DataSrc init

class DataSrc(object):
    """Acts as data provider for each new episode."""

    def __init__(self, df, steps=252, scale=True, scale_extra_cols=True, augment=0.00, window_length=50):
        """
        DataSrc.
        df - csv for data frame index of timestamps
             and multi-index columns levels=[['LTCBTC'],...],['open','low','high','close',...]]
             an example is included as an hdf file in this repository
        steps - total steps in episode
        scale - scale the data for each episode
        scale_extra_cols - scale extra columns by global mean and std
        augment - fraction to augment the data by
        """
        self.steps = steps + 1
        self.augment = augment
        self.scale = scale
        self.scale_extra_cols = scale_extra_cols
        self.window_length = window_length

        # get rid of NaN's
        df = df.copy()
        df.replace(np.nan, 0, inplace=True)
        df = df.fillna(method="pad")

        # dataframe to matrix
        self.asset_names = df.columns.levels[0].tolist()
        self.features = df.columns.levels[1].tolist()
        data = df.as_matrix().reshape(
            (len(df), len(self.asset_names), len(self.features)))
        self._data = np.transpose(data, (1, 0, 2))
        self._times = df.index

        self.price_columns = ['close', 'high', 'low', 'open']
        self.non_price_columns = set(
            df.columns.levels[1]) - set(self.price_columns)

        # Stats to let us normalize non price columns
        if scale_extra_cols:
            x = self._data.reshape((-1, len(self.features)))
            self.stats = dict(mean=x.mean(0), std=x.std(0))
            # for column in self._data.columns.levels[1].tolist():
            #     x = df.xs(key=column, axis=1, level='Price').as_matrix()[:, :]
            #     self.stats["mean"].append(x.mean())
            #      = dict(mean=x.mean(), std=x.std())

        self.reset()