1 MiB
1 MiB
Tristan Hoellinger
Institut d'Astrophysique de Paris
tristan.hoellinger@iap.fr
Non-regression tests towards implementing P3M gravity¶
Set up the environment and parameters¶
In [1]:
# pyright: reportWildcardImportFromLibrary=false
from wip3m import *
In [2]:
workdir = ROOT_PATH + "results/"
output_path = OUTPUT_PATH
L = 32 # Box size in Mpc/h
N = 64 # Density grid size
Np = 32 # Number of dark matter particles per spatial dimension
Npm = 64 # PM grid size
n_Tiles = 8 # Make sure Npm/n_Tiles >= 6
go_beyond_Nyquist_ss = True # for the summary statistics
force = True
force_hard = True
run_id = "notebook1"
# Good set of parameters for the force diagnostic
# nPairsForceDiagnostic = 5
# nBinsForceDiagnostic = 30
# maxTrialsForceDiagnostic = int(2e9)
# Faster force diagnostic
nPairsForceDiagnostic = 3
nBinsForceDiagnostic = 20
maxTrialsForceDiagnostic = int(1e8)
# Simulation parameters
nsteps_pm = 50
nsteps_cola1 = 10
nsteps_cola2 = 3
nsteps_spm = 50
nsteps_p3m = 50
In principle nothing needs to be changed below this cell.
In [3]:
# Automatic reloading of modules
%load_ext autoreload
%autoreload 2
from os.path import isfile
from pathlib import Path
import numpy as np
from pysbmy.power import PowerSpectrum
from pysbmy.fft import FourierGrid, read_FourierGrid
from pysbmy.field import read_field
from pysbmy.correlations import get_autocorrelation
from wip3m.tools import get_k_max, generate_sim_params, generate_white_noise_Field
from wip3m.params import params_planck_kmax_missing, cosmo_small_to_full_dict, z2a, BASELINE_SEEDPHASE
from wip3m.plot_utils import * # type: ignore
In [ ]:
corner = -L / 2.0
RedshiftLPT = 19.0
RedshiftFCs = 0.0
ai = z2a(RedshiftLPT)
af = z2a(RedshiftFCs)
k_max = get_k_max(L, N) # k_max in h/Mpc
print(f"k_max = {k_max}")
cosmo = params_planck_kmax_missing.copy()
cosmo["k_max"] = k_max
TimeStepDistribution_pm = 0
TimeStepDistribution_cola1 = 0
TimeStepDistribution_cola2 = 0
TimeStepDistribution_spm = 0
TimeStepDistribution_p3m = 0
wd = workdir + run_id + "/"
simdir = output_path + run_id + "/"
logdir = simdir + "logs/"
if force_hard:
import shutil
if Path(simdir).exists():
shutil.rmtree(simdir)
if Path(wd).exists():
shutil.rmtree(wd)
Path(wd).mkdir(parents=True, exist_ok=True)
Path(logdir).mkdir(parents=True, exist_ok=True)
input_white_noise_file = simdir + "input_white_noise.h5"
input_seed_phase_file = simdir + "seed"
ICs_path = simdir + "initial_density.h5"
simpath = simdir
# Path to the input matter power spectrum (generated later)
input_power_file = simdir + "input_power.h5"
# Paths the the force diagnostic CSVs
OutputForceDiagnostic_pm = simdir + "force_diagnostic_pm.txt"
OutputForceDiagnostic_cola1 = simdir + "force_diagnostic_cola1.txt"
OutputForceDiagnostic_cola2 = simdir + "force_diagnostic_cola2.txt"
OutputForceDiagnostic_spm = simdir + "force_diagnostic_spm.txt"
OutputForceDiagnostic_p3m = simdir + "force_diagnostic_p3m.txt"
Generate the parameter files¶
The first preparatory step is to generate all the parameter files required for all the simulations.
To this end we use the generate_sim_params function defined in params.py.
In [ ]:
common_params = {
"Np": Np,
"N": N,
"L": L,
"corner0": corner,
"corner1": corner,
"corner2": corner,
"h": cosmo["h"],
"Omega_m": cosmo["Omega_m"],
"Omega_b": cosmo["Omega_b"],
"n_s": cosmo["n_s"],
"sigma8": cosmo["sigma8"],
}
lpt_params = common_params.copy()
lpt_params["method"] = "lpt"
lpt_params["InputPowerSpectrum"] = input_power_file
lpt_params["ICsMode"] = 1
# 0 : the codes generates white noise, then initial conditions
# 1 : external white noise specified, the code multiplies by the power spectrum
# 2 : external initial conditions specified
lpt_params["InputWhiteNoise"] = input_white_noise_file
common_params_num = common_params.copy()
common_params_num["ai"] = ai
common_params_num["af"] = af
common_params_num["RedshiftLPT"] = RedshiftLPT
common_params_num["RedshiftFCs"] = RedshiftFCs
common_params_num["Npm"] = Npm
common_params_num["RunForceDiagnostic"] = True
common_params_num["nBinsForceDiagnostic"] = nBinsForceDiagnostic
common_params_num["nPairsForceDiagnostic"] = nPairsForceDiagnostic
common_params_num["maxTrialsForceDiagnostic"] = maxTrialsForceDiagnostic
pm_params = common_params_num.copy()
pm_params["method"] = "pm"
pm_params["TimeStepDistribution"] = TimeStepDistribution_pm
pm_params["nsteps"] = nsteps_pm
pm_params["OutputForceDiagnostic"] = OutputForceDiagnostic_pm
cola1_params = common_params_num.copy()
cola1_params["method"] = "cola"
cola1_params["TimeStepDistribution"] = TimeStepDistribution_cola1
cola1_params["nsteps"] = nsteps_cola1
cola1_params["OutputForceDiagnostic"] = OutputForceDiagnostic_cola1
cola2_params = common_params_num.copy()
cola2_params["method"] = "cola"
cola2_params["TimeStepDistribution"] = TimeStepDistribution_cola2
cola2_params["nsteps"] = nsteps_cola2
cola2_params["OutputForceDiagnostic"] = OutputForceDiagnostic_cola2
spm_params = common_params_num.copy()
spm_params["method"] = "spm"
spm_params["EvolutionMode"] = 5
spm_params["TimeStepDistribution"] = TimeStepDistribution_spm
spm_params["nsteps"] = nsteps_spm
spm_params["n_Tiles"] = n_Tiles
spm_params["OutputForceDiagnostic"] = OutputForceDiagnostic_spm
p3m_params = common_params_num.copy()
p3m_params["method"] = "p3m"
p3m_params["EvolutionMode"] = 4
p3m_params["TimeStepDistribution"] = TimeStepDistribution_p3m
p3m_params["nsteps"] = nsteps_p3m
p3m_params["n_Tiles"] = n_Tiles
p3m_params["OutputForceDiagnostic"] = OutputForceDiagnostic_p3m
In [6]:
reset_plotting() # Default style for Simbelmynë
generate_sim_params(lpt_params, ICs_path, wd, simdir, None, force)
print(f"PM nsteps = {nsteps_pm}:")
file_ext = f"nsteps{nsteps_pm}" # "pm" is already in the filename
generate_sim_params(pm_params, ICs_path, wd, simdir, file_ext, force)
print(f"COLA1 nsteps = {nsteps_cola1}:")
file_ext = f"nsteps{nsteps_cola1}" # "cola" is already in the filename
generate_sim_params(cola1_params, ICs_path, wd, simdir, file_ext, force)
print(f"COLA2 nsteps = {nsteps_cola2}:")
file_ext = f"nsteps{nsteps_cola2}" # "cola" is already in the filename
generate_sim_params(cola2_params, ICs_path, wd, simdir, file_ext, force)
print(f"SPM nsteps = {nsteps_spm}:")
file_ext = f"nsteps{nsteps_spm}" # "spm" is already in the filename
generate_sim_params(spm_params, ICs_path, wd, simdir, file_ext, force)
print(f"P3M nsteps = {nsteps_p3m}:")
file_ext = f"nsteps{nsteps_p3m}" # "p3m" is already in the filename
generate_sim_params(p3m_params, ICs_path, wd, simdir, file_ext, force)
setup_plotting() # Reset plotting style for this project
Generate the initial phase¶
In [7]:
generate_white_noise_Field(
L=L,
size=N,
corner=corner,
seedphase=BASELINE_SEEDPHASE,
fname_whitenoise=input_white_noise_file,
seedname_whitenoise=input_seed_phase_file,
force_phase=force,
)
Generating the input power spectrum¶
The second preparatory step is to compute the initial power spectrum to be used in the simulations, given the cosmological parameters and prescription specified in params.py. The power spectrum is saved in input_power_file.
In [8]:
# If cosmo["WhichSpectrum"] == "class", then classy is required.
if not isfile(input_power_file) or force:
Pk = PowerSpectrum(L, L, L, N, N, N, cosmo_small_to_full_dict(cosmo))
Pk.write(input_power_file)
In [9]:
# k grid used to compute the final overdensity power spectrum
Pinit = 100
trim_threshold = 100 # Merge bins until this minimum number of modes per bin is reached
log_kmin = np.log10(2 * np.pi / (np.sqrt(3) * L)) # Minimum non-zero k in h/Mpc
if go_beyond_Nyquist_ss:
k_max_ss = get_k_max(L, N)
else:
k_max_ss = get_k_max(L, N) / np.sqrt(3) # 1D Nyquist frequency
Pbins_left_bnds = np.logspace(log_kmin, np.log10(k_max_ss), Pinit + 1, dtype=np.float32)
Pbins_left_bnds = Pbins_left_bnds[:-1]
input_ss_file = simdir + "input_ss_k_grid.h5"
Gk = FourierGrid(
L,
L,
L,
N,
N,
N,
k_modes=Pbins_left_bnds,
kmax=k_max_ss,
trim_bins=True,
trim_threshold=trim_threshold,
)
Gk.write(input_ss_file)
Running the simulations¶
We are now ready to run the actual simulations using the Simbelmynë executable.
In [10]:
%%capture
if not isfile(ICs_path) or not isfile(simdir + "lpt_density.h5") or not isfile(simdir + "lpt_particles.gadget3") or force:
!simbelmyne {wd}example_lpt.sbmy {logdir}lpt.txt
file_ext = f"nsteps{nsteps_pm}" # "pm" is already in the filename
if not isfile(simdir + f"{file_ext}_final_density_pm.h5") or force:
!simbelmyne {wd}{file_ext}_example_pm.sbmy {logdir}{file_ext}_pm.txt
file_ext = f"nsteps{nsteps_cola1}" # "cola" is already in the filename
if not isfile(simdir + f"{file_ext}_final_density_cola1.h5") or force:
!simbelmyne {wd}{file_ext}_example_cola.sbmy {logdir}{file_ext}_cola1.txt
file_ext = f"nsteps{nsteps_cola2}" # "cola" is already in the filename
if not isfile(simdir + f"{file_ext}_final_density_cola2.h5") or force:
!simbelmyne {wd}{file_ext}_example_cola.sbmy {logdir}{file_ext}_cola2.txt
file_ext = f"nsteps{nsteps_spm}" # "spm" is already in the filename
if not isfile(simdir + f"{file_ext}_final_density_spm.h5") or force:
!simbelmyne {wd}{file_ext}_example_spm.sbmy {logdir}{file_ext}_spm.txt
file_ext = f"nsteps{nsteps_p3m}" # "p3m" is already in the filename
if not isfile(simdir + f"{file_ext}_final_density_p3m.h5") or force:
!simbelmyne {wd}{file_ext}_example_p3m.sbmy {logdir}{file_ext}_p3m.txt
The logs can be monitored in the corresponding files in the logdir directory.
Plot results¶
Plot the evolved dark matter density fields¶
In [11]:
slice_ijk = (N // 2, slice(None), slice(None))
DELTA_LPT = read_field(simdir + "lpt_density.h5").data[slice_ijk]
DELTA_COLA1 = read_field(simdir + f"nsteps{nsteps_cola1}_final_density_cola.h5").data[slice_ijk]
DELTA_COLA2 = read_field(simdir + f"nsteps{nsteps_cola2}_final_density_cola.h5").data[slice_ijk]
DELTA_PM = read_field(simdir + f"nsteps{nsteps_pm}_final_density_pm.h5").data[slice_ijk]
DELTA_SPM = read_field(simdir + f"nsteps{nsteps_spm}_final_density_spm.h5").data[slice_ijk]
DELTA_P3M = read_field(simdir + f"nsteps{nsteps_p3m}_final_density_p3m.h5").data[slice_ijk]
diff_p3m_pm = DELTA_P3M - DELTA_PM
diff_p3m_spm = DELTA_P3M - DELTA_SPM
In [12]:
print(f"max(DELTA_PM) = {np.max(DELTA_PM)}, min(DELTA_PM) = {np.min(DELTA_PM)}")
print(f"max(DELTA_P3M) = {np.max(DELTA_P3M)}, min(DELTA_P3M) = {np.min(DELTA_P3M)}")
print(f"max(diff) = {np.max(diff_p3m_pm)}, min(diff) = {np.min(diff_p3m_pm)}")
In [13]:
# fields = ["pm", "spm", "p3m", "diff_p3m_pm"] # fields to plot
fields = ["lpt", "cola1", "cola2", "pm", "spm", "p3m", "diff_p3m_pm", "diff_p3m_spm"] # fields to plot
figname = "_".join(fields)
slices_dict = {
"lpt": DELTA_LPT,
"cola1": DELTA_COLA1,
"cola2": DELTA_COLA2,
"pm": DELTA_PM,
"spm": DELTA_SPM,
"p3m": DELTA_P3M,
"diff_p3m_pm": diff_p3m_pm,
"diff_p3m_spm": diff_p3m_spm,
}
titles_dict = {
"lpt": "LPT",
"cola1": f"COLA1 $n_\\mathrm{{steps}}={nsteps_cola1}$",
"cola2": f"COLA2 $n_\\mathrm{{steps}}={nsteps_cola2}$",
"pm": f"PM $n_\\mathrm{{steps}}={nsteps_pm}$",
"spm": f"sPM $n_\\mathrm{{steps}}={nsteps_spm}$",
"p3m": f"P3M $n_\\mathrm{{steps}}={nsteps_p3m}$",
"diff_p3m_pm": r"$\delta_{\rm P3M}-\delta_{\rm PM}$",
"diff_p3m_spm": r"$\delta_{\rm P3M}-\delta_{\rm sPM}$",
}
npanels = len(fields)
fig, axs = plt.subplots(1, npanels, figsize=(3 * npanels, 4), sharey=True)
ims = []
for i, key in enumerate(fields):
ax = axs[i]
data = slices_dict[key]
title = titles_dict[key]
if key.startswith("diff"):
norm = TwoSlopeNorm(vmin=-np.log(1 + np.abs(np.min(data))), vcenter=0, vmax=np.log10(1 + np.abs(np.max(data))))
im = ax.imshow(
np.sign(data) * np.log(1 + np.abs(data)), cmap="RdBu_r", norm=norm
)
else:
im = ax.imshow(np.log10(2 + data), cmap=cmap)
ims.append((im, key))
ax.set_title(title, fontsize=fs_titles)
for spine in ax.spines.values():
spine.set_visible(False)
axs[0].set_yticks([0, N // 2, N])
axs[0].set_yticklabels([f"{-L/2:.0f}", "0", f"{L/2:.0f}"], fontsize=fs)
axs[0].set_ylabel(r"Mpc/$h$", size=GLOBAL_FS_SMALL)
for i, ax in enumerate(axs):
ax.set_xticks([0, N // 2, N])
ax.set_xticklabels([f"{-L/2:.0f}", "0", f"{L/2:.0f}"], fontsize=fs)
ax.set_xlabel(r"Mpc/$h$", size=GLOBAL_FS_SMALL)
for ax, (im, key) in zip(axs, ims):
divider = make_axes_locatable(ax)
cax = divider.append_axes("bottom", size="5%", pad=0.6)
cb = fig.colorbar(im, cax=cax, orientation="horizontal")
if key.startswith("diff"):
cb.set_label(r"$\textrm{sgn}\left(\Delta\delta\right)\log_{10}(1 + |\Delta\delta|)$", fontsize=fs)
else:
cb.set_label(r"$\log_{10}(2 + \delta)$", fontsize=fs)
cb.ax.tick_params(labelsize=fs)
cax.xaxis.set_ticks_position("bottom")
cax.xaxis.set_label_position("bottom")
fig.savefig(
simdir + f"{figname}.png",
bbox_inches="tight",
dpi=300,
transparent=True,
)
fig.savefig(
simdir + f"{figname}.pdf",
bbox_inches="tight",
dpi=300,
)
plt.show()
In [14]:
full_field_p3m = np.log10(2+read_field(simdir + f"nsteps{nsteps_p3m}_final_density_p3m.h5").data)
if N <= 128:
fig = plotly_3d(full_field_p3m, size=N, L=L, colormap=thermal_plotly, limits="default")
else:
# Downsample the grid for visualisation
downsample_factor = N // 128
downsampled_field = full_field_p3m[
::downsample_factor, ::downsample_factor, ::downsample_factor
]
fig = plotly_3d(downsampled_field, size=N, L=L, colormap=thermal_plotly, limits="default")
fig.show()
clear_large_plot(fig) # Uncomment to clear the Plotly figure to avoid memory issues
Compute and plot the power spectra of the evolved dark matter fields¶
In [15]:
G = read_FourierGrid(simdir + "input_ss_k_grid.h5")
k = G.k_modes[1:]
AliasingCorr = False
DELTA = read_field(simdir + "initial_density.h5")
Pk_INI, Vk_INI = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_INI, Vk_INI = Pk_INI[1:], Vk_INI[1:]
Sk_INI = np.sqrt(Vk_INI)
DELTA = read_field(simdir + "lpt_density.h5")
Pk_LPT, Vk_LPT = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_LPT, Vk_LPT = Pk_LPT[1:], Vk_LPT[1:]
Sk_LPT = np.sqrt(Vk_LPT)
DELTA = read_field(simdir + f"nsteps{nsteps_pm}_final_density_pm.h5")
Pk_PM, Vk_PM = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_PM, Vk_PM = Pk_PM[1:], Vk_PM[1:]
Sk_PM = np.sqrt(Vk_PM)
DELTA = read_field(simdir + f"nsteps{nsteps_cola1}_final_density_cola.h5")
Pk_COLA1, Vk_COLA1 = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_COLA1, Vk_COLA1 = Pk_COLA1[1:], Vk_COLA1[1:]
Sk_COLA1 = np.sqrt(Vk_COLA1)
DELTA = read_field(simdir + f"nsteps{nsteps_cola2}_final_density_cola.h5")
Pk_COLA2, Vk_COLA2 = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_COLA2, Vk_COLA2 = Pk_COLA2[1:], Vk_COLA2[1:]
Sk_COLA2 = np.sqrt(Vk_COLA2)
DELTA = read_field(simdir + f"nsteps{nsteps_spm}_final_density_spm.h5")
Pk_sPM, Vk_sPM = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_sPM, Vk_sPM = Pk_sPM[1:], Vk_sPM[1:]
Sk_sPM = np.sqrt(Vk_sPM)
DELTA = read_field(simdir + f"nsteps{nsteps_p3m}_final_density_p3m.h5")
Pk_P3M, Vk_p3m = get_autocorrelation(DELTA, G, AliasingCorr)
Pk_P3M, Vk_p3m = Pk_P3M[1:], Vk_p3m[1:]
Sk_p3m = np.sqrt(Vk_p3m)
In [16]:
Pk_ref = Pk_PM
fig, ax = plt.subplots(figsize=(7, 4))
ax.set_xscale("log")
k = G.k_modes[1:]
kmin, kmax = k.min(), k.max()
print(f"kmin = {kmin}, kmax = {kmax}")
log_pad = 0.02
log_k_min = np.log10(kmin)
log_k_max = np.log10(kmax)
log_range = log_k_max - log_k_min
xlim_min = 10 ** (log_k_min - log_pad * log_range)
xlim_max = 10 ** (log_k_max + log_pad * log_range)
plt.xlim(xlim_min, xlim_max)
# ax.set_ylim([0.2, 1.8])
# ax.set_ylim([0.5, 1.5])
dark_grey_bnd = 0.05
light_grey_bnd = 0.1
label_ref = f"PM with $n_\\mathrm{{steps}}={nsteps_pm}$"
line1 = ax.plot([kmin, kmax], [1, 1], color="black", linestyle="-", label=label_ref)
line2 = ax.plot(k, Pk_LPT / Pk_ref, label="2LPT", color=cols[0], linestyle="-")
ax.plot(
k,
Pk_COLA1 / Pk_ref,
label=f"COLA with $n_\\mathrm{{steps}}={nsteps_cola1}$",
linestyle="-",
color=cols[2],
)
ax.plot(
k,
Pk_COLA2 / Pk_ref,
label=f"COLA with $n_\\mathrm{{steps}}={nsteps_cola2}$",
linestyle="-",
color=cols[3],
)
ax.plot(
k,
Pk_sPM / Pk_ref,
label=f"sPM with $n_\\mathrm{{steps}}={nsteps_p3m}$",
linestyle="--",
color=cols[4],
)
ax.plot(
k,
Pk_P3M / Pk_ref,
label=f"P3M with $n_\\mathrm{{steps}}={nsteps_p3m}$",
linestyle="--",
color=cols[5],
)
ax.axhspan(1 - dark_grey_bnd, 1 + dark_grey_bnd, color="grey", alpha=0.2)
ax.axhspan(1 - light_grey_bnd, 1 + light_grey_bnd, color="grey", alpha=0.1)
for i in range(1, len(k)):
ax.axvline(k[i], color="black", linestyle=":", linewidth=1, alpha=0.1)
ax.yaxis.set_major_locator(plt.MaxNLocator(6))
ax.yaxis.get_major_ticks()[0].label1.set_visible(False)
ax.set_xlabel("$k$ [$h/\\mathrm{Mpc}$]", fontsize=fs)
ax.set_ylabel("$P(k)/P_\\mathrm{ref}(k)$", fontsize=fs)
ax.tick_params(which="both", direction="in")
ax.tick_params(axis="both", which="major", labelsize=fs)
ax.tick_params(axis="both", which="minor", labelsize=fs)
# Characteristic vertical reference scales
nyquist = np.pi * N / L
nyquist_PM = np.pi * Npm / L
epsilon = 0.03 * L / Np
particle_length = 2 * epsilon
xs = 1.25 * L / Npm
xr = 4.5 * xs
particle_wavenumber = 2 * np.pi / particle_length # Too large to be shown
xs_inv = 2 * np.pi / xs
xr_inv = 2 * np.pi / xr
if nyquist <= xlim_max:
line1 = ax.axvline(
x=nyquist, color="black", linestyle="--", lw=2, label="Nyquist (density grid)", zorder=0
)
if nyquist_PM <= xlim_max:
line2 = ax.axvline(
x=nyquist_PM, color="black", linestyle="-", lw=1, label="Nyquist (PM grid)", zorder=0
)
if xs_inv <= xlim_max:
line3 = ax.axvline(
x=xs_inv, color="gray", linestyle="-.", lw=2, label=r"Split wavenumber $x_s$", zorder=0
)
if xr_inv <= xlim_max:
line4 = ax.axvline(
x=xr_inv, color="gray", linestyle=":", lw=2, label=r"Short-range reach $x_r$", zorder=0
)
empty_patch = mpatches.Patch(color="none", label="")
handles, labels = plt.gca().get_legend_handles_labels()
# handles = [empty_patch, *handles]
# labels = ["", *labels]
plt.legend(
handles,
labels,
loc="upper center",
ncol=2,
bbox_to_anchor=(0.5, -0.2),
fontsize=fs,
)
fig.savefig(
simdir + "power_spectrum.png",
bbox_inches="tight",
dpi=300,
transparent=True,
)
fig.savefig(
simdir + "power_spectrum.pdf",
bbox_inches="tight",
dpi=300,
)
plt.show()
Force exerted by particles on other particles¶
In [17]:
r1, fmag1, _ = load_force_diagnostic(OutputForceDiagnostic_pm)
r4, fmag4, _ = load_force_diagnostic(OutputForceDiagnostic_cola1)
r2, fmag2, _ = load_force_diagnostic(OutputForceDiagnostic_spm)
r3, fmag3, _ = load_force_diagnostic(OutputForceDiagnostic_p3m)
rr = np.array([r1, r4, r2, r3], dtype=object)
ff = np.array([fmag1, fmag4, fmag2, fmag3], dtype=object)
ll = np.array(["PM", "COLA", "sPM", "P3M"])
ix = [0, 1, 2, 3]
Newton_prefactor = (L / Np)**3 / (4*np.pi)
print(f"Newton prefactor = {Newton_prefactor:.2e}")
plot_force_distance_comparison(rr=rr[ix], ff=ff[ix], ll=ll[ix], L=L, Np=Np, Npm=Npm, a=Newton_prefactor, title="Particle's contributions to total force", save_path=simdir + "force_diagnostic_comparison.png")
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