#!/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"

skey="SCOX1"


plotme=False


ebands0={'B01':[0.0,0.0],
         'B02':[0.0,0.0],
         'B03':[0.0,0.0],
         'B04':[0.0,0.0],
         'B05':[0.0,0.0],
         'B06':[0.0,0.0],
         'B07':[0.0,0.0],
         'B08':[0.0,0.0],
         'B09':[0.0,0.0],
         'B10':[0.0,0.0],
         'B11':[0.0,0.0],
         'B12':[0.0,0.0],
         'B13':[0.0,0.0],
         'B14':[0.0,0.0],
         'B15':[0.0,0.0],
         'B16':[0.0,0.0],
         'B17':[0.0,0.0],
         'B18':[0.0,0.0],
         'B19':[0.0,0.0],
         'B20':[0.0,0.0],
         'B21':[0.0,0.0],
         }

"""
ebands0={'A01':[0.0,0.0],
         'A02':[0.0,0.0],
         'A03':[0.0,0.0],
         }
"""

mcrab=u.def_unit('mCrab')
ctss=u.def_unit('cts/s')
u.add_enabled_units([mcrab,ctss])

if not os.path.exists(specdir):
    os.makedirs(specdir)

fitsdir = "{}fits/".format(specdir)
if not os.path.exists(fitsdir):
    os.makedirs(fitsdir)

for enkey in ebands0.keys():
        
    fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
    dat = Table.read(proddir+fn)
    df = dat.to_pandas()

    #crab_sep_max=2.0
    query = 'PHASE > {} & PHASE < {} & SCO_SEP < {}'.format(phmin,phmax,crab_sep_max)            
    df = df.query(query)
    print("{}: {} N={}".format(enkey, query, df.shape[0]))

    t = Table.from_pandas(df)
    t.write("{}fits/SCOX1.{}.fits".format(specdir,enkey),overwrite=True)

    if not (df.shape[0]>0):
        print("continue")
        continue
        
    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)
    ebands0[enkey]=[sg_mean,sg_sem]   
    

        

fspec="{}{}.spec".format(specdir,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("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],flux,err))    
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])

    
try:
    for remfile in ["cols","cols1","cols2","header",]:
        os.remove(remfile)
except OSError:
    pass