zipline.lib.AdjustedArray

Complexity

Total Complexity

10

Size/Duplication

Total Lines	98
Duplicated Lines	0 %

6 Methods

Rating	Name	Duplication	Size	Complexity
A	_iterator_type()	0	6	1
B	__init__()	0	15	5
A	dtype()	0	6	1
A	data()	0	6	1
A	inspect()	0	18	1
A	traverse()	0	20	1

from textwrap import dedent

from numpy import (
    bool_,
    dtype,
    float32,
    float64,
    int32,
    int64,
    ndarray,
    uint32,
    uint8,
)
from zipline.errors import (
    WindowLengthNotPositive,
    WindowLengthTooLong,
)
from zipline.utils.numpy_utils import (
    datetime64ns_dtype,
    default_fillvalue_for_dtype,
    float64_dtype,
    int64_dtype,
    uint8_dtype,
)
from zipline.utils.memoize import lazyval
from zipline.utils.sentinel import sentinel

# These class names are all the same because of our bootleg templating system.
from ._float64window import AdjustedArrayWindow as Float64Window
from ._int64window import AdjustedArrayWindow as Int64Window
from ._uint8window import AdjustedArrayWindow as UInt8Window

Infer = sentinel(
    'Infer',
    "Sentinel used to say 'infer missing_value from data type.'"
)
NOMASK = None
SUPPORTED_NUMERIC_DTYPES = frozenset(
    map(dtype, [float32, float64, int32, int64, uint32])
)
CONCRETE_WINDOW_TYPES = {
    float64_dtype: Float64Window,
    int64_dtype: Int64Window,
    uint8_dtype: UInt8Window,
}


def _normalize_array(data):
    """
    Coerce buffer data for an AdjustedArray into a standard scalar
    representation, returning the coerced array and a numpy dtype object to use
    as a view type when providing public view into the data.

    Semantically numerical data (float*, int*, uint*) is coerced to float64 and
    viewed as float64.  We coerce integral data to float so that we can use NaN
    as a missing value.

    datetime[*] data is coerced to int64 with a viewtype of ``datetime64[ns]``.

    ``bool_`` data is coerced to uint8 with a viewtype of ``bool_``

    Parameters
    ----------
    data : np.ndarray

    Returns
    -------
    coerced, viewtype : (np.ndarray, np.dtype)
    """
    data_dtype = data.dtype
    if data_dtype == bool_:
        return data.astype(uint8), dtype(bool_)
    elif data_dtype in SUPPORTED_NUMERIC_DTYPES:
        return data.astype(float64), dtype(float64)
    elif data_dtype.name.startswith('datetime'):
        try:
            outarray = data.astype('datetime64[ns]').view('int64')
            return outarray, datetime64ns_dtype
        except OverflowError:
            raise ValueError(
                "AdjustedArray received a datetime array "
                "not representable as datetime64[ns].\n"
                "Min Date: %s\n"
                "Max Date: %s\n"
            ) % (data.min(), data.max())
    else:
        raise TypeError(
            "Don't know how to construct AdjustedArray "
            "on data of type %s." % dtype
        )


class AdjustedArray(object):
    """
    An array that can be iterated with a variable-length window, and which can
    provide different views on data from different perspectives.

    Parameters
    ----------
    data : np.ndarray
        The baseline data values.
    mask : np.ndarray[bool]
        A mask indicating the locations of missing data.
    adjustments : dict[int -> list[Adjustment]]
        A dict mapping row indices to lists of adjustments to apply when we
        reach that row.
    fillvalue : object, optional
        A value to use to fill missing data in yielded windows.
        Default behavior is to infer a value based on the dtype of `data`.
        `NaN` is used for numeric data, and `NaT` is used for datetime data.
    """
    __slots__ = ('_data', '_viewtype', 'adjustments', '__weakref__')

    def __init__(self, data, mask, adjustments, fillvalue=Infer):
        self._data, self._viewtype = _normalize_array(data)
        self.adjustments = adjustments
        if fillvalue is Infer:
            fillvalue = default_fillvalue_for_dtype(self.data.dtype)

        if mask is not NOMASK:
            if mask.dtype != bool_:
                raise ValueError("Mask must be a bool array.")
            if data.shape != mask.shape:
                raise ValueError(
                    "Mask shape %s != data shape %s." %
                    (mask.shape, data.shape),
                )
            self._data[~mask] = fillvalue

    @lazyval
    def data(self):
        """
        The data stored in this array.
        """
        return self._data.view(self._viewtype)

    @lazyval
    def dtype(self):
        """
        The dtype of the data stored in this array.
        """
        return self._viewtype

    @lazyval
    def _iterator_type(self):
        """
        The iterator produced when `traverse` is called on this Array.
        """
        return CONCRETE_WINDOW_TYPES[self._data.dtype]

    def traverse(self, window_length, offset=0):
        """
        Produce an iterator rolling windows rows over our data.
        Each emitted window will have `window_length` rows.

        Parameters
        ----------
        window_length : int
            The number of rows in each emitted window.
        offset : int, optional
            Number of rows to skip before the first window.
        """
        data = self._data.copy()
        _check_window_params(data, window_length)
        return self._iterator_type(
            data,
            self._viewtype,
            self.adjustments,
            offset,
            window_length,
        )

    def inspect(self):
        """
        Return a string representation of the data stored in this array.
        """
        return dedent(
            """\
            Adjusted Array ({dtype}):

            Data:
            {data!r}

            Adjustments:
            {adjustments}
            """
        ).format(
            dtype=self.dtype.name,
            data=self.data,
            adjustments=self.adjustments,
        )


def ensure_ndarray(ndarray_or_adjusted_array):
    """
    Return the input as a numpy ndarray.

    This is a no-op if the input is already an ndarray.  If the input is an
    adjusted_array, this extracts a read-only view of its internal data buffer.

    Parameters
    ----------
    ndarray_or_adjusted_array : numpy.ndarray | zipline.data.adjusted_array

    Returns
    -------
    out : The input, converted to an ndarray.
    """
    if isinstance(ndarray_or_adjusted_array, ndarray):
        return ndarray_or_adjusted_array
    elif isinstance(ndarray_or_adjusted_array, AdjustedArray):
        return ndarray_or_adjusted_array.data
    else:
        raise TypeError(
            "Can't convert %s to ndarray" %
            type(ndarray_or_adjusted_array).__name__
        )


def _check_window_params(data, window_length):
    """
    Check that a window of length `window_length` is well-defined on `data`.

    Parameters
    ----------
    data : np.ndarray[ndim=2]
        The array of data to check.
    window_length : int
        Length of the desired window.

    Returns
    -------
    None

    Raises
    ------
    WindowLengthNotPositive
        If window_length < 1.
    WindowLengthTooLong
        If window_length is greater than the number of rows in `data`.
    """
    if window_length < 1:
        raise WindowLengthNotPositive(window_length=window_length)

    if window_length > data.shape[0]:
        raise WindowLengthTooLong(
            nrows=data.shape[0],
            window_length=window_length,
        )