"""Read Phantom evolution files."""
from pathlib import Path
from typing import Dict, List, Tuple, Union
import numpy as np
import pandas as pd
from pandas import DataFrame
NAME_MAP = {
'time': 'time',
'ekin': 'energy_kinetic',
'etherm': 'energy_thermal',
'emag': 'energy_magnetic',
'epot': 'energy_potential',
'etot': 'energy_total',
'totmom': 'momentum',
'angtot': 'angular_momentum',
'rho max': 'density_max',
'rho ave': 'density_average',
'dt': 'timestep',
'dtmax': 'timestep_max',
'totentrop': 'entropy',
'rmsmach': 'mach_number_rms',
'vrms': 'velocity_rms',
'xcom': 'center_of_mass_x',
'ycom': 'center_of_mass_y',
'zcom': 'center_of_mass_z',
'rho gas max': 'gas_density_max',
'rho gas ave': 'gas_density_average',
'rho dust X': 'dust_density_max',
'rho dust A': 'dust_density_average',
'rho bdy max': 'boundary_density_max',
'rho bdy ave': 'boundary_density_average',
'rho star X': 'star_density_max',
'rho star A': 'star_density_average',
'rho dm max': 'dark_matter_density_max',
'rho dm ave': 'dark_matter_density_average',
'rho blg max': 'bulge_density_max',
'rho blg ave': 'bulge_density_average',
'alpha': 'alpha_viscosity_numerical',
'B max': 'magnetic_field_max',
'B min': 'magnetic_field_min',
'B ave': 'magnetic_field_average',
'divB max': 'magnetic_field_divergence_max',
'divB ave': 'magnetic_field_divergence_average',
'beta_P max': 'plasma_beta_max',
'beta_P min': 'plasma_beta_min',
'beta_P ave': 'plasma_beta_average',
'erot_x': 'energy_rotational_x',
'erot_y': 'energy_rotational_y',
'erot_z': 'energy_rotational_z',
'erot': 'energy_rotational_total',
'dust/gas': 'dust_gas_ratio',
't_s': 'stopping_time',
'x': 'position_x',
'y': 'position_y',
'z': 'position_z',
'vx': 'velocity_x',
'vy': 'velocity_y',
'vz': 'velocity_z',
'spinx': 'spin_x',
'spiny': 'spin_y',
'spinz': 'spin_z',
'macc': 'mass_accreted',
'fx': 'force_x',
'fy': 'force_y',
'fz': 'force_z',
'fssx': 'sink_sink_force_x',
'fssy': 'sink_sink_force_y',
'fssz': 'sink_sink_force_z',
}
NAME_MAP.update({f'DustMass{idx:03}': f'dust_mass_{idx:03}' for idx in range(100)})
def load_data_from_file(
filenames: Union[str, Path, Tuple[str], Tuple[Path], List[str], List[Path]],
):
"""Load data from Phantom .ev files."""
if isinstance(filenames, (str, Path)):
_filenames = [filenames]
elif isinstance(filenames, (list, tuple)):
_filenames = list(filenames)
else:
raise ValueError('filenames is not a known type')
_file_paths = list()
for filename in _filenames:
path = Path(filename)
_file_paths.append(path.resolve())
file_paths = tuple(_file_paths)
_check_file_consistency(file_paths, NAME_MAP)
columns = _get_columns(file_paths[0], NAME_MAP)
dataframe = _get_data(columns, file_paths)
return dataframe
def _get_data(columns: Tuple[str, ...], file_paths: Tuple[Path, ...]) -> DataFrame:
times = list()
for filename in file_paths:
times.append(np.loadtxt(filename, usecols=0))
_skiprows = [0]
if len(times) > 1:
for t1, t2 in zip(times, times[1:]):
_skiprows.append(np.where(t2 < t1[-1])[0][-1] + 2)
df = pd.concat(
(
pd.read_csv(
f,
names=columns,
skiprows=skiprows,
skipinitialspace=True,
delim_whitespace=True,
comment='#',
)
for f, skiprows in zip(file_paths, _skiprows)
)
)
df.reset_index(inplace=True, drop=True)
return df
def _get_columns(filename: Path, name_map: Dict[str, str]) -> Tuple[str, ...]:
with open(filename) as f:
column_line = f.readline().strip('\n')
_column_line = [item.strip('] ')[2:].strip(' ') for item in column_line.split('[')]
columns = _column_line[1:]
return tuple([name_map[col] if col in name_map else col for col in columns])
def _check_file_consistency(
filenames: Tuple[Path, ...], name_map: Dict[str, str]
) -> None:
columns = _get_columns(filenames[0], name_map)
for filename in filenames:
columns_previous = columns
columns = _get_columns(filename, name_map)
if columns != columns_previous:
raise ValueError('files have different columns')
