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plot_cute2pcf.py
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plot_cute2pcf.py
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'''
Plotting module for CUTE generated Two Point Correlation Functions xi(rp, pi)
'''
import numpy as np
import scipy as sp
import cosmolopy as cosmos
import matplotlib.pyplot as plt
# -- Local --
from defutility.plotting import prettyplot
from defutility.plotting import prettycolors
def plot_cute2pcf(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(-3.0, 2.5, 0.5)
elif scale == 'small':
contour_range = np.arange(-0.6, 3.0, 0.15)
elif file_flag == 'smaller':
contour_range = np.arange(-0.4, 3.2, 0.2)
for corr in corrections: # for each correction
fig = plt.figure(figsize=(15,10))
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.',
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
# contour of log(xi(r_p, pi))
cont = sub.contourf(r_p, pi, np.log10(twop_corr.T), contour_range, cmap=plt.cm.afmhot)
#sub.contour(r_p, pi, np.log10(twop_corr.T), contour_range, linewidths=4, 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'$\mathtt{'+corr.upper()+r"}\;log\xi(r_{||}, r_\perp)$", fontsize=40)
fig_name = ''.join([
'/home/users/hahn/powercode/FiberCollisions/figure/',
'2pcf_Nseries_', corr, '_', str(len(n_mocks)), 'mocks.', scale, '.png'
])
fig.savefig(fig_name, 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()
#fig.savefig(fig_name, bbox_inches="tight")
#plt.close()
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 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()
if __name__=="__main__":
#plot_cute2pcf_residual(range(1,21), 20, 20, corrections=['true', 'upweighted'], scale='verysmall')
plot_bao_cmass_cute2pcf(30, 30, xiform = 'log', cmap='hot')
#plot_bao_cute2pcf(range(1,45), 30, 30, xiform = 'none', corrections=['true'])
#plot_bao_cute2pcf(range(1,85), 30, 30, xiform = 'log', corrections=['true'], cmap='hot')
#plot_bao_cute2pcf(range(1,85), 30, 30, xiform = 'log', corrections=['true'], cmap='jet')
#plot_bao_cute2pcf(range(1,85), 30, 30, xiform = 'asinh', corrections=['true'])
#plot_bao_cute2pcf(range(1,85), 30, 30, xiform = 'asinh', corrections=['true'], cmap='jet')