"""Util functions for Projections ETL."""

from datetime import datetime, timedelta
import re

from flows import art_relations
from flows import datastore
from flows.projections import queries


def get_upc_projection_anchor_date(upc, projection_type):
    """Get the anchor date of a UPC from the datastore records.

    Args:
        upc (str): UPC to lookup.
        projection_type (str): Type of projection file we are processing.

    Returns:
        date: UPC's anchor date.
    """
    if projection_type == 'original':
        results = art_relations.query(
            queries.ORIGINAL_PROJECTIONS_ANCHOR_DATE, {'upc': upc})
    else:
        results = datastore.query(
            queries.PROJECTIONS_ANCHOR_DATE, {'upc': upc})
    row = results.fetchone()
    return row[0]


def transform(rows, anchor_date, upc, transaction_types):
    """Transform projections csv data to db table format.

    Args:
        rows (list(list)): list of rows from csv.
        anchor_date (str): anchor date in ISO format: YYYY-MM-DD.
        upc (str): release UPC.
        transaction_types (dict): transaction type mapping.

    Yields:
        tuple: row in db table format (upc, date, transaction_type, amount).
    """
    start_sate = datetime.strptime(anchor_date, '%Y-%m-%d').date()
    dates = [
        (start_sate + timedelta(days=i*7)).isoformat()
        for i in range(len(rows[0])-1)]
    for row in rows:
        if not transaction_types.get(row[0].upper()):
            continue
        for cell, date in zip(row[1:], dates):
            amount = None
            if cell:
                amount = cell.replace('$', '').replace(',', '')
                amount = round(float(amount), -1)

            yield upc, date, transaction_types.get(row[0].upper()), amount


def parse_file_name(s3_path):
    """Parse projection CSV file.

    Args:
        s3_path (str): S3 key path.

    Returns:
        (str, str): UPC value, timestamp from file name
    """
    match = re.match(
        '(?P<upc>[0-9]+)-(?P<date>[0-9]{14}).csv',
        s3_path.split('/')[-1])
    return match.group('upc'), match.group('date')