halo_comparison/halo_vis.py

from sys import argv
from typing import Tuple

import h5py
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from cic import cic_from_radius
from paths import base_dir, vis_datafile, has_1024_simulations
from read_vr_files import read_velo_halo_particles
from readfiles import read_file
from utils import waveforms

all_in_area = True

Coords = Tuple[float, float, float, float]  # radius, X, Y, Z


def load_halo_data(waveform: str, resolution: int, halo_id: int, coords: Coords):
    dir = base_dir / f"{waveform}_{resolution}_100"
    df, meta = read_file(dir / "output_0004.hdf5")
    df_halo, halo_lookup, unbound = read_velo_halo_particles(
        dir, skip_halo_particle_ids=all_in_area
    )

    halo = df_halo.loc[halo_id] if halo_id else None
    if coords:
        radius, X, Y, Z = coords
    else:
        radius = halo["R_size"]
        X = halo["Xc"]
        Y = halo["Yc"]
        Z = halo["Zc"]
        coords: Coords = radius, X, Y, Z
    if all_in_area:
        df = df[df["X"].between(X - radius, X + radius)]
        df = df[df["Y"].between(Y - radius, Y + radius)]
        halo_particles = df[df["Z"].between(Z - radius, Z + radius)]
    else:
        halo_particle_ids = halo_lookup[halo_id]
        del halo_lookup
        del unbound
        halo_particles = df.loc[list(halo_particle_ids)]
    return halo, halo_particles, meta, coords


def get_comp_id(
        ref_waveform: str,
        reference_resolution: int,
        comp_waveform: str,
        comp_resolution: int,
):
    return f"{ref_waveform}_{reference_resolution}_100_{comp_waveform}_{comp_resolution}_100_velo.csv"


def map_halo_id(
        halo_id: int,
        ref_waveform: str,
        reference_resolution: int,
        comp_waveform: str,
        comp_resolution: int,
):
    file = (
            base_dir
            / "comparisons"
            / get_comp_id(
        ref_waveform, reference_resolution, comp_waveform, comp_resolution
    )
    )
    print("opening", file)
    df = pd.read_csv(file)
    mapping = {}
    for index, line in df.iterrows():
        mapping[int(line["ref_ID"])] = int(line["comp_ID"])
    return mapping[halo_id]


def imsave(rho, file_name: str):
    # ax.scatter(Xs, Ys)
    # plt.show()
    cmap = plt.cm.viridis
    data = np.log(1.001 + rho)
    norm = plt.Normalize(vmin=data.min(), vmax=data.max())
    image = cmap(norm(data))
    print(file_name)
    plt.imsave(file_name, image)


def main():
    resolutions = [128, 256, 512]
    if has_1024_simulations:
        resolutions.append(1024)
    coords = {}
    radius = None
    if argv[1] == "box":
        initial_halo_id = 0
        first_halo = False
        radius = 15
        for wf in waveforms:
            coords[wf] = (radius, 85, 85, 85)
    else:
        initial_halo_id = int(argv[1])
        first_halo = True
        for wf in waveforms:
            coords[wf] = None

    rhos = {}
    ref_waveform = "shannon"
    ref_resolution = 128
    vmin = np.Inf
    vmax = -np.Inf
    with h5py.File(vis_datafile, "a") as vis_out:
        key = str(initial_halo_id)
        if key in vis_out:
            del vis_out[key]
        halo_group = vis_out.create_group(str(initial_halo_id))
        # use shannon first for reference and as order doesn't matter here
        for waveform in ["shannon", "DB2", "DB4", "DB8"]:
            for resolution in resolutions:
                if first_halo:
                    assert ref_resolution == resolution
                    assert ref_waveform == waveform
                    halo_id = initial_halo_id
                    first_halo = False
                else:
                    if initial_halo_id:
                        halo_id = map_halo_id(
                            initial_halo_id,
                            ref_waveform,
                            ref_resolution,
                            waveform,
                            resolution,
                        )
                    else:
                        halo_id = None
                halo, halo_particles, meta, image_coords = load_halo_data(
                    waveform, resolution, halo_id, coords[waveform]
                )
                if not radius:
                    radius = image_coords[0]
                if not coords[waveform]:
                    tmp=list(image_coords)
                    tmp[0] = radius
                    coords[waveform] = tuple(tmp)


                print(coords[waveform])
                # print("mass", halo["Mvir"])
                # print("sleep")
                # sleep(100)
                radius, X, Y, Z = coords[waveform]
                rho, _ = cic_from_radius(
                    halo_particles.X.to_numpy(),
                    halo_particles.Y.to_numpy(),
                    600,
                    X,
                    Y,
                    radius,
                    periodic=False,
                )
                rhos[(waveform, resolution)] = rho
                vmin = min(rho.min(), vmin)
                vmax = max(rho.max(), vmax)
                dataset_group = halo_group.create_group(f"{waveform}_{resolution}")
                dataset_group.create_dataset(
                    "rho", data=rho, compression="gzip", compression_opts=5
                )
                dataset_group.create_dataset("coords", data=coords[waveform])
                dataset_group.create_dataset("mass", data=meta.particle_mass)
                if halo_id:
                    dataset_group.create_dataset("halo_id", data=halo_id)
                # imsave(
                #     rho,
                #     f"out_halo{initial_halo_id}_{waveform}_{resolution}_{halo_id}.png",
                # )
        halo_group.create_dataset("vmin_vmax", data=[vmin, vmax])


if __name__ == "__main__":
    main()