f_l_lp(q*rc, krc, ell, lp)
qPqfllp_lp[i_q] = q * Pq * fllp
if mock == 'nseries':
sub.plot(q_arr, qPqfllp_lp, c=pretty_colors[i_lp], lw=2, ls=lstyle)
else:
sub.plot(q_arr, qPqfllp_lp, c=pretty_colors[i_lp], lw=2, ls=lstyle, label="l'="+str(lp))
qPqfllp += qPqfllp_lp
sub.plot(q_arr, qPqfllp, c=pretty_colors[i_col], lw=4, ls='-', label=mock)
sub.vlines(tr_k[-1], sub.axis()[2], sub.axis()[3], color='k', linestyles='--', linewidth=2)
sub.set_xlim([10.**-3, 10.**2])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=25)
sub.set_ylabel(r"$\mathtt{\sum\limits_{l'} q P(q) f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
sub.text(1.5*10**-3, 0.5*(sub.axis()[2]+sub.axis()[3]), r"k = "+str(round(k,2)), fontsize=20)
sub.legend(loc='upper right')
fig.savefig(''.join([
'figure/',
'SUMqPqf', str(ell), 'lp.nseries_nseriesbox.comparison.k', str(round(k,2)), '.png'
]), bbox_inches='tight')
plt.close()
def test_f_l_lp_est(l, lp, rc=0.43):
'''
Test the polynomial estimates of the f_l_lp integrals.
Parameter
---------
- l :
- lp :
Notes
-----
- Consistent for l, lp < 6
- Possible break-down in the estimates for l or lp = 10?
'''
q_arr = np.logspace(-3, 3, num=100)
x_arr = q_arr * rc
prettyplot()
pretty_colors = prettycolors()
fig = plt.figure(figsize=(14,8))
sub = fig.add_subplot(111)
maxmax, minmin = 0., 0.
for ik, k in enumerate([0.01, 0.05, 0.1, 0.5]):
krc = k * rc
fllp_est = []
fllp_int = []
for x in x_arr:
#poly_time = time.time()
if lp <= 10:
fllp_est.append(fourier_corr.f_l_lp_est(x, krc, l, lp))
#print 'polynomial estimate takes ', time.time() - poly_time
#int_time = time.time()
fllp_int.append(fourier_corr.f_l_lp(x, krc, l, lp))
#print 'integration takes ', time.time() - int_time
int_label = 'Integrated $ k = '+str(round(k, 2))+ '$'
if ik == 0:
est_label = 'Polynomial Estimate'
else:
est_label = None
# plot the results
sub.plot(q_arr, np.array(fllp_int), c=pretty_colors[ik+1], lw=4, ls='-', label=int_label)
if lp <= 10:
sub.plot(q_arr, np.array(fllp_est), c=pretty_colors[0], lw=3, ls='--', label=est_label)
else:
fllp_est = fllp_int
maxmax = np.max([np.max([np.max(fllp_int), np.max(fllp_est)]), maxmax])
minmin = np.min([np.min([np.min(fllp_int), np.min(fllp_est)]), minmin])
sub.set_xscale('log')
sub.set_xlabel(r"$\mathtt{q}$", fontsize=25)
sub.set_ylabel(r"$\mathtt{f_{l, l'}(q r_c, k r_c)}$", fontsize=25)
sub.set_ylim([-1.1, 1.1])
sub.text(2.*10**-3, 1.01*maxmax, r"l = "+str(l)+", l' = "+str(lp), fontsize=20)
sub.legend(loc='upper right')
fig.savefig(''.join([
'figure/',
'f_l_lp_estimate',
'.l', str(l), '.lp', str(lp), '.png'
]), bbox_inches='tight')
plt.close()
