zarr.py

import numpy as np

from .. import coding, conventions
from ..core import indexing
from ..core.dataset import Dataset
from ..core.pycompat import integer_types
from ..core.utils import FrozenDict, HiddenKeyDict, close_on_error
from ..core.variable import Variable
from .common import AbstractWritableDataStore, BackendArray, _encode_variable_name
from .plugins import BackendEntrypoint

# need some special secret attributes to tell us the dimensions
DIMENSION_KEY = "_ARRAY_DIMENSIONS"


def encode_zarr_attr_value(value):
    """
    Encode a attribute value as something that can be serialized as json

    Many xarray datasets / variables have numpy arrays and values. This
    function handles encoding / decoding of such items.

    ndarray -> list
    scalar array -> scalar
    other -> other (no change)
    """
    if isinstance(value, np.ndarray):
        encoded = value.tolist()
    # this checks if it's a scalar number
    elif isinstance(value, np.generic):
        encoded = value.item()
    else:
        encoded = value
    return encoded


class ZarrArrayWrapper(BackendArray):
    __slots__ = ("datastore", "dtype", "shape", "variable_name")

    def __init__(self, variable_name, datastore):
        self.datastore = datastore
        self.variable_name = variable_name

        array = self.get_array()
        self.shape = array.shape

        dtype = array.dtype
        self.dtype = dtype

    def get_array(self):
        return self.datastore.ds[self.variable_name]

    def __getitem__(self, key):
        array = self.get_array()
        if isinstance(key, indexing.BasicIndexer):
            return array[key.tuple]
        elif isinstance(key, indexing.VectorizedIndexer):
            return array.vindex[
                indexing._arrayize_vectorized_indexer(key, self.shape).tuple
            ]
        else:
            assert isinstance(key, indexing.OuterIndexer)
            return array.oindex[key.tuple]
        # if self.ndim == 0:
        # could possibly have a work-around for 0d data here


def _determine_zarr_chunks(enc_chunks, var_chunks, ndim, name):
    """
    Given encoding chunks (possibly None) and variable chunks (possibly None)
    """

    # zarr chunk spec:
    # chunks : int or tuple of ints, optional
    #   Chunk shape. If not provided, will be guessed from shape and dtype.

    # if there are no chunks in encoding and the variable data is a numpy
    # array, then we let zarr use its own heuristics to pick the chunks
    if var_chunks is None and enc_chunks is None:
        return None

    # if there are no chunks in encoding but there are dask chunks, we try to
    # use the same chunks in zarr
    # However, zarr chunks needs to be uniform for each array
    # http://zarr.readthedocs.io/en/latest/spec/v1.html#chunks
    # while dask chunks can be variable sized
    # http://dask.pydata.org/en/latest/array-design.html#chunks
    if var_chunks and enc_chunks is None:
        if any(len(set(chunks[:-1])) > 1 for chunks in var_chunks):
            raise ValueError(
                "Zarr requires uniform chunk sizes except for final chunk. "
                f"Variable named {name!r} has incompatible dask chunks: {var_chunks!r}. "
                "Consider rechunking using `chunk()`."
            )
        if any((chunks[0] < chunks[-1]) for chunks in var_chunks):
            raise ValueError(
                "Final chunk of Zarr array must be the same size or smaller "
                f"than the first. Variable named {name!r} has incompatible Dask chunks {var_chunks!r}."
                "Consider either rechunking using `chunk()` or instead deleting "
                "or modifying `encoding['chunks']`."
            )
        # return the first chunk for each dimension
        return tuple(chunk[0] for chunk in var_chunks)

    # from here on, we are dealing with user-specified chunks in encoding
    # zarr allows chunks to be an integer, in which case it uses the same chunk
    # size on each dimension.
    # Here we re-implement this expansion ourselves. That makes the logic of
    # checking chunk compatibility easier

    if isinstance(enc_chunks, integer_types):
        enc_chunks_tuple = ndim * (enc_chunks,)
    else:
        enc_chunks_tuple = tuple(enc_chunks)

    if len(enc_chunks_tuple) != ndim:
        # throw away encoding chunks, start over
        return _determine_zarr_chunks(None, var_chunks, ndim, name)

    for x in enc_chunks_tuple:
        if not isinstance(x, int):
            raise TypeError(
                "zarr chunk sizes specified in `encoding['chunks']` "
                "must be an int or a tuple of ints. "
                f"Instead found encoding['chunks']={enc_chunks_tuple!r} "
                f"for variable named {name!r}."
            )

    # if there are chunks in encoding and the variable data is a numpy array,
    # we use the specified chunks
    if var_chunks is None:
        return enc_chunks_tuple

    # the hard case
    # DESIGN CHOICE: do not allow multiple dask chunks on a single zarr chunk
    # this avoids the need to get involved in zarr synchronization / locking
    # From zarr docs:
    #  "If each worker in a parallel computation is writing to a separate
    #   region of the array, and if region boundaries are perfectly aligned
    #   with chunk boundaries, then no synchronization is required."
    # TODO: incorporate synchronizer to allow writes from multiple dask
    # threads
    if var_chunks and enc_chunks_tuple:
        for zchunk, dchunks in zip(enc_chunks_tuple, var_chunks):
            if len(dchunks) == 1:
                continue
            for dchunk in dchunks[:-1]:
                if dchunk % zchunk:
                    raise NotImplementedError(
                        f"Specified zarr chunks encoding['chunks']={enc_chunks_tuple!r} for "
                        f"variable named {name!r} would overlap multiple dask chunks {var_chunks!r}. "
                        "This is not implemented in xarray yet. "
                        "Consider either rechunking using `chunk()` or instead deleting "
                        "or modifying `encoding['chunks']`."
                    )
            if dchunks[-1] > zchunk:
                raise ValueError(
                    "Final chunk of Zarr array must be the same size or "
                    "smaller than the first. "
                    f"Specified Zarr chunk encoding['chunks']={enc_chunks_tuple}, "
                    f"for variable named {name!r} "
                    f"but {dchunks} in the variable's Dask chunks {var_chunks} is "
                    "incompatible with this encoding. "
                    "Consider either rechunking using `chunk()` or instead deleting "
                    "or modifying `encoding['chunks']`."
                )
        return enc_chunks_tuple

    raise AssertionError("We should never get here. Function logic must be wrong.")


def _get_zarr_dims_and_attrs(zarr_obj, dimension_key):
    # Zarr arrays do not have dimenions. To get around this problem, we add
    # an attribute that specifies the dimension. We have to hide this attribute
    # when we send the attributes to the user.
    # zarr_obj can be either a zarr group or zarr array
    try:
        dimensions = zarr_obj.attrs[dimension_key]
    except KeyError:
        raise KeyError(
            "Zarr object is missing the attribute `%s`, which is "
            "required for xarray to determine variable dimensions." % (dimension_key)
        )
    attributes = HiddenKeyDict(zarr_obj.attrs, [dimension_key])
    return dimensions, attributes


def extract_zarr_variable_encoding(variable, raise_on_invalid=False, name=None):
    """
    Extract zarr encoding dictionary from xarray Variable

    Parameters
    ----------
    variable : Variable
    raise_on_invalid : bool, optional

    Returns
    -------
    encoding : dict
        Zarr encoding for `variable`
    """
    encoding = variable.encoding.copy()

    valid_encodings = {"chunks", "compressor", "filters", "cache_metadata"}

    if raise_on_invalid:
        invalid = [k for k in encoding if k not in valid_encodings]
        if invalid:
            raise ValueError(
                "unexpected encoding parameters for zarr backend:  %r" % invalid
            )
    else:
        for k in list(encoding):
            if k not in valid_encodings:
                del encoding[k]

    chunks = _determine_zarr_chunks(
        encoding.get("chunks"), variable.chunks, variable.ndim, name
    )
    encoding["chunks"] = chunks
    return encoding


# Function below is copied from conventions.encode_cf_variable.
# The only change is to raise an error for object dtypes.
def encode_zarr_variable(var, needs_copy=True, name=None):
    """
    Converts an Variable into an Variable which follows some
    of the CF conventions:

        - Nans are masked using _FillValue (or the deprecated missing_value)
        - Rescaling via: scale_factor and add_offset
        - datetimes are converted to the CF 'units since time' format
        - dtype encodings are enforced.

    Parameters
    ----------
    var : Variable
        A variable holding un-encoded data.

    Returns
    -------
    out : Variable
        A variable which has been encoded as described above.
    """

    var = conventions.encode_cf_variable(var, name=name)

    # zarr allows unicode, but not variable-length strings, so it's both
    # simpler and more compact to always encode as UTF-8 explicitly.
    # TODO: allow toggling this explicitly via dtype in encoding.
    coder = coding.strings.EncodedStringCoder(allows_unicode=True)
    var = coder.encode(var, name=name)
    var = coding.strings.ensure_fixed_length_bytes(var)

    return var


class ZarrStore(AbstractWritableDataStore):
    """Store for reading and writing data via zarr"""

    __slots__ = (
        "ds",
        "_append_dim",
        "_consolidate_on_close",
        "_group",
        "_read_only",
        "_synchronizer",
        "_write_region",
    )

    @classmethod
    def open_group(
        cls,
        store,
        mode="r",
        synchronizer=None,
        group=None,
        consolidated=False,
        consolidate_on_close=False,
        chunk_store=None,
        append_dim=None,
        write_region=None,
    ):
        import zarr

        open_kwargs = dict(mode=mode, synchronizer=synchronizer, path=group)
        if chunk_store:
            open_kwargs["chunk_store"] = chunk_store

        if consolidated:
            # TODO: an option to pass the metadata_key keyword
            zarr_group = zarr.open_consolidated(store, **open_kwargs)
        else:
            zarr_group = zarr.open_group(store, **open_kwargs)
        return cls(zarr_group, consolidate_on_close, append_dim, write_region)

    def __init__(
        self, zarr_group, consolidate_on_close=False, append_dim=None, write_region=None
    ):
        self.ds = zarr_group
        self._read_only = self.ds.read_only
        self._synchronizer = self.ds.synchronizer
        self._group = self.ds.path
        self._consolidate_on_close = consolidate_on_close
        self._append_dim = append_dim
        self._write_region = write_region

    def open_store_variable(self, name, zarr_array):
        data = indexing.LazilyOuterIndexedArray(ZarrArrayWrapper(name, self))
        dimensions, attributes = _get_zarr_dims_and_attrs(zarr_array, DIMENSION_KEY)
        attributes = dict(attributes)
        encoding = {
            "chunks": zarr_array.chunks,
            "compressor": zarr_array.compressor,
            "filters": zarr_array.filters,
        }
        # _FillValue needs to be in attributes, not encoding, so it will get
        # picked up by decode_cf
        if getattr(zarr_array, "fill_value") is not None:
            attributes["_FillValue"] = zarr_array.fill_value

        return Variable(dimensions, data, attributes, encoding)

    def get_variables(self):
        return FrozenDict(
            (k, self.open_store_variable(k, v)) for k, v in self.ds.arrays()
        )

    def get_attrs(self):
        attributes = dict(self.ds.attrs.asdict())
        return attributes

    def get_dimensions(self):
        dimensions = {}
        for k, v in self.ds.arrays():
            try:
                for d, s in zip(v.attrs[DIMENSION_KEY], v.shape):
                    if d in dimensions and dimensions[d] != s:
                        raise ValueError(
                            "found conflicting lengths for dimension %s "
                            "(%d != %d)" % (d, s, dimensions[d])
                        )
                    dimensions[d] = s

            except KeyError:
                raise KeyError(
                    "Zarr object is missing the attribute `%s`, "
                    "which is required for xarray to determine "
                    "variable dimensions." % (DIMENSION_KEY)
                )
        return dimensions

    def set_dimensions(self, variables, unlimited_dims=None):
        if unlimited_dims is not None:
            raise NotImplementedError(
                "Zarr backend doesn't know how to handle unlimited dimensions"
            )

    def set_attributes(self, attributes):
        self.ds.attrs.put(attributes)

    def encode_variable(self, variable):
        variable = encode_zarr_variable(variable)
        return variable

    def encode_attribute(self, a):
        return encode_zarr_attr_value(a)

    def store(
        self,
        variables,
        attributes,
        check_encoding_set=frozenset(),
        writer=None,
        unlimited_dims=None,
    ):
        """
        Top level method for putting data on this store, this method:
          - encodes variables/attributes
          - sets dimensions
          - sets variables

        Parameters
        ----------
        variables : dict-like
            Dictionary of key/value (variable name / xr.Variable) pairs
        attributes : dict-like
            Dictionary of key/value (attribute name / attribute) pairs
        check_encoding_set : list-like
            List of variables that should be checked for invalid encoding
            values
        writer : ArrayWriter
        unlimited_dims : list-like
            List of dimension names that should be treated as unlimited
            dimensions.
            dimension on which the zarray will be appended
            only needed in append mode
        """
        import zarr

        existing_variables = {
            vn for vn in variables if _encode_variable_name(vn) in self.ds
        }
        new_variables = set(variables) - existing_variables
        variables_without_encoding = {vn: variables[vn] for vn in new_variables}
        variables_encoded, attributes = self.encode(
            variables_without_encoding, attributes
        )

        if len(existing_variables) > 0:
            # there are variables to append
            # their encoding must be the same as in the store
            ds = open_zarr(self.ds.store, group=self.ds.path, chunks=None)
            variables_with_encoding = {}
            for vn in existing_variables:
                variables_with_encoding[vn] = variables[vn].copy(deep=False)
                variables_with_encoding[vn].encoding = ds[vn].encoding
            variables_with_encoding, _ = self.encode(variables_with_encoding, {})
            variables_encoded.update(variables_with_encoding)

        if self._write_region is None:
            self.set_attributes(attributes)
            self.set_dimensions(variables_encoded, unlimited_dims=unlimited_dims)
        self.set_variables(
            variables_encoded, check_encoding_set, writer, unlimited_dims=unlimited_dims
        )
        if self._consolidate_on_close:
            zarr.consolidate_metadata(self.ds.store)

    def sync(self):
        pass

    def set_variables(self, variables, check_encoding_set, writer, unlimited_dims=None):
        """
        This provides a centralized method to set the variables on the data
        store.

        Parameters
        ----------
        variables : dict-like
            Dictionary of key/value (variable name / xr.Variable) pairs
        check_encoding_set : list-like
            List of variables that should be checked for invalid encoding
            values
        writer :
        unlimited_dims : list-like
            List of dimension names that should be treated as unlimited
            dimensions.
        """

        for vn, v in variables.items():
            name = _encode_variable_name(vn)
            check = vn in check_encoding_set
            attrs = v.attrs.copy()
            dims = v.dims
            dtype = v.dtype
            shape = v.shape

            fill_value = attrs.pop("_FillValue", None)
            if v.encoding == {"_FillValue": None} and fill_value is None:
                v.encoding = {}

            if name in self.ds:
                # existing variable
                zarr_array = self.ds[name]
            else:
                # new variable
                encoding = extract_zarr_variable_encoding(
                    v, raise_on_invalid=check, name=vn
                )
                encoded_attrs = {}
                # the magic for storing the hidden dimension data
                encoded_attrs[DIMENSION_KEY] = dims
                for k2, v2 in attrs.items():
                    encoded_attrs[k2] = self.encode_attribute(v2)

                if coding.strings.check_vlen_dtype(dtype) == str:
                    dtype = str
                zarr_array = self.ds.create(
                    name, shape=shape, dtype=dtype, fill_value=fill_value, **encoding
                )
                zarr_array.attrs.put(encoded_attrs)

            write_region = self._write_region if self._write_region is not None else {}
            write_region = {dim: write_region.get(dim, slice(None)) for dim in dims}

            if self._append_dim is not None and self._append_dim in dims:
                # resize existing variable
                append_axis = dims.index(self._append_dim)
                assert write_region[self._append_dim] == slice(None)
                write_region[self._append_dim] = slice(
                    zarr_array.shape[append_axis], None
                )

                new_shape = list(zarr_array.shape)
                new_shape[append_axis] += v.shape[append_axis]
                zarr_array.resize(new_shape)

            region = tuple(write_region[dim] for dim in dims)
            writer.add(v.data, zarr_array, region)

    def close(self):
        pass


def open_zarr(
    store,
    group=None,
    synchronizer=None,
    chunks="auto",
    decode_cf=True,
    mask_and_scale=True,
    decode_times=True,
    concat_characters=True,
    decode_coords=True,
    drop_variables=None,
    consolidated=False,
    overwrite_encoded_chunks=False,
    chunk_store=None,
    decode_timedelta=None,
    use_cftime=None,
    **kwargs,
):
    """Load and decode a dataset from a Zarr store.

    .. note:: Experimental
              The Zarr backend is new and experimental. Please report any
              unexpected behavior via github issues.

    The `store` object should be a valid store for a Zarr group. `store`
    variables must contain dimension metadata encoded in the
    `_ARRAY_DIMENSIONS` attribute.

    Parameters
    ----------
    store : MutableMapping or str
        A MutableMapping where a Zarr Group has been stored or a path to a
        directory in file system where a Zarr DirectoryStore has been stored.
    synchronizer : object, optional
        Array synchronizer provided to zarr
    group : str, optional
        Group path. (a.k.a. `path` in zarr terminology.)
    chunks : int or dict or tuple or {None, 'auto'}, optional
        Chunk sizes along each dimension, e.g., ``5`` or
        ``{'x': 5, 'y': 5}``. If `chunks='auto'`, dask chunks are created
        based on the variable's zarr chunks. If `chunks=None`, zarr array
        data will lazily convert to numpy arrays upon access. This accepts
        all the chunk specifications as Dask does.
    overwrite_encoded_chunks: bool, optional
        Whether to drop the zarr chunks encoded for each variable when a
        dataset is loaded with specified chunk sizes (default: False)
    decode_cf : bool, optional
        Whether to decode these variables, assuming they were saved according
        to CF conventions.
    mask_and_scale : bool, optional
        If True, replace array values equal to `_FillValue` with NA and scale
        values according to the formula `original_values * scale_factor +
        add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are
        taken from variable attributes (if they exist).  If the `_FillValue` or
        `missing_value` attribute contains multiple values a warning will be
        issued and all array values matching one of the multiple values will
        be replaced by NA.
    decode_times : bool, optional
        If True, decode times encoded in the standard NetCDF datetime format
        into datetime objects. Otherwise, leave them encoded as numbers.
    concat_characters : bool, optional
        If True, concatenate along the last dimension of character arrays to
        form string arrays. Dimensions will only be concatenated over (and
        removed) if they have no corresponding variable and if they are only
        used as the last dimension of character arrays.
    decode_coords : bool, optional
        If True, decode the 'coordinates' attribute to identify coordinates in
        the resulting dataset.
    drop_variables : str or iterable, optional
        A variable or list of variables to exclude from being parsed from the
        dataset. This may be useful to drop variables with problems or
        inconsistent values.
    consolidated : bool, optional
        Whether to open the store using zarr's consolidated metadata
        capability. Only works for stores that have already been consolidated.
    chunk_store : MutableMapping, optional
        A separate Zarr store only for chunk data.
    decode_timedelta : bool, optional
        If True, decode variables and coordinates with time units in
        {'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds'}
        into timedelta objects. If False, leave them encoded as numbers.
        If None (default), assume the same value of decode_time.
    use_cftime: bool, optional
        Only relevant if encoded dates come from a standard calendar
        (e.g. "gregorian", "proleptic_gregorian", "standard", or not
        specified).  If None (default), attempt to decode times to
        ``np.datetime64[ns]`` objects; if this is not possible, decode times to
        ``cftime.datetime`` objects. If True, always decode times to
        ``cftime.datetime`` objects, regardless of whether or not they can be
        represented using ``np.datetime64[ns]`` objects.  If False, always
        decode times to ``np.datetime64[ns]`` objects; if this is not possible
        raise an error.

    Returns
    -------
    dataset : Dataset
        The newly created dataset.

    See Also
    --------
    open_dataset

    References
    ----------
    http://zarr.readthedocs.io/
    """
    from .api import open_dataset

    if chunks == "auto":
        try:
            import dask.array  # noqa

            chunks = {}
        except ImportError:
            chunks = None

    if kwargs:
        raise TypeError(
            "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys())
        )

    backend_kwargs = {
        "synchronizer": synchronizer,
        "consolidated": consolidated,
        "overwrite_encoded_chunks": overwrite_encoded_chunks,
        "chunk_store": chunk_store,
    }

    ds = open_dataset(
        filename_or_obj=store,
        group=group,
        decode_cf=decode_cf,
        mask_and_scale=mask_and_scale,
        decode_times=decode_times,
        concat_characters=concat_characters,
        decode_coords=decode_coords,
        engine="zarr",
        chunks=chunks,
        drop_variables=drop_variables,
        backend_kwargs=backend_kwargs,
        decode_timedelta=decode_timedelta,
        use_cftime=use_cftime,
    )

    return ds


def open_backend_dataset_zarr(
    filename_or_obj,
    mask_and_scale=True,
    decode_times=None,
    concat_characters=None,
    decode_coords=None,
    drop_variables=None,
    use_cftime=None,
    decode_timedelta=None,
    group=None,
    mode="r",
    synchronizer=None,
    consolidated=False,
    consolidate_on_close=False,
    chunk_store=None,
):

    store = ZarrStore.open_group(
        filename_or_obj,
        group=group,
        mode=mode,
        synchronizer=synchronizer,
        consolidated=consolidated,
        consolidate_on_close=consolidate_on_close,
        chunk_store=chunk_store,
    )

    with close_on_error(store):
        vars, attrs = store.load()
        file_obj = store
        encoding = store.get_encoding()

        vars, attrs, coord_names = conventions.decode_cf_variables(
            vars,
            attrs,
            mask_and_scale=mask_and_scale,
            decode_times=decode_times,
            concat_characters=concat_characters,
            decode_coords=decode_coords,
            drop_variables=drop_variables,
            use_cftime=use_cftime,
            decode_timedelta=decode_timedelta,
        )

        ds = Dataset(vars, attrs=attrs)
        ds = ds.set_coords(coord_names.intersection(vars))
        ds._file_obj = file_obj
        ds.encoding = encoding

    return ds


zarr_backend = BackendEntrypoint(open_dataset=open_backend_dataset_zarr)