from datetime import date
from pathlib import Path

from fr_toolbelt.api_requests import get_documents_by_date
from fr_toolbelt.preprocessing import process_documents, AgencyMetadata
from numpy import array
from pandas import DataFrame, to_datetime
from plotnine import (
    ggplot, 
    aes, 
    geom_col, 
    labs, 
    coord_flip, 
    scale_x_discrete, 
    theme_light, 
    )

try:
    from search_columns import search_columns, SearchError
    from significant import get_significant_info
except ModuleNotFoundError:
    from .search_columns import search_columns, SearchError
    from .significant import get_significant_info


METADATA, _ = AgencyMetadata().get_agency_metadata()
START_DATE = "2024-03-01"
GET_SIGNIFICANT = True if date.fromisoformat(START_DATE) >= date(2023, 4, 6) else False


class DataAvailabilityError(Exception):
    pass


def get_date_range(start_date: str):
    start_year = date.fromisoformat(start_date).year
    end_year = start_year + 1
    date_range = {
        "start": start_date, 
        "end": f"{end_year}-01-31", 
        "transition_year": end_year, 
        }
    return date_range


def get_rules(date_range: dict) -> list[dict]:
    results, _ = get_documents_by_date(
        start_date=date_range.get("start"), 
        end_date=date_range.get("end"), 
        document_types=("RULE", )
        )
    return results


def format_documents(documents: list[dict]):
    """Format Federal Register documents to generate count by presidential year.

    Args:
        documents (list[dict]): List of documents.

    Returns:
        DataFrame: Pandas DataFrame with formatted data.
    """
    # process agency info in documents
    documents = process_documents(
        documents, 
        which=("agencies", "presidents"), 
        return_values_as_str=False
        )
    
    # create dataframe
    df = DataFrame(documents)
    
    # convert publication date to datetime format
    df.loc[:, "publication_dt"] = to_datetime(df["publication_date"])
    df.loc[:, "publication_date"] = df.apply(lambda x: x["publication_dt"].date(), axis=1)
    df.loc[:, "publication_year"] = df.apply(lambda x: x["publication_dt"].year, axis=1)
    df.loc[:, "publication_month"] = df.apply(lambda x: x["publication_dt"].month, axis=1)
    df.loc[:, "publication_day"] = df.apply(lambda x: x["publication_dt"].day, axis=1)
    
    # return dataframe
    return df


def filter_new_admin_rules(
        df: DataFrame, 
        transition_year: int, 
        date_col: str = "publication_date", 
    ):
    
    admin_transitions = {
        2001: "george-w-bush", 
        2009: "barack-obama", 
        2017: "donald-trump", 
        2021: "joe-biden", 
        }
    
    bool_date = array(df[date_col] >= date(transition_year, 1, 20))
    bool_prez = array(df["president_id"] == admin_transitions.get(transition_year))
    bool_ = bool_date & bool_prez    
    return df.loc[~bool_]


def filter_corrections(df: DataFrame):
    """Filter out corrections from Federal Register documents. 
    Identifies corrections using `corrrection_of` field and regex searches of `document_number`, `title`, and `action` fields.

    Args:
        df (DataFrame): Federal Register data.

    Returns:
        tuple: DataFrame with corrections removed, DataFrame of corrections
    """
    # get original column names
    cols = df.columns.tolist()
    
    # filter out corrections
    # 1. Using correction fields
    bool_na = array(df["correction_of"].isna())

    # 2. Searching other fields
    search_1 = search_columns(df, [r"^[crxz][\d]{1,2}-(?:[\w]{2,4}-)?[\d]+"], ["document_number"], 
                                 return_column="indicator1")
    search_2 = search_columns(df, [r"(?:;\scorrection\b)|(?:\bcorrecting\samend[\w]+\b)"], ["title", "action"], 
                                 return_column="indicator2")
    bool_search = array(search_1["indicator1"] == 1) | array(search_2["indicator2"] == 1)

    # separate corrections from non-corrections
    df_no_corrections = df.loc[(bool_na & ~bool_search), cols]  # remove flagged documents
    df_corrections = df.loc[(~bool_na | bool_search), cols]
    
    # return filtered results
    if len(df) == len(df_no_corrections) + len(df_corrections):
        return df_no_corrections, df_corrections
    else:
        raise SearchError(f"{len(df)} != {len(df_no_corrections)} + {len(df_corrections)}")


def get_significant_rules(df, start_date):
    process_columns = ("significant", "3f1_significant", )
    if date.fromisoformat(start_date) < date(2023, 4, 6):
        raise DataAvailabilityError("This program does not calculate significant rule counts prior to Executive Order 14094 of April 6, 2023.")
    else:
        document_numbers = df.loc[:, "document_number"].to_list()
        df, last_updated = get_significant_info(df, start_date, document_numbers)
        for col in process_columns:
            bool_na = df[col].isna()
            df.loc[bool_na, col] = "0"
            df.loc[:, col] = df[col].replace(".", "0").astype("int64")
        bool_3f1 = df["3f1_significant"] == 1
        bool_sig = df["significant"] == 1
        df.loc[:, "3f1_significant"] = 0
        df.loc[bool_3f1, "3f1_significant"] = 1
        df.loc[:, "other_significant"] = 0
        df.loc[(bool_sig & ~bool_3f1), "other_significant"] = 1
    return df, last_updated


def get_agency_metadata_values(
        df: DataFrame, 
        agency_column: str, 
        metadata: dict,         
        metadata_value: str, 
    ):
    if metadata_value == "acronym":
        metadata_value = "short_name"
    return df.loc[:, agency_column].apply(
        lambda x: metadata.get(x, {}).get(metadata_value)
        )


def groupby_agency(
        df: DataFrame, 
        group_col: str = "parent_slug", 
        value_col: str = "document_number", 
        aggfunc: str = "count", 
        significant: bool = True,
        metadata: dict | None = None, 
        metadata_value: str = "acronym", 
    ):
    aggfunc_dict = {value_col: aggfunc, }
    if significant:
        aggfunc_dict.update({
            "3f1_significant": "sum", 
            "other_significant": "sum", 
            })
    df_ex = df.explode(group_col, ignore_index=True)
    grouped = df_ex.groupby(
        by=group_col
    ).agg(
        aggfunc_dict
        ).reset_index()
    grouped = grouped.sort_values(value_col, ascending=False).rename(
        columns={
            group_col: "agency", 
            value_col: "rules", 
            }, errors="ignore"
        )
    if metadata is not None:
        grouped.loc[:, metadata_value] = get_agency_metadata_values(
        grouped, 
        agency_column="agency", 
        metadata=metadata, 
        metadata_value=metadata_value
        )
        cols = ["agency", metadata_value, "rules", "3f1_significant", "other_significant"]
        grouped = grouped.loc[:, [c for c in cols if c in grouped.columns]]
    return grouped


def groupby_ym(
        df: DataFrame, 
        group_col: tuple | list = ("publication_year", "publication_month", ),  
        value_col: str = "document_number", 
        aggfunc: str = "count", 
        significant: bool = True
    ):
    aggfunc_dict = {value_col: aggfunc, }
    if significant:
        aggfunc_dict.update({
            "3f1_significant": "sum", 
            "other_significant": "sum", 
            })
    grouped = df.groupby(
        by=list(group_col)
    ).agg(
        aggfunc_dict
        ).reset_index()
    grouped = grouped.rename(columns={
        value_col: "rules", 
        }, errors="ignore")
    return grouped


def save_csv(path: Path, df_all: DataFrame, df_agency: DataFrame, df_ym: DataFrame, transition_year: int):
    files = (
        f"rules_{transition_year - 1}_{transition_year}.csv", 
        f"rules_by_agency_{transition_year - 1}_{transition_year}.csv", 
        f"rules_by_month_{transition_year - 1}_{transition_year}.csv"
        )
    dataframes = (df_all, df_agency, df_ym)
    for data, file in zip(dataframes, files):
        data.to_csv(path / file, index=False)


def plot_agency(df, group_col = "acronym", value_col = "rules"):
    
    order_list = df.loc[:, group_col].to_list()[::-1]
    
    plot = (
        ggplot(
            df, 
            aes(x=group_col, y=value_col), 
            )
        + geom_col()
        + coord_flip()
        + scale_x_discrete(limits=order_list)
        + labs(y="", x="", title="Number of Rules Published by Agency")
        + theme_light()
        )
    return plot


def plot_month(df, group_cols = ("publication_year", "publication_month"), value_col = "rules"):
    
    df.loc[:, "ym"] = df[group_cols[0]].astype(str) + "-" + df[group_cols[1]].astype(str).str.pad(2, fillchar="0")
    order_list = df.loc[:, "ym"].to_list()
    
    plot = (
        ggplot(
            df, 
            aes(x="ym", y=value_col), 
            )
        + geom_col()
        + scale_x_discrete(limits=order_list)
        + labs(y="", x="", title="Number of Rules Published by Month")
        + theme_light()
        )
    return plot


def get_rules_in_window(start_date: str, get_significant: bool = True):
    date_range = get_date_range(start_date)
    transition_year = date_range.get("transition_year")
    results = get_rules(date_range)
    df = format_documents(results)
    df, _ = filter_corrections(df)
    df = filter_new_admin_rules(df, transition_year)
    if get_significant:
        df, last_updated = get_significant_rules(df, start_date)
    else:
        last_updated = date.today()
    return df, last_updated


def get_list_agencies(start_date, agency_column: str = "agency", metadata: dict | None = None, significant: bool = True):
    
    df, _ = get_rules_in_window(start_date, get_significant=significant)
    df_agency = groupby_agency(df, metadata=metadata, significant=significant)
    print(df_agency.columns)
    
    return sorted(list(set(df_agency.loc[df_agency[agency_column].notna(), agency_column].to_list())))


def main(start_date, save_data: bool = True, path: Path | None = None, metadata: dict | None = None, significant: bool = True):
    if date.fromisoformat(start_date) < date(2023, 4, 6):
        significant = False
    date_range = get_date_range(start_date)
    transition_year = date_range.get("transition_year")
    df, _ = get_rules_in_window(start_date, get_significant=significant)
    
    df_agency = groupby_agency(df, metadata=metadata, significant=significant)
    df_ym = groupby_ym(df, significant=significant)
    
    if save_data:
        if path is None:
            path = Path(__file__).parent
        save_csv(path, df, df_agency, df_ym, transition_year)
    
    return df, df_agency, df_ym


DF, LAST_UPDATED = get_rules_in_window(START_DATE, get_significant=GET_SIGNIFICANT)
AGENCIES = get_list_agencies(START_DATE, metadata=METADATA, significant=GET_SIGNIFICANT)


if __name__ == "__main__":
    
    print(DF.columns)
    print(LAST_UPDATED)