#! /usr/bin/env python

import os
import numpy as np

full_scan_mfr = [
    # (1, 1, 1), (1, 2, 0)
]

fix_sfr_mfr = [
    (1, 1, 1, 1, 2, 0),
    (1, 1, 1, 1, 0, 0),
    (1, 1, 1, 0, 1, 0),
    # (1, 1, 1, 0, 0, 1),
    # (1, 1, 0, 1, 2, 0),
    # (1, 1, 0, 1, 0, 0),
    # (1, 1, 0, 0, 1, 0),
    # (1, 0, 0, 1, 0, 0),
    # (0, 1, 0, 0, 1, 0),
    # (1, 2, 0, 1, 2, 0),
    # (1, 2, 0, 0, 1, 0),
]

GLOBAL_PARAMS = {}

# Bayes Factor
sens_eval_bin = 'all' # set to 'all' to run normally
GLOBAL_PARAMS.update(dict(
    sens_run      = 'True',
    run_method    = 'fixed_angle', # full, fixed_angle, corr_angle
    stat_method   = 'frequentist',
    sens_bins     = 10,
    seed          = 'None'
))

# MultiNest
GLOBAL_PARAMS.update(dict(
    mn_live_points = 400,
    mn_tolerance   = 0.01,
    mn_output      = './mnrun'
))

# FR
dimension         = [3, 6]
GLOBAL_PARAMS.update(dict(
    threads           = 1,
    binning           = '1e4 1e7 5',
    no_bsm            = 'False',
    scale_region      = "1E10",
    energy_dependance = 'spectral',
    spectral_index    = -2,
    fix_mixing        = 'False',
    fix_mixing_almost = 'False'
))

# Likelihood
GLOBAL_PARAMS.update(dict(
    likelihood  = 'golemfit',
    sigma_ratio = '0.01'
))

# GolemFit
GLOBAL_PARAMS.update(dict(
    ast  = 'p2_0',
    data = 'real'
))

# Plot
GLOBAL_PARAMS.update(dict(
    plot_statistic = 'True'
))

outfile = 'dagman_FR_SENS_{0}_{1}.submit'.format(
    GLOBAL_PARAMS['stat_method'], GLOBAL_PARAMS['run_method']
)
golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub'

if sens_eval_bin.lower() != 'all':
    if GLOBAL_PARAMS['run_method'].lower() == 'corr_angle':
        sens_runs = GLOBAL_PARAMS['sens_bins']**2
    else:
        sens_runs = GLOBAL_PARAMS['sens_bins']
else: sens_runs = 1

with open(outfile, 'w') as f:
    job_number = 1
    for dim in dimension:
        print 'dimension', dim
        outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}'.format(
            GLOBAL_PARAMS['likelihood'], dim
        )
        for frs in fix_sfr_mfr:
            print 'frs', frs
            output = outchain_head + '/fix_ifr/'
            if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
                output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
            output += 'fr_stat'
            for r in xrange(sens_runs):
                print 'run', r
                f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script))
                f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim))
                f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0]))
                f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1]))
                f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2]))
                f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, True))
                f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, frs[3]))
                f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, frs[4]))
                f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, frs[5]))
                if sens_eval_bin.lower() != 'all':
                    f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
                else:
                    f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all'))
                for key in GLOBAL_PARAMS.iterkeys():
                    f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key]))
                f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, output))
                job_number += 1

        for frs in full_scan_mfr:
            print 'frs', frs
            output = outchain_head + '/full/'
            if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
                output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
            output += 'fr_stat'
            for r in xrange(sens_runs):
                print 'run', r
                f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script))
                f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim))
                f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0]))
                f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1]))
                f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2]))
                f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, False))
                f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, 0))
                f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, 0))
                f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, 0))
                if sens_eval_bin.lower() != 'all':
                    f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
                else:
                    f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all'))
                for key in GLOBAL_PARAMS.iterkeys():
                    f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key]))
                f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, output))
                job_number += 1