import os
import pandas as pd
import numpy as np
from scraper.top100_extractor import programming_crime_list
from typing import Optional
from backend.api.eps import ticker_quarter_list
from dateutil.parser import isoparse

DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_comparison.csv')


def get_eps_comp(tickers: list[str]) -> list[dict]:
    df = pd.read_csv(DF_EPS_PATH)
    ticker_series = pd.Series(tickers)

    df = df.loc[df['Ticker'].isin(ticker_series), :] \
        .rename(columns={"epsDifferential": "quarterlyDifferential", "Ticker": "ticker"}) \
        .reset_index(drop=True)

    qmap = dict([ \
        [ticker, ticker_quarter_list(df.loc[df.ticker == ticker, 'quarter'])] \
        for ticker in tickers \
    ])

    def get_quart(x):
        date = isoparse(x.quarter)
        quarter = qmap[x.ticker].index(date.month) + 1
        return f"{date.year}-Q{quarter}"

    df['quarter'] = df.apply(get_quart, axis=1)
    

    df = df.pivot(index='quarter', columns='ticker', values='quarterlyDifferential').reset_index(drop=False)
    return df.replace({ np.nan: None }).to_dict('records')