auriga halo check

auriga_halo_check.py

@@ -0,0 +1,94 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from paths import auriga_dir, richings_dir, auriga_dir_new, richings_dir_new
+from read_vr_files import read_velo_halos
+from readfiles import read_file, read_halo_file
+from rockstar import read_rockstar_halos
+
+fig1 = plt.figure()
+ax1 = fig1.gca()
+fig2 = plt.figure()
+ax2 = fig2.gca()
+fig3 = plt.figure()
+ax3 = fig3.gca()
+
+for run in reversed([
+    "7_8_9",
+    "7_8_10",
+    "7_10_10",
+    "7_10_12",
+    # "auriga6_halo_arj"
+    "adrian_ref_new"
+]):
+    dir = auriga_dir_new / run
+    input_file = dir / "output_0007.hdf5"
+    print(dir.name)
+    # df_halos = read_halo_file(input_file.with_name("fof_" + input_file.name))
+    # masses = np.asarray(df_halos["Masses"])
+    # if "arj" in run or "adrian" in run:
+    #     input_file = dir / "output_0000.hdf5"
+    # vr_halo = read_velo_halos(dir, veloname="velo_out")
+    # # print(vr_halo.Structuretype)
+    # # vr_halo=vr_halo[vr_halo.Structuretype!=10]
+    # halo_pos = np.array([vr_halo.X, vr_halo.Y, vr_halo.Z]).T
+    # print(halo_pos.shape)
+    # masses = np.asarray(vr_halo["Mvir"])
+    rockstar_df, _ = read_rockstar_halos(dir)
+    print(rockstar_df.shape)
+    halo_pos = np.array([rockstar_df.x, rockstar_df.y, rockstar_df.z]).T
+    masses = np.asarray(rockstar_df.m200c) / 1e10
+    # order=np.argsort(-masses)
+    # halo_pos=halo_pos[order]
+    # masses=masses[order]
+
+    most_massive_halo_pos = halo_pos[0]
+    diff = most_massive_halo_pos - halo_pos
+    distances = np.linalg.norm(diff, axis=1)
+    filter = distances < 0.095953835
+    distances = distances[filter]
+    masses = masses[filter]
+    diff = diff[filter]
+    ax1.scatter(distances, masses, label=run, s=8)
+    theta = np.rad2deg(np.arccos(diff[::, 2] / distances))
+    phi = np.rad2deg(np.arctan2(diff[:, 1], diff[:, 0]))
+    ax2.scatter(theta, masses, label=run, s=8)
+    ax3.scatter(phi, masses, label=run, s=8)
+    if run == "7_10_12":
+        # rvir = vr_halo.iloc[1].Rvir
+        r200c = rockstar_df.iloc[1].r200c / 1000  # kpc/h to Mpc/h
+        print("r200c", r200c)
+
+    # most_massive_halo = df_halos.iloc[0]
+    # most_massive_halo_pos = np.array([most_massive_halo.X, most_massive_halo.Y, most_massive_halo.Z])
+    # print(df_halos.iloc[0])
+    # for id, halo in df_halos.items():
+    #     halo_pos = np.array([most_massive_halo.X, most_massive_halo.Y, most_massive_halo.Z])
+    #     distance=np.
+
+
+def torvir(x):
+    return x / r200c
+
+
+def fromrvir(x):
+    return x * r200c
+
+
+secax = ax1.secondary_xaxis('top', functions=(torvir, fromrvir))
+secax.set_xlabel('$r/R200c_{main}$')
+ax1.set_xlabel("r")
+ax1.set_xscale("log")
+ax2.set_xlabel("theta [deg]")
+ax3.set_xlabel("phi [deg]")
+
+for ax in [ax1, ax2, ax3]:
+    ax.set_yscale("log")
+    ax.set_ylabel("Mass")
+    ax.legend()
+# ax1.set_xlim(.01, 7.5)
+for i, fig in enumerate([fig1, fig2, fig3]):
+    fig.tight_layout()
+    fig.savefig(f"/home/lukas/tmp/halo_{i}.pdf")
+plt.legend()
+plt.show()

rockstar.py

@@ -16,7 +16,7 @@ def make_names_unique(names: list[str]) -> list[str]:
     return newlist
 
 
-def largest_halo_properties(dir: Path):
+def read_rockstar_halos(dir: Path) -> tuple[pd.DataFrame, Path]:
     file = dir / "halos_127.0.ascii"
     if not file.exists():
         file = dir / "halos_0.0.ascii"
@@ -38,6 +38,12 @@ def largest_halo_properties(dir: Path):
 
     df["con"] = df.r200c_kpc / df.Rs_kpc
 
+    return df, file
+
+
+def largest_halo_properties(dir: Path):
+    df, file = read_rockstar_halos(dir)
+
     main_halo = df.iloc[0]
 
     print(file)