Metadata-Version: 2.1
Name: tiled
Version: 0.1.0a9
Summary: Tile-based access to SciPy/PyData data structures over the web in many formats
Home-page: https://github.com/bluesky/tiled
Author: Bluesky Collaboration
Author-email: dallan@bnl.gov
License: BSD (3-clause)
Description: # Tiled
        
        *Disclaimer: This is very early work, still in the process of defining scope.*
        
        Data analysis is easier and better when we load and operate on data in simple,
        self-describing structures that keep our mind on the science rather the
        book-keeping of filenames and file formats.
        
        Tiled is a **data access** tool that enables **search** and **structured,
        chunkwise access** to data in a **variety of formats**, regardless of the
        format the data happens to be stored in at rest. Like Jupyter, Tiled can be
        used solo or deployed as a shared resource. Tiled provides data, locally or
        over the web, in a choice of formats, spanning slow but widespread
        interchange formats (e.g. CSV, JSON, TIFF) and fast, efficient ones (e.g. C
        buffers, Apache Arrow DataFrames). Tiled enables slicing and sub-selection
        for accessing only the data of interest, and it enables parallelized download
        of many chunks at once. Users can access data with very light software
        dependencies and fast partial downloads.
        
        Serve a "Catalog", a Python object backed by some generated data, directory
        of files, network resource, or database
        
        ```
        tiled serve pyobject tiled.examples.generated:demo
        ```
        
        And then access the data efficiently via the Python client, a web browser, or
        any HTTP client.
        
        ```python
        >>> from tiled.client import from_uri
        
        >>> catalog = from_uri("http://localhost:8000")
        
        >>> catalog
        <Catalog {'arrays', 'dataframes', 'xarrays', 'nested', ...} ~5 entries>
        
        >>> catalog['arrays']
        <Catalog {'large', 'medium', 'small', 'tiny'}>
        
        >>> catalog['arrays']['medium']
        <ClientDaskArrayAdapter>
        
        >>> catalog['arrays']['medium'][:]
        array([[0.21267816, 0.59685753, 0.12483017, ..., 0.74891246, 0.43889019,
                0.27761903],
               [0.95434218, 0.31376234, 0.05776443, ..., 0.53886856, 0.92855426,
                0.32506382],
               [0.0458622 , 0.0561961 , 0.3893611 , ..., 0.23124064, 0.40311252,
                0.22488572],
               ...,
               [0.91990991, 0.98361972, 0.26394368, ..., 0.86427576, 0.00436757,
                0.03021872],
               [0.26595236, 0.18207517, 0.18989639, ..., 0.16221733, 0.59052007,
                0.94255651],
               [0.4721781 , 0.01424852, 0.57294198, ..., 0.70392867, 0.69371454,
                0.228491  ]])
        
        >>> catalog['dataframes']
        <Catalog {'df'}>
        
        >>> catalog['dataframes']['df']
        <ClientDaskDataFrameAdapter ['A', 'B', 'C']>
        
        >>> catalog['dataframes']['df'][['A', 'B']]
                      A         B
        index                    
        0      0.748885  0.769644
        1      0.071319  0.364743
        2      0.322665  0.897854
        3      0.328785  0.810159
        4      0.158253  0.822505
        ...         ...       ...
        95     0.913758  0.488304
        96     0.969652  0.287850
        97     0.769774  0.941785
        98     0.350033  0.052412
        99     0.356245  0.683540
        
        [100 rows x 2 columns]
        ```
        
        Using an Internet browser or a commandline HTTP client like
        [curl](https://curl.se/) or [httpie](https://httpie.io/) you can download the
        data in whole or in efficiently-chunked parts in the format of your choice:
        
        ```
        # Download tabular data as CSV
        http://localhost:8000/dataframe/full/dataframes/df?format=csv
        
        # or XLSX (Excel)
        http://localhost:8000/dataframe/full/dataframes/df?format=xslx
        
        # and subselect columns.
        http://localhost:8000/dataframe/full/dataframes/df?format=xslx&column=A&column=B
        
        # View or download (2D) array data as PNG
        http://localhost:8000/array/full/arrays/medium?format=png
        
        # and slice regions of interest.
        http://localhost:8000/array/full/arrays/medium?format=png&slice=:50,100:200
        ```
        
        Web-based data access usually involves downloading complete files, in the
        manner of [Globus](https://www.globus.org/); or using modern chunk-based
        storage formats, such as [TileDB](https://tiledb.com/) and
        [Zarr](https://zarr.readthedocs.io/en/stable/) in local or cloud storage; or
        using custom solutions tailored to a particular large dataset. Waiting for an
        entire file to download when only the first frame of an image stack or a
        certain column of a table are of interest is wasteful and can be prohibitive
        for large longitudinal analyses. Yet, it is not always practical to transcode
        the data into a chunk-friendly format or build a custom tile-based-access
        solution. (Though if you can do either of those things, you should consider
        them instead!)
        
        In more technical language, Tiled is a service providing "tiled" chunk-based
        access to strided arrays, tablular datasets ("dataframes"), and nested
        structures thereof. It is centered on these *structures* (backed by numpy,
        pandas, xarray, various mappings in the server) rather than particular
        formats. The structures may be read from an extensible range of formats; the
        web client receives them as one of an extensible range of MIME types, and it
        can pose requests that sub-select and slice the data before it is read or
        served. The server incorporates search capability, which may be backed by a
        proper database solution at large scales or a simple in-memory index at small
        scales, as well as access controls. A Python client provides a friendly
        h5py-like interface and supports offline caching.
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: client
Provides-Extra: cache
Provides-Extra: server
Provides-Extra: array
Provides-Extra: dataframe
Provides-Extra: xarray
Provides-Extra: complete
