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Adapted power spectra evaluation

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glatterf42 2022-05-12 13:04:28 +02:00
parent ad04f7b888
commit efb271e3d4
3 changed files with 57 additions and 24 deletions
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56
agora_plot_power_spectra.py
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@ -6,6 +6,7 @@ Created on Wed Mar 16 15:12:21 2022
@author: ben
"""
from matplotlib import scale
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
@ -15,21 +16,30 @@ import numpy as np
def Dplus( lambda0, a ):
return a * np.sqrt(1.0+lambda0*a**3) * sf.hyp2f1(3.0/2.0, 5.0/6.0, 11.0/6.0, -lambda0 * a**3 )
basedir = Path("/home/ben/sims/swiftsim/examples/agora/spectra/")
basedir = Path("/home/ben/sims/swiftsim/examples/agora/")
# basedir = Path("/home/ben/sims/gadget4/examples/agora_test/output/spectra/")
#choose waveform and Lbox:
Lbox = 85.47 # 60 #only option as of now
Nres1 = 128
Nres1 = 512
# Nres2 = 256
k0 = 2 * 3.14159265358979323846264338327950 / Lbox
knyquist1 = Nres1 * k0
# knyquist2 = Nres2 * k0
times = [0.01, 0.02, 0.04, 0.08, 0.166666, 0.333333, 0.5, 0.666666, 1.0]
# #This is for 128 particles with fewer output times
# times = [0.01, 0.02, 0.04, 0.08, 0.166666, 0.333333, 0.5, 0.666666, 1.0]
# times = [0.01, 0.02, 0.04, 0.08, 0.1]
# times = [0.01, 0.02]
# scale_factor = 0 # give index of a list above
#This is for 512 particles with more output times:
time_file = basedir / 'snap_times_agora1024'
df_times = pd.read_csv(f'{time_file}.txt', sep=' ', skipinitialspace=True, header=0, engine='python')
output_numbers = [0, 1, 2, 4, 8, 16, 32]
redshifts = df_times.loc[output_numbers]
Omega_m = 0.272
Omega_L = 0.728
lambda_0 = Omega_L / Omega_m
@ -42,7 +52,13 @@ columns = ["k [Mpc]", "Pcross", "P1", "err. P1", "P2", "err. P2", "P2-1", "err.
zstart = 100 # 99.09174098173423
a_ics = 1 / (1 + zstart)
filename_ics = basedir / f'{Lbox}mpc/agora_ics_cross_spectrum'
filename_ics = basedir / f'spectra/{Lbox}mpc_{Nres1}/agora_ics_cross_spectrum'
# #just as a test for normalising wrt to the first snapshot
# zstart = 12.0
# a_ics = 1 / (1 + zstart)
# filename_ics = basedir / f'spectra/{Lbox}mpc_{Nres1}/agora_0000_cross_spectrum'
# filename_ics = basedir / 'agora_a0_cross_spectrum'
df_ics = pd.read_csv(f'{filename_ics}.txt', sep=' ', skipinitialspace=True, header=None, names=columns, skiprows=1)
#only consider rows above resolution limit
@ -53,17 +69,26 @@ Dplus0 = Dplus(lambda0=lambda_0, a=a_ics)
D_squared_ics = Dplus0 ** 2
p1_ics_noramlised = p1_ics / D_squared_ics
print(D_squared_ics)
# p1_ics_ic_normalised = p1_ics_noramlised / p1_ics_noramlised
# plt.loglog(k_ics, p1_ics_ic_normalised, label='ICs')
for scale_factor in range(len(times)):
filename = basedir / f"{Lbox}mpc/agora_a{scale_factor}_cross_spectrum"
# #This is for 128 particles with fewer output times:
# for scale_factor in range(len(times)):
# filename = basedir / f"{Lbox}mpc/agora_a{scale_factor}_cross_spectrum"
# filename = basedir / f"agora_a{scale_factor}_small_dt_cross_spectrum"
# filename = basedir / f'agora_a{scale_factor}_cross_spectrum'
# filename = basedir / f"{waveform}_{Lbox:.0f}/{waveform}_{Lbox:.0f}_ics_vsc_cross_spectrum" # for ICs
# savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/power_{waveform}_{Lbox:.0f}_ics_vsc") # for ICs
# plt.title(f"Power Spectra {waveform} L={Lbox:.0f} a=0.02 vsc") # for ICs
#This is for 512 particles with more output times:
for k in range(len(output_numbers)):
output_number = output_numbers[k]
redshift = float(redshifts.loc[output_number])
scale_factor = 1 / (1 + redshift)
filename = basedir / f'spectra/{Lbox}mpc_{Nres1}/agora_{output_number:04d}_cross_spectrum'
df = pd.read_csv(f"{filename}.txt", sep=" ", skipinitialspace=True, header=None, names=columns, skiprows=1)
@ -73,20 +98,26 @@ for scale_factor in range(len(times)):
k = df["k [Mpc]"]
p1 = df["P1"]
p1_error = df["err. P1"]
# p2 = df["P2"]
# p2_error = df["err. P2"]
# pcross = df["Pcross"]
D_squared = Dplus(lambda0=lambda_0, a=times[scale_factor]) ** 2
# D_squared = Dplus(lambda0=lambda_0, a=times[scale_factor]) ** 2
D_squared = Dplus(lambda0=lambda_0, a=scale_factor) ** 2
p1_normalised = p1 / D_squared
p1_ic_normalised = p1_normalised / p1_ics_noramlised
print(D_squared)
# Plot the power spectra:
plt.loglog(k, p1_ic_normalised, label=f"{times[scale_factor]}")
# plt.loglog(k, p1_ic_normalised, label=f"{times[scale_factor]}")
plt.loglog(k, p1_ic_normalised, label=f"{scale_factor}")
# plt.loglog(k, p2, label="P2")
plt.title(f"Power Spectra Agora 128")
savedir = Path(f"/home/ben/Pictures/swift/agora/spectra/{Lbox}mpc/")
plt.title(f"Power Spectra Agora {Nres1}")
savedir = Path(f"/home/ben/Pictures/swift/agora/spectra/{Lbox}mpc_{Nres1}")
plt.xlabel(r"k [$\mathrm{Mpc}^{-1}$]")
plt.ylabel("P")
@ -94,10 +125,11 @@ plt.vlines(knyquist1, ymin=min(p1_ic_normalised), ymax=max(p1_ic_normalised), co
# plt.vlines(knyquist2, ymin=min(p2), ymax=max(p2), color="black", linestyles="dashed", label=f"{Nres2}")
plt.legend()
# plt.ylim(1, 3)
plt.savefig(f"{savedir}/agora_{Nres1}_power.png")
plt.show()
# plt.savefig(f"{savedir}_2.png")

17
plot_cross_spectra.py
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@ -15,16 +15,17 @@ basedir = Path("/home/ben/sims/data_swift/monofonic_tests/spectra/")
#choose Nres and Lbox:
waveforms = ['DB2', "DB4", "DB8", "shannon"] #DB2, DB4, DB8, shannon are all we have right now
Lbox = 100.0 #only option as of now
Nres = 256 #128 and 256 exist for now
Nres1 = 128 #Nres1 should always be smaller than Nres2
Nres2 = 256 #128, 256 and 512 exist for now
k0 = 2 * 3.14159265358979323846264338327950 / Lbox
knyquist = Nres * k0
knyquist = Nres2 * k0 #Not used at the moment anyway except for upper limit, for which we need the larger Nres
a = [0.166666, 0.333333, 0.5, 0.666666, 1.0]
scale_factor = 4 # give index of a list above
for wave in waveforms:
# filename = basedir / f"{wave}_{Lbox:.0f}/{wave}_{Lbox:.0f}_a{scale_factor}_cross_spectrum"
filename = basedir / f"{wave}_{Lbox:.0f}/{wave}_{Lbox:.0f}_ics_local_cross_spectrum" # for ICs
# filename = basedir / f"{wave}_{Lbox:.0f}/{wave}_{Lbox:.0f}_a{scale_factor}_{Nres1}_{Nres2}_cross_spectrum"
filename = basedir / f"{wave}_{Lbox:.0f}/{wave}_{Lbox:.0f}_ics_{Nres1}_{Nres2}_cross_spectrum" # for ICs
#find columns in file manually
#is k really in Mpc? Swift doesn't use /h internally at least.
@ -46,11 +47,11 @@ for wave in waveforms:
# Plot the Cross Correlation:
plt.plot(k, pcross, label=f"{wave}")
savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/cross_{Nres}_{Lbox:.0f}_ics_local") # for ICs
plt.title(f"Cross correlation N={Nres} L={Lbox:.0f} a=0.02") # for ICs
savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/cross_{Nres1}_{Nres2}_{Lbox:.0f}_ics") # for ICs
plt.title(f"Cross correlation N=({Nres1}, {Nres2}) L={Lbox:.0f} a=0.02") # for ICs
# savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/cross_{Nres}_{Lbox:.0f}_a{scale_factor}")
# plt.title(f"Cross correlation N={Nres} L={Lbox:.0f} a={a[scale_factor]}")
# savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/cross_{Nres1}_{Nres2}_{Lbox:.0f}_a{scale_factor}")
# plt.title(f"Cross correlation N=({Nres1}, {Nres2}) L={Lbox:.0f} a={a[scale_factor]}")
plt.xscale("log")
plt.xlabel(r"k [$\mathrm{Mpc}^{-1}$]")

8
plot_power_spectra.py
View file

@ -10,20 +10,20 @@ import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
basedir = Path("/home/ben/sims/swift/monofonic_tests/spectra/")
basedir = Path("/home/ben/sims/data_swift/monofonic_tests/spectra/")
#choose waveform and Lbox:
waveform = "shannon" #DB2, DB4, DB8 or shannon
Lbox = 100.0 #only option as of now
Nres1 = 128
Nres2 = 256
Nres2 = 512
k0 = 2 * 3.14159265358979323846264338327950 / Lbox
knyquist1 = Nres1 * k0
knyquist2 = Nres2 * k0
a = [0.166666, 0.333333, 0.5, 0.666666, 1.0]
scale_factor = 4 # give index of a list above
filename = basedir / f"{waveform}_{Lbox:.0f}/{waveform}_{Lbox:.0f}_a{scale_factor}_cross_spectrum"
filename = basedir / f"{waveform}_{Lbox:.0f}/{waveform}_{Lbox:.0f}_a{scale_factor}_{Nres1}_{Nres2}_cross_spectrum"
# filename = basedir / f"{waveform}_{Lbox:.0f}/{waveform}_{Lbox:.0f}_ics_vsc_cross_spectrum" # for ICs
# savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/power_{waveform}_{Lbox:.0f}_ics_vsc") # for ICs
# plt.title(f"Power Spectra {waveform} L={Lbox:.0f} a=0.02 vsc") # for ICs
@ -50,7 +50,7 @@ plt.loglog(k, p1, label="P1")
plt.loglog(k, p2, label="P2")
plt.title(f"Power Spectra {waveform} L={Lbox:.0f} a={a[scale_factor]}")
savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/power_{waveform}_{Lbox:.0f}_a{scale_factor}")
savedir = Path(f"/home/ben/Pictures/swift/monofonic_tests/spectra/power_{waveform}_{Lbox:.0f}_{Nres1}_{Nres2}_a{scale_factor}")
plt.xlabel("k [Mpc]")
plt.ylabel("P")