# -*- coding: utf-8 -*-

# ╔════════════════════════════════════════════════════════════════════╗
# ║   Synaptans WorkforceSim™: open-source software for simulating     ║
# ║   the dynamics of a factory workforce and assessing approaches     ║
# ║   to AI-based predictive analytics in the workplace.               ║
# ║                                                                    ║
# ║   Developed by Matthew E. Gladden • ©2021-22 NeuraXenetica LLC     ║
# ║                                                                    ║
# ║   This software is made available for use under                    ║
# ║   GNU General Public License Version 3                             ║
# ║   (please see https://www.gnu.org/licenses/gpl-3.0.html).          ║
# ╚════════════════════════════════════════════════════════════════════╝

"""
This module handles the simulation’s “Level 1” logic, which simulates
the *actual* behaviors performed by workers during each day of the
simulated time period. These behaviors reflect the “reality” of the
daily productivity and interpersonal interactions of production workers
in the factory.
"""

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# ███ PRELIMINARY STEPS
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# █ Configure code analysis
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# This disables pylint convention-violation alerts that see
# variables as "constants" and thus require them to use
# the UPPER_CASE naming style, when in fact they are used
# by modules as variables.
# pylint: disable=C0103


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# █ Import standard modules
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from datetime import timedelta
import random


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# █ Import third-party modules
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import numpy as np
import pandas as pd


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# █ Import other modules from the WorkforceSim package
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import config as cfg
import wfs_utilities as utils


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# ███
# ███ DEFINE CLASSES AND FUNCTIONS
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# █ Functions for iterating the simulation through one day
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def configure_behavs_act_df():
    """
    Configures behavs_act_df -- the master DF that contains an entry for
    every actual behavior performed by workers (including a worker's
    absence, which is itself a sort of behavior) -- by creating initial
    columns needed for adding behavioral entries.
    """

    # Create as an empty DF.
    cfg.behavs_act_df = pd.DataFrame()

    # Create the DF's initial columns.
    cfg.behavs_act_df["Person ID"] = 0
    cfg.behavs_act_df["First Name"] = "" 
    cfg.behavs_act_df["Last Name"] = ""
    cfg.behavs_act_df["Sex"] = ""
    cfg.behavs_act_df["Age"] = 0
    cfg.behavs_act_df["Sphere"] = ""
    cfg.behavs_act_df["Shift"] = ""
    cfg.behavs_act_df["Team"] = ""
    cfg.behavs_act_df["Role"] = ""
    cfg.behavs_act_df["Supervisor ID"] = 0
    cfg.behavs_act_df["Supervisor Age"] = None
    cfg.behavs_act_df["Sub-Sup Age Difference"] = None
    cfg.behavs_act_df["Colleague IDs"] = []
    cfg.behavs_act_df["Same-Sex Colleagues Prtn"] = None
    #cfg.behavs_act_df["Subordinate IDs"] = []
    cfg.behavs_act_df["Behavior Datetime"] = None
    cfg.behavs_act_df["Weekday Num"] = None
    cfg.behavs_act_df["Weekday Name"] = ""
    cfg.behavs_act_df["Behavior Type"] = "" # the "main type" of the behavior (e.g., "Attendance")
    #cfg.behavs_act_df["Behavior Subtype"] = ""
    cfg.behavs_act_df["Behavior Comptype"] = "" # the "comparison type" of the behavior (e.g., for plotting)
    cfg.behavs_act_df["Actual Efficacy"] = None
    cfg.behavs_act_df["Record Type"] = ""
    cfg.behavs_act_df["Record Comptype"] = ""
    cfg.behavs_act_df["Recorded Efficacy"] = None


    # ---------------------------------------------------------------------
    # Add empty columns for one-hot encoding of the Behavior Types and
    # Comptypes for noting workers' actual behaviors.
    # ---------------------------------------------------------------------
    # Create blank new one-hot-encoding columns for Behavior Types.
    for behav_type in [
        "Attendance",
        "Good",
        "Poor",
        "Efficacy",
        ]:

        col_name = behav_type + " (Behavior Type)"
        cfg.behavs_act_df[col_name] = None

    # Create blank new one-hot-encoding columns for Behavior Comptypes.
    for behav_comptype in [
        "Presence",
        "Absence",
        "Idea",
        "Lapse",
        "Feat",
        "Slip",
        "Teamwork",
        "Disruption",
        "Sacrifice",
        "Sabotage",
        "Efficacy",
        ]:

        col_name = behav_comptype + " (Behavior Comptype)"
        cfg.behavs_act_df[col_name] = None

    # ---------------------------------------------------------------------
    # Add empty columns for one-hot encoding of the Record Types and
    # Comptypes for noting managers' records that purport to register
    # workers' behaviors.
    # ---------------------------------------------------------------------
    # Create blank new one-hot-encoding columns for Record Types.
    for behav_type in [
        "Attendance",
        "Good",
        "Poor",
        "Efficacy",
        ]:

        col_name = behav_type + " (Record Type)"
        cfg.behavs_act_df[col_name] = None

    # Create blank new one-hot-encoding columns for Record Comptypes.
    for behav_comptype in [
        "Presence",
        "Absence",
        "Idea",
        "Lapse",
        "Feat",
        "Slip",
        "Teamwork",
        "Disruption",
        "Sacrifice",
        "Sabotage",
        "Efficacy",
        ]:

        col_name = behav_comptype + " (Record Comptype)"
        cfg.behavs_act_df[col_name] = None



def print_modified_probabilities_of_a_person(
    person_u, # Person object for the person whose stats should be printed
    ):

    print("person_u.prob_modified_presence: ", person_u.prob_modified_presence)
    print("person_u.prob_modified_idea: ", person_u.prob_modified_idea)
    print("person_u.prob_modified_lapse: ", person_u.prob_modified_lapse)
    print("person_u.prob_modified_feat: ", person_u.prob_modified_feat)
    print("person_u.prob_modified_slip: ", person_u.prob_modified_slip)
    print("person_u.prob_modified_teamwork: ", person_u.prob_modified_teamwork)
    print("person_u.prob_modified_disruption: ", person_u.prob_modified_disruption)
    print("person_u.prob_modified_sacrifice: ", person_u.prob_modified_sacrifice)
    print("person_u.prob_modified_sabotage: ", person_u.prob_modified_sabotage)


def add_one_behav_to_behavs_act_df(
    person_object_u, # Person object for the person who performed the behavior
    behavior_type_u, # behavior type (string)
    behavior_comptype_u, # behavior comptype (string)
    eff_score_u, # the actual Efficacy (if relevant) demonstrated in the behavior
    ):
    """
    Adds a particular behavior by a particular person (e.g., as
    just generated by a daily behavior-generator) to behavs_act_df,
    the master DF of all actual behaviors performed by persons
    (including absences). It copies into the behavs_act_df relevant
    facts (e.g., the ID of a person's supervisor) as they exist
    *in the moment of the behavior*, thereby creating a lasting
    record of the circumstances in which the behavior occurred.
    """

    # ---------------------------------------------------------------------
    # Calculates the contents of columns containing data
    # regarding the behavior performed and person who performed it.
    # ---------------------------------------------------------------------

    # Calculate the value for the person's "Supervisor ID" field.
    # Determines whether the person has a supervisor or not (e.g., if he
    # is the factory's Production Director, who has no supervisor).
    try:
        sup_ID_to_use = person_object_u.sup.per_id
    except AttributeError:
        sup_ID_to_use = None

    # Calculate the value for the person's "Colleague IDs" field.
    # Determines whether the person has a supervisor or not (e.g., if he
    # is the factory's Production Director, who has no supervisor).
    try:
        colleague_IDs_to_use = [p.per_id for p in person_object_u.colleagues]
    except TypeError:
        colleague_IDs_to_use = None

    # Calculate the value for the "Supervisor Age" field and the
    # "Sub-Sup Age Difference" field, which is
    # the absolute value of the difference in ages between the subject
    # and his supervisor.
    # This is only relevant if a person has a supervisor (e.g.,
    # isn't the factory's Production Director).
    try:
        sup_age_to_use = person_object_u.sup.age
        sub_sup_age_diff = abs(person_object_u.sup.age - person_object_u.age)
    except:
        sup_age_to_use = None
        sub_sup_age_diff = None


    behav_to_add_df = pd.DataFrame({
        "Person ID": [person_object_u.per_id],
        "First Name": [person_object_u.f_name],
        "Last Name": [person_object_u.l_name],
        "Sex": [person_object_u.sex],
        "Age": [person_object_u.age],
        "Health": [person_object_u.stat_health],
        "Commitment": [person_object_u.stat_commitment],
        "Perceptiveness": [person_object_u.stat_perceptiveness],
        "Dexterity": [person_object_u.stat_dexterity],
        "Sociality": [person_object_u.stat_sociality],
        "Goodness": [person_object_u.stat_goodness],
        #"Sphere": [person_object_u.sphere.title],
        "Shift": [person_object_u.shift.title],
        "Team": [person_object_u.team.title],
        "Role": [person_object_u.role.title],
        "Supervisor ID": [sup_ID_to_use],
        "Supervisor Age": [sup_age_to_use],
        "Sub-Sup Age Difference": [sub_sup_age_diff],
        "Colleague IDs": [colleague_IDs_to_use],
        "Same-Sex Colleagues Prtn": [person_object_u.colleagues_of_same_sex_prtn],
        "Behavior Datetime": [cfg.current_datetime_obj],
        "Weekday Num": [cfg.current_datetime_obj.weekday()],
        "Weekday Name": [cfg.current_datetime_obj.strftime("%A")],
        "Behavior Type": [behavior_type_u],
        "Behavior Comptype": [behavior_comptype_u],
        "Actual Efficacy": [eff_score_u],
        })

    # ---------------------------------------------------------------------
    # Add empty columns for one-hot encoding of the Behavior Types and
    # Comptypes for noting workers' actual behaviors.
    # ---------------------------------------------------------------------
    # Create blank new one-hot-encoding columns for Behavior Types.
    for behav_type in [
        "Attendance",
        "Good",
        "Poor",
        "Efficacy",
        ]:

        col_name = behav_type + " (Behavior Type)"
        behav_to_add_df[col_name] = None

    # Create blank new one-hot-encoding columns for Behavior Comptypes.
    for behav_comptype in [
        "Presence",
        "Absence",
        "Idea",
        "Lapse",
        "Feat",
        "Slip",
        "Teamwork",
        "Disruption",
        "Sacrifice",
        "Sabotage",
        "Efficacy",
        ]:

        col_name = behav_comptype + " (Behavior Comptype)"
        behav_to_add_df[col_name] = None

    # ---------------------------------------------------------------------
    # Add empty columns for one-hot encoding of the Record Types and
    # Comptypes for noting managers' records that purport to register
    # workers' behaviors.
    # ---------------------------------------------------------------------
    # Create blank new one-hot-encoding columns for Record Types.
    for behav_type in [
        "Attendance",
        "Good",
        "Poor",
        "Efficacy",
        ]:

        col_name = behav_type + " (Record Type)"
        behav_to_add_df[col_name] = None

    # Create blank new one-hot-encoding columns for Record Comptypes.
    for behav_comptype in [
        "Presence",
        "Absence",
        "Idea",
        "Lapse",
        "Feat",
        "Slip",
        "Teamwork",
        "Disruption",
        "Sacrifice",
        "Sabotage",
        "Efficacy",
        ]:

        col_name = behav_comptype + " (Record Comptype)"
        behav_to_add_df[col_name] = None


    # This newly created behav_to_add_df will only have a single row.
    # It's necessary to manually one-hot encode the Behavior Type
    # and Behavior Comptype.

    # One-hot encode the Behavior Type.
    behavior_type_OHE_col_name = \
        str(behavior_type_u) + " (Behavior Type)"
    behav_to_add_df[behavior_type_OHE_col_name].values[0] = 1

    # One-hot encode the Behavior Comptype.
    behavior_comptype_OHE_col_name = \
        str(behavior_comptype_u) + " (Behavior Comptype)"
    behav_to_add_df[behavior_comptype_OHE_col_name].values[0] = 1


    # Adds a new row to cfg.behavs_act_df with relevant data
    # for the given behavior and person who performed it.
    cfg.behavs_act_df = pd.concat(
        [
            cfg.behavs_act_df,
            behav_to_add_df,
            ],
        ignore_index=True,
        axis=0,
        )


def update_modified_probabilities_for_person(
    person_u, # the Person object to update
    ):
    """
    Updates all of a person's modified probabilities for the
    given day, in preparation for determining what behaviors
    or recordings are generated by him.
    """

    person_u.prob_modified_presence = person_u.prob_modified_presence
    person_u.prob_modified_idea = person_u.prob_modified_idea
    person_u.prob_modified_lapse = person_u.prob_modified_lapse
    person_u.prob_modified_feat = person_u.prob_modified_feat
    person_u.prob_modified_slip = person_u.prob_modified_slip
    person_u.prob_modified_teamwork = person_u.prob_modified_teamwork
    person_u.prob_modified_disruption = person_u.prob_modified_disruption
    person_u.prob_modified_sacrifice = person_u.prob_modified_sacrifice
    person_u.prob_modified_sabotage = person_u.prob_modified_sabotage

    person_u.level_modified_efficacy = person_u.level_modified_efficacy


def simulate_one_day_of_behaviors():
    """
    Simulates one day's worth of workers' actual behaviors.
    """

    for p in cfg.persons:

        # Update the person's modified probabilities to take any
        # situational modifiers into account.
        update_modified_probabilities_for_person(
            cfg.persons[p], # the Person object to update
            )

#GOTOR
#        print_modified_probabilities_of_a_person(
#            cfg.persons[p], # Person object for the person whose stats should be printed
#            )

        # ---------------------------------------------------------------------
        # Calculate the person's attendance status and generate a "Presence" or
        # "Absence" behavior.
        # ---------------------------------------------------------------------

        # If the person's modified probability of a Presence is greater than or
        # equal to a random number from 0.00-1.00, the person is present (i.e.,
        # performs a Presence behavior).
        if cfg.persons[p].prob_modified_presence >= random.uniform(0.0, 1.0):
            attendance_today = "present"

            # Add the person's "Present" behavior to the behaviors DF.
            add_one_behav_to_behavs_act_df(
                cfg.persons[p], # Person object for the person who performed the behavior
                "Attendance", # behavior type (string)
                "Presence", # behavior comptype (string)
                None, # the actual Efficacy (if relevant) demonstrated in the behavior
                )

        else:
            attendance_today = "absent"

            # Add the person's "Absence" behavior to the behaviors DF.
            add_one_behav_to_behavs_act_df(
                cfg.persons[p], # Person object for the person who performed the behavior
                "Attendance", # behavior type (string)
                "Absence", # behavior comptype (string)
                None, # the actual Efficacy (if relevant) demonstrated in the behavior
                )

        # ---------------------------------------------------------------------
        # If the person is present, calculate whether (or how) he performs
        # particular types of behaviors.
        # ---------------------------------------------------------------------
        if attendance_today == "present":

            # ---------------------------------------------------------------------
            # Calculate his Efficacy level for the day
            # and generate an "Efficacy" behavior.
            # ---------------------------------------------------------------------

            eff_sco_today = round( float( ( cfg.persons[p].level_modified_efficacy ) \
                + np.random.normal(loc=0, scale=0.15, size=1) ), 3 )
            if eff_sco_today < 0:
                eff_sco_today = 0

            # Add the person's "Efficacy" behavior to the behaviors DF.
            add_one_behav_to_behavs_act_df(
                cfg.persons[p], # Person object for the person who performed the behavior
                "Efficacy", # behavior type (string)
                "Efficacy", # behavior comptype (string)
                eff_sco_today, # the actual Efficacy (if relevant) demonstrated in the behavior
                )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs an Idea
            # behavior; if so, generate an actual Idea behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate an Idea behavior...
            if cfg.persons[p].prob_modified_idea >= (random.uniform(0.0, 0.8)):

                # Add the person's "Idea" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Good", # behavior type (string)
                    "Idea", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Lapse
            # behavior; if so, generate an actual Lapse behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Lapse behavior...
            if cfg.persons[p].prob_modified_lapse >= (random.uniform(0.0, 0.8)):

                # Add the person's "Lapse" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Poor", # behavior type (string)
                    "Lapse", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Feat
            # behavior; if so, generate an actual Feat behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Feat behavior...
            if cfg.persons[p].prob_modified_feat >= (random.uniform(0.0, 0.8)):

                # Add the person's "Feat" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Good", # behavior type (string)
                    "Feat", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Slip
            # behavior; if so, generate an actual Slip behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Slip behavior...
            if cfg.persons[p].prob_modified_slip >= (random.uniform(0.0, 0.8)):

                # Add the person's "Slip" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Poor", # behavior type (string)
                    "Slip", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Teamwork
            # behavior; if so, generate an actual Teamwork behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Teamwork behavior...
            if cfg.persons[p].prob_modified_teamwork >= (random.uniform(0.0, 0.8)):

                # Add the person's "Teamwork" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Good", # behavior type (string)
                    "Teamwork", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Disruption
            # behavior; if so, generate an actual Disruption behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Disruption behavior...
            if cfg.persons[p].prob_modified_disruption >= (random.uniform(0.0, 0.8)):

                # Add the person's "Disruption" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Poor", # behavior type (string)
                    "Disruption", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Sacrifice
            # behavior; if so, generate an actual Sacrifice behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Sacrifice behavior...
            if cfg.persons[p].prob_modified_sacrifice >= (random.uniform(0.0, 0.8)):

                # Add the person's "Sacrifice" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Good", # behavior type (string)
                    "Sacrifice", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )

            # ---------------------------------------------------------------------
            # Calculate whether the person performs a Sabotage
            # behavior; if so, generate an actual Sabotage behavior.
            # ---------------------------------------------------------------------

            # If the person meets the threshold to generate a Sabotage behavior...
            if cfg.persons[p].prob_modified_sabotage >= (random.uniform(0.0, 0.8)):

                # Add the person's "Sabotage" behavior to the behaviors DF.
                add_one_behav_to_behavs_act_df(
                    cfg.persons[p], # Person object for the person who performed the behavior
                    "Poor", # behavior type (string)
                    "Sabotage", # behavior comptype (string)
                    None, # the actual Efficacy (if relevant) demonstrated in the behavior
                    )


# ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
# █ Functions for calculating metrics after the simulation's conclusion
# ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■

def calculate_metrics_for_persons_in_simulated_period():
    """
    Calculate minimum, maxiimum, and mean Efficacy scores for each
    person during the simulated period.
    """

    for p in cfg.persons:

        # Prepare a DF containing only the behaviors for the given person.
        behavs_act_df_this_pers = utils.return_df_with_rows_filtered_to_one_val_in_col(
            cfg.behavs_act_df, # the input DF
            "Person ID", # the column by which to filter rows
            cfg.persons[p].per_id, # the value to seek in the col (i.e., the restrictor)
            )

        days_present = behavs_act_df_this_pers["Presence (Behavior Comptype)"].count()
        
#GOTOR
        # NOTE! These really shouldn't be "scores". They're actual behaviors.
        min_eff_sco = behavs_act_df_this_pers["Actual Efficacy"].min()
        max_eff_sco = behavs_act_df_this_pers["Actual Efficacy"].max()
        mean_eff_sco = behavs_act_df_this_pers["Actual Efficacy"].mean()

        # NOTE! This will generate an error if there *aren't* any Good or Poor
        # behaviors in the dataset -- which is possible, if the number of persons
        # or number of days simulated is very small.
        good_count = behavs_act_df_this_pers["Good (Behavior Type)"].count()
        poor_count = behavs_act_df_this_pers["Poor (Behavior Type)"].count()

        # print("person", str(p), min_eff_sco, mean_eff_sco, max_eff_sco)

        cfg.persons[p].days_attended = days_present
        cfg.persons[p].eff_sco_act_min = min_eff_sco
        cfg.persons[p].eff_sco_act_max = max_eff_sco
        cfg.persons[p].eff_sco_act_mean = mean_eff_sco
        cfg.persons[p].good_act_num = good_count
        cfg.persons[p].poor_act_num = poor_count


def return_behavs_act_df_for_person_for_DpmN(
    person_u, # the Person object whose data should be sought
    DpmN_N_value_u, # number of the earlier day to seek (-1, 1, etc.)
    ):

    # Restrict the DF to only entries for the given subject.
    temp_df = cfg.behavs_act_df.copy()
    temp_df = temp_df[ temp_df["Person ID"] == person_u.per_id ]

    # Restrict the DF to only entries for the targeted date.
    target_date = cfg.current_datetime_obj + timedelta(days = DpmN_N_value_u)
    temp_df = temp_df[ temp_df["Person ID"] == person_u.per_id ] 

    temp_df = utils.return_df_with_rows_filtered_to_one_val_in_col(
        temp_df, # the input DF
        "Behavior Datetime", # the column by which to filter rows
        target_date, # the value to seek in the col (i.e., the restrictor)
        )

    return temp_df







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