#
# Copyright (c) Daniel Sheffield 2021 - 2022
#
# All rights reserved
#
# THIS SOFTWARE IS PROVIDED AS IS WITHOUT WARRANTY
from sqlite3 import Cursor
import time
from typing import Any, Callable
from .txn_view import (
    get_table_statement,
    get_transactions_statement,
    get_session_transactions_statement,
)
from .price_view import(
    get_historic_prices_statement,
)
from dateutil.parser import parse as parse_time
import pandas as pd
NON_IDENTIFIER_COLUMNS = [
    'ts',
    'store',
    'quantity',
    'unit',
    'price',
    'organic',
]

display_map = {
    'ts': lambda x: f"{time.strftime('%Y-%m-%d %H:%M', (x.year, x.month, x.day, x.hour, x.minute, 0, 0, 0, 0))}",
    'price': lambda x: f'{x:.4f}',
    'quantity': lambda x: f'{x:.2f}',
    'organic': lambda x: 'yes' if x else 'no',
}
display_mapper: Callable[
    [Any, str], str
] = lambda data, name: display_map[name](data) if name in display_map else data

def cursor_as_dict(cur):
    _col_idx_map=dict(map(lambda col: (col[1].name, col[0]), enumerate(cur.description)))
    for row in map(lambda row, _map=_col_idx_map: dict([
        (name, row[i]) for name, i in _map.items()
    ]), cur.fetchall()):
        #print(row)
        yield row

def get_data(cursor, statement, display):
    cursor.execute(statement)
    yield from  map(lambda x: dict([
        (k, display(v, k)) for k,v in x.items()
    ]), cursor_as_dict(cursor))

def get_session_transactions(cursor, statement, date, store):
    #print(cur.mogrify(statement).decode("utf-8"))
    #input()
    cursor.execute(statement)
    df = pd.DataFrame(cursor_as_dict(cursor))
    if df.empty:
        return ''
    return df.drop(labels=[
        'id', 'ts', 'store', 'code',
    ], axis=1).to_string(header=[
        'Description', 'Volume', 'Unit', 'Price', '$/unit', 'Total',
        'Group', 'Category', 'Product', 'Organic',
    ], justify='justify-all', max_colwidth=60, index=False)


def record_matches(record, strict=None, **kwargs):
    strict = strict or []
    for k,v in kwargs.items():
        if not v:
            continue
        
        if k in strict and v.lower() != record[k].lower():
            return False
        
        if v.lower() not in record[k].lower():
            return False
        
    return True

def unique_suggestions(cur, statement, name, display, exclude=NON_IDENTIFIER_COLUMNS, **kwargs):
    exclude = filter(
        lambda x: x != name or name == 'ts',
        exclude,
    )
    [ kwargs.pop(k) for k in exclude if k in kwargs]
    items = suggestions(cur, statement, name, display, exclude=exclude, **kwargs)
    ret = sorted(set(map(lambda x: x[name], items)))
    tables = {
        'product',
        'category',
        'group',
        'unit',
        'store',
    }
    if len(ret) > 0 or name not in tables:
        return ret
        
    items = (i for i in filter(lambda x: record_matches(x, **{ name: kwargs[name] }),
    get_data(cur, get_table_statement(name), display)))
    return sorted(set(map(lambda x: x[name], items)))

def suggestions(cur, statement, name, display, exclude=NON_IDENTIFIER_COLUMNS, **kwargs):
    exclude = filter(
        lambda x: x != name or name == 'ts',
        exclude,
    )
    [ kwargs.pop(k) for k in exclude if k in kwargs]
    yield from filter(lambda x: record_matches(
        x, strict=[ k for k in kwargs if k != name ], **kwargs
    ), get_data(cur, statement, display))

def get_insert_product_statement(product, category, group):
    return f'CALL insert_product($prod${product}$prod$, $category${category}$category$, $group${group}$group$)'

class QueryManager(object):
    
    def __init__(self, cursor: Cursor, display: Callable[
        [Any, str], str
    ]):
        self.display = display
        self.cursor = cursor
    
    def get_historic_prices(self, rating_cb, sort, product, unit, organic=None, limit='365 days'):
        statement = get_historic_prices_statement(sort, product, unit, organic=organic, limit=limit)
        #print(self.cursor.mogrify(statement).decode('utf-8'))
        #input()
        
        df = pd.DataFrame(get_data(self.cursor, statement, self.display))
        if df.empty:
            rating_cb(None, None, None)
            return ''
        
        _avg, _min, _max = [ x for x in df[['avg','min','max']].iloc[0].apply(float) ]
        rating_cb(_avg, _min, _max)
        
        return df.drop(labels=[
            'id', 'avg', 'min', 'max'
        ], axis=1).to_string(header=[
            'Date', 'Store', '$/unit', 'Org',
        ], justify='justify-all', max_colwidth=16, index=False)
    
    def get_session_transactions(self, date, store):
        return get_session_transactions(
            self.cursor, get_session_transactions_statement(
                parse_time(date), store, full_name=True, exact_time=True
            ), date, store)

    def unique_suggestions(self, name, **kwargs):
        statement = get_transactions_statement()
        return unique_suggestions(self.cursor, statement, name, self.display, **kwargs)

    def insert_new_product(self, product, category, group):
        self.cursor.execute(get_insert_product_statement(product, category, group))