from os import environ
import sys


## Settings.py Änderung damit bei Behnud das gleiche steht
SESSION_CONFIGS = [
    dict(
        name='count_numbers_group',
        display_name='count_numbers_group',
        num_demo_participants=2,
        app_sequence=['count_numbers_group'],
        second_time_counting=False,
        second_time_counting2 = True,
    ),
    dict(
        name='tax',
        display_name='Tax Evasion',
        num_demo_participants=1,
        app_sequence=['taxevasion','dohme_lotterie'],
        treatment='selection',#'control', ## 'info' , 'selection'
        high_rate=False, ##
    ),

    dict(
        name='Unmasking_Corp',
        display_name='Unmasking Corporate Hypocrisy',
        num_demo_participants=2,
        app_sequence=['unmasking_corporate_hypocrisy'],
    ),
    dict(
        name='Unmasking_Corp2',
        display_name='Unmasking Corporate Questionaire',
        num_demo_participants=4,
        app_sequence=['unmasking_corporate_questionaire'],
    ),
    dict(
        name='Unmasking_Complete',
        display_name='Unmasking inkl Konsenz & Fragebogen',
        num_demo_participants=4,
        app_sequence=['consent_check', 'unmasking_corporate_hypocrisy', 'unmasking_corporate_questionaire', 'EndScreen'],
    ),
    dict(
        name='consenz',
        display_name='Konsenzcheck Willkommensseite',
        num_demo_participants=3,
        app_sequence=['consent_check'],
    ),
dict(
        name='dohme_lotterie',
        display_name='dohme_lotterie',
        num_demo_participants=1,
        app_sequence=['dohme_lotterie'],
    ),
    dict(
        name='awesome_norms',
        display_name='awesome_norms',
        num_demo_participants=2,
        app_sequence=['awesome_norms'],
    ),
    dict(
        name='dictator_pretest',
        display_name='dictator_pretest',
        num_demo_participants=2,
        app_sequence=['dictator_pretest'],
    ),
    dict(
        name='receiver_pretest',
        display_name='receiver_pretest',
        num_demo_participants=2,
        app_sequence=['receiver_pretest'],
    ),
    dict(
        name='social_proximity_neu_6_fragebogen',
        display_name='Social Proximity Einfachsitzung --- 6 Participants MIT FRAGEBOGEN',
        num_demo_participants=6,
        app_sequence=['social_proximity_single_session', 'social_proximity_single_questionaire'],
        treatment='social_interaction',
        ## control (entsprich ohne interaction (similarity) / social_interaction mit Similarity
        type_of_sevens='old',
        ### Nur für die NEU Version
        attempts_per_puzzle=1,
        width=10,
        height=20,
        text_size=15,
        counted_char='7',  # '←',
        ignored_chars='012345689',  # '↔↑↓→',
        amount_of_counted_chars=2,
        second_time_counting=False,
        skip_questionaire=False
    ),
    dict(
        name='poc',
        display_name='Proof of concept',
        num_demo_participants=6,
        app_sequence=['proof_of_concept'],
    ),
    dict(
        name='7er',
        display_name='Environmental 7er test',
        num_demo_participants=2,
        app_sequence=['Environmental_count_numbers'],
    ),

    dict(
        name='Environmental_Screening',
        display_name='Environmental Screening',
        num_demo_participants=2,
        app_sequence=['Environmental_Screening'],
    ),
    dict(
        name='Environmental_Screening_Task',
        display_name='Environmental Screening Task',
        num_demo_participants=2,
        app_sequence=['Environmental_Screening_Task'],
    ),
    dict(
       name='Environmental_Employee',
        display_name='Environmental Employee',
        num_demo_participants=2,
        app_sequence=['Environmental_Employee', 'Environmental_count_numbers', 'Environmental_Questionnaire_Employee'],
    ),
    dict(
        name='Environmental_Manager',
        display_name='Environmental Manager',
        num_demo_participants=2,
        app_sequence=['Environmental_Manager', 'Environmental_Questionnaire_Manager'],
    ),

    dict(
        name='btae',
        display_name='Better-than-average',
        num_demo_participants=1,
        app_sequence=['btae'],
    ),
    dict(
        name='hrd',
        display_name='Human Robot Dishonesty',
        num_demo_participants=5,
        app_sequence=['hrd'],
    ),
    dict(
        name='social_proximity_neu_6',
        display_name='Social Proximity Einfachsitzung --- 6 Participants',
        num_demo_participants=6,
        app_sequence=['social_proximity_single_session', 'social_proximity_single_questionaire'],
        treatment='social_interaction',
        ## control (entsprich ohne interaction (similarity) / social_interaction mit Similarity
        type_of_sevens='old',
        ### Nur für die NEU Version
        attempts_per_puzzle=1,
        width=10,
        height=20,
        text_size=15,
        counted_char='7',  # '←',
        ignored_chars='012345689',  # '↔↑↓→',
        amount_of_counted_chars=2,
        second_time_counting=False,
        skip_questionaire=True
    ),

    dict(
        name='social_proximity_single_questionaire',
        display_name='Social Proximity Einfachsitzung - Ex-Post Fragebogen',
        num_demo_participants=1,
        app_sequence=['social_proximity_single_questionaire']
    ),
    dict(
        name='Poker_Bargain',
        display_name='Chip Bargain Alle Variablen',
        num_demo_participants=2,
        app_sequence=['chip_bargain'],
        treatment='online',
        university=True,  ### True Students Only
        payment='machine',
        first='green',
        cash=True,  ## True or False
        numcards=2,  ## keine ahnung
        forcedwait=False,
        enter_number=False,
        doc="""
        Variable - values : def
        university - True/False  : True Studies Only 
        Cash - True/False : True Cash directly - False transaction 
        payment - machine|human : machine = Münzwurf ; human is standard
        first - green|red|random : green or red first, random will use either  
        treatment - online|on_premise : on_premise= 
        numcards - keine Info - Zahl
        enter_number: Numberstage extra oder nicht 
        """
    ),
    dict(
        name='social_proximity_neu_24_23_XX',
        display_name='Social Proximity Einfachsitzung --- 24/23/22 Participants im Moment eingestellt auf 23: in settings.py anpassen',
        num_demo_participants=6,
        app_sequence=['social_proximity_single_session', 'social_proximity_single_questionaire'],
        treatment='social_interaction',
        ## control (entsprich ohne interaction (similarity) / social_interaction mit Similarity
        type_of_sevens='old',
        ### Nur für die NEU Version
        attempts_per_puzzle=1,
        width=10,
        height=20,
        text_size=15,
        counted_char='7',  # '←',
        ignored_chars='012345689',  # '↔↑↓→',
        amount_of_counted_chars=2,
        second_time_counting=False,
        skip_questionaire=True,
        skip_test_rounds=False,
        show_providers_performance_average=True,
    ),
    dict(
        name='poker_chip_red',
        display_name='ChipBargain HNI Rot Zuerst',
        num_demo_participants=2,
        app_sequence=['chip_bargain'],
        adult=True,
        treatment='on_premise',  ## Lab hinzufügen ; Auszahlungszahlungsperiode = random
        first='red'  ##random -- zufügen
    ),
    dict(
        name='poker_chip_green',
        display_name='ChipBargain HNI Grün Zuerst',
        num_demo_participants=2,
        app_sequence=['chip_bargain'],
        adult=True,
        treatment='on_premise',
        first='green'
    ),
    dict(
        name="wb",
        display_name="Whistleblowing social norms",
        num_demo_participants=4,
        #app_sequence=["baerlab_intro", "count_numbers_group", "whistleblowing","count_numbers_group_symbolic", "wb_total_results", "questionnaire"],
        app_sequence=["baerlab_intro", "count_numbers_group", "whistleblowing","count_numbers_group2", "wb_total_results", "questionnaire"],
        treatment='wb',
        wb_treatment = 'T1', # 'Control', T1' oder 'T2' eingeben für die drei Treatments
        intro_for_experiment='wb',
        show_partner_page=True,
        online=False,
        doc='''
        Treatment can be Control (Control) , T1 (Treatment 1) or T2(Treatment 2)
        '''
        ),
    dict(
        name='Fragebogen',
        display_name='Fragebogen WB social norms',
        num_demo_participants=1,
        app_sequence=['questionnaire'],
    ),
    dict(
        name="wb_norm_app",
        display_name="Whistleblowing Appropriateness",
        num_demo_participants=4,
        app_sequence=["WB_Intro", "count_numbers_group", "WB_Norm_Appropriateness", "WB_Norm_Appropriateness_Part2",
                      "count_numbers_group2", "wb_norm_app_total_results", "questionnaire_wb_norm_app"],
        treatment='wb_norm_app',
        wb_treatment='Control',  # 'Control', T1' oder 'T2' oder 'T3' eingeben für die vier Treatments
        intro_for_experiment='wb_norm_app',
        show_partner_page=True,
        doc='''
        Treatment can be Control (Control) , T1 (norm message for wb/non-embezzle) or T2(norm message against remaining silent/embezzle) or T3 (norm message for wb/non-embezzle + norm message against remaining silent/embezzle)
        T4 (norm elicitation after the Wrongdoing/WB Decision), T5 (norm message both, without previous norm elicitation)
        ''',
        second_time_counting = False,
        second_time_counting2 = True,
        keep_groups_as_before = False,
        keep_groups_as_before2 = True,
        retry_delay=1.0,
        puzzle_delay=1.0,
        attempts_per_puzzle=1,
    ),
    dict(
        name='Fragebogen_Norm_Appropriateness',
        display_name='Fragebogen WB Norm Appropriateness',
        num_demo_participants=4,
        app_sequence=['questionnaire_wb_norm_app'],
    ),
    dict(
        name="wb_norm_app2",
        display_name="Whistleblowing Appropriateness 2",
        num_demo_participants=4,
        app_sequence=["WB_Intro", "count_numbers_group", "WB_Norm_Appropriateness_2", "WB_Norm_Appropriateness_Part2",
                      "count_numbers_group2", "wb_norm_app_total_results", "questionnaire_wb_norm_app"],
        treatment='wb_norm_app_2',
        wb_treatment='Control',  # 'Control', T1' oder 'T2' oder 'T3' eingeben für die vier Treatments
        intro_for_experiment='wb_norm_app',
        show_partner_page=True,
        doc='''
    Treatment can be Control (Control) , T1 (norm message for wb/non-embezzle) or T2(norm message against remaining silent/embezzle) or T3 (norm message for wb/non-embezzle + norm message against remaining silent/embezzle)
    T4 (norm elicitation after the Wrongdoing/WB Decision), T5 (norm message both, without previous norm elicitation)
    ''',
        second_time_counting=False,
        second_time_counting2=True,
        keep_groups_as_before=False,
        keep_groups_as_before2=True,
        retry_delay=1.0,
        puzzle_delay=1.0,
        attempts_per_puzzle=1,
    ),


    #    display_name="Whistleblowing Appropriateness 2 BEHNUD",

        #       app_sequence=["WB_Intro", "count_numbers_group", "count_numbers_group_WB a 4 Runden", "wb_norm_app_total_results", "questionnaire_wb_norm_app"],



]

# if you set a property in SESSION_CONFIG_DEFAULTS, it will be inherited by all configs
# in SESSION_CONFIGS, except those that explicitly override it.
# the session config can be accessed from methods in your apps as self.session.config,
# e.g. self.session.config['participation_fee']

SESSION_CONFIG_DEFAULTS = dict(
    real_world_currency_per_point=0.07,
    participation_fee=2.50,
    doc=""
)
POINTS_CUSTOM_NAME = 'Taler'
PARTICIPANT_FIELDS = ['quiz_num_correct', 'sp_task_timer', 'total_points_test', 'count_numbers_together',
                      'estimate_correct', 'spieler', 'playertype', 'player_embezzle', 'player_wb',
                      'player_team_donation', 'expiry', 'social_proximity_order_customer','sp_playertype',
                      'social_proximity_order_seller', 'social_proximity_list', 'matched_before', 'employee' ,
                      ]  # ob Einschätzung korrekt ist
SESSION_FIELDS = ['params', 'second_time_counting', 'second_time_counting2','total_donation', 'mymatrix', 'social_proximity_anbieter',
                  'social_proximity_kunde', 'social_proximity_anbieter_not_chosen_round1']

# ISO-639 code
# for example: de, fr, ja, ko, zh-hans
#LANGUAGE_CODE = 'en'
LANGUAGE_CODE = 'de'

# e.g. EUR, GBP, CNY, JPY
REAL_WORLD_CURRENCY_CODE = 'EUR'
#REAL_WORLD_CURRENCY_CODE = 'GBP' #for WB_App_Norm Experiment which runs in Prolific;
USE_POINTS = True
#USE_POINTS = False ## einkommentieren wenn realworldcurrency benutzt wird !


ROOMS = [
    dict(
        name='control',
        display_name='control',
        participant_label_file='_rooms/lab.txt',
        use_secure_urls=False
    ),
    dict(
        name='treatment',
        display_name='treatment',
       # participant_label_file='_rooms/lab.txt',
       # use_secure_urls=False
    )
]
# OTREE_AUTH_LEVEL = DEMO  # Set to "STUDY" for actual experiment run

ADMIN_USERNAME = 'admin'
# for security, best to set admin password in an environment variable
ADMIN_PASSWORD = environ.get('OTREE_ADMIN_PASSWORD')

DEMO_PAGE_INTRO_HTML = """ """

SECRET_KEY = '6962056274088'

######################## Komplette settings.py- dict Einträge schlank halten, wenn Experiment
#### im Moment nicht gebraucht dann auskommentieren.

'''
dict(
        name='QuizTest',
        display_name='Quiz Test',
        num_demo_participants=5,
        app_sequence=['quiz'],
    ),
    dict(
        name='nim',
        display_name='VHB NIM',
        num_demo_participants=2,
        app_sequence=['nim']
    ),
    dict(
        name='vhbbeauty',
        display_name='VHB Beautycontest',
        num_demo_participants=3,
        app_sequence=['beautycontestVHB']
    ),
    dict(
        name='rockpaperscissors',
        display_name='VHB Rock Paper Scissors',
        num_demo_participants=2,
        app_sequence=['rockpaperscissorsVHB']
    )
    dict(
        name="matrices",
        display_name="7er Zählen neu",
        num_demo_participants=10,
        app_sequence=["count_numbers_group"],
        attempts_per_puzzle=1,
        width=10,
        height=20,
        text_size=15,
        counted_char='7',
        ignored_chars='012345689',
        amount_of_counted_chars=2,
        second_time_counting=False,
    ),

   
    
    dict(
        name='MontyHall',
        display_name='FMontyHall',
        num_demo_participants=1,
        app_sequence=['monty_hall'],
    ),
    dict(
        name='schere',
        display_name='Schere Stein Papier',
        num_demo_participants=2,
        app_sequence=['paper_scissor_rock'],
    ),

    dict(
        name='tax',
        display_name='taxevasion',
        num_demo_participants=1,
        app_sequence=['taxevasion']
    ),
    dict(
        name='analogientest',
        display_name='Analogientest',
        num_demo_participants=2,
        app_sequence=['analogientest'],
    ),    




'''