import os,sys

import numpy as np

from plasma.utils.performance import *

from plasma.conf import conf

#mode = 'test'

file_num = 0

save_figure = True

pred_ttd = False

# cut_shot_ends = conf['data']['cut_shot_ends']

# dt = conf['data']['dt']

# T_max_warn = int(round(conf['data']['T_warning']/dt))

# T_min_warn = conf['data']['T_min_warn']#int(round(conf['data']['T_min_warn']/dt))

# if cut_shot_ends:

# T_max_warn = T_max_warn-T_min_warn

# T_min_warn = 0

T_min_warn = 30 #None #take value from conf #30

verbose=False

if len(sys.argv) > 1:

results_dir = sys.argv[1]

else:

results_dir = conf['paths']['results_prepath']

shots_dir = conf['paths']['processed_prepath']

analyzer = PerformanceAnalyzer(conf=conf,results_dir=results_dir,shots_dir=shots_dir,i = file_num,

T_min_warn = T_min_warn, verbose = verbose, pred_ttd=pred_ttd)

analyzer.load_ith_file()

P_thresh_opt = analyzer.compute_tradeoffs_and_print_from_training()

#P_thresh_opt = 0.566#0.566#0.92# analyzer.compute_tradeoffs_and_print_from_training()

linestyle="-"

P_thresh_range,missed_range,fp_range = analyzer.compute_tradeoffs_and_plot('test',save_figure=save_figure,plot_string='_test',linestyle=linestyle)

np.savez('test_roc.npz',"P_thresh_range",P_thresh_range,"missed_range",missed_range,"fp_range",fp_range)

analyzer.compute_tradeoffs_and_plot('train',save_figure=save_figure,plot_string='_train',linestyle=linestyle)

analyzer.summarize_shot_prediction_stats_by_mode(P_thresh_opt,'test')

normalize = True

analyzer.example_plots(P_thresh_opt,'test','any',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'test',['FP'],extra_filename='test',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'test',['FN'],extra_filename='test',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'test',['TP'],extra_filename='test',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'test',['late'],extra_filename='test',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'train',['TN'],extra_filename='train',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'train',['FP'],extra_filename='train',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'train',['FN'],extra_filename='train',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'train',['TP'],extra_filename='train',normalize=normalize)

analyzer.example_plots(P_thresh_opt,'train',['late'],extra_filename='train',normalize=normalize)

alarms,disr_alarms,nondisr_alarms = analyzer.gather_first_alarms(P_thresh_opt,'test')

analyzer.hist_alarms(disr_alarms,'disruptive alarms, P thresh = {}'.format(P_thresh_opt),save_figure=save_figure,linestyle=linestyle)

np.savez('disruptive_alarms_test.npz',"disr_alarms",disr_alarms,"P_thresh_opt",P_thresh_opt)

print('{} disruptive alarms'.format(len(disr_alarms)))

print('{} seconds mean alarm time'.format(np.mean(disr_alarms[disr_alarms > 0])))

print('{} seconds median alarm time'.format(np.median(disr_alarms[disr_alarms > 0])))

analyzer.hist_alarms(nondisr_alarms,'nondisruptive alarms, P thresh = {}'.format(P_thresh_opt))

print('{} nondisruptive alarms'.format(len(nondisr_alarms)))