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https://github.com/Findus23/halo_comparison.git
synced 2024-09-19 16:03:50 +02:00
support ramses SPH plots
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5e3531e465
commit
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GPG key ID:
54DE4D798D244853
2 changed files with 91 additions and 15 deletions
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@ -3,6 +3,7 @@ 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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@ -12,6 +13,7 @@ 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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@ -19,7 +21,7 @@ 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
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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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@ -34,6 +36,11 @@ 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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@ -56,7 +63,7 @@ def main():
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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=3, ncols=5, sharex="all", sharey="all", figsize=(10, 4))
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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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@ -94,8 +101,10 @@ def main():
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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 is_ramses:
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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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@ -132,7 +141,7 @@ def main():
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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_00007")
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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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@ -234,23 +243,52 @@ def main():
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if has_baryons:
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interpolation_method = "nearest" # "linear"
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extent = [46, 52, 54, 60] # xrange[0], xrange[-1], yrange[0], yrange[-1]
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extent = [42, 62, 50, 70]
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for ii, property in enumerate(["cic", "Densities", "Entropies", "InternalEnergies", "Temperatures"]):
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key = f"grid_{property}_{interpolation_method}"
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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:
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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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grid, _ = cic_range(X + center[0], Y + center[1], 1000, *extent, periodic=False)
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grid = grid.T
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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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grid = create_2d_slice(input_file, center, property=property,
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extent=extent, method=interpolation_method)
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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 = ax_baryon.imshow(
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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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@ -289,6 +327,7 @@ def main():
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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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@ -297,7 +336,6 @@ def main():
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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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38
ramses.py
38
ramses.py
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@ -1,4 +1,5 @@
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from pathlib import Path
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from typing import List
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import numpy as np
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import pynbody
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@ -25,3 +26,40 @@ def load_ramses_data(ramses_dir: Path):
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particles_meta = ParticlesMeta(particle_mass=high_res_mass)
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center = np.median(hr_coordinates, axis=0)
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return hr_coordinates, particles_meta, center
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def get_slice_argument(extent: List[float], center: List[float], ramses_dir: Path, depth: float):
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xmin, xmax, ymin, ymax = extent
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_, _, zcenter = center
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arguments = {
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"x": (xmin + xmax) / 2,
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"y": (ymin + ymax) / 2,
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"z": zcenter,
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"w": xmax - xmin,
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"h": ymax - ymin,
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"d": depth,
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"l": 12
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}
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from paths import ramses_imager
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args = [str(ramses_imager)]
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for k, v in arguments.items():
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args.append(f"-{k} {v}")
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args.append(str(ramses_dir / "info_00009.txt"))
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return args, ramses_imager.parent
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def load_slice_data(file: Path):
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with file.open("rb") as infile:
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np.fromfile(file=infile, dtype=np.int32, count=1)
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[nx, ny] = np.fromfile(file=infile, dtype=np.int32, count=2)
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np.fromfile(file=infile, dtype=np.int32, count=1)
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np.fromfile(file=infile, dtype=np.int32, count=1)
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data: np.ndarray = np.fromfile(file=infile, dtype=np.float32, count=nx * ny)
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np.fromfile(file=infile, dtype=np.int32, count=1)
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print("NEGATIVE", (data < 0).sum())
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# np.fromfile(file=infile, dtype=np.int32, count=1)
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# cm_per_px = np.fromfile(file=infile, dtype=np.float64, count=1)[0]
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# np.fromfile(file=infile, dtype=np.int32, count=1)
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return data.reshape((nx, ny))
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