#! /usr/bin/env python
# ==========================================================================
# This script illustrates how to build an unbinned analysis pipeline using
# the ctools.
#
# Copyright (C) 2011-2014 Juergen Knoedlseder
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# ==========================================================================
from ctools import *
from gammalib import *
from math import *
import os
import glob
import sys

# ============================= #
# Analysis pipeline - version 1 #
# ============================= #
def pipeline_v1():
    """
    Unbinned analysis pipeline - save intermediate results.
 
    This function implements an analysis pipeline that successively calls
    ctobssim, ctselect and ctlike by saving the intermediate results as FITS
    files on disk. This replicates the way how the analysis would be done
    in a ftools-like approach.
    """
    # Set script parameters
    model_name           = "data/crab.xml"
    events_name          = "events.fits"
    selected_events_name = "selected_events.fits"
    result_name          = "results.xml"
    caldb                = "dummy"
    irf                  = "cta_dummy_irf"
    ra                   =   83.63
    dec                  =   22.01
    rad_sim              =   10.0
    tstart               =    0.0
    tstop                = 1800.0
    emin                 =    0.1
    emax                 =  100.0
    rad_select           =    3.0
    
    # Initialise timing
    wall_seconds = 0.0
    cpu_seconds  = 0.0
    
    # Simulate events
    sim = ctobssim()
    sim.logFileOpen()
    sim["infile"].filename(model_name)
    sim["outfile"].filename(events_name)
    sim["caldb"].string(caldb)
    sim["irf"].string(irf)
    sim["ra"].real(ra)
    sim["dec"].real(dec)
    sim["rad"].real(rad_sim)
    sim["tmin"].real(tstart)
    sim["tmax"].real(tstop)
    sim["emin"].real(emin)
    sim["emax"].real(emax)
    sim.execute()
    sys.stdout.write(".")
    
    # Select events
    select = ctselect()
    select.logFileOpen()
    select["infile"].filename(events_name)
    select["outfile"].filename(selected_events_name)
    select["ra"].real(ra)
    select["dec"].real(dec)
    select["rad"].real(rad_select)
    select["tmin"].real(tstart)
    select["tmax"].real(tstop)
    select["emin"].real(emin)
    select["emax"].real(emax)
    select.execute()
    sys.stdout.write(".")
    
    # Perform maximum likelihood fitting
    like = ctlike()
    like.logFileOpen()
    like["infile"].filename(selected_events_name)
    like["srcmdl"].filename(model_name)
    like["outmdl"].filename(result_name)
    like["caldb"].string(caldb)
    like["irf"].string(irf)
    like.execute()
    sys.stdout.write(".")
    
    # Return
    return


# ============================= #
# Analysis pipeline - version 2 #
# ============================= #
def pipeline_v2(model_name="data/crab.xml", \
                ra_sim=83.63, dec_sim=22.01, rad_sim=10.0, \
                ra_select=83.63, dec_select=22.01, rad_select=3.0, \
                tstart=0.0, tstop=1800.0, \
                emin=0.1, emax=100.0, \
                ref={}):
    """
    Unbinned analysis pipeline - keep intermediate results in memory.
 
    This function implements an analysis pipeline that successively calls
    ctobssim, ctselect and ctlike without saving the intermediate results as
    FITS files on disk. All data is only hold in memory.
    """
    # Set script parameters
    caldb = "dummy"
    irf   = "cta_dummy_irf"
    
    # Simulate events
    sim = ctobssim()
    sim["infile"].filename(model_name)
    sim["caldb"].string(caldb)
    sim["irf"].string(irf)
    sim["ra"].real(ra_sim)
    sim["dec"].real(dec_sim)
    sim["rad"].real(rad_sim)
    sim["tmin"].real(tstart)
    sim["tmax"].real(tstop)
    sim["emin"].real(emin)
    sim["emax"].real(emax)
    sim.run()
    sys.stdout.write(".")
    
    # Select events
    select = ctselect(sim.obs())
    select["ra"].real(ra_select)
    select["dec"].real(dec_select)
    select["rad"].real(rad_select)
    select["tmin"].real(tstart)
    select["tmax"].real(tstop)
    select["emin"].real(emin)
    select["emax"].real(emax)
    select.run()
    sys.stdout.write(".")
    
    # Perform maximum likelihood fitting
    like = ctlike(select.obs())
    like.run()
    sys.stdout.write(".")

    # Analyse results
    errors = 0
    models = like.obs().models()
    #print(models)
    for model in models:
        for key in ref:
            if key == model.name():
                for par in model:
                    for value in ref[key]:
                        if value == par.name():
                            if abs(float(ref[key][value])-par.value()) < 0.0001:
                                sys.stdout.write(".")
                            else:
                                sys.stdout.write("E")
                                errors += 1

    # Return number of errors
    return errors


#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
    """
    Test ctools Python modules.
    """
    # Dump header
    print("")
    print("***********************************")
    print("* ctools Python interface testing *")
    print("***********************************")
    
    # Set PFILES environment variable
    try:
        os.mkdir("pfiles")
    except:
        pass
    os.system("cp -r ../src/*/*.par pfiles/")
    os.environ['PFILES'] = "pfiles"
    
    # Remove any existing result files
    list = [glob.glob("*.fits"), glob.glob("*.xml")]
    for files in list:
        for file in files:
            os.remove(file)

    # Save intermediate results on disk
    sys.stdout.write("Test executable analysis: ")
    pipeline_v1()
    print(" ok")

    # Keep results in memory
    sys.stdout.write("Test in-memory analysis: ")
    err = pipeline_v2()
    if err == 0:
        print(" ok")
    else:
        print(" NOK")

    # Test diffuse map cube with constant spectrum
    sys.stdout.write("Test diffuse map cube with constant analysis: ")
    ref = {'CTABackgroundModel': {'Value': 0.242717}}
    err = pipeline_v2(model_name="data/model_background_cube_const.xml", \
                      ra_sim=84.17263, dec_sim=22.01444, rad_sim=2.0, \
                      ra_select=83.80, dec_select=22.20, rad_select=1.0, \
                      tstart=0.0, tstop=1.0, \
                      emin=0.3, emax=8.0, \
                      ref=ref)
    if err == 0:
        print(" ok")
    else:
        print(" NOK")

    # Test diffuse map cube with power law spectrum
    sys.stdout.write("Test diffuse map cube with power law analysis: ")
    ref = {'CTABackgroundModel': {'Prefactor': 0.513946, 'Index': -1.22388}}
    err = pipeline_v2(model_name="data/model_background_cube.xml", \
                      ra_sim=84.17263, dec_sim=22.01444, rad_sim=2.0, \
                      ra_select=84.00, dec_select=22.20, rad_select=1.0, \
                      tstart=0.0, tstop=1.0, \
                      emin=0.3, emax=8.0, \
                      ref=ref)
    if err == 0:
        print(" ok")
    else:
        print(" NOK")