diff options
Diffstat (limited to 'fg21sim/extragalactic/pointsources/base.py')
| -rw-r--r-- | fg21sim/extragalactic/pointsources/base.py | 203 |
1 files changed, 0 insertions, 203 deletions
diff --git a/fg21sim/extragalactic/pointsources/base.py b/fg21sim/extragalactic/pointsources/base.py deleted file mode 100644 index 1544f5b..0000000 --- a/fg21sim/extragalactic/pointsources/base.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) 2016 Zhixian MA <zxma_sjtu@qq.com> -# MIT license - -""" -Simulation of point sources for 21cm signal detection - -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) - -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/ -""" -import os -import numpy as np -import pandas as pd -import healpy as hp - -from .psparams import PixelParams - - -class BasePointSource: - """ - The basic class of point sources - - Parameters - ---------- - 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. - lat: au.deg; - The colatitude angle in the spherical coordinate system - lon: au.deg; - The longtitude angle in the spherical coordinate system - area: au.sr; - Area of the point sources, sr = rad^2 - - """ - # Init - - def __init__(self, configs): - # configures - self.configs = configs - # PS_list information - 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 - 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") - - def calc_number_density(self): - pass - - def calc_cdf(self): - """ - Calculate cumulative distribution functions for simulating of - samples with corresponding reshift and luminosity. - - Parameter - ----------- - rho_mat: np.ndarray rho(lumo,z) - The number density matrix (joint-distribution of z and flux) - of this type of PS. - - Returns - ------- - cdf_z, cdf_lumo: np.ndarray - Cumulative distribution functions of redshift and flux. - """ - # Normalization - rho_mat = self.rho_mat - rho_sum = np.sum(rho_mat) - rho_norm = rho_mat / rho_sum - # probability distribution of redshift - pdf_z = np.sum(rho_norm, axis=0) - pdf_lumo = np.sum(rho_norm, axis=1) - # Cumulative function - cdf_z = np.zeros(pdf_z.shape) - cdf_lumo = np.zeros(pdf_lumo.shape) - for i in range(len(pdf_z)): - cdf_z[i] = np.sum(pdf_z[:i]) - for i in range(len(pdf_lumo)): - cdf_lumo[i] = np.sum(pdf_lumo[:i]) - - return cdf_z, cdf_lumo - - def get_lumo_redshift(self): - """ - Randomly generate redshif and luminosity at ref frequency using - the CDF functions. - - Paramaters - ---------- - df_z, cdf_lumo: np.ndarray - Cumulative distribution functions of redshift and flux. - zbin,lumobin: np.ndarray - Bins of redshif and luminosity. - - Returns - ------- - z: float - 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 - dist_z = np.abs(self.cdf_z - rnd_z) - idx_z = np.where(dist_z == dist_z.min()) - z = self.zbin[idx_z[0]] - # Get luminosity - 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) - - def gen_single_ps(self): - """ - Generate single point source, and return its data as a list. - """ - # Redshift and luminosity - self.z, self.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] - # 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] - # 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 - - 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))) - - def save_as_csv(self): - """Save the catalog""" - if not os.path.exists(self.output_dir): - os.mkdir(self.output_dir) - - pattern = "{prefix}.csv" - filename = pattern.format(prefix=self.prefix) - - # save to csv - if self.save: - file_name = os.path.join(self.output_dir, filename) - self.ps_catalog.to_csv(file_name) - - return file_name |