# Inspecting Dask objects¶

Dask itself is just a specification on top of normal Python dictionaries.
Objects like `dask.Array`

are just a thin wrapper around these dictionaries
with a little bit of shape metadata.

Users should only have to interact with the higher-level `Array`

objects.
Developers may want to dive more deeply into the dictionaries/task graphs
themselves

`dask`

attribute¶

The first step is to look at the `.dask`

attribute of an array

```
>>> import dask.array as da
>>> x = da.ones((5, 15), chunks=(5, 5))
>>> dict(x.dask)
{('wrapped_1', 0, 0): (ones, (5, 5)),
('wrapped_1', 0, 1): (ones, (5, 5)),
('wrapped_1', 0, 2): (ones, (5, 5))}
```

This attribute becomes more interesting as you perform operations on your Array objects

```
>>> dict((x + 1).dask)
{('wrapped_1', 0, 0): (ones, (5, 5)),
('wrapped_1', 0, 1): (ones, (5, 5)),
('wrapped_1', 0, 2): (ones, (5, 5))
('x_1', 0, 0): (add, ('wrapped_1', 0, 0), 1),
('x_1', 0, 1): (add, ('wrapped_1', 0, 1), 1),
('x_1', 0, 2): (add, ('wrapped_1', 0, 2), 1)}
```

Note

In this example we use simple names like `x_1`

, `ones`

, and
`add`

for demonstration purposes. However in practice these names may be
more complex and include long hashed names.

## Visualize graphs with DOT¶

If you have basic graphviz tools like `dot`

installed then dask can also
generate visual graphs from your task graphs.

```
>>> d = (x + 1).dask
>>> from dask.dot import dot_graph
>>> dot_graph(d)
Writing graph to mydask.pdf
```

The result is shown to the right.