# FILE: counselling_interval_script.py
# CREATED: 1/12/25
# AUTHOR: Vincent Allen
# CONTACT: vincent@vtallen.com valle276@live.kutztown.edu
# PURPOSE:

# This library file implements control code needed to take the input yearly satisfaction survey data obtained through
# the satisfaction survey scorecard in Neoserra and pass it to the library functions that generate the graph images.

# No data cleaning is required for the files in this dataset as the scorecard does that for us. We are just taking the data and showing it per
# center instead of network wide. The network wide stat is retained through a horizontal line.

# Third party libraries
import pandas as pd

# Python modules
import sys
import os.path
import argparse
import json

# Custom modules
from section_1_graph_library_module import ( #pyright:ignore
    make_interval_snapshot_chart
)

from pasbdc_data_cleaning import clean_center_name #pyright:ignore
from constants_module import NEOSERRA_COLUMNS, OUT_COLUMNS

def parse_args():
    parser = argparse.ArgumentParser()

    parser.add_argument("--signuptostartcsv",
                        type=str,
                        required=True,
                        help='The path to the dataset containing the "Average # of Days between Client Sign-up and Client Start Date" from the Neoserra scorecard. If provided the graph will be generated')
    parser.add_argument("--signuptostartfigure",
                        type=str,
                        required=False,
                        default="signuptostart",
                        help='The filename to give the sign up to client start date graph.')

    parser.add_argument("--signuptocounsellingcsv",
                        type=str,
                        required=True,
                        help='The path to the dataset containing the "Average # of Days between Client Sign-up and 1st Counseling Session" from the Neoserra scorecard. If provided the graph will be generated')

    parser.add_argument("--signuptocounsellingfigure",
                        type=str,
                        required=False,
                        default="signuptocounselling",
                        help='The filename to give the sign up to first counselling graph.')

    parser.add_argument("--starttocounsellingcsv",
                        type=str,
                        required=True,
                        help='The path to the dataset containing the "Average # of Days between Client Start Date to 1st Counseling Session" from the Neoserra scorecard. If provided the graph will be generated')

    parser.add_argument("--starttocounsellingfigure",
                        type=str,
                        required=False,
                        default="starttocounselling",
                        help='The filename to give the sign up to first counselling graph.')

    parser.add_argument("--initialtofollowupcsv",
                        type=str,
                        required=True,
                        help='The path to the dataset containing the "Average # of Days between Initial Counseling in the period to 1st Followup Counseling" from the Neoserra scorecard. If provided the graph will be generated')

    parser.add_argument("--initialtofollowupfigure",
                        type=str,
                        required=False,
                        default="initialtofollowup",
                        help='The filename to give the inital session to follow up graph.')

    parser.add_argument("--trainingtocounsellingcsv",
                        type=str,
                        required=True,
                        help='The path to the dataset containing the "Avg. # of Days between Training in the period to 1st Counseling Session" from the Neoserra scorecard. If provided the graph will be generated')

    parser.add_argument("--trainingtocounsellingfigure",
                        type=str,
                        required=False,
                        default="trainingtocounselling",
                        help='The filename to give the inital session to follow up graph.')

    parser.add_argument("--fiscalyear",
                        type=str,
                        required=True,
                        help="The fiscal year tag to place at the end of graph titles to indicate which year the data came from.")

    parser.add_argument("--outpath",
                        type=str,
                        required=True,
                        help="The base directory path to place generated files into.")

    parser.add_argument("--report",
                        type=str,
                        required=False,
                        default="counselling-interval",
                        help="The name to give the graphs of this report. Will be used by the word code to determine what report the grpahs belong to."
                        )

    parser.add_argument("--mapping",
                        type=str,
                        required=False,
                        help="Path to a JSON file to override default column names mappings.")

    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()

    # Handle optional JSON mapping override
    if args.mapping:
        NEOSERRA_COLUMNS.apply_json_mapping(args.mapping)
        OUT_COLUMNS.apply_json_mapping(args.mapping)

    # Ensure output directory exists
    if not os.path.exists(args.outpath):
        try:
            os.makedirs(args.outpath)
        except OSError as e:
            print(f"Error creating output directory: {e}")
            sys.exit(1)


    if args.signuptostartcsv:
        signup_to_start_df = pd.read_csv(args.signuptostartcsv)
        # Filter out a testing record that can appear in this data set
        signup_to_start_df = signup_to_start_df[signup_to_start_df[NEOSERRA_COLUMNS.center] != 'API Testing Sandbox']
        clean_center_name(signup_to_start_df )
        fig = make_interval_snapshot_chart(
            signup_to_start_df,
            title=f"Average Days between Client Sign-up and Client Start Date",
            fiscal_year_tag=args.fiscalyear,
            col_interval_data_value=NEOSERRA_COLUMNS.interval_data_value,
            col_neo_center=NEOSERRA_COLUMNS.center
        )
        fig.write_image(os.path.join(args.outpath, f"{args.report}_{args.signuptostartfigure}_.png"))


    if args.signuptocounsellingcsv:
        signup_to_counselling_df = pd.read_csv(args.signuptocounsellingcsv)
        clean_center_name(signup_to_counselling_df )
        fig = make_interval_snapshot_chart(
            signup_to_counselling_df ,
            title=f"Average Days between Client Sign-up and 1st Counseling Session",
            fiscal_year_tag=args.fiscalyear,
            col_interval_data_value=NEOSERRA_COLUMNS.interval_data_value,
            col_neo_center=NEOSERRA_COLUMNS.center
        )
        fig.write_image(os.path.join(args.outpath, f"{args.report}_{args.signuptocounsellingfigure}_.png"))

    if args.starttocounsellingcsv:
        start_to_counselling_df = pd.read_csv(args.starttocounsellingcsv)
        clean_center_name(start_to_counselling_df )
        fig = make_interval_snapshot_chart(
            start_to_counselling_df,
            title=f"Average Days between Client Start Date and 1st Counseling Session",
            fiscal_year_tag=args.fiscalyear,
            col_interval_data_value=NEOSERRA_COLUMNS.interval_data_value,
            col_neo_center=NEOSERRA_COLUMNS.center
        )
        fig.write_image(os.path.join(args.outpath, f"{args.report}_{args.starttocounsellingfigure}_.png"))

    if args.initialtofollowupcsv:
        inital_to_followup_df = pd.read_csv(args.initialtofollowupcsv)
        clean_center_name(inital_to_followup_df )
        fig = make_interval_snapshot_chart(
            inital_to_followup_df ,
            title=f"Average Days between Initial Counseling and 1st Followup Counseling",
            fiscal_year_tag=args.fiscalyear,
            col_interval_data_value=NEOSERRA_COLUMNS.interval_data_value,
            col_neo_center=NEOSERRA_COLUMNS.center
        )
        fig.write_image(os.path.join(args.outpath, f"{args.report}_{args.initialtofollowupfigure}_.png"))

    if args.trainingtocounsellingcsv:
        training_to_counselling_df = pd.read_csv(args.trainingtocounsellingcsv)
        clean_center_name(training_to_counselling_df )
        fig = make_interval_snapshot_chart(
            training_to_counselling_df,
            title=f"Average Days between Training and 1st Counseling Session",
            fiscal_year_tag=args.fiscalyear,
            col_interval_data_value=NEOSERRA_COLUMNS.interval_data_value,
            col_neo_center=NEOSERRA_COLUMNS.center
        )
        fig.write_image(os.path.join(args.outpath, f"{args.report}_{args.trainingtocounsellingfigure}_.png"))