TileDB Python API Reference¶

Modules¶

Typical usage of the Python interface to TileDB will use the top-level module tiledb, e.g.

import tiledb

There is also a submodule libtiledb which contains the necessary bindings to the underlying TileDB native library. Most of the time you will not need to interact with tiledb.libtiledb unless you need native-library specific information, e.g. the version number:

import tiledb
tiledb.libtiledb.version()  # Native TileDB library version number

Getting Started¶

Arrays may be opened with the tiledb.open function:

tiledb.open(uri, mode='r', key=None, attr=None, config=None, timestamp=None, ctx=None)¶

Open a TileDB array at the given URI

Parameters:

uri – any TileDB supported URI
timestamp – array timestamp to open, int or None. See the TileDB time traveling documentation for detailed functionality description.
key – encryption key, str or None
mode (str) – (default ‘r’) Open the array object in read ‘r’ or write ‘w’ mode
attr – attribute name to select from a multi-attribute array, str or None
config – TileDB config dictionary, dict or None

Returns:

open TileDB {Sparse,Dense}Array object

Data import helpers¶

tiledb.from_numpy(uri, array, config=None, ctx=None, **kwargs)¶

Write a NumPy array into a TileDB DenseArray, returning a readonly DenseArray instance.

Keyword Arguments:
Parameters:	uri (str) – URI for the TileDB array (any supported TileDB URI) array (numpy.ndarray) – dense numpy array to persist config – TileDB config dictionary, dict or None ctx (tiledb.Ctx) – A TileDB Context kwargs – additional arguments to pass to the DenseArray constructor
Return type:	tiledb.DenseArray
Returns:	An open DenseArray (read mode) with a single anonymous attribute
Raises:	TypeError – cannot convert `uri` to unicode string
Raises:	`tiledb.TileDBError`
	full_domain - Dimensions should be created with full range of the dtype (default: False) mode - Creation mode, one of ‘ingest’ (default), ‘schema_only’, ‘append’ append_dim - The dimension along which the Numpy array is append (default: 0). start_idx - The starting index to append to. By default, append to the end of the existing data. timestamp - Write TileDB array at specific timestamp.

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     # Creates array 'array' on disk.
...     with tiledb.DenseArray.from_numpy(tmp + "/array",  np.array([1.0, 2.0, 3.0])) as A:
...         pass

tiledb.from_csv(uri: str, csv_file: Union[str, List[str]], **kwargs)¶

Create TileDB array at given URI from a CSV file or list of files

Keyword Arguments:
Parameters:	uri – URI for new TileDB array csv_file – input CSV file or list of CSV files. Note: multi-file ingestion requires a chunksize argument. Files will be read in batches of at least chunksize rows before writing to the TileDB array.
	Any pandas.read_csv supported keyword argument ctx - A TileDB context sparse - (default True) Create sparse schema index_dims (`List[str]`) – List of column name(s) to use as dimension(s) in TileDB array schema. This is the recommended way to create dimensions. (note: the Pandas `read_csv` argument `index_col` will be passed through if provided, which results in indexes that will be converted to dimnesions by default; however `index_dims` is preferred). allows_duplicates - Generated schema should allow duplicates mode - Creation mode, one of ‘ingest’ (default), ‘schema_only’, ‘append’ attr_filters - FilterList to apply to Attributes: FilterList or Dict[str -> FilterList] for any attribute(s). Unspecified attributes will use default. dim_filters - FilterList to apply to Dimensions: FilterList or Dict[str -> FilterList] for any dimensions(s). Unspecified dimensions will use default. offsets_filters - FilterList to apply to all offsets full_domain - Dimensions should be created with full range of the dtype tile - Dimension tiling: accepts either an int that applies the tiling to all dimensions or a dict(“dim_name”: int) to specifically assign tiling to a given dimension row_start_idx - Start index to start new write (for row-indexed ingestions). fillna - Value to use to fill holes column_types - Dictionary of {`column_name`: dtype} to apply dtypes to columns varlen_types - A set of {dtypes}; any column wihin the set is converted to a variable length attribute capacity - Schema capacity. date_spec - Dictionary of {`column_name`: format_spec} to apply to date/time columns which are not correctly inferred by pandas ‘parse_dates’. Format must be specified using the Python format codes: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior cell_order - (default ‘row-major) Schema cell order: ‘row-major’, ‘col-major’, or ‘hilbert’ tile_order - (default ‘row-major) Schema tile order: ‘row-major’ or ‘col-major’ timestamp - Write TileDB array at specific timestamp.
Returns:	None

Example:

>>> import tiledb
>>> tiledb.from_csv("iris.tldb", "iris.csv")
>>> tiledb.object_type("iris.tldb")
'array'

tiledb.from_pandas(uri: str, dataframe: pd.DataFrame, **kwargs)¶

Create TileDB array at given URI from a Pandas dataframe

Supports most Pandas series types, including nullable integers and bools.

Keyword Arguments:
Parameters:	uri – URI for new TileDB array dataframe – pandas DataFrame
	Any pandas.read_csv supported keyword argument ctx - A TileDB context sparse - (default True) Create sparse schema chunksize - (default None) Maximum number of rows to read at a time. Note that this is also a pandas.read_csv argument which tiledb.read_csv checks for in order to correctly read a file batchwise. index_dims (`List[str]`) – List of column name(s) to use as dimension(s) in TileDB array schema. This is the recommended way to create dimensions. allows_duplicates - Generated schema should allow duplicates mode - Creation mode, one of ‘ingest’ (default), ‘schema_only’, ‘append’ attr_filters - FilterList to apply to Attributes: FilterList or Dict[str -> FilterList] for any attribute(s). Unspecified attributes will use default. dim_filters - FilterList to apply to Dimensions: FilterList or Dict[str -> FilterList] for any dimensions(s). Unspecified dimensions will use default. offsets_filters - FilterList to apply to all offsets full_domain - Dimensions should be created with full range of the dtype tile - Dimension tiling: accepts either an int that applies the tiling to all dimensions or a dict(“dim_name”: int) to specifically assign tiling to a given dimension row_start_idx - Start index to start new write (for row-indexed ingestions). fillna - Value to use to fill holes column_types - Dictionary of {`column_name`: dtype} to apply dtypes to columns varlen_types - A set of {dtypes}; any column wihin the set is converted to a variable length attribute capacity - Schema capacity. date_spec - Dictionary of {`column_name`: format_spec} to apply to date/time columns which are not correctly inferred by pandas ‘parse_dates’. Format must be specified using the Python format codes: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior cell_order - (default ‘row-major) Schema cell order: ‘row-major’, ‘col-major’, or ‘hilbert’ tile_order - (default ‘row-major) Schema tile order: ‘row-major’ or ‘col-major’ timestamp - Write TileDB array at specific timestamp.
Raises:	`tiledb.TileDBError`
Returns:	None

Context¶

class tiledb.Ctx(config=None)¶

Class representing a TileDB context.

A TileDB context wraps a TileDB storage manager.

Parameters:	config (tiledb.Config or dict) – Initialize Ctx with given config parameters

config(self)¶: Returns the Config instance associated with the Ctx.

get_stats(self, print_out=True, json=False)¶

Retrieves the stats from a TileDB context.

Parameters:	print_out – Print string to console (default True), or return as string json – Return stats JSON object (default: False)

set_tag(self, key, value)¶: Sets a (string, string) “tag” on the Ctx (internal).

tiledb.default_ctx(config: Union[Config, dict] = None) → Ctx¶

Returns, and optionally initializes, the default tiledb.Ctx context variable.

This Ctx object is used by Python API functions when no ctx keyword argument is provided. Most API functions accept an optional ctx kwarg, but that is typically only necessary in advanced usage with multiple contexts per program.

For initialization, this function must be called before any other tiledb functions. The initialization call accepts a tiledb.Config object to override the defaults for process-global parameters.

Parameters:	config – `tiledb.Config` object or dictionary with config parameters.
Returns:	Ctx

Config¶

class tiledb.Config(params=None, path=None)¶

TileDB Config class

The Config object stores configuration parameters for both TileDB Embedded and TileDB-Py.

For TileDB Embedded parameters, see:

https://docs.tiledb.com/main/how-to/configuration#configuration-parameters

The following configuration options are supported by TileDB-Py:

py.init_buffer_bytes:

Initial allocation size in bytes for attribute and dimensions buffers. If result size exceed the pre-allocated buffer(s), then the query will return incomplete and TileDB-Py will allocate larger buffers and resubmit. Specifying a sufficiently large buffer size will often improve performance. Default 10 MB (1024**2 * 10).

py.use_arrow:

Use pyarrow from the Apache Arrow project to convert query results into Pandas dataframe format when requested. Default True.

py.deduplicate:

Attempt to deduplicate Python objects during buffer conversion to Python. Deduplication may reduce memory usage for datasets with many identical strings, at the cost of some performance reduction due to hash calculation/lookup for each object.

Unknown parameters will be ignored!

Parameters:	params (dict) – Set parameter values from dict like object path (str) – Set parameter values from persisted Config parameter file

clear(self)¶: Unsets all Config parameters (returns them to their default values)

dict(self, prefix=u'')¶

Returns a dict representation of a Config object

Parameters:	prefix (str) – return only parameters with a given prefix
Return type:	dict
Returns:	Config parameter / values as a a Python dict

from_file(self, path)¶

Update a Config object with from a persisted config file

Parameters:	path – A local Config file path

get(self, key, raise_keyerror=True)¶

items(self, prefix=u'')¶

Returns an iterator object over Config parameters, values

Parameters:	prefix (str) – return only parameters with a given prefix
Return type:	ConfigItems
Returns:	iterator over Config parameter, value tuples

keys(self, prefix=u'')¶

Returns an iterator object over Config parameters (keys)

Parameters:	prefix (str) – return only parameters with a given prefix
Return type:	ConfigKeys
Returns:	iterator over Config parameter string keys

static load(uri)¶

Constructs a Config class instance from config parameters loaded from a local Config file

Parameters:	uri (str) – a local URI config file path
Return type:	tiledb.Config
Returns:	A TileDB Config instance with persisted parameter values
Raises:	TypeError – uri cannot be converted to a unicode string
Raises:	`tiledb.TileDBError`

save(self, uri)¶

Persist Config parameter values to a config file

Parameters:	uri (str) – a local URI config file path
Raises:	TypeError – uri cannot be converted to a unicode string
Raises:	`tiledb.TileDBError`

update(self, odict)¶

Update a config object with parameter, values from a dict like object

Parameters:	odict – dict-like object containing parameter, values to update Config.

values(self, prefix=u'')¶

Returns an iterator object over Config values

Parameters:	prefix (str) – return only parameters with a given prefix
Return type:	ConfigValues
Returns:	iterator over Config string values

Array Schema¶

class tiledb.ArraySchema(domain=None, attrs=(), cell_order='row-major', tile_order='row-major', capacity=0, coords_filters=None, offsets_filters=None, validity_filters=None, allows_duplicates=False, sparse=False, Ctx ctx=None)¶

Schema class for TileDB dense / sparse array representations

Parameters:

domain – Domain of schema
cell_order ('row-major' (default) or 'C', 'col-major' or 'F' or 'hilbert') – TileDB label for cell layout
tile_order ('row-major' (default) or 'C', 'col-major' or 'F') – TileDB label for tile layout
capacity (int) – tile cell capacity
offsets_filters (tiledb.FilterList) – (default None) offsets filter list
validity_filters (tiledb.FilterList) – (default None) validity filter list
allows_duplicates (bool) – True if duplicates are allowed
sparse (bool) – True if schema is sparse, else False (set by SparseArray and DenseArray derived classes)
ctx (tiledb.Ctx) – A TileDB Context

Raises:

tiledb.TileDBError

allows_duplicates¶: Returns True if the (sparse) array allows duplicates.

attr(self, key)¶

Returns an Attr instance given an int index or string label

Parameters:	key (int or str) – attribute index (positional or associative)
Return type:	tiledb.Attr
Returns:	The ArraySchema attribute at index or with the given name (label)
Raises:	TypeError – invalid key type

attr_or_dim_dtype(self, unicode name)¶

capacity¶

The array capacity

Return type:	int
Raises:	`tiledb.TileDBError`

cell_order¶: The cell order layout of the array.

check(self)¶

Checks the correctness of the array schema

Return type:	None
Raises:	`tiledb.TileDBError` if invalid

coords_compressor¶

The compressor label and level for the array’s coordinates.

Return type:	tuple(str, int)
Raises:	`tiledb.TileDBError`

coords_filters¶

The FilterList for the array’s coordinates

Return type:	tiledb.FilterList
Raises:	`tiledb.TileDBError`

domain¶

The Domain associated with the array.

Return type:	tiledb.Domain
Raises:	`tiledb.TileDBError`

dump(self)¶: Dumps a string representation of the array object to standard output (stdout)

static from_file(uri=None, Ctx ctx=None)¶: Create an ArraySchema for a Filestore Array from a given file. If a uri is not given, then create a default schema.

has_attr(self, name)¶

Returns true if the given name is an Attribute of the ArraySchema

Parameters:	name – attribute name
Return type:	boolean

static load(uri, Ctx ctx=None, key=None)¶

nattr¶

The number of array attributes.

Return type:	int
Raises:	`tiledb.TileDBError`

ndim¶

The number of array domain dimensions.

Return type:	int

offsets_compressor¶

The compressor label and level for the array’s variable-length attribute offsets.

Return type:	tuple(str, int)
Raises:	`tiledb.TileDBError`

offsets_filters¶

The FilterList for the array’s variable-length attribute offsets

Return type:	tiledb.FilterList
Raises:	`tiledb.TileDBError`

shape¶

The array’s shape

Return type:	tuple(numpy scalar, numpy scalar)
Raises:	TypeError – floating point (inexact) domain

sparse¶

True if the array is a sparse array representation

Return type:	bool
Raises:	`tiledb.TileDBError`

tile_order¶

The tile order layout of the array.

Return type:	str
Raises:	`tiledb.TileDBError`

validity_filters¶

The FilterList for the array’s validity

Return type:	tiledb.FilterList
Raises:	`tiledb.TileDBError`

version¶

The array’s scehma version.

Return type:	int

:raises tiledb.TileDBError

tiledb.empty_like(uri, arr, config=None, key=None, tile=None, ctx=None)¶

Create and return an empty, writeable DenseArray with schema based on a NumPy-array like object.

Parameters:	uri – array URI arr – NumPy ndarray, or shape tuple config – (optional, deprecated) configuration to apply to new Ctx key – (optional) encryption key, if applicable tile – (optional) tiling of generated array ctx – (optional) TileDB Ctx
Returns:

Attribute¶

class tiledb.Attr(name=u'', dtype=np.float64, fill=None, var=None, nullable=False, filters=None, Ctx ctx=None)¶

Class representing a TileDB array attribute.

Parameters:	ctx (tiledb.Ctx) – A TileDB Context name (str) – Attribute name, empty if anonymous dtype (bool) – Attribute value datatypes nullable – Attribute is nullable fill – Fill value for unset cells. var – Attribute is variable-length (automatic for byte/string types) filters (FilterList) – List of filters to apply
Raises:	TypeError – invalid dtype
Raises:	`tiledb.TileDBError`

compressor¶

String label of the attributes compressor and compressor level

Return type:	tuple(str, int)
Raises:	`tiledb.TileDBError`

dtype¶

Return numpy dtype object representing the Attr type

Return type:	numpy.dtype

dump(self)¶: Dumps a string representation of the Attr object to standard output (stdout)

fill¶

Fill value for unset cells of this attribute

Return type:	depends on dtype
Raises:	`tiledb.TileDBERror`

filters¶

FilterList of the TileDB attribute

Return type:	tiledb.FilterList
Raises:	`tiledb.TileDBError`

isanon¶

True if attribute is an anonymous attribute

Return type:	bool

isascii¶

True if the attribute is TileDB dtype TILEDB_STRING_ASCII

Return type:	bool
Raises:	`tiledb.TileDBError`

isnullable¶

True if the attribute is nullable

Return type:	bool
Raises:	`tiledb.TileDBError`

isvar¶

True if the attribute is variable length

Return type:	bool
Raises:	`tiledb.TileDBError`

name¶

Attribute string name, empty string if the attribute is anonymous

Return type:	str
Raises:	`tiledb.TileDBError`

ncells¶

The number of cells (scalar values) for a given attribute value

Return type:	int
Raises:	`tiledb.TileDBError`

Filters¶

class tiledb.FilterList(filters: Sequence[tiledb.filter.Filter] = None, chunksize: int = None, ctx: Ctx = None, is_capsule: bool = False)¶

An ordered list of Filter objects for filtering TileDB data.

FilterLists contain zero or more Filters, used for filtering attribute data, the array coordinate data, etc.

Parameters:	ctx (tiledb.Ctx) – A TileDB context filters – An iterable of Filter objects to add. chunksize (int) – (default None) chunk size used by the filter list in bytes

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     # Create several filters
...     gzip_filter = tiledb.GzipFilter()
...     bw_filter = tiledb.BitWidthReductionFilter()
...     # Create a filter list that will first perform bit width reduction, then gzip compression.
...     filters = tiledb.FilterList([bw_filter, gzip_filter])
...     a1 = tiledb.Attr(name="a1", dtype=np.int64, filters=filters)
...     # Create a second attribute filtered only by gzip compression.
...     a2 = tiledb.Attr(name="a2", dtype=np.int64,
...                      filters=tiledb.FilterList([gzip_filter]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1, a2))
...     tiledb.DenseArray.create(tmp + "/array", schema)

__getitem__(idx)¶

Gets a copy of the filter in the list at the given index

Parameters:	idx (int or slice) – index into the
Returns:	A filter at given index / slice
Raises:	IndexError – invalid index
Raises:	`tiledb.TileDBError`

__len__() → int¶

Return type:	int
Returns:	Number of filters in the FilterList

append(filter: tiledb.filter.Filter)¶

Parameters:	filter (Filter) – the filter to append into the FilterList
Raises:	ValueError – filter argument incorrect type

class tiledb.GzipFilter(level: int = -1, ctx: Ctx = None)¶

Filter that compresses using gzip.

Parameters:	ctx (tiledb.Ctx) – TileDB Ctx level (int) – (default None) If not None set the compressor level

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.GzipFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.ZstdFilter(level: int = -1, ctx: Ctx = None)¶

Filter that compresses using zstd.

Parameters:	ctx (tiledb.Ctx) – TileDB Ctx level (int) – (default None) If not None set the compressor level

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.ZstdFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.LZ4Filter(level: int = -1, ctx: Ctx = None)¶

Filter that compresses using lz4.

Parameters:	ctx (tiledb.Ctx) – TileDB Ctx level (int) – (default None) If not None set the compressor level

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.LZ4Filter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.Bzip2Filter(level: int = -1, ctx: Ctx = None)¶

Filter that compresses using bzip2.

Parameters:	level (int) – (default None) If not None set the compressor level

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.Bzip2Filter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.RleFilter(level: int = -1, ctx: Ctx = None)¶

Filter that compresses using run-length encoding (RLE).

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.RleFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.DoubleDeltaFilter(level: int = -1, ctx: Ctx = None)¶

Filter that performs double-delta encoding.

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.DoubleDeltaFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.BitShuffleFilter(ctx: Ctx = None)¶

Filter that performs a bit shuffle transformation.

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.BitShuffleFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.ByteShuffleFilter(ctx: Ctx = None)¶

Filter that performs a byte shuffle transformation.

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.ByteShuffleFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.BitWidthReductionFilter(window: int = -1, ctx: Ctx = None)¶

Filter that performs bit-width reduction.

param ctx: A TileDB Context

type ctx: tiledb.Ctx

param window: (default None) max window size for the filter

type window: int

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.BitWidthReductionFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

class tiledb.PositiveDeltaFilter(window: int = -1, ctx: Ctx = None)¶

Filter that performs positive-delta encoding.

Parameters:	ctx (tiledb.Ctx) – A TileDB Context window (int) – (default None) the max window for the filter

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     a1 = tiledb.Attr(name="a1", dtype=np.int64,
...                      filters=tiledb.FilterList([tiledb.PositiveDeltaFilter()]))
...     schema = tiledb.ArraySchema(domain=dom, attrs=(a1,))
...     tiledb.DenseArray.create(tmp + "/array", schema)

Dimension¶

class tiledb.Dim(name=u'__dim_0', domain=None, tile=None, filters=None, dtype=np.uint64, var=None, Ctx ctx=None)¶

Class representing a dimension of a TileDB Array.

Parameters:	name (str) – the dimension name, empty if anonymous domain (tuple(int, int) or tuple(float, float)) – tile (int or float) – Tile extent filters (FilterList) – List of filters to apply ctx (tiledb.Ctx) – A TileDB Context
Dtype:	the Dim numpy dtype object, type object, or string that can be corerced into a numpy dtype object
Raises:	ValueError – invalid domain or tile extent TypeError – invalid domain, tile extent, or dtype type
Raises:	`TileDBError`

domain¶

The dimension (inclusive) domain.

The dimension’s domain is defined by a (lower bound, upper bound) tuple.

Return type:	tuple(numpy scalar, numpy scalar)

dtype¶

Numpy dtype representation of the dimension type.

Return type:	numpy.dtype

filters¶

FilterList of the TileDB dimension

Return type:	tiledb.FilterList
Raises:	`tiledb.TileDBError`

isanon¶

True if the dimension is anonymous

Return type:	bool

isvar¶

True if the dimension is variable length

Return type:	bool
Raises:	`tiledb.TileDBError`

name¶

The dimension label string.

Anonymous dimensions return a default string representation based on the dimension index.

Return type:	str

shape¶

The shape of the dimension given the dimension’s domain.

Note: The shape is only valid for integer and datetime dimension domains.

Return type:	tuple(numpy scalar, numpy scalar)
Raises:	TypeError – floating point (inexact) domain

size¶

The size of the dimension domain (number of cells along dimension).

Return type:	int
Raises:	TypeError – floating point (inexact) domain

tile¶

The tile extent of the dimension.

Return type:	numpy scalar or np.timedelta64

Domain¶

class tiledb.Domain(Ctx ctx=None, *dims)¶

Class representing the domain of a TileDB Array.

Parameters:	dims – one or more tiledb.Dim objects up to the Domain’s ndim ctx (tiledb.Ctx) – A TileDB Context
Raises:	TypeError – All dimensions must have the same dtype
Raises:	`TileDBError`

dim(self, dim_id)¶

Returns a Dim object from the domain given the dimension’s index or name.

Parameters:	dim_d – dimension index (int) or name (str)
Raises:	`tiledb.TileDBError`

dtype¶

The numpy dtype of the domain’s dimension type.

Return type:	numpy.dtype

dump(self)¶: Dumps a string representation of the domain object to standard output (STDOUT)

has_dim(self, name)¶

Returns true if the Domain has a Dimension with the given name

Parameters:	name – name of Dimension
Return type:	bool
Returns:

homogeneous¶: Returns True if the domain’s dimension types are homogeneous.

ndim¶

The number of dimensions of the domain.

Return type:	int

shape¶

The domain’s shape, valid only for integer domains.

Return type:	tuple
Raises:	TypeError – floating point (inexact) domain

size¶

The domain’s size (number of cells), valid only for integer domains.

Return type:	int
Raises:	TypeError – floating point (inexact) domain

Array¶

class tiledb.libtiledb.Array(uri, mode='r', key=None, timestamp=None, attr=None, Ctx ctx=None)¶

Base class for TileDB array objects.

Defines common properties/functionality for the different array types. When an Array instance is initialized, the array is opened with the specified mode.

Parameters:

uri (str) – URI of array to open
mode (str) – (default ‘r’) Open the array object in read ‘r’ or write ‘w’ mode
key (str) – (default None) If not None, encryption key to decrypt the array
timestamp (tuple) – (default None) If int, open the array at a given TileDB timestamp. If tuple, open at the given start and end TileDB timestamps.
attr (str) – (default None) open one attribute of the array; indexing a dense array will return a Numpy ndarray directly rather than a dictionary.
ctx (Ctx) – TileDB context

attr(self, key)¶

Returns an Attr instance given an int index or string label

Parameters:	key (int or str) – attribute index (positional or associative)
Return type:	`Attr`
Returns:	The array attribute at index or with the given name (label)
Raises:	TypeError – invalid key type

close(self)¶: Closes this array, flushing all buffered data.

consolidate(self, Config config=None, key=None, timestamp=None)¶

Consolidates fragments of an array object for increased read performance.

Overview: https://docs.tiledb.com/main/concepts/internal-mechanics/consolidation

Parameters:	config (tiledb.Config) – The TileDB Config with consolidation parameters set key (str or bytes) – (default None) encryption key to decrypt an encrypted array timestamp (tuple (int, int)) – (default None) If not None, consolidate the array using the given tuple(int, int) UNIX seconds range (inclusive)
Raises:	`tiledb.TileDBError`

Rather than passing the timestamp into this function, it may be set with the config parameters “sm.vacuum.timestamp_start”`and `”sm.vacuum.timestamp_end” which takes in a time in UNIX seconds. If both are set then this function’s timestamp argument will be used.

coords_dtype¶

Returns the numpy record array dtype of the array coordinates

Return type:	numpy.dtype
Returns:	coord array record dtype

create(type cls, uri, ArraySchema schema, key=None, overwrite=False, Ctx ctx=None)¶

Creates a TileDB Array at the given URI

Parameters:

uri (str) – URI at which to create the new empty array.
schema (ArraySchema) – Schema for the array
key (str) – (default None) Encryption key to use for array
oerwrite (bool) – (default False) Overwrite the array if it already exists
Ctx (ctx) – (default None) Optional TileDB Ctx used when creating the array, by default uses the ArraySchema’s associated context (not necessarily tiledb.default_ctx).

df¶

Retrieve data cells as a Pandas dataframe, with multi-range, domain-inclusive indexing using multi_index.

Parameters:	selection (list) – Per dimension, a scalar, `slice`, or list of scalars or `slice` objects. Scalars and `slice` components should match the type of the underlying Dimension.
Returns:	dict of {‘attribute’: result}. Coords are included by default for Sparse arrays only (use Array.query(coords=<>) to select).
Raises:	IndexError – invalid or unsupported index selection
Raises:	`tiledb.TileDBError`

df[] accepts, for each dimension, a scalar, slice, or list of scalars or slice objects. Each item is interpreted as a point (scalar) or range (slice) used to query the array on the corresponding dimension.

** Example **

>>> import tiledb, tempfile, numpy as np, pandas as pd
>>>
>>> with tempfile.TemporaryDirectory() as tmp:
...    data = {'col1_f': np.arange(0.0,1.0,step=0.1), 'col2_int': np.arange(10)}
...    df = pd.DataFrame.from_dict(data)
...    tiledb.from_pandas(tmp, df)
...    A = tiledb.open(tmp)
...    A.df[1]
...    A.df[1:5]
      col1_f  col2_int
   1     0.1         1
      col1_f  col2_int
   1     0.1         1
   2     0.2         2
   3     0.3         3
   4     0.4         4
   5     0.5         5

dim(self, dim_id)¶

Returns a Dim instance given a dim index or name

Parameters:	key (int or str) – attribute index (positional or associative)
Return type:	`Attr`
Returns:	The array attribute at index or with the given name (label)
Raises:	TypeError – invalid key type

domain¶: The Domain of this array.

dtype¶: The NumPy dtype of the specified attribute

dump(self)¶

isopen¶: True if this array is currently open.

iswritable¶: This array is currently opened as writable.

static load_typed(uri, mode='r', key=None, timestamp=None, attr=None, Ctx ctx=None)¶: Return a {Dense,Sparse}Array instance from a pre-opened Array (internal)

meta¶

Return array metadata instance

Return type:	tiledb.Metadata

mode¶: The mode this array was opened with.

multi_index¶

Retrieve data cells with multi-range, domain-inclusive indexing. Returns the cross-product of the ranges.

Parameters:	selection (list) – Per dimension, a scalar, `slice`, or list of scalars or `slice` objects. Scalars and `slice` components should match the type of the underlying Dimension.
Returns:	dict of {‘attribute’: result}. Coords are included by default for Sparse arrays only (use Array.query(coords=<>) to select).
Raises:	IndexError – invalid or unsupported index selection
Raises:	`tiledb.TileDBError`

multi_index[] accepts, for each dimension, a scalar, slice, or list of scalars or slice objects. Each item is interpreted as a point (scalar) or range (slice) used to query the array on the corresponding dimension.

Unlike NumPy array indexing, multi_index respects TileDB’s range semantics: slice ranges are inclusive of the start- and end-point, and negative ranges do not wrap around (because a TileDB dimensions may have a negative domain).

** Example **

>>> import tiledb, tempfile, numpy as np
>>>
>>> with tempfile.TemporaryDirectory() as tmp:
...    A = tiledb.DenseArray.from_numpy(tmp, np.eye(4) * [1,2,3,4])
...    A.multi_index[1]
...    A.multi_index[1,1]
...    # return row 0 and 2
...    A.multi_index[[0,2]]
...    # return rows 0 and 2 intersecting column 2
...    A.multi_index[[0,2], 2]
...    # return rows 0:2 intersecting columns 0:2
...    A.multi_index[slice(0,2), slice(0,2)]
OrderedDict([('', array([[0., 2., 0., 0.]]))])
OrderedDict([('', array([[2.]]))])
OrderedDict([('', array([[1., 0., 0., 0.], [0., 0., 3., 0.]]))])
OrderedDict([('', array([[0.], [3.]]))])
OrderedDict([('', array([[1., 0., 0.], [0., 2., 0.], [0., 0., 3.]]))])

nattr¶: The number of attributes of this array.

ndim¶: The number of dimensions of this array.

nonempty_domain(self)¶

Return the minimum bounding domain which encompasses nonempty values.

Return type:	tuple(tuple(numpy scalar, numpy scalar), ..)
Returns:	A list of (inclusive) domain extent tuples, that contain all nonempty cells

reopen(self, timestamp=None)¶

Reopens this array.

This is useful when the array is updated after it was opened. To sync-up with the updates, the user must either close the array and open again, or just use reopen() without closing. reopen will be generally faster than a close-then-open.

schema¶: The ArraySchema for this array.

set_query(self, serialized_query)¶

shape¶: The shape of this array.

subarray(self, selection, attrs=None, coords=False, order=None)¶

timestamp¶

Deprecated in 0.9.2.

Use timestamp_range

Returns the timestamp the array is opened at

Return type:	int
Returns:	tiledb timestamp at which point the array was opened

timestamp_range¶

Returns the timestamp range the array is opened at

Return type:	tuple
Returns:	tiledb timestamp range at which point the array was opened

uri¶: Returns the URI of the array

view_attr¶: The view attribute of this array.

tiledb.consolidate(uri, key=None, Config config=None, Ctx ctx=None, timestamp=None)¶

Consolidates TileDB array fragments for improved read performance

Parameters:	uri (str) – URI to the TileDB Array key (str) – (default None) Key to decrypt array if the array is encrypted config (tiledb.Config) – The TileDB Config with consolidation parameters set ctx (tiledb.Ctx) – (default None) The TileDB Context timestamp – (default None) If not None, consolidate the array using the given tuple(int, int) UNIX seconds range (inclusive)
Return type:	str or bytes
Returns:	path (URI) to the consolidated TileDB Array
Raises:	TypeError – cannot convert path to unicode string
Raises:	`tiledb.TileDBError`

Rather than passing the timestamp into this function, it may be set with the config parameters “sm.vacuum.timestamp_start”`and `”sm.vacuum.timestamp_end” which takes in a time in UNIX seconds. If both are set then this function’s timestamp argument will be used.

tiledb.vacuum(uri, Config config=None, Ctx ctx=None, timestamp=None)¶

Vacuum underlying array fragments after consolidation.

Parameters:	uri (str) – URI of array to be vacuumed config – Override the context configuration for vacuuming. Defaults to None, inheriting the context parameters. (ctx – tiledb.Ctx, optional): Context. Defaults to tiledb.default_ctx().
Raises:	TypeError – cannot convert uri to unicode string
Raises:	`tiledb.TileDBError`

This operation of this function is controlled by the “sm.vacuum.mode” parameter, which accepts the values fragments, fragment_meta, and array_meta. Rather than passing the timestamp into this function, it may be set by using “sm.vacuum.timestamp_start”`and `”sm.vacuum.timestamp_end” which takes in a time in UNIX seconds. If both are set then this function’s timestamp argument will be used.

Example:

>>> import tiledb, numpy as np
>>> import tempfile
>>> path = tempfile.mkdtemp()
>>> with tiledb.from_numpy(path, np.random.rand(4)) as A:
...     pass # make sure to close
>>> with tiledb.open(path, 'w') as A:
...     for i in range(4):
...         A[:] = np.ones(4, dtype=np.int64) * i
>>> paths = tiledb.VFS().ls(path)
>>> # should be 12 (2 base files + 2*5 fragment+ok files)
>>> (); len(paths); () # doctest:+ELLIPSIS
(...)
>>> () ; tiledb.consolidate(path) ; () # doctest:+ELLIPSIS
(...)
>>> tiledb.vacuum(path)
>>> paths = tiledb.VFS().ls(path)
>>> # should now be 4 ( base files + 2 fragment+ok files)
>>> (); len(paths); () # doctest:+ELLIPSIS
(...)

Dense Array¶

class tiledb.DenseArray¶

Class representing a dense TileDB array.

Inherits properties and methods of tiledb.Array and implements __setitem__ and __getitem__ for dense array indexing and assignment.

__getitem__(selection)¶

Retrieve data cells for an item or region of the array.

Parameters:	selection (tuple) – An int index, slice or tuple of integer/slice objects, specifying the selected subarray region for each dimension of the DenseArray.
Return type:	`numpy.ndarray` or `collections.OrderedDict`
Returns:	If the dense array has a single attribute then a Numpy array of corresponding shape/dtype is returned for that attribute. If the array has multiple attributes, a `collections.OrderedDict` is returned with dense Numpy subarrays for each attribute.
Raises:	IndexError – invalid or unsupported index selection
Raises:	`tiledb.TileDBError`

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     # Creates array 'array' on disk.
...     A = tiledb.DenseArray.from_numpy(tmp + "/array",  np.ones((100, 100)))
...     # Many aspects of Numpy's fancy indexing are supported:
...     A[1:10, ...].shape
...     A[1:10, 20:99].shape
...     A[1, 2].shape
(9, 100)
(9, 79)
()
>>> # Subselect on attributes when reading:
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.DenseArray.create(tmp + "/array", schema)
...     with tiledb.DenseArray(tmp + "/array", mode='w') as A:
...         A[0:10] = {"a1": np.zeros((10)), "a2": np.ones((10))}
...     with tiledb.DenseArray(tmp + "/array", mode='r') as A:
...         # Access specific attributes individually.
...         A[0:5]["a1"]
...         A[0:5]["a2"]
array([0, 0, 0, 0, 0])
array([1, 1, 1, 1, 1])

__setitem__(selection, value)¶

Set / update dense data cells

Parameters:	selection (tuple) – An int index, slice or tuple of integer/slice objects, specifiying the selected subarray region for each dimension of the DenseArray. value (dict or `numpy.ndarray`) – a dictionary of array attribute values, values must able to be converted to n-d numpy arrays. if the number of attributes is one, then a n-d numpy array is accepted.
Raises:	IndexError – invalid or unsupported index selection ValueError – value / coordinate length mismatch
Raises:	`tiledb.TileDBError`

Example:

>>> import tiledb, numpy as np, tempfile
>>> # Write to single-attribute 2D array
>>> with tempfile.TemporaryDirectory() as tmp:
...     # Create an array initially with all zero values
...     with tiledb.DenseArray.from_numpy(tmp + "/array",  np.zeros((2, 2))) as A:
...         pass
...     with tiledb.DenseArray(tmp + "/array", mode='w') as A:
...         # Write to the single (anonymous) attribute
...         A[:] = np.array(([1,2], [3,4]))
>>>
>>> # Write to multi-attribute 2D array
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(
...         tiledb.Dim(domain=(0, 1), tile=2, dtype=np.uint64),
...         tiledb.Dim(domain=(0, 1), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.DenseArray.create(tmp + "/array", schema)
...     with tiledb.DenseArray(tmp + "/array", mode='w') as A:
...         # Write to each attribute
...         A[0:2, 0:2] = {"a1": np.array(([-3, -4], [-5, -6])),
...                        "a2": np.array(([1, 2], [3, 4]))}

query(self, attrs=None, attr_cond=None, dims=None, coords=False, order='C', use_arrow=None, return_arrow=False, return_incomplete=False)¶

Construct a proxy Query object for easy subarray queries of cells for an item or region of the array across one or more attributes.

Optionally subselect over attributes, return dense result coordinate values, and specify a layout a result layout / cell-order.

Parameters:	attrs – the DenseArray attributes to subselect over. If attrs is None (default) all array attributes will be returned. Array attributes can be defined by name or by positional index. attr_cond – the QueryCondition to filter attributes on. dims – the DenseArray dimensions to subselect over. If dims is None (default) then no dimensions are returned, unless coords=True. coords – if True, return array of coodinate value (default False). order – ‘C’, ‘F’, ‘U’, or ‘G’ (row-major, col-major, unordered, TileDB global order) use_arrow – if True, return dataframes via PyArrow if applicable. return_arrow – if True, return results as a PyArrow Table if applicable. return_incomplete – if True, initialize and return an iterable Query object over the indexed range. Consuming this iterable returns a result set for each TileDB incomplete query. See usage example in ‘examples/incomplete_iteration.py’. To retrieve the estimated result sizes for the query ranges, use: A.query(…, return_incomplete=True)[…].est_result_size() If False (default False), queries will be internally run to completion by resizing buffers and resubmitting until query is complete.
Returns:	A proxy Query object that can be used for indexing into the DenseArray over the defined attributes, in the given result layout (order).
Raises:	ValueError – array is not opened for reads (mode = ‘r’)
Raises:	`tiledb.TileDBError`

Example:

>>> # Subselect on attributes when reading:
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(tiledb.Dim(domain=(0, 9), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.DenseArray.create(tmp + "/array", schema)
...     with tiledb.DenseArray(tmp + "/array", mode='w') as A:
...         A[0:10] = {"a1": np.zeros((10)), "a2": np.ones((10))}
...     with tiledb.DenseArray(tmp + "/array", mode='r') as A:
...         # Access specific attributes individually.
...         A.query(attrs=("a1",))[0:5]
OrderedDict([('a1', array([0, 0, 0, 0, 0]))])

static from_numpy(uri, array, ctx=None, **kwargs)¶: DenseArrayImpl.from_numpy(uri, ndarray array, Ctx ctx=None, **kw) Implementation of tiledb.from_numpy for dense arrays. See documentation

of tiledb.from_numpy

Sparse Array¶

class tiledb.SparseArray¶

Class representing a sparse TileDB array.

Inherits properties and methods of tiledb.Array and implements __setitem__ and __getitem__ for sparse array indexing and assignment.

__getitem__(selection)¶

Retrieve nonempty cell data for an item or region of the array

Parameters:	selection (tuple) – An int index, slice or tuple of integer/slice objects, specifying the selected subarray region for each dimension of the SparseArray.
Return type:	`collections.OrderedDict`
Returns:	An OrderedDict is returned with dimension and attribute names as keys. Nonempty attribute values are returned as Numpy 1-d arrays.
Raises:	IndexError – invalid or unsupported index selection
Raises:	`tiledb.TileDBError`

Example:

>>> import tiledb, numpy as np, tempfile
>>> # Write to multi-attribute 2D array
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(
...         tiledb.Dim(name="y", domain=(0, 9), tile=2, dtype=np.uint64),
...         tiledb.Dim(name="x", domain=(0, 9), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom, sparse=True,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.SparseArray.create(tmp + "/array", schema)
...     with tiledb.SparseArray(tmp + "/array", mode='w') as A:
...         # Write in the twp cells (0,0) and (2,3) only.
...         I, J = [0, 2], [0, 3]
...         # Write to each attribute
...         A[I, J] = {"a1": np.array([1, 2]),
...                    "a2": np.array([3, 4])}
...     with tiledb.SparseArray(tmp + "/array", mode='r') as A:
...         # Return an OrderedDict with values and coordinates
...         A[0:3, 0:10]
...         # Return just the "x" coordinates values
...         A[0:3, 0:10]["x"]
OrderedDict([('a1', array([1, 2])), ('a2', array([3, 4])), ('y', array([0, 2], dtype=uint64)), ('x', array([0, 3], dtype=uint64))])
array([0, 3], dtype=uint64)

With a floating-point array domain, index bounds are inclusive, e.g.:

>>> # Return nonempty cells within a floating point array domain (fp index bounds are inclusive):
>>> # A[5.0:579.9]

__setitem__(selection, value)¶

Set / update sparse data cells

Parameters:	selection (tuple) – N coordinate value arrays (dim0, dim1, …) where N in the ndim of the SparseArray, The format follows numpy sparse (point) indexing semantics. value (dict or `numpy.ndarray`) – a dictionary of nonempty array attribute values, values must able to be converted to 1-d numpy arrays. if the number of attributes is one, then a 1-d numpy array is accepted.
Raises:	IndexError – invalid or unsupported index selection ValueError – value / coordinate length mismatch
Raises:	`tiledb.TileDBError`

Example:

>>> import tiledb, numpy as np, tempfile
>>> # Write to multi-attribute 2D array
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(
...         tiledb.Dim(domain=(0, 1), tile=2, dtype=np.uint64),
...         tiledb.Dim(domain=(0, 1), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom, sparse=True,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.SparseArray.create(tmp + "/array", schema)
...     with tiledb.SparseArray(tmp + "/array", mode='w') as A:
...         # Write in the corner cells (0,0) and (1,1) only.
...         I, J = [0, 1], [0, 1]
...         # Write to each attribute
...         A[I, J] = {"a1": np.array([1, 2]),
...                    "a2": np.array([3, 4])}

query(self, attrs=None, attr_cond=None, dims=None, index_col=True, coords=None, order='U', use_arrow=None, return_arrow=None, return_incomplete=False)¶

Construct a proxy Query object for easy subarray queries of cells for an item or region of the array across one or more attributes.

Optionally subselect over attributes, return dense result coordinate values, and specify a layout a result layout / cell-order.

Parameters:

attrs – the SparseArray attributes to subselect over. If attrs is None (default) all array attributes will be returned. Array attributes can be defined by name or by positional index.
attr_cond – the QueryCondition to filter attributes on.
dims – the SparseArray dimensions to subselect over. If dims is None (default) then all dimensions are returned, unless coords=False.
index_col – For dataframe queries, override the saved index information, and only set specified index(es) in the final dataframe, or None.
coords – (deprecated) if True, return array of coordinate value (default False).
order – ‘C’, ‘F’, or ‘G’ (row-major, col-major, tiledb global order)
use_arrow – if True, return dataframes via PyArrow if applicable.
return_arrow – if True, return results as a PyArrow Table if applicable.

Returns:

A proxy Query object that can be used for indexing into the SparseArray over the defined attributes, in the given result layout (order).

Example:

>>> import tiledb, numpy as np, tempfile
>>> # Write to multi-attribute 2D array
>>> with tempfile.TemporaryDirectory() as tmp:
...     dom = tiledb.Domain(
...         tiledb.Dim(name="y", domain=(0, 9), tile=2, dtype=np.uint64),
...         tiledb.Dim(name="x", domain=(0, 9), tile=2, dtype=np.uint64))
...     schema = tiledb.ArraySchema(domain=dom, sparse=True,
...         attrs=(tiledb.Attr(name="a1", dtype=np.int64),
...                tiledb.Attr(name="a2", dtype=np.int64)))
...     tiledb.SparseArray.create(tmp + "/array", schema)
...     with tiledb.SparseArray(tmp + "/array", mode='w') as A:
...         # Write in the twp cells (0,0) and (2,3) only.
...         I, J = [0, 2], [0, 3]
...         # Write to each attribute
...         A[I, J] = {"a1": np.array([1, 2]),
...                    "a2": np.array([3, 4])}
...     with tiledb.SparseArray(tmp + "/array", mode='r') as A:
...         A.query(attrs=("a1",), coords=False, order='G')[0:3, 0:10]
OrderedDict([('a1', array([1, 2]))])

Query Condition¶

class tiledb.QueryCondition(expression: str, ctx: tiledb.libtiledb.Ctx = <factory>)¶

Class representing a TileDB query condition object for attribute filtering pushdown.

A query condition is set with a string representing an expression as defined by the grammar below. A more straight forward example of usage is given beneath.

When querying a sparse array, only the values that satisfy the given condition are returned (coupled with their associated coordinates). An example may be found in examples/query_condition_sparse.py.

For dense arrays, the given shape of the query matches the shape of the output array. Values that DO NOT satisfy the given condition are filled with the TileDB default fill value. Different attribute types have different default fill values as outlined here (https://docs.tiledb.com/main/background/internal-mechanics/writing#default-fill-values). An example may be found in examples/query_condition_dense.py.

BNF:

A query condition is made up of one or more Boolean expressions. Multiple Boolean expressions are chained together with Boolean operators. The or_op Boolean operators are given lower presedence than and_op.

query_cond ::= bool_term | query_cond or_op bool_term

bool_term ::= bool_expr | bool_term and_op bool_expr

Logical and and bitwise & Boolean operators are given equal precedence.

and_op ::= and | &

Likewise, or and | are given equal precedence.

or_op ::= or | |

We intend to support not in future releases.

A Boolean expression may either be a comparison expression or membership expression.

bool_expr ::= compare_expr | member_expr

A comparison expression contains a comparison operator. The operator works on a TileDB attribute name and value.

``compare_expr ::= attr compare_op val

val compare_op attr

val compare_op attr compare_op val``

All comparison operators are supported.

compare_op ::= < | > | <= | >= | == | !=

A memership expression contains the membership operator, in. The operator works on a TileDB attribute and list of values.

member_expr ::= attr in <list>

TileDB attribute names are Python valid variables or a attr() casted string.

attr ::= <variable> | attr(<str>)

Values are any Python-valid number or string. They may also be casted with val().

val ::= <num> | <str> | val(val)

Example:

>>> with tiledb.open(uri, mode="r") as A:
>>>     # Select cells where the attribute values for `foo` are less than 5
>>>     # and `bar` equal to string "asdf".
>>>     # Note precedence is equivalent to:
>>>     # tiledb.QueryCondition("foo > 5 or ('asdf' == attr('b a r') and baz <= val(1.0))")
>>>     qc = tiledb.QueryCondition("foo > 5 or 'asdf' == attr('b a r') and baz <= val(1.0)")
>>>     A.query(attr_cond=qc)
>>>
>>>     # Select cells where the attribute values for `foo` are equal to
>>>     # 1, 2, or 3.
>>>     # Note this is equivalent to:
>>>     # tiledb.QueryCondition("foo == 1 or foo == 2 or foo == 3")
>>>     A.query(attr_cond=tiledb.QueryCondition("foo in [1, 2, 3]"))

Group¶

class tiledb.Group(uri: str, mode: str = 'r', ctx: Ctx = None)¶

Support for organizing multiple arrays in arbitrary directory hierarchies.

Group members may be any number of nested groups and arrays. Members are stored as tiledb.Objects which indicate the member’s URI and type.

Groups may contain associated metadata similar to array metadata where keys are strings. Singleton values may be of type int, float, str, or bytes. Multiple values of the same type may be placed in containers of type list, tuple, or 1-D np.ndarray. The values within containers are limited to type int or float.

See more at: https://docs.tiledb.com/main/background/key-concepts-and-data-format#arrays-and-groups

Parameters:	uri (str) – The URI to the Group mode (str) – Read mode (‘r’) or write mode (‘w’) ctx (tiledb.Ctx) – A TileDB context

Example:

>>> # Create a group
>>> grp_path = "root_group"
>>> tiledb.Group.create(grp_path)
>>> grp = tiledb.Group(grp_path, "w")
>>>
>>> # Create an array and add as a member to the group
>>> array_path = "array.tdb"
>>> domain = tiledb.Domain(tiledb.Dim(domain=(1, 8), tile=2))
>>> a1 = tiledb.Attr("val", dtype="f8")
>>> schema = tiledb.ArraySchema(domain=domain, attrs=(a1,))
>>> tiledb.Array.create(array_path, schema)
>>> grp.add(array_path)
>>>
>>> # Create a group and add as a subgroup
>>> subgrp_path = "sub_group"
>>> tiledb.Group.create(subgrp_path)
>>> grp.add(subgrp_path)
>>>
>>> # Add metadata to the subgroup
>>> grp.meta["ints"] = [1, 2, 3]
>>> grp.meta["str"] = "string_metadata"
>>> grp.close()
>>>
>>> grp.open("r")
>>> # Dump all the members in string format
>>> mbrs_repr = grp
>>> # Or create a list of Objects in the Group
>>> mbrs_iter = list(grp)
>>> # Get the first member's uri and type
>>> member_uri, member_type = grp[0].uri, grp[0].type
>>> grp.close()
>>>
>>> # Remove the subgroup
>>> grp.open("w")
>>> grp.remove(subgrp_path)
>>> grp.close()

__getitem__(member)¶

Retrieve a member from the Group as an Object.

Parameters:	member (Union[int, str]) – The index or name of the member
Returns:	The member as an Object
Return type:	Object

__delitem__(uri)¶

Remove a member from the group.

Parameters:	uri (str) – The URI to the member

__contains__(member)¶

Returns:	Whether the Group contains a member with the given name
Return type:	bool

__len__() → int¶

Return type:	int
Returns:	Number of members in the Group

class GroupMetadata(group: tiledb.group.Group)¶

Holds metadata for the associated Group in a dictionary-like structure.

clear() → None. Remove all items from D.¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶: If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair¶: as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D¶

add(uri: str, name: str = None, relative: bool = False)¶

Adds a member to the Group.

Parameters:	uri (str) – The URI of the member to add relative (bool) – Whether the path of the URI is a relative path (default=relative: False) name (str) – An optional name for the Group (default=None)

close()¶: Close a Group.

static create(uri: str, ctx: Ctx = None)¶

Create a new Group.

Parameters:	uri (str) – The URI to the to-be created Group ctx (tiledb.Ctx) – A TileDB context

isopen¶

Returns:	Whether or not the Group is open
Return type:	bool

meta¶

Returns:	The Group’s metadata as a key-value structure
Return type:	GroupMetadata

mode¶

Returns:	Read mode (‘r’) or write mode (‘w’)
Return type:	str

open(mode: str = 'r')¶

Open a Group in read mode (“r”) or write mode (“w”).

Parameters:	mode (str) – Read mode (‘r’) or write mode (‘w’)

remove(member: str)¶

Remove a member from the Group.

Parameters:	member (str) – The URI or name of the member

uri¶

Returns:	URI of the Group
Return type:	str

class tiledb.Group.GroupMetadata(group: tiledb.group.Group)

Holds metadata for the associated Group in a dictionary-like structure.

__setitem__(key, value)¶

Parameters:	key (str) – Key for the Group metadata entry value (Union[int, float, str, bytes, np.ndarray]) – Value for the Group metadata entry

__getitem__(key)¶

Parameters:	key (str) – Key of the Group metadata entry
Return type:	Union[int, float, str, bytes, np.ndarray]
Returns:	The value associated with the key

__delitem__(key)¶

Removes the entry from the Group metadata.

Parameters:	key (str) – Key of the Group metadata entry

__contains__(key)¶

Parameters:	key (str) – Key of the Group metadata entry
Return type:	bool
Returns:	True if the key is in the Group metadata, otherwise False

__len__() → int¶

Return type:	int
Returns:	Number of entries in the Group metadata

clear() → None. Remove all items from D.

pop(k[, d]) → v, remove specified key and return the corresponding value.: If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair: as a 2-tuple; but raise KeyError if D is empty.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

Object¶

class tiledb.Object(type: tiledb.cc.ObjectType, uri: str, name: Optional[str] = None)¶

Represents a TileDB object which may be of type Array, Group, or Invalid.

uri¶

Returns:	URI of the Object.
Return type:	str

type¶

Returns:	Valid TileDB object types are Array and Group.
Return type:	type

Object Management¶

tiledb.array_exists(uri, isdense=False, issparse=False)¶

Check if arrays exists and is open-able at the given URI

Optionally restrict to isdense or issparse array types.

tiledb.group_create(uri: str, ctx: Ctx = None)¶

Create a new Group.

Parameters:	uri (str) – The URI to the to-be created Group ctx (tiledb.Ctx) – A TileDB context

tiledb.object_type(uri, Ctx ctx=None)¶

Returns the TileDB object type at the specified path (URI)

Parameters:	path (str) – path (URI) of the TileDB resource ctx (tiledb.Ctx) – The TileDB Context
Return type:	str
Returns:	object type string
Raises:	TypeError – cannot convert path to unicode string

tiledb.remove(uri, Ctx ctx=None)¶

Removes (deletes) the TileDB object at the specified path (URI)

Parameters:	uri (str) – URI of the TileDB resource ctx (tiledb.Ctx) – The TileDB Context
Raises:	TypeError – uri cannot be converted to a unicode string
Raises:	`tiledb.TileDBError`

tiledb.move(old_uri, new_uri, Ctx ctx=None)¶

Moves a TileDB resource (group, array, key-value).

Parameters:	ctx (tiledb.Ctx) – The TileDB Context old_uri (str) – path (URI) of the TileDB resource to move new_uri (str) – path (URI) of the destination
Raises:	TypeError – uri cannot be converted to a unicode string
Raises:	`TileDBError`

tiledb.ls(path, func, Ctx ctx=None)¶

Lists TileDB resources and applies a callback that have a prefix of path (one level deep).

Parameters:	path (str) – URI of TileDB group object func (function) – callback to execute on every listed TileDB resource, URI resource path and object type label are passed as arguments to the callback ctx (tiledb.Ctx) – TileDB context
Raises:	TypeError – cannot convert path to unicode string
Raises:	`tiledb.TileDBError`

tiledb.walk(path, func, order='preorder', Ctx ctx=None)¶

Recursively visits TileDB resources and applies a callback to resources that have a prefix of path

Parameters:	path (str) – URI of TileDB group object func (function) – callback to execute on every listed TileDB resource, URI resource path and object type label are passed as arguments to the callback ctx (tiledb.Ctx) – The TileDB context order (str) – ‘preorder’ (default) or ‘postorder’ tree traversal
Raises:	TypeError – cannot convert path to unicode string ValueError – unknown order
Raises:	`tiledb.TileDBError`

Fragment Info¶

class tiledb.FragmentInfoList(array_uri, include_mbrs=False, ctx=None)¶

Class representing an ordered list of FragmentInfo objects.

Parameters:

array_uri (str) – URI for the TileDB array (any supported TileDB URI)
include_mbrs (bool) – (default False) include minimum bounding rectangles in FragmentInfo result
ctx (tiledb.Ctx) – A TileDB context

Variables:

uri – URIs of fragments
version – Fragment version of each fragment
nonempty_domain – Non-empty domain of each fragment
cell_num – Number of cells in each fragment
timestamp_range – Timestamp range of when each fragment was written
sparse – For each fragment, True if fragment is sparse, else False
has_consolidated_metadata – For each fragment, True if fragment has consolidated fragment metadata, else False
unconsolidated_metadata_num – Number of unconsolidated metadata fragments in each fragment
to_vacuum – URIs of already consolidated fragments to vacuum
mbrs – (TileDB Embedded 2.5.0+ only) The mimimum bounding rectangle of each fragment; only present when include_mbrs=True
array_schema_name – (TileDB Embedded 2.5.0+ only) The array schema’s name

Example:

>>> import tiledb, numpy as np, tempfile
>>> with tempfile.TemporaryDirectory() as tmp:
...     # The array will be 4x4 with dimensions "rows" and "cols", with domain [1,4] and space tiles 2x2
...     dom = tiledb.Domain(
...         tiledb.Dim(name="rows", domain=(1, 4), tile=2, dtype=np.int32),
...         tiledb.Dim(name="cols", domain=(1, 4), tile=2, dtype=np.int32),
...     )
...     # The array will be dense with a single attribute "a" so each (i,j) cell can store an integer.
...     schema = tiledb.ArraySchema(
...         domain=dom, sparse=False, attrs=[tiledb.Attr(name="a", dtype=np.int32)]
...     )
...     # Set URI of the array
...     uri = tmp + "/array"
...     # Create the (empty) array on disk.
...     tiledb.Array.create(uri, schema)
...
...     # Write three fragments to the array
...     with tiledb.DenseArray(uri, mode="w") as A:
...         A[1:3, 1:5] = np.array(([1, 2, 3, 4, 5, 6, 7, 8]))
...     with tiledb.DenseArray(uri, mode="w") as A:
...         A[2:4, 2:4] = np.array(([101, 102, 103, 104]))
...     with tiledb.DenseArray(uri, mode="w") as A:
...         A[3:4, 4:5] = np.array(([202]))
...
...     # tiledb.array_fragments() requires TileDB-Py version > 0.8.5
...     fragments_info = tiledb.array_fragments(uri)
...
...     "====== FRAGMENTS  INFO ======"
...     f"number of fragments: {len(fragments_info)}"
...     f"nonempty domains: {fragments_info.nonempty_domain}"
...     f"sparse fragments: {fragments_info.sparse}"
...
...     for fragment in fragments_info:
...         f"===== FRAGMENT NUMBER {fragment.num} ====="
...         f"is sparse: {fragment.sparse}"
...         f"cell num: {fragment.cell_num}"
...         f"has consolidated metadata: {fragment.has_consolidated_metadata}"
...         f"nonempty domain: {fragment.nonempty_domain}"
'====== FRAGMENTS  INFO ======'
'number of fragments: 3'
'nonempty domains: (((1, 2), (1, 4)), ((2, 3), (2, 3)), ((3, 3), (4, 4)))'
'sparse fragments: (False, False, False)'
'===== FRAGMENT NUMBER 0 ====='
'is sparse: False'
'cell num: 8'
'has consolidated metadata: False'
'nonempty domain: ((1, 2), (1, 4))'
'===== FRAGMENT NUMBER 1 ====='
'is sparse: False'
'cell num: 16'
'has consolidated metadata: False'
'nonempty domain: ((2, 3), (2, 3))'
'===== FRAGMENT NUMBER 2 ====='
'is sparse: False'
'cell num: 4'
'has consolidated metadata: False'
'nonempty domain: ((3, 3), (4, 4))'

class tiledb.FragmentInfo(fragments: tiledb.fragment.FragmentInfoList, num)¶

Class representing the metadata for a single fragment. See tiledb.FragmentInfoList for example of usage.

Variables:

uri – URIs of fragments
version – Fragment version of each fragment
nonempty_domain – Non-empty domain of each fragment
cell_num – Number of cells in each fragment
timestamp_range – Timestamp range of when each fragment was written
sparse – For each fragment, True if fragment is sparse, else False
has_consolidated_metadata – For each fragment, True if fragment has consolidated fragment metadata, else False
unconsolidated_metadata_num – Number of unconsolidated metadata fragments in each fragment
to_vacuum – URIs of already consolidated fragments to vacuum
mbrs – (TileDB Embedded 2.5.0+ only) The mimimum bounding rectangle of each fragment; only present when include_mbrs=True
array_schema_name – (TileDB Embedded 2.5.0+ only) The array schema’s name

Exceptions¶

exception tiledb.TileDBError¶

VFS¶

class tiledb.VFS(config: Union[Config, dict] = None, ctx: Ctx = None)¶

TileDB VFS class

Encapsulates the TileDB VFS module instance with a specific configuration (config).

Parameters:	ctx (tiledb.Ctx) – The TileDB Context config (tiledb.Config or dict) – Override ctx VFS configurations with updated values in config.

close(file: tiledb.cc.FileHandle)¶

Closes a VFS FileHandle object.

Parameters:	file (FileIO) – An opened VFS FileIO
Return type:	FileIO
Returns:	closed VFS FileHandle
Raises:	`tiledb.TileDBError`

config() → Config¶

Return type:	tiledb.Config
Returns:	config associated with the VFS object

copy_dir(old_uri: str, new_uri: str)¶

Copies a TileDB directory from an old URI to a new URI.

Parameters:	old_uri (str) – Input of the old directory URI new_uri (str) – Input of the new directory URI

copy_file(old_uri: str, new_uri: str)¶

Copies a TileDB file from an old URI to a new URI.

Parameters:	old_uri (str) – Input of the old file URI new_uri (str) – Input of the new file URI

create_bucket(uri: str)¶

Creates an object store bucket with the input URI.

Parameters:	uri (str) – Input URI of the bucket

create_dir(uri: str)¶

Check if an object store bucket is empty.

Parameters:	uri (str) – Input URI of the bucket

ctx() → Ctx¶

Return type:	tiledb.Ctx
Returns:	context associated with the VFS object

dir_size(uri: str) → int¶

Parameters:	uri (str) – Input URI of the directory
Return type:	int
Returns:	The size of a directory with the input URI

empty_bucket(uri: str)¶

Empty an object store bucket.

Parameters:	uri (str) – Input URI of the bucket

file_size(uri: str) → int¶

Parameters:	uri (str) – Input URI of the file
Return type:	int
Returns:	The size of a file with the input URI

is_bucket(uri: str) → bool¶

Parameters:	uri (str) – Input URI of the bucket
Return type:	bool
Returns:	True if an object store bucket with the input URI exists, False otherwise

is_dir(uri: str) → bool¶

Parameters:	uri (str) – Input URI of the directory
Return type:	bool
Returns:	True if a directory with the input URI exists, False otherwise

is_empty_bucket(uri: str) → bool¶

Parameters:	uri (str) – Input URI of the bucket
Return type:	bool
Returns:	True if an object store bucket is empty, False otherwise

is_file(uri: str) → bool¶

Parameters:	uri (str) – Input URI of the file
Return type:	bool
Returns:	True if a file with the input URI exists, False otherwise

ls(uri: str) → List[str]¶

Retrieves the children in directory uri. This function is non-recursive, i.e., it focuses in one level below uri.

Parameters:	uri (str) – Input URI of the directory
Return type:	List[str]
Returns:	The children in directory uri

move_dir(old_uri: str, new_uri: str)¶

Renames a TileDB directory from an old URI to a new URI.

Parameters:	old_uri (str) – Input of the old directory URI new_uri (str) – Input of the new directory URI

move_file(old_uri: str, new_uri: str)¶

Renames a TileDB file from an old URI to a new URI.

Parameters:	old_uri (str) – Input of the old file URI new_uri (str) – Input of the new file URI

open(uri: str, mode: str = 'rb')¶

Opens a VFS file resource for reading / writing / appends at URI.

If the file did not exist upon opening, a new file is created.

Parameters:	uri (str) – URI of VFS file resource str (mode) – ‘rb’ for opening the file to read, ‘wb’ to write, ‘ab’ to append
Return type:	FileHandle
Returns:	TileDB FileIO
Raises:	TypeError – cannot convert uri to unicode string ValueError – invalid mode
Raises:	`tiledb.TileDBError`

read(file: tiledb.cc.FileHandle, offset: int, nbytes: int) → bytes¶

Read nbytes from an opened VFS FileHandle at a given offset.

Parameters:	file (FileHandle) – An opened VFS FileHandle in ‘r’ mode offset (int) – offset position in bytes to read from nbytes (int) – number of bytes to read
Return type:	`bytes()`
Returns:	read bytes
Raises:	`tiledb.TileDBError`

remove_bucket(uri: str)¶

Deletes an object store bucket with the input URI.

Parameters:	uri (str) – Input URI of the bucket

remove_dir(uri: str)¶

Removes a directory (recursively) with the input URI.

Parameters:	uri (str) – Input URI of the directory

remove_file(uri: str)¶

Removes a file with the input URI.

Parameters:	uri (str) – Input URI of the file

supports(scheme: str) → bool¶

Returns true if the given URI scheme (storage backend) is supported.

Parameters:	scheme (str) – scheme component of a VFS resource URI (ex. ‘file’ / ‘hdfs’ / ‘s3’)
Return type:	bool
Returns:	True if the linked libtiledb version supports the storage backend, False otherwise
Raises:	ValueError – VFS storage backend is not supported

touch(uri: str)¶

Touches a file with the input URI, i.e., creates a new empty file.

Parameters:	uri (str) – Input URI of the file

write(file: tiledb.cc.FileHandle, buff: Union[str, bytes])¶

Writes buffer to opened VFS FileHandle.

Parameters:	file (FileHandle) – An opened VFS FileHandle in ‘w’ mode buff – a Python object that supports the byte buffer protocol
Raises:	TypeError – cannot convert buff to bytes
Raises:	`tiledb.TileDBError`

class tiledb.FileIO(vfs: tiledb.vfs.VFS, uri: str, mode: str = 'rb')¶

TileDB FileIO class that encapsulates files opened by tiledb.VFS. The file operations are meant to mimic Python’s built-in file I/O methods.

__len__()¶

Return type:	int
Returns:	Number of bytes in file

closed¶

Return type:	bool
Returns:	True if the file is closed, otherwise False

flush()¶: Force the data to be written to the file.

mode¶

Return type:	str
Returns:	Whether the file is in read mode (“rb”), write mode (“wb”), or append mode (“ab”)

read(size: int = -1) → bytes¶

Read the file from the current pointer position.

Parameters:	size (int) – Number of bytes to read. By default, size is set to -1

which will read until the end of the file. :rtype: bytes :return: The bytes in the file

readable() → bool¶

Return type:	bool
Returns:	True if the file is readable (ie. “rb” mode), otherwise False

seek(offset: int, whence: int = 0)¶

Parameters:	offset (int) – Byte position to set the file pointer whence (int) – Reference point. A whence value of 0 measures from the

beginning of the file, 1 uses the current file position, and 2 uses the end of the file as the reference point. whence can be omitted and defaults to 0.

seekable()¶

All tiledb.FileIO objects are seekable.

Return type:	bool
Returns:	True

tell() → int¶

Return type:	int
Returns:	The current position in the file represented as number of bytes

writable() → bool¶

Return type:	bool
Returns:	True if the file is writable (ie. “wb” or “ab” mode), otherwise False

write(buff: bytes)¶

Parameters:	buff (bytes) – Write the given bytes to the file

Filestore¶

class tiledb.Filestore(uri: str, ctx: Ctx = None)¶

Functions to set and get data to and from a TileDB Filestore Array.

Parameters:	uri (str) – The URI to the TileDB Fileshare Array ctx (tiledb.Ctx) – A TileDB context

__len__() → int¶

Return type:	int
Returns:	Bytes in the Filestore Array

static copy_from(filestore_array_uri: str, file_uri: str, mime_type: str = 'AUTODETECT', ctx: Ctx = None) → None¶

Copy data from a file to a Filestore Array.

Parameters:	filestore_array_uri (str) – The URI to the TileDB Fileshare Array file_uri (str) – URI of file to export mime_type (str) – MIME types are “AUTODETECT” (default), “image/tiff”, “application/pdf” ctx (tiledb.Ctx) – A TileDB context

static copy_to(filestore_array_uri: str, file_uri: str, ctx: Ctx = None) → None¶

Copy data from a Filestore Array to a file.

Parameters:	filestore_array_uri (str) – The URI to the TileDB Fileshare Array file_uri (str) – The URI to the TileDB Fileshare Array ctx (tiledb.Ctx) – A TileDB context

read(offset: int = 0, size: int = -1) → bytes¶

Parameters:	offset (int) – Byte position to begin reading. Defaults to beginning of filestore. size (int) – Total number of bytes to read. Defaults to -1 which reads the entire filestore.
Return type:	bytes
Returns:	Data from the Filestore Array

write(buffer: ByteString, mime_type: str = 'AUTODETECT') → None¶

Import data from an object that supports the buffer protocol to a Filestore Array.

Parameters:	ByteString (buffer) – Data of type bytes, bytearray, memoryview, etc. mime_type (str) – MIME types are “AUTODETECT” (default), “image/tiff”, “application/pdf”

Version¶

tiledb.libtiledb.version()¶

Return the version of the linked libtiledb shared library

Return type:	tuple
Returns:	Semver version (major, minor, rev)

Statistics¶

tiledb.stats_enable()¶: Enable TileDB internal statistics.

tiledb.stats_disable()¶: Disable TileDB internal statistics.

tiledb.stats_reset()¶: Reset all TileDB internal statistics to 0.

tiledb.stats_dump(version=True, print_out=True, include_python=True, json=False, verbose=True)¶

Return TileDB internal statistics as a string.

Parameters:	include_python – Include TileDB-Py statistics print_out – Print string to console (default True), or return as string version – Include TileDB Embedded and TileDB-Py versions (default: True) json – Return stats JSON object (default: False) verbose – Print extended internal statistics (default: True)