generated from erosita/uds
126 lines
3.0 KiB
Python
Executable File
126 lines
3.0 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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skey="SCOX1"
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plotme=False
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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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"""
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ebands0={'A01':[0.0,0.0],
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'A02':[0.0,0.0],
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'A03':[0.0,0.0],
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}
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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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if not os.path.exists(specdir):
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os.makedirs(specdir)
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fitsdir = "{}fits/".format(specdir)
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if not os.path.exists(fitsdir):
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os.makedirs(fitsdir)
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for enkey in ebands0.keys():
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fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
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dat = Table.read(proddir+fn)
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df = dat.to_pandas()
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#crab_sep_max=2.0
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query = 'PHASE > {} & PHASE < {} & SCO_SEP < {}'.format(phmin,phmax,crab_sep_max)
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df = df.query(query)
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print("{}: {} N={}".format(enkey, query, df.shape[0]))
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t = Table.from_pandas(df)
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t.write("{}fits/SCOX1.{}.fits".format(specdir,enkey),overwrite=True)
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if not (df.shape[0]>0):
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print("continue")
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continue
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sg_mean,sg_sem,skew_val,skew_err = get_spec_src(df, sigma=3, grxe_err_cut=grxe_err_cut, enkey=enkey, plotme=True, gaussfit=True)
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ebands0[enkey]=[sg_mean,sg_sem]
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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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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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