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utils.py
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utils.py
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import healpy as hp
from astropy.coordinates import SkyCoord
import numpy as np
from astropy.table import Table
import os
import astropy.units as u
import pandas as pd
import matplotlib.pyplot as plt
#####################################################
# 检验
def readfits(id,root):
f = f'triout_128_{id}.fits'
t = Table.read(os.path.join(root,f))
t = t.to_pandas()
t = t[['gall','galb']]
t = t[0::1000]
_coor = SkyCoord(l=t['gall']*u.degree,b=t['galb']*u.degree,unit='deg',frame='galactic')
_coor = _coor.transform_to('icrs')
_ra, _dec = _coor.ra.degree, _coor.dec.degree
t['ra'] = _ra
t['dec'] = _dec
return t
def simulation_stractor_single(ipix_list,ra,dec,distance,root):
target = pd.DataFrame()
columns = ['M_H', 'logTe', 'logg', 'mu0',
'NUVmag', 'umag', 'gmag', 'rmag', 'imag', 'zmag', 'ymag',
'uSmag', 'gSmag', 'rSmag', 'iSmag', 'zSmag',
'PMracos', 'PMdec', 'Vrad', 'ra', 'dec', 'gall', 'galb']
for id in ipix_list:
f = f'triout_128_{id}.fits'
t = Table.read(os.path.join(root,f))
t = t.to_pandas()
_coor = SkyCoord(l=t['gall']*u.degree,b=t['galb']*u.degree,unit='deg',frame='galactic')
_coor = _coor.transform_to('icrs')
_ra, _dec = _coor.ra.degree, _coor.dec.degree
t['ra'] = _ra
t['dec'] = _dec
# t['parallax'] = 10**(0.2*t['mu0']+1) * u.pc
# t['parallax'] = 2*u.au/t['parallax'].to(u.au)
t = t[(t['ra']-ra)**2+(t['dec']-dec)**2<=distance**2]
# target = pd.concat([target,t])
target = pd.concat([target,t[columns]])
return target
def sim_stractor(ra,dec,radius,save_path):
nside = 128
# root path
root = '/media/hyz/travaller/CSSTsim/CSSTsim.20211214.pSDSSpPS1'
# turn icrs to gal
cencoor = SkyCoord(ra=ra,dec=dec,unit='deg',frame='icrs')
galc = cencoor.transform_to('galactic')
gall, galb = galc.l.degree, galc.b.degree
ipix_list = hp.query_disc(nside, hp.pixelfunc.ang2vec(gall,galb,lonlat=True), np.radians(radius), nest=True)
# 如果范围太小，可能只有一个pix，换用单pix函数
if len(ipix_list) == 0:
ipix_list = [hp.ang2pix(nside,gall,galb,nest=True,lonlat=True)]
print(ipix_list)
# 检验
print(readfits(ipix_list[0],root))
selection = simulation_stractor_single(ipix_list,ra,dec,radius,root)
selection.to_csv(save_path[:-4]+'_sim'+save_path[-4:])
#####################################################
def gaia_stractor_single(ipix_list,ra,dec,distance):
target = pd.DataFrame()
columns = ['phot_g_mean_mag', 'phot_rp_mean_mag', 'phot_bp_mean_mag',
'l', 'b', 'ra', 'ra_error', 'dec', 'dec_error',
'pmra', 'pmra_error', 'pmdec', 'pmdec_error',
'parallax', 'parallax_error', 'radial_velocity', 'radial_velocity_error',
'teff_gspphot', 'logg_gspphot', 'mh_gspphot']
for id in ipix_list:
t = pd.read_csv('/media/hyz/dwarfcave/data/gaiaDR3/ms/%s'%id,comment='#',usecols=columns)
t = t[(t['ra']-ra)**2+(t['dec']-dec)**2<=distance**2]
# target = pd.concat([target,t])
target = pd.concat([target,t[columns]])
return target
def gaia_stractor(ra,dec,radius,save_path):
nside = 256
target_pix = hp.query_disc(nside, hp.pixelfunc.ang2vec(ra,dec,lonlat=True), np.radians(radius), nest=True)
if len(target_pix) == 0:
target_pix = [hp.ang2pix(nside,ra,dec,nest=True,lonlat=True)]
plt.figure(figsize=(3,3))
plt.hist(target_pix)
print(len(target_pix))
target_file = []
for pix in target_pix:
for i in os.listdir('/media/hyz/dwarfcave/data/gaiaDR3/ms'):
if i.endswith('.csv') and i not in target_file:
if int(i.split('-')[1].split('.csv')[0]) >= pix and int(i.split('-')[0].split('_')[1]) <= pix:
target_file.append(i)
break
print(target_file)
selection_g = gaia_stractor_single(target_file,ra,dec,radius)
selection_g.to_csv(save_path[:-4]+'_gaia'+save_path[-4:])
print(len(selection_g))
#####################################################
def cat_merge_padding(save_path, J2000=False):
sim = pd.read_csv(save_path[:-4]+'_sim'+save_path[-4:])
if J2000:
gaia = pd.read_csv(save_path[:-4]+'_gaia_j2000'+save_path[-4:])
else:
gaia = pd.read_csv(save_path[:-4]+'_gaia'+save_path[-4:])
sim = sim.sort_values('gmag')
gaia = gaia.sort_values('phot_g_mean_mag')
sim.index = np.arange(len(sim))
gaia.index = np.arange(len(gaia))
sim_columns_raw = sim.columns
print(len(sim), len(gaia))
gi_sdss = sim['gSmag'] - sim['iSmag']
ri_sdss = sim['rSmag'] - sim['iSmag']
sim['gmag_gaia'] = -0.074189 - 0.51409*gi_sdss - 0.080607*gi_sdss**2 + 0.0016001*gi_sdss**3 + sim['gSmag'] # Gaia-SDSS12 relationship
sim['rmag_gaia'] = -0.029869 - 1.1303*ri_sdss + sim['rSmag']
sim['bmag_gaia'] = 0.1463 + 1.7244*ri_sdss - 1.1912*ri_sdss**2 + 0.22004*ri_sdss**3 + sim['rSmag']
sim['pmra_gaia'] = sim['PMracos']
sim['parallax_gaia'] = u.AU.to(u.pc)/np.power(10, 0.2*sim['mu0']+1)/(1/3600*0.01*u.degree.to(u.rad))
sim['radial_velocity_gaia'] = sim['Vrad']
ssim_list = ['ra_error_gaia','dec_error_gaia','pmra_error_gaia','pmdec_error_gaia','parallax_error_gaia','radial_velocity_error_gaia']
num_sim, num_gaia = len(sim), len(gaia)
for isim in ssim_list:
_hist,_edge = np.histogram(gaia[isim[:-5]],bins=100,range=[gaia[isim[:-5]].min(),gaia[isim[:-5]].max()])
sample = np.empty(1)
for i in range(len(_hist)):
sample = np.concatenate([sample, np.random.rand(int(num_sim*_hist[i]/num_gaia)) * (_edge[i+1]-_edge[i]) + _edge[i]], axis=0)
if len(sample)<num_sim:
sample = np.concatenate([sample,np.ones(num_sim-len(sample))*np.nan])
np.random.shuffle(sample)
print(len(sim),len(sample))
sim[isim] = sample[:num_sim]
_hist,_edge = np.histogram(gaia['phot_g_mean_mag'].dropna(),bins=50)
truncate_mag = _edge[(_hist[1:]-_hist[:-1]).argmin()+1]
sim.drop(sim[sim['gmag_gaia']<truncate_mag].index,inplace=True)
columns = ['gmag_gaia','rmag_gaia','bmag_gaia',
'gall_gaia','galb_gaia','ra_gaia','ra_error_gaia','dec_gaia','dec_error_gaia',
'pmra_gaia','pmra_error_gaia','pmdec_gaia','pmdec_error_gaia','parallax_gaia','parallax_error_gaia','radial_velocity_gaia','radial_velocity_error_gaia']
columns_gaia = ['phot_g_mean_mag','phot_rp_mean_mag','phot_bp_mean_mag',
'l','b','ra','ra_error','dec','dec_error',
'pmra','pmra_error','pmdec','pmdec_error','parallax','parallax_error','radial_velocity','radial_velocity_error']
columns_sim = ['gmag_gaia','rmag_gaia','bmag_gaia',
'gall','galb','ra','ra_error_gaia','dec','dec_error_gaia',
'pmra_gaia','pmra_error_gaia','PMdec','pmdec_error_gaia','parallax_gaia','parallax_error_gaia','radial_velocity_gaia','radial_velocity_error_gaia']
target = pd.DataFrame()
for (_,e) in enumerate(columns):
# print(_,e,columns_sim[_],columns_gaia[_])
target[e] = np.concatenate([sim[columns_sim[_]].to_numpy(),gaia[columns_gaia[_]].to_numpy()])
# print(sim[columns_sim[_]][::10000],gaia[columns_gaia[_]][::10000],target[e][::10000])
for e in sim_columns_raw:
target[e] = np.concatenate([sim[e].to_numpy(),np.ones(num_gaia)*np.nan])
target.loc[:len(sim),'if_gaia_true'] = int(0)
target.loc[len(sim):,'if_gaia_true'] = int(1)
print('len(sim_truncated),len(gaia),len(target)',len(sim),len(gaia),len(target))
def insert_na(t):
for col in t.columns:
if not col.endswith('_gaia'):
continue
data = t[col]
num_na = data.isna().sum()
idx_na = data[data.isna()].index
if num_na == 0:
continue
if 'mag' in col:
insert_data = t[['gmag_gaia','rmag_gaia','bmag_gaia']].mean(axis=1)[idx_na]
insert_data_ = pd.Series(np.random.rand(insert_data.isna().sum())+t['gmag_gaia'].max())
insert_data_.index = insert_data[insert_data.isna()].index
insert_data = insert_data.fillna(insert_data_)
insert_data = np.random.rand(num_na) * 2 + insert_data
elif 'error' in col:
insert_data = np.random.rand(num_na) * data.std() + data.max()
else:
insert_data = np.random.randn(num_na) + data.mean()
insert_data = pd.Series(insert_data)
insert_data.index = idx_na
t[col] = data.fillna(insert_data)
return t
insert_na(target)
# insert_na(sim)[::20,'gmag_gaia':]
# sim[['gmag_gaia','rmag_gaia','bmag_gaia']][-20:]
target.to_csv(save_path[:-4]+'_concat'+save_path[-4:])
return target
##############################################################
def cat_merge_forsim(save_path, J2000=False):
sim = pd.read_csv(save_path[:-4]+'_sim'+save_path[-4:])
if J2000:
gaia = pd.read_csv(save_path[:-4]+'_gaia_j2000'+save_path[-4:])
else:
gaia = pd.read_csv(save_path[:-4]+'_gaia'+save_path[-4:])
sim = sim.sort_values('gmag')
gaia = gaia.sort_values('phot_g_mean_mag')
sim.index = np.arange(len(sim))
gaia.index = np.arange(len(gaia))
print(len(sim), len(gaia))
sim['RA'] = sim['ra']
sim['Dec'] = sim['dec']
sim['app_sdss_g'] = sim['gSmag']
sim['teff'] = np.power(10,sim['logTe'])
sim['feh'] = sim['M_H']
sim['grav'] = sim['logg']
sim['pmra'] = sim['PMracos']
sim['pmdec'] = sim['PMdec']
sim['parallax'] = u.AU.to(u.pc)/np.power(10, 0.2*sim['mu0']+1)/(1/3600*0.01*u.degree.to(u.rad))
sim['RV'] = sim['Vrad']
gaia_bprp = gaia['phot_bp_mean_mag'] - gaia['phot_rp_mean_mag']
gaia['sdss_g'] = gaia['phot_g_mean_mag']-(0.13518-0.46245*gaia_bprp-0.25171*gaia_bprp**2+0.021349*gaia_bprp**3)
_hist,_edge = np.histogram(gaia['sdss_g'].dropna(),bins=50)
truncate_mag = _edge[(_hist[1:]-_hist[:-1]).argmin()+1]
sim.drop(sim[sim['app_sdss_g']<truncate_mag].index,inplace=True)
# num_gaia = len(gaia)
# sim.loc[:num_gaia,'RA'] = gaia.loc[:num_gaia,'ra']
# sim.loc[:num_gaia,'Dec'] = gaia.loc[:num_gaia,'dec']
# sim.loc[:num_gaia,'g'] = gaia.loc[:num_gaia,'phot_g_mean_mag']
# sim.loc[:num_gaia,'teff'] = gaia.loc[:num_gaia,'teff_gspphot']
# sim.loc[:num_gaia,'grav'] = gaia.loc[:num_gaia,'logg_gspphot']
# sim.loc[:num_gaia,'feh'] = gaia.loc[:num_gaia,'mh_gspphot']
# sim.loc[:num_gaia,'pmra'] = gaia.loc[:num_gaia,'pmra']
# sim.loc[:num_gaia,'pmdec'] = gaia.loc[:num_gaia,'pmdec']
# sim.loc[:num_gaia,'parallax'] = gaia.loc[:num_gaia,'parallax']
# sim.loc[:num_gaia,'rv'] = gaia.loc[:num_gaia,'radial_velocity']
columns = ['RA','Dec','app_sdss_g',
'teff','grav','feh',
'pmra','pmdec','parallax','RV']
columns_gaia = ['ra','dec','sdss_g',
'teff_gspphot','logg_gspphot','mh_gspphot',
'pmra','pmdec','parallax','radial_velocity']
target = pd.DataFrame()
for (_,e) in enumerate(columns):
target[e] = np.concatenate([sim[e].to_numpy(),gaia[columns_gaia[_]].to_numpy()])
# sim = sim[columns]
# sim.to_csv(save_path[:-4]+'_merge_forsim'+save_path[-4:])
target.to_csv(save_path[:-4]+'_merge_forsim'+save_path[-4:])
return target
##############################################################
import numpy as np
from astropy.time import Time
from astropy.table import Table, hstack
from astropy.coordinates import SkyCoord,Distance
import astropy.units as u
def epoch_trans(save_path,t0,t1):
data_c = Table.read(save_path[:-4]+'_gaia'+save_path[-4:])
#天体的基本信息
Alpha_t1 = np.array(data_c['ra'])
Delta_t1 = np.array(data_c['dec'])
pmra1 = np.array(data_c['pmra'])
pmdec1 = np.array(data_c['pmdec'])
para1 = np.array(data_c['parallax'])
radial_velocity1 = np.zeros(len(data_c))
ra_2000 = []
dec_2000 = []
c = SkyCoord(ra=Alpha_t1 * u.deg,
dec=Delta_t1 * u.deg,
distance=Distance(parallax=abs(para1) * u.mas),
pm_ra_cosdec=pmra1 * u.mas/u.yr, #np.cos(np.radians(table['dec'])
radial_velocity=radial_velocity1*u.km/u.s,
pm_dec=pmdec1 * u.mas/u.yr,
obstime=Time(2016, format='jyear',
scale='tcb'), frame="icrs" )
epoch_now = Time(t0, format='jyear', scale='tcb')
obstime=Time(t1, format='jyear', scale='tcb')
c_epoch_now = c.apply_space_motion(epoch_now)
ra_2000 = c_epoch_now.ra.degree
dec_2000 = c_epoch_now.dec.degree
RA_l = Table([ra_2000],names='q')
DEC_l = Table([dec_2000],names='w')
RA_l['q'].name='ra_j2000'
DEC_l['w'].name='dec_j2000'
All_data = hstack([data_c, RA_l, DEC_l])
All_data.write(save_path[:-4]+'_gaia_j2000'+save_path[-4:],format="ascii.csv",overwrite=True)