# Copyright (c) 2017 Weitian LI <weitian@aaronly.me>
# MIT license
"""
Simulate the extended radio emissions from the galaxy cluster,
e.g., giant radio halos, radio relics.
NOTE
----
There are other types of extended radio emissions not considered
yet, e.g., mini-halos, roundish radio relics, etc.
References
----------
.. [cassano2012]
Cassano et al. 2012, A&A, 548, A100
http://adsabs.harvard.edu/abs/2012A%26A...548A.100C
"""
import os
import logging
from collections import OrderedDict
import numpy as np
import pandas as pd
from .psformalism import PSFormalism
from .formation import ClusterFormation
from .halo import RadioHalo
from ...share import CONFIGS, COSMO
from ...utils.io import dataframe_to_csv, pickle_dump
from ...utils.ds import dictlist_to_dataframe
from ...utils.convert import JyPerPix_to_K
from ...sky import get_sky
from . import helper
logger = logging.getLogger(__name__)
class GalaxyClusters:
"""
Simulate the extended radio emissions from the galaxy clusters.
NOTE
----
Currently, only the *giant radio halos* are considered, while
other types of extended emissions are missing, e.g., mini-halos,
elongated relics, roundish relics.
Attributes
----------
configs : `~ConfigManager`
A `ConfigManager` instance containing default and user configurations.
For more details, see the example configuration specifications.
halo_configs : dict
A dictionary containing the configurations for halo simulation.
sky : `~SkyBase`
The sky instance to deal with the simulation sky as well as the
output map.
XXX: current full-sky HEALPix map is NOT supported!
"""
# Component name
compID = "extragalactic/clusters"
name = "galaxy clusters (halos)"
def __init__(self, configs=CONFIGS):
self.configs = configs
self._set_configs()
self.sky = get_sky(configs)
self.sky.add_header("CompID", self.compID, "Emission component ID")
self.sky.add_header("CompName", self.name, "Emission component")
self.sky.add_header("BUNIT", "K", "[Kelvin] Data unit")
self.sky.creator = __name__
def _set_configs(self):
"""
Load the configs and set the corresponding class attributes.
"""
comp = self.compID
self.catalog_outfile = self.configs.get_path(comp+"/catalog_outfile")
self.halos_dumpfile = self.configs.get_path(comp+"/halos_dumpfile")
self.prefix = self.configs.getn(comp+"/prefix")
self.output_dir = self.configs.get_path(comp+"/output_dir")
self.merger_mass_min = self.configs.getn(comp+"/merger_mass_min")
self.ratio_major = self.configs.getn(comp+"/ratio_major")
self.tau_merger = self.configs.getn(comp+"/tau_merger")
self.frequencies = self.configs.frequencies
self.filename_pattern = self.configs.getn("output/filename_pattern")
self.clobber = self.configs.getn("output/clobber")
logger.info("Loaded and set up configurations")
def _simulate_catalog(self):
"""
Simulate the (z, mass) catalog of the cluster distribution
according to the Press-Schechter formalism.
Catalog columns
---------------
* ``z`` : redshifts
* ``mass`` : cluster total mass; unit: [Msun]
"""
logger.info("Simulating the clusters (z, mass) catalog ...")
psform = PSFormalism(configs=self.configs)
counts = psform.calc_cluster_counts(coverage=self.sky.area)
self.catalog, self.catalog_comment = psform.sample_z_m(counts)
logger.info("Simulated cluster catalog of counts %d." % counts)
def _process_catalog(self):
"""
Do some basic processes to the catalog:
* Generate random positions within the sky for each cluster;
* Generate random elongated fraction;
* Generate random rotation angle.
Catalog columns
---------------
* ``lon`` : longitudes; unit: [deg]
* ``lat`` : latitudes; unit: [deg]
* ``felong`` : elongated fraction, defined as the ratio of
elliptical semi-major axis to semi-minor axis;
restricted within [0.3, 1.0]
* ``rotation`` : rotation angle; uniformly distributed within
[0, 360.0); unit: [deg]
NOTE
----
felong (elongated fraction) ::
Adopt a definition (felong = b/a) similar to the Hubble
classification for the elliptical galaxies. As for the
elliptical galaxies classification, E7 is the limit (e.g.,
Wikipedia), therefore felong is also restricted within
[0.3, 1.0], and sampled from a cut and absolute normal
distribution centered at 1.0 with sigma ~0.7/3 (<= 3σ).
"""
logger.info("Preliminary processes to the catalog ...")
num = len(self.catalog)
lon, lat = self.sky.random_points(n=num)
self.catalog["lon"] = lon
self.catalog["lat"] = lat
self.catalog_comment.append(
"lon, lat : longitudes and latitudes [deg]")
logger.info("Added catalog columns: lon, lat.")
felong_min = 0.3
sigma = (1.0 - felong_min) / 3.0
felong = 1.0 - np.abs(np.random.normal(scale=sigma, size=num))
felong[felong < felong_min] = felong_min
self.catalog["felong"] = felong
self.catalog_comment.append(
"felong : elongated fraction (= b/a)")
logger.info("Added catalog column: felong.")
rotation = np.random.uniform(low=0.0, high=360.0, size=num)
self.catalog["rotation"] = rotation
self.catalog_comment.append(
"rotation : ellipse rotation angle [deg]")
logger.info("Added catalog column: rotation.")
def _simulate_mergers(self):
"""
Simulate the *recent major merger* event for each cluster.
First simulate the cluster formation history by tracing the
merger and accretion events of the main cluster, then identify
the most recent major merger event according to the mass ratio
of two merging clusters. And the properties of the found merger
event are appended to the catalog.
NOTE
----
There may be no such recent major merger event satisfying the
criteria, since we only tracing ``tau_merger`` (~3 Gyr) back.
On the other hand, the cluster may only experience minor merger
or accretion events.
Catalog columns
---------------
* ``rmm_mass1``, ``rmm_mass2`` : masses of the main and sub
clusters upon the recent major merger event; unit: [Msun]
* ``rmm_z``, ``rmm_age`` : redshift and cosmic age; unit: [Gyr]
of the recent major merger event.
"""
logger.info("Simulating the galaxy formation to identify " +
"the most recent major merger event ...")
num = len(self.catalog)
mdata = np.zeros(shape=(num, 4))
mdata.fill(np.nan)
num_major = 0 # number of clusters with recent major merger
for i, row in zip(range(num), self.catalog.itertuples()):
ii = i + 1
if ii % 50 == 0:
logger.info("[%d/%d] %.1f%% ..." % (ii, num, 100*ii/num))
z0, M0 = row.z, row.mass
age0 = COSMO.age(z0)
zmax = COSMO.redshift(age0 - self.tau_merger)
clform = ClusterFormation(M0=M0, z0=z0, zmax=zmax,
ratio_major=self.ratio_major,
merger_mass_min=self.merger_mass_min)
clform.simulate_mergertree(main_only=True)
mmev = clform.recent_major_merger
if mmev:
num_major += 1
mdata[i, :] = [mmev["M_main"], mmev["M_sub"],
mmev["z"], mmev["age"]]
mdf = pd.DataFrame(data=mdata,
columns=["rmm_mass1", "rmm_mass2",
"rmm_z", "rmm_age"])
self.catalog = self.catalog.join(mdf, how="outer")
self.catalog_comment += [
"rmm_mass1 : main cluster mass at recent major merger; [Msun]",
"rmm_mass2 : sub cluster mass at recent major merger; [Msun]",
"rmm_z : redshift of the recent major merger",
"rmm_age : cosmic age of the recent major merger; [Gyr]",
]
logger.info("Simulated and identified recent major merger events.")
logger.info("%d (%.1f%%) clusters have recent major mergers." %
(num_major, 100*num_major/num))
def _simulate_halos(self):
"""
Simulate the radio halo properties for each cluster with
recent major merger event.
Attributes
----------
halos : list[dict]
Simulated data for each cluster with recent major merger.
halos_df : `~pandas.DataFrame`
The Pandas DataFrame converted from the above ``halos`` data.
"""
# Select out the clusters with recent major mergers
idx_rmm = ~self.catalog["rmm_z"].isnull()
num = idx_rmm.sum()
logger.info("Simulating halos for %d merging clusters ..." % num)
self.halos = []
i = 0
for row in self.catalog[idx_rmm].itertuples():
i += 1
if i % 50 == 0:
logger.info("[%d/%d] %.1f%% ..." % (i, num, 100*i/num))
logger.info("[%d/%d] " % (i, num) +
"M1[%.2e] & M2[%.2e] @ z[%.3f] -> M[%.2e] @ z[%.3f]" %
(row.rmm_mass1, row.rmm_mass2, row.rmm_z,
row.mass, row.z))
halo = RadioHalo(M_obs=row.mass, z_obs=row.z,
M_main=row.rmm_mass1, M_sub=row.rmm_mass2,
z_merger=row.rmm_z, configs=self.configs)
n_e = halo.calc_electron_spectrum()
emissivity = halo.calc_emissivity(frequencies=self.frequencies)
power = halo.calc_power(emissivity)
flux = halo.calc_flux(emissivity)
Tb_mean = halo.calc_brightness_mean(emissivity, self.frequencies,
pixelsize=self.sky.pixelsize)
data = OrderedDict([
("z0", halo.z_obs),
("M0", halo.M_obs), # [Msun]
("lon", row.lon), # [deg] longitude
("lat", row.lat), # [deg] longitude
("felong", row.felong), # Fraction of elongation
("rotation", row.rotation), # [deg] ellipse rotation angle
("z_merger", halo.z_merger),
("M_main", halo.M_main), # [Msun]
("M_sub", halo.M_sub), # [Msun]
("time_crossing", halo.time_crossing), # [Gyr]
("gamma", halo.gamma), # Lorentz factors
("radius", halo.radius), # [kpc]
("angular_radius", halo.angular_radius), # [arcsec]
("volume", halo.volume), # [kpc^3]
("B", halo.magnetic_field), # [uG]
("kT_merger", halo.kT_merger), # [keV] ICM kT at z_merger
("Ke", halo.injection_rate), # [cm^-3 Gyr^-1]
("chi", halo._chi_acceleration()), # [Gyr^-1]
("n_e", n_e), # [cm^-3]
("frequency", self.frequencies), # [MHz]
("emissivity", emissivity), # [erg/s/cm^3/Hz]
("power", power), # [W/Hz]
("flux", flux), # [Jy]
("Tb_mean", Tb_mean), # [K]
])
self.halos.append(data)
logger.info("Simulated radio halos for merging cluster.")
logger.info("Converting halos data to be a Pandas DataFrame ...")
keys = list(self.halos[0].keys())
# Ignore the ``gamma`` and ``n_e`` items
for k in ["gamma", "n_e"]:
keys.remove(k)
self.halos_df = dictlist_to_dataframe(self.halos, keys=keys)
logger.info("Done halos data conversion.")
def _draw_halos(self):
"""
Draw the template images for each halo, and cache them for
simulating the superimposed halos at requested frequencies.
NOTE
----
The drawn template images are append to the dictionaries of
the corresponding halo within the ``self.halos``.
The templates are normalized to have *mean* value of 1.
"""
num = len(self.halos)
logger.info("Draw template images for %d halos ..." % num)
i = 0
for hdict in self.halos:
i += 1
if i % 100 == 0:
logger.info("[%d/%d] %.1f%% ..." % (i, num, 100*i/num))
theta_e = hdict["angular_radius"] / self.sky.pixelsize
rprofile = helper.halo_rprofile(re=theta_e)
template = helper.draw_halo(rprofile, felong=hdict["felong"],
rotation=hdict["rotation"])
hdict["template"] = template
logger.info("Done drawn halo template images.")
def _outfilepath(self, frequency, **kwargs):
"""
Generate the path/filename to the output file for writing
the simulate sky images.
Parameters
----------
frequency : float
The frequency of the output sky image.
Unit: [MHz]
Returns
-------
filepath : str
The generated filepath for the output sky file.
"""
filename = self.filename_pattern.format(
prefix=self.prefix, frequency=frequency, **kwargs)
filepath = os.path.join(self.output_dir, filename)
return filepath
def preprocess(self):
"""
Perform the preparation procedures for the later simulations.
Attributes
----------
_preprocessed : bool
This attribute presents and is ``True`` after the preparation
procedures have been done.
"""
if hasattr(self, "_preprocessed") and self._preprocessed:
return
logger.info("{name}: preprocessing ...".format(name=self.name))
self._simulate_catalog()
self._process_catalog()
self._simulate_mergers()
self._simulate_halos()
self._draw_halos()
self._preprocessed = True
def simulate_frequency(self, freqidx):
"""
Simulate the superimposed radio halos image at frequency (by
frequency index) based on the above simulated halo templates.
Parameters
----------
freqidx : int
The index of the frequency in the ``self.frequencies`` where
to simulate the radio halos image.
Returns
-------
sky : `~SkyBase`
The simulated sky image of radio halos as a new sky instance.
"""
freq = self.frequencies[freqidx]
logger.info("Simulating radio halo map at %.2f [MHz] ..." % freq)
sky = self.sky.copy()
sky.frequency = freq
# Conversion factor for [Jy/pixel] to [K]
JyPP2K = JyPerPix_to_K(freq, sky.pixelsize)
for hdict in self.halos:
center = (hdict["lon"], hdict["lat"])
template = hdict["template"] # normalized to have mean of 1
Npix = template.size
flux = hdict["flux"][freqidx] # [Jy]
Tmean = (flux/Npix) * JyPP2K # [K]
Timg = Tmean * template # [K]
sky.add(Timg, center=center)
logger.info("Done simulate map at %.2f [MHz]." % freq)
return sky
def simulate(self):
"""
Simulate the sky images of radio halos at each frequency.
Returns
-------
skyfiles : list[str]
List of the filepath to the written sky files
"""
logger.info("Simulating {name} ...".format(name=self.name))
skyfiles = []
for idx, freq in enumerate(self.frequencies):
sky = self.simulate_frequency(freqidx=idx)
outfile = self._outfilepath(frequency=freq)
sky.write(outfile)
skyfiles.append(outfile)
logger.info("Done simulate {name}!".format(name=self.name))
return skyfiles
def postprocess(self):
"""
Do some necessary post-simulation operations.
"""
logger.info("{name}: postprocessing ...".format(name=self.name))
# Save the final resulting clusters catalog
logger.info("Save the resulting catalog ...")
if self.catalog_outfile is None:
logger.warning("Catalog output file not set; skip saving!")
else:
dataframe_to_csv(self.catalog, outfile=self.catalog_outfile,
comment=self.catalog_comment,
clobber=self.clobber)
# Dump the simulated clusters data
logger.info("Dumping the simulated halos data ...")
if self.halos_dumpfile is None:
logger.warning("Missing dump outfile; skip dump cluster data!")
else:
pickle_dump(self.halos, outfile=self.halos_dumpfile,
clobber=self.clobber)
# Also save converted DataFrame of halos data
outfile = os.path.splitext(self.halos_dumpfile)[0] + ".csv"
dataframe_to_csv(self.halos_df, outfile, clobber=self.clobber)
logger.info("Saved DataFrame of halos data to file: %s" % outfile)