Serve a Directory of Files

In this tutorial, we will use Tiled to browse a directory of spreadsheets and image files from Python and read the data as pandas DataFrames and numpy arrays.

Generate a directory of example files using a utility provided by Tiled. (Or use your own, if you have one to hand.)

python -m tiled.examples.generate_files example_files/

This created a directory named example_files with some files and subdirectories.

$ ls example_files
another_table.csv  a.tif  b.tif  c.tif  even_more	more  tables.xlsx

The full structure looks like

├── another_table.csv
├── a.tif
├── b.tif
├── c.tif
├── more
│   └── d.tif
│   └── even_more
│       ├── e.tif
│       └── f.tif
└── tables.xlsx

We can serve this directory using Tiled.

tiled serve directory --public example_files

Tiled walks the directory, identifies files that it recognizes and has Readers for. It watches the directory for additions, removals, and changes to the file.

In a Python interpreter, connect with the Python client.

from tiled.client import from_uri

tree = from_uri("http://localhost:8000")

The tree has the same tree structure as the directory on disk, and we can slice and access the data.

>>> client
<Node {'more', 'b', 'a', 'c', ...} ~7 entries>

>>> client['more']
<Node {'d'}>

>>> client['more']['d']
<ArrayClient>

>>> client['more']['d'].read()
array([[1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       ...,
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.]])

>>> client['tables']
<Node {'Sheet 1', 'Sheet 2'}>

>>> client['tables']['Sheet 1']
<DataFrameClient ['A', 'B']>

>>> client['tables']['Sheet 1'].read()
   A  B
0  1  4
1  2  5
2  3  6

Try deleting, moving, or adding files, and notice that the client object updates its structure. It continually watches the filesystem for changes in an efficient fashion.