generated from erosita/uds
221 lines
6.5 KiB
Python
Executable File
221 lines
6.5 KiB
Python
Executable File
#!/usr/bin/env python
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__author__ = "Roman Krivonos"
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__copyright__ = "Space Research Institute (IKI)"
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import numpy as np
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import pandas as pd
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from astropy.io import fits
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from astropy.table import Table, Column
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from astropy import units as u
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import matplotlib.pyplot as plt
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import math, sys, os
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import pickle
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from sklearn.linear_model import LinearRegression
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from sklearn.linear_model import HuberRegressor
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from sklearn.linear_model import RANSACRegressor
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from sklearn.linear_model import TheilSenRegressor
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from sklearn.model_selection import cross_val_score
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from sklearn.model_selection import RepeatedKFold
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#from statsmodels.robust.scale import huber
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from astropy.stats import sigma_clip
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from astropy.stats import sigma_clipped_stats
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from scipy.stats import norm
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from scipy.stats import describe
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from scipy.stats import sem
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import subprocess
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from numpy import absolute
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from numpy import arange
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from ridge.utils import *
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from ridge.config import *
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inkey="ALL"
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sigma=3
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plotme=False
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"""
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ebands0={#'E02':[0.0,0.0],
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'E03':[0.0,0.0],
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'E04':[0.0,0.0],
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'E05':[0.0,0.0],
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'E06':[0.0,0.0],
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'E07':[0.0,0.0],
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'E08':[0.0,0.0],
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'E09':[0.0,0.0],
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'E10':[0.0,0.0],
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'E11':[0.0,0.0],
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'E12':[0.0,0.0],}
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"""
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ebands0={'B01':[0.0,0.0],
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'B02':[0.0,0.0],
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'B03':[0.0,0.0],
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'B04':[0.0,0.0],
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'B05':[0.0,0.0],
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'B06':[0.0,0.0],
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'B07':[0.0,0.0],
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'B08':[0.0,0.0],
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'B09':[0.0,0.0],
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'B10':[0.0,0.0],
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'B11':[0.0,0.0],
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'B12':[0.0,0.0],
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'B13':[0.0,0.0],
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'B14':[0.0,0.0],
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'B15':[0.0,0.0],
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'B16':[0.0,0.0],
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'B17':[0.0,0.0],
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'B18':[0.0,0.0],
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'B19':[0.0,0.0],
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'B20':[0.0,0.0],
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'B21':[0.0,0.0],
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}
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mcrab=u.def_unit('mCrab')
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ctss=u.def_unit('cts/s')
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u.add_enabled_units([mcrab,ctss])
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#skey='Geminga'
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if len(sys.argv) > 1:
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skeys = [sys.argv[1]]
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else:
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skeys = list(skyreg.keys())
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# don't plot, if all regions are taken
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plotme=False
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if not os.path.exists(specdir):
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os.makedirs(specdir)
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with open(proddir+'detcnts.B21.ignored_rev.resid.pkl', 'rb') as fp:
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ignored_rev = pickle.load(fp)
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print("{} orbits ignored".format(len(ignored_rev)))
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ign=ignored_rev.tolist()
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for skey in skeys:
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if not skey in skyreg.keys():
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print("{} not found in {}".format(skey,list(skyreg.keys())))
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sys.exit()
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# generate array for bootstrap
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ebands_sim={}
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for enkey in ebands0.keys():
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ebands_sim[enkey] = []
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for enkey in ebands0.keys():
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#bkg_fn="detcnts.{}.BKG.resid.fits".format(enkey,inkey)
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#syserr, bkg_sem = get_syserror(proddir+bkg_fn)
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fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
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#d1 = fits.getdata(proddir+fn)
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#d2=np.array(d1)
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#df=pd.DataFrame(d2.view(d2.dtype.newbyteorder()))
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#df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
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#with fits.open(proddir+fn) as data:
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# df = pd.DataFrame(data[1].data)
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dat = Table.read(proddir+fn, unit_parse_strict='silent')
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df = dat.to_pandas()
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#print(df.columns)
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#sys.exit()
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#df = df.query("REV == @ign")
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query = "LON > {} & LON < {} & LAT > {} & LAT < {} & REV != @ign".format(
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skyreg[skey]['lon'] - skyreg[skey]['wlon']/2,
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skyreg[skey]['lon'] + skyreg[skey]['wlon']/2,
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skyreg[skey]['lat'] - skyreg[skey]['wlat']/2,
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skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
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#sys.exit()
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df = df.query(query)
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print("{}, {}: {} N={}".format(skey, enkey, query, df.shape[0]))
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t = Table.from_pandas(df)
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t.write("{}fits/{}.{}.fits".format(specdir,skey,enkey),overwrite=True)
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texp = np.array(df['TEXP'])
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with open("{}fits/{}.{}.livetime".format(specdir,skey,enkey), 'w') as fp:
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fp.write("{} {} ScWs: {} Texp: {:.2f} Ms\n".format(skey,enkey,df.shape[0],np.sum(texp)/1e6))
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if not (df.shape[0]>0):
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continue
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#plt.scatter(df['LON'],df['LAT'])
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#plt.show()
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#print("*** {} {} Data Frame size {} ***".format(skey, enkey, df.size))
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sg_mean,sg_sem = get_spec(df, sigma=2, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=False)
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ebands0[enkey]=[sg_mean,sg_sem]
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nsel = int(df.shape[0]/simfrac)
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for n in range(nsim):
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df0=df.sample(nsel)
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sg_mean,sg_sem = get_spec(df0, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
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ebands_sim[enkey].append(sg_mean)
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#ebands_sim[enkey][1].append(sg_sem)
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###
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fspec="{}{}.spec".format(specdir,skey)
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with open(fspec, 'w') as fp:
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for enkey in ebands0.keys():
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flux=ebands0[enkey][0]
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err=ebands0[enkey][1]
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print("[DATA] {}: {} {:.6f} {:.6f}".format(skey,enkey,flux,err))
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fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],flux,err))
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subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
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###
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fspec="{}{}.sim.spec".format(specdir,skey)
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with open(fspec, 'w') as fp:
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for enkey in ebands_sim.keys():
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data=ebands_sim[enkey]
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(mu, sg) = norm.fit(data)
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fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],mu,sg))
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print("[BOOT] {}: {} {:.6f} {:.6f}".format(skey,enkey,mu,sg))
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if(plotme):
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n, bins, patches = plt.hist(data, 60, density=True, facecolor='green', alpha=0.75)
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# add a 'best fit' line
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y = norm.pdf(bins, mu, sg)
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l = plt.plot(bins, y, 'r--', linewidth=2)
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#plot
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plt.axvline(mu, color="black")
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plt.axvline(ebands0[enkey][0], color="black", linestyle="dashed")
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#plt.axvline(mu+sg_sem, color="black", linestyle="dashed")
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#plt.axvline(mu-sg_sem, color="black", linestyle="dashed")
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plt.axvline(mu+sg, color="blue", linestyle="dashed")
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plt.axvline(mu-sg, color="blue", linestyle="dashed")
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plt.xlabel('Flux, mCrab')
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plt.ylabel('Probability')
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plt.title("[BOOT] {}: {:.2f} {:.2f}".format(enkey, mu, sg))
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plt.grid(True)
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plt.show()
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subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
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try:
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for remfile in ["cols","cols1","cols2","header",]:
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os.remove(remfile)
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except OSError:
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pass
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