use crate::utils::misc::*;
use crate::utils::qdf::core::*;
use polars::prelude::*;
use std::error::Error;

/// The required columns for a Quantamental DataFrame.
const QDF_INDEX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];

/// Filter a dataframe based on the given parameters.
/// - `cids`: Filter by cross-sectional identifiers
/// - `xcats`: Filter by extended categories
/// - `metrics`: Filter by metrics
/// - `start`: Filter by start date
/// - `end`: Filter by end date
/// - `intersect`: If true, intersect only return `cids` that are present for all `xcats`.
/// Returns a new DataFrame with the filtered data, without modifying the original DataFrame.
/// If no filters are provided, the original DataFrame is returned.
pub fn reduce_dataframe(
    df: DataFrame,
    cids: Option<Vec<String>>,
    xcats: Option<Vec<String>>,
    metrics: Option<Vec<String>>,
    start: Option<String>,
    end: Option<String>,
    intersect: bool,
) -> Result<DataFrame, Box<dyn Error>> {
    check_quantamental_dataframe(&df)?;
    // df_size
    let df_size = df.shape();
    let mut new_df = df.clone();

    let ticker_col: Column = get_ticker_column_for_quantamental_dataframe(&new_df)?;

    // if cids is not provided, get all unique cids
    let u_cids: Vec<String> = get_unique_cids(&new_df)?;
    let u_xcats: Vec<String> = get_unique_xcats(&new_df)?;
    let u_tickers: Vec<String> = _get_unique_strs_from_str_column_object(&ticker_col)?;

    let cids_vec = cids.unwrap_or_else(|| u_cids.clone());
    let specified_cids: Vec<&str> = cids_vec.iter().map(AsRef::as_ref).collect();
    let xcats_vec = xcats.unwrap_or_else(|| u_xcats.clone());
    let specified_xcats: Vec<&str> = xcats_vec.iter().map(AsRef::as_ref).collect();

    let non_idx_cols: Vec<String> = new_df
        .get_column_names()
        .iter()
        .filter(|&col| !QDF_INDEX_COLUMNS.contains(&col.as_str()))
        .map(|s| s.to_string())
        .collect();

    let specified_metrics: Vec<String> =
        metrics.unwrap_or_else(|| non_idx_cols.iter().map(|s| s.to_string()).collect());

    let specified_tickers: Vec<String> = create_interesecting_tickers(
        &specified_cids
            .iter()
            .map(AsRef::as_ref)
            .collect::<Vec<&str>>(),
        &specified_xcats
            .iter()
            .map(AsRef::as_ref)
            .collect::<Vec<&str>>(),
    );

    let keep_tickers: Vec<String> = match intersect {
        // true => get_intersecting_cids_str_func(&specified_cids, &specified_xcats, &u_tickers),
        true => {
            let int_cids = get_intersecting_cids_str_func(
                &specified_cids
                    .iter()
                    .map(|&s| s.to_string())
                    .collect::<Vec<String>>(),
                &specified_xcats
                    .iter()
                    .map(|&s| s.to_string())
                    .collect::<Vec<String>>(),
                &u_tickers,
            );
            create_interesecting_tickers(
                &int_cids.iter().map(AsRef::as_ref).collect::<Vec<&str>>(),
                &specified_xcats
                    .iter()
                    .map(AsRef::as_ref)
                    .collect::<Vec<&str>>(),
            )
        }
        false => specified_tickers.clone(),
    };

    let kticks: Vec<&str> = keep_tickers
        .iter()
        .map(AsRef::as_ref)
        .collect::<Vec<&str>>();

    // Create a boolean mask to filter rows based on the tickers
    let mut mask = vec![false; ticker_col.len()];
    for (i, ticker) in ticker_col.str()?.iter().enumerate() {
        if let Some(t) = ticker {
            if kticks.contains(&t) {
                mask[i] = true;
            }
        }
    }
    let mask = BooleanChunked::from_slice("mask".into(), &mask);
    new_df = new_df.filter(&mask)?;

    // Apply date filtering if `start` or `end` is provided

    if let Some(start) = start {
        let start_date = chrono::NaiveDate::parse_from_str(&start, "%Y-%m-%d")?;
        new_df = new_df
            .lazy()
            .filter(
                col("real_date")
                    .gt_eq(lit(start_date))
                    .alias("real_date")
                    .into(),
            )
            .collect()?;
    }

    if let Some(end) = end {
        let end_date = chrono::NaiveDate::parse_from_str(&end, "%Y-%m-%d")?;
        new_df = new_df
            .lazy()
            .filter(
                col("real_date")
                    .lt_eq(lit(end_date))
                    .alias("real_date")
                    .into(),
            )
            .collect()?;
    }

    // Filter based on metrics if provided
    assert!(specified_metrics.len() > 0);

    // remove columns that are not in the specified metrics
    let mut cols_to_remove = Vec::new();
    for col in new_df.get_column_names() {
        if !specified_metrics.contains(&col.to_string())
            && !QDF_INDEX_COLUMNS.contains(&col.as_str())
        {
            cols_to_remove.push(col);
        }
    }
    new_df = new_df.drop_many(
        cols_to_remove
            .iter()
            .map(|s| s.to_string())
            .collect::<Vec<String>>(),
    );
    // check if the df is still the same size
    let new_df_size = new_df.shape();
    if df_size != new_df_size {
        println!(
            "Reduced DataFrame from {} to {} rows",
            df_size.0, new_df_size.0
        );
    }
    Ok(new_df)
}