generated from erosita/uds
154 lines
4.9 KiB
Python
Executable File
154 lines
4.9 KiB
Python
Executable File
#!/usr/bin/env python
|
|
|
|
__author__ = "Roman Krivonos"
|
|
__copyright__ = "Space Research Institute (IKI)"
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from astropy.io import fits
|
|
from astropy.table import Table, Column
|
|
from astropy import units as u
|
|
|
|
import matplotlib.pyplot as plt
|
|
import math, sys, os
|
|
import pickle
|
|
|
|
from sklearn.linear_model import LinearRegression
|
|
from sklearn.linear_model import HuberRegressor
|
|
from sklearn.linear_model import RANSACRegressor
|
|
from sklearn.linear_model import TheilSenRegressor
|
|
from sklearn.model_selection import cross_val_score
|
|
from sklearn.model_selection import RepeatedKFold
|
|
|
|
#from statsmodels.robust.scale import huber
|
|
from astropy.stats import sigma_clip
|
|
from astropy.stats import sigma_clipped_stats
|
|
|
|
from scipy.stats import norm
|
|
from scipy.stats import describe
|
|
from scipy.stats import sem
|
|
import subprocess
|
|
|
|
from numpy import absolute
|
|
from numpy import arange
|
|
|
|
from ridge.utils import *
|
|
from ridge.config import *
|
|
|
|
inkey="ALL"
|
|
|
|
|
|
sigma=3
|
|
plotme=False
|
|
|
|
ebands0={
|
|
'E01':[0.0,0.0], # 25-60 keV
|
|
'E14':[0.0,0.0], # 60-80 keV
|
|
'E13':[0.0,0.0], # 80-200 keV
|
|
}
|
|
|
|
|
|
if len(sys.argv) > 1:
|
|
skeys = [sys.argv[1]]
|
|
else:
|
|
skeys = list(skyreg.keys())
|
|
|
|
if not os.path.exists(fluxdir):
|
|
os.makedirs(fluxdir)
|
|
|
|
with open(ignored_rev_file, 'rb') as fp:
|
|
ignored_rev = pickle.load(fp)
|
|
print("{} orbits ignored".format(len(ignored_rev)))
|
|
ign=ignored_rev.tolist()
|
|
|
|
|
|
for skey in skeys:
|
|
if not skey in skyreg.keys():
|
|
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
|
sys.exit()
|
|
|
|
# generate array for bootstrap
|
|
ebands_sim={}
|
|
for enkey in ebands0.keys():
|
|
ebands_sim[enkey] = []
|
|
|
|
for enkey in ebands0.keys():
|
|
#bkg_fn="detcnts.{}.BKG.resid.fits".format(enkey,inkey)
|
|
#syserr, bkg_sem = get_syserror(proddir+bkg_fn)
|
|
|
|
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
|
|
print("Reading {}".format(proddir+fn))
|
|
dat = Table.read(proddir+fn, unit_parse_strict='silent')
|
|
df = dat.to_pandas()
|
|
|
|
query = "LON > {} & LON < {} & LAT > {} & LAT < {} & REV != @ign".format(
|
|
skyreg[skey]['lon'] - skyreg[skey]['wlon']/2,
|
|
skyreg[skey]['lon'] + skyreg[skey]['wlon']/2,
|
|
skyreg[skey]['lat'] - skyreg[skey]['wlat']/2,
|
|
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
|
|
|
|
df = df.query(query)
|
|
print("{}, {}: {} N={}".format(skey, enkey, query, df.shape[0]))
|
|
|
|
t = Table.from_pandas(df)
|
|
t.write("{}fits/{}.{}.fits".format(fluxdir,skey,enkey),overwrite=True)
|
|
|
|
texp = np.array(df['TEXP'])
|
|
with open("{}fits/{}.{}.livetime".format(fluxdir,skey,enkey), 'w') as fp:
|
|
fp.write("{} {} ScWs: {} Texp: {:.2f} Ms\n".format(skey,enkey,df.shape[0],np.sum(texp)/1e6))
|
|
|
|
if not (df.shape[0]>0):
|
|
continue
|
|
|
|
sg_mean,sg_sem = get_spec(df, sigma=sigma, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=plotme)
|
|
ebands0[enkey]=[sg_mean,sg_sem]
|
|
|
|
nsel = int(df.shape[0]/simfrac)
|
|
for n in range(nsim):
|
|
df0=df.sample(nsel)
|
|
sg_mean,sg_sem = get_spec(df0, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
|
|
ebands_sim[enkey].append(sg_mean)
|
|
|
|
|
|
|
|
###
|
|
fspec="{}{}.dat".format(fluxdir,skey)
|
|
with open(fspec, 'w') as fp:
|
|
for enkey in ebands0.keys():
|
|
flux=ebands0[enkey][0]
|
|
err=ebands0[enkey][1]
|
|
print("[DATA] {}: {} {:.6f} {:.6f}".format(skey,enkey,flux,err))
|
|
fp.write("{} {} {} {:.6f} {:.6f}\n".format(skey,enkey,bands[enkey],flux,err))
|
|
|
|
|
|
###
|
|
fspec="{}{}.sim.dat".format(fluxdir,skey)
|
|
with open(fspec, 'w') as fp:
|
|
for enkey in ebands_sim.keys():
|
|
data=ebands_sim[enkey]
|
|
|
|
(mu, sg) = norm.fit(data)
|
|
fp.write("{} {} {} {:.6f} {:.6f}\n".format(skey,enkey,bands[enkey],mu,sg))
|
|
print("[BOOT] {}: {} {:.6f} {:.6f}".format(skey,enkey,mu,sg))
|
|
|
|
if(plotme):
|
|
n, bins, patches = plt.hist(data, 60, density=True, facecolor='green', alpha=0.75)
|
|
# add a 'best fit' line
|
|
y = norm.pdf(bins, mu, sg)
|
|
l = plt.plot(bins, y, 'r--', linewidth=2)
|
|
#plot
|
|
plt.axvline(mu, color="black")
|
|
plt.axvline(ebands0[enkey][0], color="black", linestyle="dashed")
|
|
#plt.axvline(mu+sg_sem, color="black", linestyle="dashed")
|
|
#plt.axvline(mu-sg_sem, color="black", linestyle="dashed")
|
|
plt.axvline(mu+sg, color="blue", linestyle="dashed")
|
|
plt.axvline(mu-sg, color="blue", linestyle="dashed")
|
|
|
|
plt.xlabel('Flux, mCrab')
|
|
plt.ylabel('Probability')
|
|
plt.title("[BOOT] {}: {:.2f} {:.2f}".format(enkey, mu, sg))
|
|
plt.grid(True)
|
|
plt.show()
|
|
|
|
|