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authorAaron LI <aaronly.me@outlook.com>2016-10-28 21:33:43 +0800
committerAaron LI <aaronly.me@outlook.com>2016-10-28 21:33:43 +0800
commitdbfab5d3ce42f99df4a76c165d6300d09c65e9e4 (patch)
treee2110bb09d9b807ef36e532859d436d9ad262663 /fg21sim
parent1c2e43ebdc199136584099ec1c5ef743aff2457c (diff)
downloadfg21sim-dbfab5d3ce42f99df4a76c165d6300d09c65e9e4.tar.bz2
pointsources/base.py: Partially rewrite and FIXME marksps
NOTE: There are many "FIXME" needs further rewrite
Diffstat (limited to 'fg21sim')
-rw-r--r--fg21sim/extragalactic/pointsources/base.py237
1 files changed, 151 insertions, 86 deletions
diff --git a/fg21sim/extragalactic/pointsources/base.py b/fg21sim/extragalactic/pointsources/base.py
index 1544f5b..101b6cb 100644
--- a/fg21sim/extragalactic/pointsources/base.py
+++ b/fg21sim/extragalactic/pointsources/base.py
@@ -1,97 +1,125 @@
# Copyright (c) 2016 Zhixian MA <zxma_sjtu@qq.com>
+# Copyright (c) 2016 Weitian LI <liweitianux@live.com>
# MIT license
"""
-Simulation of point sources for 21cm signal detection
+Simulation the radio emissions from various types of point sources (PS)
-Point sources types
--------------------
-1. Star forming (SF) galaxies
-2. Star bursting (SB) galaxies
-3. Radio quiet AGN (RQ_AGN)
-4. Faranoff-Riley I (FRI)
-5. Faranoff-Riley II (FRII)
+Currently supported PS types:
+
+* Star-forming (SF) galaxies
+* Star-bursting (SB) galaxies
+* Radio-quiet AGNs
+* Radio-loud AGNs: Fanaroff-Riley type I (FRI)
+* Radio-loud AGNs: Fanaroff-Riley type II (FRII)
References
----------
-[1] Wilman et al.,
- "A semi-empirical simulation of the extragalactic radio continuum
- sky for next generation radio telescopes",
- 2008, MNRAS, 388, 1335-1348.
- http://adsabs.harvard.edu/abs/2008MNRAS.388.1335W
-[2] Jelic et al.,
- "Foreground simulations for the LOFAR-Epoch of Reionization
- Experiment",
- 2008, MNRAS, 389, 1319-1335.
- http://adsabs.harvard.edu/abs/2008MNRAS.389.1319W
-[3] Spherical uniform distribution
- https://www.jasondavies.com/maps/random-points/
+.. [Wilman2008]
+ Wilman et al.,
+ "A semi-empirical simulation of the extragalactic radio continuum
+ sky for next generation radio telescopes",
+ 2008, MNRAS, 388, 1335-1348,
+ http://adsabs.harvard.edu/abs/2008MNRAS.388.1335W
+
+.. [Jelic2008]
+ Jelic et al.,
+ "Foreground simulations for the LOFAR-Epoch of Reionization
+ Experiment",
+ 2008, MNRAS, 389, 1319-1335,
+ http://adsabs.harvard.edu/abs/2008MNRAS.389.1319W
"""
+
+
import os
+import logging
+
import numpy as np
+import astropy.units as au
+from astropy.cosmology import FlatLambdaCDM
import pandas as pd
import healpy as hp
-from .psparams import PixelParams
+from ...utils.random import spherical_uniform
+
+
+logger = logging.getLogger(__name__)
class BasePointSource:
"""
- The basic class of point sources
+ Base class for point sources simulation
+
+ FIXME: rewrite this doc string!
- Parameters
+ Attributes
----------
- z: float;
+ z: float
Redshift, z ~ U(0,20)
dA: au.Mpc;
Angular diameter distance, which is calculated according to the
- cosmology constants. In this work, it is calculated by module
- basic_params
- lumo: au.Jy;
- Luminosity at the reference frequency.
+ cosmology constants.
+ lumo: au.Jy;
+ Luminosity at the reference frequency
lat: au.deg;
- The colatitude angle in the spherical coordinate system
+ The latitude in the spherical coordinate system
lon: au.deg;
- The longtitude angle in the spherical coordinate system
+ The longitude in the spherical coordinate system
area: au.sr;
- Area of the point sources, sr = rad^2
-
+ Angular size/area of the point sources
"""
- # Init
+ # Identifier of the PS component
+ comp_id = "extragalactic/pointsources"
+ # ID of this PS type
+ ps_type = None
+ # Name of this PS type
+ name = None
def __init__(self, configs):
# configures
self.configs = configs
- # PS_list information
+ # FIXME: get rid of this `columns`
+ # Basic PS catalog columns
self.columns = ['z', 'dA (Mpc)', 'luminosity (Jy)', 'Lat (deg)',
'Lon (deg)', 'Area (sr)']
- self.nCols = len(self.columns)
self._set_configs()
def _set_configs(self):
- """
- Load the configs and set the corresponding class attributes.
- """
- comp = "extragalactic/pointsources/"
- # common
+ """Load the configs and set the corresponding class attributes."""
+ # Configurations shared between all supported PS types
+ comp = self.comp_id
+ self.resolution = self.configs.getn(comp+"/resolution") * au.arcmin
+ self.catalog_pattern = self.configs.getn(comp+"/catalog_pattern")
+ self.save = self.configs.getn(comp+"/save")
+ self.output_dir = self.configs.get_path(comp+"/output_dir")
+ # Specific configurations for this PS type
+ pstype = "/".join([self.comp_id, self.ps_type])
+ self.number = self.configs.getn(pstype+"/number")
+ self.prefix2 = self.configs.getn(pstype+"/prefix2")
+ self.save2 = self.configs.getn(pstype+"/save2")
+ #
+ self.use_float = self.configs.getn("output/use_float")
+ self.clobber = self.configs.getn("output/clobber")
self.nside = self.configs.getn("common/nside")
- # resolution
- self.resolution = self.configs.getn(comp+"resolution")
- # save flag
- self.save = self.configs.getn(comp+"save")
- # Output_dir
- self.output_dir = self.configs.get_path(comp+"output_dir")
+ self.npix = hp.nside2npix(self.nside)
+ # Cosmology model
+ self.H0 = self.configs.getn("cosmology/H0")
+ self.OmegaM0 = self.configs.getn("cosmology/OmegaM0")
+ self.cosmo = FlatLambdaCDM(H0=self.H0, Om0=self.OmegaM0)
+ #
+ logger.info("Loaded and set up configurations")
def calc_number_density(self):
- pass
+ raise NotImplementedError("Sub-class must implement this method!")
+ # FIXME: directly sample from joint distribution of z and flux/luminosity
def calc_cdf(self):
"""
Calculate cumulative distribution functions for simulating of
samples with corresponding reshift and luminosity.
- Parameter
- -----------
+ Parameters
+ ----------
rho_mat: np.ndarray rho(lumo,z)
The number density matrix (joint-distribution of z and flux)
of this type of PS.
@@ -111,6 +139,7 @@ class BasePointSource:
# Cumulative function
cdf_z = np.zeros(pdf_z.shape)
cdf_lumo = np.zeros(pdf_lumo.shape)
+ # FIXME: use `cumsum()`
for i in range(len(pdf_z)):
cdf_z[i] = np.sum(pdf_z[:i])
for i in range(len(pdf_lumo)):
@@ -118,12 +147,13 @@ class BasePointSource:
return cdf_z, cdf_lumo
+ # FIXME: get rid of self.* !
def get_lumo_redshift(self):
"""
Randomly generate redshif and luminosity at ref frequency using
the CDF functions.
- Paramaters
+ Parameters
----------
df_z, cdf_lumo: np.ndarray
Cumulative distribution functions of redshift and flux.
@@ -136,8 +166,7 @@ class BasePointSource:
Redshift.
lumo: au.W/Hz/sr
Luminosity.
- """
- # Uniformlly generate random number in interval [0,1]
+ """
rnd_z = np.random.uniform(0, 1)
rnd_lumo = np.random.uniform(0, 1)
# Get redshift
@@ -148,56 +177,92 @@ class BasePointSource:
dist_lumo = np.abs(self.cdf_lumo - rnd_lumo)
idx_lumo = np.where(dist_lumo == dist_lumo.min())
lumo = 10 ** self.lumobin[idx_lumo[0]]
-
return float(z), float(lumo)
+ # FIXME: Get rid of this function!
def gen_single_ps(self):
- """
- Generate single point source, and return its data as a list.
- """
+ """Generate the information of one single point source"""
# Redshift and luminosity
- self.z, self.lumo = self.get_lumo_redshift()
+ z, lumo = self.get_lumo_redshift()
# angular diameter distance
- self.param = PixelParams(self.z)
- self.dA = self.param.dA
- # W/Hz/Sr to Jy
- dA = self.dA * 3.0856775814671917E+22 # Mpc to meter
- self.lumo = self.lumo / dA**2 / (10.0**-24) # [Jy]
+ DA = self.cosmo.angular_diameter_distance(z).to(au.Mpc).value
+ # [ W/Hz/sr ] => [ Jy ]
+ DA_m = DA * 3.0856775814671917E+22 # Mpc to meter
+ lumo = lumo / DA_m**2 / 1e-24 # [Jy]
# Position
- x = np.random.uniform(0, 1)
- self.lat = (np.arccos(2 * x - 1) / np.pi * 180 - 90) # [deg]
- self.lon = np.random.uniform(0, np.pi * 2) / np.pi * 180 # [deg]
+ theta, phi = spherical_uniform()
+ glon = np.rad2deg(phi)
+ glat = 90.0 - np.rad2deg(theta)
# Area
- npix = hp.nside2npix(self.nside)
- self.area = 4 * np.pi / npix # [sr]
-
- ps_list = [self.z, self.dA, self.lumo, self.lat, self.lon, self.area]
- return ps_list
+ area = 4 * np.pi / self.npix # [sr]
+ ps_data = [z, DA_m, lumo, glat, glon, area]
+ return ps_data
+ # FIXME: The catalog should be simulated on the column based, not on
+ # single PS based, which slows the speed!
def gen_catalog(self):
"""
Generate num_ps of point sources and save them into a csv file.
"""
- # Init
- ps_table = np.zeros((self.num_ps, self.nCols))
- for x in range(self.num_ps):
- ps_table[x, :] = self.gen_single_ps()
-
- # Transform into Dataframe
- self.ps_catalog = pd.DataFrame(ps_table, columns=self.columns,
- index=list(range(self.num_ps)))
+ shape = (self.number, len(self.columns))
+ catalog = np.zeros(shape)
+ for i in range(shape[0]):
+ catalog[i, :] = self.gen_single_ps()
+ self.catalog = pd.DataFrame(catalog, columns=self.columns)
+ logger.info("Done simulate the catalog")
- def save_as_csv(self):
+ def save_catalog(self):
"""Save the catalog"""
if not os.path.exists(self.output_dir):
os.mkdir(self.output_dir)
+ logger.info("Created output directory: %s" % self.output_dir)
+ # Append the prefix for the specific PS type
+ if self.prefix2 is not None:
+ prefix = "_".join([self.prefix, self.prefix2])
+ filepath = self._make_filepath(pattern=self.catalog_pattern,
+ prefix=prefix)
+ # Save catalog data
+ if os.path.exists(filepath):
+ if self.clobber:
+ logger.warning("Remove existing catalog file: %s" % filepath)
+ os.remove(filepath)
+ else:
+ raise OSError("Output file already exists: %s" % filepath)
+ self.catalog.to_csv(filepath, header=True, index=False)
+ logger.info("Save clusters catalog in use to: {0}".format(filepath))
- pattern = "{prefix}.csv"
- filename = pattern.format(prefix=self.prefix)
+ def output(self):
+ raise NotImplementedError("TODO")
- # save to csv
- if self.save:
- file_name = os.path.join(self.output_dir, filename)
- self.ps_catalog.to_csv(file_name)
+ def _make_filepath(self, pattern=None, **kwargs):
+ """Make the path of output file according to the filename pattern
+ and output directory loaded from configurations.
- return file_name
+ Parameters
+ ----------
+ pattern : str, optional
+ Specify the filename (without the extension) pattern in string
+ format template syntax. If not specified, then use
+ ``self.filename_pattern``.
+ **kwargs : optional
+ Other named parameters used to format the filename pattern.
+
+ Returns
+ -------
+ filepath : str
+ The full path to the output file (with directory and extension).
+ """
+ data = {
+ "prefix": self.prefix,
+ }
+ data.update(kwargs)
+ if pattern is None:
+ pattern = self.filename_pattern
+ filename = pattern.format(**data)
+ filetype = self.configs.getn("output/filetype")
+ if filetype == "fits":
+ filename += ".fits"
+ else:
+ raise NotImplementedError("unsupported filetype: %s" % filetype)
+ filepath = os.path.join(self.output_dir, filename)
+ return filepath