mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-19 16:03:50 +02:00
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
import pickle
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from dataclasses import dataclass
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from pathlib import Path
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from typing import 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 pandas import DataFrame
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@dataclass
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class ParticlesMeta:
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particle_mass: float
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def read_file(file: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
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cache_file = file.with_suffix(".cache.pickle")
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meta_cache_file = file.with_suffix(".cache_meta.pickle")
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if not (cache_file.exists() and meta_cache_file.exists()):
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reference_file = h5py.File(file)
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has_fof = "FOFGroupIDs" in reference_file["PartType1"]
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try:
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masses = reference_file["PartType1"]["Masses"]
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if not np.all(masses == masses[0]):
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raise ValueError("only equal mass particles are supported for now")
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meta = ParticlesMeta(particle_mass=masses[0])
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except KeyError:
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meta = ParticlesMeta(particle_mass=0)
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df = pd.DataFrame(
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reference_file["PartType1"]["Coordinates"], columns=["X", "Y", "Z"]
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)
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if has_fof:
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df2 = pd.DataFrame(
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reference_file["PartType1"]["FOFGroupIDs"], columns=["FOFGroupIDs"]
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).astype("category")
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df = df.merge(df2, "outer", left_index=True, right_index=True)
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del df2
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df3 = pd.DataFrame(
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reference_file["PartType1"]["ParticleIDs"], columns=["ParticleIDs"]
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)
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df = df.merge(df3, "outer", left_index=True, right_index=True)
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del df3
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df.set_index("ParticleIDs", inplace=True)
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if has_fof:
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print("sorting")
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df.sort_values("FOFGroupIDs", inplace=True)
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print("saving cache")
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with meta_cache_file.open("wb") as f:
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pickle.dump(meta, f)
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df.to_pickle(str(cache_file))
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reference_file.close()
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return df, meta
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print("from cache")
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df = pd.read_pickle(str(cache_file))
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with meta_cache_file.open("rb") as f:
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meta = pickle.load(f)
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return df, meta
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def read_halo_file(file: Path) -> DataFrame:
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# file = path / "fof_output_0004.hdf5"
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reference_file = h5py.File(file)
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df1 = pd.DataFrame(reference_file["Groups"]["Centres"], columns=["X", "Y", "Z"])
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df2 = pd.DataFrame(reference_file["Groups"]["GroupIDs"], columns=["GroupIDs"])
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df3 = pd.DataFrame(reference_file["Groups"]["Masses"], columns=["Masses"])
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df4 = pd.DataFrame(reference_file["Groups"]["Sizes"], columns=["Sizes"])
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df = df1.merge(df2, "outer", left_index=True, right_index=True)
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df = df.merge(df3, "outer", left_index=True, right_index=True)
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df = df.merge(df4, "outer", left_index=True, right_index=True)
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df.set_index("GroupIDs", inplace=True)
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return df
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def read_fof_file(path: Path):
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file = path / ""
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def read_g4_file(file: Path, zoom_type: str) -> Tuple[np.ndarray, np.ndarray, float, float]:
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with h5py.File(file) as reference_file:
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hubble_param = reference_file["Parameters"].attrs["HubbleParam"]
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masstable = reference_file['Header'].attrs['MassTable']
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if zoom_type == 'pbh':
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highres_parttype = 'PartType0'
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lowres_parttype = 'PartType1'
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highres_mass = masstable[0] / hubble_param
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lowres_mass = masstable[1] / hubble_param
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elif zoom_type == 'cdm':
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highres_parttype = 'PartType1'
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lowres_parttype = 'PartType2'
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highres_mass = masstable[1] / hubble_param
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lowres_mass = masstable[2] / hubble_param
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else:
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raise ValueError('Please select pbh or cdm as zoom_type!')
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# all coordinates in Mpc/h without adaption!
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highres_coordinates = reference_file[highres_parttype]["Coordinates"][:]
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lowres_coordinates = reference_file[lowres_parttype]["Coordinates"][:]
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return highres_coordinates, lowres_coordinates, highres_mass, lowres_mass
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