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db_fit_spec_manual_old.py
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db_fit_spec_manual_old.py
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#! /usr/bin/python3
# Filename: db_fit_spec.py
# Aim: to load the data base and then to fit spectra.
import argparse
import numpy as np
import sqlite3
from astropy.modeling import models
from astropy.modeling.models import custom_model
from astropy.modeling.fitting import LevMarLSQFitter
import matplotlib.pyplot as plt
def db_create_connection(db_file):
conn = None
try:
conn = sqlite3.connect(db_file)
conn.row_factory = sqlite3.Row
except sqlite3.Error as err:
print(err)
return conn
def db_read_sources(conn):
cur = conn.cursor()
cur.execute('select * from catalog')
return cur.fetchall()
def db_update_fitpara(conn,paras):
if len(paras) == 7:
sql = ''' UPDATE catalog
SET Flux_peak = ? ,
Flux_peak_err = ? ,
Velo_lsr = ? ,
Velo_lsr_err = ? ,
FWHM = ? ,
FWHM_err = ?
WHERE GName = ? '''
elif len(paras) == 13:
sql = ''' UPDATE catalog
SET Flux_peak1 = ? ,
Flux_peak1_err = ? ,
Velo_lsr1 = ? ,
Velo_lsr1_err = ? ,
FWHM1 = ? ,
FWHM1_err = ? ,
Flux_peak2 = ? ,
Flux_peak2_err = ? ,
Velo_lsr2 = ? ,
Velo_lsr2_err = ? ,
FWHM2 = ? ,
FWHM2_err = ?
WHERE GName = ? '''
print(paras)
cur = conn.cursor()
cur.execute(sql,paras)
conn.commit()
def gauss_fit_1p(velo,spec,toWrite=False,source='',size=0,paras=None):
plt.gcf()
if paras == None:
plt.clf()
paras = [0.1, 40., 10.]
plt.plot(velo,spec)
plt.xlim(-200,200)
plt.title(source+' - Radius {:3.0f}"'.format(size),size='x-large')
plt.xlabel('V$_{LSR}$ (km/s)',size='x-large')
plt.ylabel('Jy/beam',size='x-large')
else:
plt.gcf()
r_s2f = 2.355 # sigma to fwhm: FWHM = 2.355*sigma
gauss = models.Gaussian1D(amplitude=paras[0],mean=paras[1],stddev=paras[2]/r_s2f)
gauss.amplitude.min = 0.
gauss.mean.bounds=[0.,150.]
gauss.stddev.bounds=[5./r_s2f,40./r_s2f]
fit = LevMarLSQFitter()
sp_fit = fit(gauss,velo,spec)
fpeak = sp_fit.amplitude.value
vlsr = sp_fit.mean.value
fwhm = sp_fit.fwhm
para_err = np.sqrt(np.diag(fit.fit_info['param_cov']))
print('Flux: {:6.4f}; Vlsr: {:5.1f}; FWHM: {:4.1f}'.format(fpeak, vlsr, fwhm))
for old_line in plt.gca().lines + plt.gca().collections:
print('oldline')
old_line.remove()
plt.plot(velo,spec,color='C0')
plt.plot(velo,sp_fit(velo),color='C3')
if toWrite:
plt.savefig(source+'.png',dpi=300,bbox_inches='tight')
plt.show()
return fpeak, vlsr, fwhm, para_err[0], para_err[1], para_err[2]
def gauss_fit_2p(velo,spec,toWrite=False,source='',size=0,paras=None):
@custom_model
def gaussian_2peak(x, amplitude1=1., mean1=-1., sigma1=1.,
amplitude2=1., mean2=1., sigma2=1.):
return (amplitude1 * np.exp(-0.5 * ((x - mean1) / sigma1)**2) +
amplitude2 * np.exp(-0.5 * ((x - mean2) / sigma2)**2))
plt.gcf()
if paras == None:
plt.clf()
paras = [0.1, 40., 10., 0.1, 100., 10.]
plt.plot(velo,spec)
plt.xlim(-200,200)
plt.title(source+' - Radius {:3.0f}"'.format(size),size='x-large')
plt.xlabel('V$_{LSR}$ (km/s)',size='x-large')
plt.ylabel('Jy/beam',size='x-large')
else:
plt.gcf()
r_s2f = 2.35482 # sigma to fwhm: FWHM = 2.355*sigma
gauss2 = gaussian_2peak(amplitude1=paras[0],mean1=paras[1],sigma1=paras[2]/r_s2f,amplitude2=paras[3],mean2=paras[4],sigma2=paras[5]/r_s2f)
# print(gauss2.param_names)
gauss2.amplitude1.min = 0.
gauss2.amplitude2.min = 0.
gauss2.mean1.bounds=[0.,150.]
gauss2.mean2.bounds=[0.,150.]
gauss2.sigma1.bounds=[5./r_s2f,40./r_s2f]
gauss2.sigma2.bounds=[5./r_s2f,40./r_s2f]
fit = LevMarLSQFitter()
sp_fit = fit(gauss2,velo,spec,maxiter=50000)
print('fitting error: ',fit.fit_info['ierr'])
print(fit.fit_info['message'])
fpeak1 = sp_fit.amplitude1.value
vlsr1 = sp_fit.mean1.value
fwhm1 = sp_fit.sigma1.value*r_s2f
fpeak2 = sp_fit.amplitude2.value
vlsr2 = sp_fit.mean2.value
fwhm2 = sp_fit.sigma2.value*r_s2f
try:
if fit.fit_info['param_cov'] is not None:
para_err = np.sqrt(np.diag(fit.fit_info['param_cov']))
else:
para_err = np.zeros(len(paras))
except:
para_err = np.zeros(len(paras))
print('Flux1: {:6.4f}; Vlsr1: {:5.1f}; FWHM1: {:4.1f}'.format(fpeak1, vlsr1, fwhm1))
print('Flux2: {:6.4f}; Vlsr2: {:5.1f}; FWHM2: {:4.1f}'.format(fpeak2, vlsr2, fwhm2))
print(para_err)
for old_line in plt.gca().lines + plt.gca().collections:
print('oldline')
old_line.remove()
plt.plot(velo,spec,color='C0')
plt.plot(velo,sp_fit(velo),color='C3')
if toWrite:
plt.savefig(source+'.png',dpi=300,bbox_inches='tight')
plt.show()
return fpeak1,vlsr1,fwhm1,fpeak2,vlsr2,fwhm2,para_err[0], para_err[1], para_err[2]*r_s2f, para_err[3], para_err[4], para_err[5]*r_s2f
# return fpeak, vlsr, fwhm, para_err[0], para_err[1], para_err[2], para_err[3], para_err[4], para_err[5]
def main(args):
db_conn1 = db_create_connection(args.file_db)
db_conn2 = db_create_connection(args.file_db2)
with db_conn1:
plt.ion()
plt.figure(figsize=(6,4))
source_list = db_read_sources(db_conn1)
for source in source_list:
spec = np.frombuffer(source['Spectrum'],dtype=np.float32)
velo = np.frombuffer(source['Velocity'],dtype=np.float32)
if spec.shape == velo.shape:
paras = None
while(1):
if paras == None or len(paras) == 3:
fpeak,vlsr,fwhm,fpeak_err,vlsr_err,fwhm_err = gauss_fit_1p(velo,spec,toWrite=args.savefig,source=source['GName'],size=source['Radius'],paras=paras)
data = (fpeak,fpeak_err,vlsr,vlsr_err,fwhm,fwhm_err,source['GName'])
elif len(paras) == 6:
fpeak1,vlsr1,fwhm1,fpeak2,vlsr2,fwhm2,fpeak_err1,vlsr_err1,fwhm_err1,fpeak_err2,vlsr_err2,fwhm_err2 = gauss_fit_2p(velo,spec,toWrite=args.savefig,source=source['GName'],size=source['Radius'],paras=paras)
data = (fpeak1,fpeak_err1,vlsr1,vlsr_err1,fwhm1,fwhm_err1,fpeak2,fpeak_err2,vlsr2,vlsr_err2,fwhm2,fwhm_err2,source['GName'] )
try:
paras = eval(input("Input initial paras: amp, vlsr, fwhm (0 to quit)\n>: "))
except:
print("re-input:")
continue
if paras == 0:
break
elif paras == 1:
if len(data) == 7:
db_update_fitpara(db_conn1,data)
elif len(data) == 13:
db_update_fitpara(db_conn2,data)
break
plt.close(fig='all')
db_conn2.close()
return 0
#----------------------------------
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('file_db', type=str,help='The sqlite database file')
parser.add_argument('file_db2', type=str,help='The sqlite database file')
parser.add_argument('--savefig', action='store_true',help='save figure')
args = parser.parse_args()
main(args)