# Graphtik¶

## Lightweight computation graphs for Python¶

Graphtik is an an understandable and lightweight Python module for building and running ordered graphs of computations. The API posits a fair compromise between features and complexity, without precluding any. It can be used as is to build machine learning pipelines for data science projects. It should be extendable to act as the core for a custom ETL engine or a workflow-processor for interdependent files and processes.

Graphtik sprang from Graphkit to experiment with Python 3.6+ features.

## Quick start¶

Here’s how to install:

```pip install graphtik
```

OR with dependencies for plotting support (and you need to install Graphviz program separately with your OS tools):

```pip install graphtik[plot]
```

Here’s a Python script with an example Graphtik computation graph that produces multiple outputs (`a * b`, `a - a * b`, and `abs(a - a * b) ** 3`):

```>>> from operator import mul, sub
>>> from functools import partial
>>> from graphtik import compose, operation

# Computes |a|^p.
>>> def abspow(a, p):
...    c = abs(a) ** p
...    return c
```

Compose the `mul`, `sub`, and `abspow` functions into a computation graph:

```>>> graphop = compose(name="graphop")(
...    operation(name="mul1", needs=["a", "b"], provides=["ab"])(mul),
...    operation(name="sub1", needs=["a", "ab"], provides=["a_minus_ab"])(sub),
...    operation(name="abspow1", needs=["a_minus_ab"], provides=["abs_a_minus_ab_cubed"])
...    (partial(abspow, p=3))
... )
```

Run the graph-operation and request all of the outputs:

```>>> graphop({'a': 2, 'b': 5})
{'a': 2, 'b': 5, 'ab': 10, 'a_minus_ab': -8, 'abs_a_minus_ab_cubed': 512}
```

Run the graph-operation and request a subset of the outputs:

```>>> graphop({'a': 2, 'b': 5}, outputs=["a_minus_ab"])
{'a_minus_ab': -8}
```

As you can see, any function can be used as an operation in Graphtik, even ones imported from system modules!