from otree.api import (
    models,
    widgets,
    BaseConstants,
    BaseSubsession,
    BaseGroup,
    BasePlayer,
    Currency as c,
    currency_range,
)
from random import choice
from random import shuffle

author = 'Miguel Alberto Gomez'

doc = """
Survey experiment instrument with Gregory Winger from the University of Cincinnati.
"""


class Constants(BaseConstants):
    name_in_url = 'experiment'
    players_per_group = None
    num_rounds = 1


class Subsession(BaseSubsession):

    def creating_session(self):
        # Assign treatment conditions and measure order to participants
        for p in self.get_players():
            p.supportive = choice([True, False])
            p.partner = choice([True, False])
            p.state = choice([True, False])
            p.initial = choice([True, False])


class Group(BaseGroup):
    pass


class Player(BasePlayer):
    # Participant Consent
    consent = models.BooleanField()

    # Supportive Treatment Condition
    supportive = models.BooleanField()

    # Partner Treatment Condition
    partner = models.BooleanField()

    # State Actor Condition
    state = models.BooleanField()

    # Flag for completed experiment
    completed = models.BooleanField()

    # Browser capture, only for error testing
    browser = models.StringField()

    # Order condition for covariate measures
    initial = models.BooleanField()

    # Cooperative, Militant, Isolationist Index
    # isolationist 2 is reverse coded

    cooperative_1 = models.IntegerField(min=1, max=6)
    cooperative_2 = models.IntegerField(min=1, max=6)
    cooperative_3 = models.IntegerField(min=1, max=6)
    militant_1 = models.IntegerField(min=1, max=6)
    militant_2 = models.IntegerField(min=1, max=6)
    militant_3 = models.IntegerField(min=1, max=6)
    militant_4 = models.IntegerField(min=1, max=6)
    isolationist_1 = models.IntegerField(min=1, max=6)
    isolationist_2 = models.IntegerField(min=1, max=6)
    isolationist_3 = models.IntegerField(min=1, max=6)
    sovereign_1 = models.IntegerField(min=1, max=6)
    sovereign_2 = models.IntegerField(min=1, max=6)
    sovereign_3 = models.IntegerField(min=1, max=6)

    # Participant risk preference
#    risk = models.FloatField()

    # Partricipant attention
    attention = models.IntegerField()

    # Participant domain expertise

    knowledge_1 = models.IntegerField(min=1, max=5)
    knowledge_2 = models.IntegerField(min=1, max=5)
    knowledge_3 = models.IntegerField(min=1, max=5)
    knowledge_4 = models.IntegerField(min=1, max=5)
    knowledge_5 = models.IntegerField(min=1, max=5)
    knowledge_6 = models.IntegerField(min=1, max=5)

    # Participant Age
    age = models.IntegerField()

    # Participant Gender
    gender = models.IntegerField(
        choices=[
            [1, 'Male'],
            [2, 'Female'],
            [3, 'Other']
        ]
    )

    # Participant Educational Achievement
    education = models.IntegerField(
        choices=[
            [1, 'Secondary Education (High School Diploma)'],
            [2, 'Vocational Qualification'],
            [3, 'Undergraduate Degree (BA, BSc etc.)'],
            [4, 'Post-graduate Degree (MA, MSc etc.)'],
            [5, 'Doctorate (PhD, DBA etc.)']
        ])

    # Household Income
    income = models.IntegerField(
        choices=[
            [1, 'Much Lower than the Median'],
            [2, 'Lower than the Median'],
            [3, 'A Little Lower than the Median'],
            [4, 'Same as the Median'],
            [5, 'A Little Higher than the Median'],
            [6, 'Higher than the Median'],
            [7, 'Much Higher than the Median']
        ]
    )

    # Residency
    resident = models.BooleanField()

    # Affected by Malware
    malware = models.BooleanField()

    # Dependent Variables

    # Self-reported Anxiety (Marteau and Bekker 1992; Spielberger 1970)
    # Reverse coded: 2, 3, 5
    anxiety_1 = models.IntegerField(min=1, max=6)
    anxiety_2 = models.IntegerField(min=1, max=6)
    anxiety_3 = models.IntegerField(min=1, max=6)
    anxiety_4 = models.IntegerField(min=1, max=6)
    anxiety_5 = models.IntegerField(min=1, max=6)
    anxiety_6 = models.IntegerField(min=1, max=6)

    # Self-reported Anger (State-Trait Anger Expression Inventory; Spielberger 1988)
    anger_1 = models.IntegerField(min=1, max=6)
    anger_2 = models.IntegerField(min=1, max=6)
    anger_3 = models.IntegerField(min=1, max=6)
    anger_4 = models.IntegerField(min=1, max=6)

    # Self-reported Dread (Gomez & W Villar, 2018)
    dread_1 = models.IntegerField(min=1, max=6)
    dread_2 = models.IntegerField(min=1, max=6)
    dread_3 = models.IntegerField(min=1, max=6)
    dread_4 = models.IntegerField(min=1, max=6)
    dread_5 = models.IntegerField(min=1, max=6)
    dread_6 = models.IntegerField(min=1, max=6)
    dread_7 = models.IntegerField(min=1, max=6)
    dread_8 = models.IntegerField(min=1, max=6)
    dread_9 = models.IntegerField(min=1, max=6)
    dread_10 = models.IntegerField(min=1, max=6)

    # Censure actor
    censure = models.BooleanField()

    # Continue cooperation
    cooperate = models.BooleanField()

    # Continue cooperation
    interest = models.BooleanField()

    # Consequences for mediator
    consequences = models.BooleanField()

    # Responsible actor
    responsible = models.StringField()

    # State Target
    state_target = models.StringField()

    # Manipulation checks
    # Check for permission treatment
    supportive_check = models.BooleanField()
    # Check for partner treatment
    partner_check = models.BooleanField()
    # Check for state treatment
    state_check = models.BooleanField()