mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-19 16:03:50 +02:00
352 lines
13 KiB
Python
352 lines
13 KiB
Python
import pickle
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from dataclasses import dataclass
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from enum import Enum
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from pathlib import Path
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from pprint import pprint
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from subprocess import run
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from typing import List, Tuple
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import h5py
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from matplotlib.axes import Axes
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from matplotlib.colors import LogNorm
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from matplotlib.figure import Figure
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from matplotlib.image import AxesImage
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from cache import HDFCache
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from cic import cic_from_radius, cic_range
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from find_center import find_center
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from halo_mass_profile import halo_mass_profile
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from nfw import fit_nfw
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from paths import auriga_dir, richings_dir
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from ramses import load_ramses_data, get_slice_argument, load_slice_data
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from readfiles import read_file, read_halo_file, ParticlesMeta
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from slices import create_2d_slice
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from utils import read_swift_config, print_wall_time, figsize_from_page_fraction
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class Mode(Enum):
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richings = 1
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auriga6 = 2
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mode = Mode.richings
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cache = HDFCache(Path("auriga_cache.hdf5"))
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if mode == Mode.richings:
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boxsize = 67.77
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else:
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boxsize = None # not yet needed
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def dir_name_to_parameter(dir_name: str):
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return map(
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int,
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dir_name.lstrip("auriga6_halo")
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.lstrip("richings21_")
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.lstrip("bary_")
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.lstrip("ramses_")
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.split("_"),
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)
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def levelmax_to_softening_length(levelmax: int) -> float:
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box_size = 100
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return box_size / 30 / 2 ** levelmax
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def main():
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fig1: Figure = plt.figure(figsize=figsize_from_page_fraction())
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ax1: Axes = fig1.gca()
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fig2: Figure = plt.figure(figsize=figsize_from_page_fraction())
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ax2: Axes = fig2.gca()
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fig4, axs_baryon = plt.subplots(nrows=2, ncols=4, sharex="all", sharey="all", figsize=(10, 4))
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baryon_plot_counter = 0
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for ax in [ax1, ax2]:
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ax.set_xlabel(r"R [Mpc]")
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ax1.set_ylabel(r"M [$10^{10} \mathrm{M}_\odot$]")
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ax2.set_ylabel("density [$\\frac{10^{10} \\mathrm{M}_\\odot}{Mpc^3}$]")
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part_numbers = []
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reference_file = Path(f"auriga_reference_{mode}.pickle")
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centers = {}
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@dataclass
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class Result:
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title: str
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rho: np.ndarray
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levels: Tuple[int, int, int]
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images = []
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vmin = np.Inf
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vmax = -np.Inf
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root_dir = auriga_dir if mode == Mode.auriga6 else richings_dir
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i = 0
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for dir in sorted(root_dir.glob("*")):
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if not dir.is_dir() or "bak" in dir.name:
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continue
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is_ramses = "ramses" in dir.name
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has_baryons = "bary" in dir.name or is_ramses
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is_by_adrian = "arj" in dir.name
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print(dir.name)
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if not is_by_adrian:
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levelmin, levelmin_TF, levelmax = dir_name_to_parameter(dir.name)
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print(levelmin, levelmin_TF, levelmax)
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if not has_baryons:
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continue
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if levelmax != 11:
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continue
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# if not is_ramses:
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# continue
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input_file = dir / "output_0009.hdf5"
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if mode == Mode.richings:
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input_file = dir / "output_0004.hdf5"
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if is_by_adrian or is_ramses:
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input_file = dir / "output_0000.hdf5"
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softening_length = None
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else:
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try:
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swift_conf = read_swift_config(dir)
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print_wall_time(dir)
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except FileNotFoundError:
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continue
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gravity_conf = swift_conf["Gravity"]
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softening_length = gravity_conf["comoving_DM_softening"]
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assert softening_length == gravity_conf["max_physical_DM_softening"]
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if "max_physical_baryon_softening" in gravity_conf:
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assert softening_length == gravity_conf["max_physical_baryon_softening"]
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assert softening_length == gravity_conf["comoving_baryon_softening"]
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ideal_softening_length = levelmax_to_softening_length(levelmax)
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if not np.isclose(softening_length, levelmax_to_softening_length(levelmax)):
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raise ValueError(
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f"softening length for levelmax {levelmax} should be {ideal_softening_length} "
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f"but is {softening_length}"
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)
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print(input_file)
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if mode == Mode.richings and is_by_adrian:
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h = 0.6777
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with h5py.File(dir / "Richings_object_z0.h5") as f:
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df = pd.DataFrame(f["Coordinates"][:] / h, columns=["X", "Y", "Z"])
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particles_meta = ParticlesMeta(particle_mass=1.1503e7 / 1e10)
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center = np.array([60.7, 29, 64]) / h
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softening_length = None
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elif "ramses" in dir.name:
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h = 0.6777
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hr_coordinates, particles_meta, center = load_ramses_data(dir / "output_00009")
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df = pd.DataFrame(hr_coordinates, columns=["X", "Y", "Z"])
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softening_length = None
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else:
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df, particles_meta = read_file(input_file)
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df_halos = read_halo_file(input_file.with_name("fof_" + input_file.name))
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# vr_halo = read_velo_halos(dir, veloname="velo_out").loc[1]
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# particles_in_halo = df.loc[df["FOFGroupIDs"] == 3]
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halo_id = 1
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while True:
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particles_in_halo = df.loc[df["FOFGroupIDs"] == halo_id]
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if len(particles_in_halo) > 1:
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break
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halo_id += 1
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halo = df_halos.loc[halo_id]
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part_numbers.append(len(df) * particles_meta.particle_mass)
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# halo = halos.loc[1]
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center = np.array([halo.X, halo.Y, halo.Z])
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center = find_center(df, center)
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log_radial_bins, bin_masses, bin_densities, center = halo_mass_profile(
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df, center, particles_meta, plot=False, num_bins=100, vmin=0.002, vmax=6.5
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)
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i_min_border = np.argmax(
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0.01 < log_radial_bins
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) # first bin outside of specific radius
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i_max_border = np.argmax(1.5 < log_radial_bins)
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popt = fit_nfw(
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log_radial_bins[i_min_border:i_max_border],
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bin_densities[i_min_border:i_max_border],
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) # = rho_0, r_s
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print(popt)
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# # Plot NFW profile
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# ax.loglog(
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# log_radial_bins[i_min_border:i_max_border],
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# nfw(log_radial_bins[i_min_border:i_max_border], *popt),
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# linestyle="dotted"
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# )
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centers[dir.name] = center
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if is_by_adrian:
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with reference_file.open("wb") as f:
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pickle.dump([log_radial_bins, bin_masses, bin_densities], f)
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if is_by_adrian:
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label = "reference"
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else:
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label = f"{levelmin}, {levelmin_TF}, {levelmax}"
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ax1.loglog(log_radial_bins[:-1], bin_masses, label=label, c=f"C{i}")
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ax2.loglog(log_radial_bins[:-1], bin_densities, label=label, c=f"C{i}")
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if reference_file.exists() and not is_by_adrian:
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with reference_file.open("rb") as f:
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data: List[np.ndarray] = pickle.load(f)
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ref_log_radial_bins, ref_bin_masses, ref_bin_densities = data
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mass_deviation: np.ndarray = np.abs(bin_masses - ref_bin_masses)
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density_deviation: np.ndarray = np.abs(bin_densities - ref_bin_densities)
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ax1.loglog(log_radial_bins[:-1], mass_deviation, c=f"C{i}", linestyle="dotted")
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ax2.loglog(
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log_radial_bins[:-1], density_deviation, c=f"C{i}", linestyle="dotted"
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)
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accuracy = mass_deviation / ref_bin_masses
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print(accuracy)
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print("mean accuracy", accuracy.mean())
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if softening_length:
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for ax in [ax1, ax2]:
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ax.axvline(4 * softening_length, color=f"C{i}", linestyle="dotted")
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# for ax in [ax1, ax2]:
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# ax.axvline(vr_halo.Rvir, color=f"C{i}", linestyle="dashed")
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X, Y, Z = df.X.to_numpy(), df.Y.to_numpy(), df.Z.to_numpy()
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# shift: (-6, 0, -12)
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# if not is_by_adrian:
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# xshift = Xc - Xc_adrian
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# yshift = Yc - Yc_adrian
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# zshift = Zc - Zc_adrian
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# print("shift", xshift, yshift, zshift)
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X -= center[0]
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Y -= center[1]
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Z -= center[2]
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rho, extent = cic_from_radius(X, Z, 4000, 0, 0, 5, periodic=False)
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vmin = min(vmin, rho.min())
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vmax = max(vmax, rho.max())
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images.append(
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Result(
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rho=rho,
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title=str(dir.name),
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levels=(levelmin, levelmin_TF, levelmax) if levelmin else None,
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)
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)
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i += 1
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if has_baryons:
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interpolation_method = "nearest" # "linear"
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radius = 10
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# xrange[0], xrange[-1], yrange[0], yrange[-1]
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extent = [center[0] - radius, center[0] + radius,
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center[1] - radius, center[1] + radius]
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# extent = [42, 62, 50, 70]
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ramses_done = False
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for ii, property in enumerate(["cic", "Densities", "Entropies", "Temperatures"]):
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print("property:", property)
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key = f"grid_{property}_{interpolation_method}_{radius}"
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cached_grid = cache.get(key, str(input_file))
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if cached_grid is not None and not is_ramses:
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grid = cached_grid
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else:
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if property == "cic":
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rho, _ = cic_range(X + center[0], Y + center[1], 1000, *extent, periodic=False)
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# TODO: filter in Z-axis
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grid = 1.1 + rho.T
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else:
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if not is_ramses:
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grid = create_2d_slice(input_file, center, property=property,
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extent=extent, method=interpolation_method)
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else:
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frac_center = center / 100
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frac_extent = np.array(extent) / 100
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print(frac_extent)
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print(frac_center)
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args, imager_dir = get_slice_argument(frac_extent, frac_center, dir / "output_00009",
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depth=.001)
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print(" ".join(args))
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if not ramses_done:
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run(args, cwd=imager_dir)
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ramses_done = True
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property_map = {
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"Densities": "rhomap",
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"Entropies": "Smap", # TODO: check
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"InternalEnergies": None,
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"Temperatures": "Tmap"
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}
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fname = imager_dir / f"snapshot_{property_map[property]}_zproj_zobs-0p00.bin"
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grid = load_slice_data(fname).T
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cache.set(key, grid, str(input_file), compressed=True)
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ax_baryon: Axes = axs_baryon[baryon_plot_counter, ii]
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img: AxesImage = ax_baryon.imshow(
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grid,
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norm=LogNorm(),
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interpolation="none",
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origin="lower",
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extent=extent,
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)
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ax_baryon.set_title(property)
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# ax_baryon.set_xlabel("X")
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# ax_baryon.set_ylabel("Y")
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ax_baryon.set_aspect("equal")
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# exit()
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baryon_plot_counter += 1
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# plot_cic(
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# rho, extent,
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# title=str(dir.name)
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# )
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ax1.legend()
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ax2.legend()
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fig1.tight_layout()
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fig2.tight_layout()
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# fig3: Figure = plt.figure(figsize=(9, 9))
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# axes: List[Axes] = fig3.subplots(3, 3, sharex=True, sharey=True).flatten()
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fig3: Figure = plt.figure(
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figsize=figsize_from_page_fraction(columns=2, height_to_width=1)
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)
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axes: List[Axes] = fig3.subplots(3, 3, sharex=True, sharey=True).flatten()
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for result, ax in zip(images, axes):
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data = 1.1 + result.rho
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vmin_scaled = 1.1 + vmin
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vmax_scaled = 1.1 + vmax
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img = ax.imshow(
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data.T,
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norm=LogNorm(vmin=vmin_scaled, vmax=vmax_scaled),
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extent=extent,
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origin="lower",
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interpolation="none"
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)
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ax.set_title(result.title)
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fig3.tight_layout()
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fig3.subplots_adjust(right=0.825)
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cbar_ax = fig3.add_axes([0.85, 0.05, 0.05, 0.9])
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fig3.colorbar(img, cax=cbar_ax)
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fig1.savefig(Path(f"~/tmp/auriga1.pdf").expanduser())
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fig2.savefig(Path(f"~/tmp/auriga2.pdf").expanduser())
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fig3.savefig(Path("~/tmp/auriga3.pdf").expanduser())
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fig4.tight_layout()
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fig4.savefig(Path("~/tmp/slice.png").expanduser(), dpi=300)
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pprint(centers)
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plt.show()
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print(part_numbers)
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if __name__ == '__main__':
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main()
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