Initial import
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scripts/misc/check_bias.py
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274
scripts/misc/check_bias.py
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#+
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# ARES/HADES/BORG Package -- ./scripts/misc/check_bias.py
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# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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#
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# Additional contributions from:
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# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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#
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#+
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import read_all_h5
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from pylab import *
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n=[]
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b0=[]
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b1=[]
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b2=[]
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b3=[]
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b4=[]
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b5=[]
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b6=[]
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b7=[]
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b8=[]
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b9=[]
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b10=[]
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b11=[]
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b12=[]
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b13=[]
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b14=[]
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b15=[]
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rho_g0=[]
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rho_g1=[]
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rho_g2=[]
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rho_g3=[]
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rho_g4=[]
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rho_g5=[]
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rho_g6=[]
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rho_g7=[]
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rho_g8=[]
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rho_g9=[]
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rho_g10=[]
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rho_g11=[]
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rho_g12=[]
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rho_g13=[]
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rho_g14=[]
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rho_g15=[]
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eps_g0=[]
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eps_g1=[]
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eps_g2=[]
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eps_g3=[]
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eps_g4=[]
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eps_g5=[]
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eps_g6=[]
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eps_g7=[]
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eps_g8=[]
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eps_g9=[]
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eps_g10=[]
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eps_g11=[]
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eps_g12=[]
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eps_g13=[]
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eps_g14=[]
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eps_g15=[]
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n0=[]
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n1=[]
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n2=[]
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n3=[]
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n4=[]
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n5=[]
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n6=[]
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n7=[]
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n8=[]
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n9=[]
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n10=[]
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n11=[]
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n12=[]
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n13=[]
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n14=[]
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n15=[]
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accept=[]
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i=0
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#while True:
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for l in range(0,440,1):
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a = \
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read_all_h5.read_all_h5("mcmc_%d.h5" % l)
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try:
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n0.append(a.scalars.galaxy_nmean_0)
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n1.append(a.scalars.galaxy_nmean_1)
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n2.append(a.scalars.galaxy_nmean_2)
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n3.append(a.scalars.galaxy_nmean_3)
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n4.append(a.scalars.galaxy_nmean_4)
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n5.append(a.scalars.galaxy_nmean_5)
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n6.append(a.scalars.galaxy_nmean_6)
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n7.append(a.scalars.galaxy_nmean_7)
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n8.append(a.scalars.galaxy_nmean_8)
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n9.append(a.scalars.galaxy_nmean_9)
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n10.append(a.scalars.galaxy_nmean_10)
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n11.append(a.scalars.galaxy_nmean_11)
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n12.append(a.scalars.galaxy_nmean_12)
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n13.append(a.scalars.galaxy_nmean_13)
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n14.append(a.scalars.galaxy_nmean_14)
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n15.append(a.scalars.galaxy_nmean_15)
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b0.append(a.scalars.galaxy_bias_0)
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b1.append(a.scalars.galaxy_bias_1)
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b2.append(a.scalars.galaxy_bias_2)
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b3.append(a.scalars.galaxy_bias_3)
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b4.append(a.scalars.galaxy_bias_4)
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b5.append(a.scalars.galaxy_bias_5)
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b6.append(a.scalars.galaxy_bias_6)
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b7.append(a.scalars.galaxy_bias_7)
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b8.append(a.scalars.galaxy_bias_8)
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b9.append(a.scalars.galaxy_bias_9)
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b10.append(a.scalars.galaxy_bias_10)
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b11.append(a.scalars.galaxy_bias_11)
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b12.append(a.scalars.galaxy_bias_12)
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b13.append(a.scalars.galaxy_bias_13)
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b14.append(a.scalars.galaxy_bias_14)
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b15.append(a.scalars.galaxy_bias_15)
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rho_g0.append(a.scalars.galaxy_rho_g_0)
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rho_g1.append(a.scalars.galaxy_rho_g_1)
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rho_g2.append(a.scalars.galaxy_rho_g_2)
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rho_g3.append(a.scalars.galaxy_rho_g_3)
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rho_g4.append(a.scalars.galaxy_rho_g_4)
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rho_g5.append(a.scalars.galaxy_rho_g_5)
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rho_g6.append(a.scalars.galaxy_rho_g_6)
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rho_g7.append(a.scalars.galaxy_rho_g_7)
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rho_g8.append(a.scalars.galaxy_rho_g_8)
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rho_g9.append(a.scalars.galaxy_rho_g_9)
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rho_g10.append(a.scalars.galaxy_rho_g_10)
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rho_g11.append(a.scalars.galaxy_rho_g_11)
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rho_g12.append(a.scalars.galaxy_rho_g_12)
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rho_g13.append(a.scalars.galaxy_rho_g_13)
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rho_g14.append(a.scalars.galaxy_rho_g_14)
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rho_g15.append(a.scalars.galaxy_rho_g_15)
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eps_g0.append(a.scalars.galaxy_eps_g_0)
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eps_g1.append(a.scalars.galaxy_eps_g_1)
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eps_g2.append(a.scalars.galaxy_eps_g_2)
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eps_g3.append(a.scalars.galaxy_eps_g_3)
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eps_g4.append(a.scalars.galaxy_eps_g_4)
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eps_g5.append(a.scalars.galaxy_eps_g_5)
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eps_g6.append(a.scalars.galaxy_eps_g_6)
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eps_g7.append(a.scalars.galaxy_eps_g_7)
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eps_g8.append(a.scalars.galaxy_eps_g_8)
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eps_g9.append(a.scalars.galaxy_eps_g_9)
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eps_g10.append(a.scalars.galaxy_eps_g_10)
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eps_g11.append(a.scalars.galaxy_eps_g_11)
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eps_g12.append(a.scalars.galaxy_eps_g_12)
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eps_g13.append(a.scalars.galaxy_eps_g_13)
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eps_g14.append(a.scalars.galaxy_eps_g_14)
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eps_g15.append(a.scalars.galaxy_eps_g_14)
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accept.append(a.scalars.hades_accept_count)
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print l
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except AttributeError:
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break
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i += 1
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rate =np.cumsum(np.array(accept))
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norm =np.cumsum(np.ones(len(accept)))
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plt.plot(rate/norm)
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plt.show()
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print
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plt.plot(b0,label=str(0))
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plt.plot(b1,label=str(1))
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plt.plot(b2,label=str(2))
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plt.plot(b3,label=str(3))
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plt.plot(b4,label=str(4))
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plt.plot(b5,label=str(5))
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plt.plot(b6,label=str(6))
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plt.plot(b7,label=str(7))
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plt.plot(b8,label=str(0))
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plt.plot(b9,label=str(1))
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plt.plot(b10,label=str(2))
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plt.plot(b11,label=str(3))
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plt.plot(b12,label=str(4))
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plt.plot(b13,label=str(5))
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plt.plot(b14,label=str(6))
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plt.plot(b15,label=str(7))
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legend()
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plt.savefig('check_bias.png')
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plt.show()
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plt.plot(np.log10(rho_g0),label=str(0))
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plt.plot(np.log10(rho_g1),label=str(1))
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plt.plot(np.log10(rho_g2),label=str(2))
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plt.plot(np.log10(rho_g3),label=str(3))
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plt.plot(np.log10(rho_g4),label=str(4))
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plt.plot(np.log10(rho_g5),label=str(5))
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plt.plot(np.log10(rho_g6),label=str(6))
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plt.plot(np.log10(rho_g7),label=str(7))
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plt.plot(np.log10(rho_g8),label=str(0))
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plt.plot(np.log10(rho_g9),label=str(1))
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plt.plot(np.log10(rho_g10),label=str(2))
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plt.plot(np.log10(rho_g11),label=str(3))
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plt.plot(np.log10(rho_g12),label=str(4))
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plt.plot(np.log10(rho_g13),label=str(5))
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plt.plot(np.log10(rho_g14),label=str(6))
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plt.plot(np.log10(rho_g15),label=str(7))
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legend()
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plt.savefig('check_rho_g.png')
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plt.show()
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x=np.arange(600)*0.04+1e-12
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y0=n0[-1]*x**b0[-1]*np.exp(-rho_g0[-1]*x**(-eps_g0[-1]))
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y1=n1[-1]*x**b1[-1]*np.exp(-rho_g1[-1]*x**(-eps_g1[-1]))
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y2=n2[-1]*x**b2[-1]*np.exp(-rho_g2[-1]*x**(-eps_g2[-1]))
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y3=n3[-1]*x**b3[-1]*np.exp(-rho_g3[-1]*x**(-eps_g3[-1]))
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y4=n4[-1]*x**b4[-1]*np.exp(-rho_g4[-1]*x**(-eps_g4[-1]))
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y5=n5[-1]*x**b5[-1]*np.exp(-rho_g5[-1]*x**(-eps_g5[-1]))
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y6=n6[-1]*x**b6[-1]*np.exp(-rho_g6[-1]*x**(-eps_g6[-1]))
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y7=n7[-1]*x**b7[-1]*np.exp(-rho_g7[-1]*x**(-eps_g7[-1]))
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y8=n8[-1]*x**b8[-1]*np.exp(-rho_g8[-1]*x**(-eps_g8[-1]))
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y9=n9[-1]*x**b9[-1]*np.exp(-rho_g9[-1]*x**(-eps_g9[-1]))
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y10=n10[-1]*x**b10[-1]*np.exp(-rho_g10[-1]*x**(-eps_g10[-1]))
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y11=n11[-1]*x**b11[-1]*np.exp(-rho_g11[-1]*x**(-eps_g11[-1]))
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y12=n12[-1]*x**b12[-1]*np.exp(-rho_g12[-1]*x**(-eps_g12[-1]))
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y13=n13[-1]*x**b13[-1]*np.exp(-rho_g13[-1]*x**(-eps_g13[-1]))
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y14=n14[-1]*x**b14[-1]*np.exp(-rho_g14[-1]*x**(-eps_g14[-1]))
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y15=n15[-1]*x**b15[-1]*np.exp(-rho_g15[-1]*x**(-eps_g15[-1]))
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plt.plot(x,np.log(y0),label=str(0),color='red')
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plt.plot(x,np.log(y1),label=str(1),color='blue')
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plt.plot(x,np.log(y2),label=str(2),color='green')
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plt.plot(x,np.log(y3),label=str(3),color='orange')
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plt.plot(x,np.log(y4),label=str(4),color='yellow')
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plt.plot(x,np.log(y5),label=str(5),color='black')
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plt.plot(x,np.log(y6),label=str(6),color='gray')
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plt.plot(x,np.log(y7),label=str(7),color='magenta')
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plt.plot(x,np.log(y8),label=str(0),color='red')
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plt.plot(x,np.log(y9),label=str(1),color='blue')
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plt.plot(x,np.log(y10),label=str(2),color='green')
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plt.plot(x,np.log(y11),label=str(3),color='orange')
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plt.plot(x,np.log(y12),label=str(4),color='yellow')
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plt.plot(x,np.log(y13),label=str(5),color='black')
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plt.plot(x,np.log(y14),label=str(6),color='gray')
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plt.plot(x,np.log(y15),label=str(7),color='magenta')
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plt.plot(x,np.log(x),label=str(99))
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plt.ylim([-8,5])
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legend(loc='lower right', shadow=True)
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#plt.show()
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gcf().savefig("check_bias.png")
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26
scripts/misc/check_integrator.py
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scripts/misc/check_integrator.py
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#+
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# ARES/HADES/BORG Package -- ./scripts/misc/check_integrator.py
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# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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#
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# Additional contributions from:
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# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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#
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#+
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import h5py as h5
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import numpy as np
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import pylab as plt
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fig = plt.figure(1)
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fig.clf()
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ax = fig.add_subplot(111)
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f = h5.File("symplectic.h5")
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for k in f.keys():
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ax.semilogy(np.abs(f[k]['energy']), label=k)
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f.close()
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ax.legend()
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fig.savefig("symplectic.png")
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29
scripts/misc/check_velocities.py
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scripts/misc/check_velocities.py
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@ -0,0 +1,29 @@
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#+
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# ARES/HADES/BORG Package -- ./scripts/misc/check_velocities.py
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# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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#
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# Additional contributions from:
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# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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#
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#+
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from read_all_h5 import *
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import pylab as plt
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g=read_all_h5('dump_velocities.h5')
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V = g.scalars.L0[0]*g.scalars.L1[0]*g.scalars.L2[0]
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q = g.scalars.k_pos_test
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H=100.
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D=1.
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a=1.
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f=g.scalars.cosmology['omega_m']**(5./9)
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vref = 2* q/((q**2).sum()) / V * g.scalars.A_k_test * f * H * a**2 * D
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vborg = g.scalars.lpt_vel[:,::].max(axis=0)
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print "vref = %r" % vref
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print "vborg = %r" % vborg
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print "ratio = %r" % (vborg/vref)
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29
scripts/misc/convert_2m++.py
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29
scripts/misc/convert_2m++.py
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@ -0,0 +1,29 @@
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#+
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# ARES/HADES/BORG Package -- ./scripts/misc/convert_2m++.py
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# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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#
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# Additional contributions from:
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# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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#
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#+
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import numpy as np
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from scipy import constants as sconst
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LIGHT_SPEED = sconst.c/1000. #In km/s
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catalog = np.load("2m++.npy")
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with open("2MPP.txt", mode="w") as f:
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cond = (catalog['flag_vcmb']==1)*(catalog['flag_zoa']==0)*(catalog['best_velcmb'] > 100)
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for i,c in enumerate(catalog[cond]):
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M = c['K2MRS'] - 5*np.log10(c['best_velcmb']/100*1e5)
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zo = c['velcmb']/LIGHT_SPEED
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z = zo
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f.write(
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"%d %lg %lg %lg %lg %lg %lg\n" %
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(i, np.radians(c['ra']), np.radians(c['dec']), zo, c['K2MRS'], M, z)
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)
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42
scripts/misc/plot_power.py
Normal file
42
scripts/misc/plot_power.py
Normal file
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@ -0,0 +1,42 @@
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#+
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# ARES/HADES/BORG Package -- ./scripts/misc/plot_power.py
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# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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#
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# Additional contributions from:
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# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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#
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#+
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import read_all_h5
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from pylab import *
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P=[]
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n=[]
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b=[]
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n1=[]
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b1=[]
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i=0
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while True:
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a = \
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read_all_h5.read_all_h5("mcmc_%d.h5" % i)
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try:
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P.append(a.scalars.powerspectrum)
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n.append(a.scalars.galaxy_nmean_0[0])
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b.append(a.scalars.galaxy_bias_0[0])
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n1.append(a.scalars.galaxy_nmean_1[0])
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b1.append(a.scalars.galaxy_bias_1[0])
|
||||
except AttributeError:
|
||||
break
|
||||
i += 1
|
||||
|
||||
k = read_all_h5.read_all_h5("info.h5").scalars.k_modes
|
||||
P = np.array(P)
|
||||
|
||||
f=figure(1)
|
||||
clf()
|
||||
loglog(k[:,None].repeat(P.shape[0],axis=1),P.transpose())
|
||||
|
||||
f=figure(2)
|
||||
clf()
|
||||
plot(n)
|
||||
|
||||
85
scripts/misc/plot_void.py
Normal file
85
scripts/misc/plot_void.py
Normal file
|
|
@ -0,0 +1,85 @@
|
|||
#+
|
||||
# ARES/HADES/BORG Package -- ./scripts/misc/plot_void.py
|
||||
# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
|
||||
# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
|
||||
#
|
||||
# Additional contributions from:
|
||||
# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
|
||||
#
|
||||
#+
|
||||
from pylab import *
|
||||
from read_all_h5 import explore_chain
|
||||
|
||||
def box2sphere(x,y,z):
|
||||
#calculate radii
|
||||
r=np.sqrt(x**2+y**2+z**2)
|
||||
|
||||
print np.shape(r),np.shape(x)
|
||||
|
||||
dec=np.zeros(np.shape(r))
|
||||
'''
|
||||
foo= np.where(r>0)
|
||||
dec[foo]=np.arcsin(z[foo]/r[foo])
|
||||
'''
|
||||
ra=np.arctan2(y,x)
|
||||
|
||||
print np.shape(r),np.shape(ra)
|
||||
|
||||
return ra,dec,r
|
||||
|
||||
|
||||
chain_path="."
|
||||
|
||||
N = 256
|
||||
L = 677.7
|
||||
Nb = 128
|
||||
f = np.sqrt(3)*0.5
|
||||
|
||||
ix = np.arange(N)*L/N - 0.5*L
|
||||
|
||||
ra,dec,r=box2sphere(ix[:,None,None],ix[None,:,None],ix[None,None,:])
|
||||
|
||||
r = np.sqrt(ix[:,None,None]**2 + ix[None,:,None]**2 + ix[None,None,:]**2)
|
||||
|
||||
print np.shape(r)
|
||||
|
||||
H, b = np.histogram(r, range=(0,f*L), bins=Nb)
|
||||
|
||||
Hw_mean=np.zeros(np.shape(H))
|
||||
|
||||
cnt=0
|
||||
|
||||
mu = np.zeros(np.shape(H))
|
||||
var = np.zeros(np.shape(H))
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(chain_path, 400,4100, 10):
|
||||
d = a['BORG_final_density'][:]
|
||||
|
||||
Hw, b = np.histogram(r, weights=d, range=(0,f*L), bins=Nb)
|
||||
Hw /= H
|
||||
|
||||
mu = (nn-1.)/float(nn)*mu +1./float(nn)*Hw
|
||||
if(nn>1):
|
||||
aux = (mu-Hw)**2
|
||||
var = (nn-1.)/nn*var+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
plot(b[1:], mu, label='average', color='red')
|
||||
|
||||
fill_between(b[1:], mu, mu+np.sqrt(var), interpolate=True, color='gray', alpha='0.5')
|
||||
fill_between(b[1:], mu-np.sqrt(var), mu, interpolate=True, color='gray', alpha='0.5')
|
||||
fill_between(b[1:], mu, mu+2*np.sqrt(var), interpolate=True, color='darkgray', alpha='0.5')
|
||||
fill_between(b[1:], mu-2*np.sqrt(var), mu, interpolate=True, color='darkgray', alpha='0.5')
|
||||
|
||||
|
||||
plt.xlabel(r'$r \left[\mathrm{Mpc/h} \right]$')
|
||||
plt.ylabel(r'$\langle \delta \rangle$')
|
||||
|
||||
|
||||
axhline(0.0,lw=1.5, color='black')
|
||||
|
||||
#legend()
|
||||
ylim(-1,1)
|
||||
gcf().savefig("void.png")
|
||||
Loading…
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Reference in a new issue