xbbg/io/cached.py
def bdp_bds_cache(func, tickers, flds, **kwargs) -> ToQuery:
    """
    Find cached `BDP` / `BDS` queries

    Args:
        func: function name - bdp or bds
        tickers: tickers
        flds: fields
        **kwargs: other kwargs

    Returns:
        ToQuery(ticker, flds, kwargs)
    """
    cache_data = []
    log_level = kwargs.get('log', logs.LOG_LEVEL)
    logger = logs.get_logger(bdp_bds_cache, level=log_level)
    kwargs['has_date'] = kwargs.pop('has_date', func == 'bds')
    kwargs['cache'] = kwargs.get('cache', True)

    tickers = utils.flatten(tickers)
    flds = utils.flatten(flds)
    loaded = pd.DataFrame(data=0, index=tickers, columns=flds)

    for ticker, fld in product(tickers, flds):
        data_file = storage.ref_file(
            ticker=ticker, fld=fld, ext='pkl', **{
                k: v for k, v in kwargs.items() if k not in EXC_COLS
            }
        )
        if not files.exists(data_file): continue
        logger.debug(f'reading from {data_file} ...')
        cache_data.append(pd.read_pickle(data_file))
        loaded.loc[ticker, fld] = 1

    to_qry = loaded.where(loaded == 0)\
        .dropna(how='all', axis=1).dropna(how='all', axis=0)

    return ToQuery(
        tickers=to_qry.index.tolist(), flds=to_qry.columns.tolist(),
        cached_data=cache_data
    )
def bdp_bds_cache(func, tickers, flds, **kwargs) -> ToQuery:
    """
    Find cached `BDP` / `BDS` queries

    Args:
        func: function name - bdp or bds
        tickers: tickers
        flds: fields
        **kwargs: other kwargs

    Returns:
        ToQuery(ticker, flds, kwargs)
    """
    cache_data = []
    log_level = kwargs.get('log', logs.LOG_LEVEL)
    logger = logs.get_logger(bdp_bds_cache, level=log_level)
    kwargs['has_date'] = kwargs.pop('has_date', func == 'bds')
    kwargs['cache'] = kwargs.get('cache', True)

    tickers = utils.flatten(tickers)
    flds = utils.flatten(flds)
    loaded = pd.DataFrame(data=0, index=tickers, columns=flds)

    for ticker, fld in product(tickers, flds):
        data_file = storage.ref_file(
            ticker=ticker, fld=fld, ext='pkl', **{
                k: v for k, v in kwargs.items() if k not in EXC_COLS
            }
        )
        if not files.exists(data_file): continue
        logger.debug(f'reading from {data_file} ...')
        cache_data.append(pd.read_pickle(data_file))
        loaded.loc[ticker, fld] = 1

    to_qry = loaded.where(loaded == 0)\
        .dropna(how='all', axis=1).dropna(how='all', axis=0)

    return ToQuery(
        tickers=to_qry.index.tolist(), flds=to_qry.columns.tolist(),
        cached_data=cache_data
    )
def bdp_bds_cache ( func , tickers , flds , * * kwargs ) -> ToQuery : cache_data = [ ] log_level = kwargs . get ( 'log' , logs . LOG_LEVEL ) logger = logs . get_logger ( bdp_bds_cache , level = log_level ) kwargs [ 'has_date' ] = kwargs . pop ( 'has_date' , func == 'bds' ) kwargs [ 'cache' ] = kwargs . get ( 'cache' , True ) tickers = utils . flatten ( tickers ) flds = utils . flatten ( flds ) loaded = pd . DataFrame ( data = 0 , index = tickers , columns = flds ) for ticker , fld in product ( tickers , flds ) : data_file = storage . ref_file ( ticker = ticker , fld = fld , ext = 'pkl' , * * { k : v for k , v in kwargs . items ( ) if k not in EXC_COLS } ) if not files . exists ( data_file ) : continue logger . debug ( f'reading from {data_file} ...' ) cache_data . append ( pd . read_pickle ( data_file ) ) loaded . loc [ ticker , fld ] = 1 to_qry = loaded . where ( loaded == 0 ) . dropna ( how = 'all' , axis = 1 ) . dropna ( how = 'all' , axis = 0 ) return ToQuery ( tickers = to_qry . index . tolist ( ) , flds = to_qry . columns . tolist ( ) , cached_data = cache_data )
Find cached `BDP` / `BDS` queries

    Args:
        func: function name - bdp or bds
        tickers: tickers
        flds: fields
        **kwargs: other kwargs

    Returns:
        ToQuery(ticker, flds, kwargs)
Find cached BDP / BDS queries